Clustering 24-Hour Movement Behaviours Among Adults in 16 European Countries (2008–2015): A Behavioural Typology Approach

Objective: This study aimed to identify and characterise distinct 24-hour movement behaviour profiles among adults across 16 European countries using proportional data on moderate-to-vigorous physical activity (MVPA), light physical activity (LIPA), sedentary behaviour (SB), and sleep duration (SD) 2. Methods: Data came from the Harmonized European Time Use Surveys (HETUS, Wave 2, 2008–2015) including adults aged 18–80+ (N ≈ 181,000). Each participant completed two 24-hour diaries coded into 144 ten-minute intervals. Activities were assigned metabolic equivalent of task (MET) values based on the 2024 Compendium and aggregated 3 into MVPA, LIPA, SB, and SD. Time in each behaviour was expressed as a proportion of the 24-hour day 1. Latent Profile Analysis (LPA) was applied separately by sex and age group to identify movement behaviour profiles using the tidyLPA 4 package in R (v4.5.0). Models with two to six profiles were estimated under equal variance and zero-covariance assumptions. Bayesian Information Criterion (BIC) and entropy guided selection of the optimal number of profiles. Results: Four stable profiles consistently emerged: - Sedentary Short-Sleepers (high SB, low SD, low MVPA/LIPA) - Active Balanced Types (moderate MVPA/LIPA, balanced SB and SD) - Lightly Active Long Sleepers (high LIPA and SD, low SB) - Highly Active Short Sleepers (high MVPA, low SD) Profile distributions varied by sex and age. Younger adults showed a higher likelihood of MVPA-dominant profiles, whereas older adults tended toward light activity and long sleep profiles. Conclusions: Four distinct and recurring 24-hour movement behaviour profiles emerged among European adults. These profiles reflect diverse combinations of activity, sedentary time, and sleep, underscoring the need for population-specific approaches to movement-behaviour promotion. References Clarke, A. E., & Janssen, I. (2021). A compositional analysis of time spent in sleep, sedentary behaviour and physical activity with all-cause mortality risk. International Journal of Behavioral Nutrition and Physical Activity, 18(1), Artikel 25. https://doi.org/10.1186/s12966-021-01092-0 Dumuid, D., Stanford, T. E., Martin-Fernández, J., Pedišić, Ž., Maher, C. A., Lewis, L. K., Hron, K., Katzmarzyk, P. T., Chaput, J., Fogelholm, M., Hu, G., Lambert, E. V., Maia, J., Sarmiento, O. L., Standage, M., Barreira, T. V., Broyles, S. T., Tudor-Locke, C., Tremblay, M. S., & Olds, T. (2017). Compositional data analysis for physical activity, sedentary time and sleep research. Statistical Methods in Medical Research, 27(12), 3726–3738. https://doi.org/10.1177/0962280217710835 Herrmann, S. D., Willis, E. A., Ainsworth, B. E., Barreira, T. V., Hastert, M., Kracht, C. L., Schuna, J. M., Cai, Z., Quan, M., Tudor-Locke, C., Whitt-Glover, M. C., & Jacobs, D. R. (2024). 2024 Adult Compendium of Physical Activities: A third update of the energy costs of human activities. Journal of Sport and Health Science, 13(1), 6–12. https://doi.org/10.1016/j.jshs.2023.10.010 Rosenberg, J., Beymer, P., Anderson, D., Van Lissa, C., & Schmidt, J. (2018). TidyLPA: An R package to easily carry out latent profile analysis (LPA) using open-source or commercial software. The Journal of Open Source Software, 3(30), Artikel 978. https://doi.org/10.21105/joss.00978