We invited members of our lived experience group to contribute to targeted searches using the Atlas of Longitudinal Datasets. The team at King's College London put together a shortlist of 25 datasets on the Atlas that were identified as having lived experience involvement. Lived experience experts (LEEs) then used the Atlas to read about these studies, gather further information about how the datasets included lived experience involvement, and make some observations on lived experience involvement in longitudinal research. Below you can read about what Kelvin found in his searches.

Looking through a wide range of datasets on the Atlas of Longitudinal Datasets, it became clear that while progress has definitely been made around lived experience expert (LEE) involvement in longitudinal research, there’s still a lot of space to improve. Across different topics, regions, and population groups, there was a clear effort to involve people with lived experience. That said, how meaningful or well-embedded that involvement was depended on the context, with some areas doing much better than others.

How Lived Experience is integrated into research

A common pattern across many datasets was that LEE tended to take place in advisory or consultative roles. Participants, carers, patients, or community members were often invited to provide feedback on study materials, share perspectives through consultations, or support dissemination. These approaches undoubtedly add value and help ensure research is relevant and acceptable.

However, involvement was less frequently embedded into decision-making or governance structures.

In many cases, lived experience informed the work, but did not actively shape its direction which is a concern.

An example of innovative approaches: Cloudy with a Chance of Pain

One study that stood out for its accessibility and innovation was Cloudy with a Chance of Pain. For this study, participants recorded their daily pain intensity within a smartphone app and GPS locations of their phones would then link to local weather data. Analyzing 5.1 million pain reports, researchers compared the weather conditions on days a significant increase in pain was experienced to days no such pain increase was experienced.

The study was supported by a patient and public involvement group at various stages, including study development. Patients provided input towards the wording and display of in-app questions during a feasibility study of the app. By using this app-based model, the study allowed individuals living with chronic pain to contribute data as part of their daily lives, rather than asking them to fit into traditional research settings. Participants could also see outputs and patterns emerging from their contributions, which helped build transparency and trust, making involvement feel meaningful. It offers a useful example of how design choices can lower barriers and improve engagement.

Positive patterns of involvement across datasets

Across the datasets, trust appeared strongest where involvement was long-term and grounded in building relationships. Studies that worked with communities over extended periods often invested in consistent communication, visibility, and follow-up.

Rather than treating engagement as a task to complete, these studies embedded it into how the research operated over time.

This was particularly important in work involving marginalised communities or sensitive topics, where trust is essential for ethical research.

Mental health-focused datasets also tended to show more intentional approaches to LEE. There was often greater clarity around why lived experience mattered and how it contributed to understanding outcomes, interpretation of results, or real-world relevance. While not all of these studies included LE contributors in areas such as governance, the purpose of involvement was usually clearer and better articulated

Areas for improvement

At the same time, several gaps were visible across the datasets. LEE activity was not always well documented, making it difficult to understand its impact. In some cases, engagement appeared to focus more on participation and retention than on influence or shared learning. Opportunities to involve lived experience in areas such as data access, governance, and long-term strategy were limited.

Overall, this review reinforced that meaningful LEE involvement is less about the number of activities and more about clarity, honesty, and respect. When people understand why they are involved and how their input is appreciated, how their input will be used, and can see evidence of impact, trust and engagement are strengthened. These insights are highly relevant to the Atlas and provide useful guidance for MQ as it continues to develop and strengthen its work with LEE involvement.