Background and Objective

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.

Methods

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.

Results

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).

Conclusions

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.

中文翻译：

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自动计算 1 型糖尿病胰岛素剂量中的体力活动

背景与目的

1 型糖尿病是一种疾病，其特征在于终生服用胰岛素以补偿产生胰岛素的胰腺 β 细胞的自身免疫性破坏。最佳胰岛素剂量对 1 型糖尿病患者来说是一个挑战，因为最佳血糖控制所需的胰岛素量取决于每个受试者的不同需求。在这种情况下，身体活动是改变胰岛素需求和使治疗决策复杂化的主要因素之一。这项工作旨在开发和模拟一种数据驱动的方法，以自动将身体活动纳入日常治疗决策，以优化 1 型糖尿病患者的进餐时间血糖控制。

方法

我们利用从 23 个人收集的葡萄糖、胰岛素、膳食和体力活动数据开发了一种方法，该方法 (i) 实时跟踪和量化日常体力活动累积的血糖影响，(ii) 提取个性化的常规体力活动配置文件，以及 (iii) 根据因日常体育活动的偏差而导致的胰岛素需求的长期变化，以个性化的方式调整胰岛素剂量。我们使用弗吉尼亚大学开发的数据重放模拟框架来“重新模拟”临床数据并估计新的身体活动决策支持系统的性能，告知胰岛素剂量与标准胰岛素剂量。配对t检验用于比较剂量方法与p的性能 < 0.05 作为显着性阈值。

结果

模拟结果表明，与标准剂量相比，建议的体力活动告知胰岛素剂量可显着减少低血糖时间（15.3±8% vs. 11.1±4%，p  = 0.007）和更长的目标时间血糖范围（66.1± 11.7% 对 69.6± 12.2%，p  < 0.01）并且在高于目标范围的时间上没有显着差异（26.6± 1.4 对 27.4± 0.1，p  = 0.5）。

结论

将通过步数测量的每日身体活动纳入胰岛素剂量计算中，有可能改善 1 型糖尿病患者日常生活中的血糖控制。