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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.

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 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 (

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.

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.

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

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...

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

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: 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.