Data-driven profiling of accelerometer-determined physical behaviors and health outcomes in adults: A systematic review

ObjectiveData-driven population segmentation analysis are analytical approaches applicable for profiling physical behaviors from wearable accelerometry data. These methods rely on data rather than predefined, knowledge-driven hypotheses to first identify multiple subgroups within a sample population and then evaluate their relationships with health outcomes. This systematic review aims to describe and synthesize multidimensional physical behavior profiles derived from accelerometry data across adult populations, as identified using data-driven population segmentation analysis. MethodThree electronic databases were searched for relevant articles published up to July 2025. Peer-reviewed journal articles that applied data-driven population segmentation analysis to accelerometer-monitored physical behaviors in adult participants (>18 years) to create profiles of physical behaviors, and that examined the associations of the profiles with a health outcome, were considered. Studies conducted with clinical or specific sample populations were not considered. ResultsOf the 16,289 publications retrieved, 40 were included. The most commonly employed technique for physical behavior profiling was the machine learning K-means clustering algorithm (n=18), followed by latent profile analysis (n=8). A diverse set of descriptor variables was derived from accelerometer signals and utilized. The review of profiles of physical behavior revealed several hypothesis-generating, preliminary evidence about how different components and/or aspects of physical behaviors could cluster together and influence health outcomes. ConclusionData-driven population segmentation analysis are viable analytical approaches increasingly employed in accelerometry studies to drive physical behavior profiles. The application of these techniques to accelerometer-measured physical behaviors has generated data-driven findings regarding how various physical behavior profiles may differentially relate to health outcomes.