Machine Learning-based Meal Detection Using Continuous Glucose Monitoring on Healthy Participants: An Objective Measure of Participant Compliance to Protocol.

Abstract

Meal timing affects metabolic responses to diet, but participant compliance in time-restricted feeding and other diet studies is challenging to monitor and is a major concern for research rigor and reproducibility. To facilitate automated validation of participant self-reports of meal timing, the present study focuses on the creation of a meal detection algorithm using continuous glucose monitoring (CGM), physiological monitors and machine learning. While most CGM-related studies focus on participants who are diabetic, this study is the first to apply machine learning to meal detection using CGM in metabolically healthy adults. Furthermore, the results demonstrate a high area under the receiver operating characteristic curve (AUC-ROC) and precision-recall curve (AUC-PR). A cold-start simulation using a random forest algorithm yields .891 and .803 for AUC-ROC and AUC-PR respectively on 110-minutes data, and a non-cold start simulation using a gradient boosted tree model yields over .996 (AUC-ROC) and .964 (AUC-PR). Here it is demonstrated that CGM and physiological monitoring data is a viable tool for practitioners and scientists to objectively validate self-reports of meal consumption in healthy participants.