Machine learning algorithms using continuous glucose monitoring and physiological data accurately detected meals in healthy adults, achieving an AUC-ROC up to 0.996 in non-cold start simulations.
Observational
Does a machine learning algorithm using continuous glucose monitoring accurately detect meal consumption in metabolically healthy adults?
Machine learning applied to continuous glucose monitoring and physiological data can accurately detect meal consumption in healthy participants, providing an objective tool to validate dietary compliance in research.
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.
Palacios et al. (Mon,) conducted a observational in Metabolically healthy adults. Machine learning-based meal detection using continuous glucose monitoring (CGM) and physiological monitors was evaluated on Meal detection performance (AUC-ROC and AUC-PR). Machine learning algorithms using continuous glucose monitoring and physiological data accurately detected meals in healthy adults, achieving an AUC-ROC up to 0.996 in non-cold start simulations.
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