Evidence map›Paper›PMID 42543251›Full record

ArticleDiabetes, obesity & metabolism2026

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.

Asif Iqbal, Haithem Dahimi, Philippe Preux, Elodie Lespagnol, Angéline Melin, Pierre Morel, Joris Heyman, Rémi Rabasa-Lhoret, Elsa Heyman

Abstract read
In one paragraph

Article in Diabetes, obesity & metabolism, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Asif IqbalULR 7369 - URePSSS - Unité de Recherche Pluridisciplinaire Sport Santé Société, Univ. Lille, Univ. Artois, Univ. Littoral Côte d'Opale, Lille, France.
Haithem DahimiULR 7369 - URePSSS - Unité de Recherche Pluridisciplinaire Sport Santé Société, Univ. Lille, Univ. Artois, Univ. Littoral Côte d'Opale, Lille, France.
Philippe PreuxCNRS, Inria, Centrale Lille, UMR 9189 - CRIStAL, Université de Lille, Lille, France.
Elodie LespagnolULR 7369 - URePSSS - Unité de Recherche Pluridisciplinaire Sport Santé Société, Univ. Lille, Univ. Artois, Univ. Littoral Côte d'Opale, Lille, France.
Angéline MelinULR 7369 - URePSSS - Unité de Recherche Pluridisciplinaire Sport Santé Société, Univ. Lille, Univ. Artois, Univ. Littoral Côte d'Opale, Lille, France.
Pierre MorelULR 7369 - URePSSS - Unité de Recherche Pluridisciplinaire Sport Santé Société, Univ. Littoral Côte d'Opale, Univ. Artois, Univ. Lille, Dunkerque, France.
Joris HeymanCNRS, UMR 6118, Transferts d'eau et de Matière dans les Milieux Hétérogènes Complexes - Géosciences, Université Rennes, Rennes, France.
Rémi Rabasa-LhoretInstitut de Recherches Cliniques de Montréal (IRCM), Montréal, Québec, Canada.ORCID 0000-0003-4706-5170
Elsa HeymanULR 7369 - URePSSS - Unité de Recherche Pluridisciplinaire Sport Santé Société, Univ. Lille, Univ. Artois, Univ. Littoral Côte d'Opale, Lille, France.ORCID 0000-0002-9340-7592

Funding

Santélys Association (Lille)Université de Lille
6 · The paper itself

Abstract

aimsSome 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

methodsContinuous 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 (< 70 mg/dL) and hyperglycaemia (> 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.

resultsModels 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.

conclusionsPhase-specific thresholds across exercise and insulin domains associated with exercise-related dysglycaemia were identified.

Indexed as

Diabetes Mellitus, Type 1DietExerciseHyperglycemiaHypoglycemiaHypoglycemic AgentsInsulinMachine LearningAccelerometryAdolescentBlood GlucoseChildContinuous Glucose MonitoringFemaleHumansMaleBlood GlucoseHypoglycemic AgentsInsulindatabase researchdietary interventionexercise interventionreal‐world evidencerisk predictiontype 1 diabetes

Identifiers

PMID42543251
PMCPMC13538806

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.