title: National youth sedentary behavior and physical activity daily patterns using latent class analysis applied to accelerometry.
abstract: BACKGROUND
  Applying latent class analysis (LCA) to accelerometry can help elucidated underlying patterns. This study described the patterns of accelerometer-determined sedentary behavior and physical activity among youth by applying LCA to a nationally representative United States (US) sample.
  
  
  METHODS
  Using 2003-2006 National Health and Nutrition Examination Survey data, 3998 youths 6-17 years wore an ActiGraph 7164 accelerometer for one week, providing > =3 days of wear for > =8 h/day from 6:00 am-midnight. Cutpoints defined sedentary behavior (<100 counts/minute), light activity (100-2295 counts/minute), moderate to vigorous physical activity (MVPA; > = 2296 counts/minute), and vigorous activity (> = 4012 counts/minute). To account for wear time differences, outcomes were expressed as percent of day in a given intensity. LCA was used to classify daily (Monday through Sunday) patterns of average counts/minute, sedentary behavior, light activity, MVPA, and vigorous activity separately. The latent classes were explored overall and by age (6-11, 12-14, 15-17 years), gender, and whether or not youth attended school during measurement. Estimates were weighted to account for the sampling frame.
  
  
  RESULTS
  For average counts/minute/day, four classes emerged from least to most active: 40.9% of population (mean 323.5 counts/minute/day), 40.3% (559.6 counts/minute/day), 16.5% (810.0 counts/minute/day), and 2.3% (1132.9 counts/minute/day). For percent of sedentary behavior, four classes emerged: 13.5% of population (mean 544.6 min/day), 30.1% (455.1 min/day), 38.5% (357.7 min/day), and 18.0% (259.2 min/day). For percent of light activity, four classes emerged: 12.3% of population (mean 222.6 min/day), 29.3% (301.7 min/day), 41.8% (384.0 min/day), and 16.6% (455.5 min/day). For percent of MVPA, four classes emerged: 59.9% of population (mean 25.0 min/day), 33.3% (60.9 min/day), 3.1% (89.0 min/day), and 3.6% (109.3 min/day). For percent of vigorous activity, three classes emerged: 76.8% of population (mean 7.1 min/day), 18.5% (23.9 min/day), and 4.7% (47.4 min/day). Classes were developed by age, gender, and school attendance since some patterns differed when stratifying by these factors.
  
  
  CONCLUSIONS
  The models supported patterns for average intensity, sedentary behavior, light activity, MVPA, and vigorous activity. These latent class derived patterns can be used in other youth studies to explore correlates or outcomes and to target sedentary behavior or physical activity interventions.
authors.affiliation: Department of Epidemiology at the Gillings School of Global Public Health, Center for Health Promotion and Disease Prevention, University of North Carolina, 137 East Franklin Street, Suite 306, Chapel Hill, NC, 27514, USA. kelly_evenson@unc.edu.
authors.forename: Kelly R
authors.initials: KR
authors.lastname: Evenson
authors.affiliation: Department of Epidemiology at the Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
authors.forename: Fang
authors.initials: F
authors.lastname: Wen
authors.affiliation: Department of Nutrition at the Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
authors.forename: Derek
authors.initials: D
authors.lastname: Hales
authors.affiliation: Department of Biostatistics at the Gillings School of Global Public Health, Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA.
authors.forename: Amy H
authors.initials: AH
authors.lastname: Herring
mesh_terms: D061725:Accelerometry; D000293:Adolescent; D000294:Adolescent Behavior; D002648:Child; D002652:Child Behavior; D015444:Exercise; D005260:Female; D015438:Health Behavior; D006801:Humans; D008297:Male; D009043:Motor Activity; D005082:Physical Exertion; D012574:Schools; D057185:Sedentary Behavior; D011795:Surveys and Questionnaires; D014481:United States
keywords: Accelerometry; Children; Cluster analysis; Latent class analysis; Surveillance