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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.

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 (mg's), when using wrist-worn accelerometers and during non-locomotive activities. Objective: To assess the effect of ActiGraph sampling rate on total counts/15-sec 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-sec 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-sec (mean bias 4-43 counts/15-sec 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 hipversus 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.

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.

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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.

Background: Accelerometers measure complex movements of children’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’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’s MVPA compared to measured values. Methods: Forty children (8–12 years; 25 boys) played 6–10 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 ± 1.6 METs, 64.5 ± 24.7 steps/min, and 3318 ± 1262 vertical (V) and 5350 ± 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% (κ = −0.02) to 97.6% (κ = 0.49)), while agreement was low using cadence cut-points (range: 4.0% (κ = 0.0009) to 11.3% (κ = 0.001)). Conclusion: While measured and estimated values were well correlated, using cadence tended to misclassify children’s free-play MVPA.