Research Study Abstract

Parameterizing and validating existing algorithms for identifying out-of-bed time using hip-worn accelerometer data from older women

  • Published on April 24, 2019

To parameterize and validate two existing algorithms for identifying out-of-bed time using 24 h hip-worn accelerometer data from older women.

Overall, 628 women (80  ±  6 years old) wore ActiGraph GT3X+  accelerometers 24 h d−1 for up to 7 d and concurrently completed sleep-logs. Trained staff used a validated visual analysis protocol to measure in-bed periods on accelerometer tracings (criterion). The Tracy and McVeigh algorithms were adapted for optimal use in older adults. A training set of 314 women was used to choose two key thresholds by maximizing the sum of sensitivity and specificity for each algorithm and data (vertical axis, VA, and vector magnitude [VM]) combination. Data from the remaining 314 women were then used to test agreement in waking wear time (i.e. out-of-bed time while wearing the accelerometer) by computing sensitivity, specificity, and kappa comparing the algorithm output with the criterion. Waking wear time-adjusted means of sedentary time, light-intensity physical activity (light PA) and moderate-to-vigorous-intensity physical activity (MVPA) were then estimated and compared.

Main results
Waking wear time agreement with the criterion was high for Tracy_VA, Tracy_VM, McVeigh_VA, and highest for McVeigh_VM. Compared to the criterion, McVeigh_VM had mean sensitivity  =  0.92, specificity  =  0.87, kappa  =  0.80, and overall mean difference (±SD) of  −0.04  ±  2.5 h d−1. Minutes of sedentary time, light PA, and MVPA adjusted for waking wear time using the criterion measure and McVeigh_VM were not statistically different (p   >  0.43|all).

The McVeigh algorithm with optimal parameters using VM performed best compared to criterion sleep-log assisted visual analysis and is suitable for automated identification of waking wear time in older women when visual analysis is not feasible.


  • John Bellettiere 1,2,8,9
  • Yiliang Zhang 3
  • Vincent Berardi 2,4
  • Kelsie M Full 1
  • Jacqueline Kerr 1
  • Michael J LaMonte 5
  • Kelly R Evenson 6
  • Melbourne Hovell 2
  • Andrea Z LaCroix 1,10
  • Chongzhi Di 7,10


  • 1

    Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, United States of America

  • 2

    Center for Behavioral Epidemiology and Community Health, Graduate School of Public Health, San Diego State University, San Diego, CA, United States of America

  • 3

    Department of Biostatistics, Yale, New Haven, CT, United States of America

  • 4

    Department of Psychology, Chapman University, Orange, CA, United States of America

  • 5

    Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo-SUNY, Buffalo, NY, United States of America

  • 6

    Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina Chapel Hill, Chapel Hill, NC, United States of America

  • 7

    Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America

  • 8

    University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, United States of America

  • 9

    Author to whom any correspondence should be addressed.

  • 10

    Equally contributing senior authors.


Physiological Measurement


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