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

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 acc

SUBMITTER: Tracy JD 

PROVIDER: S-EPMC5865746 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature