Peer-Assisted Social Cognitive Physical Activity Intervention for Older Adults

Permanent Link: http://ufdc.ufl.edu/UFE0022486/00001

Material Information

Title: Peer-Assisted Social Cognitive Physical Activity Intervention for Older Adults
Physical Description: 1 online resource (166 p.)
Language: english
Creator: Buman, Matthew
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008


Subjects / Keywords: efficacy, exercise, intervention, mentor, self
Applied Physiology and Kinesiology -- Dissertations, Academic -- UF
Genre: Health and Human Performance thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation


Abstract: There is growing and profound evidence that regular and sustained physical activity has substantial physical and mental health benefits for middle-aged and older adults. In spite of this evidence aging adults represent the most inactive segment of our population. One of the most pressing contemporary public health issues is identifying ways to increase the likelihood that older adults engage in regular physical activity. The purpose of our study was to the evaluate the effectiveness of the Active Adult Mentoring Program (Project AAMP), a 16-week social cognitive physical activity intervention using peer mentors, goal setting, and mental imagery, to enhance social cognitive beliefs and attitudes regarding exercise, to increase physical activity levels, and to increase cardiorespiratory fitness. An experimental design was chosen where participants were randomized to the intervention group or a 'health hygiene' control group matched in social contact and peer mentorship. Participants were 81 previously sedentary adults age 50 and older (M = 63.42, SD = 8.62), primarily female, white, college-educated, and free of disease or disability preventing physical activity participation. Sixty-nine participants completed baseline and posttest assessments (85% retention). Social cognitive outcomes were mixed; the intervention did not increase self-efficacy in either group, yet the intervention group had marginally improved intrinsic motivation compared to the control. Physical activity, measured by minutes of moderate-to-vigorous physical activity, a metabolic estimate, and pedometer steps, showed positive, curvilinear growth such that activity monotonically increased for the first eight weeks, was sustained for an additional four weeks, and had modest declines in the final four weeks when the intervention was withdrawn. Group assignment did not moderate this time trend. Small improvements in cardiorespiratory fitness were observed in both groups. These findings provide initial support for the use of peer-assisted interventions in the physical activity domain and perhaps more broadly in behavioral and health programs. Future research should continue to explore ways to increase physical activity behavior in older adult populations through the use of peer mentors and a theoretical model that can be easily and inexpensively delivered to a wide range of population subgroups.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Matthew Buman.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Giacobbi, Peter B.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022486:00001

Permanent Link: http://ufdc.ufl.edu/UFE0022486/00001

Material Information

Title: Peer-Assisted Social Cognitive Physical Activity Intervention for Older Adults
Physical Description: 1 online resource (166 p.)
Language: english
Creator: Buman, Matthew
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008


Subjects / Keywords: efficacy, exercise, intervention, mentor, self
Applied Physiology and Kinesiology -- Dissertations, Academic -- UF
Genre: Health and Human Performance thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation


Abstract: There is growing and profound evidence that regular and sustained physical activity has substantial physical and mental health benefits for middle-aged and older adults. In spite of this evidence aging adults represent the most inactive segment of our population. One of the most pressing contemporary public health issues is identifying ways to increase the likelihood that older adults engage in regular physical activity. The purpose of our study was to the evaluate the effectiveness of the Active Adult Mentoring Program (Project AAMP), a 16-week social cognitive physical activity intervention using peer mentors, goal setting, and mental imagery, to enhance social cognitive beliefs and attitudes regarding exercise, to increase physical activity levels, and to increase cardiorespiratory fitness. An experimental design was chosen where participants were randomized to the intervention group or a 'health hygiene' control group matched in social contact and peer mentorship. Participants were 81 previously sedentary adults age 50 and older (M = 63.42, SD = 8.62), primarily female, white, college-educated, and free of disease or disability preventing physical activity participation. Sixty-nine participants completed baseline and posttest assessments (85% retention). Social cognitive outcomes were mixed; the intervention did not increase self-efficacy in either group, yet the intervention group had marginally improved intrinsic motivation compared to the control. Physical activity, measured by minutes of moderate-to-vigorous physical activity, a metabolic estimate, and pedometer steps, showed positive, curvilinear growth such that activity monotonically increased for the first eight weeks, was sustained for an additional four weeks, and had modest declines in the final four weeks when the intervention was withdrawn. Group assignment did not moderate this time trend. Small improvements in cardiorespiratory fitness were observed in both groups. These findings provide initial support for the use of peer-assisted interventions in the physical activity domain and perhaps more broadly in behavioral and health programs. Future research should continue to explore ways to increase physical activity behavior in older adult populations through the use of peer mentors and a theoretical model that can be easily and inexpensively delivered to a wide range of population subgroups.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Matthew Buman.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Giacobbi, Peter B.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022486:00001

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2 2008 Matthew P. Buman


3 ACKNOWLEDGMENTS There are many people that deserve thanks for th eir support of m e and this project. Indeed, without a whole host of individu als, this project would never have begun, let alone completed. So to everyone associated with Project AAMP I send my deepest and most sincere thanks. I am grateful to have this opportunity to pub licly express my thanks for their support. First, this project could not have been completed without numerous sources of financial support. This research has been funded in part by a Research Opportunity Fund in the College of Health and Human Performance at the University of Florida, an Age Network Multidisciplinary Research Enhancement grant at the University of Florida, a Mentorship Opportunity Grant from the Graduate Student Council at the University of Florida, and a pre-doctoral fellowship granted to a co-investigator, Adrienne Aiken Morg an, from the National Institute of Aging (#1R36AG029664-01). Also, I would like to thank Pe ter Giacobbi, Jr., Beverly Roberts, Michael Marsiske, and Christina McCrae fo r the funds they used to stim ulate this research and support my development. Second, I would like to thank the generous support of our intervention si tes. First, at the Living Well Center I would like to thank Grace Badiola, Cassandra Howard, and Aaron Majcen. Their flexibility and patience to accommodate our research participants was incredible, and I thank them for sticking with me as I learned th e hard way how to run an intervention study. At the Family Life Center of Westside Baptis t Church I would like to thank Tom Grubbs and Stefanie Smith. I thank them for giving so will ingly of their resources and time and asking for nothing return. Third, I would like to thank the mentors and participants who volunteered their time and energy to make our study a success. To the me ntors: Mary Branagan, Bill Burk, Christine Dietrich, Dyke Farrow, Gail Harr is, George Lebo, Bob Millot, and Milledge Murphey, they were


4 the central component of our study and deserve all the th anks in the world. I found great joy in learning from them and their many life experiences, and hope that they found similar joy in their experiences working with the people in their gro ups. To the participants: their excitement to make positive and difficult changes in their lives was inspiring, and I learned a great deal from them. It is my sincere hope that they learned so mething about themselves in this process as well. Fourth, I would like to thank a ll of the research assistants and colleagues who volunteered their time on this project: Anna Bazhanov, Kayl a Frimmel, Joe Gullett, Justine Haroon, Karen Harris, Jesni Mathew, Phuong Nyguyen, Jordan Robbins, Katie Smythe, Howin Tsang, Sondra Watson, and Daphna Yasova. The list here is long; but each one of them s acrificed a great deal and was offered very little in retu rn. My deepest thanks for their sa crifice and willi ngness to take on sometimes menial tasks that needed to get d one. Thanks to my office-mates, Rick Dietrich and Michael Stancil, and to the best graduate program assistant in th e world, James Milford. Fifth, I would like to thank the Project AAMP re search team. To the faculty mentors, Peter Giacobbi, Jr., Michael Marsiske, Christina McCr ae, and Beverly Roberts, their guidance and support from research design to analysis was a huge learning experience for me. I appreciated the respect, challenge, and independence they gave me. To my student colleagues, Adrienne Aiken Morgan and Joe Dzierzewski, it was a pleasure to work with both of you and I look forward to our continued collaboration. It wa s nice to know throughout this process that I was never alone with this dissertation project. The collaborativ e efforts of this research team can not be overstated. This team has taught me a great d eal about how to successfully collaborate on a project and the benefits of working together toward a common research objective. They will always be considered ment ors and colleagues to me.


5 Sixth, I would like to thank my committee me mbers who have volunteered their time and efforts. I thank Dr. Heather Hausenblas for her time and her confidence in my abilities. I thank Dr. Michael Perri for his time a nd the early conversations he ha d with me about behavioral science. I thank Dr. Michael Marsiske for his mentorship, for impacting my research philosophy, and for helping me to think critically about research design and stat istical analysis. These immeasurable thanks can only be qualified by the ent husiastic and sincere way in which he shared with me his passion for research. Most of a ll, I would like to tha nk Dr. Peter Giacobbi, Jr., my committee chair, for his collective guida nce and support throughout my Ph.D. program. Words can not express my gratitude for his re lentless efforts and sincerest care for my professional growth and development. His crit ical feedback and countless hours of guidance have motivated me to push toward my fullest potential as a researcher. Seventh, I would like to thank all my family a nd friends who have believed in me. I would like to thank my parents, Paul and Marilyn Buman, who supported from a distance and believed in me no matter what. To my other parents, Fr ank and Carolyn Sylvester, thanks for their continued encouragement and trust that somehow I would manage to take care of their daughter. I thank Mark, Emily, Kevin, Melanie, Patrick, and th eir families. I also thank my family here in Gainesville (you all know who you are), whose prayer and enc ouragement sustained me and lifted me up week in and week out. Finally, I would like to thank my wife, Chri sten, who is the real reason that this dissertation had any chance of getting finished. I thank God everyday that she chooses to love me (and do it passionately) despite my many shor tcomings. What an amazing privilege it is to call her my wife. Her sacrificia l love and immense wisdom has taught me more than any degree ever could, and so I share th is accomplishment with her.


6 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................3 LIST OF TABLES................................................................................................................. ..........9 LIST OF FIGURES.......................................................................................................................11 ABSTRACT...................................................................................................................................12 CHAP TER 1 INTRODUCTION..................................................................................................................14 2 REVIEW OF LITERATURE.................................................................................................20 Social Cognitive Underpinnings of Physical Activity Behavior............................................ 20 Self-Efficacy Theory.......................................................................................................20 Self-Determination Theory..............................................................................................22 Self-Regulatory Strategies...............................................................................................25 Social Influences............................................................................................................. 27 Guiding Theoretical Framework for Intervention.................................................................. 30 Antecedents.....................................................................................................................30 Goal Setting.....................................................................................................................32 Mental Imagery............................................................................................................... 35 Peer Mentoring................................................................................................................37 3 METHODS.............................................................................................................................40 General Design.......................................................................................................................40 Participants.............................................................................................................................40 Sample.............................................................................................................................40 Exclusion Criteria............................................................................................................41 Inclusion Criteria.............................................................................................................42 Procedures..................................................................................................................... ..........43 Intervention Protocol.......................................................................................................44 Treatment arm.......................................................................................................... 45 Control arm..............................................................................................................46 Ecological Validity.......................................................................................................... 46 Peer mentors............................................................................................................. 47 Quality control and monitoring................................................................................ 47 Sustainability............................................................................................................48 Measures.................................................................................................................................49 Social Cognitive Measures.............................................................................................. 49 Barriers self-efficacy................................................................................................50 Exercise self-efficacy...............................................................................................50


7 Self-determined behavior.........................................................................................51 Physical Activity Measures.............................................................................................52 Self-reported physical activity................................................................................. 52 Pedometer................................................................................................................. 53 Physiological Measures................................................................................................... 53 Cardiorespiratory fitness..........................................................................................53 Body mass index...................................................................................................... 54 Blood pressure..........................................................................................................55 Depression Measures....................................................................................................... 55 Geriatric Depression Scale.......................................................................................55 Beck Depression Inventory......................................................................................55 Sample Size and Power Considerations.................................................................................56 Data Analysis..........................................................................................................................58 Multilevel Models for Change......................................................................................... 58 Repeated Measures Analyses of Variance...................................................................... 60 Exploratory Predictors..................................................................................................... 60 Missing Data Handling....................................................................................................61 4 RESULTS...............................................................................................................................69 Sample Characteristics............................................................................................................69 Demographic Information............................................................................................... 69 Study Variables...............................................................................................................69 Attrition...........................................................................................................................70 Study Replicates and Mentors......................................................................................... 71 Tests of Normal Distribution........................................................................................... 72 Primary Analyses: Social Cognitive Change.......................................................................... 73 Self-Efficacy....................................................................................................................73 Self-Determined Behavior...............................................................................................76 Secondary Analyses: Physical Activ ity and Cardiorespiratory Changes ...............................76 Physical Activity Behavior..............................................................................................77 Moderate-to-vigorous physical activity...................................................................77 MET estimate........................................................................................................... 79 Pedometer................................................................................................................. 80 Cardiorespiratory Fitness.................................................................................................82 Post-Hoc Analyses: Exploratory Predictors........................................................................... 82 Social Cognitive Changes................................................................................................ 83 Physical Activity Changes...............................................................................................83 Cardiorespiratory Fitness Changes..................................................................................84 5 DISCUSSION.......................................................................................................................111 Summary of Findings........................................................................................................... 111 Changes in Social-Cognition......................................................................................... 111 Self-efficacy...........................................................................................................111 Self-determined behavior.......................................................................................114 Changes in Physical Activity......................................................................................... 116


8 Changes in Cardiorespiratory Fitness............................................................................ 121 Strengths and Limitations..................................................................................................... 122 Strengths...................................................................................................................... ..122 Limitations.................................................................................................................... .123 Future Directions..................................................................................................................127 Conclusion............................................................................................................................128 APPENDIX A RECRUITMENT FLYER: LIVING WELL........................................................................ 130 B RECRUITMENT FLYER: WESTSIDE BAPTI ST CHURCH...........................................131 C DEMOGRAPHIC AND SCREENING INSTRUMENT..................................................... 132 D NURSE/PHYSICIAN CHECKLIST AND PERMISSION................................................. 137 E STAGES OF EXERCISE CHANGE QUESTIONNAIRE.................................................. 138 F INFORMED CONSENT......................................................................................................139 G QUALITY CONTROL CHECKLIST: TREATMENT GROUP.........................................141 H QUALITY CONTROL CHECKLIST: CONTROL GROUP.............................................. 143 I BARRIERS SELF-EFFICACY............................................................................................144 J EXERCISE SELF-EFFICACY............................................................................................ 146 K EXERCISE MOTIVATION SCALE................................................................................... 148 L PHYSICAL ACTIVITY DAILY RECORD ........................................................................151 M CARDIORESPIRATORY FITNESS................................................................................... 152 LIST OF REFERENCES.............................................................................................................153 BIOGRAPHICAL SKETCH.......................................................................................................166


9 LIST OF TABLES Table page 1-1 United States physical activity prevalence data grouped by age in 2005.......................... 19 3-1 Concurrent topical schedu les for intervention groups. ......................................................64 3-2 Outline of assessment plan................................................................................................. 65 4-1 Baseline demographic characteristics of participants by random ized group ( N = 81)...... 85 4-2 Baseline psychosocial va riables by random ized group.....................................................86 4-3 Baseline physiological variables by random ized group..................................................... 87 4-4 Baseline physical activity variables by randomized group (n = 76). ................................. 88 4-5 Baseline demographic characteristics of com pleted and attrited groups...........................89 4-6 Baseline study variables of com pleted and attrited groups................................................ 90 4-7 Replicate and ment or characteristics. ................................................................................91 4-8 Assessments of normal distribution for raw and square-root transform ed dependent variables...................................................................................................................... .......92 4-9 Correlations among social cognitive variab les and m odel covariates at baseline............. 95 4-10 Self-efficacy composite score model tests with group assignment, attendan ce, site of the intervention, and group mentor as model covariates................................................... 96 4-11 Mixed between-within ANOVA table for se lf-determ ined behavior index score ( N = 68)......................................................................................................................................98 4-12 Means and standard deviations of pedome ter steps, m inutes of moderate-to-vigorous physical activity, and LTEQ score across intervention timepoints..................................100 4-13 Correlations among physical activity variab les and exploratory m odel predictors......... 101 4-14 Moderate-to-vigorous ph ysical activity m odel test s for with group assignment controlling for group attendance, site of intervention, and group mentor....................... 102 4-15 MET estimate model tests with group assignm ent controlling for group attendance, site of intervention, and group mentor.............................................................................104 4-16 Pedometer steps model tests with group assignm ent controlling for group attendance, site of intervention, and group mentor.............................................................................106


10 4-17 Mixed between-within differences in cardiorespiratory fitness by group assignm ent controlling for attendance, site of the intervention, and group mentor ( N = 68).............108 4-18 Significant fixed effects for exploratory predictors and changes in m odel fit for three separate models of moderate-to-vigorous physical activity, MET estimate, and pedometer steps................................................................................................................ 110


11 LIST OF FIGURES Figure page 2-1. Guiding theoretical framework for intervention................................................................ 39 3-1 Screening/baseline, randomization, and follow-up............................................................ 62 3-2 Study timeline............................................................................................................. .......63 3-3 Power curve for barriers self-efficacy in an eigh t-week, intervention group only physical activity intervention.............................................................................................66 3-4 Power curve for EXSE in a mixe d-between ANOVA design across a six-m onth physical activity intervention.............................................................................................67 3-5 Power curve for self-reported physical activity in an 8-week, intervention-group only physical activity intervention. ............................................................................................68 4-1 Q-Q plots for raw and sq uare-root transfor med repeated measures dependent variables...................................................................................................................... .......93 4-2 Self-efficacy composite score empirical data and model-based estimates by group assignment adjusted for attendance, s ite of the interven tion, and mentor.........................97 4-3 Model-based estimates of self-determ ined behavior index score by group assignment adjusted for group attendance, site, and group mentor...................................................... 99 4-4 Moderate-to-vigorous physical activity em pirical data and m odel-based estimates by group assignment adjusted for group a ttendance, site, and group mentor....................... 103 4-5 MET estimate empirical data and m odel-based estim ates by group assignment adjusted for group attendance, site, and group mentor.................................................... 105 4-6 Pedometer steps empirical data and m odel-based estim ates by group assignment adjusted for group attendance, site, and group mentor.................................................... 107 4-7 Model-based estimates of cardiorespiratory fitness by gr oup assignm ent adjusted for group attendance, site of the in tervention, and group mentor ( N = 68)...........................109


12 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy PEER-ASSISTED SOCIAL COGNITIVE PHYS ICAL ACTIVITY INTERVENTION FOR OLDER ADULTS By Matthew P. Buman August 2008 Chair: Peter R. Giacobbi, Jr. Major: Health and Human Performance There is growing and profound evidence that regular and sustained physical activity has substantial physical and mental health benefits fo r middle-aged and older adults. In spite of this evidence aging adults represent the most inact ive segment of our population. One of the most pressing contemporary public health issues is id entifying ways to increase the likelihood that older adults engage in regular physical activity. The purpose of our study was to the evaluate the effectiveness of the Active Adult Mentoring Program (Project AAMP), a 16-week social cognitive physical activity intervention using p eer mentors, goal setting, and mental imagery, to enhance social cognitive beliefs and attitudes regarding exercise to increase physical activity levels, and to increase cardiorespiratory fitn ess. An experimental design was chosen where participants were randomized to the intervention group or a health hygiene control group matched in social contac t and peer mentorship. Participants were 81 previously se dentary adults age 50 and older ( M = 63.42, SD = 8.62), primarily female, white, college-educated, and free of disease or disabil ity preventing physical activity participation. Sixty-nine participants completed baseline and posttest assessments (85% retention). Social cognitive outcomes were mixed; the intervention did not increase self-efficacy in either group, yet the inte rvention group had marginally improved intrinsic motivation


13 compared to the control. Physical activity, measured by minutes of moderate-to-vigorous physical activity, a metabolic estimate, and pedomete r steps, showed positiv e, curvilinear growth such that activity monotonically increased for the first eight weeks, was sustained for an additional four weeks, and had m odest declines in the final four weeks when the intervention was withdrawn. Group assignment di d not moderate this time trend. Small improvements in cardiorespiratory fitness were observed in both groups. These findings provide initial support for the use of peer-assisted interven tions in the physical activity domain and perhaps more broadly in behavioral and health programs. Future research should continue to explore ways to increase physical activity behavior in older adult popula tions through the use of peer mentors and a theoretical model that can be eas ily and inexpensively delivered to a wide range of population subgroups.


14 CHAPTER 1 INTRODUCTION There is growing and profound evidence that re gular and sustained phys ical activity (P A) has substantial physical and psychol ogical health benefits for olde r adults. Individuals age 50 and older who engage in regular PA have improved cardiovascular health, in creased metabolism, and slowed declines in bone mineral density (Singh, 2002). Regular exercise can reduce adults risk of coronary heart disease (Haskell et al., 1992) cancer (Blair et al ., 1989), offer protection against non-insulin dependent diabetes (Helmrick, Ragland, Leung, & Paffenbarger, 1991), and reduce hypercholesterolemia (Harris et al., 1991). Studies have also linked exercise with diminished declines of functional capacity due to age, reduced risk factors associated with falls in the elderly (DiPietro, 2001; Liu-Ambrose et al., 2004), while older adults who engage in regular exercise reduce their risk for mortality associated wi th chronic disease states and premature mortality (Bean, Vora, & Front era, 2004; Bokovoy & Blair, 1994; Lee & Paffenbarger, 1996; Paffenbarger et al., 1993; Wagner, LaCroi x, Buchner, & Larson, 1992). When exercise is initiated later in life there is still a strong impact on health and mortality rates even when accounting for factors such as smokin g, family history, weight gain, and hypertension (Blair et al., 1995). Additionally, re gular exercise benefits older a dults with chronic conditions as varied as cancer, cardiovascular disease, resp iratory disease, and dementia (Bath & Morgan, 1998; Kiely, Wolf, Cupples, Be iser, & Kannel, 1994; Lakka et al., 1994; Leveille, Guralnik, Ferrucci, & Langlois, 1999; Morgan & Bath, 1998; Shephard & Balady, 1999). Regular exercise, whether involv ing structured or unstructured programs, is also associated with psychological health and well-being for older adults (Arent, La nders, & Etnier, 2000; Brown, 1992; Colcombe et al., 2004; Dunn, Triv edi, & O'Neal, 2001; Fukukawa et al., 2004; McAuley, 1993a; McAuley, Marquez, Jerome, B lissmer, & Katula, 2002; McAuley & Rudolph,


15 1995; Morgan & Bath, 1998; Pescatello & Di Pietro, 1993; Plante & Rodin, 1990; Rejeski & Mihalko, 2001; Spano, 2001). More recent literatur e has linked fitness outcomes with improved cognitive functioning for older adults (Colco mbe et al., 2004; McAuley, Kramer, & Colcombe, 2004). In summary, it is generally be lieved that exercise is linked to a variety of positive physical health outcomes for older a dults (McAuley & Rudolph, 1995). In spite of evidence that regul ar PA slows the aging process and that physical health is essential to quality-of-life and the maintenance of functional independence, many older adults remain sedentary. Active involvement in the reco mmended levels of PA declines considerably with age (CDC, 2005; see Table 1-1) and the per centage of adults who are completely inactive increases throughout the age span with approximately 3/4 of all individuals in the United States over the age of 50 being either inactive or not gain ing sufficient activity to derive health benefits (Schiller, Adams, & Coriaty, 2005). In response to these concerns, the American College of Sports Medicine (Cress et al., 2004), the National Institutes of Health (NIH, 1995), and the United States Surgeon Generals office have c onsensus statements and planning documents to promote exercise behaviors for older adults. It is clear that one of the most pressing contemporary research and public health issues should focus on ways to increase the likelihood that older adults engage in leisure time exercise. In the past two decades there has been a larg e number of PA interventions developed and implemented with older populations. Van der Bi j, Laurant, and Wensing (2002) reported on a systematic review of the literature, identifying 38 randomized controlled trials that targeted adults 65 years or older that included 57 PA interventions. The great majority of these interventions have focused on modifying factors associated with the individual, and in fewer cases the modification of the community and environment (Satariano & McAuley, 2003).


16 Individual-level strategies include the modification of cognitive, affective, and social influences such as self-efficacy, locus of control, and inte ntion. The majority of these behavior change strategies have been adapted from the general psychology literature and use the following theoretical perspectives: theory of planned behavior (Ajzen, 1991), transtheoretical model (Prochaska & Velicer, 1997), self-efficacy theory (Bandura, 1997), and self-determination theory (Deci & Ryan, 1985). Community and environmental approaches to PA promotion, on the other hand, aim to address mesoand macro-environmen tal factors such as urban planning and design, public transportation, and policy d ecisions. These strategies have been influenced largely by social-ecological models of health pr omotion (King, Stokols, Talen, Brassington, & Killingsworth, 2002; McLeroy, Bibeau, Steckler, & Glanz, 1988). Satariano and McAuley (2003) have suggest ed that the community/environmental and individual-level approaches to PA promotion have evolved distin ctively and antithetically from each other. In many cases, individual-level th eories are not able to fully account for environmental factors and how th ey directly influence behavior. Community and environmental approaches may neglect the transactional nature of the individual and his/her environment and the volitional nature of health behaviors. Social cognitive theory, traditionally viewed as an individual-level meta-theory, holds at its core a reciprocal, tr iadic interplay of individual, environmental, and behavioral factors (Bandur a, 1986). Indeed, ecological and social cognitive approaches to PA promotion are compatible and can be viewed as complementary (King et al., 2002). For instance, individuals within communities may help to shape each others views about exercise which may then impact choices made at the individual and/or community level. Satariano and McAuley (2003) point out that individuals may be sufficiently motivated and confident to inspire and convince others in their neighborhoods and communities by word or


17 deed to engage in physical activity (p. 188). Su ch a transtheoretical approach is needed to address the complex nature of PA adoption in older populations where barriers are experienced more variably and commonly than their younger counterparts (CDC, 2005). Project AAMP, or the Active Adult Mentoring Project is a pilot research project designed and implemented by a multidisciplinary team of researchers at the University of Florida who sought not only to promote PA in older popu lations, but also to examine gains in other health-related psychosocial domains (i.e., cogn ition, sleep) as a function of improved PA behavior and fitness. The purpos e of the current study was to ev aluate the primary outcomes of Project AAMP (self-efficacy and intrinsic mo tivation, PA behavior, and fitness). These important outcomes were hypothesized to mediat e change in secondary outcomes including sleep, cognition (particularl y executive functioning), emoti onal well-being, and physical functioning. Project AAMP adopted a social cognitive theore tical framework to enhance self-efficacy beliefs and intrinsic motivation, which are importa nt mediators of PA behavior and well-being (Netz, Wu, Becker & Tenenbaum, 2005). Throu gh a multi-component approach to behavior change (concurrent use of peer mentors, goal setting, and mental imag ery), Project AAMP was designed to address behavior change at multiple levels of influence. These levels of influence interact reciprocally to influence behavi or (Bandura, 1997). The following levels were addressed: Intrapersonal: The intervention sought to modify be liefs and attitudes (i.e., self-efficacy, intrinsic motivation) that ha ve theoretical and empirical linkages to PA behavior. Intervention strategies that address this leve l of influence included the use of goal setting and mental imagery. Interpersonal: Central to the intervention was the use of physically active older adults as trained same aged peer mentors to deliver th e intervention. This delivery took place within


18 a group-based context designed to foster a supportive enviro nment conducive to sustainable behavior change. Environmental: Setting and context are recognized as important factors in the evaluation of health behavior interventions (Glasgow Vogt, & Boles, 1999). The intervention was expressly designed to be highl y replicable and ecologically valid at a low cost to participants and utilize a relatively small amount of community resources. The basic question addressed in our study was whether a multi-component, 16-week PA intervention for sedentary adults aged 50 years a nd older can effectively e nhance social cognitive variables (i.e., self-efficacy, se lf-determined behavior) which have demonstrated theoretical linkages to PA behavior and f itness outcomes. Therefore, the purposes of the study were as follows: Primary purpose: To examine the effects of a multi-component 16-week exercise intervention for sedentary adults 50 years of age and older on changes in exercise selfefficacy and intrinsic motivation toward exercise. Secondary purpose: To evaluate subsequent improveme nt in PA behavior (i.e.; selfreported PA and pedometer count) and fitness parameters (i.e.; cardiorespiratory fitness).


19 Table 1-1. United States physical activity prevalence data grouped by age in 2005. Age group (years)18-2425-3435-4445-5455-6465+ Recommended (%)59.451.750.345.544.739.0 Insufficient (%)21.127.928.328.428.726.9 No physical activity (%)19.520.421.423.126.634.1Note. Behavioral Risk Factor Surveillance System. The recommended amount of physical activity for older adults is 30 minutes or more of moderate physical activity five or more days per week, or vigorous physical activity for 20 minutes or more three or more days per week (Nelson et al., 2007). Data includes District of Columbia, Guam, Puerto Rico, and the U.S. Virgin Islands


20 CHAPTER 2 REVIEW OF LITERATURE The following review of the literature has been divided into two sections. First, the social cognitive underpinnings of PA behavior will be reviewed and the specific theoretical perspectives utilized within the Project AAMP intervention will be presented. Second, a guiding theoretical framework will be presented and litera ture relevant to the intervention strategies chosen will be reviewed. Social Cognitive Underpinnings of Physical Activity Behavior Social cognitive theory (Bandura, 1986) fo rm s the theoretical foundation for Project AAMP. Social cognitive theory, broadly defined, is concerned with the social psychological links between cognitions, beliefs, attitudes and behavior and is deeply rooted in expectancyvalue motivation (cf. Atkinson, 1964). Social cogn itive theory contends that behavior is not approached passively; rather, individuals plan, set goals, and make decisions that affect the volition of behavioral involvement. More specif ically, self-efficacy theory (Bandura, 1997) and self-determination theory (Deci & Ryan, 1985) we re chosen as guiding frameworks for our study because both theories place emphasis on the reci procal interplay of individual and socialcontextual factors in the deve lopment of goal directed behavior and psychological well-being (Bandura, 1997; McAuley, Elavsky, Jerome, Ko nopack, & Marquez, 2005; Ryan & Deci, 2000). Both theories also emphasize the importance of perceived competence or beliefs in ones capabilities as the primary predic tor of the adoption and maintena nce of PA behavior (Standage & Duda, 2004). Self-Efficacy Theory Self-efficacy beliefs can be defined as differentiated and domain specific self-beliefs that operate within various a nd distinct realms of human functioning (Bandura, 1997). Self-


21 efficacy theory is concerned with how individuals effectively organize cognitive, social, emotional, and behavioral sub-skills that underp in successful attempts to initiate and maintain behavior. Self-efficacy expecta tions are derived from four sources: (a) past performance accomplishments (i.e., PA history), (b) vicari ous experiences (i.e., peer modeling); (c) physiological states (i.e., PA-induced positive affect ); and (d) verbal persuasion (i.e., physician advice). Information received from past perf ormance accomplishments is hypothesized to be the strongest predictor of self-efficacy beliefs. Ther efore, it is through incremental and repeated practice of a health behavior that results in e nhanced self-efficacy expectations and subsequent initiation, persistence, and maintenance of the behavior (Baranowski, Perry, & Parcel, 2005). Self-efficacy in the PA domain includes confidence to perform tasks as well as overcoming barriers that prevent behavior. Information recei ved from past success or failure in performing PA tasks serves to inform future efforts and decisions regarding behavior. McAuley (1992) has developed a measure of barriers self-efficacy (BSE) that has been widely used and validated in older adult samples (McAuley et al., 2005; McAuley, Jerome, Elavsky, Marquez, & Ramsey, 2003). Items on this measure reflect judgments of ones perceived capabilities to maintain regular PA despite barriers (e.g., if I exercise alone; felt pain or discomfort). BSE has been found to be the most consistent and proxi mal predictor of PA be havior to date as demonstrated by prospective and correlationa l studies (Conn, 1998; McAu ley, 1992; McAuley et al., 2003; McAuley et al., 2005; Rhodes et al., 1999; Wilcox & Storandt, 1996). Long-term maintenance of PA and BSE has received less attention and shown weaker and less consistent relationships (Maddison & Prap avessis; 2004; McAuley et al., 2003). McAuley et al. (2003) reported on the effect s of a 6-month randomized exercise trial on short-term (6 months) and long-term (18 months) maintenance of PA in a sample of previously sedentary


22 community-dwelling adults aged 60 to 75 years. The researchers f ound that BSE at baseline was predictive of short-term PA at 6 months ( = .27) and long-term maintenance at 18 months ( = .25) in a structural model. These long-term effects of BSE on adherence held even after controlling for PA behavior in the first 6 months of the intervention ( = .52). Maddison and Prapavessis (2004) examined the shortand long-term role of BSE on two additional objective measures of PA: (a) attendance of an 18-week aerobic (walking) and stretching supervised cardiac-rehabilitation exercise program; and (b) energy expenditure during the prescribed exercise sessions. The participants incl uded 41 adults ( M age = 63.65 years) who were recently diagnosed with ischemic heart di sease and prescribed exercise. The researchers found in a cross-lag path analysis that BSE at baseline was predictive of attendance across the first 6 weeks of the intervention ( = .44). BSE was again measured after 13 weeks in the intervention and found to be predic tive of attendance in the final six weeks of the intervention ( = .29), after controlling for attendance and BSE at previous time points. Energy expenditure was not associated with BSE at any of the time points. The role of self-efficacy in overcoming comm only experienced barriers of older adults remains an important relationship to explore. Bandura (1997) posits that self-efficacy is particularly useful when challenging situations or barrier s arise as it determines persistence in which we approach the obstacles. While relati onships between BSE at baseline and short-term behavior appear robust, additi onal questions remain regarding how BSE may impact PA beyond the initiation phase. Self-Determination Theory Self-determ ination theory is broadly focuse d and seeks to explain human motivation and psychological well-being within social contexts. Motivation is viewed along a self-determination continuum where behavior is driven by extrinsic and intrinsic rewards. Extrinsic motivation is


23 driven by outcomes independent of the behavior and external awards. Intrinsic motivation, on the other side of continuum, is driven by the pleasure and satisfaction inherent in the activity. Ryan and Deci (2000) contend that humans are lib erally endowed with intrinsic motivational tendencies (p. 70) and it is not necessary to determine causal factors that foster intrinsic motivation. Instead, efforts should be devoted towards understanding contexts and conditions that promote and sustain the natural prope nsity to be intrinsically motivated. Developed as a sub-theory of SDT, Cognitive Evaluation Theory (CET) is focused on the social-contextual conditions wh ere intrinsic motivation is fostered and healthy development occurs. CET states that intrinsic motivation is fostered when three basic, innate psychological needs are met. These needs include: (a) competen cy, feeling capable in relationship to ones environment; (b) relatedness, shared experiences with others; and (c) autonomy, a sense of choice. Maladjustment occurs and self-determi ned behavior is undermined when these three human needs are not met. In the PA domain, Deci and Ryan (1985) conte nd that the natural course of human activity is to be intrinsically motivated toward mainta ining physical capacities. Ryan, Frederick, Lepes, Rubio, and Sheldon (1997) found, however, that older adults typically initia te PA for extrinsic reasons (e.g., to lose weight, improve appearan ce, avoid health problems). This incongruence may explain why extrinsic motivation is sufficien t to initiate regular PA but undermines the development of fun and enjoyment, thus re ducing PA sustainability (Wankel, 1993). SDT predicts that autonomy-supportive envi ronments, versus controlling environments, foster more self-determined forms of intrinsic motivation in PA settings. There is evidence to support this notion with co llege-aged women (Wilson & Rodgers, 2004), adolescents (Vansteenkiste, Simons, Soenens, & Lens, 2004), adults (Chatzisarantis & Biddle, 1998; Levy &


24 Cardinal, 2004), and older adults (Losier, Bourque, & Vallerand, 1993). Researchers using the SDT framework with adults in assisted-living facilities have also demonstrated that autonomysupportive environments tend to be associated wi th intrinsic forms of motivation toward PA (Vallerand & OConnor, 1989; Vallerand, OConnor, & Hamel, 1995). Generally, perceptions of competence, autonomy, and relatedness appear to be associated with PA participation in various populations. Losier et al. (1993) tested a SDT-based motivational model of leisure behavior in adults 65 years and older. The cross-secti onal, stratified sample of 102 Fr ench-speaking Canadian elders completed questionnaires that examined how dem ographic variables, perceived constraints and opportunities, and self-determine d motivation predicted leisure satisfaction and participation. The regression-based path model indi cated that perceived constraints ( = -0.26) and opportunities ( = 0.36) predicted self-determined motiv ation, with a greater perception of opportunities and fewer perceptions of constraints predicting more intrinsically motivated behavior. Self-determined motivation also positivel y predicted participation in leisure activities ( = 0.30), even after controlli ng for significant demographic va riables (e.g., marital status, gender) and leisure satisfaction. The final mode l predicted 30% of th e variance in leisure participation. This study demonstrated, in a sample of elders, that individuals more intrinsically motivated to participate in leis ure activities were more likely to report spending time engaged in such activities. This study did not, however, include mediating variables such as autonomy, relatedness, and autonomy that Ryan and Deci (2000) contend underpin th e process and context in which self-determined behavior is fostered. Levy and Cardinal (2004) addressed how these mediating variables may be influenced over the course of a 2-month, mail-mediated PA in tervention of 59 women be tween the ages of 22


25 and 79. Participants were assigned to either a control (an educationally-based packet on health and exercise), intervention (f our-page packet designed to promote a sense of autonomy, competence, and relatedness), or intervention plus booster (intervention packet plus a mailed postcard re-iterating sup portive messages) condition. The res earchers found that the women in all three conditions increased exercise behavi or across the intervention. Of the mediating variables, women in all three groups also dem onstrated significant improvements in perceived autonomy. No changes were observed in competence or relatedness in any of the conditions. More research is needed that seeks to examine these changes in the cont ext of a longer, more intensive intervention. It is likel y that specific intervention strategies and contexts may be important factors to consider in fostering competence (e.g., goal setting) and relatedness (e.g., group-based). Self-Regulatory Strategies Bandura (1986) has defined se lf-regulation as personal co ntrol toward goal-directed behavior or perform ance. Self-re gulation is a prominent construct in the social cognitive theory and is generally hypothesized to mediate the relationship between proximal social cognitive indicators and behavior. For exam ple, in self-efficacy theory, se lf-regulatory strategies are the active efforts employed to translate self-efficacy into actual behavior (Bandura, 1986; Rhodes & Plotnikoff, 2006). In SDT, self-regulatory be havior is viewed as healthy psychological adaptations under conditions where innate psyc hological needs are being met (Ryan & Deci, 2000). In general, self-regulator y strategies are any active efforts taken to regulate affect, behavior, and cognition with the purpose of overcoming challenge s in behavior adoption. These strategies may include goal setting, self-monitoring, and reinfo rcement. Bandura (2001) has theorized that individuals with higher levels of self-efficacy to perform PA are more likely to


26 engage in self-regulatory strate gies to overcome common barriers and maintain behavior over time. Anderson, Wojcik, Winett, and Williams (2006) st udied self-regulation in a cross-sectional sample of 999 adults ( M age = 52.70, SD = 14.60) recruited as part of a church-based health promotion study. To assess self-regulation, Anderson et al. (2006) tapped into strategies used in the past three months related to PA engagement These strategies included setting aside time for PA, taking breaks for PA, walking instead of dr iving, parking further aw ay to walk, getting together with someone else for PA, writing down on a calendar their PA plans, and making contingency plans for bad weather. The research ers found, using a structural model, that selfregulation was the strongest pr edictor of PA behavior ( = .36). Self-efficacy was not a direct predictor of PA; instead, was pr edictive of self-regulation ( = .18), supporting the notion that self-regulation mediates the se lf-efficacy-behavior relationship. While there is theoretical (Bandura, 2001) and empirical (Anderson et al., 2006; Resnick, 2001; Rovniak, Anderson, Winett, & Stephen, 2002) support for the notion that self-regulation is an important mediator for behavior, little is understood about the dem ographic and contextual circumstances where self-regulation is most commonly used. Umstattd, Saunder, Wilcox, Valois, and Dowda (2006), in a convenience sample of 296 adults aged 50 and over, studied th e value of self-reported PA, demographics (gender, age, r ace, education, income, BMI, self-reported health status), and other social cogniti ve variables (social support, self-efficacy) in predicting selfregulation. Umstattd et al. (2006) used a more comprehensive measurement of self-regulation compared to Anderson et al. (2006) that incl uded self-monitoring, goal setting, social support, reinforcement, time management, and relapse prevention. The authors reported that selfregulatory strategies were positiv ely predicted by female gender ( = .11), social support ( =


27 .13), self-efficacy ( = .27), and current PA engagement ( = .44). The final model explained 36% of the overall variance in self-regulation. The empirical studies discussed above collectively provide support for the importance of including self-regulatory strate gies in PA interventions for older adults. However, our understanding of self-regulat ion is limited due to a lack of pr ospective studies that track selfregulatory processes over time and other methodologi cal inconsistencies. First, self-regulatory variables are seldom included in longitudinal models that include other social cognitive variables. By including self-regulation in these models we can understand how these processes change during initiation and mainte nance phases of PA and the eff ectiveness of these strategies to mitigate different barriers. As shown by Anderson et al (2006) and Umstaddt et al. (2006), researchers are inconsistent in how they measure self-regulation in cross-sectional studies. These self-regulatory strategies measured in cross-sectional studies are also not necessarily consistent with strategies used in intervention studies. This makes it difficult to assess the implications of cross-sectional studies for intervention design. Social Influences Central to all the so cial cognitive theories disc ussed here is the role that social context plays in the formation and mani festation of attitudes and beli efs. Ajzen (1991) noted that information and influence of others plays heavily in the formation of personal control. Bandura (1997) contends that attitudes a nd behaviors are formed from a c ontinuous, reciprocal interplay of social and individual factors. In self-efficacy theory, social support is a proximal predictor of self-efficacy and outcome expectancy in older adults (Duncan & McAuley, 1993; Resnick, 2001, Resnick, Orwig, Magaziner, & Wynne, 2002). In se lf-determination theor y, intrinsic motivation and self-determined forms of extr insic motivation is fostered only when the need for relatedness, or being properly in relations hip with others, is met.


28 Social support, like other social cognitive variables, has been shown to be an important predictor of exercise and PA behaviors in cross-sectional (Brassington, Atienza, Perczek, Lilorenzo, & King, 2002; Chogahara, Cousins, & Wankel, 1998), prospective (Castro, Sallis, Hickman, Lee, & Chen, 1999), and randomized contro lled trials (McAuley et al., 2003; Rejeski et al., 2003). Links have also been found between social support and more distal PA outcomes including behavior (Resnick, 2001, Resnick et al., 2002; Estabrooks & Carron, 1999) and subjective well-being (McAuley et al., 2000). Social support for olde r adults has the potential to promote PA in a variety of ways. Berkman (1995) descri bes at least four ways in which social support can aid health promotion: instrumental (e.g., providing access to facilities), emotional (e.g., providing encouragement to sustain activity) appraisal (e.g., providing evaluative feedback regarding normative progress), and informationa l (e.g., providing fitness/exercise knowledge). Social support can provide tangible and instrumental benefits to improve adherence that reduce common barriers for older adults. Estabrooks and Carron (1999) examined group c ohesion (task and social) within a theory of planned behavior framework. Theory of pl anned behavior constructs and group exercise participation over four weeks were measured in a cross-sectional st udy of 179 volunteer older adults (M age = 67, SD = 7.77). The researchers found that task cohesion was significantly related to behavioral attitudes ( = .135) and perceived behavioral control ( =.281). Perceived behavioral control also mediated the relations hip between task cohesion and intention and had direct effects on attendance to the group after four weeks ( = .127). Social cohesion, on the other hand, was not directly linke d to perceived behavioral cont rol but did predict behavioral attitude ( = .16). The link between elements of task cohesion and perceived behavioral control indicate that older adu lts who perceive themselves closer to the group may also perceive greater


29 social support to overcome potential barriers to PA that may arise. Moreover, because the link was exclusive to task cohesion over social cohesion, this may indicate that older adults rely more on instrumental, appraisal, and informational sources of support that are pr esent in task-oriented settings. It should also be noted that the small beta weights reported in this study suggest that other individualand environmenta l-level factors are at play that may determine intention toward behavior. It cannot be assumed th at group cohesion, or other interp ersonal factors, can determine behavior alone. The interaction of a task-ori entated group environment, appropriately-tailored, individual-level strategies (i.e ., goal setting, mental imagery), delivered within an environment conducive to change is needed to modify psychosocial mediators of behavior change. McAuley et al. (2003) reported on the long-term role that so cial support from a group plays in PA maintenance among 174 adults betw een the ages of 60 and 75. The researchers found, in a structural model, that perceptions of social support at the end of a 6-month exercise intervention predicted self -efficacy (a composite measure of BSE and EXSE; = 0.30), which in turn predicted PA at the end of the intervention ( = 0.27) and at an 18-month follow-up ( = 0.25). The findings also showed that social support during the inte rvention was also related to exercise frequency and positive affect. In other words, those individuals who perceived greater amounts of support from other group members ex ercised more and reported more positive experiences throughout the intervention. Despite the important role that social support plays in the formation of efficacy beliefs and behavior, more work is needed to fully unders tand the nature and makeup of successful support networks in the exercise domain. Israel (1982) describes six importa nt characteristics of effective social networks and relationships. These include (a) reciprocity, the extent to which resources and support are both given and received; (b) intens ity, the extent to whic h social relationships


30 offer emotional closeness; (c) complexity, exte nt to which social re lationships serve many functions; (d) density, extent to which network members know and in teract with each other; (e) homogeneity, extent to which network members ar e demographically similar; and (f) geographic dispersion, extent to which ne twork members live in close pr oximity to a focal person. Additionally, the social psychological literature also suggests that older adults prefer smaller, more intimate social support networks as co mpared to younger age cohorts (Carstensen, 1995; Carstensen, Pasupathi, Mayr, & Ne sselroade, 2000). This is especi ally true when adults have emotional regulation as the goal (Carstensen, 1995). The existing intervention literature is likely to be formally and informally addressing thes e factors in social support provision, yet few reports are available in how these characteristic s are actually being utili zed. The explicit use of these characteristics within Project AAMP will be elaborated upon in subsequent sections of this report. Guiding Theoretical Fram ew ork for Intervention The primary purpose of Project AAMP was to enhance older adults self-efficacy and intrinsic motivation towards exercise. Figure 21 depicts the theoretical framework that was chosen to guide the intervention strategies employed. This framework includes relevant antecedents (Box 1), the active elements of th e intervention (Boxes 2 to 4), the theoretical mediators of behavior change (Boxes 5 to 7), and the predicte d psychosocial, behavioral, and fitness outcomes (Boxes 8 to 9). E ach of the elements of the inte rvention was derived from selfefficacy theory, self-determination theory, and prev ious research, and will be described in more detail below. Antecedents As shown in Figure 1 (B ox 1), the linkages betw een an individuals readiness to engage in exercise behavior, or stage of change (DiClemente & Prochaska, 1982), and self-efficacy


31 expectations have important implications for ex ercise interventions. Self-efficacy expectations appear to be most important for individuals who are just beginning an exercise program (McAuley, 1992; Oman & King, 1998). Similarly, self-efficacy expectations increase almost linearly as individuals progress from initially c ontemplating a behavior to actively maintaining these behaviors (Gorely & Gordon, 1995; Hellma n, 1997; Herrick, Stone, & Mettler, 1997). Hence, previously sedentary adults typically have low levels of self-efficacy in their capability to begin and maintain an exercise program while th ose who have been activ e most of their lives usually have higher self-efficacy expect ations with regard to exercise. Perhaps the greatest advantage of adopting a stages of change framework here is the ability to understand how psychosocial mediators (e.g., efficacy beliefs, intrinsic motivation) change across stages of behavior change. In the precontemplation stage, for example, individuals may rate perceived barriers as being more importa nt than the benefits of activity. For individuals in the action and maintenance stages, day-to-d ay impediments (e.g., weather, time, stress) represent proximal challenges to completion of individual bouts of PA. Individuals who successfully move throu gh the stages of change consistent ly report increases of perceived benefits and a reduction of perceived barri ers (Gorely & Gordon, 1995; Herrick, Stone, & Mettler, 1997; Marcus, Rossi, Selby, Niaura & Abrams, 1992; Marcus & Owen, 1992; Nigg & Courneya, 1998). Jordan and Nigg (2002) offered a review of how to tailor interventions for older adults based upon their specific stage of change. Broadly, they posited that individuals will change their behaviors if and when they are ready and individuals vary in their level of readiness. For instance, pre-contemplators often have no intention of increasing their exercise behavior and they may be under-informed or completely uninformed about the health risks of a sedentary


32 lifestyle. They are perhaps demora lized about their abil ity to begin an exercise program based upon their physical limitations and/or previous exer cise experiences. They may also be avoidant of information about their behavi ors and individuals at this stag e do not think of themselves as exercisers. The primary intervention goal for individuals in the pre-contemplation stage is to increase their awareness of the need to change (Jordan & Nigg, 2002). Enc ouraging these individuals to think about the benefits of exercise behavior and facing their fears about their sedentary behaviors may achieve this task. Individuals shou ld be encouraged to think about the possible health risks associated with in activity and also feel assured to know they can change their behaviors. Individuals in the c ontemplation stage have indicated their intention to exercise (Jordan & Nigg, 2002). Contemplators are often indecisive or ambivalent about exercise but are aware of the need to begin an exercise regimen. I ndividuals at this stage should be encouraged to pay attention to news, information about exer cise, and opportunities in their community to exercise. Central to Project AAMP is the notion that indi viduals in these early stages can begin the process of self-evaluation (i.e., co ntemplation, preparation) most e ffectively within the context of a supportive, peer-based interv ention. The combination of an autonomy supportive environment where exercise goals and behaviors are modele d and actively supported by physically active, same aged cohort peers would be predicted to enhance self-efficacy exp ectations and intrinsic motivation to exercise. Goal Setting The use of goal setting to m otivate behavior ha s a long and rich theoretical and empirical tradition and goal setting strategies are an establis hed part of many behavioral interventions used by health care providers and ps ychologists (Hill, 2001). Goals ar e cognitive mechanisms that


33 pertain to what an individual is striving to attain, an objective, or aim (Burton, Naylor, & Holliday, 2001). Goals often enter and recede from conscious awareness depending on the context and they can provide focus for specific be havioral efforts (Burton et al., 2001). The most widely accepted theoretical framework guiding go al setting research has been offered by Locke and Latham (1990) and their principles inform Project AAMP. In addition, extensive theorizing and research conducted within the sport and exercise scienc es (Weinberg, 1994; Weinberg & Weigand, 1993) justify other specific goal setting practices. To begin, Locke and Latham predicted that goals enhance performance because they (a) focus attention on the task at hand; (b) increase effort and intensity of behavior; (c) encourage persistence even when failure occurs; and (d ) promote the development of learning and/or problem-solving strategies. From their work, rese archers have shown that specific and difficult goals have been linked to increased performan ce across a variety of ta sks, populations, and time frames (Burton et al., 2001; Kyllo & Landers 1995; Locke & Latham, 1990; Weinberg, 1994). Specific and difficult goals in an exercise setting might involve a previously sedentary individual striving to reach and maintain national guidelines for PA (Pate et al., 1995) for four consecutive weeks. Researchers in the exercise sciences ha ve also distinguished between performance and outcome goals with the former re ferring to form, technique, pr ocess orientation, and attaining specific performance standards re lative to previous performa nces; outcome goals are more product-oriented and focused on objective outc omes (Kingston & Hardy, 1997; Burton, 1989). While meeting PA guidelines is clearly an outcome goal, a performance oriented goals for this individual would be to walk on a treadmill in an increasingly progressive (e.g., faster pace, using an incline) manner 5 times per week. Finally, a general recommendation from the exercise sciences is to use a combination of shorta nd long-term goals (Kyllo & Landers, 1995). Short-


