Enhancing self-management in type 1 diabetes with wearables and deep learning

Journal article

People living with type 1 diabetes (T1D) require lifelong self-management to maintain glucose levels in a safe range. Failure to do so can lead to adverse glycemic events with short and long-term complications. Continuous glucose monitoring (CGM) is widely used in T1D self-management for real-time glucose measurements, while smartphone apps are adopted as basic electronic diaries, data visualization tools, and simple decision support tools for insulin dosing. Applying a mixed effects logistic regression analysis to the outcomes of a six-week longitudinal study in 12 T1D adults using CGM and a clinically validated wearable sensor wristband (NCT ID: NCT03643692), we identified several significant associations between physiological measurements and hypo- and hyperglycemic events measured an hour later. We proceeded to develop a new smartphone-based platform, ARISES (Adaptive, Real-time, and Intelligent System to Enhance Self-care), with an embedded deep learning algorithm utilizing multi-modal data from CGM, daily entries of meal and bolus insulin, and the sensor wristband to predict glucose levels and hypo- and hyperglycemia. For a 60-minute prediction horizon, the proposed algorithm achieved the average root mean square error (RMSE) of 35.28 ± 5.77 mg/dL with the Matthews correlation coefficients for detecting hypoglycemia and hyperglycemia of 0.56 ± 0.07 and 0.70 ± 0.05, respectively. The use of wristband data significantly reduced the RMSE by 2.25 mg/dL (p < 0.01). The well-trained model is implemented on the ARISES app to provide real-time decision support. These results indicate that the ARISES has great potential to mitigate the risk of severe complications and enhance self-management for people with T1D

Authors: Zhu, T; Uduku, C; Li, K; Herrero, P; Oliver, N

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: npj Digital Medicine
• Volume: 5
• Issue: 1
• Pages: 78-78
• Article number: 78
• Publication date: 2022-06-27
• DOI: 10.1038/s41746-022-00626-5
• EISSN: 2398-6352
• ISSN: 2398-6352
• Language: English
• Keywords: Statistics; Glycemic; Diabetes mellitus; Mean squared error; Computer science; Diabetes management; Internal medicine; Continuous glucose monitoring; Embedded system; Insulin; Machine learning; Type 2 diabetes; Mathematics; Wearable computer; Artificial pancreas; Hypoglycemia; Medicine; Endocrinology; Type 2 Diabetes Mellitus; Logistic regression; Type 1 diabetes