Preventing cancer and cancer disparities with weight-management support – using hospital patient records and electronic signposting (eSIGN)
Abstract
Mixed method research project: This project incorporates cutting-edge implementation science methods, quality improvement techniques and design principles into robust health service research. Overarching aim: To co-design, iteratively test, evaluate and scale-up eSignposting to an existing weight-management DHI, to prevent cancer and cancer disparities. Objectives 1. To review the literature on the impact of e-signposting, including what works, when, for whom and in what circumstances 2. To explore the needs of disadvantaged communities interested in weight-management support 3. To co-design, assess and refine text-message content (informed by Behaviour Change Techniques - BCTs) and ‘teachable moment’ delivery 4. To investigate the effectiveness of text-message content and delivery mechanisms 5. To facilitate and evaluate spread and adoption of eSignposting across 1) regional acute NHS Trusts, 2) regional Primary Care Networks 6. To co-design an evaluation toolkit to support adoption of eSignposting in different contexts Expected findings: Findings will inform the design of subsequent studies. Mixed-methods analysis will use a theoretical framework to synthesise findings across research methods, which will feed into a programme theory, logic model, implementation blueprint and evaluation toolkit. Expected impact: Patient benefits from access to weight-management support • NHS benefits from complementary service • Implementation guide with animation facilitates scale-up • Population health benefits from application to other health behaviours and settings to prevent cancer, cancer disparities and cancer deaths
Fields of Study & Subjects
Obesity and Health Practices
Mobile Health and mHealth Applications
Health Policy Implementation Science
Publication details
- Journal / source
- OSF Preprints (OSF Preprints)
- Publication date
- 12 January 2026
- Language
- EN
- Document type
- Preprint