Integration of Time-Based Recommendations with Current Pediatric Health Behavior Guidelines: Implications for Obesity Prevention and Treatment in Youth.

PURPOSE OF REVIEW: Youth-onset obesity is associated with negative health outcomes across the lifespan including cardiovascular diseases, type 2 diabetes, obstructive sleep apnea, dyslipidemias, asthma, and several cancers. Pediatric health guidelines have traditionally focused on the quality and quantity of dietary intake, physical activity, and sleep. RECENT FINDINGS: Emerging evidence suggests that the timing (time of day when behavior occurs) and composition (proportion of time spent allocated to behavior) of food intake, movement (i.e., physical activity, sedentary time), and sleep may independently predict health trajectories and disease risks. Several theoretically driven interventions and conceptual frameworks feature behavior timing and composition (e.g., 24 h movement continuum, circadian science and chronobiology, intermittent fasting regimens, structured day hypothesis). These literatures are, however, disparate, with little crosstalk across disciplines. In this review, we examine dietary, sleep, and movement guidelines and recommendations for youths ages 0-18 in the context of theoretical models and empirical findings in support of time-based approaches. The review aims to inform a unifying framework of health behaviors and guide future research on the integration of time-based recommendations into current quantity and quality-based health guidelines for children and adolescents.

Effects of a wearable technology-based physical activity intervention on sleep quality in breast cancer survivors: the ACTIVATE Trial.

INTRODUCTION: Physical activity interventions can improve sleep quality in breast cancer survivors. This paper examines the effects of the ACTIVATE Trial, a wearable-based physical activity intervention (Garmin Vivofit2® coupled with behavioral feedback, goal setting, and health coaching) on sleep outcomes. METHODS: Post-primary treatment, inactive, postmenopausal breast cancer survivors were recruited and randomized to primary intervention or waitlist. Wrist-worn actigraphy (sleep onset latency, SOL; total sleep time, TST; sleep efficiency, SE; wake after sleep onset, WASO; and number of awakenings, NWAKE) and questionnaire-derived sleep measures (Pittsburgh Sleep Quality Index) were assessed at baseline (T1), 12 weeks (end of primary intervention and start of waitlist intervention, T2), and at 24 weeks (T3). RESULTS: Eighty-three women (mean age = 62 years) were randomized; trial retention was 94% at T2 and 87% at T3. At T2, primary intervention participants had greater improvements in WASO (- 5.7 min, 95% CI - 11.7 to - 0.2) and NWAKE compared with the waitlist arm (- 2.0, 95% CI - 3.6 to - 0.4). At T3, within-group improvements were observed for SE (both groups), WASO (both groups), NWAKE (primary intervention group only), total PSQI score (primary intervention group), and sleep efficacy (primary intervention group). CONCLUSIONS: The intervention reduced actigraphy-measured sleep disturbances. Within-group analyses suggest that improvements in sleep quality are sustained over a longer duration, and there may be similar benefits from an abridged intervention (wearable device only). Actigraphy-measured effects appeared stronger in participants who were poor sleepers at study entry. IMPLICATIONS FOR CANCER SURVIVORS: Wearable technology can increase physical activity and improve sleep for breast cancer survivors.

Global Public Health Guidelines on Physical Activity and Sedentary Behavior for People Living With Chronic Conditions: A Call to Action.

BACKGROUND: In 2020, the World Health Organization (WHO) released global guidelines on physical activity (PA) and sedentary behavior, for the first time providing population-based recommendations for people living with selected chronic conditions. This article briefly presents the guidelines, related processes and evidence, and, importantly, considers how they may be used to support research, practice, and policy. METHODS: A brief overview of the scope, agreed methods, selected chronic conditions (adults living with cancer, hypertension, type 2 diabetes, and human immunodeficiency virus), and appraisal of systematic review evidence on PA/sedentary behavior is provided. Methods were consistent with World Health Organization protocols for developing guidelines. RESULTS: Moderate to high certainty evidence (varying by chronic condition and outcome examined) supported that PA can reduce the risk of disease progression or premature mortality and improve physical function and quality of life in adults living with chronic conditions. Direct evidence on sedentary behavior was lacking; however, evidence extrapolated from adult populations was considered applicable, safe, and likely beneficial (low certainty due to indirectness). CONCLUSIONS: Clinical and public health professionals and policy makers should promote the World Health Organization 2020 global guidelines and develop and implement services and programs to increase PA and limit sedentary behavior in adults living with chronic conditions.