34 term goals are effective because they provide more frequent evaluation of success, they are more flexible and controllable, and they focus attent ion less on social comparisons with others and more on ones own accomplishments (Burton et al., 2001). Using the above theory and research as a guide, the following specific goal setting guidelines were followed in Project AAMP design: (a) the participants were encouraged to set a combination of performance and outcome goals; (b ) all goals were specific and difficult; (c) all goals were measurable and timed; (d) goals we re positive and focused on desired behavioral outcomes; and (e) a combination of short and long-term goals were implemented. Peer mentors were trained extensively to deliver the educational, psychosocial and discussion elements of the goal setting interv ention. Mentors were taught how to engage in discussions that promote the formation of shortand long-term goals that were both performance and outcome oriented. In addition, goal setting wa s addressed early in the intervention (Week 2) to allow opportunities for participan ts to immediately begin attempts at goal-directed behaviors. Goals set at the beginning of the intervention were regularly re-visited both formally and informally with additional focus given to setting long-term maintenance goals at the end of the intervention. Goal-setting is a commonly employed self-re gulatory strategy (Anderson et al., 2006; Bandura, 2001) in social cognitive theory. As sh own in Figure 2-1 (Boxes 2 and 5), goal setting was hypothesized to increase efficacy beliefs an d intrinsic motivation by providing successful performance accomplishments (effective shortand long-term goals) and fostering perceptions of autonomy (freedom to choose goals important to the individual) and competence (successful attempts at behavior change). Throughout the intervention, peer mentors en couraged participants to re-visit the goal setting pro cess and gave praise to partic ipants who reached behavioral


35 milestones. Successes were shared with the group as a whole in order to foster relatedness and enhance performance accomplishment. Mental Imagery Mental im agery, also known as guided imag ery or visualization, is a quasi-sensory experience whereby individuals recreate previous experiences, imagine future anticipated experiences, or create new experience (Hall, 2001) Mental imagery is an important part of many aspects of life including language developmen t, enhancing motivation, learning motor skills, coping with stress, and improving sport performa nce (Kyllo & Landers, 1995; Weinberg et al., 1990; Weinberg, Bruya, & Jackson, 1985). Recently, researchers have also implicated mental imagery in the exercise domain (Gammage Hall, & Rodgers, 2000; Giacobbi, Hausenblas, Fallon & Hall, 2003; Hall, 1995; Hausenblas, Hall, Rodgers, & Munroe, 1999; Munroe-Chandler & Gammage, 2005). Specifically, it has been theorized that mental imager y may be associated with increased self-efficacy and motivational processes within exercise settings (MunroeChandler & Gammage, 2005). Ba sed upon social cognitive theory (Bandura, 1997), it has been proposed that imagery may increase motivation through its influence on self-efficacy and outcome expectancies (Hall, 2001). Hall suggest ed that as exercisers image themselves accomplishing a certain outcome, there is an incr eased likelihood the event will occur (outcome likelihood) and the importance of that outcome (outcome value) may be influenced. These two variables in turn lead to positive outcome expectancies, enhanced motivation to exercise, and increased frequency of exercise behavior. For instance, an individual may imagine himself exercising, completing a workout, an d improving his level of fitness. These images would in turn lead to increased motivation to exercise. It has also been proposed that imagery may influence exercise participation through its effects on self-efficacy and outcome expectations (Hall, 1995). Th at is, the use of imagery by


36 exercisers may allow individuals to imagine accomplishing desirable outcomes such as improved appearance or fitness, whic h may increase the likelihood of continued involvement or participation. In support of this contention, ev idence has revealed positive associations between exercise imagery and self-efficacy related cognitions and beliefs with regard to exercise behavior (Hausenblas et al., 1999; Rodgers & Gauvin, 1998) Additionally, individuals who are more active and participate in exercise more frequen tly report using a greater degree of appearance, energy, and technique imagery than less frequent exercisers (Hausenblas et al., 1999; Gammage et al., 2000). Because most everyone engages in some form of mental imagery (Hall, 2001), and because this is an easily learned skill that is empirically supported fo r targeted behavioral change interventions with a variety of conditions, be haviors, and populations (Hall, Anderson, & OGrady, 1994), this applied tool ma y be useful with older adults to promote exercise behavior. Mental imagery has been effectively used for th e treatment of pain, depr ession, eating disorders, phobic disorders, and other health complaints (Sheikh, 2003). Additionally, mental imagery is a key component within Suinns cognitive-behavioral model for the treatment of anxiety and is used widely by practitioners for the treatment of stress and behavioral disorders (Suinn, 1990, 1996). For Project AAMP, the use of mental imager y was hypothesized to have an influence on individuals self-efficacy expectations through effects on physiological arousal and vicarious experiences sources of efficacy information (Fi gure 2-1, Box 7). This process is facilitated by allowing participants to observe their peer mentors exercising w ithin exercise settings. Peer mentors were taught to ask open-en ded questions and help participants formally and informally use imagery to stimulate mental images of successfully completing exercise routines and


37 behaviors. Formal imagery situations included guided imagery during group sessions (Sessions 6, 7, and 11) and self-directed imagery outside of group sessions. Informal imagery occurred during the sessions while discus sing future goals, spontaneous discussions about exercise experiences, as well as images about exercise that took place before, during, or after exercise bouts. It was hypothesized that these images would stimulate physiological and affective responses linked to exercise behavior. Peer Mentoring The provisio n of social support in Pr oject AAMP was accomplished by the use of physically active, same-aged peer mentors as gro up leaders/facilitators to create a socially supportive environment conducive to behavior ch ange (Figure 2-1, Box 3). Theoretically, the linkage between the use of same age peers as intervention agents with elements of social cognitive theory include relatedness, verbal persuasion, and vicarious experience (Figure 2-1, Box 6). Specifically, meaningful so cial relationships were hypothe sized to satisfy the need for relatedness while the intervention agents and ot her members of the exercise group engaged in verbal persuasion to encourage goal achievement st rategies, adherence to exercise routines, and increased exercise frequency and intensity. Anothe r important theoretical source of self-efficacy expectations derived from peer intervention agents is vicarious experience. By having same age active adult peers, disc ussing and in some cases modeling exercise behaviors and demonstrating how to use exercise machines (e.g., treadmills) and resistance training exercises, it was hypothesized that previously sedentary adults would experience increased exercise self-efficacy (Figure 2-1, Box 8). As mentioned previously, speci fic guidelines for social network characteristics described by Israel (1982) as well as support types desc ribed by Berkman (1995) were implemented to effectively engage participants into group proce sses and the support network. First, peer mentors


38 were trained to engage participants in open and transparent discussions regarding exercise behavior. These discussions served to progressively fo ster emotional closeness (i.e., intensity) and interaction (i.e., density). Moreover, all members of the group were considered equal contributors that were expected to share personal experiences and anecdotes in order to increase reciprocity in the relationships. Finally, peer mentors were carefully selected to match demographic (i.e., homogeneity) and geographic (i.e., dispersion) characteristics of the participants but this proved to be an interesting challenge throughout our study. All peer mentors met the following characteristics: (a) were 50 year s and older; (b) were physically active and adequately trained to deliver the intervention; and (c) roughly matched to the demographic characteristics of the enrolled participants (e.g., race/ethnicity, socio-economic background). Peer mentors, when possible, attempted to pr ovide emotional, instrumental, informational, and appraisal support for participants. Peer me ntors were trained to identify and encourage engagement of potential sources of support for pa rticipants. For example, peer mentors helped participants identify other indivi duals in their lives that may serv e as support agents (e.g., family members, friends). Emotional and appraisal suppo rt were integral portio ns of the group process and weekly discussions. These types of s upport were provided through open-ended and transparent discussions throughout the interventi on. For example, appraisal support was provided weekly as individuals discussed unique challenges a nd barriers that they f aced in the past week. Instrumental support was provided in the form of a free exercise facility membership. While peer mentors were not specifically tr ained to provide informational or educational support, mentors were encouraged to enter into constructive conversations identifying and accessing reliable informational support from individual (e.g., fa mily members) and community (e.g., community centers, exercise facilitie s, internet) resources.


39 Initial Readiness, Motivation, and SelfEfficacy towards Exercise Goal Setting Social Support Mental Imagery Autonomy Perceptions of Competence Previous Performance Accomplishments Relatedness Verbal Persuasion Vicarious Experience Vicarious Experience Physiological Arousal Increased Exercise SelfEfficacy Increased Intrinsic Motivation Increased Physical Activity Improved Physical Fitness Box 1 Box 2 Box 3 Box 4 Box 5 Box 6 Box 7 Box 8Box 9 Figure 2-1. Guiding theoretical framework for intervention


40 CHAPTER 3 METHODS General Design The basic question addressed in our study was whether the Project AAMP intervention (specif ically the use of peer mentors, goal setting, and mental imagery) can effectively enhance social cognitive variables (i .e., self-efficacy, self-determined behavior), which have demonstrated theoretical linka ges to PA behavior and fitn ess outcomes, among previously sedentary adults age 50 and ol der. Therefore, the purposes of the study were as follows: Primary purpose: To examine the effects of Project AAMP, a multi-component 16-week PA intervention for sedentary adults 50 years of age and older, on changes in self-efficacy and intrinsic motivation toward PA. Secondary purpose: To evaluate subsequent improveme nt in PA behavior (i.e.; selfreported PA and pedometer count) and fitness parameters (i.e.; cardiorespiratory fitness). To address these purposes, an experimental de sign was chosen where participants were randomized to one of two experimental groups : (a) an PA promotion group where subjects participated in a 16-week psycho-educational inte rvention tailored for older adults and focused on goal setting, mental imagery, and delivered by a same aged peer mentor; or (b) a health hygiene control group matched in so cial contact and peer leadersh ip to the intervention group that discussed topics related to general health for older populations. Measurement of study outcomes occurred daily for behavioral measures, weekly for self-efficacy, and at baseline and posttest for self-determined behavi or and the fitness parameters. Participants Sample The study identified 433 individual s aged 50 and older. Figure 3-1 depicts participant flow and recruitm ent throughout the study period. Participants were drawn from the Gainesville/Alachua County area. Recruitment efforts included announcements in a local


41 newspaper, the University of Florida Older A dult participant registr y, and flyers for study locations (Appendices A & B) in community gathering places (e.g., grocery stores, recreation/community centers, re tirement communities). To maximize outreach to diverse populations, additional recruitment strategies via several local media outlets focusing on older adults and African American elders were used such as guest columns in the Senior Times as well as active participation in local health fairs. According to the U. S. Census Bureau (2005), approximately 72% of the Alachua County population is White, 20% are Black or African-American, 5% are Asian, and 3% are Other. Seven percent report Hispanic or Latino (of any r ace) descent. Recruitment strategies attempted to include members of these racia l/ethnic groups at the same rate with which they are represented in the Alachua County population. Racial/ethnic distribution and other demographic information of the final sample are reported in the results se ction. Institutional approv al was obtained for all aspects of the study protocol (#2005-U-0813). Exclusion Criteria In total, 160 participants were excluded from participation in the study. Exclusion criteria likely to adversely affect the safety of older adults in the PA intervention are included below along with number of participants excluded based on these criteria: Terminal illness with life expectancy of less than 12 months ( n = 0). Cardiovascular disease (myocardial infarction the last year, chronic heart failure, aortic stenosis, history or cardiac arrest, pace maker, implanted cardiac defibrillator and uncontrolled angina) ( n = 4). History of epilepsy, head injury requiring hospitaliza tion, or diagnosed st roke in the last year ( n = 16). Chemotherapy or radiation treatmen t for cancer in the last year ( n =3). Pulmonary disease requiring oxyg en or steroid treatment ( n = 4).


42 Ambulation with assistive device (e.g., cane, walker, wheelchair) ( n = 14). Exclusion criteria for factors adversely affec ting compliance with study protocols include: Unwillingness to be randomly assigned to one of the two experimental conditions or comply with study procedures ( n = 0). Diagnosis of schizophrenia, clinical depression, bipolar disorder, or other major psychiatric illness (n = 10). Cognitively impaired (n = 15). History of medical problems, legal problems, or withdrawal symptoms associated with alcohol or drug use ( n = 4). Hearing or speech impairments that make verbal communication difficult ( n = 10). Prescription of heart rate a ttenuating medication (e.g., beta -blockers; calcium channelblockers) for high blood pressure ( n = 36). Unable to commit for the entire study peri od or comply with all study procedures ( n = 4). Exclusionary criteria and demographic information (e.g., age, marital status, education level, etc) were collected by phone with a stan dardized protocol (Appendix C). Finally, all participants were required to pr ovide a doctors note verifying ex clusionary criteria and stating they were cleared to engage in ca rdiovascular exercise (Appendix D). Inclusion Criteria The Stages of Exercise Change Questi onnaire (S ECQ; Marcus et al., 1992) was administered during initial phone contact to assess part icipants stage of change in the PA domain (Appendix E). The SECQ contains five ordered-category items that assess change readiness along a continuum as follows: pre-co ntemplation, contemplation, preparation, action, and maintenance. This algorithm was used as a prescreening tool to exclud e participants already in the action and maintenance stages. PA was defined by meeting national recommendations set forth by the American College of Sports Medicine and Centers for Disease Control (Pate et al., 1995). This resulted in a sample pool that included those of primary interest individuals in the


43 pre-contemplation, contemplation, and preparation stages of PA behavior. Individuals who were screened and fell into the action and maintenance stages of PA were excluded from the protocol ( n = 44). Procedures The study tim eline for a single participant was divided into four pha ses over 20 weeks: initial screening/eligibility, baseline, interventi on, and posttest (Figure 3-2). During the initial screening/eligibility phase (n = 306), participants were screen ed and nurse/physician permission forms were collected. Eligibility was determined and eligible persons were enrolled into the study ( n = 146). Baseline appointment was scheduled dur ing this period for eligible participants who remained interested in the study (n = 91). During the baseline phase, participants were administered the informed consent documents (A ppendix F), baseline soci al cognitive measures, baseline cardiorespiratory fitness test, and PA was monitored daily by pedometer and self-report for one week. During this period, participants were randomized to one of the two intervention arms of the study. Randomization occu rred in replicates of approxima tely 5 to 16 participants to ensure equal group size of 4 to 8 pa rticipants for th e intervention (n = 44) and health hygiene ( n = 47) groups. Program staff was blinded to group assignment during pret esting to avoid bias. Participants were blinded to their group assignm ent throughout pretesting and the baseline phase of measurements (until the intervention phase began) to avoid bias in levels of baseline PA and self-efficacy and to decrease initial dropout in the health hygiene control. Ten individuals failed to attend any group sessions, despite not knowing group assignment, and were therefore excluded from all analyses. The intervention phase involved 13 weekly sessions over 16 weeks with peer mentors (approximately 1 hour in length) and access to an exerci se facility (during the first 12 weeks). Twelve additiona l individuals were lost to fo llow-up during the intervention phase of the trial (7 in inte rvention and 5 in control). Fina lly, during the posttest phase


44 procedures were repeated from the baseline phase for those part icipants who were not lost to follow-up in the intervention ( n = 34) and health hygiene ( n = 35) groups. Intervention Protocol Table 3-2 shows the schedule of intervention topics for both the treatm ent and control arms. Unlimited access to the exercise facility wa s granted to participants in both arms of the intervention. In order to maintain internal valid ity, it was essential that participants in both the treatment and control conditions ha d identical opportunities to engage in PA behavior. Therefore, a no-contact control group was deem ed inappropriate to answer the specific aims of the project. The key difference between these groups was that control group particip ants did not receive individualized instruction and discussion in PA-related goal se tting and mental imagery. It should be noted, however, that control group particip ants could have been motivated to begin PA due to the general health-related information presented, social contact, self-monitoring, and provision of an exercise membership. These changes in behavior were expect ed to be short-lived, while such a control group design was considered necessary to localize changes in behavior specifically to intervention-sp ecific factors. Intervention se ssions were conducted by highly active, trained mentors who had successfully comp leted a previous iterat ion of the intervention and been given additional training related to goal setting, mental imagery, and the provision of support messages. Both treatment and control ar ms received an introduction to the research program and orientation to the f itness facility by program staff during the first week. All other meetings were conducted by the trained peer mentors. In an effort to wean participants from the group meetings and address issues related to sustainabili ty and maintenance of PA routines, both intervention arms met only once over the final four weeks of the intervention protocol.


45 Treatment arm During the f irst week, participants in the treat ment arm met with an exercise professional at the facility of which they were enrolled. This individual provided the following: (a) orientation to the facility including familiarizing the partic ipant will the exercise equipment; (b) introduction to facility staff and operations; and (c) completion of facility enrollment procedures. This person also implemented a major educational com ponent of the intervention. They presented information to the participants on the benefits of regular PA tailored specifical ly for older adults. Information included the components of fitness including cardiorespirator y fitness, resistance training, and flexibility. This indivi dual also taught the participant to calculate their target heart rate and work with the participant to develop a personalized plan of PA. During the Week 2 session of th e intervention, partic ipants met with the peer mentor for the first time. The primary purpose of this sessi on was to begin to deve lop trust and rapport between the participants and the mentor. Mentor s were trained to ask open-ended questions and participants were invited to share information about their physical activ ity history during this session. The sessions during Week 3 through Week 10 were semi-structured in nature, focusing on various components of behavior change includ ing creating an PA support system, goal setting, mental imagery, and explorations of barriers to PA. These compone nts were delivered within the context of a mentoring relationship, relyi ng upon a combination of supportive questions, statements, discussions, and educat ion. Readings were assigned that were designed to inform the participants of the benefits of PA and the tenets of mental imagery and goal setting. Weeks 3, 6, and 7 had a specific educational focus that taught the tenets of goal setting and mental imagery. The final sessions between Week 10 a nd Week 16 focused on sustainability and maintenance of PA behavior. Specifically, the main emphases for these sessions were to wean


46 participants from the peer mentoring relationshi p and the group-based in tervention. As part of this process, the participants were educated abou t ways to PA and maintain an active lifestyle at home. As such, the participants were given specific homework assignmen ts related to setting goals for specific, measurable, and quantifiable home-based PA behaviors, outcomes, and routines. Participants were given the opportunity to practice these sustainability skills after Week 12 when the exercise facility membership was wi thdrawn. Participants discussed the challenges and barriers of maintenance during W eek 14, the final intervention session. Control arm The purpose of the control arm of the intervention was to control for potential social contact effects. Therefore, these sessions were also delivered by peer me ntors. Discussion topics centered on basic health hygiene and were broadranging, geriatrically-tailored topics selected from National Institutes of Health senior health topical guide ( http://nihseni orhealth.gov ; developed by the National Institute on Agi ng and National L ibrary of Medicine, 2004). Homework assignments, discussion questions, and appropriate praise and reinforcement were elicited by the peer mentors in a similar framew ork as the treatment arm. Peer mentors were selected from a similar pool of community vol unteers and appropriate training and quality control took place prior to and during the study. Ecological Validity Careful attention was given to design recomm endations set forth by the RE-AIM framework (Reach, Efficacy, Adoption, Implementation, Maintenance) described by Glasgow et al. (1999). The RE-AIM framework was establishe d to guide the evaluation of public health interventions and clinical trials and to improve the ecological validity and impact of health behavior interventions. Some of the unique elements of Projec t AAMP address these factors and are outlined below.


47 Peer mentors First, peer m entors were selected to be used throughout the intervention instead of highlyeducated behavioral counselors in order to meet the adoption recommendation from RE-AIM (Glasgow et al., 1999). Peer mentor s, if effective, offer a relativ ely inexpensive alternative to health promotion compared to research personne l and highly-trained couns elors. This increases the potential for future adoption of Project AAMP in a variety of contexts and settings where resources may be insufficient (e.g., community centers, YMCAs). Alth ough these individuals may possess varying levels of knowledge about PA promotion and behavior change, all mentors were selected based on their successful adopti on of a consistent and long-term personal PA regime as well as participation in a previous iteration of Project AAMP. The primary purpose of these mentors were to: (a) provide affirming soci al support messages; (b) engage participants in in-depth discussions about PA behaviors and barriers; and (c) intr oduce and explore with participants the psycholo gical skills of mental imagery and goal setting. In othe r words, one of the major goals of Project AAMP was to apply th e guiding theoretical mode l (Figure 2-1) to nonresearch personnel. Additionally, using intervention agents matche d in age and experience to participants enhances the social integration of the peer mentors to provide the social support and encouragement needed to promote positive behavior change in the PA domain. Characteristics of the mentors that were used are discu ssed in detail in the results section. Quality control and monitoring Quality con trol measures were taken to a ddress the implementation recommendation from the RE-AIM framework. Implementation refers to the extent to which an intervention is delivered in the manner in which it was inte nded (Glasgow et al., 1999). All intervention sessions in treatment and control arms were vi deoor audio-recorded and monitored by the research team for content, proper delivery of psycho-educational materials, and potential


48 digression from the specified content. Quality control checklists and scoring procedures were used to give the peer mentors, in both the treatment (Appendix G) and control (Appendix H) conditions, feedback about ways to improve their efforts to facilitate group meetings. Tailored quality control feedback was given for the edu cation sessions where the goal setting and mental imagery components are implemented. Program staff met weekly with the mentor after each of the first five sessions to give feedback a nd coaching. Additional training and feedback was provided as needed throughout the intervention. All feedback was recorded and discussed with the peer mentor prior to the next weekly group session. Monitoring of group sessions was maintained by program staff throughout the protocol. Sustainability Another im portant design feature of Project AAMP was that all participants were given a 12-week free membership to one of two exercise facilities: (a) a staff and faculty gym sponsored by the College of Health and Human Performance at the University of Florida; or (b) a gym facility at a suburban church in west Gainesville, Florida. The participants did not have access to these facilities after Week 12 of the intervention, however social cognitive, PA behavior, and fitness measurements were assessed after Week 12 and again at the end of the 16-week study protocol. The rationale for withdrawing access to the facility at this time was to determine the extent to which the participants sustain PA when not offered membership to an exercise facility. This was an effort to meet Glasgow and co lleagues maintenance recommendation focused on the extent to which the target health behavior is maintained over time (Glasgow et al., 1999). Similarly, peer mentors were not specifically relied upon to give recommendations or impart knowledge of specific PA routines or plans; rather, mentors encouraged, reinforced, and supported the participants effort s to seek out information about PA from credible outside sources (e.g., trained facility staff, ACSM webs ite). Participants were encouraged to access


49 available resources at the exercise facilities a nd trained staff persons w ith questions concerning how to properly use exercise equipment. Such efforts were intended to mimic the challenges posed by adults within the PA domain. Measures Study m easures were selected to specifically address the primary and secondary purposes of the study and the guiding theoretical framewor k (Figure 2-1). To address the primary purpose of the study (i.e.; the impact of the intervention on self-efficacy and intrinsic motivation toward PA), a series of social cognitive measures were used to assess changes across the intervention. To address the secondary purpose of the study (i.e ., the subsequent changes in physical activity behavior and fitness outcomes) both self-report and objective measures of behavior and cardiorespiratory fitness were administered. Additionally, measures of depression, body mass index (BMI), and blood pressure were measured to explore potential covariates of intervention adoption and behavioral change. Please see Tabl e 3-2 for a detailed schedule of the assessment timeline. Social Cognitive Measures Measures of self-efficacy were adm inister ed weekly throughout the intervention. The repeated testing schedule was used to address short-term sustainability and temporality of the intervention. In addition, this repe ated assessment allowed for formal testing of the effects of the withdrawal of the exercise faci lity membership and the reduced contact with the peer mentors and group environment during Week 12. It was not possible to administer the measure of selfdetermined behavior each week due to its length. This measure was administered only at preand post-test. All social cognitive measures were give n within the same testing period and computeradministered to increase standardization.


50 Barriers self-efficacy The BSE (McAuley, 1992; Appendix I) is a 13item m easure that taps participants perceptions of confidence to maintain regular PA (three times per week) despite commonly identified barriers. This scale was developed using attributional analysis to identify commonly cited barriers that impede PA maintena nce (McAuley, Poag, Gleason, & Wraith, 1990). McAuley and colleagues (e.g., Duncan & McAuley, 1993; McAuley, 1992, 1993; McAuley, Courneya, Rudolph, & Lox, 1994) then used this instrument with numerous samples and observed that bad weather, time management, lack of interest/boredom, pain and discomfort, location/accessibility, personal stress, and exercising alone were the most commonly cited barriers to PA. These researchers then integrat ed theoretical and meas urement considerations espoused by Bandura (1986, 1997) to create this 13-item scale. Respondents indicate their confidence on a scale of 0% ( no confidence at all ) to 100% ( completely confident ); their responses are summed and divided by the total numb er of items to provide a score that can range between 0% to 100% with higher scores indicating great er self-efficacy to overcome barriers to PA. The BSE has been shown to be predictive of PA behavior and contain adequate internal consistency across a number of diverse research contexts and populations, including use with older adults (McAuley et al., 2005) With the current sample, the internal consistency of this measure was excellent ( = .95). Exercise self-efficacy The exercise self-efficacy m easure (EXSE; McAuley, 1993b; Appendix J) is an 8-item measure that taps confidence to maintain exercise (three times per week at moderate intensity for 40 minutes) consecutively for a progression from 1 week to 8 weeks. Respondents indicate their confidence on a scale of 0% ( no confidence at all ) to 100% ( completely confident ); their responses are summed and divided by the total numb er of items to provide a score that can range


51 between 0% to 100% with higher scores indicating great er self-efficacy toward exercise. This measure has demonstrated excellent re liability across a number of studies ( s > .90) and has been used widely with older populat ions (Blissmer & McAuley, 2002; McAuley et al., 2003). With the current sample, the internal consistency of this measure was also excellent ( = .99). The BSE was combined with the EXSE measure to form a composite self-efficacy variable (SE) to reflect overall self-efficacy toward exercise behavior. This composite was formed to address the first study purpose. This composite technique was used due to high correlations among the measures across all timepoints (.66 to .85, ps < .001). The composite variable was calculated by transforming scale scores into z-scores and summing across the two measures. Self-determined behavior The Exercise Motivation Scale (EMS; Li, 1999; Appendix K) is a 31-item m easure of exercise motivation designed to assess motivational tendencies in the exercise context. It was developed based upon Deci and Ry ans (1985) theorizing about the nature of the motivation construct. As such, the EMS consists of eight subscales: amotivation, external regulation, introjected regulation, identified regulation, integrated regulation, intrinsic motivation to learn, intrinsic motivation to accomplish tasks, intrinsi c motivation to experience sensations. The EMS has shown adequate factorial evid ence to support its 8-factor structure, but can also be weighted across the 8-subscales to form a single indicato r of self-determined behavior along the selfdetermination continuum (Vallerand & Rousseau, 2001). The EMS recently demonstrated good internal consistency reliability estimates on its subscales (.75 to .90) and evidence of divergent (i.e., social desirability) and convergent validity (i.e., stages of change) in a sample of adults (Wininger, 2007). With the current sample, the in ternal consistencies of the measure subscales were adequate ( s > .75), with the exception of introj ected regulation, which demonstrated poor internal consistency ( = .49). The overall measure intern al consistency was very good ( = .85).


52 Physical Activity Measures Both a self-report and ob jective measure of PA was selected to assess changes in freeliving movement throughout the intervention as well as reports of structur ed exercise bouts. It was expected that these measures would tap asp ects of PA uniquely and complement one another to form a more comprehensive measure of change s in activity level. The measures were assessed daily and averaged across week and phases of the intervention. Self-reported physical activity The Leisure-Tim e Exercise Questionnaire (LTEQ) is a three-item scale that asks participants to rate how ofte n they engaged in mild, moderate, and strenuous leisure-time exercise (Godin, Jobin, Bouilon, 1986; Godin & Shepherd, 1985). The LTEQ allows researchers to calculate a total MET estimation (MET-mins /wk) by weighting the intensity level and summing for a total score using the followi ng formula: 3(mild), +5(moderate), and +9(strenuous). Although typically us ed as a 7-day recall of PA be havior, in the present study the LTEQ was used as a daily measure and summed across the week to re duce recall bias. Recent literature has chosen to not incl ude mild minutes in calculations of PA (Karvinen et al., 2007) given that moderate or vigorous activity is needed for health benefits (Pate et al., 1995). Minutes of moderate-to-vigorous physical activity (MVPA) were comput ed from the LTEQ by adding the number of moderate and strenuous bouts reported and multiplying by 20. This value was then summed across the week to represent minutes of MVPA per week. Both the MET estimate (MET-mins/wk) and minutes of MVPA were used as dependent variables for self-reported PA. Minutes of MVPA was also used to categorize individuals into three baseline groups based upon PA public health recommendations for older adults: (a) those who reported 150 minutes of MVPA met national guidelines; (b) those who accumulated some but < 150 minutes of MVPA were considered insufficiently active; and (c) th ose who reported no moderate or vigorous PA


53 were considered inactiv e (Nelson et al., 2007). Previous rese arch has supported the validity and reliability of LTEQ score interpretations w ith adult (Godin, Jobin, & Bouilon, 1986; Jacobs, Ainsworth, Hartman, & Leon, 1993) and older adu lt populations (Karvinen et al., 2007; Ruppar & Schneider, 2007). Please see Appendix L for the daily record where LTEQ and pedometer data were collected. Pedometer Participants were asked to wear the AE 120 pedom eter (Yamax SW200 engine). This pedometer is a small device worn on the hip to measure activity level by counting steps (works by a horizontal, spring-suspe nded lever arm, which moves up and down with vertical accelerations of the hip). Participants wore the pedometer during the entire waking day, and were asked to register the final number of steps in a log at wake ti me regarding the previous days activity. They were instructed, af ter recording, to rese t the number of steps to zero before the next day. Convergent validity of pedometers has demonstrated correlations with accelerometers, observation, energy expenditure, a nd self-report measures of PA, while divergent validity is available for sedentary behavior (see Tudor-Loc ke, Williams, Reis, & Pluto, 2002 for a review). Tudor-Locke et al. (2005) examined the number of days needed to estimate mean steps/day counts in adults and found that a minimum of three days (preferably with the inclusion of Sunday) is needed for accurate mean step coun ts. The current protocol collected continuous use data throughout the interventi on and averaged over the week and phase to estimate total steps/day. Physiological Measures Cardiorespiratory fitness A subm aximal graded exercise test employing a modified Balke treadmill protocol (ACSM, 2000) with continuous he art rate monitoring was used to obtain estimations of


54 cardiorespiratory fitness (VO2 max). The modified Balke is a treadmill protocol that involves increases in slope while speed is kept at a constant. Participants heart rate was monitored throughout the protocol and for two minutes prior to testing. With in the first minute of testing participants were asked to find a comfortable walking pace on the treadmill. After the first minute, treadmill grade increased 1% and continued to increase at 90-second intervals until heart rate reached 85% of age-approximated maximal heart rate. The following equation was used to estimate cardiorespiratory fitness: VO2max = (0.1 x speed) + (1.8 x Final Grade x Speed) + 3.5. Please see Appendix M for the data collection tool that was used. Permission from the participants physician was granted prior to administration. The measure of cardiorespiratory fitness was assessed during the pr etest week prior to the interv ention and again at posttest concurrently with the administra tion of the social cognitive measures. It should be noted that such an approach is a less accurate method of ascertaining cardiorespiratory fitness than employing a maximal graded exercise stress test ; however, for a population of sedentary older adults it is considered safe and adequate and has been used elsewhere (McAuley, 1992). Body mass index Weight and height were collect ed f rom participants at base line and posttest. Weight was measured to the nearest kilogram using a regularly calibrated balance beam scale. Height was measured by self-report. Body mass index (BMI) was calculated using the following equation: weight (kg) / height2 (m2). The following guidelines have been published for major classification: underweight (<18.5), normal (18.5 to 25), overweight (25 to 30), and obese (>30) (WHO, 2005). BMI was used to characterize the sample at baseline and as an exploratory predictor of intervention-related outcomes.


55 Blood pressure Autom ated non-invasive blood pressure was assessed using the Omron HEM-711AC blood pressure monitor weekly throughout the in tervention. Systolic, diastolic, and pulse measurements were recorded. Automated measurem ent was used to standardize the procedure. Measures at baseline and post-test were taken twice. For the first measurement, participants were asked to sit upright in a chair wi th his or her feet flat on the floor and without limbs crossed for five minutes. Participants were asked to repeat this posture for two additional minutes after which the second measurement was taken. These va lues were averaged. Blood pressure was used to characterize the sample at baseline and wa s not explored as an outcome of the study. Depression Measures Geriatric Depression Scale The Geriatric Depression Scale (GDS; Yesava ge et al., 1983) was adm inistered to assess depressive symptomatology. The GDS is a 30-item self-report scale of yes/no questions about symptoms of depression (e.g., Do you feel that y our life is empty?). One point was given for each depressive symptom endorsed. This measure has been shown to be a reliable and valid measure of depressive mood in adults 65 years and older. Beck Depression Inventory The Beck Depress ion Inventory-II (BDI-II; Beck, Steer, & Brown, 1996) was also administered to assess depressive symptomatology. The BDI-II consists of 21 groups of statements related to cognitive and somatic depression symptoms (e.g., sadness, changes in sleeping pattern). Individuals select one of four statements that describes the severity of their symptoms over the past two weeks (e.g., 0 = I do not feel sad, 1 = I feel sad much of the time, 2 = I am sad all the time, 3 = I am so sad or unhappy that I cant stand it). The scale has a clinical


56 range of 14 to 19 points for mild depression symptoms and 20 to 28 for moderate depression symptoms. Measures of depression were administered at baseline and posttest timepoints. Two measures of depression were selected to capture both broad-level and agespecific dimensions of depressive symptomatology. Both measures were used to charac terize the sample at baseline. The BDI-II was chosen as an exploratory predic tor of intervention-related outcomes given its established reliability an d validity with adult samples above and below 65 years of age (Beck et al., 1996; Brink, Yesavage, Lum, Heersema, Adey, & Rose, 1982). Sample Size and Power Considerations Effect size estim ates are available for thr ee of the primary outcomes of the current protocol: exercise self-efficacy (BSE and EXSE), self-reported exercise behavior (LTEQ), and cardiorespiratory fitness (VO2max). Given that self-efficacy and ot her social cognitive constructs are the primary outcomes of the study, the curren t protocol relied on these sample estimates to power the present protocol. Additional power analyses are provided here to estimate expected effect sizes in other study outcomes to addr ess the secondary purpose of this paper (i.e., subsequent changes in PA behavior and cardiorespiratory fitness). Sample size estimates for BSE were calculate d based upon the results of a previous pilot study that implemented a similar theoretical fr amework as proposed here on a sample of 24 individuals. Our intervention differed in the following ways: (a) it was delivered by trained graduate assistants (not peer mentors); (b) it did not include a control group; and (c) it was abbreviated in length (8 weeks opposed to 16 weeks). Changes in BSE immediately following the reduced 8-week intervention and a 12-w eek follow-up were impressive. Single-group repeated measures effect sizes were estimated for BSE at 0.78 and a power curve indicates a


57 sample size of 21 participants needed to reach st atistical significance with desirable power (0.80) and alpha (0.05). Figure 33 displays this power curve graphically. While the previous study was similar in the th eoretical nature and intensity of the current intervention, it was limited in that it did not include a control group (an important factor given the expected improvements in our health hygiene control group) and did not provide estimates for EXSE. To address these limitations, an additional power analysis was conducted based upon means and standard deviations reported by Mc Auley et al. (1999). McAuley and colleagues conducted a 6-month, randomized controlled trial ex amining change trajectories in EXSE in two experimental groups of adults 55 and over ( N = 174): (a) stretching/toni ng; and (b) walking. Both groups improved EXSE scores over the course of the 6 months; however, the walking group exhibited significantly greater improvements co mpared to the stretching/toning group. The mixed between-within repeated measures eff ect size was estimated for EXSE at 0.38 and a power curve indicated a sample size of 60 partic ipants (30/cell) needed to reach statistical significance with desirable power (0.80) and alpha (0.05). Figure 3-4 displays this power curve graphically. It should be noted that the stretching/toning group in the McAuley et al. (1999) study experienced significant gains in EXSE acr oss the intervention. W ithin the current study, the control group participants could have been mo tivated to begin PA due to the general healthrelated information presented, social contact, and provision of an exercise membership. Therefore, some improvement in this group is ex pected. This power analysis accounts for this improvement and calculates a sample size to produce a significant effect in the intervention group above and beyond the expected gains in the control group. The sample size estimates for self-reported PA behavior were derived from improvements in LTEQ scores observed from the same 8-w eek, intervention-group only pilot study reported


58 earlier. Changes in LTEQ immediately following the reduced 8-week intervention and a 12-week follow-up were impressive. Singlegroup repeated measures effect sizes were estimated for BSE at 1.28 and a power curve indicated a sample size of 7 participants needed to reach statistical significance with desirable power (0.80) and alpha (0.05). Figure 3-5 displays this power curve graphically. Finally, for cardiorespiratory fitness (VO2max), means and standard deviations were reported by Kramer, Hahn, and McAuley (2001) a nd small to moderate improvements were achieved ( ES = 0.34) for a 6-month cardiorespiratory focused intervention. Based upon this effect size and conventionally de sirable power (0.80) and alpha ( 0.05), statistical significance is expected with a total of 70 particip ants (35/cell). It should be noted that this effect size estimate and subsequent power analysis s hould be interpreted with caution given that the intervention was 6-months in length (2 months longer than the curr ent intervention) and the specific focus of the physical training was exclusively ca rdiovascular in nature. This is not, however, viewed as the primary outcome of the intervention and more appropriate effect size estimates will be used for future grant proposals. Data Analysis Frequencies, m eans, and standa rd deviations were calculated on all demographic, social cognitive, behavioral, and physiological measurements to describe participant characteristics at baseline. Independent samples t-tests were condu cted to identify predictors of dropout or incomplete data. Alpha criterion was set at p < .05, with marginal values reported up to p < .10. All statistical procedures were conducted using SPSS 15.0 (SPSS, 2006). Multilevel Models for Change Multilevel models for change (Sing er & Willett, 2003) were used to examine changes in self-efficacy (a BSE and EXSE composite), self-reported PA (MET estimation and minutes of


59 MVPA separately), and pedometer steps. These models were used to address dependent variables in which repeated assessments occurred. Data was aggregated across five study phases to tap unique aspects of behavioral adop tion: week 0 (baseline), weeks 1 to 4 (initiation), weeks 5 to 8 (maintenance), weeks 9 to 12 (maintenance), and weeks 13 to 16 (sustainability). All analyses were conducted separately using the aggregated study phases and treating time in weeks as a continuous predictor. Model estimat es were similar in both models for all dependent variables, and so the more parsimonious treatment of time (aggregated phases) are presented and discussed here. The fundamental question addressed in these analyses was whether group assignment moderated withinand between-person changes in self-efficacy and PA behavior. Because group attendance, site of the intervention, and group mentor were likely to affect protocol adherence, these variables were included as model covariates to control for these effects. Group mentor was controlled for using a series of six dichotom ized variables (i.e., dummy-coding procedure). Stepwise, nested model testing pr ocedures included the following: The unconditional means model was performed to assess levels of withinand betweenperson variability in the study variables. The unconditional growth model was tested w ith fixed and random effects of multiple polynomial specifications of time (e .g., linear, quadratic, cubic) to determine the best fit to the data. The main effects conditional growth model was tested with the addition of group assignment and the model covariates to examin e level differences (initial status) in the dependent variable. The final interactions conditional growth model was tested where model covariates were examined for their moderating effects on the ti me trend. This final step addressed both primary and secondary purposes of this investigation; whether the intervention group displayed greater improvements in self-effi cacy, self-reported PA, and pedometer steps compared to the health hygiene control. The -2 log likelihood (-2LL), Akaikes Info rmation Criterion (AIC), and Bayesian Information Criterion (BIC) were used to dete rmine model goodness-of-fit at each level of model testing. Chi-square difference tests were conducted to formally assess model improvement.


60 The rationale for using multilevel models for change is the simultaneous modeling of within-person and between-person change in the study outcomes. On e fundamental characteristic of PA behavior is between-pers on variability, at daily, weekly, and seasonal levels of analysis. By accounting for these individual fluctuations we were able to produce more precise modelbased population estimates (Singer & Willett, 2003). Repeated Measures Analyses of Variance Repeated measures analyses of variance (R-ANOVA) were conducted to assess changes f rom baseline to posttest in se lf-determined exercise behavior and cardiorespiratory fitness (VO2max). The fundamental question addre ssed here was whether group assignment significantly moderated improvements in the depe ndent variables such that the intervention group improved more than the health hygiene control group. Group attendance, site of the intervention, and group mentor were included as model covariates to control for protocol adherence. The rationale for using R-ANOVA was that only tw o timepoints of measurement were included for these analyses. While multilevel models can accommodate baseline-posttest data, intrinsic motivation and cardiorespiratory fitness were considered relatively stable measures. Modeling within-person fluctuations in these measures were not considered vital in addressing the study purposes. Exploratory Predictors Finally, exploratory analyses were perfor m ed for all dependent variables using the analytical approaches discussed above. The analyses were conducted using baseline predictors that were statistically associated with dropout status (BMI, depression ) or where theoretical support existed for examin ing the relationship (gender, age, baseline VO2max) (Mazzeo et al., 1998). Because these relationships were viewed as exploratory, they were computed only after controlling for the planned analyses and are discussed separately.


61 Missing Data Handling The percentage of m issing data was 19.9% fo r the self-efficacy composite, 6.5% for the MET estimation and minutes of MVPA, and 8.5% for the pedometer steps. Missing data clustered toward the end of the intervention when group meetings were less regular. To accommodate missing data in the multilevel m odels for change, full-information maximum likelihood estimation (FIML) was used as part of the SPSS 15.0 computer program (SPSS, 2006). FIML estimation has been shown to be mo re appropriate when simultaneously assessing fixed and random effects and performing nested model tests (Singer & Willett, 2003). For the current study, FIML estimation allowed the inclus ion of partial data fr om participants who dropped out of the study ( n = 12) as well as the estimation of daily or weekly data that was missing from completed participants. All model tests were replicated with the smaller sample of completers ( n = 69) and were found to yield similar model estimates compared to the total sample ( N = 81), but with less statistical power. FI ML estimation is not available for R-ANOVA and therefore traditional listwise deletion procedures were us ed and the analyses were conducted with the sample of completers ( n = 69).


62 433 Participants Identified (111/322) 306 Initially Interested (78/228) 146 Eligible (30/116) 91 Randomized (16/75) 47 Assigned to Health Hygiene Control (9/38) 35 Included in primary analyses (7/28) 34 included in primary analyses (4/30) 12 Did not complete all assessments (2/10)7 Did not attend any sessions (1/6) 5 Lost to follow-up (1/4) Caregiving(0/1) Illness (0/3) Too busy (1/0) 10 Did not complete all assessments (3/7)3 Did not attend any sessions (1/2) 7 Lost to follow-up (2/5) Caregiving(0/3) Illness (0/2) Too busy (2/0) 44 Assigned to Intervention (7/37) 127 not interested or <50 years of age (33/94) 160 ineligible (48/112) 55 dropped out prior to randomization (15/41) Figure 3-1. Screening/baseline, randomization, and follow-up. Note Data are reported as number of men/women.


63 Phase 1234567891011121314151617181920 Screening/ Eli g ibilit y Baseline Intervention Posttest Week Figure 3-2. Study timeline.


64 Table 3-1. Concurrent topical sc hedules for intervention groups. Week Interventio n Health H yg iene Control 2Getting to Know You Goal Settin g Part I Exercise 3Goal Settin g Part II Osteo p orosis 4Definin g Exercise Behavior Alzheimers Disease 5Barriers to Exercise Cancer Screenin g 6Mental Ima g er y I Hearin g Loss Mental Ima g er y II Pro g ress toward Stated Goals 8Gettin g Good at Exercise Vision Loss 9Revisitin g Barriers and Goals Sleep 10Exercise Behavior and Accom p lishments Balance Problems 11Sustainability: Continuing Progress and Maintainin g Success Nutrition 12Setting Maintenance Goals ( Exercise facilit y membershi p ends ) COPD/Heart Failure ( Exercise facilit y membershi p ends ) 13 No meetin g No meetin g 14Sharin g Future Goals and Social Conclusion and Social 15 No meetin g No meetin g 16 No meetin g No meetin g Introduction to program and orientation to fitness facilit y Note. In the event of unavoidable session cancellations (e.g., observed holidays), session content was adjusted over subsequent weeks to maintain a consistent 16week intervention length.1 Introduction to program and orientation to fitness facilit y 7 Arthritis


65 Table 3-2. Outline of assessment plan Measure TypeDaily Weekly BL/Posttest Social cognitive BSE EXSE EMS Physical activityLTEQ Pedometer Physiological VO2max BMI Blood p ressure Depression GDS BDI-IINote. BL = baseline; BSE = Barriers Self-efficacy; EXSE = Exercise Selfefficacy; EMS = Exercise Motivation Scale; LTEQ = Leisure-time Exercise Questinnaire; BMI = body mass index; GDS = Geriatric Depression Scale; BDI-II = Beck Depression Inventory-II.


66 Total Sample Size 0.0 0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 1.0 0510152025Power Total Sample Size 0.0 0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 1.0 0510152025Power Figure 3-3. Power curve for barriers self-efficacy in an eight-week, intervention group only physical activity intervention.


67 Group (G) Occasion (O) G x O 0.0 0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 1.0 050100150200Total Sample SizePower Group (G) Occasion (O) G x O 0.0 0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 1.0 050100150200Total Sample SizePower Figure 3-4. Power curve for EXSE in a mi xed-between ANOVA design across a six-month physical activity intervention.


68 Power 0.0 0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 1.0 0 510152025 Power 0.0 0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 1.0 0 510152025 Figure 3-5. Power curve for self-reported physical activity in an 8-week, intervention-group only physical activity intervention. Total Sample Size


69 CHAPTER 4 RESULTS Sample Characteristics Demographic Information Sixty-nin e participants completed baseline a nd posttest assessments. Baseline demographic characteristics were similar in the randomized groups (Table 4-1). Mean age was approximately 63 to 64 years of age, with a slight majority of participants being 65 years or older. More than half of the sample was married, with progressi vely fewer numbers of participants divorced, widowed, or never married respectively. The sa mple was primarily female, white, collegeeducated, and of non-Hispanic descent. By desig n, all participants were considered physically inactive (Pate et al., 1995), were 50 years or older, and were free of disease or disability preventing PA participation or study protocol compliance. Study Variables Baseline m easurements of study variables we re relatively similar in the randomized groups. Psychosocial (Table 4-2), physiological (Table 4-3), and PA (Table 4-4) measures are displayed as a function of randomized group. The intervention group displayed significantly greater EXSE scores at baseline compared to the health hygiene control ( t (78) = -2.45, p = .017) (Table 4-2). BS E scores at baseline trended in this direction, but were not significant ( t (78) = -1.31, p = .194). The self-efficacy composite score, formed by combining the BSE and EXSE measures in a z -metric, was also significantly higher in th e intervention group (t (78) = -2.15, p = .035). Self-determined PA behavior, measured by the EMS, did not displa y significant differences between groups. Both measures of depression were consis tent in their indications of a sample with minimal depressive symptomatology (Beck et al., 1996; Brink, Yesavage, Lum, Heersema, Adey, & Rose, 1982).


70 Measurement of cardiorespiratory fitness (VO2max) indicated the sample was untrained based upon age and gender classifications discussed by the Am erican College of Sports Medicine (2000) (Table 4-3). BMI measurem ent indicated the sample was moderately overweight based upon published guidelines (W HO, 2005). Mean blood pressure indicated that the sample was primarily prehypertensi ve and hypertensive (Chobanian et al., 2003). PA levels were characteristic of an inactiv e sample (Table 4-4). Individuals reported on average 65 minutes of MVPA in the baseline week. Few individuals reported meeting PA recommendations during the baseline week of observation, with most reporting insufficient levels of activity, and some reported no PA. As can be seen by the MET estimation, most reported PA was in the mild or moderate range during this initial wee k. Daily pedometer steps indicated a low active sample based upon published guidelines (Tudor-Locke, 2005). Group attendance was tracked to assess adherence to the study protocol Thirteen of the 16 sessions contained intervention-re lated material delivered by the peer mentors. Sessions 13, 15, and 16 were testing only and were therefor e not included in atte ndance tracking. Study completers attended 10.75 (SD = 1.87) out of 13 possible sessions No differences in attendance were present between intervention and health hygiene control groups. Group attendance was included as a covariate in the primary and secondar y analyses to control for protocol adherence. Attrition Overall, 85% of participants (83% in interv ention and 87% in health hygiene control) com pleted baseline and post-test assessments, with a total of 12 individu als who were lost to follow-up. Analyses were conducted to compare demographic and baseline assessments of study variables among study completers and those who dropped out. No demographic variables were significantly different (Table 4-5). Am ong baseline study variables, the GDS ( t (78) = -2.211, p = .038), BDI-II (t (78) = -2.25, p = .027), and BMI ( t (72) = -2.67, p = .009) were found to be


71 significantly lower for study completers compared to those who dropped out (Table 4-6). Post hoc exploration indicated that BMI difference was due to a single extreme value (BMI = 47.80), who reported attrition due to caregiving responsibilities. The elimin ation of this value from the analysis yielded a non significant BMI difference. Given the statistical as sociations of these variables to protocol adherence, these variables were used in exploratory analyses as predictors of intervention-related outcomes. The small sample size precluded analyses of differential dropout by group assignment. Post-hoc reasons provided by the participants illust rated that caregiving responsibilities, injury or illness unassociated with the study protocol, a nd busyness were the reasons reported for dropout (see Figure 3-1). Caregiving and injury we re more common among women while busyness was reported exclusively by the men who were lost to follow-up. The distribution of these reasons across intervention and hea lth hygiene control groups appeared equal. Study Replicates and Mentors A total of eight study replicates were com ple ted. One replicate consisted of concurrently held intervention and health hyg iene control groups. Site of the intervention was balanced by replicate four replicates at th e University facility and four re plicates at the community church. Replicate size ranged from 5 to 14 participants, with group size ranging from 2 to 7. Table 4-7 contains descriptions of group assignment by gender, retention, and mentor characteristics. In total, 7 mentors completed the 8 replicates (16 total groups). Ment ors, on average, were 67.29 4.19 years of age (range = 61 to 72), ha d 18.29 1.80 years of education (Masters degree equivalent), and completed 2.29 1.38 groups. Although small sample sizes among groups based on mentor made statistical compar isons difficult, cursory observations indicated that differences between mentors existed in dropout rate and baseline levels of PA. Mentor and


72 site of the intervention were cont rolled for in the primary and s econdary analyses to account for these differences. Tests of Normal Distribution Visual inspection of the repeated m easures dependent variables sugge sted the presence of skewness and kurtosis in the data. Formal test s of normal distribution confirmed significant skewness and kurtosis that violated the assumpti on of normal distribution for parametric tests (Table 4-8). Positive skewness was present in the PA variables and negative skewness was present in the self-efficacy composite score. Positive kurtosis was present in the self-report measures of PA and mild ne gative kurtosis was present in the pedometer counts. The Kolmogorov-Smirnov test suggested, for all dependent variables, significant departures from normal distribution for the raw values. To adjust for these violations of normality, square-root and loglinear transformations of the data were conducted. Square -root transformations appeared to provide the greatest improveme nts in skewness and kurtosis and these data transformations are presented alongside the raw estimates in Figure 4-1. The square-root transformations yielded marked improvements in the formal tests for sk ewness and kurtosis for the PA variables, but no improvements in the self-efficacy composite score were observed. It should be noted that the self-efficacy composite score had previously undergone transformations when combining BSE and EXSE scores in a z -metric that likely improved normal distribution diagnostics. Evidence of significant departures from normality persisted even after these transformations; however, visual inspection of the square-root transformed descriptive data and Q-Q plots compared to the raw values (Figure 4-1) indicated improvements that approximated a normal distribution for the PA variables, but not the self-efficacy composite. The square-root transformed data was consequently used in the secondary analyses for PA changes, while the raw self-efficacy composite score was used in the primary analysis of self-efficacy changes.


73 Primary Analyses: Social Cognitive Change The prim ary question addressed was whether th e 16-week PA intervention resulted in significantly greater social cogni tive changes in the intervention group compared to the health hygiene control group. This was addressed at the weekly level through repeated assessment of BSE and EXSE formed into a self-efficacy composite measure. At the pre-post level, selfdetermined behavior was assessed with the self-determined index score of the EMS. Table 4-9 displays correlations among the social cogni tive dependent variables and the exploratory predictors at baseline. Self-Efficacy A m ultilevel model for change (Singer & Wille tt, 2003) was used to assess withinand between-person changes in the composite self-e fficacy scores across the 16-week intervention (Table 4-9). The fundamental question addr essed was whether group assignment moderated withinand between-person cha nges in self-efficacy. The self-efficacy composite score was collected at the weekly level, however time was conceptualized across five phases of the intervention: baseline, weeks 1 to 4, weeks 5 to 8, weeks 9 to 12, and weeks 13 to 16. To control for potential confounds of program participation, group attendance, group mentor, and site of the intervention were included as model covariates. First, the unconditional means model was tested with no predictors in order to understand withinand between-person vari ation in self-efficacy. The unconditional means model in Table 4-10 (first column) shows that significant variab ility at the withinand between-person levels exists in self-efficacy. The intraclass coefficien t was 0.831, indicating the majority of variance was between-persons. In other words, 83.1% of the variance in self-efficacy was betweenpersons. Within-person fluctuations in self-efficacy were small.


74 The next model, the unconditional growth model (Table 4-10, second column), added time as a fixed and random effect, but remained unconditional as no Level-2 predictors were added. Series of nested model tests were conducted to examine the effects of linear, quadratic, and cubic time trends on self-efficacy. Higher-order polynomia l time trends did not significantly improve model fit compared to the linear trend and were therefore not retained for subsequent models. The fixed effect of time showed a modest, ye t significant, decrease in self-efficacy over the course of the intervention. The random effect of time was also si gnificant, indicating that there were significant individual differences in growth trajectories as a function of linear time. The addition of time to the model explained 7% and 20% of the betweenand within-person variance respectively. The unconditional growth model had significantly improved model fit compared to the unconditional means model, 2 (2) = 261.29, p <.001. The main effects conditional growth model (Table 4-10, third column) represents the addition of fixed effects of four time-invarian t predictors of self-efficacy: group assignment, attendance, site of th e intervention, and mentor The main effects c onditional growth model assesses level (initial status) differences in se lf-efficacy as a function of these predictors. The community church site showed marginally higher self-efficacy compared to the university facility, while group assignment, group attend ance, and mentor were not significant. The addition of the fixed effects of these predictors explained 10% of the remaining between-person variance. Because these variables were time-i nvariant, random effects were not tested and within-person variance did not change. Signi ficant withinand between-person variance remained unexplained. Finally, model fit statistics indicated the conditional growth model with main effects did not signif icantly improve model fit.


75 The interactions conditional growth model (T able 4-10, fourth column) tested interaction effects of the covariates on the linear time tre nd reported in the main ef fects conditional growth model. The negative linear time trend remained si gnificant, as did the marginal main effect for site of the interventi on. The time x group assignment effect, our main hypothesized test, was not significant. Significant and positive time x attenda nce fixed effect was observed, indicating that individuals who attended group sessions more regularly increa sed self-efficacy significantly more than those who did not. Site of the interv ention and mentor interactions did not moderate the time trend. Because interaction effects include the time predictor, random effects estimates of these variables can be studied. The random ef fects of time x group were not significant, indicating that individual differe nces in the linear time trend in self-efficacy as a function of group assignment were not present. The addition of the random effects of the model covariates on the linear time trend caused the model to not converge, and were therefore omitted from the analyses. No additional withinor between-pers on variance was explained with the inclusion of these interaction effects. Finally, the conditi onal growth model with interactions did not significantly improve model fit. In summary, the final model displayed a mode st, yet significant, negative time trend for self-efficacy across the intervention. Our hypothesis that individuals in the intervention group would display significantly greater gains in self-e fficacy over the course of the intervention was not confirmed. The addition of the main effects and interactions of model c ovariates yielded only modest differences in initial status and time trend. Significan t amount of with inand betweenperson variation in self-efficacy remained une xplained by the model predictors. Figure 4-2 displays graphically the empirical data and model-based estimates of self-efficacy by group assignment.


76 Self-Determined Behavior A m ixed between-within analysis of variance was conducted with time (baseline, post-test) being the within-subjects factor and group assign ment (intervention, health hygiene control) as the between-subjects factor. Group attendance, site, and mentor we re included as covariates. The Levenes tests for the homogeneity of variance assumption were not significant at both timepoints. Mauchlys test was not performed b ecause there were only two timepoints. Table 411 shows a summary table of the analysis of va riance with main effects and possible withinbetween interactions. To assess change in self-determined beha vior across the intervention, the time x group interaction effect wa s not significant, but trended in the hypothesized direction. The effect size was small however (Cohen, 1992). Fi gure 4-3 shows this pattern graphically. Post-hoc analyses were conducte d to understand what aspects of self-determined behavior were impacted by the intervention. Mixed between -within analyses of variance were conducted for each of the EMS subscales to determine which level of extrinsic and intrinsic behavior was impacted. No significant differe nces were observed on the indi vidual subscales after including the model covariates. Secondary Analyses: Physical Activity and Cardiorespiratory Changes The secondary purpose of this research was to evaluate subsequent im provement in PA behavior (i.e.; self-reported PA and pedometer count) and fitness parameters (cardiorespiratory fitness) as a result of the in tervention. PA was assessed daily throughout the intervention by selfreported daily logs and use of pedometer. The self-reported PA (measured by the LTEQ) was converted into minutes of MVPA and a MET esti mate (see Methods secti on). At the baselineposttest level, changes in cardiorespiratory fitness were measured by sub-maximal oxygen consumption (VO2max).


77 Physical Activity Behavior Self-reported PA and pedom eter counts were assessed concurrently and daily throughout the intervention. These measures we re averaged at the weekly leve l and time was conceptualized across the five phases of the in tervention: baseline, weeks 1 to 4, weeks 5 to 8, weeks 9 to 12, and weeks 13 to 16. Means and standard deviations for PA dependent variables at each phase are displayed in Table 4-12. Correla tions are displayed in Table 4-13. As expected, minutes of MVPA and the MET estimate were highly correlated given their origination from the same selfreport measure. Pedometer steps were uncorre lated with either self-report measure. Moderate-to-vigorous physical activity A m ultilevel model for change (Singer & Wille tt, 2003) was used to assess withinand between-person changes in minutes of MVPA acr oss the 16-week intervention. The fundamental question addressed was whether group assignm ent moderated withinand between-person changes in minutes of MVPA. Stepwise and nested multilevel model tests are displayed in Table 4-14. The unconditional means model (Table 4-14, firs t column) showed si gnificant variability at the withinand between-person levels exists in minutes of MVPA. The intraclass correl ation indicated 59.3% of the variance in MVPA was between-persons. The unconditional growth model (Table 4-14, second column) indicated a signifi cant, positive linear time trend and a negative, quadratic time trend. This would indicate an increase in MVPA followed by a modest, curvilinear decline. The random effect of linear time was also signifi cant, indicating that there were individual differences in growth trajectories. The addition of the random effect for quadratic time caused the model not to converge and was therefore remove d from subsequent model tests. The addition of time to the model explained 25% and 19% of the betweenand w ithin-person variance


78 respectively. The unconditional growth model significantly improved model fit compared to the unconditional means model, 2 (3) = 141.63, p <.001. In the main effects conditional growth model (Table 4-14, third column), the negative and quadratic time trends remained significant. Participants at the community church facility displayed higher MVPA scores across all timepoint s. The addition of the main effects explained 17% and 0% of the betweenand within-person variability respectively. This model did not result in improved model fit compared to the previous model. In the interactions conditional growth model (Table 4-14, four th column), the linear time trend was no longer significant; however, the quadra tic time trend and the main effect for site retained significance. Significant time x attenda nce interaction effect emerged, indicating that individuals who attended more group sessions, regardless of gr oup assignment, reported more minutes of MVPA compared to those who a ttended fewer sessions. The addition of the interaction effects explained no additional betweenor within-per son variability. This model was marginally improved from the previous model, 2 (9) = 16.37, p = .060. In summary, both groups increased the number of minutes of MVPA over the course of the intervention. The time trend suggests curvilinear change in minutes of MVPA. Our hypothesis that individuals in the interv ention group would display signifi cantly greater gains in MVPA over the course of the intervention was not c onfirmed, although empirical data and model-based estimates suggest modest differences may exist if adequate power were present (Figure 4-4). Both groups were unable to achieve the minimal PA recommendations (Nelson et al., 2007; Pate et al., 1995), although interventi on group participants maintained activity levels within 20 minutes of the recommendations during weeks 8 to 12 and after.


79 MET estimate A m ultilevel model for change (Singer & Wille tt, 2003) was used to assess withinand between-person changes in MET estimate across the 16-week intervention. The MET estimate differed from minutes of MVPA in that it included mild forms of PA and incorporated both intensity and duration into the calculation. The fundamental question addressed was whether group assignment moderated withinand be tween-person changes in the overall MET expenditure (MET-mins/day). Nested, multilevel model tests are displayed in Table 4-15. The unconditional means model (Table 4-15, first column) showed signifi cant variability at the withinand betweenperson levels existed in MET-mins/day. The intraclass coefficient indicated 72.0% of the variance in MET-mins/day was between-persons. The unconditional growth model (Table 4-14, second column) indicated a signifi cant positive linear time tre nd and a negative quadratic time trend. This would indicate curvilinear increase in MET-mins/day followed by a modest decline. The random effect of linear time was also sign ificant, indicating that there were significant individual differences in growth trajectories. The addition of the random effect for quadratic time caused the model not to converge and was therefore removed from subsequent model tests. The addition of time to the model explained 10% and 19% of the between and within-person variance respectively. The unc onditional growth model significantly improved model fit compared to the UMM, 2 (3) = 124.65, p <.001. The main effects conditional gr owth model (Table 4-15, third column) and interactions conditional growth model (Table 415, fourth column) followed a similar pattern of results as the models for minutes of MVPA. The linear time trend was significant in the main effects model, but was no longer significant wh en the interactions were a dded. The quadratic time trend retained significance in both models. The main effects for intervention site showed that


80 participants at the community church site disp layed marginally higher levels of MET-mins/day, regardless of time, compared to the university facility participants. No additional withinor between-person variability was e xplained with these models. Neith er of these models improved model fit compared to the unconditional growth model. In summary, the addition of mild forms of PA and more sensitive calculations for PA intensity in the MET-mins/day calculations yielded similar findings to the MVPA model. Our hypothesis that individuals in th e intervention group would display significantly greater gains in MET-mins/day over the course of the intervention was not confirmed. Figure 4-5 indicates similar patterns of growth in MET-mins/day for both groups. Pedometer A m ultilevel model for change (Singer & Wille tt, 2003) was also used to assess withinand between-person changes in pedometer steps across the 16-w eek intervention. Stepwise and nested multilevel model tests are displayed in Table 4-16. The unconditional means model (Table 4-16, firs t column) showed significant variability at the withinand between-person levels existed in pedometer steps. The intraclass coefficient indicated 71.5% of the variance in steps was between-persons. The unconditional growth model (Table 4-16, second column) indicated a signifi cant positive linear time trend and a negative quadratic time trend, similar to what was observed in minutes of MVPA and MET-mins/day. The random effect of linear time was also significan t, indicating individual differences in growth trajectories existed. The addition of the random effect for quadratic time caused the model not to converge and was therefore removed from subsequent model tests. The addition of time to the model explained 16% of the within-person variance and no additional between-person variance. The unconditional growth model significantly improved model fit compared to the UMM, 2 (3) = 87.00, p <.001.


81 The main effects conditional grow th model (Table 4-16, third co lumn) showed that none of the predictors that were introduced were signific antly predictive of pedometer steps. The linear and quadratic effects retained significance. The addition of the main effects explained 8% and less than 1% of the betweenand within-person variability. Model fit di d not improve compared to the previous model. The interactions conditional growth model (T able 4-16, fourth column) tested whether the model covariates moderated the time trend for pe dometer steps. First, the linear and quadratic time trends retained significance in this final model. The fixed effects interactions were not significant, indicating that mode l predictors did not moderate the significant time trends. However, after controlling for th e interactions, main effects fo r group and attendance emerged. This indicated that regardless of time, intervention group part icipants and those who attended more group sessions reported more pedometer st eps. The random effects for time x group and time x site interactions were able to be in cluded and the model c onverged. Both of these estimates were positive and significant, indicati ng individual difference in growth trajectories existed based upon group assignment and site of the intervention. The addition of these interaction effects explained only 2% and less than 1% of the betweenand within-person variability respectively. Model fit was significan tly improved compared to the main effects conditional growth model, 2 (9) = 18.45, p = .030. In summary, both groups increased the number of pedometer steps taken over the course of the intervention. The time trend suggests curvilinear change in steps. Our hypothesis that individuals in the intervention group would display significantly gr eater gains in steps taken over the course of the intervention was not confirmed. In fact, Figure 4-6 suggests a decline after weeks 8 to 12 in the intervention that was not present in the control group.


82 Cardiorespiratory Fitness A m ixed between-within analys is of variance was conducte d to examine changes in cardiorespiratory fitness, as measured by a submaximal oxygen consumption test (VO2max) using the Balke protocol. Time (baseline, posttest) was the within-subjects factor and group assignment (intervention, health hygiene contro l) was the between-subjects factor. The Levenes tests for the homogeneity of variance assump tion were not significan t at both timepoints. Mauchlys test was not performed because there were only tw o timepoints. The first test examined VO2max without including model covariates (a ttendance, site, and group mentor). The main effect of time was significant, F (1) = 10.173, p = .002, 2 = .151. Both groups significantly improved in cardiorespiratory fitness from baseline to posttest, although the effect size was small in magnitude (Cohen, 1992). The time x group intera ction was not significant indicating that the intervention group did not improve more than the health hygiene control, contrary to our hypothesis. Model covariates of intervention site, attendance, and group mentor were added. Table 4-17 displays the results of this hypothesized m odel. After adding th ese covariates, the main effect of time was no longer significant. Figure 4-7 displays the adjusted means of VO2max graphically. Post-Hoc Analyses: Exploratory Predictors Post-hoc analyses were conduc ted to explore the effect of other predictors on the intervention. These predictors were selected based upon their associatio ns with dropout status (i.e., BMI, depression) or thei r potential utility in understanding dem ographic characteristics of those who improved as a result of intervention (i .e., gender, age, fitness level). Because these were viewed as exploratory predictors, they we re added to the models after controlling for the hypothesized predictors and covari ates presented previously, and are presented separately here.


83 Social Cognitive Changes The exploratory predictors were added to the m odels displayed in Table 4-9 for the selfefficacy composite score. The fixed effect of female gender emerged as a significant and negative predictor of self-efficac y, estimate (SE) = -1.165 (.557), p = .040. This indicates that females displayed lower levels of self-efficacy compared to males, regardless of time. No significant predictors moderated the linear time tr end, meaning that changes in self-efficacy were not significantly different as a function of the exploratory predictors. The addition of the model predictors did improve overall model fit compared to the final interact ions conditional growth model presented earlier, 2 (9) = 320.188, p <.001. The exploratory predictors were adde d to the mixed between-within ANOVA model displayed in Table 4-10 for self-determined behavior. Age emerged as a marginal betweensubject predictor of sel f-determined behavior, ( F (1) = 3.761, p = .059). Inspection of group means indicated older individuals had lower levels of self-determined behavior. No interaction effects were significant. Physical Activity Changes The explora tory predictors were added to the final multilevel models of change for minutes of MVPA (see Table 4-14), MET-mins/day (see Ta ble 4-15), and pedometer steps (see Table 416). For brevity, only significant main effects, interactions, and model fit improvements are reported in Table 4-18. For all three models, the addition of the exploratory predictors significantly improved mode l fit. For the minutes of MVPA model, the main effect of VO2max was significant and positive, indi cating individuals with higher VO2max at baseline reported more minutes of MVPA over the course of the in tervention, regardless of timepoint. None of the main effects or interactions were significant in the MET estimate model. Finally, age emerged as


84 a negative and significant main effect pred ictor of pedometer st eps, indicating younger individuals reported more pedometer steps, regardless of timepoint. Cardiorespiratory Fitness Changes The exploratory predictors were added to the m ixed between-within model for VO2max (see Table 4-16). As would be expected, the main effects of gender ( F (1) = 12.776, p <.001, 2 = .233), age (F (1) = 17.250, p <.001, 2 = .291), and BMI ( F (1) = 6.940, p = .012, 2 = .142), were significant. Regardless of group assignment or time of VO2max assessment, females, older participants, and individuals with higher BMI disp layed lower cardiorespiratory fitness. The time x attendance interaction was also marginally significant ( F (1) = 3.162, p =.083, 2 = .070), indicating individuals who atte nded more group sessions (rega rdless of group assignment) experienced greater improvements in VO2max. No other predictors moderated the time trend. Finally, attendance, gender, age, and BMI were test ed in three-way interactions to see if they moderated the time x group effect These were not significant.


85 Table 4-1. Baseline demographic characteri stics of participants by randomized group ( N = 81). Characteristic A g e SD, y ears 63.35 9.0763.49 8.26 63.42 8.62 A g e g roup, n (%) 50 to 64 y ears 20(50.0) 19(46.3) 39(48.1) 65 y ears and over 20 ( 50.0 ) 22 ( 53.7 ) 42 ( 51.9 ) Gender, n (%) Female32 ( 80.0 ) 35 ( 85.4 ) 67 ( 82.7 ) Male 8(20.0) 6(14.6) 14(17.3) Marital status, n ( % ) Marrie d 22(55.0) 22(53.7) 44(54.3) Divorce d 12 ( 30.0 ) 11 ( 26.8 ) 23 ( 28.4 ) Widowe d 2(5.0) 5(12.2) 7(8.6) Sin g le, or never married 4(10.0) 3(7.3) 7(8.6) Education SD, y ears 16.38 2.3115.93 2.20 16.15 2.25 Educational status, n ( % ) Hi g h school/GED 2(5.0) 3(7.3) 5(6.2) Some college or vocational trainin g 11(27.5) 10(24.4) 21(25.9) College graduate 8(20.0) 11(26.8) 19(23.5) Some postbaccalaureate or Master's de g ree 14(35.0) 15(36.6) 29(35.8) Doctoral de g ree 5(12.5) 2(4.9) 7(8.6) Race, n (%) White 37(92.5) 37(90.2) 74(91.4) African-American 1 ( 2.5 ) 2 ( 4.9 ) 3 ( 3.7 ) Asian 1(2.5) 0(0.0) 1(1.2) Biracial 0(0.0) 0(0.0) 0(0.0) Other 0(0.0) 2(4.9) 2(2.5) Ethnicity, n (%) Hispanic/Latino 1(2.5) 2(4.9) 3(3.7) Total SampleNote. All group differences were not significant ( p > .10).Health Hygiene control (n = 40) Intervention group (n=41)


86 Table 4-2. Baseline psychosocial variables by randomized group. Baseline assessment Self-efficacy composite-0.41 1.90* 0.45 1.68* 0.02 1.84 Barriers Self-efficac y 63.65 24.13 70.38 21.98 67.06 23.17 Exercise Self-efficac y 67.25 27.86* 81.69 24.82* 74.47 27.20 Self-determined index score15.84 5.58 16.86 6.77 16.35 6.19 Intrinsic 4.31 0.76 4.47 0.92 4.39 0.84 Inte g rated re g ulation 4.63 0.76 4.73 0.85 4.68 0.81 Identified re g ulation 5.41 0.60 5.37 0.61 5.39 0.60 Intro j ected re g ulation 3.44 0.90 3.28 0.94 3.36 0.92 External re g ulation 1.93 0.82 1.94 0.94 1.94 0.88 Amotivation 1.48 0.68 1.40 0.79 1.44 0.74 Geriatric De p ression Scale5.13 4.89 4.85 4.29 4.99 4.56 Beck Depression Inventor y -II6.87 5.80 5.63 4.79 6.24 5.31 Health Hygiene control (n = 40) Intervention group (n=41) Total SampleNote. p < .05.


87 Table 4-3. Baseline physiological variables by randomized group. Baseline assessment VO2max 27.93 7.95 27.55 7.12 27.74 7.50 BMI, k g / m 226.77 5.68 28.39 6.53 27.56 6.12 Blood pressure Systolic, mmHg134.43 17.16 139.04 18.29 136.73 17.77 Diastolic, mmHg79.86 9.81 80.51 11.12 80.19 10.42 Pulse, bpm 75.29 11.96 75.06 11.93 75.18 11.87 Blood pressure classification, n (%) Normal 8(20.0) 5(12.2) 13(16.0) Prehypertensive 16(40.0) 17(41.5) 33(40.7) Hypertensive 16(40.0) 19(46.3) 35(43.2)Note. All group differences were not significant ( p >.10). BMI = body mass index; Blood pressure classifications were as follows: Normal = systolic < 120 and diastolic < 80; Prehypertensive = systolic 120-139.99 or diastolic 80-89.99; Hypertensive = systolic 140+ or diastolic 90+ (Chobanian et al., 2003). Health Hygiene control (n = 40) Intervention group (n=41) Total Sample


88 Table 4-4. Baseline physical activ ity variables by randomized group (n = 76). Baseline assessment MVPA, mins/wk76.81 106.9155.25 60.3465.46 85.70 Meetin g g uidelines 6 ( 16.7 ) 3 ( 7.5 ) 9 ( 11.8 ) Insufficient PA 17(47.2) 27(67.5) 44(57.9) No PA 13(36.1) 10(25.0) 23(30.3) LTEQ, MET-min/day Mild 2.85 2.57 2.84 3.10 2.84 2.84 Moderate 3.28 5.69 1.72 1.74 2.47 4.18 Strenuou s 0.50 1.36 0.55 2.16 0.52 1.81 Total 6.63 7.12 5.10 4.46 5.83 5.90 Pedometer, steps/day 5796.82 2865.626123.55 3016.385966.72 2929.71Note. All group differences were not signficant ( p > .10); Baseline physical activity data was missing for five participants; All values were reported daily and summed across the baseline week of observation; LTEQ = Leisure-time Exercise Questionnaire; MVPA = moderate-to-vigorous physical activity; PA = physical activity; National physical activity guidelines were defined as 150 or more minutes of moderate-to-vigorous physical during the week (Nelson et al., 2007).Health Hygiene control (n = 36) Intervention group (n=40) Total Sample National PA Guidelines, n (%)


89 Table 4-5. Baseline demographic characteri stics of completed and attrited groups. Characteristic Age SD, years63.91 8.7160.58 7.8063.42 8.62 Age group, n (%) 50 to 64 years 31(44.9) 8(66.7) 39(48.1) 65 years and over 38(55.1) 4(33.3) 42(51.9) Gender, n (%) Females 58(84.1) 9(75.0) 67(82.7) Males 11(15.9) 3(25.0) 14(17.3) Marital status, n (%) Married 37(53.6) 7(58.3) 44(54.3) Divorced 19(27.5) 4(33.3) 23(28.4) Widowed 7(10.1) 0(0.0) 7(8.6) Single, or never married 6(8.7) 1(8.3) 7(8.6) Education SD, years 16.19(2.2) 15.92(2.5) 16.15 2.25 Educational status, n (%) High school/GED 4(5.8) 1(8.3) 5(6.2) Some college or vocational training 18(26.1) 3(25.0) 21(25.9) College graduate 16(23.2) 3(25.0) 19(23.5) Some postbaccalaureate or Master's degree 25(36.2) 4(33.3) 29(35.8) Doctoral degree 6(8.7) 1(8.3) 7(8.6) Race, n (%) White 63(91.3) 11(91.7) 74(91.4) African-American 3(4.3) 0(0.0) 3(3.7) Asian 1(1.4) 0(0.0) 1(1.2) Biracial 0(0.0) 1(8.3) 1(1.2) Other 2(2.9) 0(0.0) 2(2.5) Ethnicity, n (%) Hispanic/Latino 3(4.3) 0(0.0) 3(3.7)Note. All group differences were not significant ( p > .10).Completed sample (n = 69) Attrited Sample (n = 12) Total Sample


90 Table 4-6. Baseline study variables of completed and attrited groups. Characteristic SE0.03 1.87-0.03 1.700.02 1.84 BSE67.57 23.4864.10 22.0167.06 23.17 EXSE73.99 27.2077.19 28.2874.47 27.20 Self-determined behavior16.39 6.2016.15 6.4516.35 6.19 GDS4.54 4.49*7.50 4.30*4.99 4.56 BDI-II5.69 5.22*9.33 4.91*6.24 5.31 LTEQ, METS/day Mild3.05 2.921.46 1.812.84 2.84 Moderate2.44 4.232.67 4.042.47 4.18 Strenuous0.60 1.930.00 0.000.52 1.81 Total6.09 5.954.13 5.555.83 5.90 Pedometer, steps/day6125.06 2934.324908.52 2866.935962.86 2935.93 VO2max 27.90 7.51 26.85 7.74 27.74 7.50 BMI, kg/m226.79 5.74**31.93 6.66**27.56 6.12 Blood pressure Systolic, mmHg136.00 18.09141.32 15.56136.73 17.77 Diastolic, mmHg79.62 9.1583.73 16.5280.19 10.42 Pulse, bpm74.93 12.4076.68 8.0775.18 11.87 Physical Activity Measures Physiological Measures* p < .05; ** p < .01. Note. SE = self-efficacy composite score; BSE = barriers selfefficacy; EXSE = exercise self-efficacy; GDS = geriatric depression scale; BDI-II = Beck depression inventory-II; LTEQ = leisure-time exercise questionnaire; BMI = body mass index.Completed sample (n = 69) Attrited Sample (n = 12) Total Sample Psychosocial Measures


91 Table 4-7. Replicate and mentor characteristics. ReplicateSite Group Assi g nmentRandomizedAttritedCompletedGenderAge Years o f Education 1 Intervention0/20/00/2 M 7220 Control0/30/00/3 M 6920 2Intervention0/50/10/4 M 6920 Control2/30/12/2F6718 3Intervention0/40/00/4F6116 Control0/40/10/3 M 6718 4Intervention2/21/11/1 M 6920 Control0/60/20/4 M 7220 5Intervention2/51/11/4 M 6718 Control1/61/00/6F6316 6Intervention2/40/02/4 M 6718 Control1/20/01/2F6316 7 Intervention1/50/01/5 M 6718 Control1/40/01/4F6116 8 Intervention0/70/20/5 M6718 Control3/40/03/4 F6718Note. Data are reported as the number of men/women; Randomization includes participants who attended 1 or more group sessions; M = male; F = female. 16 years of education is equivalent to a Bachelor's-level college degree.Mentor characteristics University facility University facility University facility University facility Community church Community church Community church Community church


92Table 4-8. Assessments of normal distribution for raw and square-root transforme d dependent variables. RawSquare-root transformed RawSquare-root transformed RawSquare-root transformed RawSquare-root transformed Skewness1.600.201.520.320.490.03-0.64-1.19 Skewness SE0. Skewness Z 22.212.83 21.044.50 6.750.44 -8.69-16.09 p <.001.005 <.001<.001 <.001.660 <.001<.001 Kurtosis 3.02-0.52 2.520.15 -0.31-0.51 -0.171.52 Kurtosis SE 0.140.14 0.140.14 0.140.14 0.150.15 Kurtosis Z 20.96-3.71 17.501.03 -2.13-3.56 -1.1410.26 p <.001<.001 <.001.303 .033<.001 .254<.001 Kolmo g orov-Smirnov Z 5.914.03 4.8 1.95 2.081.14 2.3 3.41 As y m p totic p <.001<.001 <.001.001 <.001.151 <.001<.001Note. p < .05 indicate departures from normality; Raw data represents ztransformed composite variable. MVPA = moderate-tovigorous physical activity.MVPA MET Estimate Pedometer Self-efficacy


93 Panel A Panel B Figure 4-1. Q-Q plots for raw a nd square-root transformed repeated measures dependent variables. Panel A) Minutes of moderate -to-vigorous physical activity; Panel B) MET estimate; Panel C) Pedometer steps; Pa nel D) Self-efficacy composite score.


94Panel C Panel D Figure 4-1. Continued.


95 Table 4-9. Correlations among soci al cognitive variables and m odel covariates at baseline. 1234567 1. SE1 2. SD.323**1 3. Female-.204.1021 4. Age.063-0.272*-.0461 5. BMI.116.091-.068-0.264*1 6. VO2max.014.145-.377**-.357**-0.251*1 7. BDI-II-0.236*. *p < .05; **p < .01. SE = self-efficacy composite score; SD = selfdetermined behavior index score; BMI = body mass index; BDI-II = Beck Depression Inventory-II.


96 Table 4-10. Self-efficacy composite score model test s with group assignment, attendance, site of the intervention, and group me ntor as model covariates. Unconditional Means Model Unconditional Growth Model CGM Main effects CGM Interactions Fixed effects Initial status Intercept -.09 (.19).04 (.02)-1.56 (1.01)-1.26 (1.02) Group .80 (.86) .75 (.86) Attendance .08 (.07) .06 (.07) Site 1.01 (.58).98 (.59)Rate of change Time (linear) -.08 (.04)*-.08 (.04)*-.48 (.24)* Time x group.03 (.18) Time x attendance.04 (.02)* Time x site-.02 (.11) Random effects Level-1 Within-person.58 (.03)***.46 (.02)***.46 (.02)***.46 (.02)*** Level-2 Intercept2.99 (.48)***2.79 (.50)***2.51 (.42)***2.54 (.42)*** Time.09 (.02)***.05 (.02)** Time x group.04 (.04) Time x attendance -a Time x site -aFit statistics -2LL2830.122568.832560.742546.77 n parameters 3 5 14 24 AIC 2836.12 2578.83 2588.74 2594.77 BIC 2851.10 2603.47 2657.74 2713.06 Change in -2LL from previous model 261.29*** 8.09 13.97Note. p < .10; p < .05; ** p < .01 ; *** p <.001. aModel did not converge when added; standard errors are between parentheses; Group mentor was included in the model but not reported; CGM = conditional growth model; -2LL = -2 log likelihood; AIC = Akaike's information criterion; BIC = Bayesian information criterion.


97 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 BaselineWeek 4 Week 8Week 12Week 16 TimeSelf-efficacy composite scor e Figure 4-2. Self-efficacy composite score empiri cal data and model-based estimates by group assignment adjusted for attendance, site of the interventi on, and mentor. Note. Empirical data are noted with dotted lines ; Model-based estimates are noted with smoothed solid lines; Intervention group is noted with solid square s; Control group is noted with solid diamonds.


98 Table 4-11. Mixed between-within ANOVA table for self-determi ned behavior index score ( N = 68). Source Sum of squares d f Mean Square F p 2Between-subject Group 94.31194.311.550.220.02 Site 38.10138.100.650.420.01 Attendance 20.28120.280.340.560.01 Within-subject Time 1.1011.100.090.760.00 Time x group30.35130.352.48(0.12)0.04 Time x site 17.33117.331.430.240.02 Time x attendance13.34113.341.100.300.02Note. Marginal significance noted in parentheses. Group mentor included in the model but not reported.


99 Figure 4-3. Model-based estimates of self-determined behavior index score by group assignment adjusted for group attendance, site, and group mentor.

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100 Table 4-12. Means and standard deviations of pe dometer steps, minutes of moderate-to-vigorous physical activity, and LTEQ score across intervention timepoints. BL Week 4Week 8Week 12Week 16 M 5966.726485.386533.416573.676497.88 SD 2929.712786.812726.822391.332638.13 M 65.46120.60129.47128.60124.39 SD 85.70118.33134.86132.63143.57 M 5.43 8.52 8.74 8.53 8.67 SD 4.77 7.22 7.29 6.91 7.70Note. BL = baseline. Week 4 = aggregated weeks 1 to 4; Week 8 = aggregated weeks 5 to 8; Week 12 = aggregated weeks 9 to 12; Week 16 = aggregated weeks 13 to 16.Time Pedometer, steps/day MVPA, mins/wk MET estimate, MET-min/day

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101 Table 4-13. Correlations among physi cal activity variables and e xploratory model predictors. Measure12345678 1. MVPA1 2. Pedometer -.0151 3. MET estimate .906***-.0141 4. Female.040-.200.0441 5. Age .201-0.321**.237*-.0481 6. BMI .031.011-.082-.064-.265*1 7. VO2max-.153.263*-.088-.381**-.364**-.250*1 8. BDI-II.109-. *p < .05; **p < .01; ***p < .001; MVPA = moderate-to-vigorous physical activity (mins/wk); MET estimate = metabolic estimate (MET-min/day); Pedometer is in steps/day; BMI = body mass index; BDI-II = Beck Depression Inventory-II.

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102 Table 4-14. Moderate-to-vigorous physical activity model te sts for with group assignment controlling for group attendance, site of intervention, and group mentor. Fixed effects Initial status Intercept9.06(.56)***6.88(.58)***3.48(2.76)4.62(2.86) Group3.22(2.22)2.98(2.29) Attendance.06(.18)-.03(.19) Site3.33(1.55)*3.35(1.59)* Rate of change Time (linear)2.41(.37)***2.40(.37)***.60(1.08) Time (quadratic)-.50(.08)***-.50(.08)***-.52(.08)*** Time x group .47(.75) Time x attendance .16(.08)* Time x site -.03(.48) Random effects Level-1 Within-person15.74(.68)***12.81(.57)***12.83(.58)***12.78(.57)*** Level-2 Intercept22.98(3.87)***17.20(3.25)***14.34(2.79)***14.49(2.78)*** Time (linear)1.57(.35)***1.53(.35)***1.00(1.01) Time (quadratic) Time x Group Time x Attendance .00 ( .01 ) Time x site Fit statistics -2LL n p arameters AIC BIC Change in -2LL from p revious model 3 Unconditional Growth Model 6687.43 6566.94Note. p < .10; p < .05; **p < .01; *** p <.001; aModel did not converge when added; Standard errors are between parentheses; Group mentor was included in the model but not reported; CGM = conditional growth model; -2LL = -2 log likelihood; AIC = Akaike's information criterion; BIC = Bayesian information criterion. -a -aUnconditional Means Model 6666.29 6679.29 6524.66 6 6536.66 -a24 6546.45 6542.82 -a6496.45 CGM Main effects CGM Interactions 6512.82 141.63*** -a6618.52 11.84 15 6672.61 16.37

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103 50 60 70 80 90 100 110 120 130 140 150 BaselineWeek 4 Week 8Week 12Week 16 TimeMVPA (min/wk) Figure 4-4. Moderate-to-vigorous ph ysical activity empirical data and model-based estimates by group assignment adjusted for group atte ndance, site, and group mentor. Note. MVPA = moderate-to-vigorous physical activity; Empiri cal data are noted with dotted lines; Model-based estim ates are noted with smoothe d solid lines; Physical activity guideline of 150 minutes of MVPA/week (Nels on et al., 2007) is noted with solid line with no markers; Intervention gr oup is noted with solid squares; Control group is noted with solid diamonds.

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104 Table 4-15. MET estimate model tests with group assignment controlling for group attendance, site of intervention, and group mentor. Fixed effects Initial status Intercept 2.60(.12)***2.22(.12)***1.05(.63)1.15(.64) Group .32(.51) .28(.52) Attendance .05(.04) .04(.04) Site .68 ( .36 ) .68 ( .36 ) Rate of change Time (linear) .38(.06)***.37(.06)***.22(.18) Time (quadratic) -.07(.01)***-.07(.01)***-.07(.01)*** Time x group .09(.12) Time x attendance .01(.01) Time x site .02(.08) Random effects Level-1 Within-person .42(.02)***.34(.02)***.34(.02)***.34(.02)*** Level-2 Intercept 1.08(.18)***.97(.17)***.84(.15)***.84(.15)*** Time (linear) .04(.01)***.04(.01)***.03(.01)* Time (quadratic) Time x Group Time x Attendance Time x site .01 ( .02 ) Fit statistics -2LL n parameters AIC BIC Change in -2LL from previous modelNote. p < .10; p < .05; **p < .01; *** p <.001; aModel did not converge when added; Standard errors are between parentheses; Group mentor was included in the model but not reported; CGM = conditional growth model; -2LL = -2 log likelihood; AIC = Akaike's information criterion; BIC = Bayesian information criterion. 124.65*** 10.56 8.48 2555.08 2451.67 2504.53 2566.51 2539.94 2421.39 2428.83 2440.35 361 52 4 -a2533.94 2409.39 2398.83 2390.35 -a -a -a -aUnconditional Means Model Unconditional Growth Model CGM Main effects CGM Interactions

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105 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 BaselineWeek 4Week 8Week 12Week 16 TimeMET estimate (MET-min/day) Figure 4-5. MET estimate empirical data and model-based estimates by group assignment adjusted for group attendance, site, and gr oup mentor. Note. Empirical data are noted with dotted lines and diamond markers; Model-based estimates are noted with smoothed solid lines; Intervention group is noted with solid square s; Control group is noted with solid diamonds.

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106 Table 4-16. Pedometer steps model tests with gr oup assignment controlling for group attendance, site of intervention, and group mentor. Fixed effects Initial status Intercept 78.21(1.65)***75.33(1.87)***58.10(9.70)***55.44(9.74)*** Group 11.80(7.93)14.23 ( 7.94 ) Attendance 0.88(.63) 1.06(.64) Site 6.36(5.51) 7.49(5.51) Rate of change Time (linear) 3.23(.88)***3.20(.88)***6.49(2.40)** Time (quadratic) -0.65(.18)***-0.64(.18)***-0.68(.18)*** Time x group -1.92(1.42) Time x attendance -0.27(.17) Time x site -1.05(.97) Random effects Level-1 Within-person82.95(3.60)***69.70(3.17)***69.63(3.16)***69.56(3.15)*** Level-2 Intercept 208.31(34.39)***225.77(38.80)***207.19(35.81)***203.10(34.98)*** Time (linear) 7.99(1.97)***8.15(2.00)***0.06(1.65) Time (quadratic) Time x group 4.95(3.00) Time x attendance Time x site 7.61(3.65)* Fit statistics -2LL n parameters AIC BIC C h ange i n -2LL from previous Unconditional Means Model Unconditional Growth Model CGM Main effects CGM Interactions -a -a -a -a8560.00 8473.00 8466.67 8448.22 361 52 4 8566.00 8485.00 8496.67 8500.22Note. p < .10; *p < .05; ** p < .01; *** p <.001; aModel did not converge when added; Standard errors are between parentheses; Group mentor was included in the model but not reported; CGM = conditional growth model; -2LL = -2 log likelihood; AIC = Akaike's information criterion; BIC = Bayesian information criterion.18.45* 8581.12 8515.24 8572.26 87.00*** 6.33 8631.45

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107 5400 5600 5800 6000 6200 6400 6600 6800 7000 BaselineWeek 4Week 8Week 12Week 16 TimePedometer (steps/day) Figure 4-6. Pedometer steps empirical data a nd model-based estimates by group assignment adjusted for group attendance, site, and gr oup mentor. Note. Empirical data are noted with dotted lines and diamond markers; Model-based estimates are noted with smoothed solid lines; Intervention group is noted with solid square s; Control group is noted with solid diamonds.

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108 Table 4-17. Mixed between-within differences in cardiorespiratory fitness by group assignment controlling for attendance, site of the intervention, and group mentor ( N = 68). Source Sum of squares df Mean Square F p 2Between-subject Group 66.47166.470.610.440.01 Attendance 30.74130.740.280.600.00 Site 206.921206.921.890.170.03 Within-subject Time 22.18122. Time x group11.92111.920.620.430.01 Time x attendance34.07134.071.780.190.03 Time x site 37.59137.591.970.170.03Note. Group mentor was included in the model but not reported.

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109 Figure 4-7. Model-based estimates of cardiorespiratory fitness by group assignment adjusted for group attendance, site of the in tervention, and group mentor ( N = 68).

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110 Table 4-18. Significant fixed effects for exploratory predictors and changes in model fit for three separate models of moderate-to-vigorous physical activity, MET estimate, and pedometer steps. Estimate SEdftp Change in -2LL from final hypothesized model MVPA model 721.68*** VO2max 0.050.0982.782.000.05 MET estimate model 259.26*** Pedometer model 946.55*** Age -0.900.2675.63-3.450.001Note. ***p < .001; MVPA = moderate-to-vigorous physical activity.

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111 CHAPTER 5 DISCUSSION The general objective of our study w as to determine whether a peer-assisted PA intervention, based largely upon social cognitive and social ecol ogical foundations of behavior change, could lead to changes in beliefs, attit udes, and behaviors regarding PA. Social cognitive based beliefs and attitudes (e.g., self-efficacy) were measured along with self-reported PA and pedometer steps over a 16-week randomized trial. Collectively the result s of our study indicated mixed support for the hypotheses that the interv ention impacted social cognition and PA levels. Summary of Findings Changes in Social Cognition The prim ary purpose of the study was to examin e whether the intervention could improve social cognitive beliefs and at titudes regarding PA behavior over a control group matched for social contact and peer mentorship. Social cognitive beliefs and attitudes measures were selfefficacy and self-determined behavior. For self -efficacy (BSE and EXSE composite), both groups exhibited modest, yet significant, declines in self-efficacy. This finding did not support a primary research hypothesis. For self-determined behavior, a marginally significant time x group interaction was observed, indicating that the in tervention group moved toward more intrinsic forms of motivation for exercise and PA behavior compared to the control group. This marginal effect showed tenuous support for anot her primary research hypothesis. Self-efficacy The lack of improvem ents, and modest dec lines in fact, regarding self-efficacy were surprising given the large body of evidence demonstrating short-term and long-term relationships between self-efficacy and PA behavior with older adults (McAuley et al., 2005). Because improvements in PA were observed in both group s in our intervention, changes in self-efficacy

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112 were to be expected. The negative time tre nd suggests that perhaps participants became increasingly discouraged by unsuccessful attempts at PA initiation. This may have occurred if participants set unrealistic goals, were not able to effectively pl an for and overcome barriers, or were simply not prepared for the challenge of ad opting a new behavior. This would be consistent with Banduras (1986, 1997) theorizing that past performance accomplishments strongly predict self-efficacy toward a behavior. Despite not observing the broad time x group ef fects expected, the weekly assessment of self-efficacy allowed us to observe temporal ch anges across different phases of the intervention. Unfortunately, the study lacked sufficient power to pick up these small effects and suggests a need for additional data. As shown by the empirical data plots in Figure 4-2, self-efficacy in the intervention group had a marked decline across the first phase of the trial (weeks 1 to 4), followed by a steady, yet modest increase from week 4 to the end of the intervention. The health hygiene control displayed mirrored results, within initial increase follo wed by a steady, modest decline. The short-term changes observed from ba seline through the first pha se reveal interesting findings about the PA behavior initiation process. Because intervention group participants set realistic and challenging personal go als, it is possible that early a ttempts at reaching those goals were unsuccessful, resulting in initial declines in self-efficacy. In later weeks, as these goals were appropriately modified (see Table 3-1; goa ls were re-visited in weeks 7, 9, 12, and 14) and group social support became stronger, self-efficacy began to rise. If this were true, one would expect long-term assessments of self-efficacy (i.e., 6-months, 1-year ) would result in increases in self-efficacy that exceed baseline levels. A couple of methodological concerns may also help to disentangle these unexpected results. First, because these measures were give n repeatedly over the intervention (18 times total)

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113 and were part of a larger battery of weekly psychosocial and neuropsychological measures, it is possible that fatigue or boredom was experienced by participants and artificial uniformity in responses resulted. This was evidenced by internal consistencies for BSE ( = .95) and EXSE ( = .99) that were abnormally high and higher th an previously published reports using these measures (McAuley et al., 1999; McAuley et al ., 2005). Additionally, the intraclass coefficient for the self-efficacy composite was markedly hi gh, indicating little week-t o-week within-person fluctuations. This calls into question the validi ty of weekly assessment of the BSE and EXSE measures. While the repeated assessment of these measures is a novel methodological strategy that is worthy of continued study, care must be taken in choosing the appropriate temporal spacing of assessments. To our knowledge no previ ous studies have assessed changes in selfefficacy at the weekly level. In the current study, four-week timelags that had conceptual meaning (i.e., initiation, maintenance) may have been more appropriate and would have avoided fatigue and boredom while still capturing impor tant processes of behavioral change. Second, both measures of self-efficacy included specific behavioral ta rgets that were not consistent or explicitly reinforced by the inte rvention. The behavioral target stated in the directions for the BSE measure is to exercise three times per week fo r three months, with no specifications of intensity or duration (see Appe ndix I). The behavior target for EXSE, on the other hand, is to exercise for 40 or more minut es on three days of the week (see Appendix J). The mismatch of these behavior targets may have caused confusion for participants leading to a certain degree of measurement error. Moreover, neither of these beha vioral targets were consistent with the instructions given to par ticipants on the appropriate intensity, duration, or mode of PA. Because both measur es have been widely used with general and older adult populations (Blissmer & McAuley, 2002; McAuley et al., 2003; McAuley et al., 2005), have

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114 undergone some psychometric testing (D uncan & McAuley, 1993; McAuley, 1992, 1993; McAuley, 1993b; McAuley et al., 19 94), and have demonstrated some remarkable consistencies in reliability and validity evidence for many years, future peer-assisted interventions should ensure that these measures are modified to more clearly and directly tap in to specific behavioral targets established for the intervention (i.e., ACSM/CDC physical activity recommendations). In our study, the self-directed and autonomous natu re of the intervention made this type of behavioral target difficult to achieve, which make conclusions about how self-efficacy toward PA changed over the course of the intervention difficult to ascertain. It should also be noted that the addition of model covariates and exploratory predictors explained further variance in initial status (i.e., main effects) and the time trend (i.e., interactions) in self-efficacy. The intervention site appeared to play a marginal role, with individuals at the community church displaying higher self-efficacy compared to the university facility. While reasons for this disparity are tenuous, ecological differences between the facilities such as parking, traffic, and general aesthetics may have pl ayed a role. This is an interesting finding to note, confirming previous theore tical discussions rega rding the impact of ecological factors on individual-level constructs su ch as self-efficacy (King et al ., 2002; Satariano & McAuley, 2003). Also, attendance positively moderated the time tr end in self-efficacy. This suggests that a doseresponse relationship may exist between some co mponent of the intervention and self-efficacy. Because this effect was not loca lized to the intervention group (i .e., a three-way interaction), it remains unclear if the discussions about PA, the peer mentoring re lationship, or merely the social contact were responsib le for this effect. Self-determined behavior While only m arginally significant, baseline-pos ttest changes in self-determined behavior indicated that interven tion group participants significantly moved toward more intrinsically-

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115 motivated PA behavior over the course of the intervention. This was pe rhaps due to the selfdetermined environments that were created as part of the AAMP inte rvention. Specifically, perceptions of autonomy and competence were fo stered through self-directed behavior where encouragement was given for setting realistic a nd attainable goals. Relatedness was fostered through same aged peers that created supportive e nvironments where behavioral change could be achieved. It should be noted, however, that the eff ect size for this change was small. This latter finding may have been due to a ceiling effect, as self-determined behavior at baseline fell well above the midpoint of the self-determination c ontinuum, indicating individuals began the study with more intrinsic forms of motiv ation toward PA (see Figure 4-3). The implications of motivation toward PA that is more intrinsic in nature (e.g., having fun while exercising) compared to extrinsic (e.g., exercisi ng to improve appearance) are valuable. While Frederick and Ryan (1993) suggest that extrinsic motivation may foster PA initiation, intrinsic motivation is clearly linked to long-term maintenance of PA behavior (Ryan et al., 1997). Given this, it is important to note that age was inversely asso ciated with intrinsic behavior in the current study. It may be that older indivi duals interpreted physical activities available to them as not enjoyable or their reasons for PA were targeted exclusively upon avoidance of certain health conditions and therefore they felt compelled to engage in these activities. Younger individuals in the study may have had reasons for PA that were mo re intrinsically motivated (i.e., identified regulation, integrated regulation, in trinsic motivation), such as PA for health promotion or PA for enjoyment. This suggests that when establishing an intervention where autonomy is encouraged, the PA options presente d to individuals must take into account the preferences and capabilities of th e target group. In this case, intr insic motivation may have been

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116 fostered more in the older participants had act ivities been present that they preferred and enjoyed, specifically supervised lower intensitie s activities (e.g., chair dancing, water aerobics). Unlike self-efficacy, self-determined behavior was only measured at baseline and posttest. The absence of a Week 12 assessment makes it difficult to understand th e full effect of the intervention on self-determined behavior. It may be that intrinsic motivation was at its height at Week 12 and declined after the peer mentor and group meetings were reduced in weeks 13 to 16. On the other hand, intrinsic motivation may take time to be developed and the monotonic increase as pictured in Figure 4-2 is more accu rate. This leaves additional questions to be answered about the process of change in self-determined behavi or and how the intervention may have impacted that change. Future research s hould address when changes in self-determined behavior occur through repeated assessment and if these changes are su stained long-term. Changes in Physical Activity The results of all three d epe ndent variables for PA (minut es of MVPA, MET-mins/day, and pedometer steps/day) indi cated significant and curvilinea r improvements in PA over the course of the intervention. PA levels, across a ll three models, could be characterized with the following time trend: (a) lowest levels of PA were at baselin e; (b) monotonic increase during weeks 1 to 4 and weeks 5 to 8 phases; (c) an ap parent lev eling-off during the weeks 9 to 12 phase; and (d) a modest, yet sign ificant curvilinear decrease dur ing the weeks 13 to 16 phase. While descriptive observations indicated slightly more favor able improvements in PA as measured by the MVPA and MET estimates for the intervention group, no significant time x group interactions were observed in any of the three PA models. This indicated that the intervention group did not experience statistically significant increases in PA as compared to the control group. Nevertheless, the significant time trend indicates a couple of important points regarding the intervention. First, despite diffe rences in magnitude between measures of PA,

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117 significant improvements in PA were observed in all three measures and these improvements required up to 12 weeks to reach their peak. These significant time trends suggest that participants in both conditions we re indeed sedentary at baseline and experienced meaningful improvements in PA behaviors. Few individuals during the baseline week of observation met national PA recommendations. Moreover, baseline pedometer valu es were considerably lower (6,125 v. 7,473) and more variable (2,934 v. 1,385) compared to a recently published metaanalysis of 26 intervention and observational studie s of middle-aged and older adults (Bravata et al., 2007). This can be contrasted with model-ba sed estimates at their peak at 130 minutes of MVPA, 8.5 MET-mins/wk for LTEQ scores, and 6600 pedometer steps/day, indicating not only statistical improvements but also improvements with practical significance. For example, individuals moved from less than 60 minutes a week of MVPA to almost reaching the ACSM/CDC recommendations of 150 minutes of MVPA each week (Nelson et al., 2007). It should be noted, however, percentage of improvement in PA from base line to peak varied markedly by PA measurement (116% for MVPA, 55% for MET-mins/wk, and 10% for pedometer steps/day). These disparate change s by PA measurement would indicate that behavioral change occurred most dramatica lly among minutes of MVPA (the most sensitive measure of moderate and vigor ous activities), and less drama tically among MET-mins/day and pedometer steps (more sensitive to all types of leisure-time activity and movement patterns). This finding would suggest that the intervention had its greatest impact on the types of activity suggested by the ACSM/CDC physical activity gui delines for older adults (Nelson et al., 2007) and have demonstrated links w ith important health outcomes in cluding improved cardiovascular health (Mazzeo et al., 1998; Singh, 2002), reduction in coronary hear t disease risk (Haskell et al., 1992), and premature mortality (Bean et al., 2004; Lee & Paffenbarger, 1996).

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118 Second, the maintenance effect that occurred during the week s 9 to 12 phase demonstrates that gains in PA behavior can be sustained at least initially if behavioral reinforcements and social ecological determinants remain constant While more research is clearly needed, both groups benefited from regular meeting with a gro up mentor, access to an exercise facility, and continuous behavioral feedback and self-monito ring (i.e., use of pedome ter, recording daily activity). The gains that were achieved initially were largely maintained during the maintenance phase, a finding that may have implications for long-term interventions th at seek to encourage maintenance of behaviors. Researchers should focus on establishing environments with the following characteristics: (a) re gular and appropriate social influences that cue behavioral patterns from initiation stage; (b) ensuring when possible that financial and environmental constraints to PA are minimized; and (c ) self-monitoring skills are in place. Finally, the modest decline in PA during the final phase provides another line of reasoning as to the plausibility of using peer mentors as behavior change agents. During this final phase, two important elements of the intervention we re modified: the exercise membership was withdrawn from the participants and contact with the peer me ntors was reduced significantly (only one session compared to four sessions during previous phases) All self-monitoring continued throughout this phase. One may argue that the decline in ac tivity could be attributed to the lack of an exercise facility. While the extent of facility use was not monitored in our study, and participants were encourage to engage in PA both within a nd outside the facility, cursory observations indicated that the facility equi pment was seldom used at any point in the intervention. Participants pref erred to be physically active at home or at other community locations. This is consistent with past literature that suggests older adu lts may prefer home-based

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119 PA (King, Haskell, Taylor, Kraemer, & DeBus k, 1991; King, Haskell, Young, Oka, & Stefanick, 1995; Perri, Martin, Notelovitz Leermakers, & Sears, 1997). The alternative explanation then is that the d eclines can be attributed to reduced contact with the peer mentor and the group environment. Th is suggests that the use of a peer mentor may account for at least a portion of the original improvements in PA, and may also aid in maintenance of PA behavior. The delivery of the intervention by a peer mentor may have fostered an environment of relatedness that was conducive to change according to selfdetermination theory (Deci & Ryan, 1985). Li kewise, peer mentors may have served as significant social influences by discussing or demonstrating PA behaviors (i.e., vicarious experience), educating participants about the be nefits of PA (i.e., verbal persuasion), or encouraging participants that th ey can reach their PA goals (i.e ., perceptions of competence). Even without gains in self-efficac y, there is evidence to suggest that social support may directly influence PA behavior (Resnick, 2001, Resn ick et al., 2002; Esta brooks & Carron, 1999). Finally, peer mentors may have also have been able to influence indivi duals perceptions of competence through making encouraging statements, reminding participant of past success in behavioral change, and modeling positive behavior s. Deci and Ryan (1985) have suggested that perceptions of competence is the strongest mediator of intrinsic motivation, so the influence of these statements on PA behavior may have been important. When contact with the peer mentors was then withdrawn in the final phase of the intervention, some declines in PA behavior were observed. Moreover, the fact that peer mentors in the health hygiene group, who simply engaged participants in discussions about age-appropr iate health topics (e.g., cancer screening, osteoporosis), were able to increase PA significan tly (with a similar modest decline in the final phase) provides even more support for the use of peer mentors as behavior change agents.

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120 Finally, differences in PA as a function of the measure used were evident throughout the study. Indeed, low correlations between pedometer steps/day and both minutes of MVPA and MET-mins/day were observed. These low correlati ons have been observed in other studies that have measured self-report and objective measures of PA concurrently. It is important, however, to understand that these measures theoretically tap into distinct aspects of PA behavior, and exclude and underestimate others. MET-mins/day includes mild, moderate, and vigorous activity and simultaneously can account for both intensity and duration of activity. This MET estimate however may overestimate activity as it includes mild activities that require minimal effort and have minimal health benefits (Pate et al., 1995). Minutes of MVPA has the advantage of allowing comparisons in activity directly to the ACSM/CDC PA guidelines (Pate et al., 1995; Nelson et al., 2007) by only including mode rate and vigorous forms of activity. The disadvantage of the MVPA calculation is that it underestimates ener gy expenditure and the added benefit of vigorous over moderate types of activity. Both MET-mins/day and minutes of MVPA have the disadvantage of self-report bias although the current study tried to minimize this bias by having daily instead of weekly re call. Finally, while pedometer counts have the advantage of being objective, they have a num ber of well-documented limitations, including the inability to capture intensity of movement and wa ter activities, diminishing calibration over time, and biased readings when not worn appropria tely (Tudor-Locke et al ., 2002). All of these limitations could have lead to measurement error and biased the results. Future research should carefully consider the use of accelerometers and other methods to minimize measurement error for PA and health-related research. Accelerometer s can capture movement of all types and have the capability of recording movement at second and minute intervals. Additionally, accelerometer cut-points have now been establis hed that allow for calculations of minute-by-

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121 minute classifications of moderate and vigorous activities (Bassett, Ainsworth, Swartz, Strath, OBrien, & King, 2000; Melanson, Melanson, & Sirar d, 1998). This makes comparisons with PA guidelines possible. Changes in Cardiorespiratory Fitness W ithout controlling for the model covariates, individuals (regardles s of group assignment) significantly improved their cardio respiratory fitness over the cour se of the intervention. This was consistent with the observed changes in PA over the intervention. These effects diminished after including the model covariates, however the time trend continued in the hypothesized direction. Given the observed gains in PA ove r the course of the intervention, modest improvements in cardiorespiratory fitness were e xpected, despite the relatively short period in which these effects were observed. Future extensions of peer-assi sted research should consider adding measurement timepoints of six months and one year to demonstrate longer-term maintenance of PA resultant improvements in cardior espiratory fitness. The lack of significance after adding model covariates indicates that our sample was under-powered to detect such small changes in fitness, and perhaps, there was considerable between-subject variability in improvement that was captured by the model covariates. The implications of detecting changes in cardiorespiratory fitness are essential in demonstrating the efficacy of any PA intervention. Cardiorespiratory fitne ss has established links to premature mortality, reductions in chroni c disease, and longevity (DiPietro, 2001; LiuAmbrose et al., 2004; Lee & Paffenbarger, 1996). In addition to fitness outcomes, future research should include other secondary outcomes that are linked to physical functioning and quality of life including perceptions of physical and ps ychological health, life satisfaction, physical performance, and instrumental activities of daily living and activities of daily living.

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122 Strengths and Limitations Strengths Perhaps the greatest strength of our study was the novelty of its intervention com ponents. To our knowledge this is the only study to examine the effects of a peer-assisted PA intervention within a group-based setting. Peer mentors have recently been used in one-on-one behavioral counseling setting (Castro, Pruitt, French, & King, 2008), however Project AAMP is the first study where mentors have been recruited, trained, and delivered a social cognitive based PA intervention to a group of sedentar y adults within such an autono mous framework. This is also the first PA intervention that has made the use of mental im agery a major component of the intervention. The guiding theore tical framework (see Figure 2-1) represents a novel integration of goal setting, social support, and mental imagery designed to impact social cognitive beliefs and attitudes that underpin PA behavior. Another strength of our interv ention was its close adherence to recommendations of the RE-AIM framework (Glasgow et al., 1999). Th e purpose of these recommendations was to improve the ecological validity and impact of health behavior interventions. The current intervention addressed these recommendations by using peer mentors (a doption: peer mentors are a relatively inexpens ive alternative to highly-trained behavioral counselors), monitoring quality control (implementation: peer mentor s were monitored and provided feedback for delivery of intervention conten t), and assessing short-term sustainability (maintenance). Overall retention of program participants was a strength of the study as 85% of those who began the study completed the final assessments. Even after accounting for the 10 individuals who were randomized but dropped out prior to their knowledge of group assignment, this retention rate was 76%. This retention rate is similar to recently published interventions of autonomous lifestyle interventions (Dunn, Marc us, Kampert, Garcia, Kohl, & Blair, 1999;

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123 Jancey et al., 2006; Jancey, Lee, Howat, Cl arke, Wang, & Shilton, 2007) and lower than more structured, center-based trials (Cox, Burke, Gorely, Beilin, & Puddey, 2003; Life Study Investigators, 2006). Missing data among study comp leters was a minimal concern as adherence to daily logs was excellent, despite some dr opoff during the final phase of the intervention. Incomplete data among study dropouts was accommodated with FIML estimation procedures that produced less biased estimates compared to listwise deletion or mean replacement procedures. Finally, the use of repeated, c oncurrent assessment of the so cial cognitive and behavioral outcomes was another design strength of the stud y. Repeated assessment of self-efficacy allowed us to understand the course of change in self-effi cacy across initiation and maintenance stages of behavioral adoption for the current sample. Also, repeated assessments of self-efficacy allowed us to determine the appropriate temporal spac ing of measurement for future studies. Daily assessment of PA reduced recall bias. Concurrent assessment of self-report and objective forms of PA behavior allowed us to study how different dimensions of movement were impacted by the intervention. Repeated assessment also allowed us, for both self-efficacy and PA, to simultaneously explain both withinand between-person differences in study outcomes. While our multilevel models primarily were able to predict between-person differences, this design feature is essential in understa nding how to reduce w ithin-person fluctuations (i.e., increase consistency) in PA behavior over time. Limitations A few i mportant limitations of this research bear mention. First, great efforts were taken in the selection of an appropriate control condition to match for social contact effects. This required the use of peer mentors to deliv er age-tailored health information to control group participants. This information and social cont act, along with a desire from pa rticipants to initiate PA and

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124 monitor activity with the daily logs, perhaps created a contro l group where participants were motivated to increase PA and therefore poorly representative of a usual care condition. This made firm conclusions about the intervention (par ticularly the utility of peer mentors) and its effects on PA behavior difficult to establish. Future researchers that seek to examine the use of peer mentors and the guiding integrative social cognitive model should take care in research design to avoid this pitfall. The utility of peer mentors may best be examined in future research with a three-group design that includes a usual-care control, a previously efficacious intervention delivered by trained professionals, and an identical interv ention delivered by peer mentors. Traditional time x group tests could examine if improvements for both intervention groups exceed those of the control condition whil e equivalence testing could examine if peerassisted delivery was equivalent to delivery by tr ained professions. Future studies that seek to examine the intervention framework presented he re (see Figure 2-1) may consider a waitlist control design to examine how the active componen ts of the intervention (goal setting, mental imagery, and social support) uniquely impacted PA behavior free of the social contact influences of peer-assisted delivery format and compared to a true no-contact control. Moreover, researchers are encouraged to step back and sepa rately evaluate the individual components of the intervention (e.g., peer mentoring, goal setting, me ntal imagery) before moving forward to the test the packaged, multi-component intervention. A second limitation was the exclusion of speci fic behavioral targets for the intervention group participants. This was done purposefully to support the self -determined foundations of the intervention and to encourage autonomy in behavi or change. Likewise, the lack of direct PA supervision was put in place to encourage long -term maintenance and sustainability after program completion and any supervision was wi thdrawn. These two factors may have left

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125 participants with insufficient gu idance to initiate favorable leve ls of PA compared to a robust control group. It should be noted, ho wever, that despite a lack of behavioral targets, results of minutes of MVPA indicated that individuals in the inte rvention group were able to come close to achieving the ACSM/CDC PA guide lines (Pate et al., 1995; Nels on et al., 2007) while still providing choice and flexibility in the amount, intensity, and mode of PA. This is an important observation because autonomy was a central char acteristic of our intervention but perhaps a greater degree of structure and gui dance is necessary in order to increase PA levels toward public health recommendations. The third limitation of this research deals with the lack of generalizability of its findings. Of the 433 individuals who expressed in itial interest in the study, only 16% ( N = 69) completed the study protocol (see Figure 3-1) and the majority of these individuals were white females. Because many individuals lost interest before th e study began, it is difficult to ascertain the full range of potential influences on program participation. Exclusionary criteria also limited our generalizability, with the largest number of participants were fo r being on heart-rate attenuating medications, PA levels, and cogni tive status respectively. Finall y, efforts were made during the recruitment process to over-recruit non-Whites (particularly Blacks) and men. These efforts proved unsuccessful as only 8.6% of the fi nal sample was non-White and 15.9% were men. Future researchers should focus on recruiting and retaining a more diverse sample consistent with population demographics in the United States. The fourth limitation of this research deals with assessing the effec tiveness of individual mentors. While continuous mon itoring by research staff was conduc ted to ensure adherence to the guiding model and research protocol by the mentors, little was done to assess what distinguished effective from ineffective mentor s. The underlying assumption was that same aged

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126 peer mentors would more effectively engage pa rticipants, yet it was clear that individual differences between mentors may have determ ined a large range of group and participant outcomes. Although we were able to statistically control for th ese variant outcomes and address intervention-level changes free of mentor-speci fic differences, a question we were unable to address was what characteristics made a mentor pa rticularly effective at enhancing self-efficacy beliefs and PA behavior. This is an important question to address fo r future studies that use peer mentors. Social cognitive th eory (Bandura, 1986, 1997) sugges ts that the match between participant and modeling agent characteristics is an important determinant of the formation of self-efficacy beliefs through vicarious experien ces, verbal persuasion, and perceptions of performance accomplishments. There was some suggestion throughout the intervention process that attrition among women increased in groups where the mentor was male. Likewise, groups where mentors were perceived as more supportive and empathetic resulted in the greatest PA gains. Because formal examinations of these observations were not possible, future social cognitive interventions should focus on and collec t additional information about the mentors and the mentor-participant interactions. Furtherm ore, recordings of the group meetings and interviews of the participants at posttest were collected and may, in the future, clarify what individuals perceived to be e ffective strategies and characte ristics of the peer mentors. The final limitation deals with the imprecision of measurement and the relative large variability associated with the PA and self-efficacy measurements. By nature, day-to-day and week-to-week fluctuations in PA behavior are normal and can be efficiently estimated with multilevel modeling approaches. This was evidenced by large standard deviations in the PA measures after the baseline phase, indicated that as individuals became more active variability in the activity also increased. This large amount of vari ability however made interpretations of

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127 estimates at the week-level difficult. Consequent ly time was aggregated across behavioral phases (initiation, maintenance, sustaina bility), which resulted in sl ightly less powerful models (however estimates remained similar). Perhaps another source of power loss was the inflated standard errors that were pres ent in the PA measures. Unfortuna tely, normal variability in PA behavior not accounted for by the model predictors and measurement error associated with selfreported PA and pedometer could not be distingui shed from one another. In conclusion, the imprecision of self-report and objective measurem ents of PA in our study made small and moderate effects (e.g., time x group interactions) di fficult to detect in the models. More accurate assessments of PA (i.e., accelerometers) in future studies could potentially detect small effects that were observed in the descriptive data. Future Directions While specif ic research recommendations ha ve been made throughout this discussion section to extend various lines of research or address limitations of the current study, a few broad recommendations are presented below as future di rections that build upo n the strengths of the current intervention. First, the guiding theoretical framework (s ee Figure 2-1) presented in the current intervention is flexible and has the potential to be utilized across a wide range of health behaviors (weight management, sm oking cessation), with a wide ra nge of targeted populations (e.g., children, chronically ill), in a variety of communication and delivery formats (e.g., internet, telephone, mail-based). Future researchers interested in ta rgeting specific sub-groups may consider adapting the current inte rvention, or various components of the intervention, in ways to enhance adoption and minimize relevant barriers. Second, the results of our study, along with a growing body of literature in other health domains (Riegel & Carlson, 2004; Sheppard, et al., 2008; Zimmerman & Bingenheimer, 2002),

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128 provides preliminary evidence to support the use of peer mentors as a viable alternative to standard delivery formats. Theoretically-driven intervention strategies that use peer-assisted delivery formats may reduce program resources and increase ecological validity of the intervention. While a lack of time x group inte ractions precludes us from making definitive recommendations about peer mentors and the th eoretical model, the increases in intrinsic motivation and the positive improvements in behavi or established these strategies as viable alternatives to more traditional approaches. Finally, future iterations of the current intervention would be improved by more fully engaging macroand meso-environmental factors th at are associated with PA. Social ecological approaches (King et al., 2002) th at account for environmental, individual, and transactional influences on volitional behavi or are needed. This can be a ccomplished by situating future intervention efforts in a variety of community se ttings and adapting the interventions to reduce relevant barriers for that co mmunity. Community-based participatory approaches may help to inform the specific modifications that need to be made for the intervention to be successful. The engagement of community resources and ecologi cal influences may boos t the overall power of the intervention. Conclusion In conclusion, this project sought to exam ine the utility of a peer-a ssisted PA intervention for increasing social cognitive beliefs and attitudes regarding PA and increasing PA behavior. The findings showed mixed results. The interv ention did not increase se lf-efficacy, marginally increased intrinsic motivation in the intervention group, and showed positive, curvilinear growth for PA and small improvements in cardiorespira tory fitness in both gr oups. Future research should continue to explore ways to increase PA behavior in older adu lt populations through the

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129 use of peer mentors and a theoretical model that can be easily and inexpensively delivered to a wide range of population subgroups.

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130 APPENDIX A RECRUITMENT FLYER: LIVING WELL ADULTS AGED 50 YEARS AND OLDER VOLUNTEERS NEEDED FOR EX ERCISE AND HEALTH STUDY FOR OLDER ADULTS 1 CONNECT WITH OTHER OLDE R ADULTS WHO SHARE YOUR CONCERN FOR EXERCI SE AND HEALTH Some details : Seeking seniors who do not currently exercise regularly. All senior volunteers will participate in small groups. Participants will receive mentoring and education about ei ther exercise or health issues. Participants will attend 18 weekly se ssions, including regular testing and the health promotion classes All participants will receive a limited membership to the University of Floridas Living Well fitness facility, at no cost. The non-renewable membership is limited to twelve weeks. Weekly testing will monitor exercise pa rticipation, as well as any changes in fitness, health, emotiona l and mental functioning. Participants will also wear an activi ty monitoring devi ce, and complete a daily one-page exercise and sleep log. If you would like more information, please contact Matt Buman for more information at (352) 392-0584 ext. 1236 or via e-mail at mbuman@hhp.ufl.edu This study has been approved by the Institutional Re view Board (IRB02) at the University of Florida.

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131 APPENDIX B RECRUITMENT FLYER: WESTSIDE BAPTIST CHURCH ADULTS AGED 50 YEARS AND OLDER VOLUNTEERS NEEDED FOR EXERCISE AND HEALTH STUDY FOR MIDLIFE AND OLDER ADULTS 1 CONNECT WITH OTHER MIDLIFE AND OLDER ADULTS THE STUDY WILL BE CONDUCTED HERE IN WEST GAINESVILLE AT WESTSIDE BAPTIST CHURCH Some details : Seeking adults and seniors who do not currently exercise regularly. All volunteers will participate in sma ll groups. Participants will receive mentoring and education about eith er exercise or health issues. Participants will attend 18 weekly se ssions, including regular testing and the health promotion classes All participants will receive a limited membership to the Westside Baptist Church fitness facility at no cost. You will also have the option to pay to extend your membership after the study is completed. Weekly testing will monitor exercise pa rticipation, as well as any changes in fitness, health, emotiona l and mental functioning. Participants will also wear an activi ty monitoring devi ce, and complete a daily one-page exercise and sleep log. If you would like more information, please contact Matt Buman for more information at (352) 392-0584 ext. 1236 or via e-mail at mbuman@hhp.ufl.edu T his stud y has been a pp roved b y the Institutional Review Board ( IRB02 ) at the Universit y of Florida.

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132 APPENDIX C DEMOGRAPHIC AND SCREENING INSTRUMENT ASK GENDER (2) ONLY IF NOT KNOWN OR UNABLE TO DETERMINE. OTHERWISE, CODE QUEST. 2 AND NO W ASK PREFERRED TITLE QUESTION. 2. Are you male or female? MALE........................... 1 [Is that Mr., Dr., Rev., or other?] RECORD PREFERRED TITLE ON CONTACT RECORD. FEMALE.......................2 [Is that Mrs., Miss, Ms., Dr., Rev., or other?] RECORD PREFERRED TITLE ON CONTACT RECORD. 3. What is your date of birth? .___/____/._______ IS PARTICIPANT'S AGE WI THIN 6 WEEKS OF 50th BIRTHDAY OR OLDER TODAY? YES ..............................................................1 NO ...............................................................2 INELIGIBLE: READ SCRIPT BELOW AND END INTERVIEW AGE INELIGIBILITY CLOSE-OUT SCRIPT: These are the only questions I need to ask. This research study is designed for people who are age 50 or older. I would like to thank you for th e time you have taken to speak with me. We will not need to contact you again for this study, but could we contact you in the future for other studies? Thank you. 3a. Is English your fi rst language? YES NO .2 4. What is your marital status? [READ RESPONSE CATEGORIES IF UNABLE TO ANSWER] MARRIED, .........................................................................1 LIVING AS MARRIED,.....................................................2 SEPARATED,.....................................................................3 DIVORCED,........................................................................4 WIDOWED, ........................................................................5 SINGLE, OR NEVER MARRIED?....................................6 5. Does anyone live in the home with you? YES..........1 NO..........2

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133 6. What is the highest grade of school or level of education that you completed? [CODE ONLY ONE RESPONSE] DID NOT GO TO SCHOOL 00 GRADE 1 01 GRADE 2 02 GRADE 3 03 GRADE 4 04 GRADE 5 05 GRADE 6 06 GRADE 7 07 GRADE 8 08 GRADE 9 09 GRADE 10 10 GRADE 11 11 GRADE 12/GED 12 VOCATIONAL/TRAINING/ SOME COLLEGE AFTER HS GRAD 13 ASSOCIATE DEGREE 14 COLLEGE GRAD/BA-BS 16 SOME PROFESSIONAL SCHOOL AFTER COLLEGE GRAD 17 MASTER'S DEGREE 18 DOCTORAL DEGREE (PhD, MD, DVM, DDS, JD, etc.) 20 7. What race do you consider yourself? (PRO BE: White, Black/Afri can American, Asian, Native Hawaiian/Pacific Islander, American I ndian/Alaskan Native, or another race?) WHITE/CAUCASIAN .........................................................1 BLACK/AFRICAN AMERICAN ........................................2 ASIAN...................................................................................3 NATIVE HAWAIIAN/PACIFIC ISLANDER......................4 AMERICAN INDIAN/ALASKAN NATIVE.......................5 BIRACIAL.............................................................................6 SPECIFY:_______________________________________ OTHER .................................................................................7 SPECIFY: ______________________________________ DON'T KNOW......................................................................8 IF PARTICIPANT IS UNABLE TO ANSWER 7, PROBE: Which race do you most identify with or consider yourself to be?

