title: Methods to estimate aspects of physical activity and sedentary behavior from high-frequency wrist accelerometer measurements.
abstract: This investigation developed models to estimate aspects of physical activity and sedentary behavior from three-axis high-frequency wrist-worn accelerometer data. The models were developed and tested on 20 participants (n = 10 males, n = 10 females, mean age = 24.1, mean body mass index = 23.9), who wore an ActiGraph GT3X+ accelerometer on their dominant wrist and an ActiGraph GT3X on the hip while performing a variety of scripted activities. Energy expenditure was concurrently measured by a portable indirect calorimetry system. Those calibration data were then used to develop and assess both machine-learning and simpler models with fewer unknown parameters (linear regression and decision trees) to estimate metabolic equivalent scores (METs) and to classify activity intensity, sedentary time, and locomotion time. The wrist models, applied to 15-s windows, estimated METs [random forest: root mean squared error (rSME) = 1.21 METs, hip: rMSE = 1.67 METs] and activity intensity (random forest: 75% correct, hip: 60% correct) better than a previously developed model that used counts per minute measured at the hip. In a separate set of comparisons, the simpler decision trees classified activity intensity (random forest: 75% correct, tree: 74% correct), sedentary time (random forest: 96% correct, decision tree: 97% correct), and locomotion time (random forest: 99% correct, decision tree: 96% correct) nearly as well or better than the machine-learning approaches. Preliminary investigation of the models' performance on two free-living people suggests that they may work well outside of controlled conditions.
authors.affiliation: Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts; and jstauden@math.umass.edu.
authors.forename: John
authors.initials: J
authors.lastname: Staudenmayer
authors.affiliation: Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts; and.
authors.forename: Shai
authors.initials: S
authors.lastname: He
authors.affiliation: Department of Kinesiology, University of Massachusetts Amherst, Amherst, Massachusetts.
authors.forename: Amanda
authors.initials: A
authors.lastname: Hickey
authors.affiliation: Department of Kinesiology, University of Massachusetts Amherst, Amherst, Massachusetts.
authors.forename: Jeffer
authors.initials: J
authors.lastname: Sasaki
authors.affiliation: Department of Kinesiology, University of Massachusetts Amherst, Amherst, Massachusetts.
authors.forename: Patty
authors.initials: P
authors.lastname: Freedson
mesh_terms: D056044:Actigraphy / Q000295:instrumentation*; D000203:Activities of Daily Living; D000328:Adult; D001696:Biomechanical Phenomena; D015992:Body Mass Index; D002153:Calorimetry, Indirect; D003663:Decision Trees; D004734:Energy Metabolism; D004868:Equipment Failure; D005260:Female; D015438:Health Behavior; D006801:Humans; D016014:Linear Models; D000069550:Machine Learning; D008297:Male; D015233:Models, Statistical; D009043:Motor Activity; D015203:Reproducibility of Results; D057185:Sedentary Behavior; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D014953:Wrist / Q000502:physiology*; D055815:Young Adult
keywords: ActiGraph; GT3X+; high frequency; triaxial