Advancing the global physical activity agenda: recommendations for future research by the 2020 WHO physical activity and sedentary behavior guidelines development group.

BACKGROUND: In July, 2019, the World Health Organization (WHO) commenced work to update the 2010 Global Recommendations on Physical Activity for Health and established a Guideline Development Group (GDG) comprising expert public health scientists and practitioners to inform the drafting of the 2020 Guidelines on Physical Activity and Sedentary Behavior. The overall task of the GDG was to review the scientific evidence and provide expert advice to the WHO on the amount of physical activity and sedentary behavior associated with optimal health in children and adolescents, adults, older adults (> 64 years), and also specifically in pregnant and postpartum women and people living with chronic conditions or disabilities. METHODS: The GDG reviewed the available evidence specific to each sub-population using systematic protocols and in doing so, identified a number of gaps in the existing literature. These proposed research gaps were discussed and verified by expert consensus among the entire GDG. RESULTS: Evidence gaps across population sub-groups included a lack of information on: 1) the precise shape of the dose-response curve between physical activity and/or sedentary behavior and several of the health outcomes studied; 2) the health benefits of light-intensity physical activity and of breaking up sedentary time with light-intensity activity; 3) differences in the health effects of different types and domains of physical activity (leisure-time; occupational; transportation; household; education) and of sedentary behavior (occupational; screen time; television viewing); and 4) the joint association between physical activity and sedentary time with health outcomes across the life course. In addition, we acknowledge the need to conduct more population-based studies in low- and middle-income countries and in people living with disabilities and/or chronic disease, and to identify how various sociodemographic factors (age, sex, race/ethnicity, socioeconomic status) modify the health effects of physical activity, in order to address global health disparities. CONCLUSIONS: Although the 2020 WHO Guidelines for Physical Activity and Sedentary Behavior were informed by the most up-to-date research on the health effects of physical activity and sedentary time, there is still substantial work to be done in advancing the global physical activity agenda.

New global guidelines on sedentary behaviour and health for adults: broadening the behavioural targets.

BACKGROUND: In 2018, the World Health Organisation (WHO) commenced a program of work to update the 2010 Global Recommendations on Physical Activity for Health, for the first-time providing population-based guidelines on sedentary behaviour. This paper briefly summarizes and highlights the scientific evidence behind the new sedentary behaviour guidelines for all adults and discusses its strengths and limitations, including evidence gaps/research needs and potential implications for public health practice. METHODS: An overview of the scope and methods used to update the evidence is provided, along with quality assessment and grading methods for the eligible new systematic reviews. The literature search update was conducted for WHO by an external team and reviewers used the AMSTAR 2 (Assessment of Multiple Systematic Reviews) tool for critical appraisal of the systematic reviews under consideration for inclusion. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) method was used to rate the certainty (i.e. very low to high) of the evidence. RESULTS: The updated systematic review identified 22 new reviews published from 2017 up to August 2019, 14 of which were incorporated into the final evidence profiles. Overall, there was moderate certainty evidence that higher amounts of sedentary behaviour increase the risk for all-cause, cardiovascular disease (CVD) and cancer mortality, as well as incidence of CVD, cancer, and type 2 diabetes. However, evidence was deemed insufficient at present to set quantified (time-based) recommendations for sedentary time. Moderate certainty evidence also showed that associations between sedentary behaviour and all-cause, CVD and cancer mortality vary by level of moderate-to-vigorous physical activity (MVPA), which underpinned additional guidance around MVPA in the context of high sedentary time. Finally, there was insufficient or low-certainty systematic review evidence on the type or domain of sedentary behaviour, or the frequency and/or duration of bouts or breaks in sedentary behaviour, to make specific recommendations for the health outcomes examined. CONCLUSIONS: The WHO 2020 guidelines are based on the latest evidence on sedentary behaviour and health, along with interactions between sedentary behaviour and MVPA, and support implementing public health programmes and policies aimed at increasing MVPA and limiting sedentary behaviour. Important evidence gaps and research opportunities are identified.

