title: Comparison of Accelerometry Methods for Estimating Physical Activity.
abstract: This study aimed to compare physical activity estimates across different accelerometer wear locations, wear time protocols, and data processing techniques.
  
  
  
  A convenience sample of middle-age to older women wore a GT3X+ accelerometer at the wrist and hip for 7 d. Physical activity estimates were calculated using three data processing techniques: single-axis cut points, raw vector magnitude thresholds, and machine learning algorithms applied to the raw data from the three axes. Daily estimates were compared for the 321 women using generalized estimating equations.
  
  
  
  A total of 1420 d were analyzed. Compliance rates for the hip versus wrist location only varied by 2.7%. All differences between techniques, wear locations, and wear time protocols were statistically different (P < 0.05). Mean minutes per day in physical activity varied from 22 to 67 depending on location and method. On the hip, the 1952-count cut point found at least 150 min·wk of physical activity in 22% of participants, raw vector magnitude found 32%, and the machine-learned algorithm found 74% of participants with 150 min of walking/running per week. The wrist algorithms found 59% and 60% of participants with 150 min of physical activity per week using the raw vector magnitude and machine-learned techniques, respectively. When the wrist device was worn overnight, up to 4% more participants met guidelines.
  
  
  
  Estimates varied by 52% across techniques and by as much as 41% across wear locations. Findings suggest that researchers should be cautious when comparing physical activity estimates from different studies. Efforts to standardize accelerometry-based estimates of physical activity are needed. A first step might be to report on multiple procedures until a consensus is achieved.
authors.affiliation: 1Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA; 2Graduate School of Public Health, San Diego State University, San Diego, CA; 3Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA; 4Department of Parks, Recreation, and Tourism Management, North Carolina State University Center for Geospatial Analytics, Center for Human Health and the Environment, North Carolina State University, Raleigh, NC; 5Perelman School of Medicine and School of Nursing, University of Pennsylvania, Philadelphia, PA; 6Division of Gastroenterology, Hepatology and Nutrition, Children's Hospital of Philadelphia, Philadelphia, PA; 7Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; 8Departments of Environmental Health and Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; 9Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; and 10National Cancer Institute, National Institutes of Health, Bethesda, MD.
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Kerr
authors.forename: Catherine R
authors.initials: CR
authors.lastname: Marinac
authors.forename: Katherine
authors.initials: K
authors.lastname: Ellis
authors.forename: Suneeta
authors.initials: S
authors.lastname: Godbole
authors.forename: Aaron
authors.initials: A
authors.lastname: Hipp
authors.forename: Karen
authors.initials: K
authors.lastname: Glanz
authors.forename: Jonathan
authors.initials: J
authors.lastname: Mitchell
authors.forename: Francine
authors.initials: F
authors.lastname: Laden
authors.forename: Peter
authors.initials: P
authors.lastname: James
authors.forename: David
authors.initials: D
authors.lastname: Berrigan
mesh_terms: D061725:Accelerometry / Q000295:instrumentation / Q000379:methods*; D000465:Algorithms; D001330:Electronic Data Processing / Q000379:methods; D015444:Exercise; D005260:Female; D006615:Hip; D006801:Humans; D000069550:Machine Learning; D008875:Middle Aged; D012420:Running / Q000502:physiology; D057185:Sedentary Behavior; D013997:Time Factors; D016138:Walking / Q000502:physiology; D014953:Wrist