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Expanding the science of successful aging

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Title:
Expanding the science of successful aging older adults living in continuing care retirement communities (ccrcs)
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Book
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English
Creator:
Petrossi, Kathryn H
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University of South Florida
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Subjects / Keywords:
Successful aging
Continuing care retirement communities
CCRC
Physical health
Social engagement
Intellectual challenge
Bmi
Fruit and vegetable consumption
Volunteerism
Exercise
Self-rated health
Mobility
Productive activities
Social support
Spiritial fulfillment
Dissertations, Academic -- Aging Studies -- Doctoral -- USF   ( lcsh )
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government publication (state, provincial, terriorial, dependent)   ( marcgt )
bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )

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Summary:
ABSTRACT: Rowe and Kahns theory of successful aging identifies three main components of aging successfully: reducing the risk of disease and disability, maintaining high cognitive and physical function, and engagement with life. While there is compelling evidence that suggests the legitimacy of this concept in the 50-75 year old community dwelling population, three areas of expansion are necessary: 1.) programmatic research; 2.) extending the existing research samples to include older samples and those living in continuing care retirement communities (CCRCs); and 3.) the integration of data collection and analysis to move beyond investigation of just one successful aging outcome to include elements of all three components of successful aging.Longitudinal analysis utilizing hierarchical linear modeling (HLM) was conducted on a convenience sample of 136 older adults (mean age = 80.8 years at baseline) participating in a pilot community-wide successful aging program over a 26-month period. Results indicate the sample reported exercising frequently, ate recommended levels of fruits and vegetables, had healthy BMIs, had positive ratings of health, were highly involved in productive activities, and were satisfied with their ability to give and receive social support at baseline. High levels of mobility were measured in the sample. Participants maintained this picture of successful aging over time for the majority of outcome variables, though significant declines in self-reported health were observed. Participants also reported improvements in their satisfaction with receiving social support.Results support four major conclusions: 1.) The three criteria of successful aging identified by Rowe and Kahn (1997) were observed among older adults living in CCRCs who were enrolled in a successful aging program. 2.) Stability was observed on a number of the outcomes over 26 months in this convenience sample, which has implications for intervention/programmatic research. Despite the traditional improvement-oriented focus of programmatic research, stability or maintenance of well-being over time should be viewed as a positive outcome in older age, particularly when compared to national data depicting trends of decline. 3.) The interdependence of current results support the notion that successful aging programming needs to include multi-disciplinary intervention strategies, as supported by the finding that modifiers of physical, social, and intellectual well-being include constructs from each of the components of successful aging.
Thesis:
Thesis (Ph.D.)--University of South Florida, 2005.
Bibliography:
Includes bibliographical references.
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Statement of Responsibility:
by Kathryn H. Petrossi.
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Title from PDF of title page.
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Document formatted into pages; contains 245 pages.
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Includes vita.

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oclc - 62278407
usfldc doi - E14-SFE0001195
usfldc handle - e14.1195
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Expanding the science of successful aging: Older adults living in continuing care retirement communities (CCRCs) by Kathryn H. Petrossi A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Aging Studies College of Arts and Sciences University of South Florida Co-Major Professor: Kathryn Hyer, Ph.D. Co-Major Professor: Cathy McEvoy, Ph.D. Laurence Branch, Ph.D. Robert Kahn, Ph.D. Brent Small, Ph.D. Date of Approval: July 25, 2005 Keywords: physical health, social engage ment, intellectual challenge, spiritual fulfillment, fruit and vegetable consumption, bmi, exercise, self-rated health, mobility, productive activities, volunt eerism, social support Copyright 2005 Kathryn H Petrossi

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DEDICATION I’d like to thank my mother, Bonnie B. Hammo nd, who raised me to balance intelligence and compassion. She has filled my life with love and support; always encouraging me to go after my dreams and reach for the stars. I’d also like to thank Jim Hammond and Marjorie Hammond for the analytical genes that made this quest possible. They have always instilled in me the importance of e ducation, and that regardless of what life may bring, your education is uniquely yours and something that ca n never be taken away. A special thanks to my grandparents, Chester and Ruth Baylor. Thei r life is a shining example of the values we hold so dear: love, family, hard work, perseverance, and selflessness. This dissertati on is also dedicated to my l oving husband, Dan Petrossi. He has been a constant source of support and understanding throughout my doctoral education, and is thrilled to be “Dr. and Mr. Petrossi.” I have been blessed with numerous positive influences in my life including friends and extended family; without their encouragement throughout the past five ye ars, none of this would be possible.

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ACKNOWLEDGEMENTS I would like to acknowledge my committee me mbers, Kathy Hyer, Cathy McEvoy, Larry Branch, Bob Kahn, and Brent Small. Without their support and expertise, my successful completion of the doctoral program would not be possible. I would also like to acknowledge Westport Advisors, Ltd., and pa rticularly Roger Landry, M.D. for the opportunity to participate in the development of the Masterpiece Living program. Westport Advisors has been generous in their funding of my doctoral studies, and for that I am very appreciative.

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i TABLE OF CONTENTS LIST OF TABLES.............................................................................................................iii LIST OF FIGURES............................................................................................................v ABSTRACT....................................................................................................................... vi INTRODUCTION..............................................................................................................1 Precursors of the Successful Aging Concept..................................................................1 Rowe and Kahn's Model of Successful Aging ..............................................................2 Component #1: Minimizing Risk of Disease and Disability ........................................8 Component #2: Maintaining High Physical and Cognitive Function ......................14 Component #3: Active Engagement with Life ...........................................................18 Criticisms of Successful Aging Theory........................................................................23 Stages of Motivational Readiness to Change...............................................................24 Summary.......................................................................................................................2 5 Hypotheses....................................................................................................................2 6 METHODS.......................................................................................................................3 0 Research Setting: CCRCs............................................................................................30 The Masterpiece Living Program.................................................................................32 Study Population...........................................................................................................35 Data Collection.............................................................................................................36 Instruments.................................................................................................................... 37 Outcome Measures.......................................................................................................38 Statistical Analyses.......................................................................................................46 Power.......................................................................................................................... ..50 RESULTS........................................................................................................................ .54 Baseline Sample Characteristics...................................................................................54 Mean Level Changes over Time on Outcome Measures..............................................59 Successful Aging Component #1: Reducing Risk of Disease and Disability..............63 Fruit and Vegetable Consumption .............................................................................64 Stage of Change for Fruit and Vegetable Consumption ............................................66 Exercise Participation ...............................................................................................68 Light Exercise .......................................................................................................69 Vigorous Exercise .................................................................................................71 Strength Training Exercise ...................................................................................73

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ii Stage of Change for Exercise Participation ..............................................................74 Body Mass Index ........................................................................................................74 Stage of Change for Weight Loss ...............................................................................77 Discussion of Successful Aging Component #1 .........................................................78 Successful Aging Component #2: Maintain ing High Physical and Cognitive Function ............................................................................................................................... .......87 Self-Rated Health .......................................................................................................87 Mobility ......................................................................................................................90 Discussion of Successful Aging Component #2 .........................................................92 Successful Aging Component #3: Active Engagement with Life...............................99 Formal Volunteerism ...............................................................................................100 Volunteerism Inside the CCRC ...........................................................................100 Volunteerism Outside the CCRC ........................................................................101 Informal Helping ......................................................................................................102 Helping Inside the CCRC ....................................................................................102 Helping Outside the CCRC .................................................................................104 Social Support ..........................................................................................................105 Giving Social Support .........................................................................................105 Receiving Social Support ....................................................................................106 Discussion of Successful Aging Component #3 .......................................................108 Relationships Among Changing Outcome Variables.................................................112 DISCUSSION.................................................................................................................115 Summary of Findings..................................................................................................115 Limitations..................................................................................................................11 9 Future Directions for Successful Aging Research......................................................126 APPENDICES................................................................................................................148 Appendix A: Univariate Models in Chart Form........................................................149 ABOUT THE AUTHOR...............…...….........…..................…......…..................End Page

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iii LIST OF TABLES Table 1 Outcomes for Successful Aging Component #1: Reducing the Risk of Disease and Disability 40 Table 2 Outcomes for Successful Aging Component #2: Maintaining High Physical and Cognitive Function 40 Table 3 Outcomes for Successful Aging Component #3: Active Engagement with Life 41 Table 4 Sample sizes needed for power = .80 in a single-group repeated measures design (adapted from Stevens, 2001) 52 Table 5 Number of Participants by Assessment Instrument and Data Collection Wave (2001 – 2003) 55 Table 6 Participation Rates and Reasons for Attrition (n=136) 56 Table 7 Baseline Characteristics of Masterpiece Living Participants 58 Table 8 Characteristics of Masterpiece Living Participants 58 Table 9 Fixed Effect Portion of Unconditional Growth Models 60 Table 10 Mean Level Change Over Time in Outcome Variables 62 Table 11 Unconditional Growth Models (Random Effects Only) for Component #1: Reducing Risk of Disease and Disability 64 Table 12 Multivariate Model for Fruit and Vegetable Consumption 66 Table 13 Multivariate Model for Fru it and Vegetable Stage of Change 68 Table 14 Multivariate Model for Light Exercise 71 Table 15 Multivariate Model for Vigorous Exercise 72 Table 16 Multivariate Model for Strength Training 73

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iv Table 17 Multivariate Model for Body Mass Index 76 Table 18 Multivariate Model for Stage of Change for Weight Loss 78 Table 19 Unconditional Growth Models (Random Effects Only) for Component #2: Maintaining High Physical and Cognitive Function 87 Table 20 Multivariate Model for Physical Health 89 Table 21 Multivariate Model for Mental Health 89 Table 22 Multivariate Model for Mobility 92 Table 23 Unconditional Growth Models (Random Effects Only) for Component #3: Active Engagement with Life 99 Table 24 Multivariate Model for Volunteering Inside 101 Table 25 Multivariate Model for Volunteering Outside 102 Table 26 Multivariate Model for Helping Inside 104 Table 27 Multivariate Model for Helping Outside 105 Table 28 Multivariate Model for Giving Social Support 106 Table 29 Multivariate Model for Receiving Social Support 107 Table 30 Bivariate Correlati ons for Difference Scores 113

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v LIST OF FIGURES Figure 1. Rowe and Kahn’s Model of Successful Aging 3 Figure 2. Structure of Succe ssful Aging Literature 7 Figure 3. Gaps in Causal Sequence 33 Figure 4 Assessment Schedule for Ma sterpiece Living Participants 36 Figure 5 Effect of Marital Status on Ba seline Fruit and Vegetable Consumption 65 Figure 6 Effect of Age on Light Exer cise Participation Over Time 70 Figure 7 Comparison of Masterpiece Da ta to National Norms for SF-8 93 Figure 8 National Trend of Decline in Functional Capacity 95

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vi Expanding the science of successful aging: Older adults living in continuing care retirement communities (CCRCs) Kathryn H. Petrossi ABSTRACT Rowe and Kahn’s theory of successful aging identifies thr ee main components of aging successfully: reducing th e risk of disease and disabi lity, maintainin g high cognitive and physical function, and engagement with life While there is compelling evidence that suggests the legitimacy of this concept in the 50 – 75 year old community dwelling population, three areas of expansion are n ecessary: 1.) progra mmatic research; 2.) extending the existing research samples to include older samples and those living in continuing care retirement communities ( CCRCs); and 3.) the in tegration of data collection and analysis to move beyond inve stigation of just one successful aging outcome to include elements of all three co mponents of successful aging. Longitudinal analysis utilizing hierarchical linear m odeling (HLM) was conducted on a convenience sample of 136 older adults (mean age = 80.8 ye ars at baseline) participating in a pilot community-wide successful aging program over a 26-month period. Results indicate the sample reported exercising frequently, ate reco mmended levels of fruits and vegetables, had healthy BMIs, had positive ratings of health, were highly involved in productive activities, and were satisfied with their ab ility to give and receive social support at baseline. High levels of mobility were measured in the sample. Participants maintained this picture of successful ag ing over time for the majority of outcome variables, though

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vii significant declines in self-reported health we re observed. Participants also reported improvements in their satisfact ion with receiving social s upport. Results support four major conclusions: 1.) The three criteria of successful aging identified by Rowe and Kahn (1997) were observed among older adults living in CCRCs who were enrolled in a successful aging program. 2.) Stability wa s observed on a number of the outcomes over 26 months in this convenience sa mple, which has implications for intervention/programmatic resear ch. Despite the traditiona l improvement-oriented focus of programmatic research, stab ility or maintenance of we ll-being over time should be viewed as a positive outcome in older age, particularly when compared to national data depicting trends of decline. 3.) The inte rdependence of current results support the notion that successful aging programming needs to include multi-disciplinary intervention strategies, as supported by the finding that modifiers of physical, social, and intellectual well-being include constructs from each of the components of successful aging. 4.) Participants of the current study were largel y in the precontemplat ion and contemplation stages of change. Readiness to change need s to be factored into the design of any successful aging program, as the Transtheoretic al Model could be a powerful tool for the identification of readiness to change and the development of appropriate and effective successful aging programming.

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1 INTRODUCTION Precursors of the Successful Aging Concept The first discussions of successful aging can be traced to 1948, when the World Health Organization defined health as not just the absence of disease, but also a fuller sense of wellbeing, including phys ical, mental, and social health. This is one of the first records of a slow and gradua l departure from a narrow, medical conceptualization of health, and from cl assic gerontological theories such as disengagement theory (Henry & Cummings, 1961) and activity theory (Havighurst, 1957). The first quantitative conceptualizations of successful aging can be seen in the work of Fries' (1980) compression of morb idity and Katz et al.'s (1983) active life expectancy. “Successful Aging” as a model was prompted by Rowe and Kahn in their 1987 Science article describing the need to distinguish usual and successful aging, then presented formally in 1997 with an article in The Gerontologist which was followed by the publication of a book in 1998 with strong appeal to researchers and older adults alike. Successful aging (regardless of version/author) promotes person-driven continued participation in roles and activities through older age that promote a long and healthy life, thus keeping the process of final decline and death in as short a period as possible.

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2 Rowe and Kahn's Model of Successful Aging While this author feels that Rowe and Kahn's (1997) theory of successful aging is the most comprehensive, it is one of seve ral. This dissertation will be limited to Rowe and Kahn's theory of successful agi ng however, as it was the conceptual basis for the successful aging program on which th is dissertation dataset was collected. Rowe and Kahn's conceptualization of successful aging represents a breakthrough in the way gerontologists a nd others looked at old age. Much of gerontology had been focused on the study of decline; distinguishing specifically between the "diseased" and the "normally" aging. While th is type of approach has much utility for studying the disease process, it also has th ree distinct limitations: (1) It has ignored the heterogeneity among older adults, partic ularly among those who are non-diseased; (2) The existence of only two categories for the health of older adults assumes that someone is either diseased, or healthy and without risk; (3) Whatever is not formally diseased is therefore normal and natural, and not in need of modification (Rowe & Kahn, 1987). To challenge these assumptions about the current study of aging, Rowe and Kahn suggested an additional category that could be used when examining the health of older adults. Specifically, they suggested further breaking down the "normal" aging group into: (1) Those who are not diseased, yet at high risk for developing future health conditions, and (2) those who are not dise ased and also at low risk for developing future health c onditions: those aging successfully. This

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3 distinction between the "usual" aging and the "successful" aging represents a new way to look at the heterogeneity of age in our society: what was once thought to be the effect of aging might now be the re sult of lifestyle choices (Rowe & Kahn, 1987). As a result of inves tigating this heterogeneity am ong older adults to make the distinction among pathological, usual, a nd successful, Rowe and Kahn developed a conceptual model that details three compone nts of successful aging: (1) Minimizing the risk of disease and disa bility (2) Maintaining physical and cognitive function and (3) Engagement with life. The body of knowledge on successful aging has grown rapidly over the past 18 years, but there are three main areas in which more knowledge is necessary, which this dissertation will address: 1. Expanding the age range of studies on successful aging 2. Integrative intervention research ag endas that incorporate all three components of successful aging 3. Research on residents of CCRCs. Figure 1. Rowe and Kahn’s Model of Successful Aging

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4 Perhaps the most easily identifiable a nd remediable area of expansion pertains to age: the vast majority of the successful aging research to date has been conducted on 50-75 year olds. At the other end of the spectrum, considerable attention has been paid to the characteristics of centenarians. As a scientific comm unity that recognizes the population explosion in the oldest-old, ther e is a need to begin to test the saliency of currently accepted research findings by extending the age range studied to include those over age 75 years. Research advances in this older age group present a true opportunity for advancement of the fiel d. This area for expansion suggests a multitude of potential analyses and publica tions that would answer the following: What do adults whose average age is 80 y ears look like in terms of variables known to be important to successful aging? Ho w does their performance on these variables change over the course of twenty-six months ? Do the predictors of successful aging currently seen in young-old adults rema in salient for this older age group? The second area where the research c ould be expanded is the need for integrated research programs. To date, the majority of successful aging research has taken place in isolated, tightly controlled, and narrowl y-focused interventions. The next step must be the extension and application of this research to additional types of environments that older adults live i n, and the development of programs and interventions that are as comprehensive a nd complex as the notion of successful aging itself and the older adults who hope to achieve it.

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5 The third area for development in the successful aging field is research on residents of continuing care retirement communities (CCRCs). Residents of CCRCs are largely overlooked in the gerontological research, despite the fact that this population is growing larger every da y, up from 700 CCRCs serving 100,000 older adults in 1986, to recent estimates of 2,200 CCRCs providing care to 613,000 residents (Cohen et al., 1998; American Asso ciation for Homes and Services for the Aged, 2003). The impetus for research on this group of older adults goes beyond their rising population. CCRCs share some common traits with living in the community: adults live in a fully-functional home or apartment setting, maintain their freedom to drive if they choose, and can come and go as they please, participating (or not) in any number of activities both inside and outside the CCRC. Residents of CCRCs are distinguishable from older adults living in seniors-only communities or those living in their own homes throughout the country in th eir access to lifestyle and health care related services, if needed. Residents of CCRCs also sta nd to gain much from health promotion efforts, and the CCRC environment may even serve as a valuable microcosm for the larger aging population. CCRCs provide efficient access to large numbers of older adults in a small physical location, thus streamlining some of the difficulties of part icipant recruitment, assessment, follow-up, and retention. The CCRC setting is also a supportive environment; a community-oriented culture offering a varying array of services, programs, and resources (AAHSA, 2003) that ca n be tailored to meet research needs.

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6 It is this supportiv e nature of the CCRC that id entifies it as a resource for addressing the second major area for development: the need for multi-dimensional, interdisciplinary research projects that ar e consistent with the whole-person approach to successful aging. Much of the succe ssful aging research to date has been traditional research which involves baseline testing on a narrow concept that is a component of successful aging, followed by a uni-disciplinary clinical intervention, and follow-up testing. While this is appr opriate for determining whether lower body strength increased, or depressive sympto ms decreased, it is not a comprehensive measurement of the complex concept of successful aging. The research has not expanded beyond traditional protocols to incl ude projects that address each of the three areas of successful aging together. Furthermore, while there has been much research on interventions to change specific behaviors, particular ly exercise (Dun et al., 1999; King, 2001; Lazowski et al., 1999; Messier et al., 2000; Wolfson et al., 1996), none have tackled multi-faceted behavi or changes such as those advocated by Rowe and Kahn’s successful aging theory. Figure 2 provides a pictorial repres entation of the rationale for the current analyses. The bolded text details the current research focus, indicating that there is much existing research on successful aging. The majority of this research is observational (non-interventio n) in nature and has been conducted on communitydwelling samples (typically age 50 – 75 years). The italic ized text indicates where research is lacking: succe ssful aging programs and interv entions, particularly those involving multiple components of the successf ul aging model, on adults living in

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7 CCRCs (who, according to industry reports (S anders, 1997), have an average age of 81.2 years). A search of the literature using PsycInfo re vealed 272 journal articles containing the term “successful aging,” but only 23 with the additional terms of “program” or “intervention.” Only thr ee of these articles examined multiple components of successful aging (vs. a singl e research goal such as sleep problems, depression, strength training, etc). Parker et al. (2002) describes a multi-church sponsored conference to educate older adults on successful topics related to physical, social, intellectual, and spiritual well-bei ng, though no measures of behavior change were collected. Parker et al. (2001) applies Rowe and Kahn’s (1997) model in military personnel and their families. The third multi-component article (Ramamurti, Jamuna, & Reddy, 1992) describes a small intervention study (n = 20) targeting older men. Successful Aging Literature (272 articles) Observational (Non-Intervention) Research (249 articles) Intervention or Programmatic Research (23 articles) SNF or ALF (0 articles) Community (3 articles) Figure 2 Structure of Successful Aging Literature Single SA component (21 articles) Multiple SA components ( 3 articles ) SNF, ALF, or CCRC (0 articles) Community (21 articles) Single SA component (249 articles) CCRC (0 articles) Multiple SA components ( 0 articles ) SNF, ALF, or CCRC (0 articles) Community (249 articles)

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8 This dissertation utilizes data collected as part of a multi-faceted successful aging program for older adults living in CCRCs with an average age of 80 years – similar to the italicized path of Figure 2. The l ack of a comparison group for the program prohibits its official labeli ng as an intervention and a ny formal evaluation of the program (attribution of any findings to the programs itself). Therefore, the context for the analysis and interpretation of the results will be more similar to the bolded path, as the sample is a convenience samp le of older adults living in a CCRC who signed up for a successful aging program. This dissertation examines a group of older adults livi ng in a CCRC who have participated in a successful aging lifestyle program, refe rred to as Masterpiece Living. The sample for the dissertation will confound the effects of older age and residence in a CCRC, but this expansion of the research li terature is worthwhile and critical to the implementation of successful aging principles. Before beginning analysis, it is importa nt to review the literature: the research conducted on one component of successful aging; on older adults aged 50 – 75 years; and on those living in the community (not in assisted living or skilled nursing). Component #1: Minimizing Risk of Disease and Disability Successful aging is somewhat hier archical (Rowe & Kahn, 1997), with the most important of the three compone nts being minimizing the risk of disease and disability, which includes reducing your risk factor s for developing new health conditions. Disability is not an inev itable part of aging, evid enced by its relatively low

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9 prevalence: between the ages of 75-84 years, 73% of adults report no disability, and after age 85 years, 40% remain functi onally independent (Rowe & Kahn, 1998). Additionally, scientific research indicates that only 30-40% of differences in functioning with age are determined by gene tics (Rowe & Kahn, 1997). They cite the Swedish Twin Study's use of heritability indexes to determine the proportion of variance attributable to genetics for the most common risk factors for disease: 6670% of body mass index (BMI), 28-78% of cholesterol, and 34-44% of blood pressure values can be attri buted to genetic factors. While these percentage s are not negligible, they point out the dramatic degree to which health as people age is determined by behavioral and lifestyle choices. Furthermore, there is evidence to suggest that the relative importance of genetics varies across the lifespan, usually decreas ing in importance with age (Rowe & Kahn, 1997). This means the effect of lifestyle choices that promote good health such as varied and balanced nutriti on (particularly lower in sa turated fat), exercise, not smoking, and preventive health screenings become increasingly apparent as people age. There are seven habits of healthy pe ople, necessary to improve health and avoid disease and disability: regular exercise weight management, proper nutrition, not smoking, adequate rest/sleep, stress manage ment, and preventive health screenings (Belloc & Breslow, 1972; Peel, Roderick, & Bartlett, 2005). Th is dissertation will address the first three habits.

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10 Exercise Numerous research studies indicate that exercise is perhaps the most important behavior a person can engage in to promot e health by preventing the development of chronic conditions and their associated ri sk factors (obesity, decreased muscle strength, cardiovascular nonfitness, poor balance, etc.) and lower the risk of mortality. Low levels of fitness are a predictor of dependence (Paterson, Govindasamy, Vidmar, Cunningham, & Koval, 2004) and can double the mortality risk (Blair, Kampert, Kohl, Barlow, M acers, Paffenbarger, & Gibbons, 1996), while high levels of fitness can pr otect against the impact of other risk factors such as smoking, high blood pressure, and poor percepti on of health (Blair et al 1996; Wei, Kampert, Barlow, Nichman, Gibbons, Paffe nbarger Jr, & Blair, 1999). These findings suggest that it is never too late to start exercising, regardless of current health condition: even recent changes in physical activity can show positive health benefits (Gregg, Cauley, Stone, Thompson, Bauer, Cummings, & Ensrud, 2003). Overall, the exercise message is pos itive. There is no age by exercise interaction, indicating that the inverse relationship between exerci se and mortality is not dependent upon age: older people can demons trate the benefits of exercise just like younger people can (Kushi, Fee, Folsom, Mink, Anderson, & Sellers, 1997). The benefits of exercising can be demonstrated through participation as infrequently as once per week doing moderate and strenuous le vels of exercise (K ushi et al., 1997). Receiving benefits from exercising, even if infrequently, is important for those with chronic conditions that might prevent them from frequent participation in vigorous

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11 activity. The evidence also suggests that exercise goes beyond k eeping the healthy in their current state. There is an abundance of research that indicates that the effects of exercise can be manifested in non-healthy populations as well (B inder, Schechtman, Ehsani, Steger-May, Brown, Sinacore, Yarasheski, & Holloszy, 2002; Messier, Royers, Craven, O'Toole, Burns, & Ettinger, 2000). Body Mass Index Maintaining a healthy weight is another health prom otion behavior, one that is closely tied to exercise. In fact, there has been a recent focus on the importance of “fitness” over “fatness.” Nonetheless, controlling wei ght or Body Mass Index (BMI) is a worthy outcome for those attempting to age successfully. In both cross-sectional and longitudinal studies, high BMI among older adults has been associated with a greater likelihood of declining perceived h ealth, a lower likelihood of improvement in mobility, a higher likelihood of mobility decl ine, and greater likelihood of functional limitation (Damush, Stump, & Clark, 2002; Kr ahnstoever-Davison, Ford, Cogswell, & Dietz 2002, Rahrig Jenkins, 2004; Zamboni Turxcato, Santana, Maggi, Harris, Pietrobelli, Heymsfield, Micciollo, & Bosello, 1999). For children and younger adults, the primary goal is to prevent obes ity or to determine avenues for lowering BMI. While this is still the case for obese older adults, there is an additional area of concern: a declining BMI is often indicative of an underl ying disease process. In longitudinal studies, decreases in BMI are th e predominant trend in older adults, and are associated with increasing chronic h ealth conditions, func tional disability, and

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12 higher mortality risk. However, those who exhibited slower decreases in BMI also showed slower increases in chronic c onditions and disability (Kahng, Dunkle & Jackson, 2004; Reynolds, Fredman, Lange nberg, & Magaziner, 1999). These findings indicate maintaining a healthy BMI (nei ther too high or t oo low) is important for multiple measures of health and well -being. More longitudinal research is necessary to examine the impact of changes in BMI on health, as well as the reverse (the impact of changing health on BMI). Nutrition Nutrition is another important co mponent of successful aging, although it has received less consideration in the gerontol ogical literature. Nutr ition influences the development of disease (e.g. cancer, cardiov ascular disease, stroke; Hyson, 2002). For example, consuming three or more se rvings of vegetables per day has been associated with a 40% reduction in risk for Non-Hodgkins Lymphoma (Kelemen, 2004). More broadly, it is estimated that ne arly 1/3 of cancers can be attributed to dietary intake (Kelemen, 2004). Kell er, Ostbye, and Goy (2004) found an independent effect of nutriti onal risk on quality of life: those at high nu tritional risk had consistently lower satisfaction with life over time, compared to their low and moderate risk counterparts, and reported an average of 31 fewer “good health days” per year (or approximately 2.5 fewer good health days per month). Despite the demonstrated importance of eating a balanced a nd varied diet, those aged 71 years and older clearly need gui dance achieving proper nutrition (Foote,

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13 Giuliano & Harris, 2000). While almost 75% of older adults ate adequate meats/proteins, only 12% consumed the reco mmended daily servings of grains, only 40-50% ate enough fruits and vegetables, a nd less than 4% ate enough dairy (Foote, Giuliano & Harris, 2000). Studies demons trate that interventions designed to improve nutrition in older adults can be su ccessful, but may be influenced by lack of social support, distress, worry, type A personality, and competing behaviors (Danhauer et al, 2004; Sorensen, Stoddard & Macario, 1998). It is possible that nutrition influences quality of life through both physiologi cal (nutrient absorption) and psychosocial (social suppor t, self-efficacy) mechanisms, making it an issue that deserves more attention in social science research agendas. The findings of Fraser and Shav lik (2001) summarize the impact of healthy behaviors on physical health and longevity. They found that those who are physically active, frequently consume nuts, are vege tarian, or have medium BMI show an increase in life expectancy of 1.5-2.5 years. The gap in life expectancy extension widens as you compound/multiply the positive health behaviors. These results are encouraging because life expectancy advantag es were demonstrated in medium risk categories for most of the health behaviors m easured, not just the lo w risk categories. Older people can be relieved by the notion that they do not have to be perfect in all areas simultaneously to experience extended life expectancy. There is also evidence that psychol ogical variables such as self-efficacy (one’s self-confidence, or belief in their ability to complete a task; often involves elements of control) and positive affect can help reduce disease, disability, and mortality risk.

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14 High self-efficacy protects against the perceptio n of disability (self-rated disability), independent of actual physical incapaciti es (performance-based measures) (Seeman, Unger, McAvay, & Medes de Leon, 1999) a nd decreases mortality risk (Ostir, Markides, Black, & Goodwin, 2000). These findings are important because selfefficacy is generally seen as a modifiable variable. Increasing self-efficacy may be a key path to increasing the quality of life of older adults by expanding the array of functions they believe they can complete, and will subsequently engage in. These findings may indicate that t hose with high positiv e affect have an outlook on life that promotes healthy living, or maybe those with high positive affect have a strong social network, which has been shown to produce positive health benefits. The massive body of literature (o nly briefly reviewed here) suggests three conclusions: (1) genetic factors alone do not account for risk in ol der age, lifestyle variables also play an important role in de termining risk for dise ase and disability; (2) as people age, the relative c ontribution of genetics decrease s and the role of lifestyle variables increases; (3) the risk factors that make up the "usual" aging segment of the population can be modified to produce pos itive health outc omes (Rowe & Kahn, 1997). Component #2: Maintaining High Physical and Cognitive Function The second component of Rowe and Kahn's (1997) model of successful aging is maintaining high physical and cognitive f unction, which can be viewed as one’s ability to do the tasks that keep them inde pendent. As in the case of disease and

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15 disability, physical and cognitive impair ments are not the norm for aging: the prevalence of ADL difficulty is estimated to be 20% in those 65 and older, and only 35% in those aged 70 years and older (B lack & Rush, 2002). Cognitive impairment is estimated at 4% in those 65-69 years and 36% in those 85 and older (Black & Rush, 2002). While these prevalence rates do indicate an increase in impairment with age, the percentages who are impaired are still a minority. Maintaining physical function pertains to the maintenance of strength, balance, and other measures of performance that al low older adults to carry out the tasks involved in their daily, indepe ndent lives. Age is not th e only explanatory variable for functional decline in ol der age, disease and lifestyle choices also play an important role. There is much research on the predictors of functional illness. Predictors of declining physical function include: BMI (too low or too high), trouble walking, poor vision, low income (less th an 10K annually), age (being older), high blood pressure, depression, dementia, and low baseline cognitive performance (Ferraro & Booth, 1999; Rowe & Ka hn, 1997; Vaillant & Mukamal, 2001). Predictors of maintaining high physical function include participation in moderate and/or strenuous leisure activity, and em otional support from friends and family (Rowe & Kahn, 1997). Maintaining physical function th rough physical fitness also has important implications for disease risk. Rogers et al. (1990) found that p hysical activity after retirement was associated with sustained cer ebral blood flow (similar to that of when the individual is working for pay), comp ared to those who retired and became

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16 sedentary (who experienced lessened flow). Sustained cerebral blood flow lowers the risk of stroke and Alzheimer's disease both of which would impair physical and cognitive function over time (Rogers et al. (1990). So how do older adults maintain thei r physical functioning? The vast majority of intervention research indicates positive outco mes as a result of participation in group or individual exercise sessions designed to increase stre ngth, cardiovascu lar function, and balance (Conn et al., 2002; Wolfson et al ., 1996). It is important to make sure that physical activity interven tions are designed to challe nge the physical capabilities of older adults, however, and are not focu sed merely on range of motion exercises (Lazowski et al., 1999). Ho rtobagyi et al. (2003) suggests the mechanism by which physical activity interventions help older adults mainta in physical function: older adults were found to be performing their ADLs at a higher level of effort in reference to their maximal capacity than are younger a dults (presumably due to ageand mostly lifestyle-related declines in strength). Ther efore, interventions th at help to restore physical strength, balance, and endurance bri ng the level of effort exerted back down to a manageable/negligible level. Physical activity is not the only factor that prom otes maintenance of physical function. Psychological or personality characte ristics may also play a role. Ostir et al. (2000) found that high positive affect scor es were associated with decreased risk of developing ADL impairment at follow-up. Weak self-efficacy at baseline has also been shown to predict declines in self -rated function in men, regardless of their actual/objective functional st atus changes (Seeman, 1999). There is evidence

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17 however that the impact of psychosocial va riables on physical functioning is diseasespecific (Seeman & Chen, 2002) Maintenance of function is not just limited to the physical do main; it also includes maintenance of high cognitive function. As mentioned before, the prevalence of cognitive impairments increases with age, but the percentages remain a minority, particularly for Alzheimer’s disease, a major area of worry for older adults. Speed of information processing and explicit memory show declines with age, but other cognitive skills, such as the ability to use words and numbers accurately, to see relationships between shapes, and to draw appropriate conc lusions from sets of facts are maintained into extreme old age (Rowe & Kahn, 1998). Also, older adults maintain their ability to recognize and, to a lesser extent, recall information previously seen or heard. Despite the fact that some feel Rowe and Kahn (1998) interpret this research in an overly optimistic fashion, a nd some research casts doubts on elders’ ability to increas e cognitive performance in all spheres (Hultsch et al.,1999; Rebok & Plude, 2001), their intent is to assuage the fears that many elders have about losing their cognitive capacities. Rowe and Kahn focus on the positive (discussing the research in terms of how to prevent or reverse incremental declines not painting a picture of total losses ). Nonetheless, research suggests that older adults are right to fear cognitive loss, as it is a ssociated with loss of independence, lowered quality of life, higher health care utilizat ion, risk of institutionalization, and higher mortality (Black & Rush, 2002).

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18 Research suggests that predictors of maintained c ognitive function (across several domains) include education as the strongest predictor, st renuous activity, being white, high peak expiratory flow rate, fewer chronic conditions, and high self-efficacy (Rowe & Kahn, 1997; Whitfield et al., 2000) Predictors of increased cognitive function include physical activity (Rogers et al, 1990). Higher levels of cognitive activity have been associated with a 33% lower risk of Alzheimer's disease and slower/lower rates of cognitive decline ove r time (Wilson et al., 2002). The overlap between the reviewed predic tors of physical and cogni tive function support the findings of Black and Rush (2002), whic h indicate that the two domains are intricately intertwined. Their results indicate that baseline cognitive status predicts functional decline and baseline functional status predicts cognitive decline. These findings indicate both modifiable and nonmodifiable risk factors for cognitive decline, which suggests that cognitive declines are not in evitable with age, and in some cases may be preventable. Component #3: Active Engagement with Life Rowe and Kahn's (1997) theory of successful aging represents a departure from disengagement theory and the activity theory of aging. Th ey suggest that there are two components of active engagement with life: staying connect ed with others and participating in meaningful a nd productive activities. The notion of connectedness with others is based on the premise that having social support and networks has positive impacts on health, while losing social support has negative impacts on health. Seeman et al. (1995) found that having high emotional

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19 support is a predictor of increased physical performance, especially among those with low instrumental support. Rowe and Ka hn's synopsis of the MacArthur Foundation studies indicates that marital status (pre sumably a source of emotional support) was protective against reductions in productive activity, while no significant associations were found for instrumental support. Perhap s it is the case that emotional forms of social support allow you to exercise the appropriate and desired amounts of control over daily tasks, whereas instrumental s upport may provide too much or too little assistance, thus taking away control or producing frustratio n as the effect of social support, not helpful and healthful outcomes. Not having, or losing social support can be detrimental to health. The Alameda County Study (as cited by Rowe & Kahn, 1987) found that men and women with low social network index scores were at a 2.3 2.8 higher risk of death after nine years, compared to those with high social suppor t scores. Bereavement, which can be conceptualized as the loss of a major source of social support, has also been associated with higher mortality for the surviving spouse (Rowe & Kahn, 1987). They also suggest that the re location process, such as m oving from the community to a long term care setting (which may involve dissolution of not only family networks, but also neighborhood and leisure networks) is also associated w ith higher mortality, although mediated by preparation for the move and the level of control the elder has over the move. Moen et al (2002) supports these findings, but indi cates that changes in social support levels through reloca tion may be dependent upon the type and number of roles w ith which you identify.

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20 With this evidence in mind, how shoul d programs to help older adults increase their social networks and the resultant positive health outc omes be structured? What does it mean to "increase" social networks does this imply quantity or quality? Does it vary by the person? The research literature suggests that these interventions should modify role perceptions as well as actual role-related be haviors (Moen et al., 2000). Furthermore, contact with netw ork members and satisfaction are not necessarily connected (Lansford et al, 1998). It is possible that the importance of the quality of the social ties may be of the same importance as the overall number of social network members. Further support for this notion of quality comes from Jang (2002), which suggests that it is not th e actual amount of social support but the subjective satisfaction with that support that mediates the relationship between disability and negativ e health outcomes such as depr ession. This evidence suggests that social network interventions need to try to match the su pport needs (objective and perceived) and the kinds of support need ed (instrumental and/or emotional) to produce the strongest benefits for health and well-being. Engagement with life is more than just staying connected with others. Having a strong social network connects older adults to other indivi duals, and to larger social entities such as the job market, opportunities to volunteer, and their extended families (Jackson, Antonucci, & Gibson, 1990, as re viewed by Glass et al., 1995) which makes it easier to participate in meaningful and productive activities. Meaningful activities are self-explanatory: activitie s that are fulfilling and rewarding to the individual participating in them. Rowe and Kahn (1997) provide a more structured

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21 definition of productive activities, to incl ude anything that produ ces goods or services of value. In earlyand mid-life, people are likely to think of their careers (paid employment for most) as their primary pr oductive activity. As people age and fewer people are working, it is im portant to re-conceptualize this concept as something more than paid activities, to include hous ework, childcare, providing assistance with personal care and transportation, and volunteer work, among other things. The current body of research seems to divide meaningful and productive activities into two separate domains of study: pa rticipation in pers onal care and leisure activities, and voluntee rism. Horgas et al. (1998) s ought to describe in detail how older adults spend their day in terms of frequency, duration, and variety of activities. They found that older adults most frequently did activities related to personal care, but these activities did not take the most time in their day: TV watching and resting did. Successful aging recognizes the importa nce of personal care activities: without competence on these items, one cannot pursu e other levels of meaningful and productive activities. However, successful aging seeks to promote a level of health and function that moves beyond a focus on oblig atory activities, where one is free to pursue discretionary activities. Strain et al. (2002) suggests age and change in functional status (not baseline functional status) are predictors of leisure activity participation. Findings that changes in activity are the resu lt of changes in functional status is further evidence for the in teraction among the three components of successful aging: strength in one area prom otes strength in the other areas, and vice versa.

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22 Volunteering is a common source of meaningful and productive activities for the old and young alike. The Metropolitan Life Foundation and Independent Sector Research Report (2000) indicates that 48% of those age 55 years and older volunteered at least once in 1998, and th at number remains high (43%) when considering only those aged 75 years and olde r. Older volunteers gave an average of 3.1 hours per week, totaling over 1.1 billi on hours annually. Musick, Herzog, and House (1999) report slightly lower voluntee rism rates at 35%, while Van Willigan (2000) reports 50% using ACL data. Of those who did not volunteer, 43% cited health-related reasons, while 18% cited age as the reason they chose not to volunteer (Met Life, 2000). Rahrig Jenkins (2002) examined partic ipation in three types of activities (passive, active, and outside community activities) in CCRCs and fo und that active activity participation was associated with good h ealth on 7 of the 8 domains of the SF-36 (Ware & Sherbourne, 1992), while inactiv e activities were not significantly associated with good health on any of the domains of the SF-36. Volunteerism does more than just fill discretionary time or enha nce social networks it is also associated with health-related quality of life (although as in the case of intellectually stimulating activities, more longitudinal research u tilizing randomization is necessary to determine more solidly the causal directi on of the effect). Van Willigan's (2000) research is promising however, finding th at although functional impairment was inversely related to volunteer commitment psychological and physical well-being did not predict the act of volunteering itself. Glass et al. (1995) determined that among

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23 high functioning older adults, some increas ed their productive activity, while others decreased over time (again demonstrati ng the heterogeneity among older adults). Predictors of improvement (higher leve ls) of productive ac tivity included being African American, having high mastery, and hi gh life satisfaction. Two of the three are modifiable. Predictors of decline in productive activity in cluded hospitalization and a new occurrence of stroke, while be ing older, married, having a previous disability, and increasing mastery were pr otective against declines in productivity. Again, according to successful aging theory, many of these predictors are modifiable. Other research suggests that there ma y be limits to the benefits of volunteerism, however. Musick et al. (1999) found that volunteering for a limited number of hours for one organization was protective agai nst mortality, and Van Willigan (2000) discovered that the benefits of volunteer ing on perceived health diminish after approximately 100-140 hours per year, but there is no upper limit to the positive relationship between volunteer hours and life satisfaction. Criticisms of Successful Aging Theory Despite the compelling evidence presen ted about the heterogene ity of older adults and the impact of lifestyle and behavioral variables in determining health outcomes, there are critics of successful aging theor y. The criticisms can be summarized into three main issues: (1) Prevalence and elig ibility disapproval: what the criteria for successful aging are, who qualifies as a pe rson who is successfully aging, and the impact of being labeled as aging successfully (or not aging successfully) (Vaillant & Mukamal, 2001; Binstock, 2002; Bootsma-van der Weil, 2002; Strawbridge,

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24 Wallhagen, & Cohen, 2002); (2) Doubts a bout the underlying assumptions of the theory from a biological and spiritual perspe ctive: Is it possible to make it to old age disease-free and without substa ntial physical deterioration? What role does genetic research play in successful aging? Does be lieving in successful aging facilitate denial of the humanity and finality of the last stages of life? (Masoro, 2001; Moody, 2002); and (3) Concern that the theory is overly focused on individuals, to the neglect of social/structural influences (Riley, 1998). The program (Masterpiece Living) from which the dissertation datase t originates hopes to addre ss criticism #1 by broadening the available body of research on successful aging, thus identifying at least a larger age range of potential successful agers. Masterpiece Living’s focus not only on the individual, but also on the culture of the CCRC, is one way to address criticism #3. Stages of Motivational Readiness to Change While Rowe and Kahn’s model of su ccessful aging provides the theoretical basis for this dissertation, the Stages of Motiva tional Readiness to Ch ange, part of the Transtheoretical Model (Prochaska & DiClemente, 1986), provides an additional framework for studying the be havior change needed for successful aging. This framework is incorporated into the current analyses as both an outcome and as a potential moderator of change over time in successful aging behaviors. Individuals can be in one of five stages relative to making a specific behavior change: precontemplation (not doing target behavi or and not intending to make changes), contemplation (considering change within next six months), preparation (having a plan or making small changes within the ne xt 30 days), action (active engagement in

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25 the new behavior, for less than 6 months), and maintenance (sustained action for more than six months). By knowing an indi vidual’s stage, one can better determine intervention appropriateness (what interventions they are likely to participate in and benefit from). People in th e first two stages (precontem plation and contemplation), are best suited for cognitively-based interven tions, while people in the latter three stages (preparation, action, and maintenance) are thought to have better success with behaviorally-based interventions. This appr oach also allows a more precise measure of intervention success than the dichotomous definition of did they change the behavior or not. It recognizes smalle r successes and attempts to influence the precursors to change, such as change s in knowledge about the behavior, and recognizing barriers to change. The stag es model was applied first to smoking cessation (Prochaska & DiClemente, 1986) and since been applied to diet, sun exposure, weight loss, alcohol abuse, screening mammography, reduction of stroke and osteoporosis risk, arthritis self-manag ement, exercise, and case management in older adults (Bock, Marcus, Rossi, & Redding, 1998; Burbank, Reibe, Padula, & Nigg, 2002; Enguidanos, 2001; Godin, Lamber t, Owen, Nolin, & Prud’homme, 2004; Keefe et al., 2000; LaForger et al, 1998; Lee, 1993; Miller & Spilker, 2003; Molaison, 2002; Nigg et al., 1999; Popa, 2005; Prochaska & Velicer, 1997; Resnick & Nigg, 2003; Zimmerman, Olsen, & Bosworth, 2000). Summary In summary, Rowe and Kahn's successful aging theory represents an advance in the gerontological vision of aging, emphasi zing the need to go beyond distinguishing

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26 between pathological and normal aging, to also distinguish between usual and successful aging. The qualities of succe ssful aging include avoiding disease and disability, maintaining high cognitive a nd physical function, and active engagement with life. The vast major ity of the research reviewed above was conducted on adults aged 50-75 years living in the community. This dissertation seeks to expand the literature by determining if the above factor s known to be important to successful aging remain salient for a group of older ad ults living in CCRCs who were enrolled in a successful aging program. Hypotheses Based on the successful aging re search reviewed previously, the following hypotheses are proposed for a group of ol der adults living in a CCRC who were enrolled in a successful aging program. For reducing the risk of disease and disability, there are three outcomes or dependent variables of interest: 1) fru it and vegetable consumption, 2) exercise participation, and 3) body ma ss index (BMI). For fruit and vegetable consumption, it is hypothesized that consumption will increase over time among individuals enrolled in a successful aging program and those with lower baseline fruit and vegetable consumption, higher self-rated health and lif e satisfaction, more frequent exercisers, and those in the preparation or action stage of change will be more likely to increase their fruit and vegetable consumption (Be lloc & Breslow, 1972; Danhauer et al, 2004;

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27 Foote, Giuliano & Harris, 2000; Keller, Ostbye, & Goy, 2004; Peel, Roderick, & Bartlett, 2005; Rowe & Kahn, 1998; Sore nsen, Stoddard & Macario, 1998). For exercise participation, it is hy pothesized that exercise frequency will increase among participants of a successful aging progr am. It is anticipated that those who exercise but do so infrequen tly, have higher baseline hea lth, higher mobility scores, fewer chronic conditions, higher health -related self-efficacy, and are in the preparation or action stage of change will be more likely to increase their physical activity participation (Bello c & Breslow, 1972; Kushi et al., 1997; Peel, Roderick, & Bartlett, 2005; Rowe & Kahn, 1998). For BMI, it is hypothesized that BMI will not change significantly over time in a group of people enrolled in a successful aging program. Those who have normal or slightly high BMIs, consume suggested servin gs of fruits and ve getables, have higher self-rated health, part icipate regularly in physical activity, have higher mobility scores, higher health-related self-efficacy, a nd are in the preparation or action stage of change will be more likely to mainta in BMI over time (Belloc & Breslow, 1972; Damush, Stump, & Clark, 2002; Ferraro & Booth, 1999; Kahng, Dunkle & Jackson, 2004; Krahnstoever-Davison et al, 2002; P eel, Roderick, & Bartlett, 2005; Rahrig Jenkins, 2004; Reynolds, Fredman, Langenbe rg, & Magaziner, 1999; Rowe & Kahn, 1998; Zamboni et al, 1999). The second set of outcome variables pertain to maintainin g high physical function and include: 1) functional stat us, and 2) self-reported hea lth. For functional status, it is hypothesized that mobility review scores will remain stable over time among

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28 participants of a successful aging prog ram. For self-reported health, it is hypothesized that SF-8 scores will remain st able over time. The predictors of both outcomes are quite similar, and it is anticip ated that those with higher baseline health and mobility, fewer significant life events, normal BMI, fewer chronic conditions, more frequent exercise participation, higher levels of social support and self-efficacy, better diet, and those who drive will be more likely to maintain over time. (Belloc & Breslow, 1972; Krahnstoever-Davison et al, 2002; Paterson et al., 2004; Rahrig Jenkins, 2002; Rowe & Kahn, 1998; Seeman et al, 1995; Seeman, 1999; Strain et al., 2002; Vaillant & Mukamal, 2001). The third set of outcome variables c oncentrate on active engagement with life, and include: 1) participation in productive activities, and 2) social connectedness. For participation in productive activ ities, it is hypothesized that participation will either improve or remain stable among participants of a successful aging program. It is anticipated that those with higher self-rated health, fewer significant life events, healthy BMI, fewer chronic conditions, nor mal blood pressure, regular exercisers, those who participate in many activities, ha ve higher social support and self-efficacy, and consume healthy amounts of fruits and ve getables will be more likely to maintain their productive activities (Glass et al., 1995; Metropolitan Life Foundation & Independent Sector Research Report, 2000; Musick, Herzog, & House, 1999; Rahrig Jenkins, 2002; Rowe & Kahn, 1998; Van Willig an, 2000). For social connectedness, it is hypothesized that feelings of conn ectedness will increase in a group of people enrolled in a successful aging program. It is anticipated that those who have not

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29 experienced a significant life event or hospitalization, those who are younger, married, have higher self-rated-health and mobility, have higher self-efficacy, and those who drive will be more likely to in crease their social connectedness (Jang, 2002; Rowe & Kahn, 1998; Sorens en, Stoddard & Macario, 1998).

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30 METHODS Research Setting: CCRCs Residents of continuing care re tirement communities (CCRCs) are largely overlooked in the gerontological research, de spite the fact that this population is growing larger every day. The numbe r of CCRCs has grown from 700 CCRCs serving 100,000 older adults in 1986, to recent estima tes of 2,200 CCRCs providing care to 613,000 residents (Cohen et al., 1998; American Association for Homes and Services for the Aged, 2003). A CCRC is a type of long-term care that offers housing, residential services, and limited health care (a changing need over ti me) to its residents (AAHA definition, as cited in Spears, 1992). CCRCs meet the changing needs of their residents through multiple levels of care: independent living un its (usually in the form of villas, condos, etc), and higher levels of care such as assisted living, skilled nursing, and possibly dementia care. Forty-three percent of CCRCs are “lifecare communities” (also known as an extensive contract), which gua rantee to provide all necessary nursing care for little or no increase in the mont hly payment (Sanders, 1997). Others offer a modified contract, whereby a specific amount of services is o ffered, after which the resident pays the full price for additional se rvices. The remaining option is a fee-forservice contract that guarante es access to nursing care, but with no discounted rate for service delivery (Sanders, 1997; Spears 1992). Access to care in a CCRC usually

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31 involves paying a large entrance fee, and a monthly maintenance/rental fee for the unit occupied. Communities vary in the am ount of the initial entrance fee that is returned to the estate upon death or otherwise leavi ng the community. The data for the present analyses were collected at two CCRCs in Florida: Freedom Village in Bradenton, and Universi ty Village in Tamp a. The communities are similar: both have approximately 500 independent living units, just over 100 assisted living rooms, 120 skilled nursing beds, and provide modified lifecare to their residents. Entrance fees average $15 9,000, with monthly fees of $1,500. Fees may vary according to the percentage of the entr ance fee returned to the estate upon death. The two CCRCs participating in the current research provide options for either 40% or 90% to be returned, and the decision betw een the two return levels is made by the resident at the time the lifecare contract is executed. Studying residents of CCRCs can offe r additional insights into how older adults could gain from health promotion efforts. Similar to the customary practice of animal models preceding human experimentati on to understand complex biological and behavioral processes, research conducted in the CCRC environment may serve as a precursor to larger scale research initiatives. As such, research in this setting may be valuable microcosm for the larger agi ng population. CCRC residents share some common traits with th eir counterparts living in the larg er community: most live in a fully-functional home or apartment setting, main tain their freedom to drive if they choose, and can come and go as they please, participating (or not ) in any number of activities both inside and outside the CCRC.

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32 CCRCs provide research ers with efficient access to la rge numbers of older adults in a defined physical location, thus streamlin ing some of the difficulties of participant recruitment, assessment, follow-up, and re tention. The CCRC setting is also a supportive environment; a comm unity-oriented culture offering a varying array of services, programs, and resources (AAHSA, 2003) that can be tailored to meet research needs. These characteristics may encourage the piloting of intervention studies otherwise thought to be unfeasible in the larger community of older adults. The Masterpiece Living Program While the current research project is not a program evaluation of the successful aging programming (known as Masterpiec e Living) at these CCRCs, a brief description of the program will inform the setting in which the data were collected and provide a context for in terpretation of findings (though the program cannot define causation because there is no randomized design). Masterpiece Living is based on the pr inciples of Rowe and Kahn's Successful Aging (Random House, 1998), and seeks to ac hieve two main goals: change the culture of CCRCs, and encourag e individual health /lifestyle behavior changes among residents living in those CCR Cs. Masterpiece Living is an example of a successful aging program that attempts to bridge the gap between scientific knowledge and public knowledge, as well as the gap be tween public knowledge and individual behavior change (Figure 3).

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33 On an individual level, Masterpi ece Living seeks to promote successful aging through education, assessment, feedback, a nd programs. Education begins with presentations on successful aging in gene ral, and is continued throughout the study through a variety of mechanisms utilizi ng internal and external expertise. Masterpiece Living also ra ises awareness of successful aging through its comprehensive assessment of resident pa rticipants with the Lifestyle Review, Mobility Review, and the Mayo Clinic Health Risk Assessment. Additional details of the three assessment instruments are descri bed later in this Methods section. Participants then engage in sma ll discussion groups (approximately 15 people), where they are given customized feedback to facilitate behavior change. Their responses on the assessment tools are revi ewed and used to cr eate feedback that identifies strengths and areas for improve ment. Although not a planned part of the program, the discussion groups have served as an opportunity for the CCRC staff to review current programming and to get re sidents involved in creating and further defining program offerings that promote succ essful aging. Consistent with the notion of successful aging, many of these new progr ams are requested, organized, and run by residents for residents. Masterpiece Living also seek s to change individual Basic Research Scientific Knowledge Public Knowledge Changes in Individual Behavior Health and Well-being Successful Aging Programming Like Masterpiece Living Figure 3 Gaps in Causal Sequence

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34 behaviors/lifestyle choices by providing pr ogramming in each of the four areas of health, to help individuals reach their successful aging goals. Masterpiece Living goes beyond just i ndividual behavior change. It changes the environment as well, in recognition that it is difficult to change one's behavior without a support system that values the prin ciples of successful aging. The goal is to implement proactive programs and policie s that maintain or improve resident functioning across multiple dimensions, not to use lifecare contract s to react to the increasing medical needs of residents. A ll direct-contact and administrative staff members are trained on the concept of successf ul aging and given the tools they need to facilitate the culture change and become an advocate for individuals participating in Masterpiece Living. Changes to the physical environment include new seniorfriendly exercise rooms and equipment, he althy meal offerings, and a variety of programs targeted to promote the components of successful aging. The Masterpiece Living program is not, nor is it intended to be, a tightly controlled research intervention with iden tical protocols across communities. If researchers manipulate only one variable at a time, the gain in knowledge is limited to this one area under isolated conditions, and the applicability of findings to the real world may be restricted. Instead, Mast erpiece Living is a CCRC-wide communitybased initiative, tailored to meet the need s of each participating community. This limits the current study to observational re search that monito rs the self-reported performance of a group of older adults part icipating in a succe ssful aging program. This design also prohibits any inference of causality, and its li mited participation

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35 structure inhibits generalization for resear ch purposes. Nonetheless, the successful aging program itself is beneficial for esta blishing the feasibility of such a widereaching initiative, even if the current re search cannot address program evaluation or intervention research issues. Study Population Subjects participated in the Mast erpiece Living pilot program for approximately 26-months beginning in 2001. Three CCRCs init ially participated in the pilot study: University Village in Tampa, FL; Freedom Village in Bradenton, FL; and Lambeth House in New Orleans, LA. Lambeth House di d not wish to continue its participation in the program and as a result their data will be excluded from the analyses. Study participants were a self-selec ted convenience sample of male and female residents in the independent living level of the CCRC, with an average age of 80.8 years (range 63 – 99 years). Participants were voluntarily enro lled in a successful aging program conducted at their CCRCs. Th ey were recruited primarily through the resident board/council and their spouses, and then through volunteers after a community-wide presentation on successf ul aging. Roughly one-third of the volunteers were part of the re sident council; the remaining two-thirds were spouses of the resident board members and other in terested residents. To reduce the administrative burden on local staff, part icipants were enrolled in two cohorts (hereafter “cohort 1” and “cohort 2”), approxi mately 6-8 months apart. There were more volunteers than could be included in th e pilot test. The names of the additional volunteers were noted by local staff and re-approached fo r enrollment approximately

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36 one year later, when formal enrollment in Masterpiece Living was opened to the entire community. This author is unaware wh at percentage of thos e interested parties enrolled at a later date. Data Collection The Masterpiece dataset consists of data collected at four points over an approximately two year period: baselin e (August October 2001 for cohort 1, and June – July 2002 for cohort 2), with followup data collected at roughly 6-10 months (July and August 2002 for cohort 1, and Janua ry 2003 for cohort 2), just over one year (January 2003 for cohort 1 and July 2003 for cohort 2), and two years (January and February 2004 for cohort 1, and August and September 2004 for cohort 2). During these assessments, three instruments were used to collect data on successful aging: the Lifestyle Review, Mobility Re view, and the Mayo Clinic Health Risk Assessment. August September October November December January February March April May June July August September October November December January February March April May June July August September October November December January February March April May June JulyLifestyle Review Mobility Review Mayo HRA Lifestyle Review Mobility Review Mayo HRA 2004 20012003 2002 Figure 4. Assessment Schedule for Master piece Living Participants (2001 2004) Cohort 2 Cohort 1

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37 Instruments The Lifestyle Review (LR) is a 134-item self-report questionnaire measuring demographic characteristics, health-relat ed quality of life (using the SF-8 Health Survey, Ware et al., 2001), beliefs, paid a nd unpaid activities, social network/support, life overall, transportation, satisfaction with staff and services, moving and transitions, and additional health questions such as significant life events, memory, incontinence, sensory acuity, and advanced di rectives. This questi onnaire is a subset of items from several established and valid ated instruments from resources such as the World Health Organization (WHOQOL -100), The John D. and Catherine T. MacArthur Foundation Research Network on Midlife Development (MIDMAC) and Midlife in the United States (MIDUS), the Americans Changing Lives Survey Research Center (ACL), The Multipha sic Environmental Assessment Procedure (MEAP; Moos & Lemke 1996), The Short Form 8 (SF-8) Health Survey (Ware et al, 2001), and The Charlotte Count y Healthy Aging Study. The Mobility Review (MR) is a 24 -item instrument administered by a physical therapist (or other trained pr ofessional), measuring gait and balance (using the Tinetti Scale (Tinetti, 1986) and the functional reach test), speed of locomotion (using timed walk test), and upper body strength (using the timed bicep curl test). The Mayo Clinic Health Risk Assessment (HRA) is an online, self-report assessment of health risk offered by the Mayo Clinic. It measures approximately 250 total variables, including demographic variab les, medical risk fa ctors (blood pressure,

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38 cholesterol levels, triglycer ides, blood glucose level, weight), medical conditions (arthritis, asthma, cancer, diab etes, heart disease, lung can cer, serious back problems, and migraine headaches), lifesty le risk factors (alcohol use, dietary fat, exercise, fruit and vegetable consumption, seatbelt use, stress/coping, tobacco use), and Prochaska and DiClemente’s (1986) stages of readiness of change for each. All three assessment tools were administered at each follow-up period. The Lifestyle Review, Mobility Review, and Ma yo Clinic Health Risk Assessment are copyrighted materials. For more informa tion about their availability, please contact Roger Landry, M.D. of Masterpiece A lliance Foundation at rlandry120@aol.com. Outcome Measures Indicators reviewed in the Introduction which were demonstrated to be important to successful aging in community-dwelling older adults aged 50 – 75 years were examined as outcomes to determine if they are relevant for those living in CCRCs with an average age of 80 years. Exercise participation, stage of change for exercise, body mass index, stage of change for weight loss, fruit and vegetable consumption, and stage of change for fruit and vegetable consumption were measured to represent successful aging components #1: reducing risk of disease and disabili ty (Belloc & Breslow, 1972; Damush, Stump, & Clark, 2002; Danhauer et al, 2004; Fe rraro & Booth, 1999; Foote, Giuliano & Harris, 2000; Kahng, Dunkle & Jackson, 2004; Keller, Ostbye, & Goy, 2004; Krahnstoever-Davison et al, 2002; Kushi et al., 1997; Rahr ig Jenkins, 2004;

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39 Reynolds, Fredman, Langenberg, & Mag aziner, 1999; Rowe & Kahn, 1998; Peel, Roderick, & Bartlett, 2005; Sorense n, Stoddard & Macario, 1998; Vaillant & Mukamal, 2001; Zamboni et al, 1999). A summary of the outcome variables for successful aging component #1, their calculatio n from the original instrument items, and response codes are presented in Table 1. Self-rated health and mobility were measured as indicators of component #2: maintaining high physical and cognitive f unction (Belloc & Breslow, 1972; Paterson et al., 2004; Rahrig Jenkins, 2002; Rowe & Kahn, 1998; Seeman et al, 1995; Seeman, 1999; Strain et al., 2002). A summary of the outcome variables for successful aging component #2, their calculation from the origin al instruments, and response codes are presented in Table 2. To examine component #3 (active e ngagement with life), productive activities were examined through helping and volunt eerism, while social connectedness was measured via satisfaction with giving and r eceiving social support (Glass et al., 1995; Jang, 2002; Metropolitan Life Foundation & Independent Se ctor Research Report, 2000; Musick, Herzog, & House, 1999; Rahrig Jenkins, 2002; Rowe & Kahn, 1998; Sorensen, Stoddard & Macario, 1998; Va n Willigan, 2000). A summary of the outcome variables for successful aging component #3, their calculation from the original instruments, and respons e codes are presented in Table 3. The main effect of interest is chan ge in the outcome variables over time, measured in months. On average, data were colle cted at 0.0 months (baseline), 7.2 months, 13.8 months, and 26.5 months.

