• We are sorry, but NCBI web applications do not support your browser and may not function properly. More information
Logo of amjphAmerican Journal of Public Health Web SiteAmerican Public Health Association Web SiteSubmissionsSubscriptionsAbout Us
Am J Public Health. 2006 July; 96(7): 1201–1209.
PMCID: PMC1483857

Results of the First Year of Active for Life: Translation of 2 Evidence-Based Physical Activity Programs for Older Adults Into Community Settings


Objectives. Translating efficacious interventions into practice within community settings is a major public health challenge. We evaluated the effects of 2 evidence-based physical activity interventions on self-reported physical activity and related outcomes in midlife and older adults.

Methods. Four community-based organizations implemented Active Choices, a 6-month, telephone-based program, and 5 implemented Active Living Every Day, a 20-week, group-based program. Both programs emphasize behavioral skills necessary to become more physically active. Participants completed pretest and posttest surveys.

Results. Participants (n=838) were aged an average of 68.4 ±9.4 years, 80.6% were women, and 64.1% were non-Hispanic White. Seventy-two percent returned posttest surveys. Intent-to-treat analyses found statistically significant increases in moderate-to-vigorous physical activity and total physical activity, decreases in depressive symptoms and stress, increases in satisfaction with body appearance and function, and decreases in body mass index.

Conclusions. The first year of Active for Life demonstrated that Active Choices and Active Living Every Day, 2 evidence-based physical activity programs, can be successfully translated into community settings with diverse populations. Further, the magnitudes of change in outcomes were similar to those reported in the efficacy trials.

A comprehensive review concluded that individually adapted behavior-change programs are effective and strongly recommended for increasing physical activity at the community level.1 The efficacy of these programs has been documented in older populations,2,3 yet evidence-based physical activity interventions have not been widely applied and tested in public health practice and other nonresearch settings.4 It is unclear whether evidence-based programs can produce similar outcomes and reach broad target populations when delivered in community practice settings.5 It is necessary to understand and evaluate translational efforts of efficacious physical activity interventions58 to determine their public health impact.9

The National Blueprint10 was developed in 2001 with input from 46 organizations. It outlines broad strategies that are expected to increase physical activity among older adults. Despite being an influential document in the area of physical activity and aging, physical activity programming for older adults in the community is typically not evidence-based, does not incorporate behavior-change theory, and does not include evaluation.11 Increased physical activity at the population level is only likely to occur when efficacious interventions are translated for widespread use in community settings.6

Active for Life was designed to address major gaps in the science and practice literatures by examining the translation of 2 efficacious, theory-based12,13 physical activity interventions to community settings. The primary aims of the outcome evaluation were (1) to evaluate 2 evidence-based physical activity interventions on self-reported physical activity in community settings, (2) to evaluate the impact of these interventions on specific quality-of-life outcomes related to physical activity, and (3) to determine whether the program reaches a more diverse sample of older adults than was reached in controlled research settings. It was hypothesized that both physical activity programs would lead to increased physical activity and improved physical activity–related outcomes and that Active for Life would recruit a more diverse sample compared with the evidence-based programs on which it was based. The outcomes for the first year are described.


Program Overview

Detailed information about Active for Life can be found at http://www.activeforlife.info. Two evidence-based programs, Active Choices (AC; Stanford University) and Active Living Every Day (ALED; The Cooper Institute, Dallas, Tex, and Human Kinetics Inc, Champaign, Ill), were selected for translation and evaluation. These 2 programs met the criteria of (1) being successful in increasing physical activity in adults aged 50 years and older, (2) being based on behavioral theories, (3) including intervention manuals, and (4) having been tested in multiple settings with a variety of participants. Both programs help participants develop behavioral skills needed to build moderate physical activity into their daily lives.

Active Choices is a 6-month program delivered through 1 face-to-face meeting followed by one-on-one telephone counseling.1417 Participants receive biweekly telephone calls for the first 2 months and monthly telephone calls for the last 4 months (up to 8 calls total). Counseling is tailored to the person’s readiness for change12 and emphasizes key social cognitive theory13 constructs (e.g., social support, self-regulation, self-efficacy).

