Background: Over the last decade use of raw acceleration metrics to assess physical activity has increased. Metrics such as Euclidean Norm Minus One (ENMO), and Mean Amplitude Deviation (MAD) can be used to generate metrics which describe physical activity volume (average acceleration), intensity distribution (intensity gradient), and intensity of the most active periods (MX metrics) of the day. Presently, relatively little comparative data for these metrics exists in youth. To address this need, this study presents age- and sex-specific reference percentile values in England youth and compares physical activity volume and intensity profiles by age and sex. Methods: Wrist-worn accelerometer data from 10 studies involving youth aged 5 to 15 y were pooled. Weekday and weekend waking hours were first calculated for youth in school Years (Y) 1&2, Y4&5, Y6&7, and Y8&9 to determine waking hours durations by age-groups and day types. A valid waking hours day was defined as accelerometer wear for ≥ 600 min·d⁻¹ and participants with ≥ 3 valid weekdays and ≥ 1 valid weekend day were included. Mean ENMO- and MAD-generated average acceleration, intensity gradient, and MX metrics were calculated and summarised as weighted week averages. Sex-specific smoothed percentile curves were generated for each metric using Generalized Additive Models for Location Scale and Shape. Linear mixed models examined age and sex differences. Results: The analytical sample included 1250 participants. Physical activity peaked between ages 6.5–10.5 y, depending on metric. For all metrics the highest activity levels occurred in less active participants (3^rd-50^th percentile) and girls, 0.5 to 1.5 y earlier than more active peers, and boys, respectively. Irrespective of metric, boys were more active than girls (p < .001) and physical activity was lowest in the Y8&9 group, particularly when compared to the Y1&2 group (p < .001). Conclusions: Percentile reference values for average acceleration, intensity gradient, and MX metrics have utility in describing age- and sex-specific values for physical activity volume and intensity in youth. There is a need to generate nationally-representative wrist-acceleration population-referenced norms for these metrics to further facilitate health-related physical activity research and promotion.

This study aimed to: (1) compare acceleration output between ActiGraph (AG) hip and wrist monitors and GENEActiv (GA) wrist monitors; (2) identify raw acceleration sedentary and stationary thresholds for the two brands and placements; and (3) validate the thresholds during a free-living period. Twenty-seven from 9- to 10-year-old children wore AG accelerometers on the right hip, dominant- and non-dominant wrists, GA accelerometers on both wrists, and an activPAL on the thigh, while completing seven sedentary and light-intensity physical activities, followed by 10 minutes of school recess. In a subsequent study, 21 children wore AG and GA wrist monitors and activPAL for two days of free-living. The main effects of activity and brand and a significant activity × brand × placement interaction were observed (all p < 0.0001). Output from the AG hip was lower than the AG wrist monitors (both p < 0.0001). Receiver operating characteristic (ROC) curves established AG sedentary thresholds of 32.6 mg for the hip, 55.6 mg and 48.1 mg for dominant and non-dominant wrists respectively. GA wrist thresholds were 56.5 mg (dominant) and 51.6 mg (non-dominant). Similar thresholds were observed for stationary behaviours. The AG non-dominant threshold came closest to achieving equivalency with activPAL during free-living.

This study aimed to validate the Sedentary Sphere posture classification method from wrist-worn accelerometers in children. Twenty-seven 9–10-year-old children wore ActiGraph GT9X (AG) and GENEActiv (GA) accelerometers on both wrists, and activPAL on the thigh while completing prescribed activities: five sedentary activities, standing with a phone, walking (criterion for all 7: observation) and 10-min free-living play (criterion: activPAL). In an independent sample, 21 children wore AG and GA accelerometers on the non-dominant wrist and activPAL for two days of free-living. Per cent accuracy, pairwise 95% equivalence tests (±10% equivalence zone) and intra-class correlation coefficients (ICC) analyses were completed. Accuracy was similar, for prescribed activities irrespective of brand (non-dominant wrist: 77–78%; dominant wrist: 79%). Posture estimates were equivalent between wrists within brand (±6%, ICC > 0.81, lower 95% CI ≥ 0.75), between brands worn on the same wrist (±5%, ICC ≥ 0.84, lower 95% CI ≥ 0.80) and between brands worn on opposing wrists (±6%, ICC ≥ 0.78, lower 95% CI ≥ 0.72). Agreement with activPAL during free-living was 77%, but sedentary time was underestimated by 7% (GA) and 10% (AG). The Sedentary Sphere can be used to classify posture from wrist-worn AG and GA accelerometers for group-level estimates in children, but future work is needed to improve the algorithm for better individual-level results.

To examine the backward comparability of a range of wrist-worn accelerometer estimates of sedentary time (ST) with ActiGraph 100countmin-1 waist ST estimates. Cross-sectional, secondary data analysis METHODS: One hundred and eight 10-11-year-old children (65 girls) wore an ActiGraph GT3X+ accelerometer (AG) on their waist and a GENEActiv accelerometer (GA) on their non-dominant wrist for seven days. GA ST data were classified using a range of thresholds from 23 to 56mg ST estimates were compared to AG ST 100countmin-1 data. Agreement between the AG and GA thresholds was examined using Cronbach's alpha, intraclass correlation coefficients (ICC), limits of agreement (LOA), Kappa values, percent agreement, mean absolute percent error (MAPE) and equivalency analysis. Mean AG total ST was 492.4min over the measurement period. Kappa values ranged from 0.31 to 0.39. Percent agreement ranged from 68 to 69.9%. Cronbach's alpha values ranged from 0.88 to 0.93. ICCs ranged from 0.59 to 0.86. LOA were wide for all comparisons. Only the 34mg threshold produced estimates that were equivalent at the group level to the AG ST 100countmin-1 data though sensitivity and specificity values of ∼64% and ∼74% respectively were observed. Wrist-based estimates of ST generated using the 34mg threshold are comparable with those derived from the AG waist mounted 100countmin-1 threshold at the group level. The 34mg threshold could be applied to allow group-level comparisons of ST with evidence generated using the ActiGraph 100countmin-1 method though it is important to consider the observed sensitivity and specificity results when interpreting findings.

This secondary data analysis aimed to demonstrate the utility of physical activity (PA) wrist accelerometer outcome reference values by identifying the PA volume (average acceleration) and intensity distribution (intensity gradient) centiles and values associated with body mass index (BMI) status (normal weight, overweight, and obese) and cardiorespiratory fitness (CRF, multistage shuttle runs test) status (low, moderate, and high) in children and adolescents. We assessed the dose–response associations between average acceleration and intensity gradient with BMI and CRF outcomes using restricted cubic spline linear mixed models. To aid translation of the findings, we calculated the increases in average acceleration needed to shift exemplar participants to “healthy” weight and CRF status. For boys and girls, there was a nonlinear inverse association between average acceleration and BMI. In both sexes, a positive dose–response was observed between average acceleration and intensity gradient with CRF. The values and centiles of average acceleration and intensity gradient that aligned with BMI and CRF statuses were identified. To move from an average acceleration associated with overweight to healthy weight, 10‐year‐old boys and girls would need to increase daily average acceleration by 23 m g (~30‐min running) and 16 m g (~18‐min running), respectively. These findings further demonstrate the importance of PA in relation to BMI and CRF and the utility of PA reference values for the translation of accelerometer outcomes into meaningful information. Additional studies demonstrating how PA reference values can be used to track behaviors and provide insights into health associations could inform practice further.

Purpose: Accurately measuring sedentary behavior (SB) in children is challenging by virtue of its complex nature. While self-report questionnaires are susceptible to recall errors, accelerometer data lacks contextual information. This study aimed to explore the efficacy of using accelerometry combined with the Digitising Children’s Data Collection (DCDC) for Health application (app), to capture SB comprehensively. Methods: 74 children (9–10 years old) wore ActiGraph GT9X accelerometers for 7 days. Each received a SAMSUNG Galaxy Tab4 (SM-T230) tablet, with the DCDC app installed and a specially designed sedentary behavior study downloaded. The app uses four data collection tools: 1) Questionnaire, 2) Take a photograph, 3) Draw a picture, and 4) Record my voice. Children self-reported their SB daily. Accelerometer data were analyzed using R-package GGIR. App data were downloaded and individual participant profiles created. SBs reported were grouped into categories and reported as frequencies. Results: Participants spent, on average, 629 min (i.e., 73% of their waking time) sedentary. App data revealed most of their out-of-school SB consisted of screen time (112 photos, 114 drawings, and screen time mentioned 135 times during voice recordings). Playing with toys, reading, arts and crafts, and homework were also reported across all four data capturing tools on the app. On an individual level, data from the app often explained irregular patterns in physical activity and SB observed in accelerometer data. Conclusion: This mixed methods approach to assessing SB adds context to accelerometer data, providing researchers with information needed for intervention design.