Culturally sensitive approaches in digital mental health research

Lessons from co-developing the Core Mental Health Dataset

GM.Digital worked with a Lived Experience Advisory Group to pressure-test our mental health data collection tool.

Mental health questionnaires are usually built for scientific rigour, validated, standardised, consistent. But standardisation can come at a cost: if a tool doesn't reflect how different communities actually understand and talk about mental health, it risks producing data that misses the people it's meant to help.

That tension sits at the heart of our latest paper, published in Frontiers in Digital Health. The paper reflects on what the team learned while co-developing the Core Mental Health Dataset (CMHDS), a tool built to make it simple, safe and acceptable for people to share mental health information within physical health research, so we can better understand the links between the two.

Why cultural sensitivity matters

Mental health is understood and talked about differently across cultures. Stigma, family reputation, gender norms, and beliefs about what counts as a "personal" condition versus something to be managed practically can all affect whether someone recognises, discloses, or seeks support for a mental health difficulty. A research tool that doesn't account for this risks excluding the very communities it should be reaching, and producing findings that don't generalise across the population.

Listening to lived experience

To pressure-test the CMHDS, the team worked with the DATAMIND Lived Experience Advisory Group, eight people with personal experience of mental health services, across two consultations in 2024 and 2025. Their feedback shaped three key changes:

1. Rethinking terminology

Words like "mental health problem" and "mental illness" don't mean the same thing to everyone, and can reinforce stigma in some contexts. The advisory group proposed "mental health condition" as a more neutral, culturally sensitive term, a reminder that language choices actively shape how people engage with research, not just how questions are worded.

2. Recognising different ways of expressing distress

Across cultures, people express and disclose mental health difficulties very differently, sometimes staying silent to protect family honour, or using different concepts of distress altogether. The team adapted the CMHDS to include questions about religion and spirituality alongside mental health support, and paired clinical diagnostic labels (like "anorexia") with plain-language explanations to make the tool more accessible without losing scientific validity.

3. Balancing richer data with participant safety

The advisory group suggested free-text questions could capture more meaningful responses than tick-box scales, which can feel easy to click through without real engagement. But free text brings real risks too: participants could unintentionally share identifiable information, or find themselves re-visiting distressing memories without support in place. In response, the team built an optional free-text version of the CMHDS, giving individual research teams the flexibility to decide what's appropriate for their own participants and context.

What this means going forward

The overarching message from the research is that cultural sensitivity can't be a one-off design decision — it has to be an ongoing practice. As digital tools play an increasingly central role in mental health research and treatment, the people reflected in that research need to be genuinely representative of the people who will one day use the services it informs.

The team's recommendation: sustained co-production with diverse communities, and a willingness to keep revisiting and adapting digital research tools as understanding deepens.

📄 Read the full paper in Frontiers in Digital Health: Culturally sensitive approaches in digital mental health research: lessons learned from co-developing the core mental health dataset

Authors: Heidi Tranter, Madeleine France Ratcliffe, Thomas Price, Auden Edwardes, Pauline Whelan, Kathryn M. Abel. Funded by the NIHR (NIHR201104) and the MRC via DATAMIND, the HDR UK mental health data hub.