title: Ability of thigh-worn ActiGraph and activPAL monitors to classify posture and motion.
abstract: OBJECTIVE
  This study compared sitting, standing, and stepping classifications from thigh-worn ActiGraph and activPAL monitors under laboratory and free-living conditions.
  
  
  METHODS
  Adults wore both monitors on the right thigh while performing activities (six sitting, two standing, nine stepping, and one cycling) and writing on a whiteboard with intermittent stepping under laboratory conditions (n = 21) and under free-living conditions for 3 d (n = 18). Percent time correctly classified was calculated under laboratory conditions. Between-monitor agreement and weighted κ were calculated under free-living conditions.
  
  
  RESULTS
  In the laboratory, both monitors correctly classified 100% of standing time and >95% of the time spent in four of six sitting postures. Both monitors demonstrated misclassification of laboratory stool sitting time (ActiGraph 14% vs. activPAL 95%). ActivPAL misclassified 14% of the time spent sitting with legs outstretched; ActiGraph was 100% accurate. Monitors were >95% accurate for stepping, although ActiGraph was less so for descending stairs (86%), ascending stairs (92%), and running at 2.91 m·s(-1) (93%). Monitors classified whiteboard writing differently (ActiGraph 83% standing/15% stepping vs. activPAL 98% standing/2% stepping). ActivPAL classified 93% of cycling time as stepping, whereas ActiGraph classified <1% of cycling time as stepping. During free-living wear, monitors had substantial agreement (86% observed; weighted κ = 0.77). Monitors classified similar amounts of time as sitting (ActiGraph 64% vs. activPAL 62%). There were differences in time recorded as standing (ActiGraph 21% vs. activPAL 27%) and stepping (ActiGraph 15% vs. activPAL 11%).
  
  
  CONCLUSIONS
  Differences in data processing algorithms may have resulted in the observed disagreement in posture and activity classification between thigh-worn ActiGraph and activPAL. Despite between-monitor agreement in classifying sitting time under free-living conditions, ActiGraph appears to be more sensitive to free-living upright walking motions than activPAL.
authors.affiliation: 1Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute, Rockville, MD; 2Risk Factor Monitoring and Methods Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD; 3Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, Rockville, MD; and 4Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.
authors.forename: Jeremy A
authors.initials: JA
authors.lastname: Steeves
authors.forename: Heather R
authors.initials: HR
authors.lastname: Bowles
authors.forename: James J
authors.initials: JJ
authors.lastname: McClain
authors.forename: Kevin W
authors.initials: KW
authors.lastname: Dodd
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Brychta
authors.forename: Juan
authors.initials: J
authors.lastname: Wang
authors.forename: Kong Y
authors.initials: KY
authors.lastname: Chen
mesh_terms: D061725:Accelerometry / Q000379:methods*; D000203:Activities of Daily Living; D000328:Adult; D000465:Algorithms; D001330:Electronic Data Processing; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009043:Motor Activity; D011187:Posture; D013848:Thigh; D055815:Young Adult