Improving Glucose Prediction Accuracy in Physically Active Adolescents With Type 1 Diabetes.
Improving Glucose Prediction Accuracy in Physically Active Adolescents With Type 1 Diabetes.
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DOI：
10.1177/1932296818820550
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发表时间：
2019-07-01
影响因子：
5
通讯作者：
Cinar, Ali
中科院分区：
文献类型：
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作者：
Hobbs, Nicole;Hajizadeh, Iman;Cinar, Ali
背景：体力活动对1型糖尿病患者的血糖控制是一个重大挑战。由于准确的血糖预测是自动化决策系统(如人工胰腺)成功的关键，在代谢状态估计中加入额外的生理变量可能会提高运动过程中血糖预测的准确性。方法：将基于预测器的子空间识别应用于动态血糖预测模型，该模型包括心率测量以及代表碳水化合物消耗和胰岛素推注的变量。为了证明额外的心率变量对预测能力的改善，使用滑雪营地青少年的实验数据，在有(SID-HR)和没有心率(SID-2)的情况下评估了所提出的建模技术的性能。结果：在基于子空间的模型(SID-HR)中加入心率，与SID-2模型(P<；.001)和标准MSO(P<；.001)相比，均方根误差有显著的改善。此外，考虑到复杂性的增加，SID-HR模型比SID-2和MSO模型表现更好。结论：通过在葡萄糖动力学模型中加入心率作为额外的输入变量，直接考虑体力活动水平对血糖动态的影响，提高了血糖预测的准确性。该方法可以改进人工胰腺系统中基于运动信息模型的预测控制算法。
BACKGROUND: Physical activity presents a significant challenge for glycemic control in individuals with type 1 diabetes. As accurate glycemic predictions are key to successful automated decision-making systems (eg, artificial pancreas, AP), the inclusion of additional physiological variables in the estimation of the metabolic state may improve the glucose prediction accuracy during exercise.METHODS: Predictor-based subspace identification is applied to a dynamic glucose prediction model including heart rate measurements along with variables representing the carbohydrate consumption and insulin boluses. To demonstrate the improvement in prediction ability due to the additional heart rate variable, the performance of the proposed modeling technique is evaluated with (SID-HR) and without heart rate (SID-2) as an additional input using experimental data involving adolescents at ski camp. Furthermore, the performance of the proposed approach is compared to that of the metabolic state observer (MSO) model currently used in the University of Virginia AP algorithm.RESULTS: The addition of heart rate in the subspace-based model (SID-HR) yields a statistically significant improvement in the root-mean-square error compared to the SID-2 model (P < .001) and the standard MSO (P < .001). Furthermore, the SID-HR model performed favorably in comparison to the SID-2 and MSO models after accounting for its increased complexity.CONCLUSIONS: Directly considering the effects of physical activity levels on glycemic dynamics through the inclusion of heart rate as an additional input variable in the glucose dynamics model improves the glucose prediction accuracy. The proposed methodology could improve exercise-informed model-based predictive control algorithms in artificial pancreas systems.