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.
Kimberly A. Clevenger, Karin A. Pfeiffer, and Cheryl A. Howe
Alexander H.K. Montoye, Kimberly A. Clevenger, Kelly A. Mackintosh, Melitta A. McNarry, and Karin A. Pfeiffer
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.
Kimberly A. Clevenger, Michael J. Wierenga, Cheryl A. Howe, and Karin A. Pfeiffer
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.
Cheryl A. Howe, Kimberly A. Clevenger, Danielle McElhiney, Camille Mihalic, and Moira A. Ragan
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.
Cheryl A. Howe, Kimberly A. Clevenger, Brian Plow, Steve Porter, and Gaurav Sinha
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.
Kimberly A. Clevenger, Aubrey J. Aubrey, Rebecca W. Moore, Karissa L. Peyer, Darijan Suton, Stewart G. Trost, and Karin A. Pfeiffer
Limited data are available on energy cost of common children’s games using measured oxygen consumption.
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.
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).
This article contributes energy expenditure data for the update and expansion of the youth compendium.
Kimberly A. Clevenger, Jan Christian Brønd, Daniel Arvidsson, Alexander H.K. Montoye, Kelly A. Mackintosh, Melitta A. McNarry, and Karin A. Pfeiffer
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.