Sociodemographic differences in 24-hour time-use behaviours in New Zealand children.

BACKGROUND: The time that children spend in physical activity, sedentary behaviour, and sleep each day (i.e., 24-h time-use behaviours), is related to physical and mental health outcomes. Currently, there is no comprehensive evidence on New Zealand school-aged children's 24-h time-use behaviours, adherence to the New Zealand 24-h Movement Guidelines, and how these vary among different sociodemographic groups. METHODS: This study utilises data from the 8-year wave of the Growing Up in New Zealand longitudinal study. Using two Axivity AX3 accelerometers, children's 24-h time-use behaviours were described from two perspectives: activity intensity and activity type. Compositional data analysis techniques were used to explore the differences in 24-h time-use compositions across various sociodemographic groups. RESULTS: Children spent on average, 31.1%, 22.3%, 6.8%, and 39.8% of their time in sedentary, light physical activity, moderate-to-vigorous physical activity, and sleep, respectively. However, the daily distribution of time in different activity types was 33.2% sitting, 10.8% standing, 7.3% walking, 0.4% running, and 48.2% lying. Both the activity intensity and activity type compositions varied across groups of child ethnicity, gender, and household income or deprivation. The proportion of children meeting each of the guidelines was 90% for physical activity, 62.5% for sleep, 16% for screen time, and 10.6% for the combined guidelines. Both gender and residence location (i.e., urban vs. rural) were associated with meeting the physical activity guideline, whereas child ethnicity, mother's education and residence location were associated with meeting the screen time guideline. Child ethnicity and mother's education were also significantly associated with the adherence to the combined 24-h Movement Guidelines. CONCLUSIONS: This study provided comprehensive evidence on how New Zealand children engage in 24-h time-use behaviours, adherence to the New Zealand 24-h Movement Guidelines, and how these behaviours differ across key sociodemographic groups. These findings should be considered in designing future interventions for promoting healthy time-use patterns in New Zealand children.

A Dual-Accelerometer System for Classifying Physical Activity in Children and Adults.

INTRODUCTION: Accurately monitoring 24-h movement behaviors is a vital step for progressing the time-use epidemiology field. Past accelerometer-based measurement protocols are either hindered by lack of wear time compliance, or the inability to accurately discern activities and postures. Recent work has indicated that skin-attached dual-accelerometers exhibit excellent 24-h uninterrupted wear time compliance. This study extends this work by validating this system for classifying various physical activities and sedentary behaviors in children and adults. METHODS: Seventy-five participants (42 children) were equipped with two Axivity AX3 accelerometers; one attached to their thigh, and one to their lower back. Ten activity trials (e.g., sitting, standing, lying, walking, running) were performed while under direct observation in a lab setting. Various time- and frequency-domain features were computed from raw accelerometer data, which were then used to train a random forest machine learning classifier. Model performance was evaluated using leave-one-out cross-validation. The efficacy of the dual-sensor protocol (relative to single sensors) was evaluated by repeating the modeling process with each sensor individually. RESULTS: Machine learning models were able to differentiate between six distinct activity classes with exceptionally high accuracy in both adults (99.1%) and children (97.3%). When a single thigh or back accelerometer was used, there was a pronounced drop in accuracy for nonambulatory activities (up to a 26.4% decline). When examining the features used for model training, those that took the orientation of both sensors into account concurrently were more important predictors. CONCLUSIONS: When previous wear time compliance results are taken together with our findings, it represents a promising step forward for monitoring and understanding 24-h time-use behaviors. The next step will be to examine the generalizability of these findings in a free-living setting.