Integrating Sleep, Physical Activity, and Diet Quality to Estimate All-Cause Mortality Risk: A Combined Compositional Clustering and Survival Analysis of the National Health and Nutrition Examination Survey 2005-2006 Cycle.

We aimed to compare all-cause mortality risk across clusters of adults ≥50 years of age (n = 1,035) with common lifestyle behaviors patterns, enrolled in the US National Health and Nutrition Examination Survey (2005-2006). Log-ratio coordinates of 24-hour movement pattern and z scores of diet quality were used as input into a model-based clustering analysis. A Cox regression model was fitted to ascertain the all-cause mortality risk associated with each cluster. Participants were clustered into 4 groups: 1) a group characterized by a better physical activity profile and longer sleep duration coupled with an average diet quality (cluster 1); 2) a group with the poorest activity profile and shortest sleep but also the best diet quality (cluster 2); 3) another group featuring lower levels of activity of either intensity and higher levels of sedentary behavior and also a poor diet quality score (cluster 3); and 4) a group with an average diet quality and the best activity profile in the sample (cluster 4). A combination of a poorer diet and activity profile increased the prospective risk of all-cause mortality. Our findings emphasize the importance of considering the combination of diet quality and 24-hour movement patterns when developing interventions to reduce the risk of premature mortality.

Development of a Control-Oriented Model of Social Cognitive Theory for Optimized mHealth Behavioral Interventions.

Mobile health (mHealth) technologies are contributing to the increasing relevance of control engineering principles in understanding and improving health behaviors, such as physical activity. Social Cognitive Theory (SCT), one of the most influential theories of health behavior, has been used as the conceptual basis for behavioral interventions for smoking cessation, weight management, and other health-related outcomes. This paper presents a control-oriented dynamical systems model of SCT based on fluid analogies that can be used in system identification and control design problems relevant to the design and analysis of intensively adaptive interventions. Following model development, a series of simulation scenarios illustrating the basic workings of the model are presented. The model's usefulness is demonstrated in the solution of two important practical problems: 1) semiphysical model estimation from data gathered in a physical activity intervention (the MILES study) and 2) as a means for discerning the range of "ambitious but doable" daily step goals in a closed-loop behavioral intervention aimed at sedentary adults. The model is the basis for ongoing experimental validation efforts, and should encourage additional research in applying control engineering technologies to the social and behavioral sciences.

Evaluating the Evidence on Sitting, Smoking, and Health: Is Sitting Really the New Smoking?

Sitting has frequently been equated with smoking, with some sources even suggesting that smoking is safer than sitting. This commentary highlights how sitting and smoking are not comparable. The most recent meta-analysis of sedentary behavior and health outcomes reported a hazard ratio of 1.22 (95% confidence interval [CI] = 1.09, 1.41) for all-cause mortality. The relative risk (RR) of death from all causes among current smokers, compared with those who have never smoked, is 2.80 (95% CI = 2.72, 2.88) for men and 2.76 for women (95% CI = 2.69, 2.84). The risk is substantially higher for heavy smokers (> 40 cigarettes per day: RR = 4.08 [95% CI = 3.68, 4.52] for men, and 4.41 [95% CI = 3.70, 5.25] for women). These estimates correspond to absolute risk differences of more than 2000 excess deaths from any cause per 100 000 persons per year among the heaviest smokers compared with never smokers, versus 190 excess deaths per 100 000 persons per year when comparing people with the highest volume of sitting with the lowest. Conflicting or distorted information about health risks related to behavioral choices and environmental exposures can lead to confusion and public doubt with respect to health recommendations.

Movement behaviours are associated with lung function in middle-aged and older adults: a cross-sectional analysis of the Canadian longitudinal study on aging.

BACKGROUND: Physical activity has been shown to attenuate the age-associated decline in lung function; however, there is little research evaluating different movement behaviours as potential correlates of lung function. Modifiable determinants need to be identified, as the prevalence of chronic respiratory disease is on the rise. The purpose of this study was to investigate associations of self-reported movement behaviours (i.e., sitting time, walking, different intensities of physical activity, and strengthening activities), with lung function in middle-aged and older adults without a respiratory disease, according to their smoking history. METHODS: Data from participants of the Canadian Longitudinal Study on Aging were used for analysis (n = 16,839). Lung function was assessed using spirometry. A modified version of the Physical Activity Scale for the Elderly was used to assess sitting time and physical activity levels. Smoking status was classified as non-smoking, < 10 pack years smoking, and 10 or more pack years of smoking. The association between movement behaviours and lung function was assessed using hierarchical linear regression models with all covariates (age, sex, smoking status, body mass index, education, retirement status, and sleep duration) entered into block 1, and all movement behaviours entered into block 2. RESULTS: All movement behaviours were associated with Forced Expiratory Volume in 1 s (FEV1) and Forced Vital Capacity (FVC) % predicted in crude and adjusted models, regardless of smoking status. Sitting time was negatively associated with both FEV1%pred (ß: -0.094, CI: -0.140, - 0.047) and FVC%pred (ß: -0.087, CI: -0.128, -0.045) among those who never smoked, and strength activity was positively associated with both FEV1%pred (ß: 0.272, CI: 0.048, 0.496) and FVC%pred (ß: 0.253, CI: 0.063,0.442) among those who smoked < 10 pack years, as well as with FVC%pred among those who smoked 10 or more pack years (ß: 0.309, CI: 0.064, 0.554). CONCLUSIONS: This is the first study to assess the association of different movement behaviours with lung function among middle-aged and older adults without a respiratory disease. These findings indicate that movement behaviours are correlates of lung function, and that they may be modifiable determinants of the age-associated decline in lung function.

Reallocating time to sleep, sedentary, and active behaviours in non-Hodgkin lymphoma survivors: associations with patient-reported outcomes.

The purpose of this study was to examine potential effects of reallocating time between sleep, sedentary and active behaviours on fatigue symptoms and quality of life in a sample of non-Hodgkin lymphoma survivors. Non-Hodgkin lymphoma survivors identified from the Western Australian Cancer Registry (N = 149) (response rate = 36%; median age = 64 years) wore an Actigraph® GT3X+ accelerometer for 7 days and completed the Fatigue Scale, the Functional Assessment of Cancer Therapy-General and the Pittsburgh Sleep Quality Index. We used isotemporal substitution methods in linear regression models to examine the potential effects of reallocating time between sleep, sedentary and activity behaviours on fatigue and quality of life. Data collection was conducted in Western Australia in 2013. Significant differences were observed for fatigue symptoms when 30 min per day of bouted moderate-to-vigorous physical activity (10 min) was reallocated from 30 min per day of sleep (5.7 points, 95% CI = 1.8, 9.7), sedentary time bouts (20 min) (5.7 points, 95% CI = 1.6, 9.7), sedentary time non-bouts (5.1 points, 95% CI = 1.0, 9.3) or light intensity activity (5.5 points, 95% CI = 1.5, 9.5). Isotemporal substitution effects of reallocating sedentary time, sleep and light physical activity with bouted physical activity was significantly associated with fatigue, but not quality of life. Findings from the present study may aid in the development and delivery of health behaviour interventions that are more likely to influence the health outcome of interest.

