Predicting sleep based on physical activity, light exposure, and Heart rate variability data using wearable devices.

ABSTRACT:

Objective

We aimed to improve the performance of sleep prediction algorithms by increasing the data amount, adding variables reflecting psychological state, and adjusting the data length.

Materials and methods

We used ActiGraph GT3X+^® and Galaxy Watch Active2^™ to collect physical activity and light exposure data. We collected heart rate variability (HRV) data with the Galaxy Watch. We evaluated the performance of sleep prediction algorithms based on different data sources (wearable devices only, sleep diary only, or both), data lengths (1, 2, or 3 days), and analysis methods. We defined the target outcome, 'good sleep', as ≥90% sleep efficiency.

Results

Among 278 participants who denied having sleep disturbance, we used data including 2136 total

SUBMITTER: Park KM 

PROVIDER: S-EPMC11413959 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature