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Identification of Activity, Insulin, and Dietary Thresholds Associated With Exercise-Related Hypoglycaemia and Hyperglycaemia in Children With Type 1 Diabetes: A Free-Living Study Using Explainable Machine Learning.

Iqbal A, Dahimi H, Preux P, Lespagnol E, Melin A, Morel P et al. · Diabetes, obesity & metabolism · 2026

Researchers tracked 36 children with type 1 diabetes for a week of normal life, combining continuous glucose monitors, accelerometers, and logs of exercise, food, and insulin, then used machine learning to find thresholds linked to low or high blood sugar during exercise, the 2 hours after, and overnight. Insulin boluses above 11% of the daily dose in the 4 hours before exercise (and above 17% during early recovery) and self-reported sessions longer than 80 minutes were linked to hypoglycaemia, while over 15 minutes of vigorous activity appeared protective against post-exercise lows and multiple sessions per day protected against overnight highs.
Takeaway: Limit insulin boluses in the hours around exercise and watch sessions longer than 80 minutes, since these were the strongest flags for exercise-related lows in kids with type 1 diabetes.
Abstract (source)

Aims: Some studies have explored associations between physical activity (PA) and hypoglycaemia in real-life in type 1 diabetes (T1D) but without fully accounting for two major confounders, diet and insulin. We aimed to identify thresholds of PA characteristics, insulin, and carbohydrates associated with dysglycaemia across the exercise-recovery cycle in children with T1D.

Materials and methods: Continuous glucose monitoring and accelerometry data, self-reported PA sessions (timing, duration and perceived intensity), diet (timing, type and quantity; optional photographs) and insulin data (doses and timing; corrective/meal boluses and basal insulin) were collected in 36 children with T1D (11.9 ± 3.3 years, injections or open-loop pumps) over seven free-living days. Accelerometer data were analysed for periods corresponding to self-reported PA sessions. Ensemble machine-learning models classified hypoglycaemia ( 180 mg/dL) during three phases: PA, 2-h post-exercise (early recovery), overnight. Shapley analysis identified risk and protection thresholds of features with high importance.

Results: Models achieved moderate to strong performance (AUC: 0.65-0.99; F1-score: 0.62-0.95) across outcomes and phases. Insulin boluses > 11% of total daily dose within 4-h pre-exercise and > 17% during early recovery were associated with hypoglycaemia risk during PA and early recovery respectively. Carbohydrate intake showed collinearity with insulin, resulting in complex associations with glycaemia. Self-reported PA > 80 min was associated with hypoglycaemia risk during PA, while accumulating > 15 min of accelerometer-derived vigorous PA was actually protective against early recovery hypoglycaemia. Multiple daily sessions were associated with nocturnal hyperglycaemia protection.

Conclusions: Phase-specific thresholds across exercise and insulin domains associated with exercise-related dysglycaemia were identified.

Primary studyRecovery & Sleep
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