Toward Detection of Nocturnal Hypoglycemia in People With Diabetes Using Consumer-Grade Smartwatches and a Machine Learning Approach.

ABSTRACT:

Background

Nocturnal hypoglycemia poses significant risks to individuals with insulin-treated diabetes, impacting health and quality of life. Although continuous glucose monitoring (CGM) systems reduce these risks, their poor accuracy at low glucose levels, high cost, and availability limit their use. This study examined physiological biomarkers associated with nocturnal hypoglycemia and evaluated the use of machine learning (ML) to detect hypoglycemia during nighttime sleep using data from consumer-grade smartwatches.

Methods

This study analyzed 351 nights of 36 adults with insulin-treated diabetes. Participants wore two smartwatches alongside CGM systems. Linear mixed-effects models compared sleep and vital signs between nights with and without hypoglycemia during early an

SUBMITTER: Mendez C 

PROVIDER: S-EPMC11851596 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature