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

John Bellettiere, Yiliang Zhang, Vincent Berardi, Kelsie M. Full, Jacqueline Kerr, Michael J. Lamonte, Kelly R. Evenson, Melbourne Hovell, Andrea Z. Lacroix, Chongzhi Di

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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

Original languageEnglish (US)
Article number075008
JournalPhysiological Measurement
Issue number7
StatePublished - Jul 30 2019

Bibliographical note

Publisher Copyright:
© 2019 Institute of Physics and Engineering in Medicine.


  • 24 hour data processing
  • accelerometer data
  • measurement
  • older adults
  • sedentary behavior
  • sleep algorithm


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