Active Living Every Day is a 20-week program delivered in a group setting.18,19 Participants come together to learn behavior-change principles consistent with social cognitive theory13 and the transtheoretical model.12 Participants are encouraged to provide support to one another and share successes and challenges. Applicant sites chose which program they wanted to implement, then justified their choice in a grant award competition (473 applications were received).

Grantees and Intervention Staff

The 9 successful grantees demonstrated their ability to recruit a diverse sample and participate in an evaluation, as well as the capacity to implement a large-scale program. Characteristics of these grantees are shown in Table 1 [triangle]. Grantees have local community partners who inform recruitment and programmatic activities. These partnerships allow grantees to enhance the representativeness of populations recruited. Most grantees had offered some type of physical activity programs previous to Active for Life.

Description of the Active for Life Grantees, Recruitment Target Populations, and Most Common Recruitment Strategies, by Program Type

Each grantee strived to recruit 100 participants in the first year of the study (beginning March 2003). Over the 4-year period, we expect that each grantee will recruit approximately 900 participants into Active for Life, for a total of approximately 8000 participants across communities.

Intervention staff were employed by the lead organization and trained and certified by program developers who were also available throughout the project to provide technical assistance. The National Program Office provided technical assistance for program marketing, recruitment, budget management, and sustainability planning. A detailed process evaluation tracks intervention fidelity and use of technical assistance and will be reported in a future paper.


Recruitment strategies during the first year were tailored by grantees to the communities they targeted (Table 1 [triangle]). Participants had to be 50 years or older (no upper limit), sedentary or underactive, and free of medical conditions or disabilities that required higher levels of supervision. Each grantee was required to design and implement a risk-management strategy that included risk assessment, participant education, and health care provider involvement as appropriate (before and during the program).

All sites used the revised Physical Activity Readiness Questionnaire20 (PAR-Q) for risk assessment. All sites educated participants on risks of physical activity and methods to minimize risks (also included in the consent form) and included safety tips and information on warning signs and symptoms. All sites advised participants to start at a low level of activity and gradually increase volume of activity over time to reduce injury risks. At 2 sites, participants who endorsed PAR-Q items were required to obtain written medical approval before starting the program. At the other sites, participants were advised to discuss positive PAR-Q items with their health care provider, but were not routinely required to obtain written medical approval. Program directors had the discretion to require medical approval if deemed appropriate. This type of approach has been shown to result in few adverse events.21


Participants reported their sociodemographic and health-related variables, including date of birth (to compute age), race, ethnicity, marital status, gender, years of formal education, income, height and weight (to compute body mass index [BMI]22 in kg/m2), health rating, and presence of chronic health conditions.23

Physical activity was determined with the Community Healthy Activities Model Program for Seniors (CHAMPS), a 41-item self-report measure.24 It includes activities typically undertaken by older adults for exercise, activities undertaken in the course of their day that are physical in nature, and recreational activities that provide physical activity. Minutes per week spent in moderate- and vigorous-intensity physical activities and in light-, moderate-, and vigorous-intensity physical activities (all physical activities) were derived. The CHAMPS measure has strong psychometric properties, including demonstrated validity,25 test–retest reliability,25 and sensitivity to change.16,17,24,26,27 Underactive participants (as defined below) have been shown to report an average of about 3.0 hours per week of moderate- and vigorous-intensity physical activity from all sources.15 A secondary 3-item measure23 assessed participation, frequency, and duration of moderate- and vigorous-intensity physical activities to classify participants as sedentary (<10 min/wk), regularly active (≥ 30 min/d, ≥ 5 d/wk), or underactive (not meeting criteria for sedentary or regularly active) according to current recommendations.28

For a measure of depressive symptoms, participants completed the widely used 10-item Center for Epidemiological Studies Depression Scale (CES-D).2931 Participants rated the frequency with which they experienced symptoms of depression during the past week. Possible scores can range from 0 to 30 with higher scores indicating greater depressive symptoms.

Participants also completed the 4-item version of the Perceived Stress Scale,32,33 an extensively used questionnaire that was designed to measure the degree to which situations in one’s life are appraised as stressful. Possible scores can range from 0 to 16 with higher scores indicating greater perceived stress.

In addition, a scale developed as part of the Activity Counseling Trial34 measured satisfaction with body function and appearance. Participants rated their satisfaction over the past 4 weeks with 9 aspects of their body’s appearance and function (e.g., overall level of physical fitness, muscle tone, shape, etc.) on a 7-point scale. Two subscales were derived from factor analysis: satisfaction with body appearance (3 items) and satisfaction with body function (6 items). Possible scores range from −9 to +9 and −18 to +18, respectively, with higher scores indicating greater satisfaction.

Design and Procedure

The original evidence-based studies of AC and ALED used rigorous randomized controlled designs. Active for Life was not designed to demonstrate efficacy of AC or ALED nor to compare the 2 programs, but, rather, to study translation of research to practice by examining whether the magnitude of effects was comparable in community settings to what has been reported in controlled research settings and to examine the representativeness (or reach) of the sample. Thus, a randomized controlled design was not deemed necessary or appropriate for these community-based settings (see Estabrooks and Gyurcsik9 for a discussion of the evaluation of translational projects). Instead, a pre–post design was used.

All participants completed an informed consent form that was approved by the institutional review boards of the University of South Carolina and Texas A&M University and review boards or legal departments at the local grantee organizations. Participants then completed a brief questionnaire that collected personal and demographic information. Finally, participants were given the pretest survey and a postage-provided envelope. Staff was available to assist participants with lower literacy levels and cognitive or sensory impairments. In some instances where literacy levels were low among most participants, staff members read survey items out loud so that surveys could be completed in a group setting with less reliance on reading. Participants were encouraged to complete the surveys without input from or discussion with other group members. Surveys were completed and mailed directly to the evaluation team, which reviewed them for completeness, accuracy, and potential response-set biases. Participants with these types of problems were contacted via telephone for clarification.

Start and end dates were used to track dates for when to send the posttest surveys. One ALED grantee that served a low literacy population administered posttest surveys during the 19th or 20th group session. For the remaining 8 grantees, the posttest surveys were sent directly to participants, along with postage-paid envelopes, 2 weeks before the scheduled completion of their program. Postcard reminders and a second survey were sent to nonresponders. Participants who returned their pretest and/or posttest survey entered a drawing for a $20 gift card to a local retail store (1-in-25 chance).

Statistical Analyses

To address changes from pretest to posttest in hours per week of moderate- and vigorous-intensity physical activity, total physical activity, depressive symptoms, perceived stress, satisfaction with body function and appearance, and BMI, repeated measures analyses of covariance (using SAS Proc Mixed software [SAS Institute Inc, Cary, NC]) were conducted that controlled for site clustering, program (AC vs ALED), race (White vs ethnic minority), gender, BMI, health rating, and education (high school or fewer years vs some college or college graduate). Moderate- and vigorous-intensity physical activity and total physical activity were highly positively skewed at pretest and somewhat skewed at posttest. A square root transformation was successfully used to normalize these variables.

Participants were recruited from 12 geographic sites; therefore, all change analyses took into account this site-level clustering and accounted for the nesting of sites within programs. All change analyses had 10 degrees of freedom.

Two types of change analyses were conducted. First, we examined changes among participants who had both pretest and posttest data available (complete data). Because of missing data for variables of interest, 33 of the 608 who completed posttests were excluded from these analyses, leaving a sample size of 575. Second, we conducted more conservative analyses using an intent-to-treat model. Because there is no control group in Active for Life, these analyses assumed no change in outcomes among those who did not return posttest surveys by carrying forward baseline values. Because of missing data for variables of interest, 49 of the 847 participants were excluded from these analyses, leaving a sample size of 798. Both types of analyses were also conducted separately for AC and ALED.

An effect size (d=[posttest mean – pretest mean]/pretest standard deviation) using means that were adjusted for site clustering, program, race, gender, BMI, health rating, and education was computed for each outcome.35 Effect sizes of d=0.2 were considered small; d=0.5, medium; and d=0.8, large.36


Description of the Sample

A total of 838 participants were recruited into Active for Life in the first year and completed pretest surveys. Recruitment numbers ranged from 76 to 107 across grantees. The screening and recruitment numbers are shown in Figure 1 [triangle]. Participant characteristics for the entire sample and separately by program are reported in Table 2 [triangle].

Recruitment and retention of participants into the Active for Life program.
Unadjusted Baseline Demographic and Health-Related Characteristics of Active for Life Participants Overall (n = 838) and by Program Type

On average, AC participants were significantly younger, heavier, and more physically active than ALED participants. Active Choices participants were also more likely than ALED participants to be non-White or Hispanic and less likely to have had a stroke. Consistent with eligibility and screening, most participants were sedentary (43.1%) or underactive (45.1%).

Analysis of Survey Nonresponders and Participant Withdrawals

A total of 608 participants (275 AC and 333 ALED) returned their posttest surveys (72% response rate). In data not shown but available upon request, participants who returned their posttest surveys were more likely to be older, White, married, and to have arthritis and osteoporosis compared with participants who did not return posttest surveys. Responders also had lower scores on depressive symptoms and perceived stress, and reported better overall health. Across both programs, responders were more likely to be White with fewer depressive symptoms. Among AC participants, responders were also more likely to be leaner, older, to have osteoporosis, and to report lower perceived stress. Among ALED participants, responders were more likely to be married, healthier (perceived rating), to have higher income, and to have arthritis. All P values were less than .05.

Sixty-nine participants (25 AC and 44 ALED; 8.2%) actively withdrew from the program or the program and evaluation. Non-Hispanic White participants were more likely than participants from ethnic minority groups to withdraw (10.0% vs 5.6%; P= .02), and people with arthritis were less likely to withdraw (5.8% vs 11.4%; P < .01). No other variables were associated with study withdrawal (data not shown).

Change Analyses

Table 3 [triangle] presents adjusted mean scores on the outcome variables for pretest and posttest and effect sizes for these changes for the entire sample and separately by program. In analyses that used complete data (n = 575), participants reported a significant increase from pretest to posttest in moderate- and vigorous-intensity physical activity (t10 = 11.67; P < .0001) and total physical activity (t10 = 13.53; P < .0001). Participants reported improvements in satisfaction with body appearance (t10 = 11.22; P < .0001), body function (t10 = 10.39; P < .0001), depressive symptoms (t10 = −3.14; P < .05), and perceived stress (t10 = −2.90; P < .05). Finally, BMI decreased (t10 = −5.19; P < .001). When analyzed separately by program, all effects remained statistically significant with 2 exceptions: depressive symptoms and perceived stress for AC participants. The intent-to-treat analyses yielded the same findings, as shown in Table 3 [triangle], although the magnitude of the changes was expectedly somewhat smaller.

Effect Sizes (d) and Adjusted Mean Pre- and Posttest Scores, With Standard Errors, for the Primary Outcomes of Interest, Complete Data, and Intent-to-Treat Analyses, by Program Type


Despite the availability of effective programs to increase physical activity in older adults, few evidence-based programs have been translated into community settings. Increasingly, researchers are being asked to document the public health implications of effective programs and move beyond purely efficacy-based studies,46,8,37 and projects like Active for Life are responsive to this call.

When implemented in diverse community settings by trained staff, both AC and ALED increased physical activity and the magnitude of increase (effect size) was comparable to that seen in the randomized trials on which they were based, where d= 0.38 to d= 0.64, depending on the outcome.15,19,24 In Active for Life, moderate- and vigorous-intensity physical activity increased by 2.12 hours per week (d = 0.50) and total activity increased by 3.84 hours per week (d= 0.51), translating into comparable medium effects. Both programs emphasize building routine lifestyle activity into one’s day, thus explaining the increases seen in both low- and moderate-intensity physical activity. It is important to note that the randomized trials followed participants for longer periods of time, and it is not known whether the changes reported at the completion of Active for Life were maintained over time.

Physical activity–related quality-of-life outcomes also improved as a result of the programs. Satisfaction with body function, a domain particularly relevant to older adults who are at increased risk of functional decline,38 moderately improved from pretest to posttest (d= 0.45). As is common in participants who volunteer for research studies, Active for Life participants had low levels of depressive symptoms and perceived stress, and, therefore, these measures were subject to floor effects.39 Furthermore, those with higher levels of depressive (for AC and ALED) and stress-related symptoms (for AC) were less likely to return posttest surveys. The smaller improvements in mental health domains are consistent with the literature on evidence-based physical activity programs. In addition, although reductions in depressive symptoms and perceived stress were significant for ALED, they were not for AC. These findings for AC are inconsistent with 2 previous studies of middle-aged and older adults15,40 and are unlikely to be caused by format differences. Findings might be attributed to lower statistical power in AC (fewer sites and participants), differential nonresponse (AC participants reporting greater stress were less likely to return a postprogram survey), or other differences in AC versus ALED participants in this particular study.

It is important for translational projects to extend evidence-based findings to more representative populations. Like most controlled research on interventions for physical activity, the original randomized trials of AC and ALED recruited sedentary or underactive older adults. However, these participants were more than 80% White, highly educated with few chronic health conditions, and, for ALED, relatively young (35–60 years).1416,18 Thus, these studies are high in internal validity but weaker in external validity. The first year of Active for Life has addressed external validity of AC and ALED, because targeted samples were enrolled that were similar to the older US population for race41 (with oversampling of Black and undersampling of White participants), chronic health conditions,42,43 and health ratings.42,43 Participants were more likely to be obese and less likely to be normal weight than the older US population.42 Although participants had higher educational levels relative to the older US population,42,43 they were less educated than participants in the AC and ALED randomized trials.

One constraint on representativeness is that individuals who seek out such programs tend to be more motivated for change. However, in the real world it is precisely such individuals that would make use of this kind of programming, and the Active for Life grantees were diverse, real-world organizations. Thus, this study extends the literature by moving from efficacy toward effectiveness and allowing us to generalize to a population that is more representative of those who could realistically be reached by physical activity programs.

Several limitations should be considered when interpreting our findings. To reduce grantee and participant burden, we relied exclusively on self-report data. Although a weakness, the primary study outcome (CHAMPS) correlated moderately with objective physical activity measures, objective measures of physical functioning, and quality of life in other studies.22,25,27 The evidence-based studies of AC and ALED used subjective as well as objective measures of physical activity, fitness, and functional performance and showed similar increases in these objective measures.1416,18 Social desirability is the major concern related to self-report data. Yet, our pattern of results does not support a social desirability bias. Because all measures were self-reported, one might expect similar effect sizes across outcomes. However, effect sizes varied by outcome and were consistent with the literature.

A second limitation is that participants who returned posttest surveys differed from those who did not (28% of the sample). Responders were significantly older, White, and college educated; more likely to have arthritis and osteoporosis yet rate their health as better; and less likely to report depressive symptoms and stress than nonresponders. If participants with greater symptoms of depression and stress remained in the study, it is likely that they would have benefited to an even greater extent than participants observed given that reductions in depression and stress were found even among a population with very low levels of these symptoms. When considering the translation of these programs into the community, the evidence base for the types of individuals who did not return surveys may not be as strong as for those who did. Future papers will examine whether intervention effectiveness differed by participant health and sociodemographic characteristics.

The third major limitation is that data regarding the maintenance of behavior change were not collected. Finally, it is not clear whether small, community-based organizations with fewer resources would show comparable success in implementing these programs.

These limitations notwithstanding, our findings have implications for community-based practice and translational research. Public health practitioners and health care providers can be assured that older adults can and do change as a result of behavioral interventions. Active for Life is also responsive to a call to conduct research that has greater public health relevance.46,8,37

Although efficacy studies are a critical first step in the research process, all too often effective behavioral programs do not impact communities because they are never translated broadly. Active for Life will reach at least 8000 diverse older adults over a 4-year period, and the community organizations funded in this initiative are currently working on sustainability plans to ensure continued reach over time. Active for Life may serve as a useful model regarding how to translate evidence-based programs and how to evaluate the translational process. Future papers will focus on recruitment, intervention moderators (including environmental factors), process evaluation, and sustainability.


Active for Life is funded by The Robert Wood Johnson Foundation. The findings and conclusions in this report are those of the authors and do not necessarily represent the views of The Robert Wood Johnson Foundation, the Centers for Disease Control and Prevention, or other institutions affiliated with the authors.

We gratefully acknowledge the many participants who took part in the first year of the Active for Life program and evaluation. We also acknowledge the involvement and significant contribution of the following program managers, facilitators/health educators, and staff at each of the grantee sites and organizations involved in Active for Life during the first year: Barbara E. Ainsworth, Vernice Davis Anthony, Jan Arnold, Vanessa Batt, Peter Bense, Susan Binkert, Esther Bishop-Files, Paul Bridgewater, Christine Bruchac, Karen Calhoun, Marvin Cato, Fabiana Cheistwer, Kate Clayton, Mary Cocanougher, Teresa Cutts, Annemarie DeFazio, Mia Earl-Clemmons, Doris Y. Estremera-Rohleder, Erica Frechman, Julie Freelove-Charton, Maude Freeman, Mary Ganzel, Russell E. Glasgow, Doris Grider, Lisa Groce, Michele Guerra, Isabel R. Guerrero, Lisa G. Hartsock, Katya Henriquez, Sharlene P. Hirsch, Justine Kaplan, Marcia Kerz, Lisa M. Klesges, Deborah Kosmont, Elise Krumholz, Diana Lattimore, Jassen Lanfair, Fran Lewickyj, H. Michelle Maloney, Angela Merlo-Rains, Cindy Merrins, Martha Milk, Dorothy Morgan-Quelch, Mindy Morgen, Michael C. Nabors, Cheryl Roberts Oliver, Russell Pate, Alisa Phillips, Shirley Pogue, Michelle D. Rice, Juan Rivas, Brigid Sanner, Amber Schickedanz, Brenda Schmachtenberger, Patricia A. Sharpe, Dennis Shepard, Tracy Slate, William Smith, Alberta Smith-Plump, Sam Stebbins, Debbie Stefanides, Madhuri Sudan, Theresa Taylor, Winifred W. Thompson, Jocelyn Tobnick, M. Renée Umstattd, Melissa Watford, John B. Waller Jr, Stacy Wegley, Gail Weisberg, Shirley Williams, Carolyn Wilson-Hall, and Jaslin Yu.

We also thank Peter Hannan, from the University of Minnesota, for his statistical consultation on this project. We thank the National Advisory Committee for its valuable contributions to Active for Life. Finally, we thank the coalitions, partnering organizations, and advisory boards at each of the grantee sites for their meaningful contributions and support of the program.

Human Participant Protection
This study was approved by the University of South Carolina and Texas A&M University System institutional review boards. It was also approved by local grantee review boards.


Peer Reviewed

S. Wilcox led the development and implementation of the evaluation, synthesized analyses, and led the writing. M. Dowda contributed to the implementation of the evaluation, led and conducted all analyses, and assisted with the writing. S. F. Griffin contributed to the implementation of the evaluation. C. Rheaume oversaw the implementation of the evaluation. M. G. Ory contributed to the origination of the study and oversaw grantee implementation of the programs. D. M. Buchner and T. Bazzarre contributed to the origination of the study. K. Campbell-Voytal and J. Bartlett-Prescott contributed to the implementation of the program and evaluation. D. Dowdy oversaw grantee implementation of the programs. R. Mockenhaupt originated the study. In addition, M. Dowda, S. F. Griffin, C. Rheaume, M. G. Ory, L. Leviton, A. C. King, A. Dunn, D. M. Buchner, C. M. Castro, and R. A. Carpenter contributed to the development of the evaluation. All authors helped to conceptualize ideas, interpret findings, and review and revise drafts of the article.


