Ozaslan Basak, Patek Stephen D, Fabris Chiara, Breton Marc D
University of Virginia, Charlottesville, VA, United States.
Dexcom, Inc., Charlottesville, VA, United States.
Comput Methods Programs Biomed. 2020 Dec;197:105757. doi: 10.1016/j.cmpb.2020.105757. Epub 2020 Sep 21.
Type 1 diabetes is a disease characterized by lifelong insulin administration to compensate for the autoimmune destruction of insulin-producing pancreatic beta-cells. Optimal insulin dosing presents a challenge for individuals with type 1 diabetes, as the amount of insulin needed for optimal blood glucose control depends on each subject's varying needs. In this context, physical activity represents one of the main factors altering insulin requirements and complicating treatment decisions. This work aims to develop and test in simulation a data-driven method to automatically incorporate physical activity into daily treatment decisions to optimize mealtime glycemic control in individuals with type 1 diabetes.
We leveraged glucose, insulin, meal and physical activity data collected from twenty-three individuals to develop a method that (i) tracks and quantifies the accumulated glycemic impact from daily physical activity in real-time, (ii) extracts an individualized routine physical activity profile, and (iii) adjusts insulin doses according to the prolonged changes in insulin needs due to deviations in daily physical activity in a personalized manner. We used the data replay simulation framework developed at the University of Virginia to "re-simulate" the clinical data and estimate the performances of the new decision support system for physical activity informed insulin dosing against standard insulin dosing. The paired t-test is used to compare the performances of dosing methods with p < 0.05 as the significance threshold.
Simulation results show that, compared with standard dosing, the proposed physical-activity informed insulin dosing could result in significantly less time spent in hypoglycemia (15.3± 8% vs. 11.1± 4%, p = 0.007) and higher time spent in the target glycemic range (66.1± 11.7% vs. 69.6± 12.2%, p < 0.01) and no significant difference in the time spent above the target range(26.6± 1.4 vs. 27.4± 0.1, p = 0.5).
Integrating daily physical activity, as measured by the step count, into insulin dose calculations has the potential to improve blood glucose control in daily life with type 1 diabetes.
1型糖尿病是一种需要终身注射胰岛素以补偿胰岛素生成胰腺β细胞自身免疫性破坏的疾病。最佳胰岛素剂量对1型糖尿病患者来说是一项挑战，因为实现最佳血糖控制所需的胰岛素量取决于每个患者的不同需求。在这种情况下，体育活动是改变胰岛素需求并使治疗决策复杂化的主要因素之一。本研究旨在开发并在模拟中测试一种数据驱动的方法，该方法可自动将体育活动纳入日常治疗决策，以优化1型糖尿病患者的餐时血糖控制。
我们利用从23名个体收集的葡萄糖、胰岛素、饮食和体育活动数据，开发了一种方法，该方法能够：（i）实时跟踪和量化日常体育活动对血糖的累积影响；（ii）提取个性化的日常体育活动概况；（iii）根据日常体育活动偏差导致的胰岛素需求的长期变化，以个性化方式调整胰岛素剂量。我们使用弗吉尼亚大学开发的数据回放模拟框架“重新模拟”临床数据，并评估新的体育活动指导胰岛素剂量决策支持系统相对于标准胰岛素剂量的性能。配对t检验用于比较给药方法的性能，显著性阈值为p < 0.05。
模拟结果表明，与标准给药相比，所提出的体育活动指导胰岛素给药可显著减少低血糖时间（15.3±8%对11.1±4%，p = 0.007），并增加处于目标血糖范围内的时间（66.1±11.7%对69.6±12.2%，p < 0.01），而在高于目标范围的时间上无显著差异（26.6±1.4对27.4±0.1，p = 0.5）。
将通过步数测量的日常体育活动纳入胰岛素剂量计算，有可能改善1型糖尿病患者的日常生活血糖控制。