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40 OutcomeScale or ItemScale ConstructionCoding of Responses Light Exercise Participation LR (single item scale): How frequently do you take walks or other light exercise? 1 = never 2 = once a month or less 3 = two or three times a month 4 = once or twice a week 5 = three times a week or more Vigorous Exercise Participation LR (single item scale): How frequently do you take part in vigorous exercise? 1 = never 2 = once a month or less 3 = two or three times a month 4 = once or twice a week 5 = three times a week or more Strength Training Particiaption LR (single item scale): How frequently do you take part in strength training exercises (lift heavy weights or use strength training equipment)? 1 = never 2 = once a month or less 3 = two or three times a month 4 = once or twice a week 5 = three times a week or more Stage of Change for Exercise Participation Stage of Change HRA (single item scale): Which statemement best describes your plans for exercise participation? 0 = no plans 1 = thinking about exercising more within the next six months 2 = making plans to exercise more within the next 30 days 3 = currently involved in an exercise program to exercise more Body Mass Index (BMI)BMI Standard formula for BMI, calculated using height and weight variables on HRA continuous variable Stage of Change for Weight Loss Stage of Change HRA (single item scale): Which statement best describes your plans for weight loss? 0 = no plans 1 = thinking about losing weight within the next six months 2 = making plans to lose weight within the next 30 days 3 = currently involved in a program to lose weight Fruit and Vegetable Consumption Fruit and Vegetable Consumption HRA (sum of two items): servings of fruits per day + servings of vegetables per day 0 = no servings 1 = 1 serving 2 = 2 servings, etc. Stage of Change for Fruit and Vegetable Consumption Stage of Change HRA (single item scale): Which statement best describes your plans for eating fruits and vegetables? 0 = no plans 1 = thinking about eating more fruits/vegetables within the next six months 2 = making plans to eat more fruits/vegetables within the next 30 days 3 = currently involved in a program to eat more fruits/vegetables Table 1. Outcomes for Successful Aging Component #1: Reducing Risk of Disease and Disability Exercise Participation OutcomeScale or ItemScale ConstructionCoding of Responses SF-8 Physical Score Eight items summed and weighted using the Quality Metrics instructionsContinuous variable ranging from 19 58 SF-8 Mental Score Eight items summed and weighted using the Quality Metrics instructionsContinuous variable ranging from 19 58 MobilityMobility Sum of Tinetti Gait and Balance Scale + functional reach scoreContinuous variable ranging from 0 30 Table 2. Outcomes for Successful Aging Component #2: Maintaining High Physical and Cognitive Function Self-Rated Health

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41OutcomeScale or ItemScale ConstructionCoding of Responses Helping Inside CCRC During the past four weeks, have you given any of the following kinds of help to residents inside this Masterpiece Community? Shop or run errands; provide transportation + help with housework/laundry + meal preparation + personal care + any other kind of help (4 item scale) Summed and recoded into categories: 0 = no helping 1 = some helping 2 = a lot of helping 3 = a great deal of helping Helping Outside CCRC During the past four weeks, have you given any of the following kinds of help to friends, relatives, or neighbors outside this Masterpiece Community? Shop or run errands; provide transportation + help with housework/laundry + childcare + meal preparation + personal care + any other kind of help (6 item scale) Summed and recoded into categories: 0 = no helping 1 = some helping 2 = a lot of helping 3 = a great deal of helping Volunteering Inside CCRC During the past four weeks, did you do any volunteer work inside this Masterpiece Community (such as committee memberships, elected office, library work, etc)? (single item scale) 0 = no 1 = yes Volunteering Outside CCRC During the past four weeks, have you done any volunteer work outside this Masterpiece Community? For a church, synangogue or other religious organization + For a school or educational organization + For a senior group or similar organization + For any other organization (United Way, hospital, etc.) (4 item scale) Summed and recoded into categories: 0 = not involved 1 = involved 2 = highly involved Giving Social Support How satisfied are you with your ability to help and give support to others? (single item scale) 1 = dissatisfied 2 = neither dissatisfied nor satisfied 3 = satisfied Receiving Social Support How satisfied are you with your ability to get the kind of help and support from others that you need? (single item scale) 1 = dissatisfied 2 = neither dissatisfied nor satisfied 3 = satisfied Productive Activities Social Connectedness Table 3. Outcomes for Successful Aging Component #3: Active Enagagement With Life As Table 1 indicates, exercise partic ipation was defined as the frequency of selfreported participation in light, vigorous, or strength traini ng activities (1 = never, 2 = once a month or less, 3 = two or three tim es a month, 4 = once or twice a week, 5 = three times a week or more, as in the MI DMAC and ACL). Stages of motivational readiness to change for exercise were self -reported (0 = precontemplation [no plans to change], 1 = contemplation [consideri ng change within next six months], 2 = preparation [making plans to change within 30 days], 3 = action [currently involved in a program]). Body mass index was cal culated from self-re ported height and

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42 weight, analyzed as a continuous variable and interpreted where <18.5 is interpreted as underweight, 18.6 – 29.9 is interpreted as no rmal, and >30.0 is interpreted as obese (personal communication w ith Masterpiece Living Op erations Workgroup, 2001). Such a classification combines the normal weight (18.5 – 24.9) and overweight (25.0 – 29.9) categories recommended by the World Health Organization (2004). Stages of motivational readiness to change for weight loss was self-reported (0 = precontemplation [no plans to change], 1 = contemplation [considering change within next six months], 2 = preparation [making pl ans to change within 30 days], 3 = action [currently involved in a program]). Fruit and vegetable consumption was defined as the self-reported number of serv ings of fruits and vegetabl es eaten on a typical day (1 = one serving, 2 = two servings, etc.). Stag es of motivational readiness to change for fruit and vegetable consumption was self-repo rted (0 = precontemplation [no plans to change], 1 = contemplation [considering change within next six months], 2 = preparation [making plans to change within 30 days], 3 = action [currently involved in a program]). As Table 2 indicates for successful aging component #2 (mai ntaining high physical and cognitive function), self-rated health was measured using the SF-8 (Ware, Kosinski, Dewey, & Gandek, 2001), yielding two sub-scales: physical and mental health (general population norms are 49.2 for physical health and mental health (range 19 58), while norms for the 75+ gr oup were 45.5 for physical health and 52.0 for mental health. Standard deviations we re less than 10 for all groups). Mobility was defined as the total of measured gait, balance, and functional reach scales, with a

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43 range of 0 – 30 and a score below 20 consid ered at risk for a fall (Tinetti, 1986; personal communication with the Masterpiece Living Oper ations Workgroup, 2001). As Table 3 indicates, successful agi ng component #3 (active engagement with life) was measured through productive activities and social connectedness. Productive activities were defined as helping and vol unteering, while social connectedness was defined as satisfaction with giving and rece iving social support. Informal helping was also conceptualized as two separate variables, depending on whether the helping was done inside or outside the CCRC. Help ing inside the CCRC is the sum of five self-reported items asking about the type of helping (0 = no helping, 1 = some helping, 2 = a lot of helping, 3 = a great de al of helping). Helping outside the CCRC is the sum of six self-reported items aski ng about the type of helping done (0 = no helping, 1 = some helping, 2 = a lot of he lping, 3 = a great deal of helping). Volunteering was conceptualized in two wa ys: self-reported vol unteer activities done both inside and outside the CCRC. Volunteering inside the CCRC is a dichotomous variable (0 = no, 1 = yes), while volunteer ing outside the CCRC is the sum of four items asking about the loca tion and type of volunteerism done (0 = not involved, 1 = involved, 2 = highly involved). Giving social support is de fined as satisfaction with ability to help and give support to others (1 = dissatisfied; 2 = neither satisfied nor dissatisfied; 3 = satisfied). Receiving soci al support is defined as satisfaction with ability to get the support and help needed (1 = dissatisfied; 2 = neither satisfied nor dissatisfied; 3 = satisfied). Coding for all aggregate volunteerism, helping, and social

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44 support outcome variables were the resu lt of personal communication with the Masterpiece Living Operat ions Workgoup (2001). Variables included in the analysis as potential predictors of baseline variability and change over time on the outcome variable s include demographic characteristics, health status variables, and several additional variables previously demonstrated by the literature to be impor tant to successful aging in younger, community dwelling elders as reviewed in th e introduction (Belloc & Bres low, 1972; Damush, Stump, & Clark, 2002; Danhauer et al, 2004; Ferraro & Booth, 1999; Foote, Giuliano & Harris, 2000; Glass et al., 1995; Ja ng, 2002; Kahng, Dunkle & Jack son, 2004; Keller, Ostbye, & Goy, 2004; Krahnstoever-Davison et al 2002; Kushi et al., 1997; Metropolitan Life Foundation & Independent Sector Research Report, 2000; Musick, Herzog, & House, 1999; Paterson et al., 2004; Peel, R oderick, & Bartlett, 2005; Rahrig Jenkins, 2002; Rahrig Jenkins, 2004; Reynolds, Fr edman, Langenberg, & Magaziner, 1999; Rowe & Kahn, 1997; Rowe & Kahn, 1998; Seeman et al, 1995; Seeman, 1999; Sorensen, Stoddard & Macario, 1998; Strain et al, 2002; Vaillant & Mukamal, 2001; Van Willigan, 2000; Zamboni et al, 1999). Demographic variables included age (meas ured in years at the baseline interview), gender (1 = female, 2 = male), marital stat us (1 = single, 2 = widowed, 3 = married), and community of residence (1 = University Village, 2 = Freedom Village). Health status variables include conditions (self-re port of the number of conditions diagnosed by a physician), medications (total number of prescription medica tions reported), and blood pressure risk (BP ove r 140/90; Chobanian, Bakris Black, Cushman, Green,

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45 Izzo, Jones, Materson, Oparil, Wright Jr., Roccella, & the National High Blood Pressure Education Program Coordinati ng Committee, 2003). A dditional variables included are health-related and non-health rela ted self-efficacy (0 = no control of any areas, 1 = little control in some areas, 2 = moderate amount of control in some areas, 3 = control over most areas, 4 = cont rol over all areas (based on personal communication with the Masterpiece Living Operations Workgroup, 2001), and significant life events. Significant life events were defined in two ways, one for use in the prediction of baseline values of th e outcome variables, and one for use in the prediction of change over time in the outcome variables. For the baseline models, significant life events were defined as the tota l number of events re ported in the past year at the time of the baseline interview. In the models examining change over time in the outcome variables, significant life even ts were defined as the total number of life events experienced with in the past year, summed over the course of the study (personal communication with the Masterpiece Living Op erations Workgroup, 2001). Driving status (0 = not driving, 1 = driving), life satisfaction (1 = very dissatisfied, 2 = dissatisfied, 3 = neither satisfied nor dissa tisfied, 4 = satisfied, 5 = very satisfied), life happiness (1 = very unhappy, 2 = pretty unhappy, 3 = not too happy, 4 = pretty happy, 5 = very happy), and net change in physic al or social activity participation (< 2 = net decline, 2 = no change, > 2 = net increase in participation) were also examined.

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46 Statistical Analyses To investigate successful aging am ong residents of CCRCs, hierarchical linear modeling (HLM; Bryk & Raudenbush, 1987, 1992) was chosen to estimate change in the physical, social, and intellectual wellbeing of 136 residents attempting to age successfully. Analyses include explorati on of changes on the outcome variables of interest, and the correlates of those changes (predictors of intraand interindividual change variability). Using the proposed anal ysis on light exercise participation as an example, HLM allows researchers to seek an swers to questions such as: What are the predictors of baseline light exercise pa rticipation (Are health, mobility, chronic conditions, self-efficacy, and st age of change predictors of baseline light exercise participation)? Did light exercise part icipation change over time? Did all who changed their level of participation do so uniformly, or did some improve while others declined? What are the predictors of improvement, stability, or decline in exercise participation over time (Did base line exercise participation, health, mobility, chronic conditions, self-efficacy, and stage of change predict individual trajectories of light exercise participation over time)? Do changes in one outcome variable predict changes in another (for example, are declines in light exercise participation associated with changes in another outcome variable, such as social activity participation)? HLM is the appropriate method to analyze data from a mixed models design, where two levels of data ar e of interest. Multilevel modeling is conceptually important when the study design is nested. Examples of nested designs are seen typically in education res earch, where students are nested within classrooms, and

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47 there is a need to disentangle the effects of classroom from that of students. This is an example of between-subjects nesting. Fo r this analysis, the nesting is withinsubjects, where time is nested within each pe rson, resulting in the need for a two level model where the effects of time, as well as person-level characteristics can be examined. There are also statistical benefits to using HLM (Luke, 2004). Specifying multiple levels acknowledges that error terms for baseline and subsequent data are likely correlated; thus HLM allows the error term to be more precise than in a multiple regression model. HLM also allows the modeling of 3 or more time points, thus allowing one to see non-linear trajectories of change, if present. Another benefit of HLM is that, unlike traditi onal analyses that requir e choosing the appropriate variance-covariance matrix for the entir e dataset, HLM allows each individual participant to specify its own matrix. More simply, this means that HLM allows each participant to have their own pattern of missing data, thus maximizing power. Following this same principle, HLM also allows for varying time intervals between assessments. This is particularly helpfu l for the Masterpiece Living dataset because people come and go from the community se asonally and may miss an assessment. There is also variability in the time be tween assessments for participants due to administrative lag between people and acr oss cohorts, which HLM is able to accommodate. Lastly, HLM pulls upon the strength of the existing data to estimate missing data for outcome variables (though cases are eliminated due to missing predictor data).

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48 The estimation of data is based on the assu mption that the data is missing at random (MAR), but it also quite robust to violat ions of this assumption (Bryk & Raudenbush, 1987; Raudenbush, 2001). The reality of most re search is that while some data is MAR, there can be also data that ar e not missing at random (i.e. incomplete longitudinal data that results from the deat h of a participant are not MAR). So while it may be reasonable from a statistical perspective to estimate their missing data, conceptually this may seem troublesome. In order to explore the impact of estimating missing data for participants, particularly those who died during the course of the study, the author examined differences in baseline performance betw een those who were alive for the whole study (n = 128) and those who participated bu t later died (n=8). There were no age or community of residence differences, nor were there baseline differences in 11 of the 14 outcome variables examined. The three areas where significant differences were observed were mobility, fruit and vegetable consumption, and satisfaction with one’s ability to give social support to others. At baseline, those who eventually died were less mobile (F = 10.27, p < .01), ate more serv ings of fruits and vegetables (F = 4.07, p = .05), and were less satisfied with their abil ity to give social support to others (F = 4.18, p = .04). While the alive and eventually deceased subjects were more similar than different at the beginning of the study, the exercise was continued to determine whether inclusion of the eventually deceased participants impacted the mean-level growth trajectory on these outcome variables. Two outcomes were selected as tests: fruit and vegetable consumption (because th e eventually deceased out-performed the

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49 survivors) and giving social support (b ecause the eventually deceased underperformed when compared to the surviving participants). For fruit and vegetable consumption, the results remained the same : a non-significant decline in consumption over time, with significant variability in inte rcept and slope (baseline score and rate of change over time). For satisfaction with giving and receiving social support the results also remained the same: a non-significant improvement over time, with significant variability in intercept and slope Since those who eventually died were for the most part similar to those who su rvived at baseline, and the differences observed did not impact the growth trajectory of the sample over time, it is reasonable to conclude that the estimation of data for subjects who eventually die is both statistically and conceptually sound. Therefor e, data for these eight participants were included in the present analysis. Initially, unconditional growth models are specified to determ ine whether there is growth over time on the outcome variable of interest, and to determine if there is variability in the baseline sc ore and the rate of change over time. The level one model is specified first, and models the within-subjects effect of time (changes in individuals over time on a pa rticular outcome variable). The level 2 model is the between subjects model, where the interc ept and slope in the level 1 model are allowed to vary as a function of the level 2 units. If the unconditional growth model establishes variability in the intercept and slope, then growth models can be specified to model inter-individual (predicting baseline

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50 scores) and intraindividual (predicting slope) variability. Essentially, there will be a different level one model estimated for each of the values of the level 2 predictors. Level 1 Model: Y = P0 + P1 (Time) + e Level 2 Model: P0 = B00 + B01(Predictor) + RO P1 = B10 + B11(Predictor) + R1 Where Y is the outcome variable, P0 is the intercept, P1 is the effect of time, and e is error. In the level 2 model, P0 and P1 are further specified where B00 is the mean value of the outcome variable, cont rolling for the level 2 predictor, B01 is the effect of the level 2 predictor, R0 is error associated with the level 2 predictor, B10 is the mean value of the level 1 slope, controlling for the level 2 predictor, B11 is the effect of the level 2 predictor, and R1 is error associated with the level 2 predictor. Power Power is the term used to describe the probability of corr ectly rejecting the null hypothesis if an alternative hypothesis is tr ue. Calculating power before collecting data is advisable to ensure that an ade quate sample size has been obtained to reduce the risk of committing a Type II error (faili ng to reject the null hypothesis when it is indeed false). Another reason to calculate pow er is to determine if there is adequate sample size to find the effect sizes previously demonstrated in the research literature.

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51 Most simply, statistical power means ma king sure that you have enough subjects to detect an effect if it occurs. Because this dissertation involves anal ysis of an existing da taset, power is being calculated after the fact. Fu rthermore, literature on succe ssful aging in older samples is limited, so there is no established effect size for the outcomes included in this dissertation. To provide some context, how ever, the effect sizes from literature on successful aging in 50 – 75 year old commun ity-dwelling elders (as reviewed in the Introduction) were found to vary widely, ranging from .01 to .74 (Glass et al., 1995; Seeman et al., 1995). Stevens (2001) provides a table to de termine sample sizes needed for 80% power for repeated measures, which takes into acc ount the correlation be tween observations over time and the anticipated effect size. A portion of that table has been adapted below (Table 4). In the current project correlations range from .21 for satisfaction with receiving social support from others to .95 for BMI, and there are four repeated measures.

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52234567 0.300.12268223192170154141 0.30453936323029 0.49191716161616 0.500.14199165142126114106 0.35343027252423 0.57141413131314 0.800.22826960545047 0.56151413131414 0.8988891010 Average Correlation Effect Size Number of repeated measures Table 4. Sample sizes needed for power = .80 (two tailed, p = .05) in a single-group repeated measures design (adapted from Stevens, 2001) For example, for an average correlati on of .30 over time, and a medium effect size of .30 with four repeated measures, a sample size of 36 subjects is needed. At the other end of the spectrum, if the correlation between observations over time is higher (.80), and the effect size is .22, a sample size of 60 is needed. But, if the effect size is .56, then only 13 subjects are necessary. The sample sizes of the multivariate model range from n = 42 to n = 130. While there should be sufficient power for most of proposed analysis, each of the predictors in the multivariate models described belo w will be examined first in a univariate model to determine significance. The multivariate model will be built from the univariate predictors that we re significant. This research project is a pilot study, and despite the possibility of being underpower ed on some outcomes, these analyses can yield important results on the salience of successful aging in an older sample with an average age of 80.8 years living in CCRCs. It is important to note that the current

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53 HLM analysis will interpret the direction of the effect only, not the magnitude, so outcome-specific determinations of power ad equacy are not necessary or relevant.

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54 RESULTS Baseline Sample Characteristics There are 136 participants in the current study. Because hierarchical linear modeling is able to estimate missing data for the outcome (dependent) variables, the inclusion criteria for participants in the present analyses is all people with any data. If participants completed any one assessment in strument across any of the four waves of data collection, their data was included in the analysis, resulting in a sample size of 136. At any given point in the study, partic ipants could have completed one of more of the three assessment tools, but not all of them. The number of completed assessment instruments at each wave of data collection (and combinations thereof) is displayed in Table 5. Of those 136 partic ipants, 133 completed a Lifestyle Review and 121 completed a Mobility Review at base line. As the fourth column indicates, there were only 120 participants w ho completed both a Lifestyle Review and a Mobility Review at baseline. Therefor e, 13 participants completed a Lifestyle Review but no Mobility Review, and one person completed a Mobility Review but not a Lifestyle Review. Tabl e 5 is intended only to be a reference tool for those interested in understandi ng how much data was estimated. The HLM approach eliminates the bias in results due to attrit ion. While HLM is able to estimate outcome

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55 variable data, it does not estim ate predictor data. As a result, sample size for the predictive models will vary throughout the analyses but be clearly marked in the multivariate tables. Lifestyle Review Mobility Review Mayo Clinic HRA LR & MR LR & HRA MR & HRA All THREE assessments Baseline13312189120888483 Six Months1061015396515049 One Year103997698757271 Two Years67675565504948 All Four Waves of Data67562154211818 Table 5. Number of Participants with Completed Assessments Over Four Waves of Data Collection (2001-2003) Table 6 displays the participation and attrition rate of par ticipants in the study. There was a high attrition rate among participants, just sh ort of 50% by time four. There were many causes for withdrawal fr om the study, including health-related concerns (10.3%), non-health related reasons such as being “t oo busy to take the assessments” (15.4%), moving out of the Masterpiece Community (3.0%), transitioning to a higher level of care (1.5 %), and death (5.9%). The independence of CCRC residents has resulted in much interrupted participation in the successful aging program and as a result, the distinct ion between those who have withdrawn permanently from those who have done so temporarily (are merely missing a data point) can be difficult to delineate. By time four, 12.6% of the sample falls into this interrupted participation category. As mentioned in the Methods section, HLM estimates the missing data for the 136 pa rticipants on the outcome variables, regardless of the reason cited (interrupted participation, withdrawal, death, etc).

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56Enrollment Status n =%n =%n =%n =% Enrolled and Participating136100.010879.410275.06950.7 Withdrew Health Reasons128.81410.31410.3 Non-Health Reasons75.2118.12115.4 Left CCRC (Moved Out)21.521.543.0 Transitioned to Higher Care00.000.021.5 Interrupted Participation42.932.21812.6 Deceased32.242.985.9 Table 6. Participation Rates and Reasons for Attrition (n=136) Time 1Time 2Time 3Time 4 Table 7 presents demographic and othe r baseline characteristics of participants included in the current analyses (n=136). The sample had a mean age of 80.8 years (52.0% of whom were octogenarians at baseline), 62.2% were female, 56.4% were married, and 60.9% of the sample had college or advanced degrees. Participants reported on average 3.2 chronic conditions at baseline and reported taking 2.6 prescription medications. Thirty-seven percent had high blood pressure (above 140/90). Just over eighty percent reported driving at baseline, and they reported moderate amounts of perceived self-efficacy and control over most health and nonhealth related matters. Life happiness a nd satisfaction were high at baselines (M = 4.2 for both, on a five point scale). Particip ants reported an aver age of 0.7 significant life events within the past year at the beginning of the study. The second column of Table 7 indicat es that at baseline, participants reported eating on average 6.0 servings of fruits and vegetables per day, and reported participating in light exercise multiple times per week (M = 4.7, SD = 0.8). While participation in vigorous and strength training exer cises were less frequent Masterpiece participants still reported e ngaging in these activities on at least a

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57 monthly basis (M = 3.3, SD = 1.6 and M = 2.3, SD = 1.7, respectively). The body mass index of participants was within healt hy limits at baseline (M = 25.6, SD = 5.5). Participants rated their physical and mental health and mobility as high at baseline (M = 49.3 and 52.6, respectively). Participants had few mobility problems (M=26.3, SD = 3.4, range = 0 30). Nearly two out of th ree (62%) of particip ants reported being involved in formal volunteerism activities inside their CCRC (including committee membership, elected office, library work, etc. ), whereas 40% of pa rticipants reported being involved in formal volunteerism out side their CCRC. Participants reported doing small amounts of informal helpi ng, doing equal amounts outside their CCRC (M = 0.8, SD = 0.8) and inside their CCRC (M = 0.8, SD = 0.8). Satisfaction with ability to both give and receive social s upport was reported quite high at baseline (M = 2.8, SD = 0.4 and M= 2.8, SD = 0.3, respectivel y). With regard to the stages of change, participants were between the contemplation and preparation phase for exercise participation (M = 1.4, SD = 0.8), but between the precontemplation and contemplation phases for fruit and vegetable consumption (M = 0.5, SD = 1.0) and weight loss (M = 0.9, SD = 1.4).

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58Variable M (SD) or % Variable M (SD) or % Demographic CharacteristicsOutcome Variables at Baseline Age (in years)80.8 (6.1) Fruit and Vegetable Consumption6.0 (2.7) Gender (% female)62.2 Exercise Participation Marital Status (% married)56.4 Light4.7(0.8) Education Vigorous3.3 (1.6) High School (%)39.1 Strength Training2.3 (1.7) College (%)35.9 Body Mass Index (BMI)25.6 (5.5) Graduate School (%)25.0 Self-Rated Health SF-8 Physical49.3 (9.0) Other Baseline Characteristics SF-8 Mental52.6 (7.2) Chronic Conditions3.2 (1.8) Mobility26.3 (3.4) Medications2.6 (1.5) Formal Volunteerism Blood Pressure Risk (% at risk)37.1 Inside CCRC0.6 (0.5) Driving Status (% driving)81.4 Outside CCRC0.5 (0.7) Self-Efficacy Informal Helping Health Related2.8 (0.5) Inside CCRC0.8 (0.8) Non-Health Related2.5 (0.6) Outside CCRC0.8 (0.8) Life Happiness4.2 (0.7) Social Support Life Satisfaction4.2 (0.8) Giving2.8 (0.4) Significant Life Events0.7 (1.1 ) Receiving2.8 (0.3) Stages of Change Exercise1.4 (0.8) Fruit/Vegetable0.5 (1.0) Weight Loss0.9 (1.4) Table 7. Baseline Characteristics of Masterpiece Living Participants (n = 136) Table 8 displays information about significant life events and changes in selfreported activity participa tion, variables that were a ggregated over the four time periods of the study. Roughly one-third of the sample reported increasing their physical, social, or intellectua l activities during th eir participation in the successful aging program. Participants also reported an average of 2.1 significant life events (death of spouse or child, accident or illn ess requiring hospitalization, other accident or illness, or spouse accident/illness) w ithin the scope of the 26 month study. Variable M (SD) or % Net Change in Activity Participation (% increasing) Physical Activity Levels37.6 Social Activity Levels26.6 Intellectual Activity Levels31.1 Significant Life Events (during study)2.1 (1.7) Table 8. Characteristics of Masterpiece Living Participants (n = 136)

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59 Mean Level Changes over Time on Outcome Measures Table 9 displays the fixed effects portions of the unconditional growth models, where the intercept represents the mean score on the outcome variable at the midpoint of the study due to time being centered in th e models. The slope displays the change per month on the outcome variable and determin es whether this change is statistically significant.

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60EstimateSEP Intercept5.880.17< .001 Slope-0.02 0.010.202 Intercept0.630.07< .001 Slope0.020.010.016 Intercept4.600.06< .001 Slope-0.010.000.118 Intercept3.200.12< .001 Slope-0.010.010.126 Intercept2.220.13< .001 Slope-0.010.010.283 InterceptN/AN/AN/A SlopeN/AN/AN/A Intercept25.120.35< .001 Slope-0.04-0.020.054 Intercept0.880.09< .001 Slope-0.000.010.636 Intercept48.600.76< .001 Slope-0.070.030.023 Intercept52.470.51< .001 Slope-0.010.040.727 Intercept26.490.35< .001 Slope0.020.020.426 Intercept0.620.04< .001 Slope0.000.000.982 Intercept0.480.05< .001 Slope-0.000.000.560 Intercept0.780.06< .001 Slope0.000.000.847 Intercept0.810.06< .001 Slope-0.000.000.809 Intercept2.750.04< .001 Slope-0.000.000.246 Intercept2.830.03< .001 Slope0.000.000.035 Receiving Social Support Volunteering Outside CCRC Helping Inside CCRC Helping Outside CCRC Giving Social Support Self-Rated Health: Physical Scale Self-Rated Health: Mental Scale Mobility Volunteering Inside CCRC BMI Stage of Change for Weight Loss Table 9: Fixed Effects Portion of Unconditional Growth Models Fixed Effects Fruit and Vegetable Consumption Stage of Change for Fruit & Vegetable Consumption Light Exercise Vigorous Exercise Strength Training Stage of Change for Exercise

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61 Table 9 is the basis for Table 10, a cal culated table that displays the mean values for each outcome variable at the four time points measured in the study. In general, there was no change over the 26 months, as evidenced by non-significant improvements or declines in fruit and vege table consumption, exercise participation, BMI, the mental health scale of the SF8, mobility, volunteerism, helping, satisfaction with giving social support to others, and stages of cha nge for exercise and weight loss. Significant changes over time included a decline in self-rate d physical health (p = .023) and an increase in sa tisfaction with receiving so cial support (p = .035). Participants also progressed through the st ages of change for fruit and vegetable consumption (p = .016).

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62Variable Time 1 (0.0 months) Time 2 (7.2 months) Time 3 (13.8 months) Time 4 (26.5 months)P SA Component #1: Reducing Risk of Disease and Disability Fruit and Vegetable Consumption6.05.95.85.6n.s. Exercise Participation Light4.74.64.64.5n.s. Vigorous3.33.23.23.0n.s. Strength Training2.32.22.22.1n.s. Body Mass Index (BMI)25.625.225.024.4n.s. SA Component #2: Maintaining High Physical Function Self-Rated Health SF-8 Physical49.348.848.347.40.023 SF-8 Mental52.652.552.452.2n.s. Mobilit y 26.326.426.626.8n.s. SA Component #3: Engagement with Life Formal Volunteerism Inside CCRC0.60.60.60.6n.s. Outside CCRC0.50.50.50.5n.s. Informal Helping Inside CCRC0.80.80.80.8n.s. Outside CCRC0.80.80.80.8n.s. Social Support Giving2.82.82.72.7n.s. Receiving2.82.92.92.90.035 Stages of Change Exercise1.41.41.41.4n.s. Fruit/Vegetable0.50.60.70.90.016 Weight Loss0.90.90.90.8n.s. Table 10. Mean Level Change Over Time for Outcome Variables After examining mean level changes over time, outcome variables were analyzed using HLM. The following results are pres ented in three cluste rs, corresponding to the three components of Rowe and Kahn’s (1997) model of successful aging: 1.) reducing the risk of disease and disabilit y, 2.) maintaining high physical and cognitive function, and 3.) active engagement with lif e. Within each component of successful aging, results will be broken down further by outcome variables. For each outcome variable, baseline performance will be reviewed (using baseline scores from Table

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63 10). Next, results of random effects porti on of the unconditional growth models will be presented to demonstrate the mean-level changes over time, and establish variability in the intercept (baseline perfor mance) and slope (performance over time). Results of the unconditional growth models are presented in Tables 11, 19, and 23 (one table for each component of successful aging: Table 11 corresponds to component #1, Table 19 to component #2, a nd Table 23 to component #3). If there was significant variability in either intercept or slope, resu lts of the predictive models will be presented (univariate models followed by multivariate models). Successful Aging Component #1: Reducing Risk of Disease and Disability Fruit and vegetable consumption, exercise participat ion, and body mass index (BMI) were chosen as the outcome variab les to measure successful aging component #1: reducing the risk of disease and disabilit y. Stage of change for fruit and vegetable consumption, exercise participation, a nd weight loss were also measured.

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64EstimateSDP Intercept 1.731.32 < .001 Slope 0.000.06 0.004 Intercept 0.180.42 < .001 Slope 0.000.02 0.194 Intercept 0.370.61 < .001 Slope 0.000.02 0.003 Intercept 1.491.22 < .001 Slope 0.000.02 0.144 Intercept 1.431.19 < .001 Slope 0.000 > .500 Intercept N/ A N/ A N/A Slope N/ A N/ A N/A Intercept 11.283.36 < .001 Slope 0.030.17 < .001 Intercept 0.470.69 < .001 Slope 0.000.03 0.110 BMI Stage of Change for Weight Loss Vigorous Exercise Strength Training Stage of Change for Exercise Table 11. Unconditional Growth Models (Random Effects Only) for Component #1: Reducing Risk of Disease and Disability Random Effects Fruit and Vegetable Consumption Stage of Change for Fruit & Vegetable Consumption Light Exercise Fruit and Vegetable Consumption At baseline, participants consumed an average 6.0 servings of fruits and vegetables per day, a healthy level of consumption th at did not change significantly over 26 months. The unconditional growth model (t op panel, Table 11) i ndicates significant variability in the intercep t (baseline consumption, est. = 1.73, p < .001) and slope (change in consumption over time, est. = 0.00, p = .004). These statistics reveal that participants in a successful aging program at e significantly different amounts of fruits and vegetables at baseline, and although th ere was no mean-level change in fruit and vegetable consumption, individual participan ts had varied patterns of fruit and

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65 vegetable consumption over time (some increased participation while others decreased). This variability was investigated initially with univariate predictive models to maximize sample size, followed by multivariate predictive models using only those variables that achieved statistica l significance in the univariate predictive models. At the univariate level, baseline fru it and vegetable consump tion was predicted by marital status only (est. = 0.57, p = .043), with married participants eating more fruits and vegetables at baseline than their c ounterparts (Appendix A). As an example, Figure 5 displays the impact of marital status on baseline fruit and vegetable consumption. The influence of age, partic ipation in light, vigorous, and strength training exercise activities, BMI, community of residence, gender, recent significant life events and stage of change for fruit and vegetable consumption were examined but not significant. As there was only one significant predictor of baseline fruit and vegetable consumption, a multivariate model is not necessary. Figure 5 Impact of Marital Status on Baseline Fruit and Vegetable Consumption9 9.5 10 10.5 11 11.5Marital StatusFruit and Vegetabl e Consumption Single Widowed Married

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66 Variability in the slope for fru it and vegetable consumption was predicted by baseline consumption (est. = -0.04, p < .001) and life events (est. = -0.02, p = .014) in the univariate predictive mode ls (Appendix A), with thos e eating more fruits and vegetables at baseline and those experienci ng more significant life events during their participation showing greater than average de clines in consumption. The influence of age, exercise participation, BMI, communit y, gender, marital status, net change in activity participation, and stage of change were examined but found to be nonsignificant. For the multivariate model predicting changes in fruit and vegetable consumption over time, results are presented in Tabl e 12. Only baseline fruit and vegetable consumption remained significant (est. = -0.07, p < .001): those who ate more at baseline showed greater than averag e declines in consumption over time. Fixed EffectsEstimateSEP Intercept Intercept5.50.56< .001 Marital Status0.140.210.520 Slope Intercept0.440.03< .001 Baseline Fruit and Vegetable Consumption-0.070.01< .001 Life Events-0.010.010.243 Table 12. Multivariate Model for Fruit and Vegetable Consumption (n = 77) Stage of Change for Fruit and Vegetable Consumption While there was no increase in fruit and vegetable consumption, measured by the number of servings per day, there is evid ence of progress/effort on this important

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67 health promotion variable: there was si gnificant progression through the stages of change for fruit and vegetable consumption (Table 10). Mean stage at baseline was 0.5 (halfway between precontemplation and contemplation), with a small but statistically significant adva ncement through the stages of change over time (est. = 0.02, p = .016). The unconditional growth model (top panel, Table 11) suggests there was significant variability in the intercep t (est. = 0.18, p < .001). The variability in slope was not significant (est. = 0.00, p = .194). In the univariate predictive models (Appendix A), baseline stage of change was predicted by age (est. = -0.03, p = .019), BMI (est. = 0.03, p = .051), community of residence (est. = -0.33, p = .016), health-r elated self-efficacy (est. = 0.44, p = .051), self-reported physical health (est. = -0.02, p = .040), and st age of change for other behaviors such as exercise participation and weight loss (est. = 0.18, p = .027 and est. = 0.20, p = .003 respectively). Participants with higher BMIs, more health-related self-efficacy, and those who were further al ong in the stages of change were more likely to be in higher stages for fru it and vegetable consumption, while older participants, those living at Freedom V illage, and those who reported poorer physical health were more likely to be early on in the stages of change. The influence of baseline fruit and vegetable consumpti on, gender, social support, exercise participation, mobility, and nonhealth related self-efficacy were tested but were not significant.

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68Fixed EffectsEstimateSEP Intercept Intercept1.241.700.467 Age-0.010.020.614 Community -0.230.160.144 Exercise Stage0.110.080.191 Weight Loss Stage0.090.080.244 BMI0.000.020.994 Physical Health-0.020.010.050 Health Self-Efficacy0.390.140.006 Table 13. Multivariate Model for Fruit and Vegetable Stage of Change (n = 77) Significant predictors from the univariate model were entered into a multivariate model to predict stage of change for fr uit and vegetable consumption at baseline (Table 13). In the multivariate model, onl y self-rated physical health (est. = -0.02, p = .050) and health self-efficacy remained significant (est. = 0.39, p = .006). Those rating their health higher were more likely to be in the earlie r stages of change, whereas those reporting higher health self -efficacy were more likely to progress further along in the stages of change for fruit and vegetable consumption. Due to the lack of variability in th e slope (Table 11), there is no need to model inter-individual differences in progression through the stages of change for fruit and vegetable consumption. Exercise Participation Exercise participation was defined in three ways: light exercise, vigorous exercise, and strength training exercise.

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69 Light Exercise Participants reported engaging in light exercise multiple times per week at baseline (M = 4.7, SD = 0.8), which is more frequently than the response of “once or twice a week” but just short of the “three time s a week or more” response, and this participation level did not change signifi cantly over time. The unconditional growth model (middle panel, Table 11) indicates significant variability in the intercept (baseline participation; est. = 0.37, p < .001) and slope (change in participation in light exercise over time est. = 0.00, p = 0.003). In the univariate models (Appendix A), higher baseline participation in light exercise was predicted by greater frequenc y of vigorous activity participation (est. = 0.18, p < .001), higher health self-efficacy (est = 0.44, p = .001), better mobility (est. = 0.05, p = .001) and higher self-rated health (est. = 0.02, p = .019). Additionally, those with more chronic condi tions reported less light activ ity at baseline than their counterparts (est. = -0.09, p = .045). The influence of age, strength training, gender, and stage of change, and recent significant life events were examined but were not significant. In the univariate analyses (Append ix A), participants who reported doing more baseline light exercise (est. = -0.01, p = .001) and those who were older (est. = -0.002, p = .002) experienced greater than average de clines in light exercise participation, while higher health-related self-efficacy (est. = 0.02, p = .007) and better mobility (est. = 0.004, p = .001) were protective against d eclines in light exer cise participation. As an example, Figure 6 displays the effect of age on light exercise participation over

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70 time. The influence of baseline streng th training and vigorous exercise, chronic conditions, gender, marital status, net change in physical activity participation, selfrated health, significant life events, and st age of change for exercise participation were examined but were non-significant mode rators of changes in self-reported light exercise over time. Figure 6. Effect of Age on Light Exercise Participation Over Time0 Months7 Months14 Months26 MonthsTimeFrequency of Light Exercise Participation 65 74 years 75 84 years 85 + years Three or more times per wee k Neve r Once a month or less Two to three times a month Once or twice a wee k Table 14 displays the results of the multivariate model, indicating that only vigorous exercise (est. = 0.09, p = 0.017) a nd health self-efficacy (est. = 0.45, p = 0.006) remained as predictors of baselin e light exercise. Those who did more vigorous exercise more frequently and had higher health self-efficacy did more light exercise at baseline. Simply, those who ex ercise do multiple types of exercise (light and vigorous). Based on the results of the univariate models (Appendix A), a multivariate model was created to examine modifiers of light exercise participati on over time. At the multivariate level, all variables remained significant except age (Table 14). Those

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71 who were more mobile (est. = 0.01, p = 0.007) and had higher health self-efficacy (est. = 0.03, p = 0.019) showed le ss decline in light exercise participation over time, whereas participants who did more frequent light exercise at baseline were more likely to decrease their participati on over time (est. = -0.05, p < .001). Fixed EffectsEstimateSEP Intercept Intercept1.210.760.117 Vigorous Exercise 0.090.040.017 Chronic Conditions0.010.030.778 Health Self-Efficacy0.450.150.006 Mobility0.040.030.094 Physical Health0.010.010.098 Slope Intercept0.120.100.242 Age-0.000.000.060 Baseline Light Exercise-0.050.01< .001 Health Self-Efficacy0.030.010.019 Mobility0.010.000.007 Table 14. Multivariate Model for Light Exercise (n = 69) Vigorous Exercise Participants reported engaging in vigorous exercise slightly less than once per week (M = 3.3, SD = 1.6), which is slightly mo re frequently than “two or three times a month” but short of “once or twice a w eek,” a level that remained constant over time. The unconditional growth model for vi gorous exercise (middle panel, Table 11) indicates significant variability in the in tercept (baseline part icipation in vigorous exercise, est. = 1.49, p < .001), but not for sl ope (est. = 0.00, p = .144). Therefore, the intercept will be modeled, but mode ling the slope is not appropriate.

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72 In the univariate models for the inte rcept (Appendix A), marri ed participants (est. = 0.47, p = .025), those who also do more li ght exercises (est. = 0.51, p < .001) and strength exercises (est. = 0.33, p < .001), rate their health as better (est. = 0.04, p = .002), suffer from fewer chronic conditions (est. = -0.17, p = .034) and are more mobile (est. = 0.10, p = .003) reported doing mo re vigorous exercise at baseline than their counterparts. The influence of age, gender, health-related self-efficacy, recent significant life events, and stage of change for exercise participation were examined but found to be non-significant. Multivariate analyses (Table 15) show that only strength training participation (est. = 0.26, p = .009) and mobility (est. = 0.15, p = .002) remained significant predictors of baseline participation in vigorous exerci se. Those who did more strength training and were more mobile at baseline engaged in vigorous exercise more frequently than did their counterparts. Fixed EffectsEstimateSEP Intercept Intercept-2.551.650.131 Light Exercise -0.030.240.891 Strength0.260.090.009 Chronic Conditions-0.030.090.777 Marital Status0.110.290.709 Mobility0.150.040.002 Physical Health0.020.020.418 Table 15. Multivariate Model for Vigorous Exercise (n = 42)

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73 Strength Training Exercise Participants reported doing strength training exercises mo nthly (M = 2.3, SD = 1.7), at a rate that is more frequently than “once a month or less” but not as frequent as “two or three times a month,” and did not significantly cha nge this level of participation over the 26 months studie d. The unconditional growth model for strength training partic ipation (middle panel, Table 11) suggests significant variability in the intercept (est. = 1.43, p < .001), but not slope (est. = 0.00, p > .500). Therefore, models will be created for the intercept, but no further modeling of the slope will be pursued. Univariate predictive modeling (T able 16) revealed that more frequent participation in strength training at base line was predicted only by frequent vigorous activity participation (est. = 0.39 p < .001). Pa rticipants who participated in vigorous exercise frequently at base line also did strength traini ng exercises more frequently than the rest of the participants. Age, baseline exercise participation, conditions, gender, health-related self-efficacy, marital status, mobility, net change in physical activity participation, self-reported health, r ecent significant life events, and stage of change for exercise participation were exam ined but found to be non-significant. As there was only one significant predictor, a multivariate model is not needed. Fixed EffectsEstimateSEP Intercept Intercept0.950.260.001 Vigorous Exercise 0.390.07< .001 Table 16. Univariate Model for Strength Training (n = 125)

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74 Stage of Change for Exercise Participation While the maintenance of physical activ ity in older adults is in itself a positive outcome, stage of motivational readiness to change for exercise participation was examined to determine if there were partic ipants who were not ch anging their activity but increased their knowledge about why exer cise is important, weighed the pros and cons of activity vs. inactivity, or developed strategies to become active that simply were not yet acted upon. Mean stage of readiness to ch ange at baseline was 1.40 (halfway between contemplation and prepara tion). The no-growth model, a precursor to the unconditional growth model which ex amines variability collapsed across time, did not find any variability in exercise st age scores over time. This suggests that scores were similar at baseline and over ti me and therefore no additional analysis was conducted on this variable. Body Mass Index At the beginning of the successful agi ng program, the average participant reported a healthy body mass index (BMI) of 25.6, whic h declined but not significantly over 26 months to 24.4 (p = .054; Table 10). The unconditional growth model for BMI (bottom panel, Table 11) yields significant variability in the intercept (est. = 11.28, p < .001) and slope (est. = 0.03, p < .001). Univariate models (Appendix A) examin ing baseline variability demonstrated that lower baseline BMIs were reported by ol der participants (est. = -0.21, p < .001), those with lower health self-efficacy (est. = 6.53, p < .001), those with higher ratings of

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75 health (est. = -0.18, p < .001), a nd those in the earlier stages of change for weight loss (est. = 1.54, p < .001). Community of reside nce, fruit and vegetable consumption, gender, exercise participation, marital stat us, mobility, and recent significant life events were examined but found to be non-significant. Variability in slope for BMI was pr edicted by self-reported health (est. = 0.01, p = .002), mobility (est. = 0.01, p = .030), and exercise participation (light est. = 0.02, p = .052; vigorous est. = 0.02, p = .052; net change in participation (est. = 0.10, p = .005). Healthier, more mobile adults, those who did more light and vigorous exercise at baseline, and those who increased their phys ical activity levels experienced slower than average rates of decline in BMI. Part icipants with higher ba seline BMIs (est. = 0.03, p < .001), who consumed more fruits and vegetables at base line (est. = -0.02, p = .014), those with higher health self-effi cacy (est. = -0.41, p < .001), and those who were further along in the stages of re adiness to change (est. = -0.06, p = .002) experienced greater than aver age declines in BMI. The influence of age, community of residence, gender, strength training, marital status, and significant life events were examined but deemed non-significant.

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76Fixed EffectsEstimateSEP Intercept Intercept36.044.03< .001 Age-0.020.040.511 Health Self-Efficacy-0.040.680.951 Physical Health-0.200.04< .001 Stage of Change1.250.350.001 Slope Intercept3.010.38< .001 Baseline BMI-0.090.01< .001 Fruit and Vegetable Consumption-0.010.010.182 Health Self-Efficacy0.030.060.599 Light Exercise-0.010.020.666 Mobility0.000.010.815 Physical Activity Participation0.040.030.301 Physical Health-0.020.00< .001 Stage of Change0.120.040.002 Vigorous Exercise0.000.010.690 Table 17. Multivariate Model for Body Mass Index (n = 63) The results of the multivariate models are presented in Table 17. Baseline selfreported physical health (est. = -0.20, p < .001) and stage of change for weight loss (est. = 1.25, p = 0.001) remained significant predictors of baseline body mass index. Participants reporting better health had lower BMIs at the beginning of the study, while those in the higher stag es of change (preparation or action vs. precontemplation or contemplation) had higher BMIs at base line. Modifiers of change in BMI over time were similar: self-reported physica l health (est. = -0.02, p < .001), stage of change for weight loss (est. = 0.12, p = 0.002), and baseline BM I (est. = -0.09, p < .001). Those in the higher stages of cha nge showed a slower decline in BMI than their counterparts, an une xpected finding. Those with higher baseline BMIs and

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77 those who reported better health showed st eeper declines in BMI over time than did those with lower baseline BMI. Stage of Change for Weight Loss Despite the slight but non-signifi cant overall decline in BMI, there was no significant progression through the stages of motivational readiness to change for weight loss (Table 10). Mean stage score at baseline was 0.9 (mostly contemplators), and participants did not ch ange significantly over time. The unconditional growth model (bottom panel, Table 11) indicates that there was si gnificant variability in the intercept (est. = 0.47, p < .001), but not the slope (est. = 0.00, p = .110). The univariate models (Appendix A) reve aled that stage of change at baseline can be predicted by age (est. = -0.06, p < .001), BMI (est. = 0.12, p < .001), stage of change for fruit and vegetable consump tion and exercise (est. = 0.44, p = 0.000 and est. = 0.31, p = .002, respectively), health self-efficacy (est. = 0.97, p = .001), and self-rated health (est. = 0.02, p = .048). Participants w ho were older and had lower self-rated health were more likely to be in the earlier stages of ch ange for weight loss, whereas those with higher BMIs, those w ho were further along in the stages of change for other behaviors like fruit and vegetable consumption and exercise, and those who had higher health self-efficacy were more likely to more likely to be in the higher stages of change. Community of residence, baseline fruit and vegetable consumption, gender, social support, exerci se participation, mari tal status, mobility, and non-health related self-efficacy were exam ined but were not significant predictors of baseline stage of change for weight loss.

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78Fixed EffectsEstimateSEP Intercept Intercept-0.171.680.921 Age-0.030.010.026 Stage of Change for Exercise0.140.080.087 Stage of Change for Fruits and Vegetables0.180.100.088 BMI0.110.02< .001 Physical Health0.010.010.527 Health Self-Efficacy0.220.140.118 Table 18. Multivariate Model for Stage of Change for Weight Loss (n = 77) The multivariate model was constructe d using the significant univariate predictors (Table 18), indicating that age (est. = -0.03, p = .026) and BMI (est. = 0.11, p < .001) were the only variables that predicted baseline stage of change for weight loss after controlling for the other variable s. Older participants were more likely to be in the earlier stages of change, while those with higher BMIs were more likely to be in the later stages of change. Discussion of Successful Aging Component #1 The findings for successful aging component #1 (reducing risk of disease and disability) revealed a trend of non-significant changes in a ll three outcomes, fruit and vegetable consumption, exercise participa tion, and BMI among older adults with a mean age of 80.8 years living in a CCRC and en rolled in a successful aging program. For the stages of readine ss to change, there was signi ficant progression through the stages for fruit and vegetable consumption, but no significant cha nge over time for exercise participation or weight loss. The findings for fruit and vegetable consumption were not consistent with the hypothesis. The finding that ba seline consumption is the main predictor of change in

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79 consumption over time is consistent with Danhauer et al. (20 04), who found greater rates of intervention success (defined as increasing fruit and vegetable intake or maintaining intake if it was at a healthy level at baseli ne) among those who ate fewer fruits and vegetables at baseline. Daily fruit and vegetable consumption at baseline is similar to that of Foote, Guiliano, and Harris (2000), who found that among males and females aged 71 – 85 years, average consumption was 5.2 to 5.7 se rvings (respectively) Larger studies using NHANES II and Behavioral Risk Factor Surveillance System Data indicated that less than a third of older adults re ported eating enough vegetables, and less than one half reported eating enough fruit for optimum health (Patterson, Block, Rosenberger, Pee, & Kahlee, 1990 (as reviewed by Wakimoto & Block, 2001); National Center for Chronic Disease Prev ention and Health Promotion Centers for Disease Control and Prevention, 2005). In the current sample, approximately 70% of participants are consuming five or more servings per day. The present findings support the pilo t research of Cluskey (2001), who found that the majority of CCRC residents reported consuming adequate amounts of fruits and vegetables each day. She asserts that the nutritional deficits and weight loss reported as common among older adults should be cl arified because much of the research documenting these deficits has taken pl ace among community-dwelling elders who may not have the access and variety in f oods that CCRCs residents have, or among nursing home residents, who may have signi ficant health problems that dictate nutritional habits.

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80 Meeting or exceeding recommen ded guidelines for fruit and vegetable consumption has important health benefits. A review of the health benefits of fruit and vegetable consumption (Hyson, 2002) details that consumption has been inversely related to lung, es ophageal, ovarian, bladder, and oral forms of cancer. This review also reported that consumption of fr uits and vegetables, as a rich source of antioxidants, folate, fiber, potassium, and fl avinoids, have been consistently linked to reduced mortality and morbidity from cardiova scular disease, maintenance of health, normal blood pressure, lowered incidence of st roke, helpful for wei ght control and the prevention of obesity, better bone healt h. The relationship between fruit and vegetable consumption and cognition (i ncluding neurodegenerative diseases) has been promising in non-human research (as reviewed by Hyson, 2002). The findings for fruit and vegetable st age of change indicated that perhaps there was a change in individuals’ internal thought process about fruit and vegetable consumption, which is presumed to be a precu rsor of behavior change. It has been suggested that our social support networ k is our primary social environment, influencing what one does, the goals one sets and what one achiev es (Sorensen et al., 1998). The absence of social support as a pr edictor of baseline st age of change for fruit and vegetable consumption is inconsiste nt with Sorensen et al.’s (1998) findings that there is a significant re lationship between some types of social support and being in the preparation phase. Th e cross-sectional nature of their study leaves one to wonder whether a person receives more suppor t in the preparation phase, or is just more receptive to hearing the support at that time. Health-self-efficacy was a

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81 significant predictor of fruit and vegetable stage of change in the current sample of CCRC residents, a finding which expands upon the research of Keefe et al., (2000) and Resnick & Nigg (2003) who found self-efficacy to be associated with stage of change for arthritis self-management and exercise. For those who consumed fewer than five servings of fruits and vegetables per day, the distribution of partic ipants across the stages of readiness to change was as follows: 54% reported being in precontem plation, 27% were in contemplation, 15% were in preparation, and only 4% reported be ing in the action stage. This distribution suggests that this group would be receptive to a more direct effort to educate about nutrition and impact consumption. Sorensen et al’s (1998) fi ndings reinforce the impact that environment has on an indi vidual’s attempts to change behavior. Successful aging programs such as Mast erpiece Living recognize this and are attempting to change the culture of CCRCs to be more supportive of individuals’ behavior change goals. The findings for exercise participat ion support the possible explanation that those who are in better physical condition (bette r self-reported healt h, more mobile) and those who have higher health self-efficacy ar e more likely to participate in physical activities because they are physically and mentally/emotionally more capable. For light exercise, it was also dem onstrated that those who were most active to begin with were the most likely to decline over time. Interestingly enough, base line self-reported health was no t a predictor of change in light exercise participation over time, suggesting that peop le with varying levels of

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82 health can maintain regular light exerci se participation (t hough only 10.6% of the current sample reported their health as fair or poor at baseline). The importance of health self-efficacy suggests feeling more in control, more confident, and more optimistic may encourage an older adult to continue exercising despite small fluctuations in health. Also of note is the non-significant age predictor, suggesting that there may be other factor s more critical to sustained participation in exercise. Predictors of particip ation in strength training exerci ses were different from those of light and vigorous exercise which could be attributable to the lower prevalence of participation in this activity. It is also possible that this sample of predominantly older women were less interested in streng th training as a form of physical activity, which is consistent with the absence of health-related variables in the prediction of baseline participation (participation base d on interest rather than ability). The findings for exercise participa tion over time were not consistent with the hypothesis of increased participation. One explanation could be measurement inadequacy. Given the high rate of partic ipation in all three types of exercise (particularly light exercise), there may be an instrument-induced ceiling effect. It is possible that the participants are doing more exercise, but the coding of the instrument (with “three or more times per week” being the highest frequency response option) is not able to capture these increases. For exampl e, if an older adult was engaging in light exercise three days pe r week at baseline, then increased to five days – this would be improvement/increased participation, but th e instrument would not be able to record this change in behavior.

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83 The research on weight gain in later life as a result of decreased metabolic rate (and often compounded by sedentary lifesty le) is well established (Wakimoto & Block, 2001). The findings that nutritiona l intake (measured by fruit and vegetable consumption) did not change significantly over time but BMI did not increase (in fact, it decreased slightly but not significantly) could make a case for a real but undetected increase in activity over time. M easurement inadequacy as an explanation is reinforced by the finding that self-reporte d change in physical activity participation variable did not predict any of the variability in light exercise participation over time. One would expect that there would be a re lationship between self -reported change in physical activity participation and changes in the self-reported fr equency of exercise participation. King (2001) suggests that the determ inants of physical activity participation among older adults can be classified into th ree categories: personal characteristics, program factors, and environmental fact ors. Personal characteristics include demographic and health variables, as we ll as knowledge, attitudes and beliefs about physical activity, and behaviors and skills that encourage and form barriers to participation. Program factors include program structure, complexity, format, intensity, convenience, and the cost of participation, both financially and psychologically (the amount of compet itiveness involved, fear of social embarrassment and self-consciousness). Envi ronmental factors include social support from friends, family, program staff, and other exercisers – both to begin and to maintain physical activity participation, phys ical activity advice from physicians, and

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84 the use of environmental cues, prompt s, and incentives to promote activity participation. This and any other successful aging program will need to examine these factors, if the goal is to provide effective programming and a supportive environment for older adults to age successfully. The importance of maintenance shoul d not be overlooked. This sample was active in multiple types of physical activity, and th eir maintenance of this activity over time should be applauded. Exercise could possi bly be the single most important health promotion behavior (Rowe & Kahn, 1998), as ex ercise participation impacts not only the avoidance of disease and disability a nd the maintenance of physical function, but also facilitates active engage ment with life if performed in a group/social setting. Since these CCRCs increased the number of group exercise classes over the 26 month study, and added fitness equipment to the co mmon areas, it is likely that exercise in these communities is occurring in a group se tting and facilitati ng active engagement. Comparison of these findings to nati onal data is not straightforward, as most research on exercise participa tion examines the physical bene fits of exercise, not selfreported frequency of participation. What can be garnered from the existing research, however, is that that older adults can increa se their cardiorespirat ory fitness, strength, and balance by participating in exercise, as infrequently as twice per week (Lazowski et al, 1999; Messier et al, 2000; Wolfson et al, 1996), and th at this participation and resultant fitness can reduce mortality risk s ubstantially (Blair et al, 1996; Kushi et al, 1997, Wei et al, 1999).

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85 When this sample is compared to BRFSS data (National Center for Chronic Disease Prevention and Health Promo tion Centers for Disease Control and Prevention, 2005), it appears this group is more active than the average Floridian over age 65 years, which may help explain why se lf-reported exercise participation did not increase as hypothesized. BRFSS data from 2003 indicates that 61.6% of those aged 65 and older do not meet recommended guide lines for physical activity (30 minutes per day, five or more days per week). Fift y-five percent of participants with a mean age of 80.8 years in the successful aging progr am were considered at risk for their failure to meet this suggested exercise guide line. It is possible that this successful aging program self-selected the more activ e portion of the CC RC resident population, but it is also possible that the CCRC envi ronment is somehow more supportive of exercise habits. Without a comparison gr oup, this explanation cannot be explored further. The lack of progression through the st ages of motivational readiness to change, coupled with the trend of cons istent exercise participation over time, suggests that the participants recruited into the successful aging program were distributed across the stages of readiness to change (not cluste red in preparation or action), and that the programs of the successful aging initiative ma y not have been stage appropriate. This is not surprising, given that the curren t successful aging program exemplified traditional intervention and programmatic research by focusing on interventions with action-oriented indicators of success.

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86 The non-significant decline in BMI over time was consistent with the hypothesis of non-significant change over time. The hypot hesis was based on two factors: most of the longitudinal literatur e on BMI spans time periods more lengthy than the current 26 month study (Himes, 2004), and the supposition that no change, perhaps even modest increases in BMI represent the positive/successful aging outcome. Though some have suggested that the optimal BMI fo r older adults is higher for older adults than younger adults (i.e. 24 – 29 kg/m2) (as reviewed in Pedersen, Ovesen, Schroll, Avlund & Era, 2002), additional research is necessary to determine benchmarks for healthy BMI in this group of adults aged 80 years and older, and the implication of changes over time. Comparison of these fi ndings to larger data sets such as the BRFSS indicate that obesity (defined as a BMI of 30.0 or greater) is 17.2% among Floridians aged 65 years and older, but slig htly lower at 12.1% in the current sample. The lack of progression through the st ages of change for weight loss is likely attributable to the profile of residents participating in th e pilot program. For the 53% of participants who reported being in the precontemplati on stage (by indicating that they have no plans to lose weight), the sl ower rate of decline is predictable: There were no stage-appropriate programs for thes e participants, so there was no reason to expect they would report changes in BMI or progress through the stages of change. For the 15% of participants who were in the action phase (alr eady involved in a weight loss program), the slight but nonsignificant decline in BMI would be achieved without progression to another stage.

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87 Successful Aging Component #2: Maintaining High Physical and Cognitive Function Self-rated health and mobility were chosen as the outcomes to measure successful aging component #2: maintaining hi gh physical and cognitive function. EstimateSDP Intercept60.917.80< .001 Slope0.000.03> .500 Intercept18.444.29< .001 Slope0.000.06> .500 Intercept9.733.12< .001 Slope0.020.130.025 Self-Rated Health: Physical Scale Self-Rated Health: Mental Scale Mobility Random Effects Table 19. Unconditional Growth Models (Random Effects Only) for Component #2: Maintaining High Physical and Cognitive Function Self-Rated Health Baseline self-reported physical he alth scores were on average 49.3 (SD = 9.0), declining significantly and uniformly over time to 47.4 (p = .023). The unconditional growth models (top panel, Ta ble 19) indicate significant variability in the intercept (baseline self-reported health score; est. = 60.91, p < .001) but not slope (change in self-reported health ov er time, est. = 0.00, p > .500). This means that participants had significant differences in th eir baseline self-reported health score, but over time nearly all participants declin ed in a similar fashion. Univariate models of baseline variab ility (Appendix A) dem onstrated self-reported physical health to be predicte d by a number of health and so cial factors. Participants with higher BMIs (est. = -0.88, p < .001), hi gher health self-efficacy (est. = -7.50, p =

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88 .007), those who reported being diagnosed with more chronic condi tions (est. = -1.86, p < .001), and those who took more medicati ons (est. = -1.52, p = .014) reported their physical health at baseline as lower than their counterparts, while those who were more satisfied with their ability to give social support to others (est. = 4.59, p = .003), those who are more mobile (est. = 0.65, p = .001), and those who did more light exercise (est. = 1.62, p = .051) or vigorous exercise (est. = 1.13, p = .013) reported themselves in better physical health than th e rest of the sample. Age, blood pressure risk, community of residence, fruit and ve getable consumption, gender, participation in group or solitary activitie s, marital status, satisfac tion with receiving social support, recent significant life events, and strength training were examined but found to be non-significant. Multivariate analyses incl uding all significant variab les from the univariate predictive models suggest that only BMI (est. = -0.67, p = .001) and chronic conditions (est. = -1.25, p = .032) influenced self-reporte d physical health (Table 20). Those with higher baseline BMI and more chroni c conditions reported poorer health than did their counterparts.

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89Fixed EffectsEstimateSEP Intercept Intercept49.2610.88< .001 BMI-0.670.190.001 Chronic Conditions-1.250.570.032 Giving Social Support0.852.060.682 Health Self-Efficacy0.241.480.871 Light Exercise0.980.930.294 Medications-0.340.690.626 Mobility0.440.240.067 Vigorous Exercise0.520.520.322 Table 20. Multivariate Model for Physical Health (n = 75) Analysis of self-reported mental hea lth (using the mental health subscale of the SF-8) was conducted only to complement th e SF-8 physical health subscale, and was not considered in the hypotheses for th e study. Univariate predictive models (Appendix A) indicated that mobility, vigor ous exercise participation, and giving social support were potentia l predictors of baseline se lf-reported mental health. Multivariate analysis (Table 21) revealed that only giving social support remained significant (est. = 2.37, p = .045). Participants who were more satisfied with their ability to give social support to others re ported better mental health at baseline than those who were less satisfied with their abilities. Fixed EffectsEstimateSEP Intercept Intercept40.153.81< .001 Giving Social Support2.371.170.045 Mobility0.190.140.182 Vigorous Exercise0.270.320.413 Table 21. Multivariate Model for Mental Health (n = 114)

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90 Mobility Participants in the successful aging program were highly mobile at baseline (M = 26.3, SD = 3.4), and this mobility did not change significantly over the 26 month study. The unconditional growth model (bottom panel, Table 19) indicated significant variability in intercept (est. = 9.73, p < .001) and slope (est. = 0.02, p = .025). Univariate models (Appendix A) rev ealed that older (est = -0.16, p = .006) and non-driving (est. = 2.80, p = .003) participants reported lower baseline mobility than did younger and driving participants, while greater mobility at baseline was observed among those who reported better health (est. = 0.16, p < .001), did more vigorous exercise (est. = 0.69, p = .002), and were more sa tisfied with their ability to give (est. = 2.39, p = .001) and receive (est. = 1.73, p = .041) social support. The influence of BMI, blood pressure risk, community, c onditions, gender, self-efficacy, exercise participation, marital status medications, and recent si gnificant life events were examined but determined to be non-significant. When investigating sources of variance in the slope for mobility, univariate models (Appendix A) indicated that older participants (est. = -0.01, p = .003) and those living at Freedom Village (est. = -0.21, p < .001) showed less improvement in mobility over time, while drivers (est. = 0.12, p = .046), those rating their physical health higher (est. = 0.01, p = .011), and people who reported doing more vigorous exercise (est. = 0.03, p = .027) showed more improvement than their counterparts. BMI, blood pressure risk, c onditions, gender, giving and receiving social support,

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91 self-efficacy, exercise participation, mar ital status, medications, net change in physical activity, and significant life even ts were examined but determined nonsignificant. Multivariate analysis of baseline m obility indicated that age (est. = -0.14, p = .013), giving social support (est. = 2.15, p = .002), and self-reported physical health (est. = 0.10, p = .006), remained significant pr edictors of baseline mobility (Table 22). Younger participants, t hose who reported better physical health, and those who were more satisfied with their ability to give social support to others had better mobility scores at baseline. Multivariate analysis of mobility ove r time revealed that only community of residence remained significant at the mu ltivariate level (est. = -0.18, p < .001). Residents of Freedom Village showed less improvement in mobility over time than did University Village residents (Table 22).

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92Fixed EffectsEstimateSEP Intercept Intercept23.435.32< .001 Age-0.140.050.013 Driving0.580.970.552 Giving Social Support2.150.670.002 Receiving Soci al Support0.640.730.386 Physical Health0.100.040.006 Vigorous Exercise0.320.190.104 Slope Intercept0.460.410.271 Age-0.000.010.443 Community-0.180.05< .001 Driving Status0.070.080.431 Physical Health0.000.000.642 Vigorous Exercise0.010.020.497 Table 22. Multivariate Model for Mobility (n = 108) Discussion of Successful Aging Component #2 The declines in self-r eported physical health were c ontrary to the hypothesis that health would not change significantly over ti me. It is possible that the author was overly-optimistic with regard to this outcome variable, and a more appropriate (yet still successful aging-friendly) hypothesis w ould have been that declines in selfreported health would be less dramatic th an the national trends demonstrate. When compared to national norms (Fi gure 7), participants in the successful aging program reported better health to begin with, more akin with that of adults 10 or more years their junior (The thr ee darker bars in the backgr ound represent their self-rated health scores at 0 months, 14 months, a nd 26 months). The national data for SF-8 scores cannot be disaggregated into smalle r age groups after age 75 years due to small sample size (personal communication with Qu ality Metrics, Inc., June, 2005). As a

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93 result, one can conclude that the current sample rates their health high, and is similar to the national norms in their trend of dec lining self-rated health over time. However, one cannot determine whether the rate of decline in self-repor ted health is less steep than the rate of decline seen in the national study. 43 44 45 46 47 48 49Self-Reported Healt h 5 0 5 4 5 5 5 9 6 0 6 4 6 5 6 9 7 0 7 4 7 5 +Age Figure 7 Self Reported Health: Comparison of Masterpiece Data to National Norms for SF-8 SF-8 National Norm Masterpiece Living It was also hypothesized that fru it and vegetable consumption would be a significant predictor of self-re ported physical health at ba seline and over time, similar to that of Keller, Ostbye, and Goy ( 2004) who found that nutritional risk was a significant predictor of good health days at baseline and follow-up. While the present study did not replicate these findings, this is possibly due to differences in measurement (a broader measure of nutri tional risk versus fruit and vegetable

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94 consumption, and good health days versus self -reported health). Nutritional risk may be better able to predict health than fruit and vegetable consumption due to its association with dependency, disability, so cial isolation, acute and chronic diseases, medication, and poverty (Saxon & Etten, 2002). The concept of nutrition, regardless of how it is measured, should not be ove rlooked as a potential primary or secondary health promotion program, given its potential health benefits (Hyson, 2002). The maintenance of health, functional abilities, and ultimate ly independence is the over-arching goal of successful aging pr ograms such as Masterpiece Living. The current findings suggest a number of ave nues for programming to influence selfreported health (programs targeting BMI, chronic disease prevention, and mobility), but the lack of variability in the slope of self-reported he alth over time leaves a void as to which one has the most impact on th e trajectory of health over time and should therefore be the priority for program development and evaluation. The finding of no significant change in mobility supported the original hypothesis. The research literature, based on both larg e and small studies, suggested that the predominant trend is decline in functiona l capacity after age 80 (Figure 8, House, 2003; Black & Rush, 2002). This study explor ed functional capacity with a measure of gait and balance rather than ADLs/IADLs, so a direct comparison is not possible. The broader interpretation th at both ADLs/IADLs and gait and balance are indicators of functional capacity is quite valuable, however. It has been suggested that changes in gait and balance may precede changes in ADL/IADL capability (personal communication with Masterpiece Livi ng Operations Workgroup, 2001 – 2005).

