Sociodemographic disparities in sedentary time among US youth vary by period of the day.

INTRODUCTION: Studies have reported sex and race/ethnicity disparities in sedentary time (ST), but none have evaluated ST by well-defined periods of the weekday (before school, during school, afterschool, and evening) and weekend day (morning, afternoon, and evening). Comparing sex and race/ethnicity disparities in ST at different periods of a weekday and weekend day can deepen our understanding of disparities and inform intervention efforts. This study tests sex and race/ethnicity disparities in ST by period of day in a representative sample of US youth. METHODS: Youth (N = 2,972) from the 2003-2006 NHANES waves reported demographic variables and wore an accelerometer for 7 consecutive days to assess ST. Linear regressions were conducted to test relationships between sex and race/ethnicity and ST (min/hour) during each period of a weekday and weekend day. ST differences by sex and race/ethnicity were calculated to identify the periods of the day presenting the largest opportunity to reduce disparities. RESULTS: Females were more sedentary than males during school (p < 0∙0001), afterschool (p < 0∙0001), and weekday evenings (p < 0∙0001) after controlling for covariates. After controlling for covariates, race/ethnicity only was a significant predictor of ST during weekend mornings (p < 0∙0001). During school and afterschool emerged as the periods with the largest opportunities to reduce sex disparities in ST. Weekend mornings were identified as the largest opportunity to reduce race/ethnic disparities in ST. CONCLUSIONS: Sex disparities in ST appear to be driven mostly by the during school period of the day, while race/ethnic disparities in ST seem to be driven by the weekend morning period. Future intervention work should consider these periods when aiming to reduce ST disparities in youth.

Intervention to increase physical activity and healthy eating among under-represented adolescents: GOAL trial protocol.

INTRODUCTION: To reduce obesity-related disparities, reaching economically disadvantaged and/or minority status adolescents to assist them in meeting physical activity (PA) and nutrition recommendations is important. To address the problem, a 16-week intervention called Guys/Girls Opt for Activities for Life (GOAL) was designed. The purpose of this randomised controlled trial is to evaluate any effect of the intervention, compared with a control condition, on improving: (1) adolescents' % body fat (primary outcome), moderate-to-vigorous PA (MVPA), diet quality and cardiorespiratory fitness from 0 to 4 months; (2) body mass index (BMI), overweight/obesity percentage and quality of life from 0 to 4 months and to 13 months; and (3) perceived social support, self-efficacy and motivation from 0 to 4 months with evaluation of any mediating effect on adolescent PA and diet quality. An exploratory aim is to evaluate any effect of the intervention, compared with the control, on improving parents'/guardians' home environment, MVPA and diet quality from 0 to 4 months; and BMI from 0 to 4 months and to 13 months. METHODS AND ANALYSIS: Adolescents (fifth to eighth grade) in 14 schools located in underserved urban communities are randomly assigned to the intervention or usual school offerings. One parent per adolescent is enrolled (882 dyads total). Cohort 1 includes four schools (2022-2023). Cohorts 2 and 3 include 5 schools in 2023-2024 and 2024-2025, respectively. The 16-week intervention has three components: (1) after-school GOAL club for adolescents to engage in PA and healthy eating/cooking activities; (2) three parent-adolescent meetings to empower parents to assist adolescents; and (3) GOAL social networking website for parents to share how they helped their adolescent. ETHICS AND DISSEMINATION: The Michigan State University Biomedical Institutional Review Board provided ethical approval for the study. Findings will be shared via the trial registration database, peer-reviewed publications, conferences and community-oriented strategies. TRIAL REGISTRATION NUMBER: NCT04213014.

The effects of contemporary redlining on the mental health of Black residents.

