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.

1 型糖尿病 (T1D) 患者需要终生进行自我管理，以将血糖水平维持在安全范围内。如果不这样做，可能会导致不良血糖事件以及短期和长期并发症。连续血糖监测 (CGM) 广泛用于 T1D 自我管理以实时测量血糖，而智能手机应用程序被用作基本的电子日记、数据可视化工具和简单的胰岛素剂量决策支持工具。对 12 名 T1D 成人使用 CGM 和临床验证的可穿戴传感器腕带（NCT ID：NCT03643692）进行的为期六周的纵向研究的结果应用混合效应逻辑回归分析，我们确定了生理测量与低血糖和高血糖之间的几个重要关联一个小时后测量的事件。我们着手开发一个基于智能手机的新平台 ARISES（增强自我保健的自适应、实时和智能系统），该平台采用嵌入式深度学习算法，利用来自 CGM 的多模态数据、每日进餐和推注胰岛素，以及用于预测血糖水平和低血糖和高血糖的传感器腕带。对于 60 分钟的预测范围，所提出的算法实现了 35.28 ± 5.77 mg/dL 的平均均方根误差 (RMSE)，用于检测低血糖和高血糖的马修斯相关系数分别为 0.56 ± 0.07 和 0.70 ± 0.05。腕带数据的使用将 RMSE 显着降低了 2.25 mg/dL（膳食和推注胰岛素的每日条目，以及用于预测葡萄糖水平和低血糖和高血糖的传感器腕带。对于 60 分钟的预测范围，所提出的算法实现了 35.28 ± 5.77 mg/dL 的平均均方根误差 (RMSE)，用于检测低血糖和高血糖的马修斯相关系数分别为 0.56 ± 0.07 和 0.70 ± 0.05。腕带数据的使用将 RMSE 显着降低了 2.25 mg/dL（膳食和推注胰岛素的每日条目，以及用于预测葡萄糖水平和低血糖和高血糖的传感器腕带。对于 60 分钟的预测范围，所提出的算法实现了 35.28 ± 5.77 mg/dL 的平均均方根误差 (RMSE)，用于检测低血糖和高血糖的马修斯相关系数分别为 0.56 ± 0.07 和 0.70 ± 0.05。腕带数据的使用将 RMSE 显着降低了 2.25 mg/dL（p  < 0.01）。训练有素的模型在 ARISES 应用程序上实现，以提供实时决策支持。这些结果表明，ARISES 具有降低严重并发症风险和增强 T1D 患者自我管理的巨大潜力。