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Purpose: The Youth Activity Profile (YAP) is a 7-day self-report designed to quantify physical activity and sedentary behaviors among youth. This study evaluated the reliability of the online version of the YAP and equivalence with the paper-based version., Method: A total of 2,490 participants from 17 schools in Iowa and Texas completed the YAP. Each classroom was randomly assigned to complete either the print or online version twice. A variance components model was used to assess the test-retest reliability and equivalence testing was also applied., Results: Both paper and online versions had similar reliability for the PA estimates in school (ICCprint = 0.69-0.91; ICConline = 0.54-0.84), at home (ICCprint = 0.72-0.83; ICConline = 0.64-0.94), PA at weekend (ICCprint = 0.33-0.72; ICConline = 0.39-0.70), and SB (ICCprint = 0.69-0.90; ICConline = 0.66-0.80). The two versions were statistically equivalent for most YAP items except for recess., Conclusion: The online YAP appears to be a reliable assessment of physical activity and sedentary behavior in youth populations., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2025 Bai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In every statistical analysis, a critical step is to determine the smallest effect size of interest, namely, the arbitrary dividing line between meaningful and negligible results. Different tests address this in different ways, and the contrasting approaches can sometimes lead to confusion. We discuss a key example of such confusion, whereby equivalence testing is perceived to be more arbitrary than difference testing. Our comments are intended to clarify that the latter methods share parallel arbitrariness, and to show how the contrary perception is fueled by the habituated use of "nil null hypotheses" in difference testing. The main premise is that nil null hypotheses give an appearance of objectivity by making the smallest effect size of interest an implicit factor in the interpretation stage of difference testing. When contrasted with the requirements of equivalence testing (where the smallest effect size of interest must be explicitly declared and justified a priori, in the form of the equivalence zone), it is therefore understandable how the misperception of greater arbitrariness could emerge. By combating the latter misperception, our comments serve to promote good practice in both difference testing and equivalence testing., Competing Interests: Conflict of interest The authors report no conflicts of interest., (Copyright © 2024 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.)

Objective: Health coaching (HC) may promote healthy behaviors in incoming college students. Participants: 187 undergraduate students (76% female; age 18.6 ± 1.6) were recruited to participate in a peer HC program. Methods: Participants received four HC sessions in eight-weeks to work toward self-selected goals of physical activity (PA), diet, or stress management. PA was assessed using the International Physical Activity Questionnaire Short-Form, diet with the Eating Habits Confidence Survey, and stress with the Perceived Stress Scale. Results: Repeated measures ANOVAs investigated lifestyle behavior changes in 130 participants. Significant effects of time were reported, with a 39.6% gain in PA, 9.9% increase in dietary habit, and 16.3% decline in stress levels with moderate to large effect sizes (η p 2 : 0.07-0.17; p< 0.01). Conclusion: The peer HC model promoted healthy lifestyles in college students. Additional research is needed to understand the effects of HC specifically for each goal, and student reactions to peer-led HC.

Background: Falls are the leading cause of injury related morbidity and mortality in older adults. Primary and secondary prevention strategies that address modifiable risk factors are critically important to reduce the number of falls and fall related injuries. A number of evidence-based fall prevention programs are available, but few offer potential for broad dissemination and public health impact due to implementation barriers, such as a need for trained program leaders and clinicians., Methods: The study will use a randomized controlled trial design to evaluate incorporating physical therapy exercises (primary prevention strategy) within an existing intervention called Walk with Ease. While Walk with Ease has an established evidence-base related to the management of arthritis pain and symptoms, the present study will determine the potential to also reduce falls and fall risk in community-dwelling older adults. The integrated process and outcome evaluation will determine the relative effectiveness of individually-prescribed exercises (compared to standardized exercises) as well as the potential of 'habit training' resources (relative to generic behavior prompts) to improve compliance with exercises in this population., Discussion: The study, conducted through a local clinical-community partnership will advance both the science and practice of community-based fall prevention programming, while also informing implementation strategies needed to promote broader dissemination., Trial Registration: ClinicalTrials.gov, NCT05693025, Registered January 20, 2023, Updated March 1, 2023., (© 2024. The Author(s).)

Older adults face unique barriers and challenges related to physical activity (PA) participation. Motivational interviewing (MI) is a commonly used health coaching strategy to support behavior change that holds potential for older adults. Previous research on MI strategies has focused primarily on face-to-face delivery, limiting insights regarding virtual programs., Objectives: The purpose of this study was to determine if MI could be delivered telephonically with high fidelity and high acceptability in older adult participants. The study is designed to inform future trials evaluating its effectiveness in supporting virtual PA programs., Methods: This study evaluated the feasibility and acceptability of telephonic MI among older adults that enrolled in an online version of the Walk with Ease program., Results: Of 39 participants referred, 29 enrolled and 27 provided feedback. Participants were highly accepting and adherent, with 74 % of patients attending at least five of six sessions, and 96 % of participants indicating satisfaction with the MI provided. Coaches improved program enjoyment by helping set effective goals and providing ongoing accountability., Conclusions: Evaluations documented adequate fidelity and high acceptability of telephonic delivery, and motivation results revealed large, significant increases in autonomous regulation (quality of motivation)., Innovation: Telephonic MI using non-healthcare professional coaches is feasibly delivered with high fidelity, and is acceptable to older adult participants. The innovative delivery format offers novel opportunities supporting virtual and telehealth interventions to reduce chronic disease risk among older adults., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Background: The influence of home obesogenic environments, as assessed by the validated Family Nutrition and Physical Activity (FNPA) tool, and child obesity during the COVID pandemic were evaluated using electronic health records in this retrospective cohort study., Methods: Historical data on BMI and the FNPA screening tool were obtained from annual well-child visits within the Geisinger Health System. The study examined youth ages 2-17 that had a BMI record and an FNPA assessment prior to the pandemic (BMI 3/1/19-2/29/20), 1 BMI record 3 months into the pandemic (6/1/20-12/31/20) and 1 BMI in the second year of the pandemic (1/1/21-12/31/21). Tertiles of obesity risk by FNPA score were examined. Mixed-effects linear regression was used to examine change in BMI slope (kg/m 2 per month) pre-pandemic to pandemic using FNPA summary and subscales scores as predictors and adjusting for confounding factors., Results: The analyses included 6,746 children (males: 51.7%, non-Hispanic white: 86.6%, overweight:14.8%, obesity:10.3%, severe obesity: 3.9%; mean(SD) age: 5.7(2.8) years). The rate of BMI change in BMI was greatest from early pandemic compared to pre-pandemic for children in lowest versus highest tertiles of FNPA summary score (0.079 vs. 0.044 kg/m 2 ), FNPA-Eating (0.068 vs. 0.049 kg/m 2 ), and FNPA-Activity (0.078 vs. 0.052 kg/m 2 ). FNPA summary score was significantly associated with change in BMI from the pre-pandemic to early pandemic period (p = 0.014), but not associated with change in BMI during the later pandemic period., Conclusions: This study provides additional insight into the changes in the rate of BMI change observed among children and adolescents in the United States during the COVID-19 pandemic. The FNPA provides ample opportunity to continue our exploration of the negative impact of the COVID-19 pandemic on the longitudinal growth patterns among children and adolescents., (© 2024. The Author(s).)

Study Objectives: Evaluate wrist-placed accelerometry predicted heartrate compared to electrocardiogram (ECG) heartrate in children during sleep., Methods: Children (n=82, 61% male, 43.9% Black) wore a wrist-placed Apple Watch Series 7 (AWS7) and ActiGraph GT9X during a polysomnogram. 3-Axis accelerometry data was extracted from AWS7 and the GT9X. Accelerometry heartrate estimates were derived from jerk (the rate of acceleration change), computed using the peak magnitude frequency in short time Fourier Transforms of Hilbert transformed jerk computed from acceleration magnitude. Heartrates from ECG traces were estimated from R-R intervals using R-pulse detection. Lin's Concordance Correlation Coefficient (CCC), mean absolute error (MAE) and mean absolute percent error (MAPE) assessed agreement with ECG estimated heartrate. Secondary analyses explored agreement by polysomnography sleep stage and a signal quality metric., Results: The developed scripts are available on Github. For the GT9X, CCC was poor at -0.11 and MAE and MAPE were high at 16.8 (SD=14.2) beats/minute and 20.4% (SD=18.5%). For AWS7, CCC was moderate at 0.61 while MAE and MAPE were lower at 6.4 (SD=9.9) beats/minute and 7.3% (SD=10.3%). Accelerometry estimated heartrate for AWS7 was more closely related to ECG heartrate during N2, N3 and REM sleep than lights on, wake, and N1 and when signal quality was high. These patterns were not evident for the GT9X., Conclusions: Raw accelerometry data extracted from AWS7, but not the GT9X, can be used to estimate heartrate in children while they sleep. Future work is needed to explore the sources (i.e., hardware, software, etc.) of the GT9X's poor performance., (© The Author(s) 2024. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

The purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n = 10 ActiGraph wGT3X-BT, n = 10 Apple Watch Series 7, n = 10 Garmin Vivoactive 4S, and n = 10 Fitbit Sense) were compared to reference accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested inter-device reliability. Pearson product moment, Lin's concordance correlation coefficient (CCC), absolute error, mean bias, and equivalence testing were calculated to assess the validity between the raw estimates from the devices and the reference metric. Estimates from Apple, ActiGraph, Garmin, and Fitbit were reliable, with ICCs = 0.99, 0.97, 0.88, and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the reference CCCs = 0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC = 0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors = 16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error = 32.5mg compared to the reference. ActiGraph produced the lowest mean bias 0.0mg (95%CI = -40.0, 41.0). Equivalence testing revealed raw accelerometry data from all devices were not statistically significantly within the equivalence bounds of the shaker speed. Findings from this study provide evidence that raw accelerometry data from Apple, Garmin, and Fitbit devices can be used to reliably estimate movement; however, no estimates were statistically significantly equivalent to the reference. Future studies could explore device-agnostic and harmonization methods for estimating physical activity using the raw accelerometry signals from the consumer wearables studied herein., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Unrelated to this work Dr. Weaver and Dr. Armstrong report board membership and ownership shares in Trackster LLC. Unrelated to this work Dr. de Zambotti reports grants from Noctrix Health and Verily Life Science LLC (Alphabet Inc.), and is a co-founder and Chief Scientific Officer at Lisa Health Inc. and has ownership of shares in Lisa Health., (Copyright: © 2024 White et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Lack of physical activity (PA) among children living in rural communities is a documented public health problem. Although studies have examined community conditions defined by a rural-urban dichotomy, few have investigated rural community conditions with a concentration of Hispanic/Latino people. This cross-sectional study examined sociodemographic characteristics associated with youth sport (YS) participation and daily PA among children living within concentrated Hispanic/Latino rural U.S. Midwest communities., Methods: During spring 2022, 97% of 3rd-6th grade children ( n  = 281, aged approximately 8-12 years) attending school in rural Midwestern communities ( n  = 2) with >50% concentration of Hispanic students participated in the Wellscapes Project, a community randomized trial. Participants completed the Youth Activity Profile and supplemental National Survey of Children's Health questions assessing PA behaviors and YS participation. Caregivers of a subsample of children ( n =  215; males, n  = 93; females, n  = 122) consented to pair their child's survey results with school enrollment records (e.g., free/reduced lunch status and race and ethnicity). Mixed models with community as a random effect examined main and interaction effects of grade, sex, ethnoracial status, and family income on YS participation and these sociodemographic characteristics and YS participation on daily moderate-to-vigorous PA (MVPA)., Results: Approximately half of children participated in YS. Non-Hispanic White children ( n  = 82) were over five times more likely to participate in YS than Hispanic peers ( n  = 133) (OR = 5.54, 95% CI = 2.64-11.61, p  < 0.001). YS participants accumulated 8.3 ± 2.3 more minutes of daily MVPA than non-participants ( p  < 0.001). Sixth graders, females, and Hispanic children reported lower daily MVPA than comparison groups ( p  < 0.05). Significant interaction effects on daily MVPA between grade and ethnoracial status ( F (3, 204) = 3.04, p  = 0.030) were also found., Discussion: Disparities in sport participation and PA outcomes based on sociodemographic characteristics exist among children living in ethnoracially diverse rural communities. Strategies to promote YS participation, including community structural changes, may help reduce PA disparities. The research provides valuable insights for policymakers, public health professionals, and community members to address YS participation barriers, not limited to cost, while considering other PA-promotion efforts to improve child population health., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Von Seggern, Rogers, Schenkelberg, Kellstedt, Welk, High and Dzewaltowski.)

Introduction: This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared with a research-grade accelerometry., Methods: Seventy-five 5- to 12-year-olds (58% male, 63% White) participated in a 60-min protocol. Children wore wrist-placed consumer wearables (Apple Watch Series 7 and Garmin Vivoactive 4) and a research-grade device (ActiGraph GT9X) concurrently with an indirect calorimeter (COSMED K5). Activity intensities (i.e., inactive, light, moderate-to-vigorous physical activity) were estimated via indirect calorimetry (criterion), and the Hildebrand thresholds were applied to the raw accelerometer data from the consumer wearables and research-grade device. Epoch-by-epoch (e.g., weighted sensitivity, specificity) and discrepancy (e.g., mean bias, absolute error) analyses evaluated agreement between accelerometry-derived and criterion estimates. Equivalence testing evaluated the equivalence of estimates produced by the consumer wearables and ActiGraph., Results: Estimates produced by the raw accelerometry data from ActiGraph, Apple, and Garmin produced similar criterion agreement with weighted sensitivity = 68.2% (95% confidence interval (CI), 67.1%-69.3%), 73.0% (95% CI, 71.8%-74.3%), and 66.6% (95% CI, 65.7%-67.5%), respectively, and weighted specificity = 84.4% (95% CI, 83.6%-85.2%), 82.0% (95% CI, 80.6%-83.4%), and 75.3% (95% CI, 74.7%-75.9%), respectively. Apple Watch produced the lowest mean bias (inactive, -4.0 ± 4.5; light activity, 2.1 ± 4.0) and absolute error (inactive, 4.9 ± 3.4; light activity, 3.6 ± 2.7) for inactive and light physical activity minutes. For moderate-to-vigorous physical activity, ActiGraph produced the lowest mean bias (1.0 ± 2.9) and absolute error (2.8 ± 2.4). No ActiGraph and consumer wearable device estimates were statistically significantly equivalent., Conclusions: Raw accelerometry estimated inactive and light activity from wrist-placed consumer wearables performed similarly to, if not better than, a research-grade device, when compared with indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables., (Copyright © 2023 by the American College of Sports Medicine.)

The purpose of this meta-analysis was to quantify the difference in physical activity and sleep estimates assessed via 1) movement, 2) heart rate (HR), or 3) the combination of movement and HR (MOVE+HR) compared to criterion indicators of the outcomes. Searches in four electronic databases were executed September 21-24 of 2021. Weighted mean was calculated from standardized group-level estimates of mean percent error (MPE) and mean absolute percent error (MAPE) of the proxy signal compared to the criterion measurement method for physical activity, HR, or sleep. Standardized mean difference (SMD) effect sizes between the proxy and criterion estimates were calculated for each study across all outcomes, and meta-regression analyses were conducted. Two-One-Sided-Tests method were conducted to metaanalytically evaluate the equivalence of the proxy and criterion. Thirty-nine studies (physical activity k = 29 and sleep k = 10) were identified for data extraction. Sample size weighted means for MPE were -38.0%, 7.8%, -1.4%, and -0.6% for physical activity movement only, HR only, MOVE+HR, and sleep MOVE+HR, respectively. Sample size weighted means for MAPE were 41.4%, 32.6%, 13.3%, and 10.8% for physical activity movement only, HR only, MOVE+HR, and sleep MOVE+HR, respectively. Few estimates were statistically equivalent at a SMD of 0.8. Estimates of physical activity from MOVE+HR were not statistically significantly different from estimates based on movement or HR only. For sleep, included studies based their estimates solely on the combination of MOVE+HR, so it was impossible to determine if the combination produced significantly different estimates than either method alone.

Background: Physical activity is an effective method of reducing fall risk among older adults. Previous evaluations of the six-week Walk with Ease (WWE) program have documented benefits to functional outcomes, but the potential effects on reducing fall risk have not been evaluated. This pilot study evaluates outcomes of a community delivered WWE program for potential suitability as a fall risk reduction program., Methods: A total of 59 older adults (age > 60) enrolled in a group version of WWE delivered by trained community-based leaders. Complete data (pre- and post-program) from functional fitness tests and behavioral instruments were obtained from 41 participants (aged 74.4 ± 6.6 years, 70% female). Functional outcomes included the 10-foot timed up and go (TUG), 30-second chair stand (CST) and 4-stage balance test (BT) included as part of STEADI, as well as a two-minute step test (ST) and normal gait speed test (GST). Survey assessments included STEADI fall risk screening, self-reported physical activity, and fear of falling measures. Analyses focused on reporting pre-post effect sizes, but paired t-tests were used to test statistical significance of differences., Results: Improvements in functional performance approached significance for both CST (d = 0.31, p = 0.06) and ST (d = 0.26, p = 0.12), but all other tests were nonsignificant. Survey results demonstrated significant increases in self-reported walking (d = 0.54, p = 0.02) and moderate-to-vigorous physical activity (MVPA; d = 0.56, p = 0.004), but perceived fear of falling and overall fall risk scores had smaller, non-significant, effects (d ranging from 0.01 to 0.31). Stratified analysis suggested that participants screened at an elevated risk for falls at baseline consistently had larger effects on all functional and survey assessments, though the analysis was underpowered to test significance., Conclusions: Walk with Ease participation significantly increased self-reported physical activity but did not significantly improve physical function or reduce fall risk. However, consistently larger effect sizes among participants screened as at-risk for falls suggest that the program may be beneficial for those with elevated risk for falls or functional limitations. Further research is needed to document the consistency of these effects among participants with elevated fall risk status., (© 2023. The Author(s).)

Background: Intake-balance assessments measure energy intake (EI) by summing energy expenditure (EE) with concurrent change in energy storage (ΔES). Prior work has not examined the validity of such calculations when EE is estimated via open-source techniques for research-grade accelerometry devices. The purpose of this study was to test the criterion validity of accelerometry-based intake-balance methods for a wrist-worn ActiGraph device., Methods: Healthy adults (n = 24) completed two 14-day measurement periods while wearing an ActiGraph accelerometer on the non-dominant wrist. During each period, criterion values of EI were determined based on ΔES measured by dual X-ray absorptiometry and EE measured by doubly labeled water. A total of 11 prediction methods were tested, 8 derived from the accelerometer and 3 from non-accelerometry methods (e.g., diet recall; included for comparison). Group-level validity was assessed through mean bias, while individual-level validity was assessed through mean absolute error, mean absolute percentage error, and Bland-Altman analysis., Results: Mean bias for the three best accelerometry-based methods ranged from -167 to 124 kcal/day, versus -104 to 134 kcal/day for the non-accelerometry-based methods. The same three accelerometry-based methods had mean absolute error of 323-362 kcal/day and mean absolute percentage error of 18.1-19.3%, versus 353-464 kcal/day and 19.5-24.4% for the non-accelerometry-based methods. All 11 methods demonstrated systematic bias in the Bland-Altman analysis., Conclusions: Accelerometry-based intake-balance methods have promise for advancing EI assessment, but ongoing refinement is necessary. We provide an R package to facilitate implementation and refinement of accelerometry-based methods in future research (see paulhibbing.com/IntakeBalance)., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Goal and Aims: Evaluate the performance of a sleep scoring algorithm applied to raw accelerometry data collected from research-grade and consumer wearable actigraphy devices against polysomnography., Focus Method/technology: Automatic sleep/wake classification using the Sadeh algorithm applied to raw accelerometry data from ActiGraph GT9X Link, Apple Watch Series 7, and Garmin Vivoactive 4., Reference Method/technology: Standard manual PSG sleep scoring., Sample: Fifty children with disrupted sleep (M = 8.5 years, range = 5-12 years, 42% Black, 64% male)., Design: Participants underwent to single night lab polysomnography while wearing ActiGraph, Apple, and Garmin devices., Core Analytics: Discrepancy and epoch-by-epoch analyses for sleep/wake classification (devices vs. polysomnography)., Additional Analytics and Exploratory Analyses: Equivalence testing for sleep/wake classification (research-grade actigraphy vs. commercial devices)., Core Outcomes: Compared to polysomnography, accuracy, sensitivity, and specificity were 85.5, 87.4, and 76.8, respectively, for Actigraph; 83.7, 85.2, and 75.8, respectively, for Garmin; and 84.6, 86.2, and 77.2, respectively, for Apple. The magnitude and trend of bias for total sleep time, sleep efficiency, sleep onset latency, and wake after sleep were similar between the research and consumer wearable devices., Important Additional Outcomes: Equivalence testing indicated that total sleep time and sleep efficiency estimates from the research and consumer wearable devices were statistically significantly equivalent., Core Conclusion: This study demonstrates that raw acceleration data from consumer wearable devices has the potential to be harnessed to predict sleep in children. While further work is needed, this strategy could overcome current limitations related to proprietary algorithms for predicting sleep in consumer wearable devices., (Copyright © 2023 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

Purpose: Harmonization of assessment methods represents an ongoing challenge in physical activity research. Previous research has demonstrated the utility of calibration approaches to enhance agreement between measures of physical activity. The present study utilizes a calibration methodology to add behavioral context from the Global Physical Activity Questionnaire (GPAQ), an established report-based measure, to enhance interpretations of monitor-based data scored using the novel Monitor Independent Movement Summary (MIMS) methodology., Methods: Matching data from the GPAQ and MIMS were obtained from adults (20-80 yr of age) assessed in the 2011-2014 National Health and Nutrition Examination Survey. After developing percentile curves for self-reported activity, a zero-inflated quantile regression model was developed to link MIMS to estimates of moderate to vigorous physical activity (MVPA) from the GPAQ., Results: Cross-validation of the model showed that it closely approximated the probability of reporting MVPA across age and activity-level segments, supporting the accuracy of the zero-inflated model component. Validation of the quantile regression component directly corresponded to the 25%, 50%, and 75% values for both men and women, further supporting the model fit., Conclusions: This study offers a method of improving activity surveillance by translating accelerometer signals into interpretable behavioral measures using nationally representative data. The model provides accurate estimates of minutes of MVPA at a population level but, because of the bias and error inherent in report-based measures of physical activity, is not suitable for converting or interpreting individual-level data. This study provides an important preliminary step in utilizing information from both device- and report-based methods to triangulate activity related outcomes; however additional measurement error modeling is needed to improve precision., (Copyright © 2023 by the American College of Sports Medicine.)

Physical activity (PA) behavior tends to decline as youth get older, especially in female adolescents. The purpose of this study was to develop an understanding of female adolescent moderate-to-vigorous physical activity (MVPA) behavior. Baseline MVPA data was collected during year one of a female-specific PA related program. The Youth Activity Profile was administered to contextualize current middle school female PA levels. Data were collected on over 600 6th-8th grade youths with even distributions by grade. No significant differences between grade, race/ethnicity, and MVPA minutes were found. The average estimated value for daily MVPA across all grades was 43.93 (+/-12.97) min, which is considerably lower than the public health recommendation of 60 min per days. Similar amounts were observed for weekend days 45.03 (+/-19.98) and weekdays 45.50 (+/-13.14); however, allocations were smaller during school (9.45 +/- 5.13 min) than at home (34.04 +/- 11.15). The findings from this study highlight the need for further investigation in developing sustainable and innovative PA interventions that target adolescent females.

Background: The School Wellness Integration Targeting Child Health (SWITCH) intervention has demonstrated feasibility as an implementation approach to help schools facilitate changes in students' physical activity (PA), sedentary screen time (SST), and dietary intake (DI). This study evaluated the comparative effectiveness of enhanced (individualized) implementation and standard (group-based) implementation., Methods: Twenty-two Iowa elementary schools participated, with each receiving standardized training (wellness conference and webinars). Schools were matched within region and randomized to receive either individualized or group implementation support. The PA, SST, and DI outcomes of 1097 students were assessed at pre- and post-intervention periods using the Youth Activity Profile. Linear mixed models evaluated differential change in outcomes by condition, for comparative effectiveness, and by gender., Results: Both implementation conditions led to significant improvements in PA and SST over time (p < 0.01), but DI did not improve commensurately (p value range: 0.02‒0.05). There were no differential changes between the group and individualized conditions for PA (p = 0.51), SST (p = 0.19), or DI (p = 0.73). There were no differential effects by gender (i.e., non-significant condition-by-gender interactions) for PA (p for interaction  = 0.86), SST (p for interaction  = 0.46), or DI (p for interaction  = 0.15). Effect sizes for both conditions equated to approximately 6 min more PA per day and approximately 3 min less sedentary time., Conclusion: The observed lack of difference in outcomes suggests that group implementation of SWITCH is equally effective as individualized implementation for building capacity in school wellness programming. Similarly, the lack of interaction by gender suggests that SWITCH can be beneficial for both boys and girls. Additional research is needed to understand the school-level factors that influence implementation (and outcomes) of SWITCH., (Copyright © 2022. Production and hosting by Elsevier B.V.)

Background: Obesity disproportionally impacts rural, lower-income children in the United States. Primary care providers are well-positioned to engage parents in early obesity prevention, yet there is a lack of evidence regarding the most effective care delivery models. The ENCIRCLE study, a pragmatic cluster-randomized controlled trial, will respond to this gap by testing the comparative effectiveness of standard care well-child visits (WCV) versus two enhancements: adding a patient-reported outcome (PRO) measure (PRO WCV) and PRO WCV plus Food Care (telehealth coaching and a grocery store tour)., Methods: A total of 2,025 parents and their preschool-aged children (20-60 months of age) will be recruited from 24 Geisinger primary care clinics, where providers are randomized to the standard WCV, PRO WCV, or PRO WCV plus Food Care intervention arms. The PRO WCV includes the standard WCV plus collection of the PRO-the Family Nutrition and Physical Activity (FNPA) risk assessment-from parents. Parents complete the PRO in the patient-portal or in the clinic (own device, tablet, or kiosk), receive real-time feedback, and select priority topics to discuss with the provider. These results are integrated into the child's electronic health record to inform personalized preventive counseling by providers. PRO WCV plus Food Care includes referrals to community health professionals who deliver evidence-based obesity prevention and food resource management interventions via telehealth following the WCV. The primary study outcome is change in child body mass index z-score (BMIz), based on the World Health Organization growth standards, 12 months post-baseline WCV. Additional outcomes include percent of children with overweight and obesity, raw BMI, BMI50, BMIz extended, parent involvement in counseling, health behaviors, food resource management, and implementation process measures., Discussion: Study findings will inform health care systems' choices about effective care delivery models to prevent childhood obesity among a high-risk population. Additionally, dissemination will be informed by an evaluation of mediating, moderating, and implementation factors., Trial Registration: ClinicalTrials.gov identifier (NCT04406441); Registered May 28, 2020., (© 2022. The Author(s).)

Objectives: To examine differences in rural community children's moderate-to-vigorous physical activity (MVPA) and participation in out-of-school activities from fall 2019 to fall 2020 and explore enacted PA opportunity modifications post initial COVID-19 disruption., Design: Mixed methods study using the validated Youth Activity Profile (YAP), administrator reports and stakeholder surveys and semistructured interviews., Setting: Children and community stakeholders from one rural US Great Plains community in the state of Nebraska were recruited., Participants: Third through fifth graders in fall 2019 (n=144) and fall 2020 (n=174) reported MVPA and participation in out-of-school activities using the YAP. School administrators reported weekly physical education (PE) and recess minutes. Community stakeholders reported pandemic-related changes in community social structures in semistructured interviews (n = 4) and surveys (n=19)., Results: Average daily MVPA minutes increased from 2019 to 2020 (75.0 vs 81.3, SE=1.6, p<0.05). Minutes of MVPA increased during: school hours (MD=2.7, SE=0.5, p<0.5); out-of-school time on weekdays (MD=3.9, SE=1.3, p<0.5); and on weekends (MD=5.5, SE=2.4, p<0.5). On average, fewer children participated in youth sport (42.5% vs 47.2%), youth clubs (10.3% vs 16.0%) and other out-of-school activities (24.1% vs 38.2%) in 2020, compared with 2019. Weekly PE/recess minutes increased from 208.3 to 241.7 from 2019 to 2020. Stakeholder surveys revealed community-driven modifications to PA opportunities, and interviews suggested children played outside more frequently, especially when school was closed and out-of-school activities were shut down., Conclusions: Increased minutes of PE and recess, and decreased out-of-school activity participation may have increased children's overall free play and MVPA during the pandemic. Free play was an important contributor to children's PA during the pandemic and should be prioritised by educators, coaches and other leaders of child PA opportunities., Trial Registration Number: NCT03380143., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Guidelines recommend primary care providers refer children with obesity to behavioral interventions, but given limited program availability, access, and parental engagement, referrals remain rare. We developed telehealth coaching interventions for families whose children received care at a health system in Pennsylvania, United States in 2019-2020. Intervention referrals were facilitated by the pediatrician and/or project team for 6-12-year-old children with obesity following well-child visits. Participants chose one of three 26-week interventions focused on healthy eating, physical activity, or a hybrid clinical/nutrition intervention. Interventions engaged parents as change agents, enhancing self-efficacy to model and reinforce behavior and providing resources to help create a healthy home environment. We enrolled 77 of 183 eligible parent/child dyads. We used mixed methods to evaluate the interventions. Repeated measures models among participants showed significant reductions in obesogenic nutrition behaviors post-intervention and at 1-year follow-up, including a reduction in sugar-sweetened beverage intake of 2.14 servings/week (95% confidence interval: -3.45, -0.82). There were also improvements in obesoprotective nutrition behaviors (e.g., frequency of family meals, parental self-efficacy related to meal management). One year post-baseline, we observed no significant differences in changes in body mass index (BMI) z-scores comparing child participants with matched controls. Given potential impacts of COVID-19 community restrictions on study outcomes, we conducted qualitative interviews with 13 participants during restrictions, which exemplified how disrupted routines constrained children's healthy behaviors but that intervention participation prepared parents by providing cooking and physical activities at home. Findings support the potential of a telehealth-delivered nutrition intervention to support adoption of healthy weight behaviors., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Poulsen, Hosterman, Wood, Cook, Wright, Jamieson, Naylor, Lutcher, Mowery, Seiler, Welk and Bailey-Davis.)

The U.S. Department of Agriculture Final Rule on School Wellness Policy requires schools to self-evaluate wellness policies and environments. To understand the utility of this information, this study evaluates the validity of school-reported wellness information against directly observed data. Wellness leaders at 10 Midwestern elementary schools completed a questionnaire spanning nine school wellness settings. School-reported information was compared against a direct observation protocol. Percent agreement and κ statistics were used to assess agreement between school reporters and direct observation. Overall percent agreement between reporters and direct observation was 77.1%. Agreement ranged from 67.3% (Lunchroom Environment) to 92.0% (School Wellness Policies) across the nine categories. κ results showed that 65.7% of the items demonstrated fair or better reporter agreement. The results provide preliminary support for the utility of schools' self-reported wellness information. Facilitation of independent reporting on wellness environments by school leaders will contribute to broader applications for school wellness programming.

Background: Numerous studies have tested school-based interventions promoting healthy behaviors in youth, but few have integrated dissemination and implementation (D&I) frameworks. Using D&I frameworks can inform if and how an evidence-based intervention is implemented and maintained and provide strategies to address contextual barriers. Such application is necessary to understand how and why interventions are sustained over time. We evaluated a school wellness initiative called SWITCH® (School Wellness Integration Targeting Child Health) to (1) assess implementation outcomes of adoption, fidelity, and penetration, (2) discern implementation determinants through the Consolidated Framework for Implementation Research (CFIR), and (3) examine differences among inexperienced and experienced schools and influential factors to sustainment., Methods: A total of 52 schools from Iowa, United States enrolled in the 2019-2020 iteration of SWITCH (22 inexperienced; 30 experienced). The CFIR guided the adaptation of mixed methods data collection and analysis protocols for school settings. Specific attention was focused on (1) fidelity to core elements; (2) adoption of best practices; and (3) penetration of behavior change practices. Determinants were investigated through in-depth qualitative interviews and readiness surveys with implementation leaders. A systematic process was used to score CFIR domains (between -2 and +2) indicating positive or negative influence. Independent t -tests were conducted to capture differences between samples, followed by a cross-case analysis to compare determinants data. Inductive coding yielded themes related to sustainment of SWITCH beyond formal implementation support., Results: Experienced schools had higher scores on fidelity/compliance ( t = -1.86 p = 0.07) and adoption ( t = -2.03 p = 0.04). CFIR determinants of innovation source, culture, relative priority, and leadership engagement were positive implementation determinants, whereas tension for change and networks and communications were negative determinants. Distinguishing factors between experienced and inexperienced schools were Readiness for Implementation and Self-efficacy (experienced significantly higher; p < 0.05). Strategies to enhance sustainability were increasing student awareness/advocacy, keeping it simple, and integrating into school culture., Conclusions: Findings provide specific insights related to SWITCH implementation and sustainability but more generalized insights about the type of support needed to help schools implement and sustain school wellness programming. Tailoring implementation support to both inexperienced and experienced settings will ultimately enhance dissemination and sustainability of evidence-based interventions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 McLoughlin, Sweeney, Liechty, Lee, Rosenkranz and Welk.)

Background: Although it is widely recognized that physical activity is an important determinant of health, assessing this complex behavior is a considerable challenge., Objective: The purpose of this systematic review and meta-analysis is to examine, quantify, and report the current state of evidence for the validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits., Methods: We conducted a systematic review and Bland-Altman meta-analysis of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate, and steps., Results: A total of 52 studies were included in the systematic review. Among the 52 studies, 41 (79%) were included in the meta-analysis, representing 203 individual comparisons between Fitbit devices and a criterion measure (ie, n=117, 57.6% for heart rate; n=49, 24.1% for energy expenditure; and n=37, 18.2% for steps). Overall, most authors of the included studies concluded that recent Fitbit models underestimate heart rate, energy expenditure, and steps compared with criterion measures. These independent conclusions aligned with the results of the pooled meta-analyses showing an average underestimation of -2.99 beats per minute (k comparison=74), -2.77 kcal per minute (k comparison=29), and -3.11 steps per minute (k comparison=19), respectively, of the Fitbit compared with the criterion measure (results obtained after removing the high risk of bias studies; population limit of agreements for heart rate, energy expenditure, and steps: -23.99 to 18.01, -12.75 to 7.41, and -13.07 to 6.86, respectively)., Conclusions: Fitbit devices are likely to underestimate heart rate, energy expenditure, and steps. The estimation of these measurements varied by the quality of the study, age of the participants, type of activities, and the model of Fitbit. The qualitative conclusions of most studies aligned with the results of the meta-analysis. Although the expected level of accuracy might vary from one context to another, this underestimation can be acceptable, on average, for steps and heart rate. However, the measurement of energy expenditure may be inaccurate for some research purposes., (©Guillaume Chevance, Natalie M Golaszewski, Elizabeth Tipton, Eric B Hekler, Matthew Buman, Gregory J Welk, Kevin Patrick, Job G Godino. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 13.04.2022.)

Background: The challenges of accurate estimation of energy intake (EI) are well-documented, with self-reported values 12%-20% below expected values. New approaches rely on gold-standard assessments of the other components of energy balance, energy expenditure (EE) and energy storage (ES), to estimate EI., Objectives: The purpose of this study was to evaluate the validity, repeatability, and measurement error of consumer devices when estimating energy balance in a free-living population., Methods: Twenty-four healthy adults (14 women, 10 men; mean ± SD age: 30.7 ± 8.2 y) completed two 14-d assessment periods, including assessments of EE and ES using gold-standard [doubly labeled water (DLW) and DXA] and commercial devices [Fitbit Alta HR activity monitor (Alta) and Fitbit Aria wireless body composition scale (Aria)], and of EI by dietician-administered recalls. Accuracy and validity were assessed using Spearman correlation, interclass correlation, mean absolute percentage error, and equivalency testing. We also applied linear measurement error modeling including error in gold-standard devices and within-subject repeated-measures design to calibrate consumer devices and quantify error., Results: There was moderate to strong agreement for EE between the Fitbit Alta and DLW at each time point (rs = 0.82 and 0.66 for Times 1 and 2, respectively). There was weak agreement for ES between the Fitbit Aria and DXA (rs = 0.15 and 0.49 for Times 1 and 2, respectively). Correlations between methods to assess EI ranged from weak to strong, with agreement between the DXA/DLW-calculated EI and dietary recalls being the highest (rs = 0.63 for Time 1 and 0.73 for Time 2). Only EE from the Fitbit Alta at Time 1 was equivalent to the DLW value using equivalency testing., Conclusions: Commercial devices provide estimates of energy balance in free-living adults with varying degrees of validity compared to gold-standard techniques. EE estimates were the most robust overall, whereas ES estimates were generally poor., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.)

The present study examines the temperature variability in physical activity (PA), sedentary behavior (SB), and sleep in a free-living population. A representative sample of 1235 adults (ages 21-70) from Iowa, U.S.A., wore a SenseWear Mini Armband (SWA) for a randomly assigned day. Koppen's weather climate classification was used to precisely classify the temperature: cold (-13 to 32 °F), cool (32 to 50 °F), mild (50 to 64 °F), warm (64 to 73 °F), and hot (73 to 95 °F). The main effect of three-way ANOVA (age × gender × temperature) had differences for SB and sleep, with older adults having higher levels than younger adults ( p < 0.05). However, moderate to vigorous PA (MVPA) did not vary systematically by age or gender, and contrary to expectations, the main effect of the weather was not significant for MVPA ( p > 0.05). Participants spent more time participating in PA at cold than at hot temperatures. The results clarify the impact of temperature on shaping PA and SB patterns in adults. The variable impacts and differential patterns by age suggest that weather should be considered when interpreting differences in PA patterns in research or surveillance applications.

Background: Active commuting (AC) to and from school can contribute to physical activity, although it has recently seen a global decline. The purpose of this study was to examine the agreement between parent and child perceptions of barriers to school AC., Methods: Participants were parents (N = 152, M age  = 40.6 ± 6.3 years) and elementary school children (N = 98, M age  = 10.0 ± 1.2 years). School commute type/frequency and barriers to AC were collected via surveys. Intraclass correlation coefficients (ICCs) were used to assess relative agreement between parent and child perceptions (N = 98 dyads). Paired t tests and equivalence testing were employed to assess group-level agreement. Bland-Altman analysis was used to assess individual-level agreement. Partial correlations of AC with perceptions were also assessed., Results: All parent and child perceptions of barriers to AC to school had low agreement. Bland-Altman Plots indicated negative bias for all but 3 barrier perceptions. Paired t tests indicated significant differences between parent and child perceptions for 8 out of 15 barriers while equivalence testing deemed no parent-child perception equivalent. Partial correlations with AC frequency were significant for 7 parent perceptions and 2 child perceptions., Conclusions: Parent and child perceptions have low agreement. Programs aimed at promoting AC to and from school should account for these discrepancies., (© 2021 American School Health Association.)

Background: The purpose of this study was to examine and compare parent preferences of before and after school physical activity program components in rural and suburban elementary schools., Methods: A discrete choice experiment was conducted to measure parent preferences for components of before/after school programs. A total of 183 parents (age = 37.2 [8.2] y, 155 females) sampled from 15 elementary schools (K-6 grades) in the Western United States took part in the study, half of which were from a rural community (n = 93, 50.8%). Results from the discrete choice experiment were analyzed using hierarchic Bayesian methodology, which estimated utility scores and was used to calculate important scores for program components., Results: The specific goal of the before/after school program was the strongest determinant of parents' stated choice overall, followed by leaders, time of day, length, and main focus. Learning sports as the physical activity goal was the top-rated attribute. Subgroup analyses revealed discrepancies between suburban and rural parents and parents of boys and girls., Conclusion: This study extends the application of discrete choice experiments to school-based programming, providing a unique way to design empirically based, stakeholder informed school programs, specifically within before and after school settings.

Disparities in physical activity and health outcomes exist between urban and rural youth. School settings can be utilized to promote physical activity in youth regardless of urban-rural status. This systematic review and meta-analysis aimed to assess and compare the effect of rural and urban/suburban school-based physical activity programs on total physical activity in youth. A search of five databases was conducted. A total of 33 studies remained after the exclusion process, 28 of which took place in urban/suburban schools and five of which took place in rural schools. The DerSimonian and Laird random effects model was employed with the estimates of heterogeneity taken from the inverse-variance fixed-effect model. For rural studies, the Hartung-Knapp-Sidak-Jonkman method was used to obtain error estimates. Results from the total sample indicated a significant but small pooled increase in daily physical activity (Hedge's g = 0.12, 95% confidence interval [CI]: 0.06-0.18), which held for interventions conducted in urban/suburban schools (Hedge's g = 0.12, 95% CI: 0.06-0.19). For rural school-based interventions, there was no significant pooled effect (Hedge's g = 0.06, 95% CI: -0.50 to 0.61). This meta-analysis provides evidence that school-based interventions can be marginally effective for increasing daily physical activity in children and adolescents; however, no effect was observed for interventions implemented in rural settings., (© 2021 World Obesity Federation.)

A balance between the feasibility and validity of measures is an important consideration for physical activity research - particularly in school-based research with youth. The present study extends previously tested calibration methods to develop and test new equations for an online version of the Youth Activity Profile (YAP) tool, a self-report tool designed for school applications. Data were collected across different regions and seasons to develop more robust, generalizable equations. The study involved a total of 717 youth from 33 schools (374 elementary (ages 9-11), 224 middle (ages 11-14), and 119 high school (ages 14-18)) in two different states in the U.S. Participants wore a Sensewear monitor for a full week and then completed the online YAP at school to report physical activity (PA) and sedentary behaviors (SB) in school and at home. Accelerometer data were processed using an R-based segmentation program to compute PA and SB levels. Quantile regression models were used with half of the sample to develop item-specific YAP calibration equations and these were cross validated with the remaining half of the sample. Computed values of Mean Absolute Percent Error (MAPE) ranged from 15-25% with slightly lower error observed for the middle school sample. The new equations had improved precision compared to the previous versions when tested on the same sample. The online version of the YAP provides an efficient and effective way to capture school level estimates of PA and SB in youth.

Accurate and effective local data collection systems are needed to inform community change on youth health behaviors such as physical activity (PA). Systematic methods are particularly important for understanding PA behaviors that may be influenced by individual, interpersonal, organizational, and regional factors. The purpose of this study was to describe a protocol for coordinating community stakeholders to implement an online youth PA surveillance instrument. The research team collaborated with local health departments (LHDs) from two rural communities to coordinate schools in implementing school-wide youth PA surveillance. A data sharing agreement was established between all partners. School administrators and teachers attended in-person training sessions for an online PA survey and how to use the data. Following the training, students were provided individualized logins to complete the survey once a semester over a two-year academic period. Across both communities, 23 teachers and administrators attended the training sessions that were facilitated by the LHDs and research team. In Year 1 (Y1), a total of 465 3rd through 6th grade students were enrolled in the participating schools (community 1 = 227; community 2 = 238). Survey response rates ranged from 86.1% to 95.4% completion, depending on the community and semester. In Year 2 (Y2), a total of 501 3rd through 6th grade students were enrolled (community 1 = 260; community 2 = 241). Response rates ranged from 86.3% to 89.6% in the fall term. A protocol for coordinating LHD and community stakeholders was an effective strategy for implementing population-level youth PA surveillance with high levels of reach., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors.)

A common way to address rural population health issues is through community stakeholders working together. Youth physical activity (PA) happens in adult-led in-school and out-of-school group opportunities that vary across communities and generally occur in isolated settings. This study explores similarities and differences in rural community system structure and collaborative process variables that help to conceptualize the collaborative impact influencing population youth PA outcomes. Stakeholders (Community 1, n  = 23; Community 2, n  = 26) and youth (Community 1, n  = 205; Community 2, n  = 213) were recruited in 2018-2019 as part of Wellscapes, a hybrid implementation-effectiveness community randomized trial. A stakeholder survey ( n  = 49) measured community system structures and collaboration processes. Youth completed the Youth Activity Profile to measure PA levels. More Community 1 respondents than Community 2 resided within city limits (73.9% vs. 34.6%). Collective efficacy was significantly greater in Community 1 ( M  = 4.0, SD  = 0.5) than in 2 ( M  = 3.2, SD  = 0.4), p  < 0.05. Perceptions of trust scored significantly greater in Community 1 ( M  = 4.1, SD  = 0.3) than in 2 ( M  = 3.3, SD  = 0.5), p  < 0.05. Though both communities met rural definitions and had stakeholder investment, Community 1 had a greater proportion of PA-implementing stakeholders and more residing within city limits which may have influenced that community's higher scores in collective efficacy and trust. Community 2 had more stakeholders in administrator roles and yielded greater youth PA levels. Unique and common variables of rural communities should be considered in understanding system factors that impact youth PA., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors.)

The adoption and dissemination of evidence-based programs is predicated on multiple factors, including the degree to which key stakeholders are motivated to implement program best practices. The present study focuses on the development of indicators that capture motivations of teachers to adopt school wellness programming since personal motivations are central to achieving sustainable impacts in these settings. The study specifically describes the measurement development and validation of the Self-Regulations for Educators Questionnaire (SREQ), designed to measure educators' autonomous and controlled motivation for adopting evidence-based programming in their schools. A naturalistic design to study motivation to adopt aspects of NFL PLAY60 programming through the NFL PLAY60 FitnessGram Partnership Project was used. A total of 1,106 teachers completed the SREQ online. Internal validity was assessed through exploratory and confirmatory factor analysis, and predictive validity using structural equation modeling (SEM). The results supported the two-factor solution with separate items capturing aspects of autonomous and controlled motivation. Both factors had good internal reliability and the item-total correlation coefficients were above 0.40 for both factors. The results also supported the predictive validity as autonomous motivation positively predicted the level of overall engagement, teaching effectiveness, fitness testing reports sent home, and completion of fitness testing with students (p < .05). Controlled motivation positively predicted whether the teachers conducted fitness testing and the engagement of Play 60 Challenge (p < .05). Findings suggest that the SREQ displays a number of psychometric characteristics that make the instrument useful for examining motivation of providers to implement evidence-based best practices., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society of Behavioral Medicine.)

Background: Physical activity, a high-frequency health behavior, varies by where children live, learn, and play. Children accumulate physical activity in adult-led in-school and out-of-school settings. Youth sport is a potential setting for physical activity, but there are differences in youth sport participation based on age, sex, and socioeconomic status. There is a gap in understanding demographic influences on youth sport participation and how these factors interact to influence physical activity. This study examines influences of grade, sex, and family income on youth sport participation and these factors and youth sport participation on moderate-to-vigorous physical activity of children in rural communities., Methods: Children (n = 418 3rd-6th graders) living in two rural communities completed the online Youth Activity Profile as part of Wellscapes, a type 3 hybrid implementation-effectiveness community randomized trial. Mixed models with community as a random effect examined main effects and interactions of grade, sex, and family income on youth sport participation and these factors and youth sport participation on moderate-to-vigorous physical activity., Results: About 80% of children engaged in youth sport, and full-pay lunch students were almost four times more likely to have youth sport participation than students with free/reduced lunch (OR = 3.91, 95% CI = 1.95, 7.8). Females and 6th graders (p < 0.05) had lower physical activity than comparison groups. Males with higher family income had greater physical activity; females with higher family income had less physical activity. For 6th graders, high family income had less effect on physical activity than similar 3rd-5th graders (p < 0.01)., Conclusions: While a fairly high percentage of children participate in youth sports, there are disparities in rural communities on youth sport participation and physical activity outcomes based on age, sex, and family income.

Considerable public health efforts across the globe have focused on promoting physical activity (PA) and minimizing sedentary behaviour (SB) in youths. However, it is important to have valid, reliable and feasible methods to assess these behaviours in youths. The purpose of this study was to analyse the feasibility and reliability of the Spanish version of the previously validated Youth Activity Profile questionnaire (YAP) in children and adolescents. The YAP-S is a 15-item self-report instrument designed to capture PA and SB in youths. A total of 604 children (5-12 years old) and 346 adolescents (12-17 years old) filled out the questionnaire twice (14 days apart). Feasibility was evaluated through required time and number of misunderstood questions by participants. The test-retest reliability was examined using the weighted kappa coefficient (κ) and intraclass correlation coefficient. The average time to complete the questionnaire was 28.85 ± 14.28 and 12.24 ± 9.84 minutes in children and adolescents, respectively. No misunderstanding of questions was reported. The questionnaire showed an adequate reliability for activity at school, out-of-school and sedentary behaviours (k = 0.61-0.77; ICC = 0.77-0.89) in children and adolescents. The YAP-S might be considered a feasible and reliable questionnaire for assessing PA and SB in Spanish children and adolescents.

The use of Comprehensive School Physical Activity Program (CSPAP) has been recommended to help students achieve 60-minutes of physical activity each day. Implementing a CSPAP requires planning, coordination, and ongoing oversight, but an understudied factor is how principal support influences CSPAP implementation. The purpose of this study was to evaluate the impact of principal support on CSPAP implementation. Method. Schools in the Iowa FitnessGram Initiative ( n = 84), a participatory network of schools committed to supporting physical education and wellness efforts, were invited to participate in the study. Physical education teachers from 42 schools completed a survey assessing CSPAP implementation and principal support for school wellness. Descriptive statistics and correlation analyses were used to report associations between the variables. A regression analysis was conducted to evaluate the impact of principal support on CSPAP implementation. Results . Almost half of the schools were reported to be fully implementing just one CSPAP component and no school was reported to be fully implementing all five. The CSPAP component with the highest reported level of implementation was quality physical education, while the lowest level of implementation was reported for family and community engagement and staff involvement. The regression analysis identified that principal support was a significant predictor of CSPAP implementation, b = 0.55, t (37) = 3.10, p < .004. Conclusions . Principal support is associated with implementation of CSPAP initiatives. Strategies that focus on how to attain principal support for CSPAP initiatives are needed and could have a significant impact on student physical activity and health.

Background/objectives: Evidence on the associations between lifestyle movement behaviors and obesity has been established without taking into account the time-constrained nature of categorized, time-based lifestyle behaviors. We examined the associations of sleep, sedentary behavior (SED), light-intensity physical activity (LPA), and moderate-to-vigorous PA (MVPA) with body mass index (BMI) using Compositional Data Analysis (CoDA), and compared the associations between a report-based method (24-h Physical Activity Recall; 24PAR) and a monitor-based method (SenseWear Armband; SWA)., Subjects/methods: Replicate data from a representative sample of 1247 adults from the Physical Activity Measurement Survey (PAMS) were used in the study. Participants completed activity monitoring on two randomly selected days, each of which required wearing a SWA for a full day, and then completing a telephone-administered 24PAR the following day. Relationships among behavioral compositional parts and BMI were analyzed using CoDA via multiple linear regression models with both 24PAR and SWA data., Results: Using 24PAR, time spent in sleep (γ = -3.58, p = 0.011), SED (γ = 3.70, p = 0.002), and MVPA (γ = -0.53, p = 0.018) was associated with BMI. Using SWA, time spent in sleep (γ = -5.10, p < 0.001), SED (γ = 8.93, p < 0.001), LPA (γ = -3.12, p < 0.001), and MVPA (γ = -1.43, p < 0.001) was associated with BMI. The SWA models explained more variance in BMI (R 2  = 0.28) compared with the 24PAR models (R 2  = 0.07). The compositional isotemporal substitution models revealed reductions in BMI when replacing SED by MVPA, LPA (not with 24PAR) or sleep for both 24PAR and SWA, but the effect estimates were larger with SWA., Conclusions: Favorable levels of relative time spent in lifestyle movement behaviors were, in general, associated with decreased BMI. The observed associations were stronger using the monitor-based SWA method compared with the report-based 24PAR method.

Background: School wellness programming is important for promoting healthy lifestyles and academic achievement in youth; however, research is needed on methods that can help schools implement and sustain such programs on their own. The purpose of this study was to investigate factors within and outside the school environment that influenced school capacity for implementation and potential sustainability of wellness programming., Methods: As part of the School Wellness Integration Targeting Child Health (SWITCH®) intervention, elementary school wellness teams (N = 30) were guided through a capacity-building process focused on promoting the adoption of healthy lifestyle behaviors in students. Data on implementation were collected through three standardized surveys and interviews (pre-mid-post) and a post-implementation interview. Indicators of organizational capacity were assessed using the School Wellness Readiness Assessment (SWRA). Paired t-tests were run to assess changes in implementation (classroom, physical education, and lunchroom settings), capacity, and stakeholder engagement over time. One-way analysis of variance (ANOVA) tests were run to examine how implementation of best practices (low, moderate, high) explained differences in capacity gains. Qualitative data were analyzed through inductive and deductive analysis, following the Consolidated Framework for Implementation Research (CFIR)., Results: Paired t-tests showed non-significant increases in school and setting-specific capacity and implementation of SWITCH best practices over time, in addition to a consistent level of engagement from key stakeholders. ANOVA results revealed non-significant associations between implementation group and gains in school capacity (F [2, 24] = 1.63; p = .21), class capacity (F [2, 24]=0.20 p = .82), lunchroom capacity (F [2, 24]=0.29; p = .78), and physical education (F [2, 24]=1.45; p = .25). Qualitative data demonstrated that factors within the outer setting (i.e., engaging community partners) facilitated programming. Inner-setting factors (i.e., relationships with administration and staff) influenced implementation. Implementation process themes (e.g., planning, adaptation of resources to meet school capacity/needs, and engaging students as leaders) were cited as key facilitators. Schools discussed factors affecting sustainability, such as school culture and knowledge of school wellness policy., Conclusions: The results from this implementation study document the importance of allowing schools to adapt programming to meet their local needs, and highlight the strengths of measuring multiple implementation outcomes. Increased support is needed for schools regarding the formation and improvement of wellness policies as a means to enhance sustainability over time.

Introduction: Data from clinical trials have justified the promotion of fitness as a means to enhance facets of cognitive control and academic achievement in youth. However, such associations, when tested under real-world conditions, are equivocal. The purpose of this study, therefore, was to evaluate longitudinal associations between aerobic capacity (AC), weight status, and academic achievement within a large urban county., Methods: Longitudinal data were obtained from a sample of third, fifth, and seventh grade students in schools within an urban county in Georgia. Data on body mass index (BMI) were available from 11,639 students; AC data from 5735 students. Data on both indicators were obtained through the established FitnessGram assessment battery with 2-yr changes calculated using standardized Z scores. Academic achievement data were available from three subjects (math, science, and reading) for third, fifth, and seventh grade students, and 2-yr changes were computed using changes in Z scores for each test. Data were analyzed using generalized logistic models to test associations between change in BMI and AC in relation to changes in academic achievement., Results: Positive associations were observed between improvements in weight status and academic achievement for the fifth grade boys and girls ([reading] odds ratio [OR], 1.47; 95% confidence interval [CI], 1.25-1.72; [science] OR, 1.22; 95% CI, 1.04-1.42). Maintaining weight status was associated with improved scores in the third grade ([math] OR, 1.16; 95% CI, 1.012-1.327; [reading] OR, 1.47; 95% CI, 1.25-1.72) and fifth grade cohorts (math OR, 1.20; 95% CI, 1.00.1.43). For AC, no significant associations were found for any age cohort., Conclusions: Modest associations between improvements in weight status, AC, and academic achievement are noteworthy, despite the lack of statistical significance for AC. The results provide a robust evaluation of associations between fitness and academic achievement.

School Wellness Integration Targeting Child Health (SWITCH ® ) is a school wellness implementation initiative focused on building capacity for schools to plan and coordinate wellness programming. Grounded in Social Cognitive Theory (SCT), the purpose of this study was to evaluate the utility of the web-based, self-regulation system on physical activity (PA) behavior outcomes. At pre-test and post-test, students in SWITCH ® schools ( n = 8) completed the online Youth Activity Profile (YAP) to assess PA and sedentary behavior (SB). Students ( n = 513) were categorized into high or low self-monitoring groups (using a median split) based on their use of the web-based self-regulation platform. Linear mixed models were used to assess differences in moderate-to-vigorous PA (MVPA) and sedentary behavior, with school, classroom, student, time-by-school, and time-by-classroom random effects and main and interaction fixed effects for student self-monitoring, gender, and time. Significant self-monitoring-by-time interactions were observed for estimates of PA F(1, 477) = 5.55, p = 0.02 and SB F(1, 477) = 4.90, p = 0.03. Students in the high self-monitoring group had larger gains in PA per day and larger declines in hours per day of sedentary screen time behavior compared to students in the low self-monitoring group. These findings support the utility of web-based self-regulation for facilitating PA change in youth.

Self-reported youth physical activity (PA) is typically overestimated. We aimed to calibrate and validate a self-report tool among English youth. Four-hundred-and-two participants (aged 9-16 years; 212 boys) wore SenseWear Armband Mini devices (SWA) for eight days and completed the self-report Youth Activity Profile (YAP) on the eighth day. Calibration algorithms for temporally matched segments were generated from the YAP data using quantile regression. The algorithms were applied in an independent cross-validation sample, and student- and school-level agreement were assessed. The utility of the YAP algorithms to assess compliance to PA guidelines was also examined. The school-level bias for the YAP estimates of in-school, out-of-school, and weekend moderate-to-vigorous PA (MVPA) were 17.2 (34.4), 31.6 (14.0), and -4.9 (3.6) min·week -1 , respectively. Out-of-school sedentary behaviour (SB) was over-predicted by 109.2 (11.8) min·week -1 . Predicted YAP values were within 15%-20% equivalence of the SWA estimates. The classification accuracy of the YAP MVPA estimates for compliance to 60 min·day -1 and 30 min·school-day -1 MVPA recommendations were 91%/37% and 89%/57% sensitivity/specificity, respectively. The YAP generated robust school-level estimates of MVPA and SB and has potential for surveillance to monitor compliance with PA guidelines. The accuracy of the YAP may be further improved through research with more representative UK samples to enhance the calibration process and to refine the resultant algorithms.

Introduction: A lack of standardization with accelerometry-based monitors has made it hard to advance applications for both research and practice. Resolving these challenges is essential for developing methods for consistent, agnostic reporting of physical activity outcomes from wearable monitors in clinical applications., Methods: This article reviewed the literature on the methods used to evaluate the validity of contemporary consumer activity monitors. A rationale for focusing on energy expenditure as a key outcome measure in validation studies was provided followed by a summary of the strengths and limitations of different analytical methods. The primary review included 23 recent validation studies that collectively reported energy expenditure estimates from 58 monitors relative to values from appropriate criterion measures., Results: The majority of studies reported weak indicators such as correlation coefficients (87%), but only half (52%) reported the recommended summary statistic of mean absolute percent error needed to evaluate actual individual error. Fewer used appropriate tests of agreement such as equivalence testing (22%)., Conclusions: The use of inappropriate analytic methods and incomplete reporting of outcomes is a major limitation for systematically advancing research with both research grade and consumer-grade activity monitors. Guidelines are provided to standardize analytic methods and reporting in these types of studies to enhance the utility of the devices for clinical mHealth applications.

This study used accelerometer and self-report measures of overall sedentary time (ST) and screen time behaviours to examine their respective associations with adiposity among UK youth. Participants (Year groups 5, 8, and 10;  n =292, 148 girls) wore the SenseWear Armband Mini accelerometer for eight days and completed the Youth Activity Profile, an online report tool designed to estimate physical activity and ST.Stature, body mass and waist circumference were measured to classify adiposity outcomes (overweight/obese and central obesity). One-way between groups ANOVA and adjusted linear, logistic and multinomial logistic regression analyses were conducted. There was a significant main effect of age on total ST across the whole week ( F (2, 289)=41.64,  p ≤0.001). ST increased monotonically across Year 5 (581.09±107.81 min·d-¹), 8 (671.96±112.59 min·d-¹) and 10 (725.80±115.20 min·d-¹), and all pairwise comparisons were significant at  p ≤0.001. A steep age-related gradient to mobile phone use was present ( p ≤0.001). ST was positively associated with adiposity outcomes independent of moderate-to-vigorous intensity physical activity (MVPA;  p ≤0.001). Engaging in >3 hours of video gaming daily was positively associated with central obesity (OR=2.12,  p ≤0.05) but not after adjustment for MVPA. Results further demonstrate the importance of reducing overall ST to maintain healthy weight status among UK youth.

Background: The last few years have seen renewed interest in use-of-time recalls in epidemiological studies, driven by a focus on the 24-h day [including sleep, sitting, and light physical activity (LPA)] rather than just moderate-vigorous physical activity (MVPA). This paper describes four different computerised use-of-time instruments (ACT24, PAR, MARCA and cpar24) and presents population time-use data from a collective sample of 8286 adults from different population studies conducted in Australia/New Zealand, Germany and the United States., Methods: The instruments were developed independently but showed a number of similarities: they were self-administered through the web or used computer-assisted telephone interviews; all captured energy expenditure using variants of the Ainsworth Compendium; each had been validated against criterion measures; and they used a domain structure whereby activities were aggregated under categories such as Personal Care and Work., Results: Estimates of physical activity level (average daily rate of energy expenditure in METs) ranged from 1.53 to 1.78 in the four studies, strikingly similar to population estimates derived from doubly labelled water. There was broad agreement in the amount of time spent in sleep (7.2-8.6 h), MVPA (1.6-3.1 h), personal care (1.6-2.4 h), and transportation (1.1-1.8 h). There were consistent sex differences, with women spending 28-81% more time on chores, 8-40% more time in LPA, and 3-39% less time in MVPA than men., Conclusions: Although there were many similarities between instruments, differences in operationalizing definitions of sedentary behaviour and LPA resulted in substantive differences in the amounts of time reported in sedentary and physically active behaviours. Future research should focus on deriving a core set of basic activities and associated energy expenditure estimates, an agreed classificatory hierarchy for the major behavioural and activity domains, and systems to capture relevant social and environmental contexts.

Objective: Integration of behavioural risk assessment into well-child visits is recommended by clinical guidelines, but its feasibility and impact is unknown., Methods: A quasi-experimental study evaluated the feasibility and effectiveness of risk assessment on body mass index (BMI) at 1-year follow-up. Children with assessments (intervention) were compared with those who did not complete assessments (non-respondent) and those who received standard care (non-exposed)., Results: Analyses included 10,647 children aged 2-9 years (2,724 intervention, 3,324 non-respondent and 4,599 non-exposed). Forty-five per cent of parents completed the assessments. Intervention and non-respondent groups differed in change in BMI z -score at 1 year by -0.05 (confidence interval [CI]: -0.08, -0.02; P  = 0.0013); no difference was observed with non-exposed children. The intervention group had a smaller increase in BMI z -score (0.07 ± 0.63) than non-respondent group (0.13 ± 0.63). For children with normal weight at baseline, intervention versus non-respondent groups differed in BMI z -score change by -0.06 (CI: -0.10, -0.02; P  = 0.0025). However, children with overweight at baseline in the intervention versus the non-exposed group differed in BMI z -score change (0.07 [CI: 0.02, 0.14]; P  = 0.016). When analysed by age, results were similar for 2- to 5-year-olds, but no differences were found for 6- to 9-year-olds., Conclusion: Automating risk assessment in paediatric care is feasible and effective in promoting healthy weight among preschool but not older children., Competing Interests: The authors declared no conflicts of interest.

Background: While widely used and endorsed, there is limited evidence supporting the benefits of activity trackers for increasing physical activity; these devices may be more effective when combined with additional strategies that promote sustained behavior change like motivational interviewing (MI) and habit development., Objective: This study aims to determine the utility of wearable activity trackers alone or in combination with these behavior change strategies for promoting improvements in active and sedentary behaviors., Methods: A sample of 91 adults (48/91 female, 53%) was randomized to receive a Fitbit Charge alone or in combination with MI and habit education for 12 weeks. Active and sedentary behaviors were assessed pre and post using research-grade activity monitors (ActiGraph and activPAL), and the development of habits surrounding the use of the trackers was assessed postintervention with the Self-Reported Habit Index. During the intervention, Fitbit wear time and activity levels were monitored with the activity trackers. Linear regression analyses were used to determine the influence of the trial on outcomes of physical activity and sedentary time. The influence of habits was examined using correlation coefficients relating habits of tracker use (wearing the tracker and checking data on the tracker and associated app) to Fitbit wear time and activity levels during the intervention and at follow-up., Results: Regression analyses revealed no significant differences by group in any of the primary outcomes (all P>.05). However, personal characteristics, including lower baseline activity levels (beta=-.49, P=.01) and lack of previous experience with pedometers (beta=-.23, P=.03) were predictive of greater improvements in moderate and vigorous physical activity. Furthermore, for individuals with higher activity levels at the baseline, MI and habit education were more effective for maintaining these activity levels when compared with receiving a Fitbit alone (eg, small increase of ~48 steps/day, d=0.01, vs large decrease of ~1830 steps/day, d=0.95). Finally, habit development was significantly related to steps/day during (r=.30, P=.004) and following the intervention (r=.27, P=.03)., Conclusions: This study suggests that activity trackers may have beneficial effects on physical activity in healthy adults, but benefits vary based on individual factors. Furthermore, this study highlights the importance of habit development surrounding the wear and use of activity trackers and the associated software to promote increases in physical activity., Trial Registration: ClinicalTrials.gov NCT03837366; https://clinicaltrials.gov/ct2/show/NCT03837366., (©Laura D Ellingson, Jeni E Lansing, Kathryn J DeShaw, Karissa L Peyer, Yang Bai, Maria Perez, L Alison Phillips, Gregory J Welk. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 14.02.2019.)

Purpose: Activity trackers are useful tools for physical activity promotion in adolescents, but robust validity evaluations have not been done under free-living conditions. This study evaluated the validity of the Garmin Vívofit 1 (G1) and Garmin Vívofit 3 (G3) in different settings and contexts., Methods: The participants (girls: 52%, age: 15.9 [1.9] y) wore the G1 and G3 on their nondominant wrist and the Yamax pedometer on their right hip for a period of 1 week. Validity was examined in 4 discrete segments (before school, in school, after school, and whole day). The criterion method was the Yamax pedometer., Results: Both the G1 and G3 could be considered equivalent to the Yamax pedometer regarding the before school, in school, and whole day segments. The G1 showed wider limits of agreement than G3., Conclusions: The G1 and G3 trackers exhibited acceptable validity for 3 of the 4 segments (before school, in school, and whole day measurements). The results were less accurate during the after-school segment. The evidence that the validity of the monitors varied depending on the setting and context is an important consideration for research on adolescent activity patterns.

Objectives: To examine the comparability of children's free-living sedentary time (ST) derived from raw acceleration thresholds for wrist mounted GENEActiv accelerometer data, with ST estimated using the waist mounted ActiGraph 100count·min -1 threshold., Design: Secondary data analysis., Method: 108 10-11-year-old children (n=43 boys) from Liverpool, UK wore one ActiGraph GT3X+ and one GENEActiv accelerometer on their right hip and left wrist, respectively for seven days. Signal vector magnitude (SVM; mg) was calculated using the ENMO approach for GENEActiv data. ST was estimated from hip-worn ActiGraph data, applying the widely used 100count·min -1 threshold. ROC analysis using 10-fold hold-out cross-validation was conducted to establish a wrist-worn GENEActiv threshold comparable to the hip ActiGraph 100count·min -1 threshold. GENEActiv data were also classified using three empirical wrist thresholds and equivalence testing was completed., Results: Analysis indicated that a GENEActiv SVM value of 51mg demonstrated fair to moderate agreement (Kappa: 0.32-0.41) with the 100count·min -1 threshold. However, the generated and empirical thresholds for GENEActiv devices were not significantly equivalent to ActiGraph 100count·min -1 . GENEActiv data classified using the 35.6mg threshold intended for ActiGraph devices generated significantly equivalent ST estimates as the ActiGraph 100count·min -1 ., Conclusions: The newly generated and empirical GENEActiv wrist thresholds do not provide equivalent estimates of ST to the ActiGraph 100count·min -1 approach. More investigation is required to assess the validity of applying ActiGraph cutpoints to GENEActiv data. Future studies are needed to examine the backward compatibility of ST data and to produce a robust method of classifying SVM-derived ST., (Copyright © 2018 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)