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134 8. Are you Hispanic or Latino? YES..........1 NO..........2 9. The next questions are about your vision. Do you wear glasses or contact lenses to read? YES..........1 NO..........2 BEGINNING WITH ITEM 10a, AND FOR ALL OTHERS DO NOT TERMINATE INTERVIEW IF INELIGIBLE COLLECT ALL DATA, TH EN READ INELIGIBILITY SCRIPT BEFORE ITEM 15. 10a. How much difficulty do you have reading ordinary print in the newspaper, [wearing glasses or contact le nses]? Would you say... no difficulty................................................................1 (11) a little or some difficulty............................................2 (11) extreme difficulty ......................................................3 = INELIGIBLE (11) you stopped reading because of your eyesight..........4 = INELIGIBLE (11) you stopped reading for other reasons or you are not interested in reading...................................................5 (11) 10b. How much difficulty do you have hearing c onversation partners, when in small groups and there is background noise? (m ultiple conversations, music, white noise) What about with your hearing aid(s) on? Would you say... no difficulty................................................................1 (11) a little or some difficulty............................................2 (11) extreme difficulty ......................................................3 = INELIGIBLE (11) you stopped participating in small group/ social settings because of your hearing......................4 = INELIGIBLE (11) you stopped participating in small group/social settings for other reasons or you are not interested in reading...................................................5 (11) The next few questions are about medical conditions you might have. 11. Has a doctor or a nurse ev er told you that you have: YES NO DK N/A a. Alzheimer's Disease, or dementia 1 = INELIGIBLE 2 8

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135 b. Huntingtons disease, with dementia symptoms 1 = INELIGIBLE 2 8 c. Parkinsons disease with dementia symptoms 1 = INELIGIBLE 2 8 d. Recurring epilepsy? 1 = INELIGIBLE 2 8 e. Stroke? 1 = ASK NEXT QUESTION 2 8 f. [Was it in the past year?] 1 = INELIGIBLE 2 8 7 g. [Do you have limb weakness or paralysis as a result?] 1= INELIGIBLE 2 8 -7 h. heart attack or myocardial infarction? 1 = ASK NEXT QUESTION 2 8 i. [Was it in the past year?] 1 = INELIGIBLE 2 8 7 j. A head injury requiring hospitalization any time in your lifetime? 1= INELIGIBLE 2 8 k. been hospitalized for psychiatric illness at any point in your lifetime, or do you currently have a psychiatric illness? 1= INELIGIBLE 2 8 l. cancer, other than skin cancer, within the past 5 years? 1= ASK NEXT QUESTION 2 (14) 8 (14) m. [Are you currently receiving chemotherapy or radiation treatment for this cancer?] 1= INELIGIBLE 2 8 -7 n. Did you ever receive radiation treatment for a cancer above the chest? 1= INELIGIBLE 2 8 -7 o. Do you have a pacemaker or internal defibrillator? 1= INELIGIBLE 2 8 p. Do you use portable oxygen? 1= INELIGIBLE 2 8

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136 q. Do you take steroids or cortisone? 1= INELIGIBLE 2 8 Meds for Asthm a,OK r. Do you use a cane or walker? 1= INELIGIBLE 2 8 s. Did you ever have medical problems as a consequence of alcohol or drug use? 1= INELIGIBLE 2 8 t. Did you ever have legal problems as a consequence of alcohol or drug use? 1= INELIGIBLE 2 8 u. Did you ever have withdrawal symptoms related to alcohol or drug use? 1= INELIGIBLE 2 8 v. Are you currently on any medication? 1 = ASK NEXT QUESTION 2 8 w. [If known, are you currently on any calcium channel blockers or beta blockers?] List Meds: (Name and purpose) __________________________ __________________________ __________________________ __________________________ __________________________ __________________________ __________________________ __________________________ __________________________ __________________________ __________________________ __________________________ __________________________ __________________________ __________________________ 1= INELIGIBLE Acebutolol (Monitan, Secral) Atenolol (ApoAtenolol, NovoAtenol, Ternormin) Betaxolol (Kerlone) Bisprolol (Zebeta) Carteolol (Cartrol) Labetalol (Normodyne) Oxprenolol (Trasicor, SlowTrasicor) Bepridi (Vascor) Diltazem (Cardizem, Cardizem CD, Cardizem LA, Cardizem SR, Dilacor-XR) Betaxolo (Kerlone) Toprol Norvasc If still unsure, look in book or Rxlist.com (clinical pharmacology) 2 8

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137 APPENDIX D NURSE/PHYSICIAN CHECKLIST AND PERMISSION Project AAMP (Active Adult Mentoring Project) College of Public Health and Health Professions Adrienne Aiken Morgan PO Box 100165 Gainesville, FL 32610-0165 Phone: (352) 273-5098 Please type or print clearly Physicians Name ___________________________ Phone # _______________________ Patients Name ______________________________ (Project AAMP participant) Program exclusion checklis t (please check any that apply to this patient): Terminal illness with lif e expectancy of < 12 months Myocardial infarction in the last 6 months Chronic heart failure (New York Classification III to IV) Aortic stenosis Cardiac arrhythmia Cardiac stent Cardiac arrest Implanted cardiac defibrillator Pacemaker Coronary artery bypass graft History of cardiac arrest Uncontrolled angina Stroke or TIA Peripheral vascular disease Pulmonary disease requiring oxygen or steroid treatment Receiving chemotherapy or radiation for cancers Ambulation with assistive devices Poorly controlled diabetes Smoked regularly (>4 cigarettes per day) in past 3 years Any of the following calcium channel or beta blockerS Beta Blockers Acebutolol (Monitan, Sectral) Atenolol (Apo-Atenolol, Novo-Atenol, Tenormin) Betaxolol (Kerlone) Bisoprolol (Zebeta) Carteolol (Cartrol) Labetalol (Normodyne, Trandate) Oxprenolol (Trasicor, Slow-Trasicor) Calcium Channel Blockers Bepridil (Vascor) Diltiazem (Cardizem, Cardizem CD, Cardizem LA, Cardizem SR, Dilacor-XR) Betaxolo (Kerlone) Note: Trade Names in parenthesis I hereby give my patient permission to: 1. Participate in an exercise program YES NO 2. Complete a health and fitness assessments* YES NO *The fitness assessment includes resting heart rate and blood pressure measurements and an 85% sub-maximal cardiovascular test (heart rate only, no EKG) Special instructions or indicated activities: ___________________________________________________ Contraindications to any activities: _________________________________________________________ ____________________________ ____________ Nurse/Physicians Signature [Required] Date

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138 APPENDIX E STAGES OF EXERCISE CHANGE QUESTIONNAIRE I am now going to ask you a few questions about your recent exercise ha bits. To do this, I will need to read to you a definition of what we m ean by regular exercis e so that we understand each other. Are you ready to hear the de finition? [WAIT UNTIL PARTICIPANT SEEMS ATTENTIVE AND READY TO LISTEN] Regular exercise is any pla nned voluntary physical ac tivity (such as brisk walking, aerobics, jogging, bicycling, swimming, basketball, etc.) perfor med to increase physical fitness. Such activity should be performed 3 to 5 times per week for a minimum of 20 minutes per session Exercise does not have to be pain ful to be effective, but should be done at a level that increases your breathing rate and causes you to break a sweat Is this definition clear to you? [IF YES, CONTINUE. IF NO, CLARIFY ANY CONF USIONS, PROBE FOR EXAMPLES OF ACTIVITIES THEY SUGGEST] (Record Persons report of Routine) _________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ 15a. Based on this definition, do you currently exercise regularly? YES.................................................................1 = INELIGIBLE NO...................................................................2 GO TO QUESTION #15b [QUESTIONS #2 AND #3 ARE CODED AS ONE ITEM] 15b. Do you intend to begin exercising regularly? YES.................................................................1 GO TO QUESTION #15c NO...................................................................2 SKIP QUESTION #15c 15c. Do you intend to begin exercising regularly in the next 30 days or the next 6 months? Next 30 days..1 Next 6 months.2APPENDIX A

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139 APPENDIX F INFORMED CONSENT PLEASE READ T HIS ENTIRE DOCUMENT CAREFULLY TO: All Research Participants FROM: Dr. Peter Giacobbi, Jr. RE: Informed Consent STUDY TITLE: Beliefs, Attitudes, and Impact of an Exercise Program for Older Adults PURPOSE OF THIS STATEMENT: The purpose of this statement is to summarize the study I am conducting, explain what I am asking you to do, and to assure you that the information you and other participants share will be kept confidential to the ex tent permitted by law. Specifically, nobody besides the Principal Investigator and a research assistant will be able to identify you in this study and your name will not be used in any research reports that result from this project. The purpose of this study is to help us understand the attitudes and be liefs adults hold about exercise and physical activity. WHAT YOU WILL BE ASKED TO DO: If you agree to participate in this study, you will be asked to participate in 13 informational sessions during a four-month period. Additionally, you will be asked to complete daily and weekly surveys along with pre and post test assessments (See Table Below). You do not have to answer any question you do not wish to answer and participation in this study will not affect your membership with the Living Well Center. In addition to the interview data, we will collect information about your height, weight, and body com position prior to interviews one and eight. You will also be asked to wear an RT3 Accelerometer and/or a pedometer. These devices are safe to wear and about the size, shape, and weight of a standard pager. Daily Measures Weekly Measures Pre/Post Measures Leisure Time Exercise Questionnaire physical activity Sleep Diary quality and quantity of sleep Wearing an RT3 accelerometer/pedometer which measures daily physical activity Symbol Digit Number Comparison Letter Series Positive and Negative Affect Scale general mood Reaction Time Task reaction time General Self-efficacy Scale overall sense of confidence Sleep Self-efficacy Scale how confident you can fall asleep and sleep effectively Exercise Self-efficacy Scale how confident you can perform exercise tasks Barriers Self-efficacy Scale how confident you can overcome barriers to exercise Letter-Number sequencing Blood Pressure Readings Task Modification Stair Climbing Late Life FDI how well you can perform everyday tasks Trail Making Test A and B N-Back your attention, memory capability, and how quickly you can process information Controlled Word Association language ability Logical Memory of WMS-III how well you can remember words Geriatric Depression Semi-structured interview about your exercise experiences Balke Protocol general measure of physical fitness State-Trait Anxiety Inventory Exercise Motivation Scale measures your motivation to exercise Task Modification Tasks

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140TIME REQUIRED: Your participation in this study will take place over a 14-week period. This will include an initial 45-minute session during the first week. Then we will meet for seven 45-minute interviews, one per week. The daily surveys should ta ke approximately 5 minutes to complete while the weekly surveys will take 20 to 30 minutes. The Pre/Post surveys will take approximately 30 minutes during each time period (the beginning and end of the study). RISKS AND BENEFITS: There are no known risks expected from pa rticipating in this study. As a result of your participation, you may develop insi ghts about yourself that could help your future development in physical activity settings. If as a resu lt of taking any of these surveys you wish to discuss anything with a counselor we will provide appropriate re ferrals to local agencies that may assist you (e.g., Alachua County Mental Health Agency). COMPENSATION: In exchange for your participation in this study you will be given access to the Living Well Faculty Fitness Center. No other compensation will be provided. CONFIDENTIALITY: Your identity will be kept confidential to the extent provided by law. Your completed survey will be assigned a code number and all surveys will be kept in my office (Room 124 Florida Gym) in a locked file cabinet. Your name will not be used in any report. VOLUNTARY PARTICIPATION: Your participation in this study is completely voluntary. There is no penalty for not participating. RIGHT TO WITHDRAW: You have the right to withdraw from the study at anytime without consequence. WHOM TO CONTACT IF YOU HAVE QUESTIONS ABOUT THIS STUDY: Dr. Peter Giacobbi, Jr., Department of Exercise and Sport Sciences, 100 Florida Gym, PO Box 118207, Gainesville, FL, 32611; ph. (352) 392-0584 WHOM TO CONTACT ABOUT YOUR RIGHTS AS A RESEARCH PARTICIPANT IN THE STUDY: UFIRB Office, Box 112250, University of Florida, Gainesville, FL 32611-2250; ph. 392-0433. AGREEMENT: I have read the procedure described above. I voluntarily agree to participate in the procedure and I have received a copy of this description. Participant:_____________________________________________Date:___________ Principal Investigator:____________________________________Date:___________

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141 APPENDIX G QUALITY CONTROL CHECKLIST: TREATMENT GROUP Mentor/Coach ______________________ Date ____________ Procedure None Part Full Score CREATING A SUPPORTIVE ENVIRONMENT 1. Reviews participants exercise during previous week. 0 5 10 _____ 2. Use of Open-Ended Questions ex.: In what ways has exercise been helpful to you? 0 5 10 _____ 3. Use of Affirmations ex.: You are a very co nscientious person. That quality will help you to begin an exercise program. 0 5 10 _____ 4. Use of Reflective Responses ex.: It sounds like you are frustrated. How do you deal with that? 0 5 10 _____ 5. Use of Summary Statements 0 5 10 _____ 6. Effective group management Keeps group on topic Manages time and pace of discussion Maintains leadership of discussion 0 5 10 _____ COMMUNICATION ROADBLOCKS 7. Avoids Lecturing 0 10 20 _____ 8. Avoids giving advice 0 10 20 _____ 9. Avoids interpreting or analyzing 0 10 20 _____ 10. Avoids questioning participant 0 10 20 _____ CONCLUSION 11. Assures participant that all instructions are in the Workbook & reminds to bring all logs and complete any homework for next session. 0 5 10 _____ 12. Makes appropriate referrals regarding questions participants may have about the study. 0 5 10 _____ 13. Makes appropriate referrals regarding mental or physical health concerns obse rved during sessions. 0 5 10 _____ Comments for Mentor: Total __________

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142 Session Specific Evaluations Procedure None Part Full Score GOAL SETTING SESSION 7. Use of OARS during discussions about goals 0 5 10 _____ 8. Distinguishes between longand short-term goals 0 5 10 _____ 9. Clearly discusses SMART goals 0 5 10 _____ 10. Encourages participants to set SMART goals in a non-judgmental manner. 0 5 10 _____ 11. Gives examples of SMART goals 0 5 10 _____ Comments for Mentor: Total __________ Mental imagery SESSION 12. Integrates mental imagery into discussions using OARS. 0 5 10 _____ 13. Makes connections between fitness/health goals and mental imagery with op en-ended questions. 0 5 10 _____ 14. Gives examples of vivid images that evoke all five senses and feelings/emotions. 0 5 10 _____ Comments for Mentor: Total __________

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143 APPENDIX H QUALITY CONTROL CHECKLIST: CONTROL GROUP Mentor/Coach ______________________ Date ____________ Procedure None Part Full Score GROUP MANAGEMENT 1. Begin sessions with discussion about last weeks topic, including quiz. 0 5 10 _____ 2. Keeps group on topic 0 5 10 _____ 3. Manages time and pace of discussion 0 5 10 _____ 4. Maintains leadership of discussion 0 5 10 _____ PRESENTATIONAL SKILLS 5. Use of Open-ended questions 0 5 10 _____ 6. Clearly presents topic to be discussed 0 5 10 _____ 7. Promotes discussion by asking questions 0 5 10 _____ 8. Actively makes efforts to include all members of the group into discussion. 0 5 10 _____ COMMUNICATION ROADBLOCKS 9. Avoids Lecturing 0 10 20 _____ 10. Avoids giving advice 0 10 20 _____ 11. Avoids interpreting or analyzing 0 10 20 _____ 12. Avoids questioning participant 0 10 20 _____ CONCLUSION 13. Assures participant that all instructions are in the Workbook & reminds to bring all logs and complete any homework for next session. 0 5 10 _____ 14. Makes appropriate referrals regarding questions participants may have about the study. 0 5 10 _____ 15. Makes appropriate referrals regarding mental or physical health concerns observ ed during sessions. 0 5 10 _____ Comments for Mentor: Total __________

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144 APPENDIX I BARRIERS SELF-EFFICACY The f ollowing items reflect situations that are listed as common reasons for preventing individuals from participating in exercise se ssions or, in some cases, dropping out. Using the scales below please indicate how confident you are that you could exercise in the event that any of the following circumstances were to occur. Please indicate the degree to which you are co nfident that you could exercise in the event that any of the following circumstances were to occur by circling the appropriate %. Select the response that most closely matches your own, remembering that there are no right or wrong answers. For example, in question #1 if you have complete confidence that you could exercise even if the weather was very bad, you would circle 100%. If, however, you had no confidence at all that you could exercise, if you failed to make or continue making progress (that is, confidence you would not exercise), you would circle 0%. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% NOT AT ALL MODERATELY HIGHLY CONFIDENT CONFIDENT CONFIDENT I BELIEVE THAT I COULD EXERCISE 3 TIMES PER WEEK FOR THE NEXT 3 MONTHS IF: 1. The weather was very bad (hot, humid, rainy, cold). 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2. I was bored by the program or activity. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 3. I was on vacation. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 4. I was not interested in the activity. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 5. I felt pain or discomfort when exercising. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

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145 Mark your answer by circling a %. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% NOT AT ALL MODERATELY HIGHLY CONFIDENT CONFIDENT CONFIDENT I BELIEVE THAT I COULD EXERCISE 3 TIMES PER WEEK FOR THE NEXT 3 MONTHS IF: 6. I had to exercise alone. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 7. It was not fun or enjoyable. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 8. It became difficult to ge t to the exercise location. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 9. I didn't like the particular activity program that I was involved in. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 10. My schedule conflicted w ith my exercise session. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 11. I felt self-conscious about my appearance when I exercised. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 12. An instructor does not offer me any encouragement. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 13. I was under personal stress of some kind. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

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146 APPENDIX J EXERCISE SELF-EFFICACY The item s listed below are designed to assess your beliefs in your ability to continue exercising on a three time per week basis at moderate intensities (upper end of your perceived exertion range), for 40+ minutes per session in the future. Usi ng the scales listed below please indicate how confident you are that you will be able to continue to exercise in the future. For example, if you have complete confidence th at you could exercise th ree times per week at moderate intensity for 40+ minutes for the next four weeks without quitting, you would circle 100% However, if you had no confidence at all that you could exercise at your exercise prescription for the next four w eeks without quitting, (that is, conf ident you would not exercise), you would circle 0% Please remember to answer honestly and accura tely. There are no right or wrong answers. Mark your answer by circling a %: 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% NOT AT ALL MODERATELY HIGHLY CONFIDENT CONFIDENT CONFIDENT 1. I am able to continue to exercise three times per week at moderate intensity, for 40+ minutes without quitting for the NEXT WEEK 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2. I am able to continue to exercise three times per week at moderate intensity, for 40+ minutes without quitting for the NEXT TWO WEEKS 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 3. I am able to continue to exercise three times per week at moderate intensity, for 40+ minutes without quitting for the NEXT THREE WEEKS 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 4. I am able to continue to exercise three times per week at moderate intensity, for 40+ minutes without quitting for th e NEXT FOUR WEEKS 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 5. I am able to continue to exercise three times per week at moderate intensity, for 40+ minutes without quitting for the NEXT FIVE WEEKS 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

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147 Please remember to answer honestly and accura tely. There are no right or wrong answers. Mark your answer by circling a %: 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% NOT AT ALL MODERATELY HIGHLY CONFIDENT CONFIDENT CONFIDENT 6. I am able to continue to exercise three times per week at moderate intensity, for 40+ minutes without quitting for the NEXT SIX WEEKS 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 7. I am able to continue to exercise three times per week at moderate intensity, for 40+ minutes without quitting for the NEXT SEVEN WEEKS 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 8. I am able to continue to exercise three times per week at moderate intensity, for 40+ minutes without quitting for the NEXT EIGHT WEEKS 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%APPENDIX B

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148 APPENDIX K EXERCISE MOTIVATION SCALE Why Are You Currently Participating In This Activity? Direction : P lease read each of the statements listed below and indicate how strongly you agree or disagree with each statement by circling the appropriate response to the right of the statement. Use the following response categories: Strongly disagree Disagree Moderately disag ree Moderately agree Agree Strongly agree (SD) (D) (MD) (MA) (A) (SA) 1 2 3 4 5 6 SD D MD MA A SA 1. For the pleasure it gives me to experience positive sensations from the activity. 1 2 3 4 5 6 2. For the satisfaction it gives me to increase my knowledge about this activity. 1 2 3 4 5 6 3. Because other people believe that its a good idea for me to exercise. 1 2 3 4 5 6 4. Because I must exercise to feel good about myself. 1 2 3 4 5 6 5. Because I believe that regular exercise is a good way to enhance my overall development. 1 2 3 4 5 6 6. Because it is consistent with what I value. 1 2 3 4 5 6 7. I cant understand why I am doing this. 1 2 3 4 5 6 8. Because I feel pressure from others to participate. 1 2 3 4 5 6 9. Because I think that exercise allows me to feel better about myself. 1 2 3 4 5 6 10. For the pleasure I experience while learning about this activity. 1 2 3 4 5 6 11. For the satisfaction I feel when I get into the flow of this activity. 1 2 3 4 5 6 12. Because I feel I have to do it. 1 2 3 4 5 6

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149 Why Are You Currently Partic ipating In This Activity? SD D MD MA A SA 13. To satisfy people who want me to exercise. 1 2 3 4 5 6 14. Because exercising is an important aspect of how I perceive myself. 1 2 3 4 5 6 15. For the pleasure of understanding this activity. 1 2 3 4 5 6 16. I have no idea. 1 2 3 4 5 6 17. For the pleasure of mastering this activity. 1 2 3 4 5 6 18. Because I think it is a good thing for my personal growth. 1 2 3 4 5 6 19. For the pleasure I experience when I feel completely absorbed in the activity. 1 2 3 4 5 6 20. For the satisfaction I feel while I try to achieve my personal goals during the course of this activity. 1 2 3 4 5 6 21. Because I would feel guilty if I did not take the time to do it. 1 2 3 4 5 6 22. Because I value the way exercise allows me to make changes in my life. 1 2 3 4 5 6 23. It is not clear to me anymore. 1 2 3 4 5 6 24. Because I think exercise contributes to my health. 1 2 3 4 5 6 25. To comply with expectations of others (e.g., friends). 1 2 3 4 5 6 26. For the enjoyment that comes from how good it feels to do the activity. 1 2 3 4 5 6 27. Because I enjoy the f eelings of discovering more about this activity. 1 2 3 4 5 6 28. Because I enjoy the feelings of improving through participating in this activity. 1 2 3 4 5 6

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150 Why Are You Currently Partic ipating In This Activity? SD D MD MA A SA 29. Because I feel that changes that are taking place through exercise are becoming part of me. 1 2 3 4 5 6 30. For the pleasure I experience while trying to become the person I want to be. 1 2 3 4 5 6 31. Because I would feel ashamed if I was not doing anything to improve my current situation. 1 2 3 4 5 6 Factor Scoring Amotivation = Items of 7, 16, 23 External regulation = Items of 3, 8, 13, 25 Introjected regulation = Items of 4, 12, 21, 31 Identified regulation = Items of 5, 9, 18, 24 Integrated regulation = Items of 6, 14, 22, 29 IM to learn = Item of 2, 10, 15, 27 IM to accomplish things = Items of 17, 20, 28, 30 IM to experience sensations = items of 1, 11, 19, 26

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151 APPENDIX L PHYSICAL ACTIVITY DAILY RECORD ID: ___________________ (Indicate Date mm /dd/yy) __________________________________________________ Pedometer Reading: ____________ Please answer this questionnaire WHEN YOU AWAKEN IN THE MORNING Please enter yesterday's day and date above, and provide the information requested below. Instructions This is a scale that measures your leisur e time exercise (i.e., exercise that was done during your free time). During the past 24 hours please indicate how many times you have engaged in strenuous, moderate, and mild exercise more than 20 minutes during your free time. Indicate how many times you did this activity for 20 minutes or longer in the past 24 hours : # times 1. Strenuous exercise : heart beats rapidl y (e.g., running, basketball, jogging, hockey, squash, judo, roller skating, vigorous sw imming, vigorous long distance bicycling, vigorous aerobic dance classe s, heavy weight training) 2. Moderate exercise: not exhausting, light sweating (e.g., fast walking, baseball, tennis, easy bicycling, volleyball, badminton, easy swimming, popular and folk dancing) 3. Mild exercise : minimal effort, no sweating (e.g., easy walking, yoga, archery, fishing, bowling, lawn bowling, shuffleboard, horseshoes, golf) 4. Total Number of exercise: add the number of strenuous, moderate, and mild exercise and write that number to the right.

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152 APPENDIX M CARDIORESPIRATORY FITNESS Participant Na me: _______________________ Which test is this? (circle one) Participant ID: _________ PRETEST POSTTEST Patient Characteristics Heart Rate Calculations Interval Conditions Age = Max Age (220-age) = Time (min.) = Height (ft in) = 85% Max HR = Grade (%) = Weight (lbs.) = VO2max = Speed (mph) = Variables: Speed (m/min) = 26.8 x mph = Final Grade = Grade at max = VO2max = (0.1 x speed) + (1.8 x Final Grade x Speed) + 3.5

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166 BIOGRAPHICAL SKETCH I, Matthew Bum an, was born in San Diego, California. I received my Bachelor of Science from the University of Utah in 2002 in exerci se and sport science and psychology. I received my Master of Science in counseling psychology from Springfield College in 2004. Since then, I have been working toward my Ph.D. degree in exerci se and sport psychology, focusing in the area of physical activity and aging. In August 2008 my wife Christen Buman, and I, will be moving to Palo Alto, California where I will begin a postdoc toral fellowship at the Stanford University Prevention Research Center. Christen and I are thrilled to begin a new chapter in our lives, but are sad to be leaving the University of Florida and the city of Gainesville. The UF community has been very good to us, and we are proud to remain a part of the Gator Nation.

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