Reallocating Time to Sleep, Sedentary Time, or Physical Activity: Associations with Waist Circumference and Body Mass Index in Breast Cancer Survivors.

BACKGROUND: Moderate-to-vigorous intensity physical activity (MVPA) is inversely associated with waist circumference and body mass index (BMI) among breast cancer survivors. Limited research has focused on behaviors that account for larger portions of the day [sleep, sedentary time, and light-intensity physical activity (LPA)]. We investigated the interdependent associations of self-reported sleep, objectively assessed prolonged and short bouts of sedentary time, total LPA, and total MVPA with waist circumference and BMI. METHODS: A cross-sectional sample of breast cancer survivors (N = 256, mean age = 60 years; mean time since diagnosis = 3 years) wore an Actigraph GT3X+ accelerometer during waking hours for 7 days. Participants completed the Pittsburgh Sleep Quality Index and self-reported their waist circumference, height, and weight. An isotemporal substitution approach was used in linear regression models to explore the associations of reallocating time to sleep, sedentary and active behaviors on waist circumference, and BMI, after adjusting for potential confounders. RESULTS: Reallocating 30 minutes to MVPA was significantly associated with lower waist circumference when allocated from sleep (-2.50 cm), prolonged sedentary time (-2.51 cm), or LPA (-2.71 cm). Reallocating 30 minutes of prolonged sedentary time to nonprolonged sedentary time was significantly associated with lower waist circumference (-0.94 cm). Similar results were observed for BMI. CONCLUSIONS: Reallocating 30 minutes to MVPA was associated with significantly lower waist circumference and BMI, as was reallocating 30 minutes of prolonged sedentary time to 30 minutes of nonprolonged sedentary time. IMPACT: Increasing MVPA levels and decreasing time spent in prolonged, unbroken sedentary bouts may be avenues for improving body composition in this population. Cancer Epidemiol Biomarkers Prev; 26(2); 254-60. ©2016 AACR.

Development of a dynamic computational model of social cognitive theory.

Social cognitive theory (SCT) is among the most influential theories of behavior change and has been used as the conceptual basis of health behavior interventions for smoking cessation, weight management, and other health behaviors. SCT and other behavior theories were developed primarily to explain differences between individuals, but explanatory theories of within-person behavioral variability are increasingly needed as new technologies allow for intensive longitudinal measures and interventions adapted from these inputs. These within-person explanatory theoretical applications can be modeled as dynamical systems. SCT constructs, such as reciprocal determinism, are inherently dynamical in nature, but SCT has not been modeled as a dynamical system. This paper describes the development of a dynamical system model of SCT using fluid analogies and control systems principles drawn from engineering. Simulations of this model were performed to assess if the model performed as predicted based on theory and empirical studies of SCT. This initial model generates precise and testable quantitative predictions for future intensive longitudinal research. Dynamic modeling approaches provide a rigorous method for advancing health behavior theory development and refinement and for guiding the development of more potent and efficient interventions.

Integrating Physical Activity in Primary Care Practice.

Based on a collaborative symposium in 2014 hosted by the Society of Behavioral Medicine (SBM) and the American College of Sports Medicine (ACSM), this paper presents a model for physical activity counseling for primary care physicians (PCPs). Most US adults do not meet national recommendations for physical activity levels. Socioecological factors drive differences in physical activity levels by geography, sex, age, and racial/ethnic group. The recent Patient Protection and Affordable Care Act incentivizes PCPs to offer patients physical activity counseling. However, PCPs have reported socioecological barriers to physical activity counseling and also patient barriers to physical activity, spanning from the individual to the environmental (eg, lack of safe spaces for physical activity), policy (eg, reimbursement policies), and organizational (eg, electronic medical record protocols, worksite norms/policies) levels. The aims of this paper are to: 1) discuss barriers to PCP counseling for physical activity; 2) provide evidence-based strategies and techniques to help PCPs address these counseling barriers; and 3) suggest practical steps for PCPs to counsel patients on physical activity using strategies and supports from policy, the primary care team, and other support networks.

