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Enregistrement W4415945984 · doi:10.1111/dme.70163

Physical activity (20 min) is a powerful adjunct to insulin for correcting hyperglycaemia in Type 1 diabetes: A paradigm shift

2025· article· en· W4415945984 sur OpenAlexaboutno aff
John Pemberton, Catherine L. Russon, Richard Pulsford, Brad Metcalf, Emma Cockcroft, Michael Allen, Anne Marie Frohock, Robert Andrews

Notice bibliographique

RevueDiabetic Medicine · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueDiabetes Management and Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInsulinAdjunctPhysical activityType 2 diabetesDiabetes mellitusHypoglycemiaContinuous glucose monitoring

Résumé

récupéré en direct d'OpenAlex

Achieving target glucose remains one of the most persistent challenges in Type 1 diabetes (T1D),1 especially postprandially, where insulin cannot match rapid carbohydrate absorption.2 In our recent publication in Diabetic Medicine, we applied a causal matched-pairs analysis to continuous glucose monitoring data, enabling comparisons of periods with and without physical activity under otherwise equivalent conditions.3 When glucose was above 10 mmol/L (180 mg/dL), about 20 minutes of everyday activity lowered levels by approximately 2 mmol/L (40 mg/dL), with hypoglycaemia risk under 2%. These findings support a simple heuristic for education—‘20 by 2’ in mmol/L, or ‘20 by 40’ in mg/dL—reframing physical activity as an acute, real-time adjunct to insulin therapy for hyperglycaemia. This commentary places these findings in historical and clinical context, highlights the methodological advance of causal inference through matched-pair analysis and outlines the guardrails needed for safe translation into practice. The glucose-lowering effects of physical activity (PA) have been recognised for decades, but variability in individual responses has often been viewed as a barrier. In a pivotal analysis, Riddell and colleagues reported steep glucose declines in 120 adolescents with T1D undertaking 45–60 minutes of moderate walking or cycling within 4 h of prandial insulin, with hypoglycaemia in 44%.4 This gave rise to the familiar phrase, ‘the higher they start, the harder they fall’. Yet their secondary analysis, limited to PA events starting above 10.6 mmol/L (190 mg/dL) (n = 41), found that activity was effective at rapidly bringing glucose back into range, with hypoglycaemia risk under 10%.3 The first structured approach using PA to reduce hyperglycaemia was introduced at Birmingham Children's Hospital in 2019. Building on Riddell's findings,4 young people and families were taught: if glucose is above 10 mmol/L, ‘15 minutes lowers it by 2 mmol/L’. In 2023, evaluation of this programme showed that those most engaged achieved the greatest time in range (TIR 3.9–10.0 mmol/L; 70–180 mg/dL) without more hypoglycaemia.5 The next step was to test whether these findings held in larger, more diverse populations. The Type 1 Diabetes Exercise Initiative (T1DEXI) adult and paediatric cohorts (T1DEXIP) provided this opportunity. Analysis of nearly 2000 bouts lasting 10–60 minutes confirmed that when started above 10 mmol/L, PA consistently lowered glucose into the target range.6 This effect was consistent across age, sex, regimen and activity type, establishing real-world evidence for PA in correcting hyperglycaemia.6 But this analysis lacked a control condition. To address this, in our analysis in this edition of Diabetic Medicine, we applied causal modelling via a within-subject matched-pairs framework, focusing on PA bouts of 10–30 minutes when starting above 10 mmol/L.3 Each event was matched to a control period from the same individual, balanced on the four strongest predictors of glucose change: (i) starting glucose, (ii) glucose rate of change, (iii) insulin on board and (iv) preceding glucose variability. Robust matching across >1500 events, with balance confirmed by a standardised mean difference (SMD) of <0.001, established PA as the key determinant. The analysis showed that 20 minutes of PA lowered glucose by approximately 2 mmol/L, an effect around 8-fold greater than matched control periods. Hypoglycaemia during or immediately after PA was very rare (less than 2%).3 These findings provide the first causal-style evidence that short bouts of everyday activity can be prescribed as an acute glucose-lowering intervention for people with T1D from age 12, across insulin regimens and activity types.3 With evidence now secure, the next question is how to use PA safely in everyday practice. PA should also be considered alongside other adjunct therapies. The newer glucagon-like peptide-1 receptor agonists (GLP-1RAs) and dual agonists are emerging as leading candidates. For example, semaglutide (Ozempic®/Wegovy®) has been shown to improve glycaemic control, reduce weight and reduce insulin requirements when added to automated insulin delivery systems in adults with T1D.9 A recent consensus outlined how