Zhu, Taiyu;
Uduku, Chukwuma;
Li, Kezhi;
Herrero, Pau;
Oliver, Nick;
Georgiou, Pantelis;
(2022)
Enhancing self-management in type 1 diabetes with wearables and deep learning.
npj Digital Medicine
, 5
, Article 78. 10.1038/s41746-022-00626-5.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Enhancing self-management in type 1 diabetes with wearables and deep learning. |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41746-022-00626-5 |
| Publisher version: | https://doi.org/10.1038/s41746-022-00626-5 |
| Language: | English |
| Additional information: | © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons. org/licenses/by/4.0/. © The Author(s) 2022. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10151366 |
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