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Given the multifaceted nature of physical activity behavior in children and adolescents, researchers have conducted myriad intervention studies designed to increase physical activity across many populations, study designs, contexts, and settings. This narrative review overviews the characteristics, conclusions, and research gaps/future directions indicated in prior reviews of interventions to promote physical activity in youth and identifies potential knowledge gaps. Seven databases were searched for articles published between January 2012 and September 2022. A predetermined list of characteristics of included reviews was extracted. Reviews (n = 68) concluded that interventions were generally effective. Little attention was paid to implementation, theoretical framework was only addressed in about half of reviews, and only a quarter specifically examined individuals from underrepresented groups. Family, community, and policy work are needed, and overarching reviews such as this study should occasionally occur given the high number of reviews focusing on specific populations or settings.

ActiGraph counts are commonly used for characterizing physical activity intensity and energy expenditure and are among the most well-studied accelerometer metrics. Researchers have recently replicated the counts processing method using a mechanical setup, now allowing users to generate counts from raw acceleration data. Purpose: The purpose of this study was to compare ActiGraph-generated counts to open-source counts and assess the impact on free-living physical activity levels derived from cut points, machine learning, and two-regression models. Methods: Children (n = 488, 13.0 ± 1.1 years of age) wore an ActiGraph wGT3X-BT on their right hip for 7 days during waking hours. ActiGraph counts and counts generated from raw acceleration data were compared at the epoch-level and as overall means. Seven methods were used to classify overall and epoch-level activity intensity. Outcomes were compared using weighted kappa, correlations, mean absolute deviation, and two one-sided equivalence testing. Results: All outcomes were statistically equivalent between ActiGraph and open-source counts; weighted kappa was ≥.971 and epoch-level correlations were ≥.992, indicating very high agreement. Bland–Altman plots indicated differences increased with activity intensity, but overall differences between ActiGraph and open-source counts were minimal (e.g., epoch-level mean absolute difference of 23.9 vector magnitude counts per minute). Regardless of classification model, average differences translated to 1.4–2.6 min/day for moderate- to vigorous-intensity physical activity. Conclusion: Open-source counts may be used to enhance comparability of future studies, streamline data analysis, and enable researchers to use existing developed models with alternative accelerometer brands. Future studies should verify the performance of open-source counts for other outcomes, like sleep.

Identifying the best analytical approach for capturing moderate-to-vigorous physical activity (MVPA) using accelerometry is complex but inconsistent approaches employed in research and surveillance limits comparability. We illustrate the use of a consensus method that pools estimates from multiple approaches for characterising MVPA using accelerometry. Participants (n = 30) wore an accelerometer on their right hip during two laboratory visits. Ten individual classification methods estimated minutes of MVPA, including cut-point, two-regression, and machine learning approaches, using open-source count and raw inputs and several epoch lengths. Results were averaged to derive the consensus estimate. Mean MVPA ranged from 33.9-50.4 min across individual methods, but only one (38.9 min) was statistically equivalent to the criterion of direct observation (38.2 min). The consensus estimate (39.2 min) was equivalent to the criterion (even after removal of the one individual method that was equivalent to the criterion), had a smaller mean absolute error (4.2 min) compared to individual methods (4.9-12.3 min), and enabled the estimation of participant-level variance (mean standard deviation: 7.7 min). The consensus method allows for addition/removal of methods depending on data availability or field progression and may improve accuracy and comparability of device-based MVPA estimates while limiting variability due to convergence between estimates.

Residents in US cities are exposed to high levels of stress and violent crime. At the same time, a number of cities have put forward “greening” efforts which may promote nature’s calming effects and reduce stressful stimuli. Previous research has shown that greening may lower aggressive behaviors and violent crime. In this study we examined, for the first time, the longitudinal effects over a 30-year period of average city greenness on homicide rates across 290 major cities in the US, using multilevel linear growth curve modeling. Overall, homicide rates in US cities decreased over this time-period (52.1–33.5 per 100,000 population) while the average greenness increased slightly (0.41–0.43 NDVI). Change in average city greenness was negatively associated with homicide, controlling for a range of variables (β = −.30, p-value = .02). The results of this study suggest that efforts to increase urban greenness may have small but significant violence-reduction benefits.

The childcare environment impacts physical activity participation, but little descriptive information is available in different types of programs. Most studies focus on the schoolyard despite children spending more time indoors. This study identified which learning centers were available, used by children, and promoted physical activity, both inside and on the schoolyard. Teachers ( N = 139; licensed centers, n = 59; home-based programs, n = 80) identified available learning centers and ranked locations where children spent free-choice time and participated in physical activity. Indoors, blocks, books, and dramatic play were frequently available, and dramatic play was ranked as an area where children spent time (87%) and were active (63%). Outdoors, open/grassy areas, fixed equipment, sandbox, and seating were available, and open/grassy areas and paved areas for balls/objects were locations where children spent time (73%–100%) and were active (87%–100%). Addition of learning centers that were unavailable but promoted physical activity (e.g. lofts) may be a point of intervention.

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.

Exposure to nature views has been associated with diverse mental health and cognitive capacity benefits. Yet, much of this evidence was derived in adult samples and typically only involves residential views of nature. Findings from studies with children suggest that when more greenness is available at home or school, children have higher academic performance and have expedited attention restoration, although most studies utilize coarse or subjective assessments of exposure to nature and largely neglect investigation among young children. Here, we investigated associations between objectively measured visible nature at school and children’s behavior problems (attention and externalizing behaviors using the Brief Problem Monitor Parent Form) in a sample of 86 children aged seven to nine years old from 15 classrooms across three schools. Images of classroom windows were used to quantify overall nature views and views of specific nature types (sky, grass, tree, shrub). We fitted separate Tobit regression models to test associations between classroom nature views and attention and externalizing behaviors, accounting for age, sex, race/ethnicity, residential deprivation score, and residential nature views (using Google Street View imagery). We found that higher levels of visible nature from classroom windows were associated with lower externalizing behavior problem scores, after confounder adjustment. This relationship was consistent for visible trees, but not other nature types. No significant associations were detected for attention problems. This initial study suggests that classroom-based exposure to visible nature, particularly trees, could benefit children’s mental health, with implications for landscape and school design.

Preschool children spend most of their day in the indoor classroom, but it is unknown which learning centers or activity types are associated with physical activity (PA) or if children even use learning centers for their intended purpose (e.g., block play in the block center). The present study aimed to describe activity types in which children participate within classroom locations and to compare location- versus activity type-based approaches to characterizing children's classroom PA. Preschoolers' (N = 50) classroom location and activity type were video-coded and matched with PA level (measured as accelerometer vector magnitude in counts/s) for one indoor free-play period. Multiple activities occurred within each location (median = 6.5), with the predominant activity type accounting for 40.7 (sociodramatic play in group location) to 88.4 (manipulative play in sensory location) percent of time in that location. Children spent the most time in the group location (17.9%) and the sociodramatic activity type (19.9%). PA varied by location and activity type with the loft location (37.3 counts/s) and gross motor activity type (46.3 counts/s) promoting the highest PA levels. Activity types elicited varying PA levels depending on location (e.g., block activity in block vs. group location: 22.8 vs. 43.9 counts/s). Children do not always participate in the type of activity for which a space was designed but this can facilitate PA. Activity type- and location-based approaches to characterizing children’s play and PA are not interchangeable but these approaches may provide additive information about classroom behavior, which is a methodological consideration for future studies.

Understanding the context of children's schoolyard activity can inform approaches by which to increase physical activity participation. Two major approaches exist for assessing this context- based on location or activity type. Location-based methods (e.g., Global Positioning Systems) may miss variations in activity type if children participate in multiple types of activities in each location (e.g., children engage in something other than playing with sand in a sandbox). Activity-type based methods for contextualizing activity (e.g., Observational System for Recording Physical Activity in Children, OSRAC) capture activity type, but not location, and children might participate in the same type(s) of activities in multiple schoolyard locations. The purpose of this study was to compare location- vs activity type-based approaches to characterizing children's schoolyard behavior. Preschoolers' (N = 50) schoolyard location and activity type (based on OSRAC) were video-coded and matched with physical activity monitor data (counts/s) for 1 outdoor period (~43 min). The results showed that multiple activities occurred within each location (median = 5), but the predominant activity typically matched the expected activity type (e.g., 66.9% fixed equipment play in the fixed equipment location). Children spent the majority of their time in open space (type or location; 37.6%–48.9%), but there were differences in the setting that promoted the most physical activity (teacher arranged type, but fixed equipment location). Activity types elicited varying physical activity levels depending on location (e.g., open space activities in open spaces vs on paths: 40.9 vs 60.7 counts/s). Activity type- and location-based approaches provide some similar, but potentially additive information about schoolyard behavior, which is an important methodological consideration for future studies.

ActiGraph sampling frequencies of more than 30 Hz may result in overestimation of activity counts in both children and adults, but research on free-living individuals has not included the range of sampling frequencies used by researchers. Objective. We compared count- and raw-acceleration-based metrics from free-living children and adolescents across a range of sampling frequencies. Approach. Participants (n = 445; 10–15 years of age) wore an ActiGraph accelerometer for at least one 10 h day. Vector magnitude counts, mean amplitude deviation, monitor-independent movement summary units, and activity intensity classified using six methods (four cut-points, two-regression model, and artificial neural network) were compared between 30 Hz and 60, 80, 90, and 100 Hz sampling frequencies using mean absolute differences, correlations, and equivalence testing. Main results. All outcomes were statistically equivalent, and correlation coefficients were ≥0.970. Absolute differences were largest for the 30 versus 80 and 30 versus 100 Hz count comparisons. For comparisons of 30 with 60, 80, 90, or 100 Hz, mean (and maximum) absolute differences in minutes of moderate-to-vigorous physical activity per day ranged from 0.1 to 0.3 (0.4 to 1.5), 0.3 to 1.3 (1.6 to 8.6), 0.1 to 0.3 (1.1 to 2.5), and 0.3 to 2.5 (1.6 to 14.3) across the six classification methods. Significance. Acceleration-based outcomes are comparable across the full range of sampling rates and therefore recommended for future research. If using counts, we recommend a multiple of 30 Hz because using a 100 Hz sampling rate resulted in large maximum individual differences and epoch-level differences, and increasing differences with activity level.

ActiGraph accelerometers are frequently used to characterize physical activity, but free-living cross-generational comparability of newer models has not been verified. Participants (N = 70) wore GT9X and wGT3X-BT accelerometers at the hip and a sub-sample (n = 54) wore GT9X and either wGT3X-BT or GT3X+ monitors at each wrist for 4 days. Vector magnitude (VM) counts, VM acceleration, Mean Amplitude Deviation (MAD), and Euclidean Norm Minus One (ENMO) were calculated (60-s epoch), and cut-points were used to determine percent of time spent in each intensity (sedentary/light, moderate, vigorous). Epoch-level correlation coefficients (

Free-living comparability of wGT3X-BT and GT9X ActiGraphs has not been verified. Participants (n = 34, 35% females, 7–17 y) wore hip-worn monitors for one week. Vector magnitude (VM; 15-s epoch), M...

Background: Children from underrepresented populations exhibit low levels of physical activity (PA), diet quality, and health-related quality of life (QoL), but participation in extracurricular activities may positively impact these health outcomes. Purpose: To examine differences in PA, dietary behavior, and QoL in underrepresented children by extracurricular activity dose (0, 1, ≥2) and type (sports, dance/martial arts, art/music). Methods: Children (N = 754; Grades 4–6) completed the Physical Activity Questionnaire for Children, KidsScreen-27 (QoL), and the School Physical Activity and Nutrition Survey and self-reported extracurricular activity participation. One-way MANOVA tests were used in this analysis. Results: Individuals participating in 1 and ≥ 2 activities (p

The authors conducted a systematic review of children’s and adolescent’s physical activity by schoolyard location. PubMed and Web of Science were searched and articles were selected that included 3- to 17-year-olds and specifically examined and reported physical activity by schoolyard location. The primary outcomes of interest were the percentage of total time or observation intervals spent in each location and percentage of time or observation intervals in each location being sedentary or participating in moderate to vigorous physical activity. Included studies (N = 24) focused on preschoolers (n = 6), children (n = 11), adolescents (n = 2), or children and adolescents (n = 5) and primarily used direct observation (n = 17). Fields, fixed equipment, and blacktop were all important locations for physical activity participation, but there were differences by age group and sex. More research is needed that uses consistent methodology and accounts for other factors such as time of year, provided equipment, and differences in schoolyard designs.

Physical inactivity is an important health concern for children Children spend the bulk of their days during most times of the year in school, and school outdoor time (recess) contributes up to 70% of children’s weekday physical activity Recess confers many benefits, such as improved academic performance, better classroom behavior, lower stress, and improved social skills Although the COVID-19 crisis has resulted in closure of schools nationwide, school officials will need to consider all aspects of keeping children healthy when they return to school Public health officials and exercise professionals may be asked to provide guidance to school officials regarding strategies for implementing recess Eliminating recess should not be one of those strategies Although a number of other strategies (e g , designating areas of the schoolyard for each class) have been proposed to limit virus transmission during recess, this commentary translates science on recess to general guidelines and specific strategies that can be followed, specifically in the context of maintaining the benefits of recess Keeping children healthy upon return to school is paramount, and allowing participation in recess is a key component to promoting children’s health in schools

State recess laws are recommended to encourage adequate and equitable access to recess and its benefits, but the downstream effects of state recess laws are unknown. We examined the association of state recess laws with district-level policy and school recess provision.This is cross-sectional analysis of the School Health Policies and Practices Survey, a US nationally representative sample of school districts (2016) and schools (2014). State-level recess laws were coded as none, recommend, or require recess. Logistic and linear regression were used to examine the association between state law with district policies and school recess provision, respectively. Data from 2000 are presented to highlight changes in recess policy and provision over time.The odds of a district policy requiring recess were 2.22 and 2.34 times greater when state recess law recommended or required recess, respectively, compared to states with no recess policy. There were no significant differences in school-level recess provision by state recess law but point estimates from 2000 indicated states without a law had the largest declines in recess provision over time.State recess laws are positively associated with district-level policy. Effects at the school level are unclear and continued surveillance is needed.

State-level laws governing recess policies vary widely across the United States. We characterize the presence of such laws and assess their associations with child-level outcomes.The presence of a state recess law was determined using the Classification of Laws Associated with School Students (CLASS) database. Parents of 6- to 11-year-old children reported physical activity, overall health, school absences, school-related problems, and ability to make/keep friends as part of the National Survey of Children's Health (NSCH). Logistic regression was used to compare outcomes in states with and without recess laws cross-sectionally in 2018 and between 2003 and 2011/2012 using a difference-in-differences analysis.In 2018, 20 states had a law recommending or requiring recess. Cross-sectionally, the odds of being physically active every day (odds ratio, 95% confidence interval: 2.8, 1.2-6.5) and having no difficulty making or keeping friends (2.9, 1.2-7.2) were significantly higher for children residing in states with versus without a recess law. There were no significant associations in the difference-in-differences model.Significant cross-sectional associations in 2018 were not confirmed by a difference-in-differences analysis of two waves of the NSCH. Short follow-up time and the apparent weakness of existing state laws warrant further assessment of state-level recess law.

INTRODUCTION/PURPOSE: In the United States, it is recommended that schools provide at least 20 minutes of daily recess, but the optimal amount for health benefits is unknown. We examined associations between amount of recess and health indicators using National Health and Nutrition Examination Survey data (NHANES; 2013–2016). METHODS: For this cross-sectional analysis, parents/guardians of 6–11 year olds (n=738) reported recess provision which was classified as low (22.8%; approximately 10–15 min, 5 days per week), medium (54.9%; approximately 16–30 min, 5 days per week), or high (22.3%; approximately >30 min, 5 days per week). Outcomes measured included parent/guardian-reported and accelerometer-measured physical activity (PA), blood pressure, cholesterol, grip strength, bone mineral content, weight status, percent body fat, vitamin D level, and C-reactive protein level. Linear and logistic regression compared outcomes by level of recess provision accounting for the NHANES complex survey design. RESULTS: The odds of meeting PA guidelines according to parent/guardian reports were 1.70 and 2.05 times higher in those with medium and high (respectively) versus low recess provision. Accelerometer-measured weekday activity was highest in those with high recess provision while weekend activity was highest in those with low recess provision (Cohen’s d = 0.40–0.45). There were no other significant associations. CONCLUSION: At least 30 minutes of daily recess is associated with two-fold greater odds of achieving recommended PA levels according to parent/guardian reports; accelerometer data suggest this is through increased weekday activity. This finding suggests current national recess recommendations are insufficient for PA promotion. More detailed data on the frequency and duration of recess are needed to quantify optimal provision more precisely.

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.

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.

Purpose: To identify associations between amount of school recess provision and children’s physical activity (PA), weight status, adiposity, cardiorespiratory endurance, muscular strength, and muscular endurance. Method: Data from 6- to 11-year-old participants (n = 499) in the 2012 National Youth Fitness Survey were analyzed. Parents/guardians reported children’s PA levels and recess provision, categorized as no/minimal (9.0%), low (26.1%), medium (46.0%), or high (18.9%). Children wore a wrist-worn accelerometer for 7 days and completed anthropometric measurements. Fitness was assessed using grip strength and treadmill, pull-up, and plank tests. Cross-sectional linear and logistic regression compared outcomes across levels of recess provision adjusting for the survey’s complex sampling design. Results: Children with high provision of recess were 2.31 times more likely to meet PA guidelines according to parent report than those with no/minimal recess. Accelerometer-measured PA followed a more U-shaped pattern, wherein PA was higher in children with high, compared to low, recess provision but comparable to those with no/minimal recess provision. There were no associations with weight status, adiposity, or fitness. Conclusion: Current recess recommendations (20 min·d−1) may be insufficient as 30 minutes per day of recess was associated with a 2-fold greater likelihood of achieving recommended PA levels. Additional research on recess quantity and quality is needed.

Hot spot analysis of linked accelerometer and Global Positioning System data is often used to identify areas of high/low activity in the schoolyard. We illustrate the potential impact of a suite of methodological decisions (i) accelerometer metric; (ii) monitor epoch; (iii) number of recess periods/days and level of aggregation; (iv) sample size; (v) distance band; (vi) spatial versus spatiotemporal weighting scheme; and (vii) time band. Accelerometer metrics resulted in different clustering patterns. Longer epochs resulted in a less detailed picture of schoolyard behavior. Level of data aggregation impacted cluster patterns due to inter-period and inter-day differences, but clusters were consistent with increasing sample size. Use of spatiotemporal weight matrices resulted in better separation of hot and cold spots and revealed potentially important temporal clustering patterns. Increasing distance or time band resulted in reallocation of small clusters to larger clusters. Hot spot analysis decisions should be clearly reported in future studies.

Despite proprietary algorithms to account for differences, output from activity trackers worn on different wrists may not be comparable because individuals vary in their reliance on each hand durin...

Introduction Individuals living in low-income neighborhoods have disproportionately high rates of obesity, Type-2 diabetes, and cardiometabolic conditions. Perceived safety in one’s neighborhood may influence stress and physical activity, with cascading effects on cardiometabolic health. Methods In this study, we examined relationships among feelings of safety while walking during the day and mental health [perceived stress (PSS), depression score], moderate-to-vigorous physical activity (PA), Body Mass Index (BMI), and hemoglobin A1C (A1C) in low-income, high-vacancy neighborhoods in Detroit, Michigan. We recruited 69 adults who wore accelerometers for one week and completed a survey on demographics, mental health, and neighborhood perceptions. Anthropometrics were collected and A1C was measured using A1CNow test strips. We compiled spatial data on vacant buildings and lots across the city. We fitted conventional and multilevel regression models to predict each outcome, using perceived safety during daytime walking as the independent variable of interest and individual or both individual and neighborhood-level covariates (e.g., number of vacant lots). Last, we examined trends in neighborhood features according to perceived safety. Results In this predominantly African American sample (91%), 47% felt unsafe during daytime walking. Feelings of perceived safety significantly predicted PSS (β = − 2.34, p = 0.017), depression scores (β = − 4.22, p = 0.006), and BMI (β = − 2.87, p = 0.01), after full adjustment. For PA, we detected a significant association for sex only. For A1C we detected significant associations with blighted lots near the home. Those feeling unsafe lived in neighborhoods with higher park area and number of blighted lots. Conclusion Future research is needed to assess a critical pathway through which neighborhood features, including vacant or poor-quality green spaces, may affect obesity—via stress reduction and concomitant effects on cardiometabolic health.

Background: ActiGraph is a commonly used, research-grade accelerometer brand, but there is little information regarding intermonitor comparability of newer models. In addition, while sampling rate has been shown to influence accelerometer metrics, its influence on measures of free-living physical activity has not been directly studied. Purpose: To examine differences in physical activity metrics due to intermonitor variability and chosen sampling rate. Methods: Adults (n = 20) wore two hip-worn ActiGraph wGT3X-BT monitors for 1 week, with one accelerometer sampling at 30 Hz and the other at 100 Hz, which was downsampled to 30 Hz. Activity intensity was classified using vector magnitude, Euclidean Norm Minus One (ENMO), and mean amplitude deviation (MAD) cut points. Equivalence testing compared outcomes. Results: There was a lack of intermonitor equivalence for ENMO, time in sedentary/light- or moderate-intensity activity according to ENMO cut points, and time in moderate-intensity activity according to MAD cut points. Between sampling rates, differences existed for time in moderate-intensity activity according to vector magnitude, ENMO, and MAD cut points, and time in sedentary/light-intensity activity according to ENMO cut points. While mean differences were small (0.1–1.7 percentage points), this would equate to differences in moderate-to vigorous-intensity activity over a 10-hr wear day of 3.6 (MAD) to 10.8 (ENMO) min/day for intermonitor comparisons or 3.6 (vector magnitude) to 5.4 (ENMO) min/day for sampling rate. Conclusions: Epoch-level intermonitor differences were larger than differences due to sampling rate, but both may impact outcomes such as time spent in each activity intensity. ENMO was the least comparable metric between monitors or sampling rates.

Despite recent popularity of wrist-worn accelerometers for assessing free-living physical behaviours, there is a lack of user-friendly methods to characterize physical activity from a wrist-worn ActiGraph accelerometer. Participants in this study completed a laboratory protocol and/or 3-8 hours of directly observed free-living (criterion measure of activity intensity) while wearing ActiGraph GT9X Link accelerometers on the right hip and non-dominant wrist. All laboratory data (n = 36) and 11 participants' free-living data were used to develop vector magnitude count cut-points (counts/min) for activity intensity for the wrist-worn accelerometer, and 12 participants' free-living data were used to cross-validate cut-point accuracy. The cut-points were:2,860 counts/min (sedentary); 2,860-3,940 counts/min (light); and ≥3,941counts/min (moderate-to-vigorous (MVPA)). These cut-points had an accuracy of 70.8% for assessing free-living activity intensity, whereas Sasaki/Freedson cut-points for the hip accelerometer had an accuracy of 77.1%, and Hildebrand Euclidean Norm Minus One (ENMO) cut-points for the wrist accelerometer had an accuracy of 75.2%. While accuracy was higher for a hip-worn accelerometer and for ENMO wrist cut-points, the high wear compliance of wrist accelerometers shown in past work and the ease of use of count-based analysis methods may justify use of these developed cut-points until more accurate, equally usable methods can be developed.

Portable metabolic units (PMUs) are used to assess energy expenditure, with the assumption that physical activity level and enjoyment are unaffected due to the light weight and small size. Purpose: To assess differences in physical activity level and enjoyment while wearing and not wearing a PMU. Method: Youth (8–17 y; N = 73) played children’s games or active video games while wearing and not wearing a PMU (crossover design). Participants wore an accelerometer and heart rate monitor and responded to questions about enjoyment on a facial affective scale. A repeated-measures analysis of variance was used to determine if accelerometer measures, heart rate, or enjoyment differed between conditions overall and by sex and weight status. Results: Steps per minute were lower while wearing the PMU than not wearing the PMU (40 vs 44, P = .03). There was an interaction between PMU condition and weight status for enjoyment (P = .01), with overweight participants reporting less enjoyment when wearing the PMU compared with not wearing the PMU (72 vs 75 out of 100). Heart rate, vector magnitude, and counts per minute were not different. Conclusion: There may be psychosocial effects of wearing the PMU, specifically in overweight participants. Activity level was minimally affected, but the practical significance for research is still unknown.

This study evaluated feasibility, acceptability, and preliminary efficacy of a 12-week Guys/Girls Opt for Activities for Life (GOAL) intervention on 10- to 13-year-old adolescents’ body mass index (BMI), percent body fat, physical activity (PA), diet quality, and psychosocial perceptions related to PA and healthy eating. Parent–adolescent dyads from two schools were enrolled. Schools were assigned to either GOAL (38 dyads) or control (43 dyads) condition. The intervention included an after-school club for adolescents 2 days/week, parent–adolescent dyad meeting, and parent Facebook group. Intervention adolescents had greater autonomous motivation for PA and self-efficacy for healthy eating than control adolescents (both p < .05). Although between-group differences were not significant, close-to-moderate effect sizes resulted for accelerometer-measured moderate-to-vigorous PA and diet quality measured via 24-hr dietary recall ( d = .46 and .44, respectively). A trivial effect size occurred for percent body fat ( d = −.10). No differences emerged for BMI. Efficacy testing with a larger sample may be warranted.

This study examined if accelerometer-based assessments of physical activity were responsive to changes in physical activity level commensurate with performing structured versus unstructured activit...

Our purpose was to examine the relationship of mindfulness with weight status, physical activity, screen time, diet (fruit, vegetable, and junk food intake), and health-related quality of life in children living in a low-socioeconomic status community. Children (N = 754; 8–13 years) completed surveys, and height and weight were measured. Regression analysis evaluated the relationship of mindfulness with weight status (body mass index percentile), physical activity, screen time, diet, health-related quality of life, and dummy-coded moderator race, grade, and sex. Average mindfulness was 23.4 ± 9.5 and was higher in black children (24.4 ± 9.3) vs. multi-racial/other children (22.1 ± 9.7), boys (24.2 ± 9.2) vs. girls (22.6 ± 9.8), and in sixth (25.1 ± 9.4) vs. fifth (23.0 ± 9.2) or fourth (21.9 ± 9.8) graders. Overall, the model was significant (adjusted R 2 = 0.201, P

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. Electronic supplementary material The online version of this article (10.1186/s40814-019-0455-0) contains supplementary material, which is available to authorized users.

Background: This study validated the How(e) Happy Scale (HHS) for measuring children's real-time physical activity (PA) enjoyment across PA type, intensity, sex, and weight status and compared state versus trait enjoyment. Methods: Children's (N = 31; 9.7 [1.7] y) PA intensity was measured during sport, play, and locomotive PA. Following each activity, children rated their perceived state (HHS) of enjoyment across 4 constructs (social engagement). Questionnaires measured trait PA enjoyment prior to play. Rasch Rating Scale analysis assessed model-data fit and probability distribution of HHS responses. Analyses of variance compared state versus trait PA enjoyment across main effects, and correlations assessed relationships between measured PA intensity versus state and trait PA enjoyment. Results: Trait PA enjoyment was neither different across sex and weight status nor correlated with PA intensity (r = -.16 to .22). By contrast, HHS responses differed across sex, weight status, and PA type and intensity and correlated with PA type (r = -.56 to -.28) and intensity (r = -.29 to -.32). HHS responses were ordered along the probability curve and showed good infit (0.76-1.22) and outfit (0.71-1.28) statistics and good person (r = .62) and item (r = .88) reliability. Conclusion: HHS is valid for detecting differences in real-time enjoyment across PA type and intensity in all children.

Sampling rate (Hz) of ActiGraph accelerometers may affect processing of acceleration to activity counts when using a hip-worn monitor, but research is needed to quantify if sampling rate affects actual acceleration (mgs), when using wrist-worn accelerometers and during non-locomotive activities. Objective: To assess the effect of ActiGraph sampling rate on total counts/15 s and mean acceleration and to compare differences due to sampling rate between accelerometer wear locations and across different types of activities. Approach: Children (n = 29) wore a hip-and wrist-worn accelerometer (sampled at 100 Hz, downsampled in MATLAB to 30 Hz) during rest/transition periods, active video games, and a treadmill test to volitional exhaustion. Mean acceleration and counts/15 s were computed for each axis and as vector magnitude. Main results: There were mostly no significant differences in mean acceleration. However, 100 Hz data resulted in significantly more total counts/15 s (mean bias 4-43 counts/15 s across axes) for both the hip-and wrist-worn monitor when compared to 30 Hz data. Absolute differences increased with activity intensity (hip: r = 0.46-0.63; wrist: r = 0.26-0.55) and were greater for hip-versus wrist-worn monitors. Percent agreement between 100 and 30 Hz data was high (97.4%-99.7%) when cut-points or machine learning algorithms were used to classify activity intensity. Significance: Our findings support that sampling rate affects the generation of counts but adds that differences increase with intensity and when using hip-worn monitors. We recommend researchers be consistent and vigilantly report the sampling rate used, but note that classifying data into activity intensities resulted in agreement despite differences in sampling rate.

Background: Machine learning may improve energy expenditure (EE) prediction from body-worn accelerometers. However, machine learning models are rarely cross-validated in an independent sample, and the use of machine learning raises additional questions including the effect of accelerometer placement and data type (count vs. raw) for optimal EE prediction. Purpose: To assess the accuracy of artificial neural network (ANN) models for EE prediction in youth using count-based or raw data from accelerometers worn on the hip, wrist, or in combination, and compare these to count-based, EE regression equations. Methods: Data were collected in two settings; one (n = 27) to calibrate the EE prediction models, and the other (n = 34) for model cross-validation. Participants wore a portable metabolic analyzer (EE criterion) and accelerometers on the left wrist and right hip while completing 30 minutes of exergames (calibration, cross-validation) and a maximal exercise test (calibration only). Six ANNs were created from the calibration data, separately by accelerometer placement (hip, wrist, combination) and data format (count-based, raw) to predict EE (15-second epochs). Three count-based linear regression equations were also developed for comparison to the ANNs. Results: The count-based, hip ANN demonstrated lower error (RMSE: 1.2 METs) than all other ANNs (RMSE: 1.7–3.6 METs) and EE regression equations (RMSE: 1.5–3.2 METs). However, all models showed bias toward the mean. Conclusion: An ANN developed for hip-worn accelerometers had higher accuracy for EE prediction during an exergame session than wrist or combination ANNs, and ANNs developed using count-based data had higher accuracy than ANNs developed using raw data.

