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

Objective Data-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.

Three 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.

Of 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.

Data-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. Competing Interest Statement The authors have declared no competing interest. Funding Statement The authors are supported by the Institute for Sport and Sport Science, TU Dortmund University. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes

has been shortened. Discussion strengthened. Figures adjusted. Data Availability All data produced in the present work are contained in the manuscript