title: A method to estimate free-living active and sedentary behavior from an accelerometer.
abstract: BACKGROUND
  Methods to estimate physical activity (PA) and sedentary behavior (SB) from wearable monitors need to be validated in free-living settings.
  
  
  OBJECTIVE
  The purpose of this study was to develop and validate two novel machine-learning methods (Sojourn-1 Axis [soj-1x] and Sojourn-3 Axis [soj-3x]) in a free-living setting.
  
  
  METHODS
  Participants were directly observed in their natural environment for 10 consecutive hours on three separate occasions. Physical activity and SB estimated from soj-1x, soj-3x, and a neural network previously calibrated in the laboratory (lab-nnet) were compared with direct observation.
  
  
  RESULTS
  Compared with lab-nnet, soj-1x and soj-3x improved estimates of MET-hours (lab-nnet: % bias [95% confidence interval] = 33.1 [25.9 to 40.4], root-mean-square error [RMSE] = 5.4 [4.6-6.2]; soj-1x: % bias = 1.9 [-2.0 to 5.9], RMSE = 1.0 [0.6 to 1.3]; soj-3x: % bias = 3.4 [0.0 to 6.7], RMSE = 1.0 [0.6 to 1.5]) and minutes in different intensity categories {lab-nnet: % bias = -8.2 (sedentary), -8.2 (light), and 72.8 (moderate-to-vigorous PA [MVPA]); soj-1x: % bias = 8.8 (sedentary), -18.5 (light), and -1.0 (MVPA); soj-3x: % bias = 0.5 (sedentary), -0.8 (light), and -1.0 (MVPA)}. Soj-1x and soj-3x also produced accurate estimates of guideline minutes and breaks from sedentary time.
  
  
  CONCLUSIONS
  Compared with the lab-nnet algorithm, soj-1x and soj-3x improved the accuracy and precision in estimating free-living MET-hours, sedentary time, and time spent in light-intensity activity and MVPA. In addition, soj-3x is superior to soj-1x in differentiating SB from light-intensity activity.
authors.affiliation: 1Department of Kinesiology, University of Massachusetts, Amherst, MA; and 2Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA.
authors.forename: Kate
authors.initials: K
authors.lastname: Lyden
authors.forename: Sarah Kozey
authors.initials: SK
authors.lastname: Keadle
authors.forename: John
authors.initials: J
authors.lastname: Staudenmayer
authors.forename: Patty S
authors.initials: PS
authors.lastname: Freedson
mesh_terms: D000054:Acceleration; D056044:Actigraphy; D000328:Adult; D000465:Algorithms; D015444:Exercise / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D055686:Metabolic Equivalent; D016571:Neural Networks, Computer; D019370:Observation; D057185:Sedentary Behavior; D013997:Time Factors; D055815:Young Adult