1. Task Force on Community Preventive Services. Recommendations to increase physical activity in communities. Am J Prev Med. 2002;22:67–72. [PubMed]
2. King AC, Rejeski WJ, Buchner DM. Physical activity interventions targeting older adults. A critical review and recommendations. Am J Prev Med. 1998;15:316–333. [PubMed]
3. van der Bij AK, Laurant MG, Wensing M. Effectiveness of physical activity interventions for older adults: a review. Am J Prev Med. 2002;22:120–133. [PubMed]
4. Green LW. From research to “best practices” in other settings and populations. Am J Health Behav. 2001;25:165–178. [PubMed]
5. Glasgow RE, Lichtenstein E, Marcus AC. Why don’t we see more translation of health promotion research to practice? Rethinking the efficacy-to-effectiveness transition. Am J Public Health. 2003;93:1261–1267. [PMC free article] [PubMed]
6. Dzewaltowski DA, Estabrooks PA, Glasgow RE. The future of physical activity behavior change research: what is needed to improve translation of research into health promotion practice? Exerc Sport Sci Rev. 2004;32:57–63. [PubMed]
7. Dzewaltowski DA, Glasgow RE, Klesges LM, Estabrooks PA, Brock E. RE-AIM: evidence-based standards and a Web resource to improve translation of research into practice. Ann Behav Med. 2004;28:75–80. [PubMed]
8. Glasgow RE, Klesges LM, Dzewaltowski DA, Bull SS, Estabrooks P. The future of health behavior change research: what is needed to improve translation of research into health promotion practice? Ann Behav Med. 2004;27:3–12. [PubMed]
9. Estabrooks P, Gyurcsik NC. Evaluating the impact of behavioral interventions that target physical activity: issues of generalizability and public health. Psychol Sport Exerc. 2003;4:41–55.
10. The Robert Wood Johnson Foundation. National Blueprint: Increasing Physical Activity Among Adults Aged 50 and Older. Princeton, NJ: The Robert Wood Johnson Foundation; 2001.
11. A National Survey of Health and Supportive Services in the Aging Network. Washington, DC: The National Council on the Aging; 2001.
12. Prochaska JO, DiClemente CC, Norcross JC. In search of how people change. Applications to addictive behaviors. Am Psychol. 1992;47:1102–1114. [PubMed]
13. Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice-Hall; 1986.
14. King AC, Haskell WL, Young DR, Oka RK, Stefanick ML. Long-term effects of varying intensities and formats of physical activity on participation rates, fitness, and lipoproteins in men and women aged 50 to 65 years. Circulation. 1995;91:2596–2604. [PubMed]
15. King AC, Baumann K, O’Sullivan P, Wilcox S, Castro C. Effects of moderate-intensity exercise on physiological, behavioral, and emotional responses to family caregiving: a randomized controlled trial. J Gerontol A Biol Sci Med Sci. 2002;57:M26–M36. [PubMed]
16. King AC, Pruitt LA, Phillips W, Oka R, Rodenburg A, Haskell WL. Comparative effects of two physical activity programs on measured and perceived physical functioning and other health-related quality of life outcomes in older adults. J Gerontol A Biol Sci Med Sci. 2000;55:M74–M83. [PubMed]
17. Stewart AL, Mills KM, Sepsis PG, et al. Evaluation of CHAMPS, a physical activity promotion program for older adults. Ann Behav Med. 1997;19:353–361. [PubMed]
18. Dunn AL, Marcus BH, Kampert JB, Garcia ME, Kohl HW III, Blair SN. Reduction in cardiovascular disease risk factors: 6-month results from Project Active. Prev Med. 1997;26:883–892. [PubMed]
19. Dunn AL, Marcus BH, Kampert JB, Garcia ME, Kohl HW III, Blair SN. Comparison of lifestyle and structured interventions to increase physical activity and cardiorespiratory fitness: a randomized trial. JAMA. 1999;281:327–334. [PubMed]
20. Thomas S, Reading J, Shephard RJ. Revision of the Physical Activity Readiness Questionnaire (PAR-Q). Can J Sport Sci. 1992;17:338–345. [PubMed]
21. Ory M, Resnick B, Jordan PJ, et al. Screening, safety, and adverse events in physical activity interventions: collaborative experiences from the behavior change consortium. Ann Behav Med. 2005;29(suppl):20–28. [PubMed]
22. Executive summary of the clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults. Arch Intern Med. 1998; 158:1855–1867. [PubMed]
23. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System, Survey Questions. Available at: http://www.cdc.gov/brfss/questionnaires/questionnaires.htm. Accessed May 9, 2005.
24. Stewart AL, Mills KM, King AC, Haskell WL, Gillis D, Ritter PL. CHAMPS physical activity questionnaire for older adults: outcomes for interventions. Med Sci Sports Exerc. 2001;33:1126–1141. [PubMed]
25. Harada ND, Chiu V, King AC, Stewart AL. An evaluation of three self-report physical activity instruments for older adults. Med Sci Sports Exerc. 2001;33:962–970. [PubMed]
26. Stewart AL. Community-based physical activity programs for adults age 50 and older. J Aging Phys Activity. 2001;9:S71–S91.
27. Stewart AL, Verboncoeur CJ, McLellan BY, et al. Physical activity outcomes of CHAMPS II: a physical activity promotion program for older adults. J Gerontol A Biol Sci Med Sci. 2001;56:M465–M470. [PMC free article] [PubMed]
28. Pate RR, Pratt M, Blair SN, et al. Physical activity and public health. A recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine. JAMA. 1995;273: 402–407. [PubMed]
29. Kohout FJ, Berkman LF, Evans DA, Cornoni-Huntley J. Two shorter forms of the CES-D (Center for Epidemiological Studies Depression) depression symptoms index. J Aging Health. 1993;5:179–193. [PubMed]
30. Irwin M, Artin KH, Oxman MN. Screening for depression in the older adult: criterion validity of the 10-item Center for Epidemiological Studies Depression Scale (CES-D). Arch Intern Med. 1999;159:1701–1704. [PubMed]
31. Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. App Psych Meas. 1977;1:385–401.
32. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983; 24:385–396. [PubMed]
33. Pbert L, Doerfler LA, DeCosimo D. An evaluation of the perceived stress scale in two clinical populations. J Psychopathol Behav Assess. 1992;14:363–375.
34. Reboussin BA, Rejeski WJ, Martin KA, et al. Correlates of satisfaction with body function and body appearance in middle- and older aged adults: The Activity Counseling Trial (ACT). Psychol Health. 2000;15: 239–254.
35. Wolf FM. Meta-analysis. Quantitative Methods for Research Synthesis. Newbury Park, Calif: Sage Publications; 1986.
36. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum; 1988.
37. Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press; 2001. [PubMed]
38. Centers for Disease Control and Prevention. The State of Aging and Health in America 2004. Available at: http://www.cdc.gov/aging/pdf/State_of_Aging_and_Health_in_America_2004.pdf. Accessed May 9, 2005.
39. Brosse AL, Sheets ES, Lett HS, Blumenthal JA. Exercise and the treatment of clinical depression in adults: recent findings and future directions. Sports Med. 2002;32:741–760. [PubMed]
40. King AC, Taylor CB, Haskell WL. Effects of differing intensities and formats of 12 months of exercise training on psychological outcomes in older adults. Health Psychol. 1993;12:292–300. [PubMed]
41. Health, United States, 2004. With Chartbook on Trends in the Health of Americans. Hyattsville, Md: National Center for Health Statistics; 2004. [PubMed]
42. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System Prevalence Data. Available at: http://apps.nccd.cdc.gov/brfss/. Accessed October 3, 2005.
43. National Center for Health Statistics. Data Warehouse on Trends in Health and Aging. Available at: http://www.cdc.gov/nchs/agingact.htm. Accessed October 3, 2005.

Articles from American Journal of Public Health are provided here courtesy of American Public Health Association
PubReader format: click here to try


Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...


  • Cited in Books
    Cited in Books
    PubMed Central articles cited in books
  • PubMed
    PubMed citations for these articles

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...