GLP-1RAs could be integrated into care pathways.10 These developments indicate a shift towards multimodal care. Within this model, PA is distinctive—safe, cost-free, accessible and deployable in real time with CGM. The evidence supports a simple rule: when glucose is above 10 mmol/L, and provided that (i) bolus insulin has been delivered in the last 4 h, and (ii) if above 15.0 mmol/L (270 mg/dL), ketones are not elevated [≥ 0.6 mmol/L (≥ + on a urine strip) on pump therapy or >1.5 mmol/L (> ++ on a urine strip) otherwise], then 20 minutes of almost any activity will lower glucose by approximately 2 mmol/L (40 mg/dL). This ‘20 by 2’ (Figure 1) or ‘20 by 40’ mg/dL (Figure 2) principle is reproducible across cohorts, therapies and demographics. While longer durations of activity may further reduce glucose, they also increase the likelihood of hypoglycaemia, particularly when insulin on board is present. Therefore, longer durations should be accompanied by more vigilance. Reframing physical activity as a powerful, real-time glycaemic optimiser—rather than only a long-term health strategy—positions it as a safe, zero-cost therapy that, in the era of continuous glucose monitoring, delivers instant feedback and reinforces a virtuous cycle of activity driving better control. Future research should also explore whether short bouts of activity can be used pre-emptively to prevent post-prandial glucose excursions, in addition to their corrective role when glucose is elevated. John Pemberton: Conceptualisation, Background research, Writing – original draft. Catherine L. Russon: Writing – review and editing. Richard Pulsford: Writing – review and editing. Bradley S. Metcalf: Writing – review and editing. Emma Cockroft: Writing – review and editing. Michael Allen: Analysis, Writing – review and editing. Anne-Marie Frohock: Writing – review and editing. Robert C. Andrews: Supervision, writing – review and editing, intellectual revision. We would like to sincerely acknowledge the invaluable contributions of colleagues who have provided critical insight, discussion and foundational work in developing the concept of using physical activity to lower glucose in people with Type 1 diabetes. We are grateful to Dr. Suma Uday (Birmingham Women's and Children's NHS Foundation Trust) for her instrumental role in reporting the first clinical use of this approach in paediatric practice at Birmingham Children's Hospital. Dr. Dessi Zaharieva (Stanford University) for her guidance in shaping this concept and for her ongoing critical appraisal to ensure the strategy is presented as safe, effective and implementable. Professor Mike Riddell (York University, Canada) has been central in refining the concept and in securing support for the subsequent T1DEXI analyses of physical activity for ameliorating hyperglycaemia. Professor Othmar Moser (University of Bayreuth, Germany) has provided valuable feedback and contributed to the inclusion of this concept in the recent EASD/ISPAD position statement on automated insulin delivery systems. Dr. Peter Adolfsson (University of Gothenburg, Sweden) was the first to formally publish this approach in the ISPAD 2022 guidelines, following critical discussions of the evidence dating back to 2019. We further wish to highlight the pioneering efforts of colleagues in curating and analysing the T1DEXI and T1DEXIP datasets, which have provided the essential infrastructure to examine this question in real-world, high-resolution data: Zoey Li, Robin L. Gal, Simon Bergford and Peter Calhoun (Jaeb Center for Health Research, Tampa, FL, USA); Lauren V. Turner and Michael C. Riddell (York University, Toronto, ON, Canada). Their sustained support has been instrumental in shaping the research direction and ensuring its translational relevance. John Pemberton reports being on the advisory board for Abbott and ROCHE and speaker fees from Abbott, Dexcom and Insulet in the last 3 years. Faculty member of Exercise for Type 1 Diabetes. Catherine L. Russon, Richard Pulsford, Bradley S. Metcalf, Emma Cockroft, and Michael Allen have no conflicts. Anne-Marie Frohock reports consultancy fees for Insulet and speaker fees from Dexcom and Insulet in the last 3 years. Faculty member of Exercise for Type 1 Diabetes. Robert C. Andrews reports research funding from NovoNordisk Healthcare Organisation in the last 3 years, honoraria from NovoNordisk, AstraZeneca and Eli Lilly for education talks on diet and exercise to health care professionals. Co-founder of Exercise for Type 1 Diabetes. John Pemberton is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Accepted for oral presentation at the European Association for the Study of Diabetes (EASD) Annual Meeting 2025, Vienna, Austria.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,576
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,026
Tête enseignante GPT0,336
Écart entre enseignants0,310 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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