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
Year: 2023
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Accurately identifying eating patterns, specifically the timing, frequency, and distribution of eating occasions (EOs), is important for assessing eating behaviors, especially for preventing and managing obesity and type 2 diabetes (T2D). However, existing methods to study EOs rely on self-report, which may be prone to misreporting and bias and has a high user burden. Therefore, objective methods are needed. METHODS: We aim to compare EO timing using objective and subjective methods. Participants self-reported EO with a smartphone app (self-report [SR]), wore the ActiGraph GT9X on their dominant wrist, and wore a continuous glucose monitor (CGM, Abbott Libre Pro) for 10 days. EOs were detected from wrist motion (WM) using a motion-based classifier and from CGM using a simulation-based system. We described EO timing and explored how timing identified with WM and CGM compares with SR. RESULTS: . All had prediabetes or moderately controlled T2D. The median time-of-day first EO (and interquartile range) for SR, WM, and CGM were 08:24 (07:00-09:59), 9:42 (07:46-12:26), and 06:55 (04:23-10:03), respectively. The median last EO for SR, WM, and CGM were 20:20 (16:50-21:42), 20:12 (18:30-21:41), and 21:43 (20:35-22:16), respectively. The overlap between SR and CGM was 55% to 80% of EO detected with tolerance periods of ±30, 60, and 120 minutes. The overlap between SR and WM was 52% to 65% EO detected with tolerance periods of ±30, 60, and 120 minutes. CONCLUSION: The continuous glucose monitor and WM detected overlapping but not identical meals and may provide complementary information to self-reported EO.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funders: National Science Foundation, American Heart Association, University of Nevada, Reno, National Institutes of Health, NYU Grossman School of Medicine, National Institute of Diabetes and Digestive and Kidney Diseases, U.S. National Library of Medicine, National Center for Advancing Translational Sciences
Awards: Harnessing Patient Generated Data to Find Causes and Effects of Diet in Pregnancy , BIGDATA: Causal Inference in Large-Scale Time Series, RII Track-1: AI-enabled Devices for the Advancement of Personalized and Transformative Healthcare in South Carolina, G8010534571, Clinical and Translational Science Award, Validating Sensor-based Approaches for Monitoring Eating Behavior and Energy Intake by Accounting for Real-World Factors that Impact Accuracy and Acceptability, Artificial Intelligence, Modeling, and Informatics for Nutrition Guidance and Systems (AIMINGS) Center
OpenAlex ID: W4387002320
