Bridging the gap from movement to meaning: Developing a standard for processing hip accelerometer data for measuring physical activity intensity in youths

Background: Accurate assessment of physical activity (PA) using hip-worn accelerometers depends heavily on data processing decisions, yet implications of these choices for health-related outcomes remain insufficiently understood in children and adolescents. Aim: This study examined how different data-processing settings (frequency filter, epoch length, and axis selection) influence estimates of PA intensity and their associations with health measures in youth. Methods: Data were drawn from four Norwegian and Swedish cohorts comprising 1842 children and 3019 adolescents, including hip-worn accelerometer measurements (GT3X and GT3X+) alongside measures of physical and psychological health. Laboratory data were used to calibrate accelerometers and to derive comparable estimates of PA intensity and time across intensity categories. Associations were analysed using partial least squares and ordinary least squares regression. Results: Overall, the level of PA was consistently associated with the three physical health measures (Body Mass Index, High density Lipoprotein Cholesterol and Cardiorespiratory Fitness) and with the four psychological measures (Stress, Cognition, Psychosomatic Symptoms and Health Realted Quality of Life), across all processing methods. Accelerometer processing choices substantially influence estimated PA distributions as well as the magnitude and pattern of associations between PA and investigated health outcomes. In general, processing approaches combining 4 Hz frequency filtering, shorter epoch lengths, and triaxial data produced stronger and more consistent associations with health measures in both children and adolescents, confirming the higher predictive validity of this combination of processing settings. Conclusions: Findings highlight the importance of carefully selecting processing settings when analysing accelerometer data in children and adolescents. This study provides a fundament for a processing standard of hip accelerometer data for more accurate characterization of PA patterns relevant to health.