Non-invasive hypoglycemia detection in people with diabetes using smartwatch data

Aim

To develop a non-invasive hypoglycemia detection approach using smartwatch data.

Research Design and Methods

We prospectively collected data from two wrist-worn wearables (Garmin vivoactive 4S; Empatica E4) and continuous glucose monitoring values in adults with diabetes mellitus on insulin treatment. Using this data, we developed a machine learning (ML) approach to detect hypoglycemia (<3.9 mmol/L) non-invasively on unseen individuals and solely based on wearable data.

Results

Twenty-two individuals were included in the final analysis (age 54.5±15.2y, HbA1c 6.9±0.6%, 16 male). Hypoglycemia was detected with an area under the receiver operating characteristic curve of 0.76±0.07 solely based on wearable data. Feature analysis revealed that the ML model associated increased heart rate, decreased heart rate variability, and increased tonic electrodermal activity with hypoglycemia.

Conclusions

Our approach may allow for non-invasive hypoglycemia detection using wearables in people with diabetes and thus complement existing methods for hypoglycemia detection and warning.