Rural Food and Physical Activity Assessment Using an Electronic Tablet-Based Application, New York, 2013-2014.

INTRODUCTION: A community's built environment can influence health behaviors. Rural populations experience significant health disparities, yet built environment studies in these settings are limited. We used an electronic tablet-based community assessment tool to conduct built environment audits in rural settings. The primary objective of this qualitative study was to evaluate the usefulness of the tool in identifying barriers and facilitators to healthy eating and active living. The second objective was to understand resident perspectives on community features and opportunities for improvement. METHODS: Participants were recruited from 4 rural communities in New York State. Using the tool, participants completed 2 audits, which consisted of taking pictures and recording audio narratives about community features perceived as assets or barriers to healthy eating and active living. Follow-up focus groups explored the audit experience, data captured, and opportunities for change. RESULTS: Twenty-four adults (mean age, 69.4 y (standard deviation, 13.2 y), 6 per community, participated in the study. The most frequently captured features related to active living were related to roads, sidewalks, and walkable destinations. Restaurants, nontraditional food stores, and supermarkets were identified in the food environment in relation to the cost, quality, and selection of healthy foods available. In general, participants found the assessment tool to be simple and enjoyable to use. CONCLUSION: An electronic tablet-based tool can be used to assess rural food and physical activity environments and may be useful in identifying and prioritizing resident-led change initiatives. This resident-led assessment approach may also be helpful for informing and evaluating rural community-based interventions.

Reallocating time to sleep, sedentary behaviors, or active behaviors: associations with cardiovascular disease risk biomarkers, NHANES 2005-2006.

Sleep and sedentary and active behaviors are linked to cardiovascular disease risk biomarkers, and across a 24-hour day, increasing time in 1 behavior requires decreasing time in another. We explored associations of reallocating time to sleep, sedentary behavior, or active behaviors with biomarkers. Data (n = 2,185 full sample; n = 923 fasting subanalyses) from the cross-sectional 2005-2006 US National Health and Nutrition Examination Survey were analyzed. The amounts of time spent in sedentary behavior, light-intensity activity, and moderate-to-vigorous physical activity (MVPA) were derived from ActiGraph accelerometry (ActiGraph LLC, Pensacola, Florida), and respondents reported their sleep duration. Isotemporal substitution modeling indicated that, independent of potential confounders and time spent in other activities, beneficial associations (P < 0.05) with cardiovascular disease risk biomarkers were associated with the reallocation of 30 minutes/day of sedentary time with equal time of either sleep (2.2% lower insulin and 2.0% lower homeostasis model assessment of ß-cell function), light-intensity activity (1.9% lower triglycerides, 2.4% lower insulin, and 2.2% lower homeostasis model assessment of ß-cell function), or MVPA (2.4% smaller waist circumference, 4.4% higher high-density lipoprotein cholesterol, 8.5% lower triglycerides, 1.7% lower glucose, 10.7% lower insulin, and 9.7% higher homeostasis model assessment of insulin sensitivity. These findings provide evidence that MVPA may be the most potent health-enhancing, time-dependent behavior, with additional benefit conferred from light-intensity activities and sleep duration when reallocated from sedentary time.

Determining who responds better to a computer- vs. human-delivered physical activity intervention: results from the community health advice by telephone (CHAT) trial.

BACKGROUND: Little research has explored who responds better to an automated vs. human advisor for health behaviors in general, and for physical activity (PA) promotion in particular. The purpose of this study was to explore baseline factors (i.e., demographics, motivation, interpersonal style, and external resources) that moderate intervention efficacy delivered by either a human or automated advisor. METHODS: Data were from the CHAT Trial, a 12-month randomized controlled trial to increase PA among underactive older adults (full trial N = 218) via a human advisor or automated interactive voice response advisor. Trial results indicated significant increases in PA in both interventions by 12 months that were maintained at 18-months. Regression was used to explore moderation of the two interventions. RESULTS: Results indicated amotivation (i.e., lack of intent in PA) moderated 12-month PA (d = 0.55, p < 0.01) and private self-consciousness (i.e., tendency to attune to one's own inner thoughts and emotions) moderated 18-month PA (d = 0.34, p < 0.05) but a variety of other factors (e.g., demographics) did not (p > 0.12). CONCLUSIONS: Results provide preliminary evidence for generating hypotheses about pathways for supporting later clinical decision-making with regard to the use of either human- vs. computer-delivered interventions for PA promotion.

