# Health Literacy Recommendations for Digital Mental Health Treatments

**Authors:** Alana C. Fisher, Atria Rezwan, Danielle M. Muscat, Julie Ayre, Madelyne Bisby, Taylor Hathway, Isabella Choi, Nickolai Titov, Blake F. Dear

PMC · DOI: 10.3928/24748307-20250714-01 · HLRP: Health Literacy Research and Practice · 2026-01-12

## TL;DR

This study created seven recommendations to improve digital mental health treatments for people with varying health literacy levels, based on user feedback and expert input.

## Contribution

The study co-developed practical, user-informed recommendations to enhance accessibility and engagement in digital mental health treatments.

## Key findings

- Seven final recommendations were developed to improve digital mental health treatments for diverse health literacy levels.
- Most recommendations were informed by feedback from both lower and higher health literacy users.
- The recommendations focus on accessibility, engagement, and emotional wellbeing in digital treatment design.

## Abstract

Digital mental health (dMH) treatments have garnered much attention for increasing access to treatment, yet real-world engagement with these treatments remains a challenge. In upscaling these treatments, we need to ensure that they are equitable and do not exclude groups who already experience inequities in health care, such as people with lower health literacy.

To co-develop recommendations for the design and delivery of dMH treatments for people with a variety of health literacy levels.

Drafted recommendations were based on a thematic analysis of 357 free-text comments (likes, dislikes/other suggestions) from 213 people (n = 80 lower, and n = 133 higher health literacy) who had completed unguided internet-delivered cognitive behaviour therapy (iCBT) for depression and anxiety, as part of a trial. The initial set of drafted recommendations were iteratively modified and refined based on a review by a multidisciplinary project team with professional and lived-experience expertise (n = 9) and focus group consultations with people with relevant lived experience (n = 8).

The co-development process resulted in seven final recommendations: (1) Focus on informative and practical content; (2) Prioritize accessibility and ease of use; (3) Structure content in a progressive, layered way; (4) Enhance interactivity and engagement; (5) Employ strategies to enhance motivation and accountability; (6) Consider participants' emotional wellbeing; (7) Incorporate diverse modes of delivering content. Most recommendations were based on comments from people with lower and higher health literacy.

These recommendations advance both research and practice by outlining a flexible and practical framework for dMH treatment developers and service providers to meet the preferences and needs of people with diverse health literacy strengths and needs. Further research is needed to determine the feasibility and impact of implementing these recommendations across different dMH treatment delivery formats, settings, and populations.

Plain Language Summary: This study developed seven recommendations to improve digital mental health treatments for people with different health literacy levels. These recommendations are based on feedback from users and experts and aim to help make digital treatments easy to access, use, and engage with. These recommendations will also guide people who develop digital treatments, but they need to be tested more.

## Linked entities

- **Diseases:** depression (MONDO:0002050), anxiety (MONDO:0005618)

## Full-text entities

- **Diseases:** depression (MESH:D003866), anxiety (MESH:D001007), Mental (MESH:D008607)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/PMC12788875/full.md

## Figures

7 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12788875/full.md

## References

46 references — full list in the complete paper: https://tomesphere.com/paper/PMC12788875/full.md

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Source: https://tomesphere.com/paper/PMC12788875