Cost and cost-effectiveness of the 'Stand and Move at Work' multicomponent intervention to reduce workplace sedentary time and cardiometabolic risk.

OBJECTIVE: Few studies have reported the cost and cost-effectiveness of workplace interventions to reduce sedentary time. The purpose of this study was to complete an economic evaluation of a multilevel intervention to reduce sitting time and increase light-intensity physical activity (LPA) among employees. METHODS: We conducted a retrospective within-trial cost and cost-effectiveness analysis (CEA) to compare a 12-month multilevel intervention with (STAND+) and without (MOVE+) a sit-stand workstation, across 24 worksites (N=630 employee participants) enrolled in a cluster randomized clinical trial. We estimated the intervention costs using activity-based costing strategy. The intervention costs were further expressed as per person and per worksite. CEA was conducted using an incremental cost-effectiveness ratio (ICER) metric, expressed as costs for additional unit of sitting time (minute/day), LPA (minutes/day), cardiometabolic risk score, and quality-adjusted life years (QALY) increased/decreased at 12 months. We assessed the cost analysis and CEA from the organizational (ie, employer) perspective with a one-year time horizon. RESULTS: Total intervention costs were $134 and $72 per person, and $3939 and $1650 per worksite for the STAND+ (N worksites = 12; N employees = 354) and MOVE+ (N worksites = 12; N employees = 276) interventions, respectively. The ICER was $1 (95% CI $0.8-1.4) for each additional minute reduction of workplace sitting time (standardized to 8-hour workday); and $4656 per QALY gained at 12 months. There was a modest and non-significant change of loss of work productivity improvement (-0.03 hours, 95% CI -4.16-4.09 hours), which was associated with a $0.34 return for every $1 invested. CONCLUSIONS: The multi-level intervention with sit-stand workstations has the potential to be widely implemented to reduce workplace sitting time. Future research into work productivity outcomes in terms of cost-benefits for employers is warranted.

Does investing in low-income urban neighborhoods improve sleep?

STUDY OBJECTIVES: Neighborhood disadvantage is associated with poor sleep, which may contribute to and exacerbate racial and socioeconomic health disparities. Most prior work has been cross-sectional and thus it has not been possible to estimate causal effects. METHODS: We leveraged a natural experiment opportunity in two low-income, predominantly African American Pittsburgh, PA neighborhoods, following a randomly selected cohort of households (n = 676) between 2013 and 2016. One of the neighborhoods received substantial public and private investments (housing, commercial) over the study period, while the other socio-demographically similar neighborhood received far fewer investments. Primary analyses used a difference-in-difference analysis based on neighborhood, to examine changes in actigraphy-assessed sleep duration, efficiency, and wakefulness after sleep onset (WASO), and self-reported sleep quality. Secondary analyses examined whether residents' proximity to investments, regardless of neighborhood, was associated with changes in sleep outcomes. RESULTS: Resident sleep worsened over time in both neighborhoods with no significant differences among residents between the two neighborhoods. Secondary analyses, including covariate adjustment and propensity score weighting to improve comparability, indicated that regardless of neighborhood, those who lived in closer proximity to investments (<0.1 mile) were significantly less likely to experience decreases in sleep duration, efficiency, and quality, or increases in WASO, compared to those who lived farther away. CONCLUSIONS: While we did not observe sleep differences among residents between neighborhoods, living closer to a neighborhood investment was associated with better sleep outcomes. Findings have relevance for public health and policy efforts focused on investing in historically disinvested neighborhoods.

Violent crime, police presence and poor sleep in two low-income urban predominantly Black American neighbourhoods.

OBJECTIVE: To examine violent crime in relation to sleep and explore pathways, including psychological distress, safety perceptions and perceived police presence, that may account for associations. METHODS: In 2018, 515 predominantly Black American (94%) adults (Pittsburgh, Pennsylvania, USA) provided survey data: actigraphy-assessed sleep duration and wakefulness after sleep onset (WASO). We estimated pathways from violent crime (2016-2018) to sleep through psychological distress, perceptions of safety and perceived adequacy of police presence. RESULTS: WASO was most strongly associated with violent crimes that were within 1/10 mile of the participant's home and within the month preceding the interview. Violent crimes were associated with lower perceived safety (ß=-0.13 (0.03), p<0.001) and greater WASO (ß=5.96 (2.80), p=0.03). We observed no indirect associations between crime and either WASO or sleep duration through any of the tested mediators. Crime was not associated with sleep duration. CONCLUSIONS: We demonstrated that more proximal and more recent violent crimes were associated with reduced perceived safety and worse WASO. Differential exposure to violent crime among Black Americans may contribute to health disparities by reducing residents' perceived safety and sleep health.

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.

Estimating energy expenditure of head-hauling water and grain grinding from heart rate monitor measurements in northern Mozambique.

OBJECTIVE: Even though sub-Saharan African women spend millions of person-hours per day fetching water and pounding grain, to date, few studies have rigorously assessed the energy expenditure costs of such domestic activities. As a result, most analyses that consider head-hauling water or hand pounding of grain with a mortar and pestle (pilão use) employ energy expenditure values derived from limited research. The current paper compares estimated energy expenditure values from heart rate monitors v. indirect calorimetry in order to understand some of the limitations with using such monitors to measure domestic activities. DESIGN: This confirmation study estimates the metabolic equivalent of task (MET) value for head-hauling water and hand-pounding grain using both indirect calorimetry and heart rate monitors under laboratory conditions. SETTING: The study was conducted in Nampula, Mozambique. PARTICIPANTS: Forty university students in Nampula city who recurrently engaged in water-fetching activities. RESULTS: Including all participants, the mean MET value for head hauling 20 litres (20·5 kg, including container) of water (2·7 km/h, 0 % slope) was 4·3 (sd 0·9) and 3·7 (sd 1·2) for pilão use. Estimated energy expenditure predictions from a mixed model were found to correlate with observed energy expenditure (r2 0·68, r 0·82). Re-estimating the model with pilão use data excluded improved the fit substantially (r2 0·83, r 0·91). CONCLUSIONS: The current study finds that heart rate monitors are suitable instruments for providing accurate quantification of energy expenditure for some domestic activities, such as head-hauling water, but are not appropriate for quantifying expenditures of other activities, such as hand-pounding grain.

Resting Energy Expenditure Relationship with Macronutrients and Gestational Weight Gain: A Pilot Study.

Resting energy expenditure (REE) comprises 60% of total energy expenditure and variations may be associated with gestational weight gain (GWG) or maternal diet. The objective of this study was to examine the impact of metabolic tracking on GWG and the association with maternal macronutrients. Pregnant women aged 29.8 ± 4.9 years (78.6% non-Hispanic, White) with gestational age (GA) < 17 week were randomized to Breezing™ (n = 16) or control (n = 12) groups for 13 weeks. REE by Breezing™ indirect calorimetry, anthropometrics and dietary intake were collected every two weeks. Early (14-21 weeks), late (21-28 weeks), and overall (14-28 weeks) changes in macronutrients and GWG were calculated. The Breezing™ group had a significantly greater rate of GWG [F (1,23) = 6.8, p = 0.02] in the latter half of the second trimester. Late (-155.3 ± 309.2 vs. 207.1 ± 416.5 kcal, p = 0.01) and overall (-143.8 ± 339.2 vs. 191.8 ± 422.2 kcal, p = 0.03) changes in energy consumption were significantly different between Breezing™ and control groups, respectively. Early changes in REE were positively correlated with overall changes in carbohydrates (r = 0.58, p = 0.02). Regular metabolism tracking alone did not have an impact on GWG. Early shifts in REE might impact GWG later in pregnancy. Investigation in a larger population from preconception through postpartum is needed.

Physical Activity Promotion: Highlights from the 2018 Physical Activity Guidelines Advisory Committee Systematic Review.

PURPOSE: This article describes effective interventions to promote regular physical activity and reduce sedentary behavior that were identified as part of the 2018 Physical Activity Guidelines Advisory Committee Scientific Report. METHODS: A comprehensive literature search was conducted of eligible systematic reviews, meta-analyses, and relevant governmental reports published between 2011 and 2016. For the physical activity promotion question, articles were first sorted by four social ecological levels of impact (i.e., individual, community, communication environment, and physical environment and policy levels) and then further sorted into more specific categories that emerged during the review process. For the sedentary behavior reduction question, the literature was sorted directly into emergent categories (i.e., youth, adult, and worksite interventions). RESULTS: Effective physical activity promotion strategies were identified at each level of impact, including those based on behavior change theories and those occurring at different settings throughout the community. Effective interventions also included those delivered in person by trained staff or peer volunteers and through different information and communication technologies, such as by phone, Web or Internet, and computer-tailored print. A range of built environment features were associated with more transit-based and recreational physical activity in children and adults. Effective sedentary reduction interventions were found for youth and in the workplace. CONCLUSIONS: A promising number of interventions with demonstrated effectiveness were identified. Future recommendations for research include investigating the most useful methods for disseminating them to real-world settings; incorporating more diverse population subgroups, including vulnerable and underrepresented subgroups; collecting cost data to inform cost-effectiveness comparisons; and testing strategies across different levels of impact to determine which combinations achieve the greatest effects on different modes of physical activity across the week.

Identifying Free-Living Physical Activities Using Lab-Based Models with Wearable Accelerometers.

The purpose of this study was to classify, and model various physical activities performed by a diverse group of participants in a supervised lab-based protocol and utilize the model to identify physical activity in a free-living setting. Wrist-worn accelerometer data were collected from ( N = 152 ) adult participants; age 18⁻64 years, and processed the data to identify and model unique physical activities performed by the participants in controlled settings. The Gaussian mixture model (GMM) and the hidden Markov model (HMM) algorithms were used to model the physical activities with time and frequency-based accelerometer features. An overall model accuracy of 92.7% and 94.7% were achieved to classify 24 physical activities using GMM and HMM, respectively. The most accurate model was then used to identify physical activities performed by 20 participants, each recorded for two free-living sessions of approximately six hours each. The free-living activity intensities were estimated with 80% accuracy and showed the dominance of stationary and light intensity activities in 36 out of 40 recorded sessions. This work proposes a novel activity recognition process to identify unsupervised free-living activities using lab-based classification models. In summary, this study contributes to the use of wearable sensors to identify physical activities and estimate energy expenditure in free-living settings.

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.

Future directions in physical activity intervention research: expanding our focus to sedentary behaviors, technology, and dissemination.

Despite the increased health risks of a sedentary lifestyle, only 49 % of American adults participate in physical activity (PA) at the recommended levels. In an effort to move the PA field forward, we briefly review three emerging areas of PA intervention research. First, new intervention research has focused on not only increasing PA but also on decreasing sedentary behavior. Researchers should utilize randomized controlled trials, common terminology, investigate which behaviors should replace sedentary behaviors, evaluate long-term outcomes, and focus across the lifespan. Second, technology has contributed to an increase in sedentary behavior but has also led to innovative PA interventions. PA technology research should focus on large randomized trials with evidence-based components, explore social networking and innovative apps, improve PA monitoring, consider the lifespan, and be grounded in theory. Finally, in an effort to maximize public health impact, dissemination efforts should address the RE-AIM model, health disparities, and intervention costs.

Leveraging Citizen Science and Information Technology for Population Physical Activity Promotion.

PURPOSE: While technology is a major driver of many of society's comforts, conveniences, and advances, it has been responsible, in a significant way, for engineering regular physical activity and a number of other positive health behaviors out of people's daily lives. A key question concerns how to harness information and communication technologies (ICT) to bring about positive changes in the health promotion field. One such approach involves community-engaged "citizen science," in which local residents leverage the potential of ICT to foster data-driven consensus-building and mobilization efforts that advance physical activity at the individual, social, built environment, and policy levels. METHOD: The history of citizen science in the research arena is briefly described and an evidence-based method that embeds citizen science in a multi-level, multi-sectoral community-based participatory research framework for physical activity promotion is presented. RESULTS: Several examples of this citizen science-driven community engagement framework for promoting active lifestyles, called "Our Voice", are discussed, including pilot projects from diverse communities in the U.S. as well as internationally. CONCLUSIONS: The opportunities and challenges involved in leveraging citizen science activities as part of a broader population approach to promoting regular physical activity are explored. The strategic engagement of citizen scientists from socio-demographically diverse communities across the globe as both assessment as well as change agents provides a promising, potentially low-cost and scalable strategy for creating more active, healthful, and equitable neighborhoods and communities worldwide.

Increasing US health plan coverage for exercise programming in community mental health settings for people with serious mental illness: a position statement from the Society of Behavior Medicine and the American College of Sports Medicine.

Adults with serious mental illness die more than 10 years earlier than the average American. Premature mortality is due to the high prevalence of preventable diseases including cardiovascular disease and diabetes. Poor lifestyle behaviors including lack of exercise and physical inactivity contribute to the epidemic levels of obesity, diabetes, and cardiovascular disease observed among adults with serious mental illness. Not surprisingly, people with serious mental illness are among the most costly consumers of health services due to increased visits for poorly managed mental and physical health. Recent studies have demonstrated that exercise interventions based on community mental health settings can significantly improve physical and mental health in people with serious mental illness. However, current funding regulations limit the ability of community mental health settings to offer exercise programming services to people with serious mental illness. Policy efforts are needed to improve the dissemination and sustainability of exercise programs for people with serious mental illness.

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.

A statistical estimation framework for energy expenditure of physical activities from a wrist-worn accelerometer.

Energy expenditure (EE) estimation from accelerometer-based wearable sensors is important to generate accurate assessment of physical activity (PA) in individuals. Approaches hitherto have mainly focused on using accelerometer data and features extracted from these data to learn a regression model to predict EE directly. In this paper, we propose a novel framework for EE estimation based on statistical estimation theory. Given a test sequence of accelerometer data, the probability distribution on the PA classes is estimated by a classifier and these predictions are used to estimate EE. Experimental evaluation, performed on a large dataset of 152 subjects and 12 activity classes, demonstrates that EE can be estimated accurately using our framework.

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.

Validity and reliability of Nike + Fuelband for estimating physical activity energy expenditure.

BACKGROUND: The Nike + Fuelband is a commercially available, wrist-worn accelerometer used to track physical activity energy expenditure (PAEE) during exercise. However, validation studies assessing the accuracy of this device for estimating PAEE are lacking. Therefore, this study examined the validity and reliability of the Nike + Fuelband for estimating PAEE during physical activity in young adults. Secondarily, we compared PAEE estimation of the Nike + Fuelband with the previously validated SenseWear Armband (SWA). METHODS: Twenty-four participants (n = 24) completed two, 60-min semi-structured routines consisting of sedentary/light-intensity, moderate-intensity, and vigorous-intensity physical activity. Participants wore a Nike + Fuelband and SWA, while oxygen uptake was measured continuously with an Oxycon Mobile (OM) metabolic measurement system (criterion). RESULTS: The Nike + Fuelband (ICC = 0.77) and SWA (ICC = 0.61) both demonstrated moderate to good validity. PAEE estimates provided by the Nike + Fuelband (246 ± 67 kcal) and SWA (238 ± 57 kcal) were not statistically different than OM (243 ± 67 kcal). Both devices also displayed similar mean absolute percent errors for PAEE estimates (Nike + Fuelband = 16 ± 13 %; SWA = 18 ± 18 %). Test-retest reliability for PAEE indicated good stability for Nike + Fuelband (ICC = 0.96) and SWA (ICC = 0.90). CONCLUSION: The Nike + Fuelband provided valid and reliable estimates of PAEE, that are similar to the previously validated SWA, during a routine that included approximately equal amounts of sedentary/light-, moderate- and vigorous-intensity physical activity.

A qualitative study of shopper experiences at an urban farmers' market using the Stanford Healthy Neighborhood Discovery Tool.

OBJECTIVE: To understand factors which enhance or detract from farmers' market shopper experiences to inform targeted interventions to increase farmers' market utilization, community-building and social marketing strategies. DESIGN: A consumer-intercept study using the Stanford Healthy Neighborhood Discovery Tool to capture real-time perceptions via photographs and audio narratives. SETTING: An urban farmers' market in a large metropolitan US city. PARTICIPANTS: Thirty-eight farmers' market shoppers, who recorded 748 unique coded elements through community-based participatory research methods. RESULTS: Shoppers were primarily women (65 %), 18-35 years of age (54 %), non-Hispanic (81 %) and white (73 %). Shoppers captured 291 photographs (7·9 (sd 6·3) per shopper), 171 audio narratives (5·3 (sd 4·7) per shopper), and ninety-one linked photograph + audio narrative pairs (3·8 (sd 2·8) per shopper). A systematic content analysis of the photographs and audio narratives was conducted by eight independent coders. In total, nine common elements emerged from the data that enhanced the farmers' market experience (61·8 %), detracted from the experience (5·7 %) or were neutral (32·4 %). The most frequently noted elements were freshness/abundance of produce (23·3 %), product presentation (12·8 %), social interactions (12·4 %) and farmers' market attractions (e.g. live entertainment, dining offerings; 10·3 %). CONCLUSIONS: While produce quality (i.e. freshness/abundance) was of primary importance, other contextual factors also appeared important to the shoppers' experiences. These results may inform social marketing strategies to increase farmers' market utilization and community-building efforts that target market venues.

