Evidence mapPaperPMID 42273193Full record

ArticlePublic health in practice (Oxford, England)2026

What works to improve health and digital health literacy in disadvantaged groups: A policy-focused evidence brief.

Sashika Harasgama, Amy Dehn Lunn, Danielle Lamb, Anna Gkiouleka, Helena Painter, Adnaan Ghanchi, John Ford

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Article in Public health in practice (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Sashika HarasgamaWolfson Institute for Population Health, Queen Mary University of London, UK.
Amy Dehn LunnWolfson Institute for Population Health, Queen Mary University of London, UK.
Danielle LambNIHR Applied Research Collaboration, University College London, UK.
Anna GkioulekaWolfson Institute for Population Health, Queen Mary University of London, UK.
Helena PainterWolfson Institute for Population Health, Queen Mary University of London, UK.
Adnaan GhanchiDepartment of Public Health and Primary Care, University of Cambridge, UK.
John FordWolfson Institute for Population Health, Queen Mary University of London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The policy challenge: Low health literacy disproportionately affects disadvantaged populations and is associated with poorer health outcomes and reduced engagement with health services. Functional literacy skills, such as reading, writing, and numeracy, underpin health literacy and is shaped by cultural and socioeconomic factors. As digital health technologies become more widespread, digital health literacy is increasingly important; however, both low digital health literacy and digital exclusion can limit access to and engagement with health information and services, potentially exacerbating existing inequalities. Here, we review key policy-relevant evidence of what works to improve health and digital literacy for disadvantaged communities. Key evidence to inform policy: This evidence brief synthesises findings across four key population groups: people from low socioeconomic backgrounds, ethnic minority communities, older people with low digital literacy, and people with mental health conditions. Across populations, successful literacy interventions tailored information to the cultural and social needs of their target users, engaged and co-produced with communities in real-world settings, used audiovisual information to improve accessibility and focused on empowering users. Effectiveness in the context of intensity, i.e. contact hours, or intervention type was dependent on population groups studied. Evidence on digital health literacy interventions highlights the importance of usability, skills development and integration with existing health services, although consideration of digital health literacy in intervention design remains inconsistent. There is moderate evidence that health literacy interventions improve knowledge and skills, with mixed evidence for effects on behaviour and service use, and limited evidence for sustained improvements in clinical outcomes. Further considerations and implications: Health and digital health literacy are important drivers of health inequalities, influencing access to, engagement with and experience of health and care services. Interventions are most effective when delivered across individual, organisational and system levels. Policymakers should prioritise culturally tailored, accessible and community-based approaches, while also addressing structural barriers such as digital exclusion. Further research is needed to strengthen the evidence base, particularly on long-term clinical outcomes and implementation in diverse contexts.

Indexed as

Digital health literacyHealth inequalitiesHealth literacy

Identifiers

PMID42273193
PMCPMC13247701

What Socratic holds

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.