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Identifying bedrest using 24-h waist or wrist accelerometry in adults.

Identifying bedrest using 24-h waist or wrist accelerometry in adults.
Author Information (click to view)

Tracy JD, Acra S, Chen KY, Buchowski MS,


Tracy JD, Acra S, Chen KY, Buchowski MS, (click to view)

Tracy JD, Acra S, Chen KY, Buchowski MS,

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PloS one 2018 03 2313(3) e0194461 doi 10.1371/journal.pone.0194461
Abstract
OBJECTIVES
To adapt and refine a previously-developed youth-specific algorithm to identify bedrest for use in adults. The algorithm is based on using an automated decision tree (DT) analysis of accelerometry data.

DESIGN
Healthy adults (n = 141, 85 females, 19-69 years-old) wore accelerometers on the waist, with a subset also wearing accelerometers on the dominant wrist (n = 45). Participants spent ≈24-h in a whole-room indirect calorimeter equipped with a force-platform floor to detect movement.

METHODS
Minute-by-minute data from recordings of waist-worn or wrist-worn accelerometers were used to identify bedrest and wake periods. Participants were randomly allocated to development (n = 69 and 23) and validation (n = 72 and 22) groups for waist-worn and wrist-worn accelerometers, respectively. The optimized DT algorithm parameters were block length, threshold, bedrest-start trigger, and bedrest-end trigger. Differences between DT classification and synchronized objective classification by the room calorimeter to bedrest or wake were assessed for sensitivity, specificity, and accuracy using a Receiver Operating Characteristic (ROC) procedure applied to 1-min epochs (n = 92,543 waist; n = 30,653 wrist).

RESULTS
The optimal algorithm parameter values for block length were 60 and 45 min, thresholds 12.5 and 400 counts/min, bedrest-start trigger 120 and 400 counts/min, and bedrest-end trigger 1,200 and 1,500 counts/min, for the waist and wrist-worn accelerometers, respectively. Bedrest was identified correctly in the validation group with sensitivities of 0.819 and 0.912, specificities of 0.966 and 0.923, and accuracies of 0.755 and 0.859 by the waist and wrist-worn accelerometer, respectively. The DT algorithm identified bedrest/sleep with greater accuracy than a commonly used automated algorithm (Cole-Kripke) for wrist-worn accelerometers (p<0.001). CONCLUSIONS
The adapted DT accurately identifies bedrest in data from accelerometers worn by adults on either the wrist or waist. The automated bedrest/sleep detection DT algorithm for both youth and adults is openly accessible as a package "PhysActBedRest" for the R-computer language.

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