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95 Maintaining higher physical function is a major part of successful aging, and it was anticipated that a successful aging pr ogram such as Masterpiece Living would encourage exercise participation, rehabilitation therapy, and learned in dependence that would do much to help older adu lts maintain their mobility (and implied, function). 1986 2001/2002 Functional Limitations by Level of Education0.2 0.4 0.6 0.8 1 30405060708090100Age in YearsProportion No Functiona l Limitations high education (16+ yrs) medium education (12-15 yrs) low education (0-11 yrs) Si4 Source: Americans Changing Lives The results of Seeman et al (1995), using a subsampl e of the MacArthur Research Network on Successful Aging data, has measures similar to that of the current study. The findings are similar in the demonstrati on of maintenance of physical performance over time, with sub-groups of individual s improving and declining over time. The MacArthur sample observed 23% of the sa mple declining and 22% improving on the Figure 8 National Trend of Decline in Functional

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96 physical performance measure. In the curr ent sample, 28% of the current sample declined and 57% improved their mobility. The larger percentage reporting improvement in the current sample was not surprising. The goal of the MacArthur studies was to follow their sample over tim e and observe change s in the upper onethird (successful agers) versus all others. Since the current sample is composed of CCRC residents enrolled in a successful aging program, so one could reasonably conclude that there was a climate for change and/or an individual desire to influence this outcome variable. Seeman et al. (1995) found that moderate and/or strenuous exercise was an independent predictor of improving mobility. This was replicated in the current study in the univariate model but exercise became non-significant after controlling for the other variables in the mu ltivariate model. Satisfaction with giving and receiving social support were significan t predictors of base line performance in the current study, but not changes in performan ce, as reported in Seeman et al (1995). Small sample sizes and differences in meas urement of social support may be potential sources of these discrepancies. The Seeman et al. (1999) data rev ealed discrepancies between the predictors of perceived and observed functional impair ments, finding no relationship between baseline self-efficacy and the developmen t of observed functional impairments. There was, however, a relationship between instrumental self-efficacy and perceived disability (as measured with self-reported Nagi and Katz items). The current study further reinforces the findings for obser ved functional impairments, but did not measure perceived disability. This disc repancy between perceived and observed

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97 disability is exactly the gap that successful aging program s are attempting to address, by encouraging individuals to take more c ontrol over their health and well-being. With greater self-efficacy may come great er willingness to participate in new activities. Success at these endeavors further builds self-confidence and life experience and creates an upward self-fulfilling prophecy. The effect of community on change in mobility is an unexpected finding, which may be attributable to differences in pers onnel continuity and qua lifications. It is possible that residents of University Village showed greater improvements in mobility over time because their Masterpiece Co ordinator is a physical therapist, and was the sole rater of performance on the Mobility Review. At Freedom Village, Mobility Reviews were conducted by a vari ety of individuals, including physical therapists not otherwise involved in the successful aging program, and Activities department personnel who did not have a ny formal physical therapy training. Ferraro and Booth (1999) suggested that age is not the cause of onset of functional impairment in later life. Instead, they at tribute functional impairment at follow-up to unhealthy BMI (either too high or too low) While the current study also did not observe any significant relationship betw een increasing impairment (measured by mobility) and age, these data do not show an effect of BMI on mobility. The lack of relationship between age and functional impa irment has important implications, as the basic science research has long taught th at advancing age is the major cause of decreasing muscle fibers over time, whic h leads to sarcopenia and eventually functional impairment (Saxon & Etten, 2002). The lack of a relationship between age

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98 and functional ability is consistent with the empowering message of successful aging theory that it is never too late to begin health promotion efforts. Black and Rush (2002) found marriage to be protective ag ainst functional dec line over time, though this finding was not supported in the curre nt analysis. The mechanism through which marriage is thought to encourage health promotion is its assumption of built-in, constantly available social support. Satis faction with giving a nd receiving social support were significant predictors of mob ility in the univariate models, but not once other variables were controlled for in the multivariate model.

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99 Successful Aging Component #3: Active Engagement with Life Successful aging component #3 (active engagement with life) can be divided into two sub-parts: participati on in productive activities and maintenance of strong social networks (Rowe & Kahn, 1997). Participati on in productive activities was measured using four variables: formal volunteeri ng (both inside and outside the CCRC), and helping (both inside and outsi de the CCRC). Maintenance of strong social ties was measured through two variables: satisfaction with giving and receiv ing social support. EstimateSDP Intercept0.120.35< .001 Slope0.000.010.063 Intercept0.250.50< .001 Slope0.000.010.116 Intercept0.290.54< .001 Slope0.000.010.032 Intercept0.250.50< .001 Slope0.000.020.289 Intercept0.170.41< .001 Slope0.000.00> .500 Intercept0.040.20< .001 Slope0.000.00> .500 Helping Inside CCRC Helping Outside CCRC Giving Social Support Receiving Social Support Random Effects Volunteering Inside CCRC Volunteering Outside CCRC Table 23. Unconditional Growth Models (Random Effects Oly) for Component #3: Active Engagement with Life

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100 Formal Volunteerism Formal volunteerism was separated into two types: volunteerism for people or groups inside the CCRC, and volunteerism for agencies and people outside the CCRC. Volunteerism Inside the CCRC Volunteerism inside the CCRC was quite common, with 62% of participants reporting volunteer activ ities at baseline, a level of involvement that remained high over the course of the study. Unconditiona l growth models (top panel, Table 23) revealed significant variability in the inte rcept (baseline volunteerism; est. = 0.12, p < .001) but not slope (change in volunteeris m over time, est. = 0.00, p = .063). This means that although some participants reporte d more volunteering than others at the beginning of the study, all maintained their volunteerism efforts similarly over time. Predicting the sources of variance for baseline volunteerism was conducted univariately first, then multivariate models using only those variables that achieved statistical significance in the univariate predictive models. The univariate analysis (Appendix A) revealed that predictors of volunteerism inside the CCRC included driving status (est. = 0.27, p = .007), self-reported health (est. 0.01, p = .015), mobility (est. = 0.03, p = .007), giving and receiving social support (est. = 0.23, p = .002 and est. = 0.16 p = .059 respectively), life happiness (est. = 0.17, p = .026), and life satisfaction (est. = 0.11, p = .012). People who drove, reported better health, were more mobile, sa tisfied with their ability to give and receive the social support th ey need, and those who were happy and satisfied with

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101 their lives did more volunt eerism inside their community. The influence of age, marital status, community, gender, and recen t significant life events were examined but found to be non-significant. All significant variables from the uni variate predictive models were entered into the multivariate model, and only mobility remained significant (est. = .05, p = .004): those with better mobility reported doing more types of volunteerism inside the CCRC than did the less mobile (Table 24). Fixed EffectsEstimateSEP Intercept Intercept-1.410.530.011 Driving-0.060.180.738 Giving Social Support-0.140.160.362 Life Happiness-0.040.150.752 Life Satisfaction0.150.120.252 Receiving Social Support0.140.130.288 Mobility0.050.020.004 Physical Health0.010.010.392 Table 24. Multivariate Model for Volunteering Inside (n = 53) Volunteerism Outside the CCRC Approximately 40% of residents repor t volunteering for organizations that operate outside their CCRC (i.e. for religious, educational, senior, or other community organization), and this level of involvement did not change significantly over time. The unconditional growth model (top panel, Ta ble 23) shows significant variability in the intercept (est. = 0.25, p < .001) bu t not slope (est. = 0.00, p = .116). Univariate predictive models (Appe ndix A) indicate that baseline volunteerism outside the CCRC can be pr edicted by giving and receiv ing social support (est. =

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102 0.24, p = .014 and est. = .35, p = .003, respectiv ely), life happiness (est. = 0.20, p = .043), and life satisfaction (est. = 0.16, p = .010). People who were more satisfied with their ability to give a nd receive the social support th ey need and those who were happy and satisfied with their lives did mo re volunteerism outside their community. Age, community, driving, gender, marital status, mobility, recent significant life events, and self-rated hea lth were examined but deemed non-significant. In the multivariate model (Table 25), only satisfaction with receiving social support (est. = 0.33, p = 0.011) remained a significant predic tor of baseline volunteerism outside the CCRC. The more sa tisfied participants were with their ability to receive the soci al support they need, the mo re volunteerism they did for those not living in th eir CCRC community. Fixed EffectsEstimateSEP Intercept Intercept-1.320.480.009 Giving Social Support0.230.130.074 Life Happiness0.070.170.679 Life Satisfaction-0.020.140.892 Receiving Social Support0.330.120.011 Table 25. Multivariate Model for Volunteering Outside (n = 68) Informal Helping Helping Inside the CCRC Over 60% of participants reported helping other reside nts inside their CCRC, a level of helping that did not change significantly over time. Unconditional growth models

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103 (middle panel, Table 23) indicate signifi cant variability in in tercept (est. = 0.29, p < .001) and slope (est. = 0.00, p = .032). Univariate analysis of baseline variability (Appendix A) indicated that widowed/single people (est. = -0.23, p = .025), drivers (est. = 0.33, p = .043), people who were more satisfied with their ability to give social support (est. = 0.41, p = .001) and receive social support (est. = 0.33, p = .015), and happier/more satisfied people (est. = 0.23, p = .034 and est. = 0.17, p = .020, resp ectively) did more helping of those living inside the CCRC. The influence of age, community, gender, mobility, recent significant life events and se lf-rated physical health were examined but were nonsignificant. Multivariate analysis of baseline helping inside the CCRC (Table 26) indicated that only receiving social support (est. = 0.44, p = .011) and marital status (est. = 0.26, p = .040) remained significant. Thos e who were more satisfied with their ability to get the kind of s upport they need from others did more types of helping inside the CCRC than those who were less sa tisfied. Married participants did fewer types of helping than did singl e or widowed participants. When examining variability in slope at the univariate level, people reporting better health were more likely to increase the sc ope of their helping behaviors inside the CCRC (est. = 0.00, p = .023). Age, communit y, driving, gender, giving and receiving social support, life happiness and satisfacti on, marital status, mobility, significant life events, and net change in social activity participation were examined but were nonsignificant. Because there was only one pred ictor of changing helping behavior over

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104 time, a multivariate model for slope is unn ecessary. As Table 26 indicates, those reporting better health were more likely to increase the scope of their helping behaviors inside the CCRC (est. = 0.00, p = .036). Fixed EffectsEstimateSEP Intercept Intercept-0.430.690.541 Driving0.070.220.755 Giving Social Support0.150.170.378 Life Happiness0.240.190.215 Life Satisfaction-0.210.180.242 Marital Status-0.260.120.040 Receiving Social Support0.440.170.011 Slope Intercept-0.070.040.051 Physical Health0.000.000.036 Table 26. Multivariate Model for Helping Inside (n = 61) Helping Outside the CCRC Sixty-four percent of participants reported some level of helping those outside their CCRC at baseline, and th is level of helping did not change significantly over time. Unconditional growth models (middle panel, Table 23) indicated significant variability in intercept (e st. = 0.25, p < .001), but not sl ope (est. = 0.00, p = .289). Univariate analysis (Appendix A) i ndicated that non-driver s (est. = 0.41, p = .010) reported less helping outside at baseline th an their counterparts. Participants who were more mobile (est. = 0.03, p = .028), ha ppier with their li fe (est. = 0.37, p = .002), and those who were more satisfied with their ability to give and receive social support to others (est. = 0.33, p = .004 and es t. = 0.35, p = .011, respectively) reported giving more help to people outside the CCRC. The influence of age, community,

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105 gender, life satisfaction, marital status, r ecent significant life ev ents, and self-rated health were examined but were not signifi cant predictors of baseline helping. Multivariate analysis (Table 27) indi cated that none of the variables examined in this study remained significant predictors of baseline helping behaviors for those living outside the CCRC. Fixed EffectsEstimateSEP Intercept Intercept-1.000.720.168 Driving0.330.270.223 Giving Social Support0.020.210.909 Life Happiness0.230.150.131 Mobility0.000.030.987 Receiving Soci al Support0.190.180.303 Table 27. Multivariate Model for Helping Outs ide (n = 57) Social Support Social support was measured with two outcome variables: sa tisfaction with giving and receiving social support. Giving Social Support Satisfaction with one’s ability to give social support to others was high at baseline and remained high over time. Unconditional growth modeling (bottom panel, Table 23) revealed significant vari ability in intercept (est. = 0.17, p < .001), but not slope (est. = 0.00 p > .500). Univariate predictive models (Appendi x A) indicated that baseline satisfaction varied among participants, with older pa rticipants (est. = -0.02, p = .017) and nondrivers (est. = 0.44, p < .001) being less satisf ied with their ability to give social

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106 support to others. People reporting be tter health (est. = 0.02, p < .001), higher mobility (est. = 0.04, p < .001), and those with greater non-health related self-efficacy (est. = 0.02, p = .025) were more satisfied with their ability to give social support to others at baseline. Community of reside nce, gender, marital status, and recent significant life events were also examined but were found to be non-significant. Significant predictors from the univariate analysis were entered into the multivariate model (Table 28) revealing that only driving status and self-reported physical health remained significant. Drivers (est. = 0.43, p = 0.003) and those reporting better physical health (est. = 0.02, p = 0.001) were mo re satisfied with their ability to give social support to others at the beginning of the study. Fixed EffectsEstimateSEP Intercept Intercept0.990.790.219 Age0.000.010.936 Driving0.430.140.003 Non-Health Self-Efficacy0.060.080.445 Physical Health0.020.000.001 Mobility0.020.010.178 Table 28. Multivariate Model for Giving Social Support (n = 87) Receiving Social Support Participants were satisfied with thei r ability to receive th e kind of social support they need from others at the beginning of the successful aging program, and this level of satisfaction increased over time (p = .035). Unconditional growth models (bottom

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107 panel, Table 23) showed signi ficant variability in baseli ne satisfaction (est. = 0.04, p < .001), but not slope (est. = 0.00, p > .500). Univariate models (Appendix A) attri buted variability in ba seline satisfaction to community of residence (Freedom Villag e residents were more satisfied with receiving social support than were Univers ity Village residents, est. = 0.13, p = .007), mobility (greater mobility was linked to greater satisfaction, est. = 0.02, p = .019), and non-health related self-efficacy (those with greater self-efficacy were more satisfied with their ability to receive th e social support they needed than were participants with lower self-efficacy, es t. = 0.11, p = .007). Age, driving status, gender, marital status, recent significant li fe events, and self-r ated health were examined also but found to be non-significant. Only community (est. = 0.10, p = .038) and non-health related self-efficacy (est. = 0.13, p = .005) remained significant in the multivariate model (Table 29). Freedom Village participants and those with higher non-health related self-efficacy were more satisfied with their ability to re ceive the social support they need. Fixed EffectsEstimateSEP Intercept Intercept2.190.18< .001 Community0.100.050.038 Non-Health Self-Efficacy0.130.040.005 Mobility0.010.010.220 Table 29. Multivariate Model for Receiving Social Support (n = 117)

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108 Discussion of Successful Aging Component #3 The results for successful aging co mponent #3 (active engagement with life) indicated that the sample of older adults with a mean age of 80.8 years living in a CCRC and enrolled in a successful aging program were active in meaningful and productive activities and were building or maintaining their social networks, as evidenced by their satisfaction with their abil ity to give and receive social support. These findings were consistent with the hypothesis that partic ipation in productive activities will remain constant or potentially increase over time. Sixty-two percent of participants reported volunt eering inside the CCRC, while 40% volunteer for outside organizations. The literature on the preval ence of formal volunteerism varies from 35 50% in studies with mean ages around 70 years (Musick, Herzog, & House, 1999; Van Willigen, 2000) to 43% in those aged 75 years and older (Metropolitan Life, 2000). High baseline performance on the outco me variables may have created little room for increased participation. Some studi es have found that there is a curvilinear effect of the benefits of civic engage ment on health and well-being: some involvement produces positive outcomes, while too many hours or too much commitment to too many organizations can ac tually be detrimental to health (Musick et al, 1999; Van Willigan, 2000). Due to meas urement restrictions, it is not possible to determine where these participants are on this curve. In the univariate models, there was considerable overlap in the predictors of baseline participation in pr oductive activities. Satisfact ion with social support, particularly satisfaction with receiving soci al support, was an important predictor of

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109 baseline engagement in productive activiti es. Perhaps satisfaction with receiving social support created a need to give back by helping others. It is also possible that the helping behaviors created relationships and access to support networks that increased satisfaction with rece iving of social support. Differences in the predictors of volunteering at the multivariate level could be attributable to different levels of physical demand or time commitment when volunteering inside versus outside the CCRC community. Volunteerism inside was predicted by mobility while voluntee rism outside the CCRC was predicted by satisfaction with receiving social support. This finding is undocumented in the literature. The opposite relationship is more common, such as Rahrig Jenkins et al. (2002), who found a positive relationship betw een health-related quality of life and participation in activities outside the CCRC. It is possible that residents who are more mobile attend more activities, are witn ess to more of the daily operation of the CCRC, and as a result are more interested and able to volunteer inside their CCRC in capacities such as the resident boa rd of directors. If this logic is plausible, however, it is surprising that self-report ed physical health was not al so a significant predictor of volunteerism inside. The role of mobility on volunteerism inside the CCRC could be explained by the policies of these communities. For example, both CCRCs in the study prohibit mobility aids in the dining rooms. These restrictions are not imposed formally by the CCRC management for other public spaces in the CCRC, but are often subtly imposed by residents. Such restrictions, whether objective or perceived, could have discouraged participation by those with mobility concerns.

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110 Unfortunately, the lack of variability in the slope for three of the four measures of productive activities over time prevents a co mprehensive examination of prediction of changes in volunteerism over time. As a re sult, findings cannot be compared to Glass et al. (1995) who found that being older, married, disabl ed, and increasing mastery were protective against declines in pr oductive activity, while hospitalizations and stroke predicted declines in productivit y. They also found that being African American, having high mastery, and high life satisfaction increased the likelihood of increasing productive activities over time. Variability in slope was significant for helping inside the CCRC however, where higher self-rated health predicted increases in the types of helping behaviors done. Wh ile causality cannot be determined in this design, this result builds on the work of Ra hrig Jenkins et al. (2002) who speculate that health self-selects particip ation in productive activities. Engagement in productive activities ha s been associated with a variety of positive outcomes such as better health, higher li fe satisfaction, lower mortality risk, higher self-efficacy and higher role definition and sa tisfaction (Musick et al., 1999; Moen et al., 2000; Van Willigen, 2000) and explains why Rowe and Kahn’s (1997) model and the current successful aging progr am include this component. The results for social support indicate th at the sample of older adults with a mean age of 80.8 years residing in a CCRC and part icipating in a successful aging program were satisfied with their ability to give and receive social support, and this satisfaction remained high over time. There were different factors associated with satisfaction with one’s ability to give and receive social support at baseline. For

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111 satisfaction with giving soci al support, it is plausibl e that those who perceive themselves as healthier and those who (thr ough their ability to dr ive) have better access to the outside community are more sati sfied with their ability to give social support to others. However, driving status was not a significan t predictor in the multivariate models for actually giving social support in the form of volunteerism and helping inside and outside the CCRC. Furtherm ore, it is conceivable that much social support is given to those inside the CCRC, where the ability to drive is inconsequential. It is possi ble that participants conceptu alize their giving of social support in ways other than helping and volunt eerism (largely instrumental), including emotional supports such as visiting, encour aging, talking, and listening to those in need of support. Satisfaction with receiving social support was associated with the CCRC of residence and non-health related self-efficacy in the multivariate model. Why Freedom Village residents would feel more satisfied with their ability to receive social support is unknown. The qualities of the Masterpiece Coordinator may explain this finding, if participants interpreted this item to include CCRC staff in the term “friends and family.” University Vill age has had two Masterpiece Coordinators during the pilot study, each with their own unique style of encouragement and program implementation, while Freedom Village has had the same Coordinator over the entire study period. The presence of se lf-efficacy in predicting satisfaction with giving and receiving social support (at the un ivariate level) rein forces the role of

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112 modifiable risk factors in determining the health and well-being of older adults (Rowe & Kahn, 1998). Relationships Among Changing Outcome Variables To answer the question about whethe r changes in one variable are related to another (i.e. are declines in health over time related to changes in physical activity participation or volunteerism?), bivariat e correlations were performed. Ideally, HLM would be used to model these rela tionships, but the process to do this analysis correctly is quite complicat ed and beyond the scope of the current project. As a result of not estimati ng missing data, the sample size for these analyses are smaller than the n=136 for the larger study. Change over time on each outcome variable was calculated by s ubtracting responses at baseline from responses at two years. Correlations be tween the outcome variables are presented in Table 30.

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113 SelfReported Physical Health Fruit and Vegetable ConsumptionBMI Light Exercise Vigorous Exercise Strength Training Mobility Volunteering Inside CCRC Volunteering Outside CCRC Helping Inside CCRC Helping Outside CCRC Giving Social Support Receiving Social Support Self-Reported Physical Health 1.000.18-0.010.180.080.50**0.080.090.18-0.20-0.060.100.00 Fruit and Vegetable Consumption 1.00-0.141.00**0.29*0.35*0.03-0.040.090.080.050.200.15 BMI 1.00-0.140.110.120.16-0.08-0.050.10-0.150.040.04 Light Exercise 1.000.29*0.35*0.03-0.040.090.080.050.200.15 Vigorous Exercise 1.000.46**-0.31*0.100.24-0.24-0.48**-0.100.22 Strength Training 1.00-0.070.120.19-0.100.030.110.28 Mobility 1.00-0.100.030.34*0.280.29*-0.22 Volunteering Inside CCRC 1.000.36*0.15-0.290.09-0.05 Volunteering Outside CCRC 1.00-0.10-0.35*-0.04-0.22 Helping Inside CCRC 1.00.37*0.090.01 Helping Outside CCRC 1.000.16-0.01 Giving Social Support 1.000.07 Receiving Social Suppor t 1.00 ** p < .01 p < .05 Table 30: Bivariate Correlations for Difference Scores

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114 Relationships between changes over time on the outcome variables suggest that older adults residing in a CCRC and enroll ed in a successful aging program were involved in multiple forms of behavior chan ge simultaneously, and that changes were not confined to one component of successful aging. For example, participants who reported increasing their participation in vigorous exercise activities also reported changing other behaviors important for the avoi dance of disease and disability such as light exercise participati on and fruit and vegetable consumption (r = .29, p < .05 for both). Unfortunately, the improvements seen for component #1 may have been at the expense of component #3, active engagement w ith life: increases in vigorous activity participation were associated with decr eases in helping behaviors outside the CCRC (r = -.48, p <.01). There were also a number of significant relations hips that suggest multiple types of behavior change within the same component of successful aging.

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115 DISCUSSION Summary of Findings In a sample of older adults with a mean age of 80.8 years living in CCRCs and enrolled in a successful aging program for 26 months, analyses examined multiple outcome measures for each of the thr ee components of Rowe and Kahn’s (1997) model of successful aging. Resu lts indicate that at baseline the part icipants exercised frequently, ate recommended levels of fru its and vegetables, had healthy BMIs, had positive ratings of health, were mobile, were involved in productive activities, and were satisfied with their ability to give and receive social support at baseline. Participants maintained this picture of su ccessful aging over time for the majority of outcome variables, though signi ficant declines in self-repo rted health and BMI were observed. Participants also reported improve ments in their satisfaction with receiving social support. There are four major conclusions of this dissertation. Firs t, the results support Rowe and Kahn’s (1997) model of succe ssful aging by addressing one of the criticisms of the theory s uggested that are limited numb ers of people who can meet the criterion (Vaillant & Mukamal, 2001; Binstock, 2002; Bootsma-van der Weil, 2002; Strawbridge, Wallhagen, & Cohen, 2002) The results suggest that, among a convenience sample of older adults livi ng in CCRCs, there are individuals meeting

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116 the criteria set forth for successful ag ing as defined by Rowe & Kahn’s three components. The sample’s frequent particip ation in exercise, h ealthy consumption of fruits and vegetables, and achievement of a healthy BMI indicate that they are taking steps to reduce their risk of disease and disability. Particip ants’ reports of good health and their high mobility scores suggest that they are maintaining high physical function, one part of succe ssful aging component #2 (maintaining high physical and cognitive function). This sample, through th eir participation in numerous types of productive activities and satisfa ction with social support, is an indication of their active engagement with life (successful ag ing component #3). This dissertation contributes to the literatu re on successful aging by expanding the age range and residential setting of research. Second, stability was inferred on a number of outcome variables over the 26 month study period by virtue of a lack of significant change. While the current analysis was neither an intervention st udy nor a program evaluation, and therefore interpretation of these findings is limited, th e broader implications of stability deserve discussion. Despite the traditional impr ovement-oriented focus of programmatic research, stability or maintenance of wellbeing over time should be viewed as a positive outcome in older age, particularly when compared to national data depicting trends of decline. Oftentimes, progr ammatic/intervention research focuses on improvement in the outcome variables as the sole indicator of the effectiveness of the intervention. While this is certainly appr opriate in many designs, there are situations where this approach is not appropriate. For most research, the null hypothesis is no

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117 change (stability), so dem onstration of improvement is necessary to label a program successful. But for a group with an averag e age of 80.8 years, where at least two examples of national data indicate a trend of decline in health and function at this age, the null hypothesis may be more appropriately thought of as declin e, rather than no change. As a result, demonstration of stabil ity over time, or even a slower rate of decline represents a deviation from the nu ll hypothesis and is th erefore a successful outcome. Stability in the form of main tenance of good dietary habits, exercise participation, healthy BMI, mobility, social support, and productive activities was observed in this sample of older adults living in a CCRC, and though it cannot be interpreted as intervention research or a program evaluation, the results are meaningful and should not be overlooked. The third conclusion is that physic al, social, and intelle ctual well-being is predicted by a mix of physical, social, and intellectual variables. For example, the univariate models for exercise participation demonstrated that there were more than just health-related variables (i.e. self-rated health and mobility) but also non-health related variables such as self-efficacy and marital status involved in participation. The models for physical functioning demonstr ate that self-reported health is an important predictor of mobili ty (and vice versa) but they also suggest the importance of satisfaction with giving and receiving soci al support. Prediction of participation in productive activities was explained by access va riables such as driving status, health variables such as mobility, but also interpersonal variables such as satisfaction with

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118 receiving social support and life satisfaction. This inte rdependence reinforces the three overlapping components of Rowe and Ka hn’s (1997) model of successful aging. The fourth and last major conclusion is that readiness to change may play a role in successful aging, particularly in future atte mpts to apply the principles of Rowe and Kahn’s (1997) theory. Results of the current study suggested that most participants were in the early stages of change for wei ght loss, exercise part icipation, and fruit and vegetable consumption. Stage of change was a moderator of BMI trajectory over time only, but the findings of no significan t change on the othe r two outcomes for which stages data were available (exerc ise participation and fruit and vegetable consumption) may have impacted the role of stages of change in these models. The implication for the results is that the St ages of Motivational Readiness to Change Model could be a powerful tool to identify the readiness of older adults to change behaviors important to succe ssful aging, which can inform the development of an effective and therefore succe ssful program. Though incorporating the stages of change model further complicates the va st array of assessment and programming required for a whole person successful aging program by requiring multiple intervention strategies be cr eated for each behavior, such an approach could have tremendous impact in terms of the number of older adults involved and impacted by the program. Furthermore, there is limited evidence that lifestyle interventions using the stages of motivational readiness to cha nge can generate similar improvements in cholesterol, blood pressure increased physical activity participation, and body fat percentage as structured exer cise groups (Dunn et al., 1999).

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119 Limitations While this study represents an import ant expansion of the research literature on successful aging, there are three main limitations which deserve acknowledgement and discussion: limitations of the sample, th e lack of either a comparison or control group, and the scope of measurement of Rowe and Kahn’s (1997) model of successful aging. The first limitation is the sample. The sample used in the current study is a small, non-representative convenience sample of residents living in CCR Cs. Both Freedom Village and University Village are located in Florida, so generalizability to other geographic regions is questionable. Both are lifecare communities with similar entrance and monthly fees, so generalizab ility to CCRCs with different business models cannot be established. Recruiti ng CCRCs from across the country would do much to improve the generalizability of the findings, as well as enable analyses on the impact of community age, size, locati on, and fee structure on successful aging programming. These limitations to generalizab ility do not negate the findings for this sample however, and this research represen ts a necessary first step in understanding the feasibility and effectiveness of a multi-faceted successful aging program for older adults living in CCRCs. Another sample-related limitation is th e potential of a bias that early adopters of new programs often exhibit. Early adopt ers are commonly characterized as people who easily accept new ideas and run with them. These people see the “new” as

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120 advancement and often become invested in promoting its success. One can speculate that the initial participants of the su ccessful aging progr am, being voluntary participants and members of the resident board, are likely early adopters. If this is the case, they may have been more likely th an the rest of the CCRC population to enroll in the program, participate in its activ ities, and perhaps ev en report positive outcomes. This is known as the social desirability bias and is a theoretical risk of any intervention that relies on self-report m easures. The early adopter bias does not present a problem for the current results, but should be considered when attempting to expand any successful aging program beyond the initial enrollees. It is possible that it will be more difficult to recruit participants, assess them, encourage them to participate and change their behavior. To ensure the long-term viability and widespread effectiveness, a protocol that accounts for different types of potential participants should be developed. Strategies for this will be discussed in the section on future directions. On a more positive note, there are be nefits to the early adopter bias. Jacobsen (1998) reports that “because early adopter s are not too far ahead of the average individual in innovativeness, they serve as a role model for many other members of a social system. The early adopter is respect ed by peers, embodies successful, discrete uses of new ideas, and makes judicious i nnovation-decisions (p. 20)” For this reason, the use of early adopters to pilot a successful aging interventi on may actually do much to promote its long-term validity a nd viability in the larger CCRC population.

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121 The significance of th is early adopter limitation shoul d not be inflated however. In actuality, this is typical of the medi cal model approach to health programming, whereby health professionals wait for interest ed individuals to seek out their services. These individuals are often the most motivat ed to make changes and may already be active in the promotion of their health. The field of public health is more aggressive in their needs assessment and recruitment of populations, and the development of tailored interventions to address th e needs of specific sub-groups. Independent of the ea rly adopter theory, is also po ssible that these participants represent the most functional and motivated residents in a CCRC. As a result, there may be a ceiling effect that minimizes variance and therefor e underestimates the effect sizes demonstrated. This undesirabl e situation is furthe r exacerbated by small sample size, which detracts from power to re liably detect these smaller effect sizes. The second major limitation of this res earch is the lack of either a comparison or a control group. It is important to acknowl edge that the Masterpiece Living program was intended to be based on successful aging research, not be research. It was designed to pilot a community-wide succe ssful aging program, with resources available to everyone in the CCRC. Therefore, there were no plans to have a either a randomized control group or a non-randomized but comparable comparison group to compare the participant results with. While this idea is now under consideration, the original design does not permit any compar ison of results to non-participants. One danger of not having a control or comparison group is the Hawthorne effect, whereby individual behavior may be altered because it is being studied. A control or

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122 comparison group is necessary to separate th is effect from that of a program or intervention. For example, in a study of successful aging, one might find that participants reported increased satisfaction wi th their ability to r eceive social support, or declines in BMI. A researcher could c onclude that these changes were more than a Hawthorne Effect and a result of the succe ssful aging program if there was a control group or comparison group for comparison pu rposes. If there was a Hawthorne effect, both groups might report these cha nges, but the magnitude of the change would be greater for those in the interventi on group if the program had an effect. A control or comparison group would also be helpful to assuage suspicions that the comprehensive assessment of the multiple domains of successful aging is somehow part of the successful aging pr ogram. Assessment should be independent from the customized feedback, group inte ractions, and particip ation in successful aging activities offered at the CCRC. Wit hout this distinction, the design of the program would be flawed and the cause of any changes demonstrated could not be attributed to the interventi on (Campbell & Stanley, 1963). To separate the effect of assessment from the successful agi ng program, a comparison group of nonparticipators within each community o ffering the successful aging program is necessary. This comparison group would f ill out the assessments, but not receive feedback or group interaction. In such a design, differences over time on the health promotion variables could be attributed so lely to the customized feedback and group interactions of the succe ssful aging program.