Behavioral impacts of sequentially versus simultaneously delivered dietary plus physical activity interventions: the CALM trial.

BACKGROUND: Few studies have evaluated how to combine dietary and physical activity (PA) interventions to enhance adherence. PURPOSE: We tested how sequential versus simultaneous diet plus PA interventions affected behavior changes. METHODS: Two hundred participants over age 44 years not meeting national PA and dietary recommendations (daily fruit and vegetable servings and percent of calories from saturated fat) were randomized to one of four 12-month telephone interventions: sequential (exercise first or diet first), simultaneous, or attention control. At 4 months, the other health behavior was added in the sequential arms. RESULTS: Ninety-three percent of participants were retained through 12 months. At 4 months, only exercise first improved PA, and only the simultaneous and diet-first interventions improved dietary variables. At 12 months, mean levels of all behaviors in the simultaneous arm met recommendations, though not in the exercise- and diet-first arms. CONCLUSIONS: We observed a possible behavioral suppression effect of early dietary intervention on PA that merits investigation.

Neighborhood Eating and Activity Advocacy Teams (NEAAT): engaging older adults in policy activities to improve food and physical environments.

Local food and physical activity environments are known to impact health, and older adults are generally more vulnerable to health-related environmental impacts due to poorer physical function and mobility impairments. There is a need to develop cost-conscious, community-focused strategies that impact local food and physical activity environment policies. Engaging older adult community residents in assessment and advocacy activities is one avenue to address this need. We describe the Neighborhood Eating and Activity Advocacy Team project, a community-based participatory project in low-income communal housing settings in San Mateo County, CA, as one method for engaging older adults in food and physical activity environment and policy change. Methods and strategies used by the "community action teams" to generate relevant neighborhood environmental data, build coalitions, prioritize complex issues, and advocate for change are presented. Advocacy groups are feasible among older adults to improve food and physical activity environments.

Physical activity program delivery by professionals versus volunteers: the TEAM randomized trial.

OBJECTIVE: Older adults have low rates of physical activity participation, but respond positively to telephone-mediated support programs. Programs are often limited by reliance on professional staff. This study tested telephone-based physical activity advice delivered by professional staff versus trained volunteer peer mentors. DESIGN: A 12-month, randomized, controlled clinical trial was executed from 2003-2008. Twelve volunteer peer mentors and 181 initially inactive adults ages 50 years and older were recruited from the San Francisco Bay Area. Participants were randomized to: (1) telephone-based physical activity advice delivered by professional staff, (2) telephone-based physical activity advice delivered by trained volunteer peers, or (3) an attention-control arm of staff-delivered telephone support for nutrition. MAIN OUTCOME MEASURES: Moderate-intensity or more vigorous physical activity (MVPA) was assessed at baseline, 6, and 12 months with the Community Healthy Activities Model Program for Seniors (CHAMPS) Questionnaire, with accelerometry validation (Actigraph) in a randomly selected subsample. Treatment fidelity was examined through analysis of quantity and quality of intervention delivery. RESULTS: At 6 and 12 months, both physical activity arms significantly increased MVPA relative to the control arm. Both physical activity arms were comparable in quantity of intervention delivery, but peers demonstrated more versatility and comprehensiveness in quality of intervention content. CONCLUSIONS: This study demonstrates that trained peer volunteers can effectively promote physical activity increases through telephone-based advice. The results support a program delivery model with good dissemination potential for a variety of community settings.