Reference values for wrist-worn accelerometer physical activity metrics in England children and adolescents

Abstract

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 (3rd-50th 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.

Accelerometer Metrics: Healthy Adult Reference Values, Associations with Cardiorespiratory Fitness, and Clinical Implications

“…They may thus be an important complement to the current PA guidelines by the WHO, which are predominantly questionnaire-based (43). This may have relevant implications for future research (PA surveillance [3] and epidemiology [5]) but also for clinical practice (2).…”

“…Further data such as the intercept and R 2 are presented in Table 1 to describe the regression models. Greater IG values (less negative) reflect a more favorable intensity profile with more time spent at moderate and high intensities and vice versa (2). Both metrics are explained graphically in Supplemental Figure 1 (Supplemental Digital Content 2, Illustration of IG and AvAcc, http://links.lww.com/ MSS/C917).…”

“…AvAcc and IG relate directly to measured acceleration, making these metrics not readily interpretable because of a lack of context (13). As more studies use cut-point-free accelerometer metrics and there is growing application of such (e.g., [2,6,16,17]), reference values and percentile curves of healthy individuals facilitating the interpretation of these metrics and providing a framework for judging the appropriateness of PA levels are required (13). Such reference values and curves are frequently used to facilitate the interpretation of other metrics that are not readily interpretable, for example, BMI (18) or CRF (19).…”

“…Accelerometer data can yield many physical activity (PA) outcomes (1) that are relevant for PA surveillance on individual and population levels (2), evaluation of interventions (3,4), epidemiology research (5,6), and informing the development of public health policies (2), among others.…”