Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data
Journal
Diabetes Care
ISSN
0149-5992
1935-5548
Type
case review (law)
Date Issued
2023-02-17
Author(s)
Vera Lehmann
;
Simon Föll
;
Martin Maritsch
;
Eva van Weenen
;
Mathias Kraus
;
Sophie Lagger
;
Katja Odermatt
;
Caroline Albrecht
;
;
Thomas Zueger
;
;
Christoph Stettler
Abstract
<jats:sec>
<jats:title>OBJECTIVE</jats:title>
<jats:p>To develop a noninvasive hypoglycemia detection approach using smartwatch data.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>RESEARCH DESIGN AND METHODS</jats:title>
<jats:p>We prospectively collected data from two wrist-worn wearables (Garmin vivoactive 4S, Empatica E4) and continuous glucose monitoring values in adults with diabetes on insulin treatment. Using these data, we developed a machine learning (ML) approach to detect hypoglycemia (<3.9 mmol/L) noninvasively in unseen individuals and solely based on wearable data.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>RESULTS</jats:title>
<jats:p>Twenty-two individuals were included in the final analysis (age 54.5 ± 15.2 years, HbA1c 6.9 ± 0.6%, 16 males). 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.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>CONCLUSIONS</jats:title>
<jats:p>Our approach may allow for noninvasive hypoglycemia detection using wearables in people with diabetes and thus complement existing methods for hypoglycemia detection and warning.</jats:p>
</jats:sec>
<jats:title>OBJECTIVE</jats:title>
<jats:p>To develop a noninvasive hypoglycemia detection approach using smartwatch data.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>RESEARCH DESIGN AND METHODS</jats:title>
<jats:p>We prospectively collected data from two wrist-worn wearables (Garmin vivoactive 4S, Empatica E4) and continuous glucose monitoring values in adults with diabetes on insulin treatment. Using these data, we developed a machine learning (ML) approach to detect hypoglycemia (<3.9 mmol/L) noninvasively in unseen individuals and solely based on wearable data.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>RESULTS</jats:title>
<jats:p>Twenty-two individuals were included in the final analysis (age 54.5 ± 15.2 years, HbA1c 6.9 ± 0.6%, 16 males). 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.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>CONCLUSIONS</jats:title>
<jats:p>Our approach may allow for noninvasive hypoglycemia detection using wearables in people with diabetes and thus complement existing methods for hypoglycemia detection and warning.</jats:p>
</jats:sec>
Publisher
American Diabetes Association
Volume
46
Number
5