Morton Samuel, Li Rui, Dibbo Sayanton, Prioleau Temiloluwa
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5557-5562. doi: 10.1109/EMBC44109.2020.9176414.
The prevalence of personal health data from wearable devices enables new opportunities to understand the impact of behavioral factors on health. Unlike consumer devices that are often auxiliary, such as Fitbit and Garmin, wearable medical devices like continuous glucose monitoring (CGM) devices and insulin pumps are becoming critical in diabetes care to minimize the occurrence of adverse glycemic events. Joint analysis of CGM and insulin pump data can provide unparalleled insights on how to modify treatment regimen to improve diabetes management outcomes. In this paper, we employ a data-driven approach to study the relationship between key behavioral factors and proximal diabetic management indicators. Our dataset includes an average of 161 days of time-matched CGM and insulin pump data from 34 subjects with Type 1 Diabetes (T1D). By employing hypothesis testing and association mining, we observe that smaller meals and insulin doses are associated with better glycemic outcomes compared to larger meals and insulin doses. Meanwhile, the occurrence of interrupted sleep is associated with poorer glycemic outcomes. This paper introduces a method for inferring disrupted sleep from wearable diabetes-device data and provides a baseline for future research on sleep quality and diabetes. This work also provides insights for development of decision-support tools for improving short- and long-term outcomes in diabetes care.
可穿戴设备产生的个人健康数据的普及为了解行为因素对健康的影响带来了新机遇。与通常作为辅助设备的消费级设备（如Fitbit和佳明）不同，连续血糖监测（CGM）设备和胰岛素泵等可穿戴医疗设备在糖尿病护理中变得至关重要，以尽量减少不良血糖事件的发生。对CGM和胰岛素泵数据进行联合分析可以为如何调整治疗方案以改善糖尿病管理结果提供无与伦比的见解。在本文中，我们采用数据驱动的方法来研究关键行为因素与近端糖尿病管理指标之间的关系。我们的数据集包括来自34名1型糖尿病（T1D）患者的平均161天时间匹配的CGM和胰岛素泵数据。通过进行假设检验和关联挖掘，我们观察到与较大的餐食和胰岛素剂量相比，较小的餐食和胰岛素剂量与更好的血糖结果相关。同时，睡眠中断的发生与较差的血糖结果相关。本文介绍了一种从可穿戴糖尿病设备数据中推断睡眠中断的方法，并为未来关于睡眠质量和糖尿病的研究提供了基线。这项工作还为开发决策支持工具以改善糖尿病护理的短期和长期结果提供了见解。