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123 The decision to incorporate a within-community comparison group of nonparticipators needs to be weighed caref ully, however. While better than no comparison group at all, the notion of non-part icipators should be inherently contrary to any successful aging program, as a we ll-designed program of this type is attempting to change not just individual be havior but also cha nge at the community level to impact the culture and environment. To achieve this, any successful aging program should be made available to all members of the community, independent of their participation in the formal assessmen t protocol. As such, a person could take part in the programming and experience improvements on the successful aging outcome of interest, but not be formally en rolled in the program. From a research perspective, this would contaminate th e comparison between the experimental participants and the within-community cont rols. From a programmatic perspective however this represents diffusion of the innovation, which is a positive outcome. To overcome this issue, the comparis on group could also be collecting control data using a between-CCRC design, by randomizing CCRCs into two groups: those who receive the successful aging assessments and programs immediately, and a second group that would serve as a control fo r a specified period of time before implementing the successful aging program With such a control group, one could attribute the cause of changes (or lack thereof) to participation in the successful aging program consisting of indivi dualized feedback, group inte raction, and goal setting – without compromising the larger comm unity goal of culture change.

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124 Since such a control group does not ex ist in this disserta tion, the next best approach is to compare any results found to national data – does this sample look like the national sample at baseline? How doe s their trajectory over time differ from national studies? While this does not es tablish causality to the successful aging program, it provides some context for the findings observed. The self-reported participation m easure represented another strategy to work within the limitations associated with the absence of a comparisonl group. As previously mentioned, participation in th e successful aging program offerings is conceptually different from participation in the surveys. It has been hypothesized that there will be a dose-response relationship be tween level of partic ipation in programs and successful aging outcomes. The current assessment protocol includes a relatively simple, self-reported measure of participation in programs. In one question with four sub-parts, participants are asked to se lf-report whether thei r physical, social, intellectual, or spiritual activities have incr eased, decreased, or stayed the same in the past six months. While better than no m easure of participation, the content and criterion/construct validity of this item is questionable. Content validity is a measure of whether item measures what it claims t o. It is possible that the question is too broad (a naming fallacy) and therefore vali dity is compromised because the question could be interpreted as something larger th an exercise particip ation. Criterion or concurrent validity is a measure of the correlation between th e item and other known or accepted measures. If the participati on variable had good criterion validity, it

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125 should have been correlated with the self-re ported frequency of ex ercise participation over time. None of the correlations were significant. Ideally, an objective yet unobtrusive sy stem to measure part icipation on a variety of levels would be availabl e to test the dose-response hypothesis. Suggestions for unobtrusive measurement of participation include a laser to count the number of individuals entering the CCRC wa lking trail, analysis of f ood and beverage orders at the community level to approximate th e popularity of successful aging-endorsed meals and monitor fruit and vegetable c onsumption at the community level, and motion detection or magnetic devices (simila r to those used in daily resident checkins) to monitor the number of users of community resources such as the weight training room. The third limitation of the current study is the limited measurement of Rowe and Kahn’s (1997) model of successful aging. While this dissertation represents an expansion of the research by measuring out comes for all three components within the same study (and including potential modifi ers of change over time from all three components), the outcomes measured ar e certainly not all-encompassing. For example, fruit and vegetable consumpti on, exercise participation, and BMI were selected as the variables to represent component #1: re ducing risk of disease and disability. While using thr ee outcomes for a particular construct has greater validity than using one outcome, it is not prudent to conclude that the results of these three variables accurately represent the to tal phenomenon of reducing disease and

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126 disability. Additionally nearly all of the data collected is self-reported, and subject to biases including socially desi rable responses and poor recall of past behavior. Future Directions for Successful Aging Research This research represents one of the first attempts to track a convenience sample of older adults living in CCRCs who participat ed in a successful aging program over a 26 month timeframe. Two future directions for successful aging research have been described already in the limitations of the current study: the need for research on larger and more representative samples, and the need for a comparison group or randomized design to determine the impact of the successful aging program being implemented at these two CCRCs. There are four additional areas that the research on successful aging should examine: 1.) improving recruitment and programming strategies, 2.) better use of technology to collect da ta, 3.) incorporating community and structural level variables into the analysis of successf ul aging, and 4.) the process of translating research findi ngs into effective programs. Future successful agi ng research (particularly intervention studies) should attempt to improve recruitment and programming strategies. The sample enrolled in the successful aging program which was the ba sis for the current analyses, though it was not an intervention study, was typical of much research where a program is involved. The participants were potentially above aver age in terms of hea lth, socialization, and eagerness to participate in successful aging activities. Though this group’s participation and support of the program was necessary to get the new program

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127 started, future studies need improved recruitm ent techniques so that a more accurate picture of the community can be analyzed. If residents who ar e not early adopters – those who may have more health concerns less support, and less contact with those outside the CCRC can be encouraged to participate formally in community-wide programs (including the assessments), the possi bility of seeing ev en greater gains and more occurrences of stability exists. It ha s been suggested that use of the community leaders in the pilot study mi ght have actually underestimate d the potential effect sizes seen. Most programmatic research and public health programming is designed for people who are in stages 3 and 4 (preparation and action) of the stages of motivational readiness to change model. Though the current research is neither an intervention study nor a program evaluation, it ca n be used to illustrate this issue of stage-appropriate programming. For example, offering an additional fitness class at a CCRC is an excellent program for those w ho need help overcoming the obstacles of getting active such as availability of classe s, or need something new and different to help them stay active. However, such a program would have the potential to impact only 31% of the CCRC residents in the curr ent study, because it is inappropriate for those in stages 1 and 2 (pre-contemplati on and contemplation), which represented approximately 70% of the participants. Peopl e in these initial stages (and all stages) need stage-appropriate programming. T hose in the precontemplation stage need education-oriented programs designed to rais e awareness of the benefits of physical fitness. Once knowledge is raised and a person moves into the contemplation phase,

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128 programs should focus on barriers to particip ation, weighing the advantages of getting active versus the risks of remaining inactiv e, and learning to set reasonable goals. Rather than measuring success traditionall y (pre-post testing maximum repetitions, VO2 max, etc.), successful programs for early stages can be measured by changes in knowledge, changes in decisional balance fo r activity/inactivity, as well as using statistical techniques to m odel individual growth patte rns through the stages of change. The second future direction for successf ul aging research is the effective use of technology to collect data. Data collec tion is a classic struggle for applied programmatic and intervention research. A cademic research standards often call for lengthy and obtrusive data coll ection protocols which are can be seen as unrealistic in an applied setting, particularly when re searching a complex, multi-faceted concept such as successful aging. As a result, a t op priority for the future of successful aging must be new technology for unobtrusive measur ement of reliable and valid data. For example, barcode software used to track me dical supplies could be adapted for use in resident services such as ex ercise classes, consultations, meal plan utilization, etc. This system is helpful for research as a measure of participa tion/utilization, while simultaneously allowing the CCRC to generate reports that establish a quantifiable value for the services offered as part of the monthly maintenance fee. Another option for using technology smartly to collect data is the use of motion sensors. For example, a motion detector that would c ount the number of tim es the door to the fitness center or chapel is opened or the walking trail is entered. This technology is

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129 quite similar to the daily “check-in” systems used at many retirement communities, and may not cause additional expense to a community. While this method does not allow individual-level analysis of part icipation, it enables a community-level investigation into the use of services and amenities over time. Some types of technology have alrea dy been incorporated into research. Many organizations leverage user-friendly, web-ba sed interfaces to facilitate seamless data collection across large numbers of research sites. Affordable products such as optically scan-able paper forms, touch-scre en computer monitors and tablet PCs can reduce the psychological and administrative bu rden of electronic data collection. The redundant workload of data collection and en try into local databases could be greatly reduced by better utilizing this technology. With less time spent on these tasks, more staff time can be dedicated to components of the successful aging program such as resident consultation on feedback, goal-se tting, and programming. This strategy of combining cognitive and behavioral strategies to produce behavior change has determined to be more successful than e ither approach alone (as reviewed by King, 2001) and should be the primary focus of Ma sterpiece Living Coordinators, not data collection and management. The third area that future successful aging research should a ddress is the collection of community and structural level data. The current research project on successful aging is typical of the field in its focus on individual level statistics, a criticism noted by Riley (1998). While the above paragra phs discuss the colle ction of communitylevel participation levels, future successful aging research will need to incorporate

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130 additional community-level statistics such as staff satisfaction and turnover rates, length of stay at each level of care, etc. Only when this data is collected can the impact of the successful aging program on the community, not just individual residents, be determined. The two must e volve simultaneously to achieve the goal of culture change. These community-level statistics ar e crucial to determining the larger policy implications of successful aging prog ramming. Newcomer, Preston, and Shock Roderick (1995) report that residents liv e in Baptist-run CCRCs an average of 7.75 years and 66% of these residents will need assisted living or skilled nursing care. Masterpiece Living reports that industry standards for CCRC residence is closer to 10 years, with 6 years spent in independent li ving, 2 years in assisted living, and 2 years in skilled nursing (persona l communication with Master piece Living, 2005). It has been hypothesized that successful aging pr ogramming could save the senior living industry tens of thousands of dollars per re sident if the combined length of stay in ALF and SNF can be reduced from four years to one year, due to the fact that lifecare residents receive subsidized care when they enter the ALF and SNF (personal communication with Masterpiece Living, 2005). Data collection at the community leve l also increases the potential that a link can be established between successful aging programs in the CCRC setting and resident and staff satisfaction rates. Resident satis faction may be impacted by participation in successful aging programs, which coul d lead to fewer non-health related

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131 vacancies/move-outs, coupled by the highly coveted marketing advantage of higher rates of resident referrals for new residents. Successful aging programs may impact staff satisfaction as well, as the theme of culture change in the two CCRCs in the current study c ontained messages of personal growth, possibility, and definiti on of staff role in the successful aging of older adults. Higher staff satisfaction ra tes, job involvement, and organizational commitment (including role clarity) has been associated with lower staff turnover rates (Hatton et al., 2000; Kiyak, Namazi, & Kahana, 1997; Sj oberg & Sverke, 2000). Collection of community-level data may help determine wh ether these findings generalizable to the independent living portion of CCRCs. Lower turnover rates could save money by reducing training costs (Waxman, Carner, & Berkenstock, 1984). Other benefits of lower turnover include creating a continuity of care not possible w ith higher turnover (Hatton et al., 2000), and the retention of expe rienced staff, which is an advantage to both the community and the residents (Hatton et al., 2000). While the industry data to create these benchmarks require additional analysis, the potential implications are obvious. Lastly, future research on succes sful aging should place a high priority on effectively translating research findings in to programs that can affect the lives of older adults. The available data on su ccessful aging, though mu ch of it is not intervention research (as is the case for the current research project), contain important implications for the design of future successful aging programs. The discussion of this issue will be limited to th e implications of the current research on

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132 future programming efforts, and cover thr ee themes: the need to match successful aging outcomes of interest with the va riables measured, the need to focus on modifiable risk factors for aging succe ssfully, and the need to consider the importance of stability when designing programs. The first priority for translation is th e need to match outcomes of interest with the variables measured. The outcome of good nutri tion as one of the actions necessary to reduce one’s risk of diseas e and disability is a good example from the current analyses. Participants involved in the successful aging program (which was the source of the data collecti on efforts on which these analyses were based) were encouraged to lower their salt intake, m onitor fat consumption, and eat more fruits and vegetables. Data was collected for each of these components of proper nutrition, but only fruit and vegetable consumption was prioritized for internal examination of the program, and for the current analyses. A broader definition of nutrition that includes multiple measures not only increases the validity of the measurement, but may also allow for more informed analys is and interpretation of the concept. The second priority for translatin g research into programming is the focus on modifiable risk factors over t hose that are non-modifiable. This is consistent with the empowering message of Rowe & Kahn’s ( 1997) theory of successful aging, which suggests that 60 70% of the variability in the way people age is due to lifestyle choices. There are examples of modifiab le risk factors th roughout the current research, particularly self-efficacy and social support. In the univariate models, both social support and self-efficacy were modi fiers of baseline performance or change

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133 over time in seven of the sixteen outco mes measured. Resnick and Nigg’s (2003) work is consistent with social cognitive theory (Bandura, 1997) which suggests that self-efficacy may mediate the relationshi p between social support and behavior change (exercise was their focus). Progr ams that recognize the interplay between these two concepts could be tremendous ly effective at changing behavior. Also important is the absence of n on-modifiable risk factors. Gender was not a significant predictor in any of the models. Age was not a modifier of baseline exercise participation, fruit and vegetable c onsumption, self-rated health, satisfaction with receiving social support, and three of the four meas ures of productive activity participation. Age did not modify fruit and vegetable consumption over time or the amount of helping done inside the CCRC. Unfortunately, age was a significant modifier of BMI and mobility at both baseline and change over time. Lastly, researchers hoping to enco urage successful agi ng should consider the importance of stability in older adults when designing programs and conducting analyses. Programs with objectives to ke ep adults active and engaged over time are as important as those that hope to incr ease performance. As mentioned before, consideration should be give n to whether the null hypothesi s is most appropriately described as no change or decline over time.

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144 Rebok, G.W., & Plude, D.J. (2001). Rela tion of physical activity to memory functioning in older adults: The memory workout program. Educational Gerontology 27 241-259. Resnick, B., & Nigg, C. (2003). Testing a transtheoretical model of exercise behavior for older adults. Nursing Research 52(2), 80-88. Reynolds, M.W., Fredman, L., Langenberg, P., & Magaziner, J. (1999). Weight, weight change, and mortality in a random sample of older community dwelling women. Journalof the American Geriatrics Society, 47 1409-1414. Riley, M.W. (1998). Successful aging Gerontologist 38, 151. Rogers, R.L., Meyer, J.S., & Mortel, K.F. (1990). After reaching retirement age physical activity sustains cerebral perfusion and cognition. Journal of the American Geriatrics Society 38, 123-128. Rowe, J.W., & Kahn, R.L. ( 1987). Human aging: usual and successful. Science, 237, 143-149. Rowe, J.W., & Kahn, R.L. (1997) Successful aging, The Gerontologist, 37, 433-440. Rowe, J.W. & Kahn, R.L. (1998). Successful Aging New York, NY: Pantheon Books. Rowe, J.W., & Seeman, T. (2000). The e ffect of race and health-related factors on naming and memory: The MacA rthur studies of successful aging. Journal of Aging & Health, 12, 69-89. Ruchlin, H.S., Morris, J., & Morris, S. (1993). Resident me dical care utilization patterns in continuous car e retirement communities. Health Care Financing Review, 14(4): 151-67.

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145 Ryff, C.D. (1982). Successful aging: a developmental approach, The Gerontologist, 22, 209-214. Sanders, J. (1997). Continuing Care Re tirement Communities: A Background and A Summary of Current Issues. U.S. Depa rtment of Health and Human Services, 1-24. Saxon, S.V., & Etten, M.J. (2002). Physical Change and Aging: A Guide for the Helping Professions (4th Ed.). New York, NY: Th e Tiresias Press. Schonfield, D. (1967). Geronting: reflections on successful aging. The Gerontologist, 7, 270-273. Seeman, T., & Chen, X. (2002 ). Risk and protective factors for physical functioning in older adults with and without ch ronic conditions: MacA rthur studies of successful aging. Journals of Gerontology, Series B, 57, S135-S144. Seeman, T.E., & Berkman, L.F. (1995). Beha vioral and psychsocial predictors of physical performance: MacArthur studies of successful aging. The Journals of Gerontology: Medical Sciences 50A, M177-M183. Seeman, T.E., Unger, J.B., McAvay, G., & Mendes de Leon, C.F. (1999). Selfefficacy beliefs and perceived declines in functional ability: Macarthur studies of successful aging. The Journals of Geront ology, Series B, 54, 214-222. Sjoberg, A. & Sverke, M. (2000). The in teractive effect of job involvement and organizational commitment on job turnover revisited: A note on the mediating role of turnover intention. Scandinavian Journal of Psychology, 41, 247-252. Sloan FA. Shayne MW. & Conover CJ. ( 1995). Continuing care retirement communities: prospects for reducing institutional long-term care Journal of Health Politics, Policy & Law. 20(1), 75-98

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148 APPENDICES

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149 Appendix A: Univariate Models in Chart Form Fruit and Vegetable Consumption Stage of Change for Fruit and Vegetable Consumption Light Exercise Participation Vigorous Exercise Participation Strength Training Exercise Participation Body Mass Index (BMI) Stage of Change for Weight Loss Self-Reported Health (Physical) Self-Reported Health (Mental) Mobility Volunteering Inside Volunteering Outside Helping Inside Helping Outside Giving Social Support Receiving Social Support

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Univariate Models all in one file APPENDIX A: UNIVARIATE RESULTS FRUIT AND VEGETABLE CONSUMPTION Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 6.109906 2.449432 2.494 129 0.014 AGE, B01 -0.003194 0.030131 -0.106 129 0.916 For TIME slope, P1 INTRCPT2, B10 0.206400 0.201078 1.026 257 0.306 AGE, B11 -0.002712 0.002458 -1.103 257 0.271 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.403617 0.292898 8.206 87 0.000 B/L FRUITVEG, B01 0.578034 0.046918 12.320 87 0.000 For TIME slope, P1 INTRCPT2, B10 0.192878 0.027600 6.988 228 0.000 B/L FRUITVEG, B11 -0.035886 0.004322 -8.302 228 0.000 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 5.950625 0.856251 6.950 128 0.000 LIGHT EX, B01 -0.012576 0.181050 -0.069 128 0.945 Page 1

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Univariate Models all in one file For TIME slope, P1 INTRCPT2, B10 0.008165 0.061273 0.133 258 0.895 LIGHT EX, B11 -0.005289 0.013007 -0.407 258 0.684 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 5.651687 0.394536 14.325 64 0.000 STRENGTH, B01 0.061764 0.127426 0.485 64 0.629 For TIME slope, P1 INTRCPT2, B10 -0.015004 0.028454 -0.527 158 0.598 STRENGTH, B11 0.003735 0.009464 0.395 158 0.693 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 5.890759 0.381313 15.449 124 0.000 VIG EX, B01 0.008575 0.103619 0.083 124 0.935 For TIME slope, P1 INTRCPT2, B10 -0.012588 0.028733 -0.438 245 0.661 VIG EX, B11 -0.002011 0.007676 -0.262 245 0.794 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 5.720414 0.195134 29.315 134 0.000 B/L SIG EVENT, B01 0.255420 0.141408 1.806 134 0.073 For TIME slope, P1 Page 2

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Univariate Models all in one file INTRCPT2, B10 0.030441 0.024521 1.241 262 0.216 SIG EVENTS, B11 -0.019358 0.007823 -2.475 262 0.014 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 4.633249 1.072204 4.321 87 0.000 BMI, B01 0.042554 0.041639 1.022 87 0.310 For TIME slope, P1 INTRCPT2, B10 0.029603 0.075130 0.394 228 0.694 BMI, B11 -0.001815 0.002933 -0.619 228 0.536 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 6.113161 0.556091 10.993 133 0.000 COMMUNITY, B01 -0.153187 0.338949 -0.452 133 0.652 For TIME slope, P1 INTRCPT2, B10 -0.006889 0.045997 -0.150 261 0.881 COMMUNITY, B11 -0.005699 0.026596 -0.214 261 0.831 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 6.044193 0.505411 11.959 133 0.000 GENDER, B01 -0.125464 0.346337 -0.362 133 0.717 For TIME slope, P1 INTRCPT2, B10 -0.003373 0.037867 -0.089 261 0.930 GENDER, B11 -0.009596 0.025861 -0.371 261 0.711 Page 3

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Univariate Models all in one file ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 4.415148 0.722537 6.111 131 0.000 MARITAL, B01 0.572922 0.280967 2.039 131 0.043 For TIME slope, P1 INTRCPT2, B10 0.015730 0.054367 0.289 256 0.772 MARITAL, B11 -0.012075 0.021362 -0.565 256 0.572 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 4.561683 0.624077 7.309 107 0.000 PHYS PARTICIP, B01 0.656322 0.297154 2.209 107 0.029 For TIME slope, P1 INTRCPT2, B10 0.046274 0.048949 0.945 251 0.346 PHYS PARTICIP, B11 -0.032154 0.023515 -1.367 251 0.173 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 5.870665 0.203116 28.903 87 0.000 FV STAGE, B01 -0.377593 0.245959 -1.535 87 0.128 For TIME slope, P1 INTRCPT2, B10 -0.020260 0.014519 -1.395 228 0.164 FV STAGE, B11 0.009082 0.018275 0.497 228 0.619 ---------------------------------------------------------------------------Page 4

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Univariate Models all in one file FRUIT AND VEGETABLE STAGE OF CHANGE Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.977574 0.986311 3.019 129 0.004 AGE, B01 -0.028897 0.012129 -2.383 129 0.019 For TIME slope, P1 INTRCPT2, B10 0.015702 0.006285 2.498 130 0.014 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.817542 0.198853 4.111 87 0.000 FRUITVEG, B01 -0.039215 0.031865 -1.231 87 0.222 For TIME slope, P1 INTRCPT2, B10 0.014870 0.006456 2.303 88 0.024 ---------------------------------------------------------------------------Final estimation of fixed effects: Page 5

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Univariate Models all in one file ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 -0.234607 0.424119 -0.553 87 0.581 BMI, B01 0.032489 0.016461 1.974 87 0.051 For TIME slope, P1 INTRCPT2, B10 0.014944 0.006466 2.311 88 0.023 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.155810 0.223466 5.172 133 0.000 COMMUNITY, B01 -0.331233 0.135138 -2.451 133 0.016 For TIME slope, P1 INTRCPT2, B10 0.017472 0.006362 2.746 134 0.007 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.438705 0.098769 4.442 87 0.000 EXERCISE STAGE, B01 0.175889 0.078087 2.252 87 0.027 For TIME slope, P1 INTRCPT2, B10 0.014886 0.006463 2.303 88 0.024 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 Page 6

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Univariate Models all in one file INTRCPT2, B00 0.779671 0.208046 3.748 133 0.000 GENDER, B01 -0.106282 0.142629 -0.745 133 0.457 For TIME slope, P1 INTRCPT2, B10 0.017114 0.006377 2.684 134 0.009 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.835340 0.375848 2.223 129 0.028 GIVING SS, B01 -0.077315 0.133607 -0.579 129 0.563 For TIME slope, P1 INTRCPT2, B10 0.014865 0.006239 2.383 130 0.019 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 -0.333219 0.472470 -0.705 87 0.482 HEALTH SE, B01 0.436248 0.220546 1.978 87 0.051 For TIME slope, P1 INTRCPT2, B10 0.014714 0.006473 2.273 88 0.025 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.520319 0.355623 1.463 128 0.146 LIGHT EX, B01 0.025496 0.075205 0.339 128 0.735 For TIME slope, P1 INTRCPT2, B10 0.017564 0.006493 2.705 129 0.008 ---------------------------------------------------------------------------Page 7

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.159363 0.302445 3.833 131 0.000 MARITAL, B01 -0.207517 0.117574 -1.765 131 0.079 For TIME slope, P1 INTRCPT2, B10 0.016629 0.006437 2.583 132 0.011 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.157557 0.535088 2.163 121 0.032 MOBILITY, B01 -0.020306 0.020533 -0.989 121 0.325 For TIME slope, P1 INTRCPT2, B10 0.013955 0.006366 2.192 122 0.030 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.491670 0.310348 1.584 127 0.115 NONHEALTH SE, B01 0.045072 0.123305 0.366 127 0.715 For TIME slope, P1 INTRCPT2, B10 0.015728 0.006335 2.483 128 0.015 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Page 8

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Univariate Models all in one file Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.808068 0.424474 1.904 129 0.059 RECEIVING SS, B01 -0.061610 0.152238 -0.405 129 0.686 For TIME slope, P1 INTRCPT2, B10 0.015418 0.006295 2.449 130 0.016 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.580010 0.549329 2.876 120 0.005 SF8 MENTAL, B01 -0.017661 0.010224 -1.727 120 0.086 For TIME slope, P1 INTRCPT2, B10 0.011979 0.006289 1.905 121 0.059 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.447152 0.396768 3.647 120 0.001 SF8 PHYSICAL, B01 -0.016179 0.007814 -2.071 120 0.040 For TIME slope, P1 INTRCPT2, B10 0.012171 0.006312 1.928 121 0.056 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.582415 0.157262 3.703 64 0.001 STRENGTH, B01 0.041192 0.050799 0.811 64 0.421 Page 9

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Univariate Models all in one file For TIME slope, P1 INTRCPT2, B10 0.027427 0.009707 2.826 65 0.007 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.356495 0.149706 2.381 124 0.019 VIGOR EX, B01 0.072799 0.040807 1.784 124 0.076 For TIME slope, P1 INTRCPT2, B10 0.015486 0.006493 2.385 125 0.019 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.423833 0.088510 4.789 87 0.000 WEIGHT STAGE, B01 0.199062 0.063751 3.122 87 0.003 For TIME slope, P1 INTRCPT2, B10 0.015735 0.006500 2.421 88 0.018 ---------------------------------------------------------------------------LIGHT EXERCISE PARTICIPATION Page 10

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 5.778811 0.880722 6.561 129 0.000 AGE, B01 -0.014507 0.010879 -1.333 129 0.185 For TIME slope, P1 INTRCPT2, B10 0.167478 0.054568 3.069 390 0.003 AGE, B11 -0.002139 0.000671 -3.187 390 0.002 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.508508 0.204911 7.362 128 0.000 LIGHT EX, B01 0.664625 0.043174 15.394 128 0.000 For TIME slope, P1 INTRCPT2, B10 0.059714 0.018640 3.203 397 0.002 LIGHT EX, B11 -0.013951 0.003917 -3.562 397 0.001 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 4.610777 0.075175 61.334 134 0.000 B/L SIGEVENT, B01 -0.001096 0.057915 -0.019 134 0.985 For TIME slope, P1 INTRCPT2, B10 -0.010819 0.007395 -1.463 400 0.144 SIGEVENT, B11 0.001758 0.002318 0.758 400 0.449 ---------------------------------------------------------------------------Page 11

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 3.991453 0.135945 29.361 124 0.000 VIGOR EX, B01 0.178651 0.035614 5.016 124 0.000 For TIME slope, P1 INTRCPT2, B10 -0.010637 0.008913 -1.193 380 0.234 VIGOR EX, B11 0.001211 0.002331 0.519 380 0.603 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 4.450967 0.144952 30.707 64 0.000 STRENGTH, B01 0.062594 0.046540 1.345 64 0.184 For TIME slope, P1 INTRCPT2, B10 -0.011453 0.008973 -1.276 202 0.204 STRENGTH, B11 0.001688 0.002826 0.598 202 0.550 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 4.840444 0.162646 29.761 87 0.000 CONDITIONS, B01 -0.089219 0.043899 -2.032 87 0.045 For TIME slope, P1 INTRCPT2, B10 -0.013427 0.009309 -1.442 286 0.150 CONDITIONS, B11 0.001764 0.002547 0.693 286 0.489 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Page 12

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Univariate Models all in one file Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 4.475143 0.188225 23.775 133 0.000 GENDER, B01 0.099211 0.127845 0.776 133 0.439 For TIME slope, P1 INTRCPT2, B10 0.003661 0.011288 0.324 400 0.746 GENDER, B11 -0.006943 0.007655 -0.907 400 0.365 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 3.377286 0.355862 9.490 128 0.000 HEALTH SE, B01 0.438995 0.124792 3.518 128 0.001 For TIME slope, P1 INTRCPT2, B10 -0.064453 0.021708 -2.969 392 0.004 HEALTH SE, B11 0.020983 0.007664 2.738 392 0.007 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 4.647421 0.279081 16.653 131 0.000 MARITAL, B01 -0.014258 0.108162 -0.132 131 0.896 For TIME slope, P1 INTRCPT2, B10 -0.012530 0.016942 -0.740 393 0.460 MARITAL, B11 0.002500 0.006610 0.378 393 0.705 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value Page 13

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Univariate Models all in one file ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 3.233787 0.408151 7.923 121 0.000 MOBILITY, B01 0.053561 0.015747 3.401 121 0.001 For TIME slope, P1 INTRCPT2, B10 -0.105663 0.029148 -3.625 370 0.001 MOBILITY, B11 0.003814 0.001113 3.426 370 0.001 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 4.885285 0.263701 18.526 107 0.000 PHYS PARTICIP, B01 -0.149554 0.124665 -1.200 107 0.233 For TIME slope, P1 INTRCPT2, B10 -0.005084 0.015178 -0.335 378 0.738 PHYS PARTICIP, B1 0.000034 0.007206 0.005 378 0.996 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 4.561049 0.450047 10.135 120 0.000 SF8 MENTAL, B01 0.000807 0.008431 0.096 120 0.924 For TIME slope, P1 INTRCPT2, B10 -0.040674 0.029708 -1.369 366 0.172 SF8 MENTAL, B11 0.000627 0.000555 1.129 366 0.260 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 Page 14

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Univariate Models all in one file INTRCPT2, B00 3.780812 0.352843 10.715 120 0.000 SF8 PHYSICAL, B01 0.016359 0.006898 2.371 120 0.019 For TIME slope, P1 INTRCPT2, B10 -0.001631 0.022786 -0.072 366 0.943 SF8 PHYSICAL, B11 -0.000114 0.000443 -0.257 366 0.797 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 4.492410 0.113691 39.514 87 0.000 EXER STAGE, B01 0.068787 0.091315 0.753 87 0.453 For TIME slope, P1 INTRCPT2, B10 -0.006382 0.006081 -1.049 286 0.295 EXER STAGE, B11 -0.001636 0.005155 -0.317 286 0.751 ---------------------------------------------------------------------------VIGOROUS EXERCISE PARTICIPATION Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------Page 15

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Univariate Models all in one file For INTRCPT1, P0 INTRCPT2, B00 3.195079 0.149075 21.433 134 0.000 B/L SIGEVENTS, B0 0.022692 0.115105 0.197 134 0.844 For TIME slope, P1 INTRCPT2, B10 -0.011649 0.013393 -0.870 386 0.385 SIG EVENTS, B11 0.000324 0.004150 0.078 386 0.938 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 5.895371 1.703739 3.460 129 0.001 AGE, B01 -0.033346 0.021071 -1.583 129 0.116 For TIME slope, P1 INTRCPT2, B10 0.082184 0.097723 0.841 376 0.401 AGE, B11 -0.001141 0.001203 -0.949 376 0.344 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.864029 0.644381 1.341 128 0.182 LIGHT EX, B01 0.507264 0.135858 3.734 128 0.000 For TIME slope, P1 INTRCPT2, B10 0.051082 0.034191 1.494 381 0.136 LIGHT EX, B11 -0.013260 0.007189 -1.844 381 0.065 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 Page 16

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Univariate Models all in one file INTRCPT2, B00 2.179019 0.284859 7.649 64 0.000 STRENGTH, B01 0.327885 0.091745 3.574 64 0.001 For TIME slope, P1 INTRCPT2, B10 0.030049 0.017654 1.702 194 0.090 STRENGTH, B11 -0.010605 0.005499 -1.929 194 0.055 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.298208 0.211239 1.412 124 0.161 VIGOR EX, B01 0.889180 0.055666 15.974 124 0.000 For TIME slope, P1 INTRCPT2, B10 0.057414 0.013704 4.190 374 0.000 VIGOR EX, B11 -0.020059 0.003569 -5.621 374 0.000 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 3.549589 0.294433 12.056 87 0.000 CONDITIONS, B01 -0.170025 0.079172 -2.148 87 0.034 For TIME slope, P1 INTRCPT2, B10 -0.016204 0.016444 -0.985 271 0.326 CONDITIONS, B11 0.002084 0.004447 0.469 271 0.639 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.828119 0.373661 7.569 133 0.000 GENDER, B01 0.274688 0.253611 1.083 133 0.281 Page 17