Background: Accelerometers measure complex movements of children&rsquo, s free play moderate-vigorous physical activity (MVPA), including step and non-step movements. Current accelerometer technology has introduced algorithms to measure steps, along with counts. Precise interpretation of accelerometer-based cadence (steps/min) cut-points is necessary for accurately measuring and tracking children&rsquo, s MVPA. The purpose of this study was to assess the relationships and agreement between accelerometer-based cut-points (cadence and counts/min) to estimate children&rsquo, s MVPA compared to measured values. Methods: Forty children (8&ndash, 12 years, 25 boys) played 6&ndash, 10 games while wearing a portable metabolic analyzer and GT3X+ to measure and estimate MVPA, respectively. Correlation, kappa, sensitivity, and specificity assessed the relationships and agreement between measured and estimated MVPA. Results: Games elicited, on average, 6.3 &plusmn, 1.6 METs, 64.5 &plusmn, 24.7 steps/min, and 3318 &plusmn, 1262 vertical (V) and 5350 &plusmn, 1547 vector-magnitude (VM) counts/min. The relationship between measured and estimated MVPA intensity was higher for cadence (r = 0.50) than V and VM counts/min (r = 0.38 for both). Agreement using V and VM counts/min for measuring PA intensity varied by cut-points (range: 6.8% (&kappa, = &minus, 0.02) to 97.6% (&kappa, = 0.49)), while agreement was low using cadence cut-points (range: 4.0% (&kappa, = 0.0009) to 11.3% (&kappa, = 0.001)). Conclusion: While measured and estimated values were well correlated, using cadence tended to misclassify children&rsquo, s free-play MVPA.

Purpose: Traditional direct observation cannot provide continuous, individual-level physical activity (PA) data throughout recess. This study piloted video direct observation to characterize children’s recess PA overall and by sex and weight status. Methods: Children (N = 23; 11 boys; 6 overweight; third to fifth grade) were recorded during 2 recess periods, coding for PA duration, intensity, location, and type. Duration of PA type and intensity across sex and weight status overall and between/within locations were assessed using 1- and 2-way analysis of variances. Results: The field elicited more sedentary behavior (39% of time) and light PA (17%) and less moderate to vigorous PA (41%) compared with the fixed equipment (13%, 7%, and 71%, respectively) or the court (21%, 7%, and 68%, respectively). Boys engaged in significantly more vigorous-intensity activity on the court (35%) than girls (14%), whereas girls engaged in more moderate to vigorous PA on the fixed equipment (77% vs 61%) and field (46% vs 35%) than boys (all Ps > .05). PA type also differed by sex and weight status. Conclusion: Video direct observation was capable of detecting and characterizing children’s entire recess PA while providing valuable context to the behavior. The authors confirmed previous findings that PA intensity was not uniform by schoolyard location and further differences exist by sex and weight status.

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.

New active videogames (AVGs) may provide youth an alternative to traditional play. The purpose of this study was to compare the physical activity energy expenditure (PAEE), intensity, and enjoyment of AVGs with those of seated videogames (SVGs).Youth (8-17 years old) volunteered to play a random selection of six (two SVGs, four AVGs) videogames for 6-10 minutes each. Prior to participation, height, weight, and resting metabolic rate (RMR) were measured. A portable metabolic analyzer was worn during the games to measure total energy expenditure, and PAEE was calculated as (total energy expenditure - measured RMR). An accelerometer was worn on the right hip to measure intensity in counts/minute and steps/minute. Youth were classified as child (8-12 years old) versus teen (13-17 years old), healthy weight (body mass index [BMI]85th percentile) versus overweight (BMI ≥85th percentile), and male versus female. A three-way mixed-model analysis of variance was used to compare differences in PAEE (metabolic equivalents [METs] and kcal/minute) with sex, weight status, and age group as main effects, including Bonferroni's adjustment.Most AVGs were moderate to vigorous intensity (4.6±0.1 METs; range, 2.8-6.6 METs), where steps/minute (lower-body movement) was positively related to PAEE (R(2)=0.68). All SVGs were classified as light intensity (1.7±0.0 METs). PAEE (kcal/minute) was significantly higher during AVGs and for teens, males, and overweight youth. There was no significant difference in enjoyment between AVGs and SVGs.AVGs elicited sufficient energy cost to be a suitable alternative for traditional play and may contribute to the recommended dose of physical activity, particularly in teens, males, and overweight youth.