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Enregistrement W7160891826 · doi:10.3310/gjst1327

Effects of physical activity and diet in pregnancy to prevent gestational diabetes: an individual participant data (IPD) meta-analysis on the differential effects of interventions with economic evaluation

2025· article· en· W7160891826 sur OpenAlexaff
John Allotey, Dyuti Coomar, Joie Ensor, Chidubem Okeke Ogwulu, Gabriel Ruiz Calvo, Mark Monahan, Valencia Kabeya, Rachel Mcneill, Anna Boath, G. Mahmoud, Cheryce Harrison, Mahnaz Bahri Khomami, Helena Teede, Nicola Heslehurst, G. A. Hitman, Sharon Anne Simpson, Krish Nirantharakumar, Julie Dodds, Kelly C. Allison, Garry Shen, Elisabetta Petrella, Fabio Facchinetti, Christina Vinter, Mireia Peláez, Dorte Møller Jensen, Narges Motahari-Tabari, Tarja I. Kinnunen, Jonatan R Ruiz, Annick Bogaerts, Kristina M. Renäult, Alka Kothari, Jose Guilherme Cecatti, Fionnuala M McAuliffe, Suzanne Phelan, Lucilla Poston, Ana Pilar Betrán, Ngawai Moss, Stamatina Iliodromiti, Frances Austin, Nuria García de la Torre, Alfonso Luis Calle Pascual, J Zamora, Tracy Roberts, Richard D Riley, Shakila Thangaratinam

Notice bibliographique

RevueHealth Technology Assessment · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueGestational Diabetes Research and Management
Établissements canadiensUniversity of Manitoba
Organismes subventionnairesHealth Technology Assessment ProgrammeUniversity of WarwickDepartment of Health and Social CareNational Institute for Health and Care Research
Mots-clésPregnancyPsychological interventionGestational diabetesGestational ageRandomized controlled trialBody mass indexObesityIntervention (counseling)Gestation

Résumé

récupéré en direct d'OpenAlex

Background Physical inactivity and suboptimal diet in pregnancy are important modifiable risk factors for gestational diabetes, a major contributor to pregnancy complications. Objectives We aimed to assess the effects of physical activity and/or diet-based lifestyle interventions during pregnancy on gestational diabetes and if these vary by maternal (body mass index, age, parity, ethnicity, education) and intervention characteristics using individual participant data meta-analysis of randomised trials, and a cost-effectiveness analysis. Data sources International Weight Management in Pregnancy Collaborative Network database was updated by searching major databases from February 2017 to March 2022. Review methods The main outcomes were gestational diabetes by any criteria and by the National Institute for Health and Care Excellence. Other outcomes were gestational diabetes as per International Association of Diabetes in Pregnancy Study Group and maternal and perinatal outcomes. We performed a two-stage random-effects individual participant data meta-analysis to obtain summary estimates (odds ratio) with 95% confidence intervals. Study quality of included trials was assessed, and heterogeneity summarised using τ 2 . Where possible, we added the aggregate data from non-individual participant data trials to the meta-analysis. We ranked interventions by effectiveness using network meta-analysis and undertook model-based economic evaluation to assess cost-effectiveness. The cost-effectiveness analysis took an NHS cost perspective compared an overall lifestyle intervention versus usual care with a time horizon covering the beginning of pregnancy until the discharge of the mother and infant from the hospital following delivery. Results Ninety-two trials (32,284 women) were included; 54 (23,698 women) provided individual participant data. Lifestyle interventions reduced the odds of gestational diabetes (any criteria) by 10% in individual participant data trials (odds ratio 0.90, 95% confidence interval 0.80 to 1.02, 54 studies, 23,361 women), and the findings reached statistical significance when non-individual participant data were included (odds ratio 0.81, 95% confidence interval 0.73 to 0.89, 92 studies, 31,947 women). Physical activity significantly reduced the odds of gestational diabetes by 36% (odds ratio 0.64; 95% confidence interval 0.48 to 0.84), and diet by 19% (odds ratio 0.81; 0.69 to 0.96), but not mixed interventions. Women with middle (odds ratio 0.68, 95% confidence interval 0.51 to 0.90) and high educational level (odds ratio 0.71, 95% confidence interval 0.54 to 0.93) benefited more than those with low educational status, and no differences by maternal body mass index, age, parity or ethnicity. There was no significant reduction in gestational diabetes defined by National Institute for Health and Care Excellence criteria (odds ratio 0.98, 95% confidence interval 0.84 to 1.13) in individual participant data trials. For gestational diabetes defined using International Association of Diabetes in Pregnancy Study Group criteria, interventions reduced gestational diabetes by 14% (odds ratio 0.86, 95% confidence interval 0.75 to 0.97, τ 2 = 0.00, 16 studies, 6174 women) in individual participant data trials and by 17% (odds ratio 0.83, 95% confidence interval 0.72 to 0.95, τ 2 = 0.01, 25 studies, 7883 women) when non-individual participant data trials were added. Overall, physical activity reduced caesarean section (odds ratio 0.83; 0.72 to 0.96), small-for-gestational age (odds ratio 0.72; 0.56 to 0.92) and large-for-gestational age babies (odds ratio 0.81; 0.71 to 0.94); diet-based interventions reduced any preterm birth (odds ratio 0.37; 0.20 to 0.68) compared to controls. No differences were observed for other outcomes. Lifestyle interventions were on average more expensive and more effective at averted gestational diabetes and major outcome averted compared to usual care. Limitations We could not identify the specific intervention components and delivery methods associated with improved outcomes, due to variations in reporting. Conclusion Lifestyle interventions in pregnancy prevent gestational diabetes, and the effects vary according to the definition of gestational diabetes. Physical activity-based interventions may be the most effective. Future work Lifestyle interventions should be implemented and evaluated in routine clinical practice to prevent gestational diabetes, with additional support for women with low socioeconomic status. Study registration This study is registered as PROSPERO CRD42020212884. www.crd.york.ac.uk/PROSPERO/view/CRD42020212884 Funding This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR129715) and is published in full in Health Technology Assessment ; Vol. 30, No. 39. See the NIHR Funding and Awards website for further award information.

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)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
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 enseignants0,842
Score d'incertitude au seuil0,335

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
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 Opus0,152
Tête enseignante GPT0,480
Écart entre enseignants0,328 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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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