Automatically accounting for physical activity in insulin dosing for type 1 diabetes.
Automatically accounting for physical activity in insulin dosing for type 1 diabetes.
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DOI：
10.1016/j.cmpb.2020.105757
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发表时间：
2020-12
影响因子：
6.1
通讯作者：
Breton MD
中科院分区：
文献类型：
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作者：
Ozaslan B;Patek SD;Fabris C;Breton MD
1 型糖尿病是一种以终生注射胰岛素来补偿产生胰岛素的胰腺 β 细胞的自身免疫破坏为特征的疾病。最佳胰岛素剂量对 1 型糖尿病患者提出了挑战，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，体力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。这项工作旨在开发和模拟测试一种数据驱动的方法，自动将身体活动纳入日常治疗决策中，以优化 1 型糖尿病患者进餐时的血糖控制。我们利用从 23 名个体收集的血糖、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概况，以及 (iii) 根据日常体力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重放模拟框架来“重新模拟”临床数据，并评估新的决策支持系统的性能，该系统针对身体活动通知的胰岛素剂量与标准胰岛素剂量的比较。配对 t 检验用于比较给药方法的性能，以 p <0.05 作为显着性阈值。模拟结果表明，与标准剂量相比，拟议的体力活动通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），并延长目标血糖范围内的时间（66.1±11.7% vs. 69.6±12.2%，p < 0.01），并且高于目标的时间没有显着差异范围（26.6±1.4 与 27.4±0.1，p=0.5）。将每日体力活动（通过步数测量）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。
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 datadriven 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.
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DOI：
10.1007/978-3-319-25913-0_8
发表时间：
2016-01-01
期刊：
PREDICTION METHODS FOR BLOOD GLUCOSE CONCENTRATION: DESIGN, USE AND EVALUATION
影响因子：
--
作者：
Patek, Stephen D.;Lv, Dayu;Breton, Marc D.
通讯作者：
Breton, Marc D.
影响因子：
3.9
作者：
Fabris, Chiara;Ozaslan, Basak;Breton, Marc D.
通讯作者：
Breton, Marc D.
影响因子：
3.4
作者：
Robertson, Kenneth;Adolfsson, Peter;Hanas, Ragnar
通讯作者：
Hanas, Ragnar
影响因子：
16.2
作者：
Manohar C;Levine JA;Nandy DK;Saad A;Dalla Man C;McCrady-Spitzer SK;Basu R;Cobelli C;Carter RE;Basu A;Kudva YC
通讯作者：
Kudva YC
影响因子：
16.2
作者：
Swan, Karena L.;Dziura, James D.;Weinzimer, Stuart A.
通讯作者：
Weinzimer, Stuart A.