Understanding how structural racism, including institutionalized practices such as redlining, influence persistent inequities in health and neighborhood conditions is still emerging in urban health research. Such research often focuses on historical practices, giving the impression that such practices are a thing of the past. However, mortgage lending bias can be readily detected in contemporary datasets and is an active form of structural racism with implications for health and wellbeing. The objective of the current study was to test for associations among multiple measures of mental health and a measure of contemporary redlining. We linked a redlining index constructed using Home Mortgage Disclosure Act data (2007-2013) to 2021 health data for Black/African American participants in the Study of Active Neighborhoods in Detroit (n = 220 with address data). We used multilevel regression models to examine the relationship between redlining and a suite of mental health outcomes (perceived stress, anxiety, depressive symptoms, and satisfaction with life), accounting for covariates including racial composition of the neighborhood. We considered three mediating factors: perceived neighborhood cohesion, aesthetics, and discrimination. Although all participants lived in redlined neighborhoods compared to the complete Detroit Metropolitan area, participants with very low income, low levels of experienced discrimination, and lower perceptions of neighborhood aesthetics resided in highly redlined neighborhoods (score ≥5). We observed that higher resident-reported neighborhood aesthetics were found in neighborhoods with lower redlining scores and were associated with higher levels of satisfaction with life. We found that lower levels of redlining were significantly associated with higher levels of perceived discrimination, which was significantly, positively associated with anxiety, depressive symptoms, and perceived stress scores. Our findings highlight that contemporary redlining practices may influence the aesthetics of the built environment because these neighborhoods experience less investment, with implications for residents' satisfaction with life. However, areas with lower redlining may be areas where Black/African American people experience increased perceived discrimination.

Developmental disparities in sedentary time by period of the day among US youth: a cross-sectional study.

BACKGROUND: Definitive evidence shows sedentary time (ST) is an independent risk factor for chronic disease, irrespective of physical activity. Despite calls to limit youth ST, studies demonstrate a spike in ST at the transition from childhood to adolescence. Identifying periods of the day (e.g., before school, during school, afterschool, and evenings) during which ST is higher in adolescents vs. children-that is, specifying when within daily routines ST disparities emerge-may be important to inform intervention strategies, as periods of the day correspond with variations in setting and supervision. The purpose of this study was to examine device-assessed ST engagement by period of day and developmental stage in a nationally representative sample of United States youth. METHODS: Youth (N = 2,972 between 6-18 years) from the 2003-2004 and 2005-2006 waves of NHANES reported demographic variables and wore an accelerometer for seven consecutive days to determine ST. Linear regression analyses were applied to study associations between ST and developmental stage (childhood or adolescence) by period of the week and weekend days, while controlling for sex, race/ethnicity, annual family income, and body mass index. RESULTS: Adjusted linear regressions (p-values < 0.0001) showed that adolescents were more sedentary than children during school, afterschool, and weekday evening periods as well as all the weekend periods. However, during school (36.3 ± 7.3 vs. 28.2 ± 7.2 min/hour; b = -7.4 [-8.1, -6.6]) and afterschool periods (31.1 ± 7.7 vs. 22.7 ± 7.0 min/hour; b = -7.8 [-8.6, -7.0]) showed the largest weekly ST disparities by developmental stage. Overall, the during school and after school hours constitute most (during school = 35% and afterschool = 16%) of the weekly ST disparity between children and adolescents. CONCLUSIONS: Our data provide interventionists with estimates of the potential for ST reduction in each setting and period of the day among US adolescents. Future research should gather information about the barriers and facilitators of ST in adolescents by period of the day to help understand factors driving disparities.

Methods for estimating physical activity and energy expenditure using raw accelerometry data or novel analytical approaches: a repository, framework, and reporting guidelines.

The proliferation of approaches for analyzing accelerometer data using raw acceleration or novel analytic approaches like machine learning ('novel methods') outpaces their implementation in practice. This may be due to lack of accessibility, either because authors do not provide their developed models or because these models are difficult to find when included as supplementary material. Additionally, when access to a model is provided, authors may not include example data or instructions on how to use the model. This further hinders use by other researchers, particularly those who are not experts in statistics or writing computer code.Objective: We created a repository of novel methods of analyzing accelerometer data for the estimation of energy expenditure and/or physical activity intensity and a framework and reporting guidelines to guide future work.Approach: Methods were identified from a recent scoping review. Available code, models, sample data, and instructions were compiled or created.Main Results: Sixty-three methods are hosted in the repository, in preschoolers (n = 6), children/adolescents (n = 20), and adults (n = 42), using hip (n = 45), wrist (n = 25), thigh (n = 4), chest (n = 4), ankle (n = 6), other (n = 4), or a combination of monitor wear locations (n = 9). Fifteen models are implemented in R, while 48 are provided as cut-points, equations, or decision trees.Significance: The developed tools should facilitate the use and development of novel methods for analyzing accelerometer data, thus improving data harmonization and consistency across studies. Future advances may involve including models that authors did not link to the original published article or those which identify activity type.

Accessibility and use of novel methods for predicting physical activity and energy expenditure using accelerometry: a scoping review.

Use of raw acceleration data and/or 'novel' analytic approaches like machine learning for physical activity measurement will not be widely implemented if methods are not accessible to researchers.Objective: This scoping review characterizes the validation approach, accessibility and use of novel analytic techniques for classifying energy expenditure and/or physical activity intensity using raw or count-based accelerometer data.Approach: Three databases were searched for articles published between January 2000 and February 2021. Use of each method was coded from a list of citing articles compiled from Google Scholar. Authors' provision of access to the model (e.g., by request, sample code) was recorded.Main Results: Studies (N = 168) included adults (n = 143), and/or children (n = 38). Model use ranged from 0 to 27 uses/year (average 0.83) with 101 models that have never been used. Approximately half of uses occurred in a free-living setting (52%) and/or by other authors (56%). Over half of included articles (n = 107) did not provide complete access to their model. Sixty-one articles provided access to their method by including equations, coefficients, cut-points, or decision trees in the paper (n = 48) and/or by providing access to code (n = 13).Significance: The proliferation of approaches for analyzing accelerometer data outpaces the use of these models in practice. As less than half of the developed models are made accessible, it is unsurprising that so many models are not used by other researchers. We encourage researchers to make their models available and accessible for better harmonization of methods and improved capabilities for device-based physical activity measurement.

Individual versus Group Calibration of Machine Learning Models for Physical Activity Assessment Using Body-Worn Accelerometers.

PURPOSE: We sought to determine if individually calibrated machine learning models yielded higher accuracy than a group calibration approach for physical activity intensity assessment. METHODS: Participants (n = 48) wore accelerometers on the right hip and nondominant wrist while performing activities of daily living in a semistructured laboratory and/or free-living setting. Criterion measures of activity intensity (sedentary, light, moderate, vigorous) were determined using direct observation. Data were reintegrated into 30-s epochs, and eight random forest models were created to determine physical activity intensity by using all possible conditions of training data (individual vs group), protocol (laboratory vs free-living), and placement (hip vs wrist). A 2 × 2 × 2 repeated-measures analysis of variance was used to compare epoch-level accuracy statistics (% accuracy, kappa [κ]) of the models when used to determine activity intensity in an independent sample of free-living participants. RESULTS: Main effects were significant for the type of training data (group: accuracy = 80%, κ = 0.59; individual: accuracy = 74% [P = 0.02], κ = 0.50 [P = 0.01]) and protocol (free-living: accuracy = 81%, κ = 0.63; laboratory: accuracy = 74% [P = 0.04], κ = 0.47 [P < 0.01]). Main effects were not significant for placement (hip: accuracy = 79%, κ = 0.58; wrist: accuracy = 75% [P = 0.18]; κ = 0.52 [P = 0.18]). Point estimates for mean absolute error were generally lowest for the group training, free-living protocol, and hip placement. CONCLUSIONS: Contrary to expectations, individually calibrated machine learning models yielded poorer accuracy than a traditional group approach. In addition, models should be developed in free-living settings when possible to optimize predictive accuracy.

Study of active neighborhoods in Detroit (StAND): study protocol for a natural experiment evaluating the health benefits of ecological restoration of parks.

BACKGROUND: Individuals living in deprived inner cities have disproportionately high rates of cancers, Type 2 diabetes and obesity, which have stress- and physical inactivity-related etiologies. This study aims to quantify effects of ecological park restoration on physical activity, stress and cardio-metabolic health outcomes. METHODS: The Study of Active Neighborhoods in Detroit is a quasi-experimental, longitudinal panel natural experiment with two conditions (restored park intervention (INT) and control (CNT)) and annual measurements at baseline and 3-years post-restoration. Individuals (sampled within 500 m of an INT/CNT park) serve as the unit of analysis. Restoration (n = 4 parks) involves replacing non-native plants and turf with native plants; creating trails; posting signage; and leading community stewardship events. The CNT condition (n = 5) is an unmaintained park, matched to INT based on specified neighborhood conditions. Recruitment involves several avenues, with a retention goal of 450 participants. Park measures include plant/avian diversity; usage of the park (SOPARC); signs of care; auditory environment recordings; and visual greenness using 360 imagery. Health outcomes include device-based physical activity behavior (primary outcome); salivary cortisol (secondary outcome); and several downstream health outcomes. Exposure to the INT will be assessed through visual contact time and time spent in the park using GPS data. Changes in health outcomes between years and INT versus CNT will be tested using generalized linear (mixed) models. DISCUSSION: Our study will examine whether restored urban greenspaces increase physical activity and lower stress, with public health planning implications, where small changes in neighborhood greenspaces may have large health benefits in low-income neighborhoods. STUDY REGISTRATION: Registration: OSF Preregistration registered March 31, 2020. Accessible from https://osf.io/surx7.

Space-time analysis of unhealthy food advertising: New Zealand children's exposure and health policy options.

Reducing children's exposure to unhealthy food advertising is an accepted strategy to end childhood obesity. This study aimed to (i) measure children's space-time exposures to unhealthy food advertising in public outdoor spaces, using GPS and wearable cameras; and (ii) test effectiveness of banning options. We compiled data (collected July 2014-June 2015) on 138 12-year-old children in Wellington, New Zealand, using wearable cameras and GPS devices worn over 4 days. In 2017-18, we linked 59 150 images taken in public outdoor spaces to GPS data. Of these, 1631 contained unhealthy food advertising exposures, defined as ≥50% of an advertisement observed in each image. We examined spatial patterns using kernel density mapping and graphed space-time trends. We interpolated a kriged exposure rate across Wellington to estimate exposure reductions for potential bans. Children were exposed to 7.4 (95% CI 7.0-7.8) unhealthy food advertisements/hour spent in outdoor public spaces. Exposures occurred in shopping centres, residential areas and sports facilities, commonly involving fast food, sugary drinks and ice cream. Peak exposure times were weekend afternoons/evenings and weekdays before/after school. In Wellington, we estimated that banning such advertising within 400 m of playgrounds would yield a 33% reduction in exposure, followed by in residential areas (27%), within 400 m of schools (25%), and 50% for a ban combining all above. This work documents the extent of children's exposure to unhealthy food advertising and the potential impact of bans. Given the ubiquity of advertising in public spaces, this New Zealand research offers innovative methods and findings likely relevant in other jurisdictions.

Accelerometer-based assessment of physical activity within the Fun For Wellness online behavioral intervention: protocol for a feasibility study.

BACKGROUND: Fun For Wellness (FFW) is an online behavioral intervention designed to promote growth in well-being and physical activity by providing capability-enhancing learning opportunities to participants. The conceptual framework for the FFW intervention is guided by self-efficacy theory. Evidence has been provided for the efficacy of FFW to promote self-reported free-living physical well-being actions in adults who comply with the intervention. The objective of this manuscript is to describe the protocol for a feasibility study designed to address uncertainties regarding the inclusion of accelerometer-based assessment of free-living physical activity within the FFW online intervention among adults with obesity in the United States of America (USA). METHOD: The study design is a prospective, double-blind, parallel group randomized pilot trial. Thirty participants will be randomly assigned to the FFW or usual care (UC) group to achieve a 1:1 group (i.e., FFW:UC) assignment. Recruitment of participants is scheduled to begin on 29 April 2019 at a local bariatric services center within a major healthcare organization in the Midwest of the USA. There are five eligibility criteria for participation in this study: (1) between 18 and 64 years old, (2) a body mass index ≥ 25.00 kg/m2, (3) ability to access the online intervention, (4) the absence of simultaneous enrollment in another intervention program promoting physical activity, and (5) willingness to comply with instructions for physical activity monitoring. Eligibility verification and data collection will be conducted online. Three waves of data will be collected over a 13-week period. Instruments designed to measure demographic information, anthropometric characteristics, acceptability and feasibility of accelerometer-based assessment of physical activity, self-efficacy, and well-being will be included in the study. Data will be analyzed using descriptive statistics (e.g., recruitment rates), Pearson's correlation coefficient, Bland-Altman analyses, and inferential statistical models under both an intent to treat approach and a complier average causal effect approach. DISCUSSION: Results are intended to inform the preparation of a future definitive randomized controlled trial. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03906942, registered 8 April 2019. TRIAL FUNDING: The Erwin and Barbara Mautner Charitable Foundation and the Michigan State University College of Education.

Energy Cost Expression for a Youth Compendium of Physical Activities: Rationale for Using Age Groups.

PURPOSE: This study compared the accuracy of physical activity energy expenditure (PAEE) prediction using 2 methods of accounting for age dependency versus 1 standard (single) value across all ages. METHODS: PAEE estimates were derived by pooling data from 5 studies. Participants, 6-18 years (n = 929), engaged in 14 activities while in a room calorimeter or wearing a portable metabolic analyzer. Linear regression was used to estimate the measurement error in PAEE (expressed as youth metabolic equivalent) associated with using age groups (6-9, 10-12, 13-15, and 16-18 y) and age-in-years [each year of chronological age (eg, 12 = 12.0-12.99 y)] versus the standard (a single value across all ages). RESULTS: Age groups and age-in-years showed similar error, and both showed less error than the standard method for cycling, skilled, and moderate- to vigorous-intensity activities. For sedentary and light activities, the standard had similar error to the other 2 methods. Mean values for root mean square error ranged from 0.2 to 1.7 youth metabolic equivalent across all activities. Error reduction ranged from -0.2% to 21.7% for age groups and -0.23% to 18.2% for age-in-years compared with the standard. CONCLUSIONS: Accounting for age showed lower errors than a standard (single) value; using an age-dependent model in the Youth Compendium is recommended.

Sources and Types of Social Support for Physical Activity Perceived by Fifth to Eighth Grade Girls.

BACKGROUND: Information is lacking on forms and sources of social support for physical activity (PA) received by adolescent girls during various pubertal stages. Two study purposes were to (a) identify the sources and forms of social support for PA perceived by adolescent girls, and (b) examine associations of pubertal stage and social support with PA. METHODS: A secondary analysis of data from a randomized trial was conducted. Fifth through eighth grade girls (N = 1,519) completed surveys on social support for PA and pubertal stage and wore an accelerometer. RESULTS: Girls in early-middle and late-post puberty most frequently received social support from their mothers. A higher proportion of girls in late-post puberty, compared to early-middle puberty, received social support from nonfamily adults (4.2% vs. 3.0%, p = .019). Girls identifying three sources participated in more moderate-to-vigorous PA than those having fewer sources (t1,512 = -3.57, p < .001). Various forms of social support, except for encouragement, were positively related to moderate-to-vigorous PA. Girls in early-middle puberty reported greater social support than those in late-post puberty (t1,512 = 3.99, p < .001). Social support was positively associated with moderate-to-vigorous PA, while girls in late-post puberty had lower moderate-to-vigorous PA than those in early-middle puberty. CONCLUSIONS: Mothers are important sources of social support for PA. Having more than two sources may result in greater PA. Encouraging girls to increase their PA may not be sufficient. CLINICAL RELEVANCE: Efforts are needed from health professionals to prevent any decline in social support for PA as girls advance across adolescence.

Evaluation of the activPAL accelerometer for physical activity and energy expenditure estimation in a semi-structured setting.

OBJECTIVES: Evaluate accuracy of the activPAL and its proprietary software for prediction of time spent in physical activity (PA) intensities (sedentary, light, and moderate-to-vigorous) and energy expenditure (EE) and compare its accuracy to that of a machine learning model (ANN) developed from raw activPAL data. DESIGN: Semi-structured accelerometer validation in a laboratory setting. METHODS: Participants (n=41 [20 male]; age=22.0±4.2) completed a 90-min protocol performing 13 activities for 3-10min each and choosing activity order, duration, and intensity. Participants wore an activPAL accelerometer (right thigh) and a portable metabolic analyzer. Criterion measures of time spent in sedentary, light, and moderate-to-vigorous PA were determined using measured MET values of ≤1.5, 1.6-2.9, and ≥3.0, respectively. Estimated times in each PA intensity from the activPAL software and ANN were compared with the criterion using repeated measures ANOVA. Window-by-window EE prediction was assessed using correlations and root mean square error. RESULTS: activPAL software-estimated sedentary time was not different from the criterion, but light PA was overestimated (6.2min) and moderate- to vigorous PA was underestimated (4.3min). ANN-estimated sedentary time and light PA were not different from the criterion, but moderate- to vigorous PA was overestimated (1.8min). For EE estimation, the activPAL software had lower correlations (r=0.76 vs. r=0.89) and higher error (1.74 vs. 1.07 METs) than the ANN. CONCLUSIONS: The ANN had higher accuracy for estimation of EE and PA than the activPAL software in this semi-structured laboratory setting, indicating potential for the ANN to be used in PA assessment.

Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data.

This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models (correlations: r = 0.71-0.88, RMSE: 1.11-1.61 METs; p > 0.05). For the thigh-worn accelerometer, there were no differences in correlations or RMSE between linear and ANN models (ANN-correlations: r = 0.89, RMSE: 1.07-1.08 METs. Linear models-correlations: r = 0.88, RMSE: 1.10-1.11 METs; p > 0.05). Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip (ANN-correlation: r = 0.88, RMSE: 1.12 METs. Linear models-correlations: r = 0.86, RMSE: 1.18-1.19 METs; p < 0.05), and both ANNs had higher correlations and lower RMSE than both linear models for the wrist-worn accelerometers (ANN-correlations: r = 0.82-0.84, RMSE: 1.26-1.32 METs. Linear models-correlations: r = 0.71-0.73, RMSE: 1.55-1.61 METs; p < 0.01). For studies using wrist-worn accelerometers, machine learning models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh-worn accelerometers and may be viable alternative modeling techniques for EE prediction for hip- or thigh-worn accelerometers.

Wrist-independent energy expenditure prediction models from raw accelerometer data.

PURPOSES: (1) Develop artificial neural network (ANN) models for wrist accelerometer data which can predict energy expenditure (EE) using data collected from either wrist. (2) Develop ANNs for detecting the wrist on which the accelerometer was worn. Forty-four adults wore GENEActiv accelerometers on the left and right wrists and a portable metabolic analyzer while participating in a 90 min semi-structured activity protocol. Participants performed 14 sedentary, lifestyle, exercise, and ambulatory activities and were allowed to choose activity order, duration, and intensity. ANNs were created to predict EE and wrist detection using a leave-one-out cross-validation. In total, 12 combinations of feature sets (mean and variance of raw, vector magnitude, and absolute value data), training methods (left- and right- wrist), and testing methods (left- and right-wrist data) were used to develop EE prediction ANNs. Accuracy of the ANNs was evaluated using correlations, root mean square error (RMSE), and bias, using metabolic analyzer data as the criterion for EE. ANNs using raw data from the same wrist (e.g. EE predicted from right wrist ANNs using accelerometer data from right wrist) had the highest accuracy for EE prediction (r = 0.84, RMSE = 1.25-1.26 METs); conversely, opposite-wrist prediction accuracy (e.g. EE predicted from right wrist ANNs using accelerometer data from left wrist) was lower (r = 0.60-0.64, RMSE = 1.93-2.01 METs). Preprocessing into absolute values prior to ANN development allowed for, high EE prediction accuracy, with no difference in accuracy for same- versus opposite-wrist prediction (r = 0.80-0.83, RMSE = 1.30-1.49 METs). Wrist detection ANNs correctly determined wrist placement 100% of the time. Highly accurate, wrist-independent EE prediction ANNs were developed by computing absolute values of raw acceleration data prior to ANN development. This method provides a potential approach for advancing predictive accuracy of wrist-worn accelerometers.

Energy Cost of Children's Structured and Unstructured Games.

BACKGROUND: Limited data are available on energy cost of common children's games using measured oxygen consumption. METHODS: Children (10.6 ± 2.9 years; N = 37; 26 male, 9 female) performed a selection of structured (bowling, juggling, obstacle course, relays, active kickball) and unstructured (basketball, catch, tennis, clothespin tag, soccer) activities for 5 to 30 minutes. Resting metabolic rate (RMR) was calculated using Schofield's age- and sex-specific equation. Children wore a portable metabolic unit, which measured expired gases to obtain oxygen consumption (VO2), youth METs (relative VO2/child's calculated RMR), and activity energy expenditure (kcal/kg/min). Descriptive statistics were used to summarize data. RESULTS: Relative VO2 ranged from 16.8 ± 4.6 ml/kg/min (bowling) to 32.2 ± 6.8 ml/kg/min (obstacle course). Obstacle course, relays, active kickball, soccer, and clothespin tag elicited vigorous intensity (>6 METs), the remainder elicited moderate intensity (3-6 METs). CONCLUSIONS: This article contributes energy expenditure data for the update and expansion of the youth compendium.

Examining reach, dose, and fidelity of the "Girls on the Move" after-school physical activity club: a process evaluation.

BACKGROUND: After-school programs represent a promising opportunity to assist adolescent girls' in attaining adequate physical activity. Although evaluating the process of intervention implementation is important for determining if an intervention was delivered and received as intended, comprehensive information about process evaluation methods and results are rarely reported. The purpose of this article was to evaluate the reach, dose, and fidelity of a 90-minute after-school physical activity club offered 3 days a week. The club is 1 of 3 components included in a 17-week intervention designed for 5th-8th grade girls, the majority of whom were of minority and/or low socioeconomic status. METHODS: A total of 24 schools (12 intervention; 12 control) and 56-67 girls per school (total N = 1519 girls) were included in the Girls on the Move group randomized controlled trial. At the beginning of each of 3 academic years (2012-2015), 8 schools per year were randomized to receive either the intervention (n = 4) or control condition (n = 4). To evaluate the club, data collected via surveys from girls, club coaches and managers, and process evaluators were analyzed. To evaluate the opportunity for physical activity provided by the coaches and managers, process evaluators used an observation tool based on the System for Observing Fitness Instruction Time and Academic Learning Time - Physical Education. Girls wore accelerometers every other week during the club time. RESULTS: Mean attendance was 41 % with the average attendance in year 3 being higher than rates for years 1 or 2. Mean moderate-to-vigorous physical activity time was 21.85 minutes measured via accelerometry and 21.81 minutes observed by process evaluators. Satisfaction with the intervention was high. For the most part, process evaluators perceived the club was delivered as planned and reflected constructs of the Health Promotion Model and Self-Determination Theory. Areas contributing to success included using incentives and offering a variety of activities. Issues negatively impacting implementation included managing behavioral problems, having limited space for moderate-to-vigorous physical activity, dealing with inclement weather, and getting coaches to actively participate in all physical activities with the girls. CONCLUSIONS: This process evaluation provides important information to guide future school-based physical activity intervention delivery. Barriers to implementation have been identified. Ways to overcome them warrant consideration when designing physical activity interventions. Research is needed to test innovative approaches for enhancing attendance and increasing girls' moderate-to-vigorous physical activity in after-school programs. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT01503333 . Registered 23 December, 2011.

Validation of a wireless accelerometer network for energy expenditure measurement.

