title: Adults' physical activity patterns across life domains: cluster analysis with replication.
abstract: OBJECTIVE
  Identifying adults' physical activity patterns across multiple life domains could inform the design of interventions and policies.
  
  
  METHODS
  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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Public Health Sciences, Pennsylvania State University College of Medicine.
authors.forename: Liza S
authors.initials: LS
authors.lastname: Rovniak
authors.affiliation: Department of Psychology, San Diego State University.
authors.forename: James F
authors.initials: JF
authors.lastname: Sallis
authors.affiliation: University of Washington.
authors.forename: Brian E
authors.initials: BE
authors.lastname: Saelens
authors.affiliation: School of Community and Regional Planning, University of British Columbia.
authors.forename: Lawrence D
authors.initials: LD
authors.lastname: Frank
authors.affiliation: School of Exercise and Nutritional Sciences, San Diego State University.
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Marshall
authors.affiliation: Department of Family & Preventive Medicine, University of California.
authors.forename: Gregory J
authors.identifier: 0000-0001-7989-9597
authors.initials: GJ
authors.lastname: Norman
authors.affiliation: Graduate School of Public Health, San Diego State University.
authors.forename: Terry L
authors.initials: TL
authors.lastname: Conway
authors.affiliation: Department of Psychology, San Diego State University.
authors.forename: Kelli L
authors.initials: KL
authors.lastname: Cain
authors.affiliation: Graduate School of Public Health, San Diego State University.
authors.forename: Melbourne F
authors.initials: MF
authors.lastname: Hovell
mesh_terms: D000328:Adult; D000368:Aged; D015142:Baltimore; D015992:Body Mass Index; D016000:Cluster Analysis; D004219:District of Columbia; D004522:Educational Status; D015444:Exercise / Q000502:physiology / Q000523:psychology*; D005190:Family; D005260:Female; D006801:Humans; D007182:Income; D007899:Leisure Activities; D008019:Life Style; D008297:Male; D008875:Middle Aged; D009043:Motor Activity; D012931:Social Environment; D012944:Social Support; D011795:Surveys and Questionnaires; D014861:Washington