Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies

Abstract

Digital health technologies may support the management and prevention of disease through personalized lifestyle interventions. Wearables and smartphones are increasingly used to continuously monitor health and disease in everyday life, targeting health maintenance. Here, we aim to demonstrate the potential of wearables and smartphones to (1) detect eating moments and (2) predict and explain individual glucose levels in healthy individuals, ultimately supporting health self-management. Twenty-four individuals collected continuous data from interstitial glucose monitoring, food logging, activity, and sleep tracking over 14 days. We demonstrated the use of continuous glucose monitoring and activity tracking in detecting eating moments with a prediction model showing an accuracy of 92.3% (87.2–96%) and 76.8% (74.3–81.2%) in the training and test datasets, respectively. Additionally, we showed the prediction of glucose peaks from food logging, activity tracking, and sleep monitoring with an overall mean absolute error of 0.32 (+/−0.04) mmol/L for the training data and 0.62 (+/−0.15) mmol/L for the test data. With Shapley additive explanations, the personal lifestyle elements important for predicting individual glucose peaks were identified, providing a basis for personalized lifestyle advice. Pending further validation of these digital biomarkers, they show promise in supporting the prevention and management of type 2 diabetes through personalized lifestyle recommendations.

Innovative Solution or Cause for Concern? The Use of Continuous Glucose Monitors in People Not Living with Diabetes: A Narrative Review.

“…For instance, a recent observational analysis within the PNLD devised a predictive model able to detect eating moments. 84 Pending validation in a larger cohort, this can potentially support food logging, for example, through artificial intelligence driven notifications, thereby reducing the risk of erroneous reporting. 84 The development of automatic techniques for determining meal macronutrient composition from CGM data would further support seamless use and thus accuracy of predictions.…”

Objective Determination of Eating Occasion Timing: Combining Self-Report, Wrist Motion, and Continuous Glucose Monitoring to Detect Eating Occasions in Adults With Prediabetes and Obesity

“…Meal detection can provide individuals with more insight into their eating behavior, thus providing opportunities for personalized feedback on eating frequency and timing of EO. 31…”

Best Time of Day for Strength and Endurance Training to Improve Health and Performance? A Systematic Review with Meta-analysis

“…One approach for objective monitoring is the remote food photography method, which has shown good feasibility and compliance with just a slight underestimation of energy intake in a free-living setting [ 122 – 125 ]. Furthermore, continuous interstitial glucose monitoring shows promise in validating reported timing of meals [ 126 ]. A rigorous monitoring of these behaviors is essential to address another research question that we think is important.…”