Optimizing an automated sleep detection algorithm using wrist-worn accelerometer data for individuals with chronic pain.

ABSTRACT:

Objective

To optimize a wrist-worn accelerometer-based, automated sleep detection methodology for chronic pain populations.

Patients and methods

A cohort of 16 patients with chronic pain underwent free-living observation for one week before participating in an Interdisciplinary Pain Management Program. Patients wore ActiGraph GT9X devices and maintained a sleep diary, documenting their nightly bedtimes and wake times. To derive sleep quality measures from accelerometry data, the Tudor-Locke sleep detection algorithm was employed. However, this algorithm had not been validated for chronic pain patients. Therefore, a sensitivity analysis of the algorithm's parameters was conducted, identifying a set of parameters which maximized the agreement between sleep periods identified b

SUBMITTER: Faust L 

PROVIDER: S-EPMC11970680 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature