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 and late sleep. A ML model was trained to detect hypoglycemia solely using smartwatch data.

Results: Sixty-six nights with spontaneous hypoglycemia were recorded. Hypoglycemic nights showed increased wake periods, heart rate, stress levels, and activity during early sleep, with weaker effects during late sleep. In nights when hypoglycemia occurred during early sleep, the ML model performed comparable or better than prior studies with an area under the receiver operator curve of 0.78 for level 1 and 0.83 for level 2 hypoglycemia, with sensitivity of 0.78 and 0.89, specificity of 0.64 for both, negative predictive value of 0.94 and 0.99, and positive predictive value of 0.25 and 0.13 for level 1 and level 2 hypoglycemia, respectively.

Conclusions: Consumer-grade smartwatches demonstrate promise for detecting nocturnal hypoglycemia, particularly during early sleep. Refining models to reduce false alarms could enhance their clinical utility as low-cost, accessible tools to complement CGM.