Conference Agenda

BACKGROUND: Despite the widespread use of wearable devices to collect detailed information of movement behaviours (e.g., accelerometers), current physical activity guidelines and policy-relevant insights remain based on simple metrics, such as time in moderate-to-vigorous physical activity. Advanced approaches, such as functional data analysis (FDA), allow researchers to fully exploit these rich datasets and better characterise patterns of movement behaviours. This study illustrates how advanced analytics can help inform policymakers for the development of targeted interventions in young people. METHODS: Accelerometer data from the Belgian Food Consumption Survey 2022 (n=750; aged 3–17y; 48% female) were analysed. FDA was used to investigate how movement behaviours vary across the intensity distribution and throughout the day, considering sociodemographic and modifiable factors. RESULTS: Girls were less active than boys, particularly on weekdays. Older participants were generally less active during school hours on weekdays but more active in the evening. Marked differences were observed among adolescents on weekends afternoon/evening. Participants with overweight (including obesity) tended to be less active during school hours and during weekend evenings. Children with parents with higher education were more active after school on weekdays and throughout most of the weekends, except in the evenings. Sport club members were more active during school breaks and after school on weekdays, and throughout the day on weekends. CONCLUSION: Findings show the potential of advanced analytics to provide a nuanced understanding of the patterns of movement behaviours and to help policymakers design tailored and effective interventions. FUNDINGS: European Union's Horizon Europe research (No 101072993)