Objective: Identifying adults' physical activity patterns across multiple life domains could inform the design of interventions and policies. Design: Cluster analysis was conducted with adults in two U.S. regions (Baltimore/Washington, DC, n = 702; Seattle, WA [King County], n = 987) to identify different physical activity patterns based on adults' reported physical activity across four life domains: leisure, occupation, transport, and home. Objectively measured physical activity, and psychosocial and built (physical) environment characteristics of activity patterns were examined. Main Outcome Measures: Accelerometer-measured activity, reported domain-specific activity, psychosocial characteristics, built environment, body mass index. Results: Three clusters replicated (κ =90-93) across both regions: Low Activity, Active Leisure, and Active Job. The Low Activity and Active Leisure adults were demographically similar, but Active Leisure adults had the highest psychosocial and built environment support for activity, highest accelerometer-measured activity, and lowest body mass index. Compared to the other clusters, the Active Job cluster had lower socioeconomic status and intermediate accelerometer-measured activity. Conclusion: Adults can be clustered into groups based on their patterns of accumulating physical activity across life domains. Differences in psychosocial and built environment support between the identified clusters suggest that tailored interventions for different subgroups may be beneficial.
All Science Journal Classification (ASJC) codes
- Applied Psychology
- Psychiatry and Mental health