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Univariate Models all in one file For TIME slope, P1 INTRCPT2, B10 0.014382 0.019920 0.722 386 0.471 GENDER, B11 -0.017874 0.013457 -1.328 386 0.185 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.997795 0.733865 2.722 128 0.008 HEALTH SE, B01 0.425847 0.256678 1.659 128 0.099 For TIME slope, P1 INTRCPT2, B10 -0.005018 0.037856 -0.133 380 0.895 HEALTH SE, B11 -0.001931 0.013382 -0.144 380 0.886 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.030222 0.535415 3.792 131 0.000 MARITAL, B01 0.469962 0.206699 2.274 131 0.025 For TIME slope, P1 INTRCPT2, B10 -0.014868 0.029945 -0.497 379 0.619 MARITAL, B11 0.001825 0.011682 0.156 379 0.876 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.744502 0.803332 0.927 121 0.356 MOBILITY, B01 0.095567 0.031113 3.072 121 0.003 For TIME slope, P1 INTRCPT2, B10 -0.039885 0.052776 -0.756 357 0.450 Page 18

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Univariate Models all in one file MOBILITY, B11 0.001111 0.002011 0.552 357 0.581 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 3.082383 0.392091 7.861 107 0.000 PHYS PARTICIP, B01 0.021040 0.070437 0.299 107 0.766 For TIME slope, P1 INTRCPT2, B10 -0.033573 0.022872 -1.468 363 0.143 PHYS PARTICIP, B11 0.003966 0.003804 1.042 363 0.298 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.972716 0.891729 2.212 120 0.029 SF8 MENTAL, B01 0.024098 0.016729 1.440 120 0.152 For TIME slope, P1 INTRCPT2, B10 -0.012262 0.049642 -0.247 353 0.805 SF8 MENTAL, B11 -0.000009 0.000930 -0.009 353 0.993 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.036467 0.676737 1.532 120 0.128 SF8 PHYSICAL, B01 0.044169 0.013303 3.320 120 0.002 For TIME slope, P1 INTRCPT2, B10 0.004069 0.039346 0.103 353 0.918 SF8 PHYSICAL, B11 -0.000333 0.000764 -0.435 353 0.663 ---------------------------------------------------------------------------Page 19

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.799149 0.208570 13.421 87 0.000 EXER STAGE, B01 0.215104 0.165211 1.302 87 0.197 For TIME slope, P1 INTRCPT2, B10 0.001300 0.010736 0.121 271 0.904 EXER STAGE, B11 -0.012565 0.008898 -1.412 271 0.159 ---------------------------------------------------------------------------STRENGTH TRAINING EXERCISE PARTICIPATION Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 4.255017 1.793216 2.373 129 0.019 AGE, B01 -0.025224 0.022235 -1.134 129 0.259 For TIME slope, P1 INTRCPT2, B10 0.128267 0.110590 1.160 326 0.247 Page 20

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Univariate Models all in one file AGE, B11 -0.001666 0.001360 -1.225 326 0.222 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.043025 0.693716 1.504 128 0.135 LIGHT EX, B01 0.258207 0.146358 1.764 128 0.080 For TIME slope, P1 INTRCPT2, B10 0.035120 0.041936 0.837 328 0.403 LIGHT EX, B11 -0.009256 0.008765 -1.056 328 0.292 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.667325 0.170375 3.917 64 0.000 STRENGTH, B01 0.624393 0.055184 11.315 64 0.000 For TIME slope, P1 INTRCPT2, B10 0.032854 0.014615 2.248 201 0.026 STRENGTH, B11 -0.018921 0.004655 -4.065 201 0.000 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.010212 0.265876 3.800 124 0.000 VIGOR EX, B01 0.375456 0.070962 5.291 124 0.000 For TIME slope, P1 INTRCPT2, B10 0.026729 0.015986 1.672 316 0.095 VIGOR EX, B11 -0.011251 0.004334 -2.596 316 0.010 ---------------------------------------------------------------------------Page 21

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.320764 0.295294 7.859 87 0.000 CONDITIONS, B01 -0.060355 0.078983 -0.764 87 0.447 For TIME slope, P1 INTRCPT2, B10 -0.009822 0.016380 -0.600 247 0.549 CONDITIONS, B11 0.000904 0.004433 0.204 247 0.839 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.818361 0.380028 4.785 133 0.000 GENDER, B01 0.292009 0.255677 1.142 133 0.256 For TIME slope, P1 INTRCPT2, B10 -0.004935 0.021958 -0.225 333 0.822 GENDER, B11 -0.002491 0.014974 -0.166 333 0.868 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.142215 0.770487 2.780 128 0.007 HEALTH SE, B01 0.026647 0.270401 0.099 128 0.922 For TIME slope, P1 INTRCPT2, B10 -0.000110 0.042524 -0.003 327 0.998 HEALTH SE, B11 -0.002411 0.015042 -0.160 327 0.873 ---------------------------------------------------------------------------Page 22

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.356829 0.560762 2.420 131 0.017 MARITAL B01 0.342186 0.214632 1.594 131 0.113 For TIME slope, P1 INTRCPT2, B10 0.026455 0.032626 0.811 327 0.418 MARITAL B11 -0.013590 0.012672 -1.073 327 0.285 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.168449 0.879735 1.328 121 0.187 MOBILITY, B01 0.039162 0.034010 1.151 121 0.252 For TIME slope, P1 INTRCPT2, B10 -0.061119 0.055853 -1.094 307 0.275 MOBILITY, B11 0.002030 0.002136 0.950 307 0.343 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.094745 0.488688 4.286 107 0.000 PHYS PARTICIP, B01 0.063555 0.232040 0.274 107 0.785 For TIME slope, P1 INTRCPT2, B10 -0.006698 0.028064 -0.239 323 0.812 PHYS PARTICIP, B11 -0.000998 0.013541 -0.074 323 0.942 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Page 23

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Univariate Models all in one file Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.886879 0.886868 1.000 120 0.320 SF8 MENTAL, B01 0.025991 0.016615 1.564 120 0.120 For TIME slope, P1 INTRCPT2, B10 -0.056119 0.058940 -0.952 306 0.342 SF8 MENTAL, B11 0.000867 0.001098 0.790 306 0.430 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.708614 0.689176 2.479 120 0.015 SF8 PHYSICAL, B01 0.011110 0.013611 0.816 120 0.416 For TIME slope, P1 INTRCPT2, B10 0.012837 0.042546 0.302 306 0.763 SF8 PHYSICAL, B11 -0.000457 0.000831 -0.550 306 0.582 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.135608 0.149302 14.304 134 0.000 B/L SIGEVENT, B01 0.130666 0.114580 1.140 134 0.257 For TIME slope, P1 INTRCPT2, B10 -0.011822 0.013912 -0.850 333 0.396 SIG EVENT, B11 0.000739 0.004445 0.166 333 0.868 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Page 24

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Univariate Models all in one file Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.995843 0.200460 9.956 87 0.000 EXER STAGE, B01 0.147038 0.162510 0.905 87 0.368 For TIME slope, P1 INTRCPT2, B10 0.000958 0.010947 0.088 247 0.931 EXER STAGE, B11 -0.009410 0.009094 -1.035 247 0.302 ---------------------------------------------------------------------------BODY MASS INDEX Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 42.152571 4.719740 8.931 129 0.000 AGE, B01 -0.210680 0.058127 -3.624 129 0.001 For TIME slope, P1 INTRCPT2, B10 -0.619854 0.299592 -2.069 257 0.039 AGE, B11 0.007081 0.003660 1.935 257 0.054 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------Page 25

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Univariate Models all in one file For INTRCPT1, P0 INTRCPT2, B00 5.842069 0.803784 7.268 87 0.000 BMI, B01 0.760295 0.031305 24.287 87 0.000 For TIME slope, P1 INTRCPT2, B10 0.818181 0.077799 10.517 228 0.000 BMI, B11 -0.033781 0.003030 -11.150 228 0.000 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 26.146664 1.114246 23.466 133 0.000 COMMUNITY, B01 -0.689312 0.686759 -1.004 133 0.318 For TIME slope, P1 INTRCPT2, B10 -0.121438 0.069848 -1.739 261 0.083 COMMUNITY, B11 0.047782 0.040059 1.193 261 0.234 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 25.640165 1.078090 23.783 87 0.000 FRUITVEG, B01 -0.067576 0.172976 -0.391 87 0.697 For TIME slope, P1 INTRCPT2, B10 0.086615 0.053306 1.625 228 0.105 FRUITVEG, B11 -0.020633 0.008309 -2.483 228 0.014 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 24.460902 1.039026 23.542 133 0.000 Page 26

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Univariate Models all in one file GENDER, B01 0.459850 0.711337 0.646 133 0.519 For TIME slope, P1 INTRCPT2, B10 0.019909 0.056130 0.355 261 0.723 GENDER, B11 -0.044479 0.038379 -1.159 261 0.248 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 11.344779 1.956651 5.798 87 0.000 HEALTH SE, B01 6.530917 0.913198 7.152 87 0.000 For TIME slope, P1 INTRCPT2, B10 0.833542 0.110011 7.577 228 0.000 HEALTH SE, B11 -0.411061 0.051415 -7.995 228 0.000 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 26.654081 1.641241 16.240 34 0.000 LIGHT EX, B01 -0.303600 0.315731 -0.962 34 0.343 For TIME slope, P1 INTRCPT2, B10 -0.197310 0.065791 -2.999 95 0.004 LIGHT EX, B11 0.024927 0.012685 1.965 95 0.052 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 26.194836 1.530835 17.111 131 0.000 MARITAL, B01 -0.421152 0.593836 -0.709 131 0.479 For TIME slope, P1 Page 27

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Univariate Models all in one file INTRCPT2, B10 0.016723 0.081867 0.204 256 0.839 MARITAL, B11 -0.022310 0.032206 -0.693 256 0.489 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 25.789775 2.629700 9.807 121 0.000 MOBILITY, B01 -0.025762 0.101379 -0.254 121 0.800 For TIME slope, P1 INTRCPT2, B10 -0.400861 0.169976 -2.358 245 0.019 MOBILITY, B11 0.013958 0.006402 2.180 245 0.030 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 26.175416 1.352277 19.357 107 0.000 PHYS PARTICIP, B01 -0.553587 0.642318 -0.862 107 0.391 For TIME slope, P1 INTRCPT2, B10 -0.237911 0.071414 -3.331 251 0.001 PHYS PARTICIP, B1 0.097960 0.034237 2.861 251 0.005 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 25.225420 2.643058 9.544 120 0.000 SF8 MENTAL, B01 0.000498 0.049334 0.010 120 0.992 For TIME slope, P1 INTRCPT2, B10 -0.151506 0.172681 -0.877 239 0.381 SF8 MENTAL, B11 0.002003 0.003185 0.629 239 0.530 Page 28

PAGE 188

Univariate Models all in one file ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 34.073335 1.777686 19.167 120 0.000 SF8 PHYSICAL, B01 -0.176774 0.034943 -5.059 120 0.000 For TIME slope, P1 INTRCPT2, B10 -0.385633 0.107907 -3.574 239 0.001 SF8 PHYSICAl, B11 0.006828 0.002120 3.220 239 0.002 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 24.745111 0.400414 61.799 134 0.000 B/L SIG EVENT, B0 0.458271 0.289197 1.585 134 0.115 For TIME slope, P1 INTRCPT2, B10 -0.016761 0.037633 -0.445 262 0.656 SIG EVENTs, B11 -0.010443 0.011926 -0.876 262 0.382 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 23.830623 0.427964 55.684 87 0.000 WEIGHT STAGE, B01 1.538847 0.313721 4.905 87 0.000 For TIME slope, P1 INTRCPT2, B10 0.011910 0.023504 0.507 228 0.612 Page 29

PAGE 189

Univariate Models all in one file WEIGHT STAGE, B11 -0.063603 0.019320 -3.292 228 0.002 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 25.122607 0.809657 31.029 64 0.000 STRENGTH, B01 0.098778 0.260188 0.380 64 0.705 For TIME slope, P1 INTRCPT2, B10 -0.086057 0.040826 -2.108 158 0.036 STRENGTH, B11 0.024231 0.013635 1.777 158 0.077 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 26.654081 1.641241 16.240 34 0.000 VIG EX, B01 -0.303600 0.315731 -0.962 34 0.343 For TIME slope, P1 INTRCPT2, B10 -0.197310 0.065791 -2.999 95 0.004 VIG EX, B11 0.024927 0.012685 1.965 95 0.052 ---------------------------------------------------------------------------Page 30

PAGE 190

Univariate Models all in one file WEIGHT LOSS STAGE OF CHANGE Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 6.027363 1.184648 5.088 129 0.000 AGE, B01 -0.063457 0.014576 -4.353 129 0.000 For TIME slope, P1 INTRCPT2, B10 -0.002288 0.006639 -0.345 130 0.731 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 -2.210759 0.441165 -5.011 87 0.000 BMI, B01 0.120237 0.017135 7.017 87 0.000 For TIME slope, P1 INTRCPT2, B10 -0.003118 0.006906 -0.452 88 0.652 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.335492 0.281377 4.746 133 0.000 COMMUNITY, B01 -0.293281 0.171573 -1.709 133 0.089 For TIME slope, P1 INTRCPT2, B10 -0.001488 0.006677 -0.223 134 0.824 ---------------------------------------------------------------------------Page 31

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.577166 0.119909 4.813 87 0.000 EXERCISE STAGE, B01 0.307078 0.095518 3.215 87 0.002 For TIME slope, P1 INTRCPT2, B10 -0.003504 0.006839 -0.512 88 0.609 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.726767 0.252240 2.881 87 0.005 FRUITVEG, B01 0.020262 0.040401 0.502 87 0.617 For TIME slope, P1 INTRCPT2, B10 -0.003664 0.006845 -0.535 88 0.593 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.666839 0.097462 6.842 87 0.000 FRUIT VEG STAGE, B01 0.443021 0.117391 3.774 87 0.000 For TIME slope, P1 INTRCPT2, B10 -0.003373 0.006793 -0.497 88 0.620 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value Page 32

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Univariate Models all in one file ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.821244 0.258525 3.177 133 0.002 GENDER, B01 0.040847 0.177081 0.231 133 0.818 For TIME slope, P1 INTRCPT2, B10 -0.002101 0.006673 -0.315 134 0.753 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.852320 0.469938 1.814 129 0.072 GIVING SS, B01 0.003518 0.166945 0.021 129 0.983 For TIME slope, P1 INTRCPT2, B10 -0.003874 0.006627 -0.585 130 0.559 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 -1.201753 0.570680 -2.106 87 0.038 HEALTH SE, B01 0.965090 0.266281 3.624 87 0.001 For TIME slope, P1 INTRCPT2, B10 -0.003749 0.006839 -0.548 88 0.585 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.790427 0.431212 1.833 128 0.069 LIGHT EX, B01 0.020753 0.091100 0.228 128 0.820 For TIME slope, P1 Page 33

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Univariate Models all in one file INTRCPT2, B10 -0.001898 0.006777 -0.280 129 0.780 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.836472 0.383053 2.184 131 0.031 MARITAL B01 0.020328 0.148824 0.137 131 0.892 For TIME slope, P1 INTRCPT2, B10 -0.001517 0.006763 -0.224 132 0.823 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.065432 0.670081 1.590 121 0.114 MOBILITY, B01 -0.007498 0.025772 -0.291 121 0.772 For TIME slope, P1 INTRCPT2, B10 -0.002503 0.006896 -0.363 122 0.717 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.400177 0.390435 1.025 127 0.308 NONHEALTH SE, B01 0.185702 0.154788 1.200 127 0.233 For TIME slope, P1 INTRCPT2, B10 -0.000908 0.006678 -0.136 128 0.892 ---------------------------------------------------------------------------Final estimation of fixed effects: Page 34

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Univariate Models all in one file ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.263593 0.526912 2.398 129 0.018 RECEIVING SS, B01 -0.133530 0.188515 -0.708 129 0.480 For TIME slope, P1 INTRCPT2, B10 -0.002259 0.006791 -0.333 130 0.740 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.358833 0.648400 3.638 120 0.001 SF8 MENTAL, B01 -0.027194 0.012067 -2.254 120 0.026 For TIME slope, P1 INTRCPT2, B10 -0.003286 0.007057 -0.466 121 0.642 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.873255 0.489628 3.826 120 0.000 SF8 PHYSICAL, B01 -0.019226 0.009634 -1.995 120 0.048 For TIME slope, P1 INTRCPT2, B10 -0.002686 0.007079 -0.379 121 0.705 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------Page 35

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Univariate Models all in one file For INTRCPT1, P0 INTRCPT2, B00 1.163461 0.192832 6.034 64 0.000 STRENGTH, B01 -0.076362 0.061972 -1.232 64 0.223 For TIME slope, P1 INTRCPT2, B10 -0.000950 0.008742 -0.109 65 0.914 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.017253 0.194777 5.223 124 0.000 VIGOR EX, B01 -0.043488 0.052510 -0.828 124 0.409 For TIME slope, P1 INTRCPT2, B10 -0.002938 0.006763 -0.434 125 0.664 ---------------------------------------------------------------------------SELF-RATED HEALTH: PHYSICAL HEALTH SUBSCALE Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 46.432266 10.393230 4.468 129 0.000 Page 36

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Univariate Models all in one file AGE, B01 0.027055 0.128602 0.210 129 0.834 For TIME slope, P1 INTRCPT2, B10 0.437835 0.513564 0.853 375 0.395 AGE, B11 -0.006330 0.006356 -0.996 375 0.320 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 49.358578 0.893365 55.250 134 0.000 B/L SIGEVENTS, B01 -0.863682 0.698176 -1.237 134 0.219 For TIME slope, P1 INTRCPT2, B10 0.026042 0.070015 0.372 384 0.710 SIG EVENTS, B11 -0.032589 0.022047 -1.478 384 0.140 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 9.359059 2.175180 4.303 120 0.000 BASESF8P, B01 0.784315 0.042629 18.399 120 0.000 For TIME slope, P1 INTRCPT2, B10 0.454413 0.186690 2.434 362 0.016 BASESF8P, B11 -0.010730 0.003625 -2.960 362 0.004 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 70.744353 5.017157 14.100 87 0.000 BMI, B01 -0.879074 0.193257 -4.549 87 0.000 Page 37

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Univariate Models all in one file For TIME slope, P1 INTRCPT2, B10 -0.267628 0.228709 -1.170 87 0.246 BMI, B11 0.007876 0.008780 0.897 87 0.372 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 48.940698 1.187609 41.209 87 0.000 BP RISK, B01 -1.851035 1.959016 -0.945 87 0.348 For TIME slope, P1 INTRCPT2, B10 -0.028747 0.046897 -0.613 271 0.540 BP RISK, B11 -0.101164 0.082406 -1.228 271 0.221 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 48.418870 2.380420 20.340 133 0.000 COMMUNITY, B01 0.223389 1.503202 0.149 133 0.882 For TIME slope, P1 INTRCPT2, B10 -0.105918 0.110741 -0.956 384 0.340 COMMUNITY, B11 0.023501 0.068618 0.342 384 0.732 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 54.180652 1.751510 30.934 87 0.000 CONDITIONS, B01 -1.855678 0.476132 -3.897 87 0.000 For TIME slope, P1 INTRCPT2, B10 -0.079359 0.081215 -0.977 271 0.330 Page 38

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Univariate Models all in one file CONDITIONS, B11 0.005766 0.022003 0.262 271 0.793 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 49.169455 2.619919 18.768 87 0.000 FRUITVEG, B01 -0.150747 0.421794 -0.357 87 0.721 For TIME slope, P1 INTRCPT2, B10 0.073228 0.111497 0.657 271 0.512 FRUITVEG, B11 -0.022941 0.017869 -1.284 271 0.201 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 46.574108 2.254795 20.656 133 0.000 GENDER, B01 1.577891 1.537817 1.026 133 0.307 For TIME slope, P1 INTRCPT2, B10 -0.088102 0.103422 -0.852 384 0.395 GENDER, B11 0.013042 0.070207 0.186 384 0.853 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 36.080223 4.206429 8.577 129 0.000 GIVING SS, B01 4.576274 1.481815 3.088 129 0.003 For TIME slope, P1 INTRCPT2, B10 -0.031395 0.241878 -0.130 129 0.897 GIVING SS, B11 -0.013951 0.084167 -0.166 129 0.869 ---------------------------------------------------------------------------Page 39

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 48.732655 2.473224 19.704 70 0.000 GROUP ACTS, B01 -0.421435 1.403674 -0.300 70 0.765 For TIME slope, P1 INTRCPT2, B10 0.050882 0.107364 0.474 207 0.636 GROUP ACTS, B11 -0.036751 0.060942 -0.603 207 0.547 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 64.258260 5.767954 11.141 87 0.000 HEALTH SE, B01 -7.503397 2.671980 -2.808 87 0.007 For TIME slope, P1 INTRCPT2, B10 -0.152608 0.238297 -0.640 271 0.522 HEALTH SE, B11 0.043013 0.109554 0.393 271 0.695 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 41.471861 3.917297 10.587 128 0.000 LIGHT EX, B01 1.622258 0.825828 1.964 128 0.051 For TIME slope, P1 INTRCPT2, B10 -0.010358 0.185221 -0.056 378 0.956 LIGHT EX, B11 -0.015482 0.038945 -0.398 378 0.691 ---------------------------------------------------------------------------Page 40

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 44.201182 3.239877 13.643 131 0.000 MARITAL, B01 1.840766 1.253176 1.469 131 0.144 For TIME slope, P1 INTRCPT2, B10 -0.269743 0.147122 -1.833 377 0.067 MARITAL, B11 0.079194 0.057867 1.369 377 0.172 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 52.206234 1.817787 28.720 87 0.000 MEDS, B01 -1.517619 0.605105 -2.508 87 0.014 For TIME slope, P1 INTRCPT2, B10 -0.044629 0.075340 -0.592 271 0.554 MEDS, B11 -0.006870 0.025163 -0.273 271 0.785 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 31.855780 4.940841 6.447 121 0.000 MOBILITY, B01 0.647612 0.191395 3.384 121 0.001 For TIME slope, P1 INTRCPT2, B10 -0.592315 0.269276 -2.200 358 0.028 MOBILITY, B11 0.019957 0.010245 1.948 358 0.052 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Page 41

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Univariate Models all in one file Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 48.057156 3.037433 15.822 107 0.000 PHYS PARTICIP, B01 0.251502 1.437532 0.175 107 0.862 For TIME slope, P1 INTRCPT2, B10 -0.128260 0.134242 -0.955 362 0.340 PHYS PARTICIP, B11 0.030067 0.063854 0.471 362 0.638 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 43.678648 5.046102 8.656 129 0.000 RECEIVING, B01 1.835441 1.787368 1.027 129 0.307 For TIME slope, P1 INTRCPT2, B10 -0.044835 0.245009 -0.183 378 0.855 RECEIVING, B11 -0.010333 0.086193 -0.120 378 0.905 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 48.802291 1.997212 24.435 64 0.000 STRENGTH, B01 -0.073527 0.645081 -0.114 64 0.910 For TIME slope, P1 INTRCPT2, B10 0.031773 0.088164 0.360 193 0.719 STRENGTH, B11 -0.014529 0.027379 -0.531 193 0.596 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value Page 42

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Univariate Models all in one file ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 45.049754 1.694267 26.590 124 0.000 VIGOR EX, B01 1.129433 0.445300 2.536 124 0.013 For TIME slope, P1 INTRCPT2, B10 -0.075493 0.083822 -0.901 364 0.369 VIGOR EX, B11 -0.001550 0.021715 -0.071 364 0.944 ---------------------------------------------------------------------------SELF-RATED HEALTH: MENTAL HEALTH SUBSCALE Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 53.426074 7.119634 7.504 129 0.000 AGE, B01 -0.011756 0.088117 -0.133 129 0.895 For TIME slope, P1 INTRCPT2, B10 0.813365 0.537251 1.514 375 0.131 AGE, B11 -0.010259 0.006646 -1.544 375 0.123 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Page 43

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Univariate Models all in one file Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 50.619332 2.873416 17.616 120 0.000 SF8 PHYSICAL, B01 0.036849 0.056340 0.654 120 0.514 For TIME slope, P1 INTRCPT2, B10 -0.340432 0.218071 -1.561 362 0.119 SF8 PHYSICAL, B11 0.006472 0.004233 1.529 362 0.127 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 53.424915 3.531040 15.130 87 0.000 BMI, B01 -0.035385 0.135845 -0.260 87 0.795 For TIME slope, P1 INTRCPT2, B10 0.208875 0.238547 0.876 271 0.382 BMI, B11 -0.010535 0.009155 -1.151 271 0.251 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 52.886206 0.748818 70.626 87 0.000 BPRISK, B01 -1.008654 1.247236 -0.809 87 0.421 For TIME slope, P1 INTRCPT2, B10 -0.035419 0.050556 -0.701 271 0.484 BPRISK, B11 -0.079204 0.088260 -0.897 271 0.371 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------Page 44

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Univariate Models all in one file For INTRCPT1, P0 INTRCPT2, B00 52.239370 1.599910 32.651 133 0.000 COMMUNITY, B01 0.187609 1.011295 0.186 133 0.853 For TIME slope, P1 INTRCPT2, B10 0.178559 0.117870 1.515 384 0.130 COMMUNITY, B11 -0.126009 0.073104 -1.724 384 0.085 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 54.370798 1.191878 45.618 87 0.000 CONDITIONS, B01 -0.571806 0.322471 -1.773 87 0.079 For TIME slope, P1 INTRCPT2, B10 -0.119128 0.086728 -1.374 271 0.171 CONDITIONS, B11 0.018041 0.023583 0.765 271 0.445 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 51.573028 1.667906 30.921 87 0.000 FRUITVEG, B01 0.165492 0.269031 0.615 87 0.540 For TIME slope, P1 INTRCPT2, B10 -0.050858 0.119954 -0.424 271 0.671 FRUITVEG, B11 -0.001909 0.019243 -0.099 271 0.922 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 51.519705 1.527006 33.739 133 0.000 Page 45

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Univariate Models all in one file GENDER, B01 0.709895 1.040634 0.682 133 0.496 For TIME slope, P1 INTRCPT2, B10 -0.122100 0.110424 -1.106 384 0.270 GENDER, B11 0.077200 0.074911 1.031 384 0.304 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 44.688400 2.887679 15.476 129 0.000 GIVING SS, B01 2.801029 1.016938 2.754 129 0.007 For TIME slope, P1 INTRCPT2, B10 0.069086 0.253537 0.272 377 0.785 GIVING SS, B11 -0.028828 0.088297 -0.326 377 0.744 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 50.551258 1.639523 30.833 70 0.000 GROUP ACTIVITY, B01 1.218685 0.931626 1.308 70 0.195 For TIME slope, P1 INTRCPT2, B10 -0.123625 0.102889 -1.202 207 0.231 GROUP ACTIVITY, B11 0.039942 0.058440 0.683 207 0.495 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 56.274676 3.768022 14.935 87 0.000 HEALTH SE, B01 -1.751554 1.741469 -1.006 87 0.318 For TIME slope, P1 Page 46

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Univariate Models all in one file INTRCPT2, B10 0.052420 0.256457 0.204 271 0.838 HEALTH SE, B11 -0.052682 0.118012 -0.446 271 0.655 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 50.079412 2.669753 18.758 128 0.000 LIGHT EX, B01 0.542945 0.563372 0.964 128 0.337 For TIME slope, P1 INTRCPT2, B10 -0.379723 0.196834 -1.929 378 0.054 LIGHT EX, B11 0.077608 0.041391 1.875 378 0.061 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 50.535903 2.248027 22.480 131 0.000 MARITAL, B01 0.794003 0.870104 0.913 131 0.364 For TIME slope, P1 INTRCPT2, B10 -0.279628 0.155822 -1.795 377 0.073 MARITAL, B11 0.107664 0.061214 1.759 377 0.079 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 54.087257 1.175487 46.013 87 0.000 MEDICATIONS, B01 -0.593871 0.390381 -1.521 87 0.132 For TIME slope, P1 INTRCPT2, B10 -0.143506 0.080705 -1.778 271 0.076 MEDICATIONS, B11 0.031651 0.026966 1.174 271 0.242 Page 47

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Univariate Models all in one file ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 44.823217 3.459543 12.956 121 0.000 MOBILITY, B01 0.300661 0.133512 2.252 121 0.026 For TIME slope, P1 INTRCPT2, B10 -0.019929 0.281516 -0.071 358 0.944 MOBILITY, B11 0.000506 0.010731 0.047 358 0.963 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 51.348886 2.012686 25.513 107 0.000 PHYS PARTICIP, B01 0.645571 0.953745 0.677 107 0.500 For TIME slope, P1 INTRCPT2, B10 0.001634 0.144743 0.011 362 0.991 PHYS PARTICIP, B11 -0.012188 0.068825 -0.177 362 0.860 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 50.500978 3.414199 14.791 129 0.000 RECEIVING SS, B01 0.747894 1.208969 0.619 129 0.537 For TIME slope, P1 INTRCPT2, B10 0.010256 0.268935 0.038 378 0.970 RECEIVING SS, B11 -0.009242 0.094643 -0.098 378 0.923 ---------------------------------------------------------------------------Page 48

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 49.927568 2.430690 20.540 68 0.000 SOLITARY ACTS, B01 1.089047 1.095373 0.994 68 0.324 For TIME slope, P1 INTRCPT2, B10 -0.029093 0.135888 -0.214 200 0.831 SOLITARY ACTS, B11 -0.013189 0.059991 -0.220 200 0.826 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 50.170693 1.383888 36.253 64 0.000 STRENGTH, B01 0.820537 0.446417 1.838 64 0.070 For TIME slope, P1 INTRCPT2, B10 0.008210 0.085891 0.096 193 0.924 STRENGTH, B11 -0.023280 0.026692 -0.872 193 0.384 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 50.183503 1.176166 42.667 124 0.000 VIGOR EX, B01 0.696880 0.308697 2.257 124 0.026 For TIME slope, P1 INTRCPT2, B10 0.000241 0.090637 0.003 364 0.998 VIGOR EX, B11 -0.002273 0.023506 -0.097 364 0.923 ---------------------------------------------------------------------------Page 49

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Univariate Models all in one file MOBILITY Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 26.083999 0.430054 60.653 134 0.000 B/L SIGEVENT, B01 0.296213 0.320314 0.925 134 0.357 For TIME slope, P1 INTRCPT2, B10 0.005173 0.043374 0.119 381 0.906 SIG EVENT, B11 0.008717 0.013685 0.637 381 0.524 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 39.720361 4.669737 8.506 129 0.000 AGE, B01 -0.163681 0.057612 -2.841 129 0.006 For TIME slope, P1 Page 50

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Univariate Models all in one file INTRCPT2, B10 0.990994 0.317798 3.118 370 0.002 AGE, B11 -0.011947 0.003915 -3.051 370 0.003 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 29.446653 2.328561 12.646 87 0.000 BMI, B01 -0.113138 0.090196 -1.254 87 0.213 For TIME slope, P1 INTRCPT2, B10 -0.045221 0.152617 -0.296 273 0.767 BMI, B11 0.001719 0.005924 0.290 273 0.772 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, B0 INTRCPT2, G00 28.055632 1.146070 24.480 133 0.000 COMMUNITY, G01 -1.112348 0.719538 -1.546 133 0.124 For TIME slope, B1 INTRCPT2, G10 0.351455 0.068952 5.097 380 0.000 COMMUNITY, G11 -0.207526 0.042534 -4.879 380 0.000 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 26.674307 0.825389 32.317 87 0.000 CONDITIONS, B01 -0.033531 0.222639 -0.151 87 0.881 For TIME slope, P1 INTRCPT2, B10 0.000257 0.055569 0.005 273 0.996 Page 51

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Univariate Models all in one file CONDITIONS, B11 -0.000625 0.015310 -0.041 273 0.968 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 24.049546 0.833476 28.855 127 0.000 DRIVING, B01 2.795074 0.915863 3.052 127 0.003 For TIME slope, P1 INTRCPT2, B10 -0.076680 0.056911 -1.347 367 0.179 DRIVING, B11 0.124081 0.062021 2.001 367 0.046 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 25.981427 1.082125 24.010 133 0.000 GENDER, B01 0.257968 0.740158 0.349 133 0.728 For TIME slope, P1 INTRCPT2, B10 -0.041296 0.066809 -0.618 380 0.537 GENDER, B11 0.052135 0.045332 1.150 380 0.251 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 19.769492 1.933692 10.224 129 0.000 GIVING SS, B01 2.394612 0.681926 3.512 129 0.001 For TIME slope, P1 INTRCPT2, B10 0.075105 0.138458 0.542 373 0.587 GIVING SS, B11 -0.015214 0.048577 -0.313 373 0.754 ---------------------------------------------------------------------------Page 52

