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 (r) were ≥0.73, and weighted kappa for intensity classifications ranged from 0.71 (ENMO, hip) to 0.98 (VM counts, non-dominant wrist). Monitors were equivalent for all outcomes, except ENMO (all locations/monitors), percent of time spent in sedentary/light (hip) and moderate (hip and non-dominant wrist) activity as classified by ENMO-based cut-points, and vigorous activity as classified by VM count cut-points (non-dominant wrist; p > 0.05). While epoch-level data were not identical, most outcomes were strongly related between models (e.g., MAD, VM) and equivalent once reduced to percent of time spent in each intensity. However, monitor output was not equivalent for the acceleration-based metric ENMO, suggesting that caution should be exercised when comparing this outcome among ActiGraph models.

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.

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.

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.

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), Mean Amplitude Deviation (MAD) and Euclidean Norm Minus One (ENMO) (5-s epoch), and percent of time spent in each activity intensity (using cut-points) were calculated. At the epoch-level, correlations were strong (r = 0.822–0.963), mean absolute percent difference (MAPD) was 41.5 ± 62.2% (VM), 40.8 ± 52.7% (MAD), and 110.9 ± 74.0%, with moderate (ENMO κ = 0.664–0.789) to strong (VM κ = 0.915) agreement for activity intensity. When collapsed to the individual level, MAPD was 4.2 ± 3.4% (VM), 6.8 ± 3.7% (MAD), and 35.9 ± 26.1% (ENMO), correlations were moderate (ENMO r = 0.618) to high (MAD and VM r = 0.996), but ENMO, MAD, and time spent in activity intensities using ENMO cut-points were not statistically equivalent (using 2 one-sided tests) between monitors. While time spent in activity intensities was often comparable between models, caution is warranted for epoch-level comparisons.

Objective: 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). Materials and Methods: 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. Results: 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 (R²=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. Conclusions: 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.