Optimising a digitally delivered behavioural weight loss programme: a factorial cluster randomised controlled trial

Abstract Digital interventions are widely used for weight management, so optimising their effectiveness and retention is vital. This 2⁴ factorial trial tested four components added to a commercial programme: a health coach introductory call, coaching webchat sessions, goal-setting statements, and food diary reviews plus feedback. The objective was to identify the optimal combination of these four intervention components to improve weight loss outcomes. Adults meeting the eligibility criterion of BMI ≥ 21 kg/m² who enrolled in the programme ( n = 1335; 90.8% with BMI ≥ 25 kg/m²) were randomised to one of 16 conditions. The primary outcome was weight change at 16-weeks; secondary and exploratory outcomes included weight change at 4- and 24-weeks, programme drop-out, and engagement. Per-protocol analyses considered effects in those who engaged with the component. The introductory call showed no effect at 16-weeks but was associated with a 1 kg greater weight loss at 24-weeks ( p = 0.002), which did not persist in the per-protocol analysis. The food diary component was associated with less weight loss ( + 0.52 kg at 16 weeks, p = 0.07; +0.74 kg at 24 weeks, p = 0.02). Factorial optimisation trials could be a useful approach for empirically testing intervention components before implementation in commercial programmes. Findings indicate that programmes may benefit from offering low-intensity human-supported components, while considering the potential burden of more complex features. Pre-registered 05/01/2024 (ISRCTN, ISRCTN14407868).

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Publication Details

Journal
npj Digital Medicine
Published
2026-09-19
DOI
https://doi.org/10.1038/s41746-026-03231-y
Primary Topic
Mobile Health and mHealth Applications
Type
article
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article

Optimising a digitally delivered behavioural weight loss programme: a factorial cluster randomised controlled trial

Danni Wang, Lia Willis, Dimitrios A. Koutoukidis, Kirstie Lawton et al.
npj Digital Medicine
Mobile Health and mHealth Applications
article

Optimising a digitally delivered behavioural weight loss programme: a factorial cluster randomised controlled trial

Danni Wang, Lia Willis, Dimitrios A. Koutoukidis, Kirstie Lawton, Michael Whitman, Gina Wren, Marianne Hennessy, Jillian Strayhorn, Susan A. Jebb, Jadine Scragg, Jemma Joel, Danni Wang
article en

Abstract

Abstract Digital interventions are widely used for weight management, so optimising their effectiveness and retention is vital. This 2⁴ factorial trial tested four components added to a commercial programme: a health coach introductory call, coaching webchat sessions, goal-setting statements, and food diary reviews plus feedback. The objective was to identify the optimal combination of these four intervention components to improve weight loss outcomes. Adults meeting the eligibility criterion of BMI ≥ 21 kg/m² who enrolled in the programme ( n = 1335; 90.8% with BMI ≥ 25 kg/m²) were randomised to one of 16 conditions. The primary outcome was weight change at 16-weeks; secondary and exploratory outcomes included weight change at 4- and 24-weeks, programme drop-out, and engagement. Per-protocol analyses considered effects in those who engaged with the component. The introductory call showed no effect at 16-weeks but was associated with a 1 kg greater weight loss at 24-weeks ( p = 0.002), which did not persist in the per-protocol analysis. The food diary component was associated with less weight loss ( + 0.52 kg at 16 weeks, p = 0.07; +0.74 kg at 24 weeks, p = 0.02). Factorial optimisation trials could be a useful approach for empirically testing intervention components before implementation in commercial programmes. Findings indicate that programmes may benefit from offering low-intensity human-supported components, while considering the potential burden of more complex features. Pre-registered 05/01/2024 (ISRCTN, ISRCTN14407868).

npj Digital Medicine
National Institute for Health and Care Research (GB), University of Oxford (GB), New York University (US)
Zero hunger
Openalex Percentile: Top 6%
Mobile Health and mHealth Applications
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