The purpose of this study was to validate a wireless network of accelerometers and compare it to a hip-mounted accelerometer for predicting energy expenditure in a semi-structured environment. Adults (n = 25) aged 18-30 engaged in 14 sedentary, ambulatory, exercise, and lifestyle activities over a 60-min protocol while wearing a portable metabolic analyser, hip-mounted accelerometer, and wireless network of three accelerometers worn on the right wrist, thigh, and ankle. Participants chose the order and duration of activities. Artificial neural networks were created separately for the wireless network and hip accelerometer for energy expenditure prediction. The wireless network had higher correlations (r = 0.79 vs. r = 0.72, P < 0.01) but similar root mean square error (2.16 vs. 2.09 METs, P > 0.05) to the hip accelerometer. Measured (from metabolic analyser) and predicted energy expenditure from the hip accelerometer were significantly different for the 3 of the 14 activities (lying down, sweeping, and cycle fast); conversely, measured and predicted energy expenditure from the wireless network were not significantly different for any activity. In conclusion, the wireless network yielded a small improvement over the hip accelerometer, providing evidence that the wireless network can produce accurate estimates of energy expenditure in adults participating in a range of activities.

Biological and Sociocultural Differences in Perceived Barriers to Physical Activity Among Fifth- to Seventh-Grade Urban Girls.

BACKGROUND: Inadequate physical activity (PA) contributes to the high prevalence of overweight and obesity among U.S. adolescent girls. Barriers preventing adolescent girls from meeting PA guidelines have not been thoroughly examined. OBJECTIVES: The threefold purpose of this study was to (a) determine pubertal stage, racial/ethnic, and socioeconomic status (SES) differences in ratings of interference of barriers to PA; (b) examine relationships between perceived barriers and age, body mass index, recreational screen time, sedentary activity, and PA; and (c) identify girls' top-rated perceived barriers to PA. METHODS: Girls (N = 509) from eight Midwestern U.S. schools participated. Demographic, pubertal stage, perceived barriers, and recreational screen time data were collected via surveys. Height and weight were measured. Accelerometers measured sedentary activity, moderate-to-vigorous PA (MVPA), and light plus MVPA. RESULTS: Girls of low SES reported greater interference of perceived barriers to PA than those who were not of low SES (1.16 vs. 0.97, p = .01). Girls in early/middle puberty had lower perceived barriers than those in late puberty (1.03 vs. 1.24, p < .001). Girls' perceived barriers were negatively related to MVPA (r = -.10, p = .03) and light plus MVPA (r = -.11, p = .02). Girls' top five perceived barriers included lack of skills, hating to sweat, difficulty finding programs, being tired, and having pain. DISCUSSION: Innovative interventions, particularly focusing on skill development, are needed to assist girls in overcoming their perceived barriers to PA.

Prevalence of Compliance with a New Physical Activity Guideline for Preschool-Age Children.

BACKGROUND: Four expert panels from Australia, Canada, the United Kingdom, and the United States issued physical activity (PA) recommendations for young children that are quite similar. The aim of this study was to determine compliance with the new PA guideline (defined as ≥15 min/hr of total PA) in two independent samples of preschool children. METHODS: We conducted a cross-sectional study of children attending preschools in Columbia, South Carolina. A total of 286 children in one sample and 337 children in a second sample participated. The main outcome of interest was total PA (sum of light, moderate, and vigorous intensity activity) measured by accelerometry. Compliance with the PA guideline was determined for both samples. Separately for each sample, mixed logistic models were used to determine whether there were differences in compliance with the PA guideline between groups based on sex, race/ethnicity, parent education, and weight status, controlling for preschool. RESULTS: Total PA was 14.5 and 15.2 min/hr in the first and second samples, respectively. The prevalence of meeting the PA guideline was 41.6% and 50.2% in the first and second samples, respectively. In both samples, more males than females met the guideline (53.5% vs. 33.5% and 57.6% vs. 45.9%) in the first and second samples, respectively (p<0.05). CONCLUSIONS: Approximately one half of children in two independent samples met the guideline for PA in young children. Policies and practices designed to increase PA among preschool children are needed, given that most children are not meeting this PA guideline.