Within-person modeling of postprandial glucose using multimodal wearable data.

The widespread adoption of continuous glucose monitoring (CGM) and wearable sensing technologies has enabled large-scale collection of high-resolution physiological and behavioral data in real-world settings. However, the analytical frameworks needed to translate these data into actionable, individualized insights remain limited. In particular, many existing approaches rely on population-level analysis or controlled experimental designs, which often fail to capture intra-individual variability in everyday life. To address this sensing-analysis gap, this study investigates within-person, meal-level predictors of postprandial glucose dynamics using multimodal data collected under free-living conditions. We analyzed outcomes including peak glucose, time to peak, and area under the glucose curve above 140 mg/dL. Predictors encompassed macronutrient composition, baseline glucose, meal timing, and short-term wrist movement variability derived from wearable sensors. Linear mixed-effects models were constructed with continuous predictors centered within individuals to explicitly capture within-person effects. Net carbohydrate intake showed the strongest association with postprandial glucose magnitude (