Comparison of weight change between face-to-face and digital delivery of the English National Health service diabetes prevention programme: An exploratory non-inferiority study with imputation of plausible weight outcomes.

(BMI), Body Mass Index CCG, Clinical Commissioning Group CI, Confidence interval Cohort studies DIPLOMA, Diabetes Prevention – Long Term Multimethod Assessment Diabetes mellitus, Type 2 Diet, healthy FPG, Fasting Blood Glucose (a test for diagnosing diabetes and the risk of diabetes) HbA1c, Haemoglobin A1c (a test for diagnosing diabetes and the risk of diabetes) IMD, Index of Multiple deprivation Method for dealing with missing data NHS DPP, National Health Service Diabetes Prevention Programme National health programs Non-inferiority Preventive health services STP, Sustainability and Transformation Partnership Self-management Weight loss eHealth: Telemedicine

Journal

Preventive medicine reports
ISSN: 2211-3355
Titre abrégé: Prev Med Rep
Pays: United States
ID NLM: 101643766

Informations de publication

Date de publication:
Apr 2023
Historique:
received: 11 10 2022
revised: 17 02 2023
accepted: 19 02 2023
entrez: 17 3 2023
pubmed: 18 3 2023
medline: 18 3 2023
Statut: epublish

Résumé

Worldwide evidence suggests face-to-face diabetes prevention programmes are effective in preventing and delaying the onset of type 2 diabetes by encouraging behaviour change towards weight loss, healthy eating, and increased exercise. There is an absence of evidence on whether digital delivery is as effective as face-to-face. During 2017-18 patients in England were offered the National Health Service Diabetes Prevention Programme as group-based face-to-face delivery, digital delivery ('digital-only') or a choice between digital and face-to-face ('digital-choice'). The contemporaneous delivery allowed for a robust non-inferiority study, comparing face-to-face with digital only and digital choice cohorts. Changes in weight at 6 months were missing for around half of participants. Here we take a novel approach, estimating the average effect in all 65,741 individuals who enrolled in the programme, by making a range of plausible assumptions about weight change in individuals who did not provide outcome data. The benefit of this approach is that it includes everyone who enrolled in the programme, not restricted to those who completed. We analysed the data using multiple linear regression models. Under all scenarios explored, enrolment in the digital diabetes prevention programme was associated with clinically significant reductions in weight which were at least equivalent to weight loss in the face-to-face programme. Digital services can be just as effective as face-to-face in delivering a population-based approach to the prevention of type 2 diabetes. Imputation of plausible outcomes is a feasible methodological approach, suitable for analysis of routine data in settings where outcomes are missing for non-attenders.

Identifiants

pubmed: 36926593
doi: 10.1016/j.pmedr.2023.102161
pii: S2211-3355(23)00052-9
pmc: PMC10011422
doi:

Types de publication

Journal Article

Langues

eng

Pagination

102161

Informations de copyright

© 2023 The Authors. Published by Elsevier Inc.

Déclaration de conflit d'intérêts

AM, MH, EB, BM and SC declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. EM is managing director of a not-for-profit Community Interest Company, HeLP-Digital, which exists to disseminate a digital diabetes self-management programme, HeLP-Diabetes, across the NHS. JV is the national clinical director for diabetes and obesity at NHS England.

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Auteurs

Antonia M Marsden (AM)

Centre for Biostatistics, School of Health Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, UK.

Mark Hann (M)

Centre for Primary Care and Health Services Research, School of Health Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, UK.

Emma Barron (E)

NHS England, London SE1 6LH, UK.

Jamie Ross (J)

Centre for Primary Care, Wolfson Institute of Population Health Science, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AD, UK.

Jonathan Valabhji (J)

NHS England, London SE1 6LH, UK.
Imperial College Healthcare NHS Trust, The Bays, S Wharf Rd, London W2 1NY, UK.
Imperial College London, Exhibition Rd, South Kensington, London SW7 2BX, UK.

Elizabeth Murray (E)

Research Department of Primary Care and Population Health, University College London, Royal Free Campus, Pond Street, London NW3 2PF, UK.

Sarah Cotterill (S)

Centre for Biostatistics, School of Health Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, UK.

Classifications MeSH