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In IEEE transactions on bio-medical engineering

Continuous glucose monitoring (CGM) enables improvements in diabetes treatment by providing frequent temporal information on glycemia, and prediction of future glucose concentration (GC) trends. The accurate prediction of the future GC trajectory is important for making meal, activity and insulin dosing decisions. Glucose concentration values are affected by various physiological and metabolic variations, such as physical activity (PA) and acute psychological stress (APS), in addition to meals and insulin. In this work, we extend our adaptive glucose modeling framework to incorporate the effects of PA and APS on the GC predictions by integrating input features derived from supplemental physiological variables measured from a wearable device. We use a wristband that is conducive of use by free-living ambulatory people. The readily obtained biosignals are used to generate new quantifiable input features for PA and APS. Machine learning techniques are used to estimate the type and intensity of the PA and APS when they occur individually and concurrently. Variables quantifying the characteristics of both PA and APS are integrated for the first time as exogenous inputs in an adaptive system identification technique for enhancing the accuracy of GC predictions. Data from clinical experiments are used to illustrate the improvement in GC prediction accuracy. The average mean absolute error (MAE) of one-hour-ahead GC predictions decreases from 35.1 to 31.9 mg/dL (p-value=0.01) for testing data with the inclusion of PA information. The average MAE of one-hour-ahead GC predictions decreases from 16.9 to 14.2 mg/dL (p-value=0.006) for testing data with the inclusion of PA and APS information.

Sevil Mert, Rashid Mudassir, Hajizadeh Iman, Park Minsun, Quinn Laurie, Cinar Ali