JMIR Publications

ABSTRACT

Background:

Type 2 diabetes mellitus (T2DM) is a major public health burden. Self-management of diabetes including maintaining a healthy lifestyle is essential for glycemic control, and to prevent diabetes complications. Mobile-based health data can play an important role in the forecasting of blood glucose levels for lifestyle management and control of T2DM.

Objective:

The objective of this work is to dynamically forecast the daily glucose levels in patients with T2DM based on their daily mobile health lifestyle data including diet, physical activity, weight, and glucose level from the day before.

Methods:

We used data from 10 T2DM patients who were overweight or obese in a behavioral lifestyle intervention using mobile tools for daily monitoring of diet, physical activity, weight, and blood glucose over 6 months. We developed a deep learning model based on long short-term memory (LSTM) based recurrent neural networks (RNN) to forecast the next day glucose levels in individual patients. The proposed neural network utilizes several layers of computational nodes to model how mobile health data (food intake including consumed calories, fat, and carbs; exercise; and weight) are progressing from one day to another from noisy data.

Results:

The model was validated based on a dataset of 10 patients who have been monitored daily for over 6 months. The proposed deep learning model demonstrates considerable accuracy in predicting the next day glucose level based on Clark Error Grid and ±10% range of the actual values.

Conclusions:

Using machine learning methodologies may leverage mobile health lifestyle data to develop effective individualized prediction plans for T2DM management. However, predicting future glucose levels is challenging as glucose level is determined by multiple factors. Future study with more rigorous study design is warranted to predict future glucose levels for T2DM management.