Validity of the Rapid Eating Assessment for Patients for assessing dietary patterns in NCAA athletes.

BACKGROUND: Athletes may be at risk for developing adverse health outcomes due to poor eating behaviors during college. Due to the complex nature of the diet, it is difficult to include or exclude individual food items and specific food groups from the diet. Eating behaviors may better characterize the complex interactions between individual food items and specific food groups. The purpose was to examine the Rapid Eating Assessment for Patients survey (REAP) as a valid tool for analyzing eating behaviors of NCAA Division-I male and female athletes using pattern identification. Also, to investigate the relationships between derived eating behavior patterns and body mass index (BMI) and waist circumference (WC) while stratifying by sex and aesthetic nature of the sport. METHODS: Two independent samples of male (n = 86; n = 139) and female (n = 64; n = 102) collegiate athletes completed the REAP in June-August 2011 (n = 150) and June-August 2012 (n = 241). Principal component analysis (PCA) determined possible factors using wave-1 athletes. Exploratory (EFA) and confirmatory factor analyses (CFA) determined factors accounting for error and confirmed model fit in wave-2 athletes. Wave-2 athletes' BMI and WC were recorded during a physical exam and sport participation determined classification in aesthetic and non-aesthetic sport. Mean differences in eating behavior pattern score were explored. Regression models examined interactions between pattern scores, participation in aesthetic or non-aesthetic sport, and BMI and waist circumference controlling for age and race. RESULTS: A 5-factor PCA solution accounting for 60.3% of sample variance determined fourteen questions for EFA and CFA. A confirmed solution revealed patterns of Desserts, Healthy food, Meats, High-fat food, and Dairy. Pattern score (mean ± SE) differences were found, as non-aesthetic sport males had a higher (better) Dessert score than aesthetic sport males (2.16 ± 0.07 vs. 1.93 ± 0.11). Female aesthetic athletes had a higher score compared to non-aesthetic female athletes for the Dessert (2.11 ± 0.11 vs. 1.88 ± 0.08), Meat (1.95 ± 0.10 vs. 1.72 ± 0.07), High-fat food (1.70 ± 0.08 vs. 1.46 ± 0.06), and Dairy (1.70 ± 0.11 vs. 1.43 ± 0.07) patterns. CONCLUSIONS: REAP is a construct valid tool to assess dietary patterns in college athletes. In light of varying dietary patterns, college athletes should be evaluated for healthful and unhealthful eating behaviors.

Exploring behavioral markers of long-term physical activity maintenance: a case study of system identification modeling within a behavioral intervention.

Efficacious interventions to promote long-term maintenance of physical activity are not well understood. Engineers have developed methods to create dynamical system models for modeling idiographic (i.e., within-person) relationships within systems. In behavioral research, dynamical systems modeling may assist in decomposing intervention effects and identifying key behavioral patterns that may foster behavioral maintenance. The Active Adult Mentoring Program was a 16-week randomized controlled trial of a group-based, peer-delivered physical activity intervention targeting older adults. Time-intensive (i.e., daily) physical activity reports were collected throughout the intervention. We explored differential patterns of behavior among participants who received the active intervention (N = 34; 88% women, 64.1 ± 8.3 years of age) and either maintained 150 minutes/week of moderate to vigorous intensity physical activity (MVPA; n = 10) or did not (n = 24) at 18 months following the intervention period. We used dynamical systems modeling to explore whether key intervention components (i.e., self-monitoring, access to an exercise facility, behavioral initiation training, behavioral maintenance training) and theoretically plausible behavioral covariates (i.e., indoor vs. outdoor activity) predicted differential patterns of behavior among maintainers and nonmaintainers. We found that maintainers took longer to reach a steady-state of MVPA. At week 10 of the intervention, nonmaintainers began to drop whereas maintainers increased MVPA. Self-monitoring, behavioral initiation training, percentage of outdoor activity, and behavioral maintenance training, but not access to an exercise facility, were key variables that explained patterns of change among maintainers. Future studies should be conducted to systematically explore these concepts within a priori idiographic (i.e., N-of-1) experimental designs.

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.