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 28.215872 2.568797 10.984 87 0.000 HEALTH SE, B01 -0.776600 1.191645 -0.652 87 0.516 For TIME slope, P1 INTRCPT2, B10 -0.099612 0.168507 -0.591 273 0.555 HEALTH SE, B11 0.046202 0.078290 0.590 273 0.555 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 25.218091 1.974926 12.769 128 0.000 LIGHT EX, B01 0.253504 0.415739 0.610 128 0.543 For TIME slope, P1 INTRCPT2, B10 -0.012038 0.120285 -0.100 374 0.921 LIGHT EX, B11 0.008792 0.025249 0.348 374 0.728 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 24.290562 1.586500 15.311 131 0.000 MARITAL, B01 0.821613 0.615833 1.334 131 0.185 For TIME slope, P1 INTRCPT2, B10 -0.142227 0.097701 -1.456 373 0.146 MARITAL, B11 0.069809 0.038244 1.825 373 0.068 ---------------------------------------------------------------------------Page 53

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 27.635838 0.804212 34.364 87 0.000 MEDS, B01 -0.410767 0.268661 -1.529 87 0.130 For TIME slope, P1 INTRCPT2, B10 0.024143 0.052749 0.458 273 0.647 MEDS, B11 -0.010277 0.017932 -0.573 273 0.567 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 26.369429 0.496130 53.150 127 0.000 NONHEALTH SE, B01 0.033317 0.102691 0.324 127 0.746 For TIME slope, P1 INTRCPT2, B10 0.010951 0.031780 0.345 369 0.730 NONHEALTH SE, B11 0.006783 0.006484 1.046 369 0.297 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 28.473831 1.357725 20.972 107 0.000 PHYS PARTICIP, B0 -0.877849 0.642510 -1.366 107 0.175 For TIME slope, P1 INTRCPT2, B10 0.032902 0.087933 0.374 359 0.708 PHYS PARTICIP, B11 -0.006148 0.041667 -0.148 359 0.883 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Page 54

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Univariate Models all in one file Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 21.517980 2.368658 9.084 129 0.000 RECEIVING SS, B01 1.731774 0.839290 2.063 129 0.041 For TIME slope, P1 INTRCPT2, B10 0.054723 0.141391 0.387 374 0.699 RECEIVING SS, B11 -0.006438 0.050153 -0.128 374 0.898 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 22.938046 2.290759 10.013 120 0.000 SF8 MENTAL, B01 0.071911 0.043018 1.672 120 0.097 For TIME slope, P1 INTRCPT2, B10 0.323352 0.157486 2.053 349 0.040 SF8 MENTAL, B11 -0.005228 0.002938 -1.779 349 0.076 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 18.789483 1.701355 11.044 120 0.000 SF8 PHYSICAL, B01 0.158017 0.033385 4.733 120 0.000 For TIME slope, P1 INTRCPT2, B10 -0.255717 0.118604 -2.156 349 0.032 SF8 PHYSICAL, B11 0.005961 0.002312 2.578 349 0.011 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value Page 55

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Univariate Models all in one file ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 26.001520 0.837825 31.035 64 0.000 STRENGTH, B01 0.190641 0.271072 0.703 64 0.484 For TIME slope, P1 INTRCPT2, B10 -0.000535 0.059438 -0.009 192 0.993 STRENGTH, B11 0.002088 0.018957 0.110 192 0.913 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 23.993104 0.797596 30.082 124 0.000 VIGOR EX, B01 0.688463 0.210122 3.276 124 0.002 For TIME slope, P1 INTRCPT2, B10 -0.074615 0.052905 -1.410 359 0.159 VIGOR EX, B11 0.031164 0.014024 2.222 359 0.027 ---------------------------------------------------------------------------VOLUNTEERING INSIDE THE CCRC Page 56

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.421694 0.488040 2.913 129 0.005 AGE, B01 -0.009952 0.006037 -1.648 129 0.101 For TIME slope, P1 INTRCPT2, B10 0.072115 0.027972 2.578 382 0.011 AGE, B11 -0.000892 0.000345 -2.588 382 0.010 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.638576 0.043010 14.847 134 0.000 B/L SIGEVENTS, B01 -0.012139 0.033317 -0.364 134 0.716 For TIME slope, P1 INTRCPT2, B10 0.008486 0.003790 2.239 391 0.026 SIG EVENTS, B11 -0.003191 0.001175 -2.716 391 0.007 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.591855 0.114096 5.187 133 0.000 COMMUNITY, B01 0.025073 0.071713 0.350 133 0.727 For TIME slope, P1 INTRCPT2, B10 0.009971 0.006216 1.604 391 0.109 COMMUNITY, B11 -0.006548 0.003803 -1.722 391 0.085 ---------------------------------------------------------------------------Final estimation of fixed effects: Page 57

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Univariate Models all in one file ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.402279 0.088257 4.558 127 0.000 DRIVING, B01 0.266323 0.096458 2.761 127 0.007 For TIME slope, P1 INTRCPT2, B10 -0.009094 0.005196 -1.750 378 0.080 DRIVING, B11 0.010046 0.005590 1.797 378 0.073 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.550013 0.108172 5.085 133 0.000 GENDER, B01 0.056655 0.073517 0.771 133 0.442 For TIME slope, P1 INTRCPT2, B10 0.001928 0.005732 0.336 391 0.737 GENDER, B11 -0.001529 0.003887 -0.393 391 0.694 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 -0.021073 0.202624 -0.104 129 0.918 GIVING SS, B01 0.232750 0.071350 3.262 129 0.002 For TIME slope, P1 INTRCPT2, B10 -0.025910 0.012348 -2.098 385 0.036 GIVING SS, B11 0.009071 0.004318 2.101 385 0.036 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Page 58

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Univariate Models all in one file Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 -0.199717 0.327507 -0.610 69 0.544 LIFE HAPPY, B01 0.175617 0.077310 2.272 69 0.026 For TIME slope, P1 INTRCPT2, B10 -0.016519 0.016092 -1.027 204 0.306 LIFE HAPPY, B11 0.005106 0.003861 1.322 204 0.188 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.147997 0.191293 0.774 129 0.441 LIFE SATISF, B01 0.113266 0.044256 2.559 129 0.012 For TIME slope, P1 INTRCPT2, B10 -0.006126 0.010181 -0.602 387 0.547 LIFE SATISF, B11 0.001358 0.002344 0.580 387 0.562 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.931789 0.157369 5.921 131 0.000 MARITAL, B01 -0.119887 0.060865 -1.970 131 0.051 For TIME slope, P1 INTRCPT2, B10 -0.004678 0.008181 -0.572 385 0.567 MARITAL, B11 0.001780 0.003223 0.552 385 0.581 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------Page 59

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Univariate Models all in one file For INTRCPT1, P0 INTRCPT2, B00 -0.020914 0.238745 -0.088 121 0.931 MOBILITY, B01 0.025375 0.009231 2.749 121 0.007 For TIME slope, P1 INTRCPT2, B10 -0.009663 0.014587 -0.662 361 0.508 MOBILITY, B11 0.000367 0.000556 0.661 361 0.509 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.195002 0.233871 0.834 129 0.406 RECEIVING SS, B01 0.157703 0.082946 1.901 129 0.059 For TIME slope, P1 INTRCPT2, B10 -0.009025 0.011944 -0.756 387 0.450 RECEIVING SS, B11 0.003056 0.004244 0.720 387 0.472 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.551236 0.266335 2.070 120 0.040 SF8 MENTAL, B01 0.001293 0.004990 0.259 120 0.796 For TIME slope, P1 INTRCPT2, B10 -0.002354 0.014734 -0.160 357 0.874 SF8 MENTAL, B11 0.000067 0.000275 0.242 357 0.809 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.117708 0.206871 0.569 120 0.570 Page 60

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Univariate Models all in one file SF8 PHYSICAL, B01 0.009975 0.004048 2.464 120 0.015 For TIME slope, P1 INTRCPT2, B10 -0.013216 0.011384 -1.161 357 0.247 SF8 PHYSICAL, B11 0.000283 0.000221 1.281 357 0.201 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.633350 0.164154 3.858 107 0.000 SOC PARTICIP, B01 0.004368 0.075819 0.058 107 0.955 For TIME slope, P1 INTRCPT2, B10 -0.006389 0.009111 -0.701 370 0.483 SOC PARTICIP, B11 0.002788 0.004260 0.655 370 0.513 ---------------------------------------------------------------------------VOLUNTEERING OUTSIDE THE CCRC: Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.311958 0.678462 1.934 129 0.055 AGE, B01 -0.010293 0.008400 -1.225 129 0.223 Page 61

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Univariate Models all in one file For TIME slope, P1 INTRCPT2, B10 -0.002722 0.037635 -0.072 313 0.943 AGE, B11 0.000014 0.000464 0.029 313 0.977 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.470752 0.060780 7.745 134 0.000 B/L SIGEVENTS, B01 0.020618 0.046810 0.440 134 0.660 For TIME slope, P1 INTRCPT2, B10 -0.006571 0.004899 -1.341 318 0.181 SIG EVENTS, B11 0.001583 0.001560 1.015 318 0.311 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.233343 0.161386 1.446 133 0.151 COMMUNIT, B01 0.165379 0.101003 1.637 133 0.104 For TIME slope, P1 INTRCPT2, B10 -0.001822 0.008010 -0.227 318 0.820 COMMUNIT, B11 0.000225 0.004954 0.045 318 0.964 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.233343 0.161386 1.446 133 0.151 COMMUNIT, B01 0.165379 0.101003 1.637 133 0.104 For TIME slope, P1 Page 62

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Univariate Models all in one file INTRCPT2, B10 -0.001822 0.008010 -0.227 318 0.820 COMMUNIT, B11 0.000225 0.004954 0.045 318 0.964 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.483736 0.154556 3.130 133 0.003 GENDER, B01 0.000340 0.103690 0.003 133 0.997 For TIME slope, P1 INTRCPT2, B10 -0.002854 0.007562 -0.377 318 0.706 GENDER, B11 0.000926 0.004996 0.185 318 0.853 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 -0.174058 0.271913 -0.640 129 0.523 GIVINGSS, B01 0.240724 0.096582 2.492 129 0.014 For TIME slope, P1 INTRCPT2, B10 -0.002341 0.013772 -0.170 314 0.865 GIVINGSS, B11 0.000320 0.004878 0.066 314 0.948 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 -0.418019 0.406520 -1.028 69 0.308 LIFEHAPP, B01 0.199419 0.096861 2.059 69 0.043 For TIME slope, P1 INTRCPT2, B10 -0.015273 0.017953 -0.851 173 0.396 LIFEHAPP, B11 0.002817 0.004417 0.638 173 0.524 ---------------------------------------------------------------------------Page 63

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 -0.178249 0.259329 -0.687 129 0.493 LIFESATI, B01 0.159327 0.060584 2.630 129 0.010 For TIME slope, P1 INTRCPT2, B10 -0.009732 0.011983 -0.812 316 0.417 LIFESATI, B11 0.001986 0.002810 0.707 316 0.480 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.698697 0.221675 3.152 131 0.002 MARITALS, B01 -0.084222 0.085847 -0.981 131 0.329 For TIME slope, P1 INTRCPT2, B10 0.008843 0.010658 0.830 314 0.408 MARITALS, B11 -0.004231 0.004208 -1.005 314 0.316 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 -0.159907 0.341497 -0.468 121 0.640 MOBILITY, B01 0.024507 0.013111 1.869 121 0.064 For TIME slope, P1 INTRCPT2, B10 -0.016473 0.019798 -0.832 298 0.406 MOBILITY, B11 0.000553 0.000750 0.737 298 0.461 ---------------------------------------------------------------------------Page 64

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 -0.488852 0.316499 -1.545 129 0.125 RECEIVIN, B01 0.352521 0.112670 3.129 129 0.003 For TIME slope, P1 INTRCPT2, B10 -0.023165 0.016143 -1.435 312 0.152 RECEIVIN, B11 0.007722 0.005767 1.339 312 0.182 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.230116 0.376660 0.611 120 0.542 SF8MENTA, B01 0.005217 0.007070 0.738 120 0.462 For TIME slope, P1 INTRCPT2, B10 -0.003633 0.018366 -0.198 288 0.844 SF8MENTA, B11 0.000048 0.000347 0.137 288 0.891 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.028030 0.288579 0.097 120 0.923 SF8PHYSI, B01 0.009570 0.005688 1.683 120 0.095 For TIME slope, P1 INTRCPT2, B10 0.006605 0.014574 0.453 288 0.650 SF8PHYSI, B11 -0.000152 0.000286 -0.532 288 0.595 ---------------------------------------------------------------------------Page 65

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.590570 0.239233 2.469 107 0.015 SOC PARTICIP, B01 -0.025774 0.110945 -0.232 107 0.817 For TIME slope, P1 INTRCPT2, B10 -0.005668 0.012147 -0.467 298 0.641 SOC PARTICIP, B11 0.001449 0.005811 0.249 298 0.803 ---------------------------------------------------------------------------HELPING INSIDE THE CCRC Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.267652 0.859666 0.311 129 0.756 AGE, B01 0.006390 0.010661 0.599 129 0.550 For TIME slope, P1 INTRCPT2, B10 0.027161 0.060732 0.447 300 0.655 AGE, B11 -0.000326 0.000756 -0.432 300 0.666 ---------------------------------------------------------------------------Final estimation of fixed effects: Page 66

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Univariate Models all in one file ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.724972 0.070990 10.212 134 0.000 B/L SIGEVENT, B01 0.084183 0.054760 1.537 134 0.126 For TIME slope, P1 INTRCPT2, B10 -0.002788 0.007774 -0.359 307 0.720 SIG EVENTS, B11 0.000886 0.002433 0.364 307 0.716 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.485287 0.186878 2.597 133 0.011 COMMUNITY, B01 0.202539 0.118671 1.707 133 0.090 For TIME slope, P1 INTRCPT2, B10 0.012364 0.012168 1.016 307 0.311 COMMUNITY, B11 -0.007554 0.007604 -0.993 307 0.322 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.534517 0.144868 3.690 127 0.001 DRIVING, B01 0.325437 0.159213 2.044 127 0.043 For TIME slope, P1 INTRCPT2, B10 -0.007819 0.011439 -0.684 295 0.495 DRIVING, B11 0.009616 0.012182 0.789 295 0.431 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Page 67

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Univariate Models all in one file Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.874276 0.182252 4.797 133 0.000 GENDER, B01 -0.061803 0.121519 -0.509 133 0.611 For TIME slope, P1 INTRCPT2, B10 -0.007059 0.011552 -0.611 307 0.541 GENDER, B11 0.005700 0.007757 0.735 307 0.463 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, B0 INTRCPT2, G00 -0.330502 0.311007 -1.063 129 0.290 GIVING SS, G01 0.407774 0.110318 3.696 129 0.001 For TIME slope, B1 INTRCPT2, G10 0.001601 0.024503 0.065 303 0.948 GIVING SS, G11 -0.000062 0.008580 -0.007 303 0.994 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 -0.295330 0.453329 -0.651 69 0.517 LIFE HAPPY, B01 0.232508 0.107298 2.167 69 0.034 For TIME slope, P1 INTRCPT2, B10 -0.011798 0.031765 -0.371 171 0.710 LIFE HAPPY, B11 0.004036 0.007637 0.529 171 0.597 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value Page 68

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Univariate Models all in one file ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.069042 0.313819 0.220 129 0.826 LIFE SATISF, B01 0.172124 0.072735 2.366 129 0.020 For TIME slope, P1 INTRCPT2, B10 -0.013031 0.021329 -0.611 304 0.541 LIFE SATISF, B11 0.003354 0.004948 0.678 304 0.498 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.367131 0.257164 5.316 131 0.000 MARITAL, B01 -0.225252 0.099183 -2.271 131 0.025 For TIME slope, P1 INTRCPT2, B10 -0.022453 0.015666 -1.433 304 0.153 MARITAL, B11 0.009362 0.006201 1.510 304 0.132 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.071266 0.407486 0.175 121 0.862 MOBILITY, B01 0.028131 0.015741 1.787 121 0.076 For TIME slope, P1 INTRCPT2, B10 -0.045084 0.029174 -1.545 286 0.123 MOBILITY, B11 0.001686 0.001100 1.532 286 0.126 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 Page 69

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Univariate Models all in one file INTRCPT2, B00 -0.133687 0.380011 -0.352 129 0.725 RECEIVING SS, B01 0.334429 0.135095 2.476 129 0.015 For TIME slope, P1 INTRCPT2, B10 0.006449 0.024098 0.268 303 0.789 RECEIVING SS, B11 -0.001703 0.008593 -0.198 303 0.843 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.390024 0.433280 0.900 120 0.370 SF8 MENTAL, B01 0.007600 0.008140 0.934 120 0.353 For TIME slope, P1 INTRCPT2, B10 -0.007642 0.027693 -0.276 281 0.783 SF8 MENTAL, B11 0.000190 0.000523 0.364 281 0.716 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.559734 0.345690 1.619 120 0.108 SF8 PHYSICAL, B01 0.004604 0.006800 0.677 120 0.499 For TIME slope, P1 INTRCPT2, B10 -0.047772 0.022270 -2.145 281 0.033 SF8 PHYSICAL, B11 0.000988 0.000433 2.280 281 0.023 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.806375 0.064051 12.589 107 0.000 SOC PARTICIP, B01 0.016077 0.127492 0.126 107 0.900 Page 70

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Univariate Models all in one file For TIME slope, P1 INTRCPT2, B10 -0.000331 0.003916 -0.085 289 0.933 SOC PARTICIP, B11 -0.011003 0.008757 -1.256 289 0.210 ---------------------------------------------------------------------------HELPING OUTSIDE THE CCRC Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.423612 0.831136 2.916 129 0.005 AGE, B01 -0.019963 0.010298 -1.939 129 0.054 For TIME slope, P1 INTRCPT2, B10 -0.025821 0.062592 -0.413 312 0.680 AGE, B11 0.000316 0.000775 0.408 312 0.683 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.726766 0.069113 10.516 134 0.000 B/L SIGEVENT, B01 0.100004 0.053040 1.885 134 0.061 For TIME slope, P1 INTRCPT2, B10 -0.009423 0.008452 -1.115 134 0.267 SIG EVENTS, B11 0.002905 0.002688 1.081 134 0.282 ---------------------------------------------------------------------------Page 71

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.810266 0.187578 4.320 133 0.000 COMMUNITY, B01 -0.006792 0.118211 -0.057 133 0.955 For TIME slope, P1 INTRCPT2, B10 -0.004331 0.013394 -0.323 319 0.746 COMMUNITY, B11 0.002557 0.008204 0.312 319 0.755 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.477854 0.143206 3.337 127 0.001 DRIVING, B01 0.413620 0.156842 2.637 127 0.010 For TIME slope, P1 INTRCPT2, B10 -0.001481 0.010477 -0.141 308 0.888 DRIVING, B11 0.001039 0.011403 0.091 308 0.928 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.821619 0.179318 4.582 133 0.000 GENDER, B01 -0.014999 0.120114 -0.125 133 0.901 For TIME slope, P1 INTRCPT2, B10 0.008543 0.012360 0.691 319 0.490 GENDER, B11 -0.006319 0.008286 -0.763 319 0.446 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Page 72

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Univariate Models all in one file Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 -0.111185 0.312531 -0.356 129 0.722 GIVING SS, B01 0.330652 0.110855 2.983 129 0.004 For TIME slope, P1 INTRCPT2, B10 -0.006878 0.023419 -0.294 316 0.769 GIVING SS, B11 0.002385 0.008291 0.288 316 0.774 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 -0.715514 0.459214 -1.558 69 0.124 LIFE HAPPY, B01 0.366640 0.109795 3.339 69 0.002 For TIME slope, P1 INTRCPT2, B10 0.031827 0.036961 0.861 69 0.392 LIFE HAPPY, B11 -0.007114 0.009090 -0.783 69 0.437 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.293042 0.309674 0.946 129 0.346 LIFE SATISF, B01 0.121790 0.071947 1.693 129 0.092 For TIME slope, P1 INTRCPT2, B10 -0.001277 0.021749 -0.059 129 0.954 LIFE SATISF, B11 0.000106 0.005088 0.021 129 0.984 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value Page 73

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Univariate Models all in one file ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.138490 0.253916 4.484 131 0.000 MARITAL, B01 -0.129540 0.098149 -1.320 131 0.189 For TIME slope, P1 INTRCPT2, B10 -0.008518 0.018883 -0.451 131 0.652 MARITAL, B11 0.002965 0.007447 0.398 131 0.691 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 -0.091450 0.404278 -0.226 121 0.822 MOBILITY, B01 0.034604 0.015596 2.219 121 0.028 For TIME slope, P1 INTRCPT2, B10 -0.031800 0.032570 -0.976 121 0.331 MOBILITY, B11 0.001193 0.001240 0.962 121 0.338 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 -0.156777 0.372032 -0.421 129 0.674 RECEIVING SS, B01 0.345208 0.132502 2.605 129 0.011 For TIME slope, P1 INTRCPT2, B10 0.024019 0.027647 0.869 129 0.387 RECEIVING SS, B11 -0.008651 0.009854 -0.878 129 0.382 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 Page 74

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Univariate Models all in one file INTRCPT2, B00 0.758443 0.420295 1.805 120 0.073 SF8 MENTAL, B01 0.001497 0.007890 0.190 120 0.850 For TIME slope, P1 INTRCPT2, B10 -0.006719 0.032671 -0.206 120 0.838 SF8 MENTAL, B11 0.000138 0.000616 0.224 120 0.823 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.730740 0.338652 2.158 120 0.033 SF8 PHYSICAL, B0 0.002171 0.006649 0.327 120 0.744 For TIME slope, P1 INTRCPT2, B10 -0.036662 0.025508 -1.437 120 0.153 SF8 PHYSICAL, B11 0.000743 0.000499 1.488 120 0.139 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 0.660785 0.265591 2.488 107 0.015 SOC PARTICIP, B01 0.060071 0.122750 0.489 107 0.625 For TIME slope, P1 INTRCPT2, B10 0.009037 0.021129 0.428 107 0.669 SOC PARTICIP, B11 -0.004606 0.010070 -0.457 107 0.648 ---------------------------------------------------------------------------Page 75

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Univariate Models all in one file GIVING SOCIAL SUPPORT Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 4.065188 0.546297 7.441 129 0.000 AGE, B01 -0.016278 0.006752 -2.411 129 0.017 For TIME slope, P1 INTRCPT2, B10 0.018582 0.028377 0.655 129 0.513 AGE, B11 -0.000252 0.000349 -0.723 129 0.471 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.749437 0.047739 57.593 134 0.000 B/L SIGEVENT, B01 0.005762 0.037016 0.156 134 0.877 For TIME slope, P1 INTRCPT2, B10 -0.000217 0.003851 -0.056 134 0.955 SIG EVENT, B11 -0.000517 0.001197 -0.432 134 0.666 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.856797 0.127461 22.413 133 0.000 Page 76

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Univariate Models all in one file COMMUNITY, B01 -0.068183 0.080288 -0.849 133 0.398 For TIME slope, P1 INTRCPT2, B10 0.003801 0.006296 0.604 133 0.547 COMMUNITY, B1 -0.003551 0.003837 -0.926 133 0.357 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.391190 0.092173 25.942 127 0.000 DRIVING, B01 0.443470 0.101318 4.377 127 0.000 For TIME slope, P1 INTRCPT2, B10 0.002284 0.005213 0.438 127 0.662 DRIVING, B11 -0.004740 0.005616 -0.844 127 0.400 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.719401 0.121054 22.464 133 0.000 GENDER, B01 0.024390 0.082485 0.296 133 0.768 For TIME slope, P1 INTRCPT2, B10 -0.008545 0.005753 -1.485 133 0.140 GENDER, B11 0.004864 0.003888 1.251 133 0.213 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.595811 0.179612 14.452 131 0.000 MARITAL, B01 0.062588 0.069455 0.901 131 0.369 For TIME slope, P1 Page 77

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Univariate Models all in one file INTRCPT2, B10 -0.023307 0.008145 -2.861 131 0.005 MARITAL, B11 0.008618 0.003196 2.696 131 0.008 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.651948 0.259355 6.369 121 0.000 MOBILITY, B01 0.042908 0.010039 4.274 121 0.000 For TIME slope, P1 INTRCPT2, B10 -0.012978 0.015334 -0.846 121 0.399 MOBILITY, B11 0.000403 0.000583 0.691 121 0.491 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.671912 0.055899 47.799 127 0.000 NONHEALTH SE, B01 0.024712 0.010882 2.271 127 0.025 For TIME slope, P1 INTRCPT2, B10 -0.002066 0.002677 -0.772 127 0.442 NONHEALTH SE, B11 0.000160 0.000552 0.289 127 0.773 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.426486 0.269544 9.002 120 0.000 SF8 MENTAL, B01 0.006588 0.005057 1.303 120 0.195 For TIME slope, P1 INTRCPT2, B10 0.001506 0.015422 0.098 120 0.923 SF8 MENTAL, B11 -0.000069 0.000287 -0.242 120 0.810 Page 78

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Univariate Models all in one file ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 1.766958 0.190956 9.253 120 0.000 SF8 PHYSICAL, B01 0.020071 0.003749 5.354 120 0.000 For TIME slope, P1 INTRCPT2, B10 -0.017837 0.011652 -1.531 120 0.128 SF8 PHYSICAL, B11 0.000309 0.000227 1.361 120 0.176 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.312278 0.191245 12.091 107 0.000 SOC PARTICIP, B01 0.200139 0.088194 2.269 107 0.025 For TIME slope, P1 INTRCPT2, B10 -0.014787 0.009140 -1.618 107 0.108 SOC PARTICIP, B11 0.006324 0.004266 1.482 107 0.141 ---------------------------------------------------------------------------RECEIVING SOCIAL SUPPORT Page 79

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.762583 0.399634 6.913 129 0.000 AGE, B01 0.000795 0.004938 0.161 129 0.873 For TIME slope, P1 INTRCPT2, B10 -0.008915 0.025459 -0.350 129 0.727 AGE, B11 0.000153 0.000314 0.489 129 0.625 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.854625 0.028104 101.573 134 0.000 B/L SIGEVENT, B01 0.000869 0.021346 0.041 134 0.968 For TIME slope, P1 INTRCPT2, B10 0.000962 0.003479 0.276 134 0.783 SIG EVENT, B11 0.000901 0.001101 0.818 134 0.415 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, B0 INTRCPT2, G00 2.663891 0.072082 36.957 133 0.000 COMMUNITY, G01 0.126106 0.045256 2.786 133 0.007 For TIME slope, B1 INTRCPT2, G10 -0.002182 0.005784 -0.377 133 0.706 COMMUNITY, G11 0.003661 0.003550 1.031 133 0.305 ---------------------------------------------------------------------------Page 80

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Univariate Models all in one file Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.822814 0.057783 48.852 127 0.000 DRIVING, B01 0.040504 0.063201 0.641 127 0.522 For TIME slope, P1 INTRCPT2, B10 0.000755 0.004698 0.161 127 0.873 DRIVING, B11 0.003348 0.005092 0.658 127 0.512 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.915705 0.070629 41.282 133 0.000 GENDER, B01 -0.043817 0.048278 -0.908 133 0.366 For TIME slope, P1 INTRCPT2, B10 0.000267 0.005329 0.050 133 0.960 GENDER, B11 0.002475 0.003631 0.682 133 0.496 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.964735 0.102690 28.871 131 0.000 MARITAL, B01 -0.042231 0.039762 -1.062 131 0.291 For TIME slope, P1 INTRCPT2, B10 -0.002411 0.007713 -0.313 131 0.755 MARITAL, B11 0.002431 0.003024 0.804 131 0.423 ---------------------------------------------------------------------------Final estimation of fixed effects: Page 81

PAGE 241

Univariate Models all in one file ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.467728 0.164709 14.982 121 0.000 MOBILITY, B01 0.015091 0.006360 2.373 121 0.019 For TIME slope, P1 INTRCPT2, B10 0.011862 0.013519 0.877 121 0.382 MOBILITY, B11 -0.000346 0.000516 -0.671 121 0.503 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.582067 0.103578 24.929 127 0.000 NONHEALTH SE, B01 0.113998 0.040912 2.786 127 0.007 For TIME slope, P1 INTRCPT2, B10 0.014343 0.008173 1.755 127 0.081 NONHEALTH SE, B11 -0.004525 0.003220 -1.405 127 0.162 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.783661 0.166389 16.730 120 0.000 SF8 MENTAL, B01 0.001482 0.003119 0.475 120 0.635 For TIME slope, P1 INTRCPT2, B10 0.009687 0.013301 0.728 120 0.468 SF8 MENTAL, B11 -0.000135 0.000248 -0.545 120 0.586 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Page 82

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Univariate Models all in one file Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.686685 0.131847 20.377 120 0.000 SF8 PHYSICAL, B01 0.003490 0.002583 1.351 120 0.179 For TIME slope, P1 INTRCPT2, B10 0.006515 0.010379 0.628 120 0.531 SF8 PHYSICAL, B11 -0.000080 0.000202 -0.395 120 0.693 ---------------------------------------------------------------------------Final estimation of fixed effects: ---------------------------------------------------------------------------Standard Approx. Fixed Effect Coefficient Error T-ratio d.f. P-value ---------------------------------------------------------------------------For INTRCPT1, P0 INTRCPT2, B00 2.786616 0.109129 25.535 107 0.000 SOC PARTICIP, B01 0.033043 0.050288 0.657 107 0.512 For TIME slope, P1 INTRCPT2, B10 -0.001698 0.008711 -0.195 107 0.846 SOC PARTICIP, B11 0.002610 0.004058 0.643 107 0.521 ---------------------------------------------------------------------------Page 83

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Univariate Models all in one file Page 84

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150

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ABOUT THE AUTHOR Kathryn H. Petrossi received a Bachelor’s Degree w ith a double major in Sociology and the Honors Program in Psyc hology from Vanderbilt University in 2000. She entered the Ph.D. in Aging Studies program at the University of South Florida in 2000. While in the Ph.D. program at the Un iversity of South Flor ida, Kathryn was active in the Student Association for Aging Studies, and served as academic advisor for the Alpha Omicron Pi women’s fraternity. Kathryn was a teaching assistant and instructor for the Physical Change a nd Aging undergraduate course. She has coauthored a book chapter on health car e policy, and an online article on the importance of lifelong learning. She has ma de several presentations on successful aging programs at national conferences su ch as the Gerontol ogical Society of America, The Association for Gerontol ogy in Higher Education, The American Society on Aging, and the American Associ ation of Homes and Services for the Aging.


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Includes vita.
520
ABSTRACT: Rowe and Kahns theory of successful aging identifies three main components of aging successfully: reducing the risk of disease and disability, maintaining high cognitive and physical function, and engagement with life. While there is compelling evidence that suggests the legitimacy of this concept in the 50-75 year old community dwelling population, three areas of expansion are necessary: 1.) programmatic research; 2.) extending the existing research samples to include older samples and those living in continuing care retirement communities (CCRCs); and 3.) the integration of data collection and analysis to move beyond investigation of just one successful aging outcome to include elements of all three components of successful aging.Longitudinal analysis utilizing hierarchical linear modeling (HLM) was conducted on a convenience sample of 136 older adults (mean age = 80.8 years at baseline) participating in a pilot community-wide successful aging program over a 26-month period. Results indicate the sample reported exercising frequently, ate recommended levels of fruits and vegetables, had healthy BMIs, had positive ratings of health, were highly involved in productive activities, and were satisfied with their ability to give and receive social support at baseline. High levels of mobility were measured in the sample. Participants maintained this picture of successful aging over time for the majority of outcome variables, though significant declines in self-reported health were observed. Participants also reported improvements in their satisfaction with receiving social support.Results support four major conclusions: 1.) The three criteria of successful aging identified by Rowe and Kahn (1997) were observed among older adults living in CCRCs who were enrolled in a successful aging program. 2.) Stability was observed on a number of the outcomes over 26 months in this convenience sample, which has implications for intervention/programmatic research. Despite the traditional improvement-oriented focus of programmatic research, stability or maintenance of well-being over time should be viewed as a positive outcome in older age, particularly when compared to national data depicting trends of decline. 3.) The interdependence of current results support the notion that successful aging programming needs to include multi-disciplinary intervention strategies, as supported by the finding that modifiers of physical, social, and intellectual well-being include constructs from each of the components of successful aging.
590
Adviser: Kathryn Hyer, Ph.D.
Co-adviser: Cathy McEvoy, Ph.D.
653
Successful aging.
Continuing care retirement communities.
CCRC.
Physical health.
Social engagement.
Intellectual challenge.
Bmi.
Fruit and vegetable consumption.
Volunteerism.
Exercise.
Self-rated health.
Mobility.
Productive activities.
Social support.
Spiritial fulfillment.
690
Dissertations, Academic
z USF
x Aging Studies
Doctoral.
773
t USF Electronic Theses and Dissertations.
4 856
u http://digital.lib.usf.edu/?e14.1195