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Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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\u2009\u00b1\u20095.77 mg\/dL with the Matthews correlation coefficients for detecting hypoglycemia and hyperglycemia of 0.56\u2009\u00b1\u20090.07 and 0.70\u2009\u00b1\u20090.05, respectively. The use of wristband data significantly reduced the RMSE by 2.25 mg\/dL (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.01). 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.<\/jats:p>","DOI":"10.1038\/s41746-022-00626-5","type":"journal-article","created":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T10:07:15Z","timestamp":1656324435000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["Enhancing self-management in type 1 diabetes with wearables and deep learning"],"prefix":"10.1038","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9782-3470","authenticated-orcid":false,"given":"Taiyu","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Chukwuma","family":"Uduku","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3073-3128","authenticated-orcid":false,"given":"Kezhi","family":"Li","sequence":"additional","affiliation":[]},{"given":"Pau","family":"Herrero","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3525-3633","authenticated-orcid":false,"given":"Nick","family":"Oliver","sequence":"additional","affiliation":[]},{"given":"Pantelis","family":"Georgiou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"626_CR1","doi-asserted-by":"publisher","first-page":"107843","DOI":"10.1016\/j.diabres.2019.107843","volume":"157","author":"P Saeedi","year":"2019","unstructured":"Saeedi, P. et al. 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