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Research Papers

A Data-Driven Personalized Model of Glucose Dynamics Taking Account of the Effects of Physical Activity for Type 1 Diabetes: An In Silico Study

[+] Author and Article Information
Jinyu Xie

Mechanical and Nuclear Engineering,
315 Leonhard Building,
Penn State University,
University Park, PA 16802
e-mail: jxx123@psu.edu

Qian Wang

Mem. ASME
Professor
Mechanical Engineering,
325 Leonhard Building,
Penn State University,
University Park, PA 16802
e-mail: quw6@psu.edu

1Corresponding author.

Manuscript received January 30, 2018; final manuscript received August 26, 2018; published online October 17, 2018. Assoc. Editor: Guy M. Genin.

J Biomech Eng 141(1), 011006 (Oct 17, 2018) (12 pages) Paper No: BIO-18-1057; doi: 10.1115/1.4041522 History: Received January 30, 2018; Revised August 26, 2018

This paper aims to develop a data-driven model for glucose dynamics taking into account the effects of physical activity (PA) through a numerical study. It intends to investigate PA's immediate effect on insulin-independent glucose variation and PA's prolonged effect on insulin sensitivity. We proposed a nonlinear model with PA (NLPA), consisting of a linear regression of PA and a bilinear regression of insulin and PA. The model was identified and evaluated using data generated from a physiological PA-glucose model by Dalla Man et al. integrated with the uva/padova Simulator. Three metrics were computed to compare blood glucose (BG) predictions by NLPA, a linear model with PA (LPA), and a linear model with no PA (LOPA). For PA's immediate effect on glucose, NLPA and LPA showed 45–160% higher mean goodness of fit (FIT) than LOPA under 30 min-ahead glucose prediction (P < 0.05). For the prolonged PA effect on glucose, NLPA showed 87% higher FIT than LPA (P < 0.05) for simulations using no previous measurements. NLPA had 25–37% and 31–54% higher sensitivity in predicting postexercise hypoglycemia than LPA and LOPA, respectively. This study demonstrated the following qualitative trends: (1) for moderate-intensity exercise, accuracy of BG prediction was improved by explicitly accounting for PA's effect; and (2) accounting for PA's prolonged effect on insulin sensitivity can increase the chance of early prediction of postexercise hypoglycemia. Such observations will need to be further evaluated through human subjects in the future.

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Figures

Grahic Jump Location
Fig. 1

Three-day training protocol. For the table on top of the timeline, second row indicates the amount of CHO being taken; first row indicates that the amount of insulin bolus = (number in the first row) ×ICR, e.g., “45 * 1” represents insulin bolus = 45×1×ICR, and “80 * 0.8” denotes insulin bolus = 80×0.8×ICR.

Grahic Jump Location
Fig. 2

Two-day validation protocol. For the table on top of the timeline, second row indicates the amount of CHO being taken; first row indicates that the amount of insulin bolus = (number in the first row) ×ICR, e.g., “90 * 0.8” represents insulin bolus = 90×0.8×ICR.

Grahic Jump Location
Fig. 3

Adult #008: 30-min ahead predictions by the NLPA, LPA, and LOPA from 1 h before exercise to about 2-h after exercise. BG corresponds to the CGM output from the simulator. FIT values are computed based on the predictions during and 2-h after exercise: (a) under the training protocol; (b) under the validation protocol.

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Fig. 4

Adolescent #005: 3-h ahead predictions by the NLPA, LPA, and LOPA under the 3-day training protocol. BG corresponds to the CGM output from the simulator. FIT values are calculated based on the predictions during and 24 h after exercise.

Grahic Jump Location
Fig. 5

Adolescent #005: 3-h ahead predictions by NLPA, LPA, and LOPA under the 2-day validation protocol. BG corresponds to the CGM output from the simulator. FIT values are calculated based on the prediction during and 24 h after exercise.

Grahic Jump Location
Fig. 6

Histogram of the model order k (where nb1=nb2=nb3=k) with the lowest AIC, evaluated with 30 subjects

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