Category: PPI

We have counted loneliness. Now we need to change it.

Our new research examines loneliness and social capital in more than 135,000 adults. The challenge now is to turn those findings into better questions – and interventions worth investing in.

A loneliness score can tell us something important about someone’s life, but it cannot tell us what would make that life less lonely. That gap is where I believe the next chapter of loneliness research belongs. In our new paper in BMC Global and Public Health, we analysed data from 135,725 adults participating in the INTERACT study in England. We examined factors associated with loneliness using two established measures, alongside a separate analysis of social capital: people’s perceptions of neighbourhood trust, cohesion and reciprocity.

Among respondents, younger age, being single, unemployment and disability were consistently associated with greater loneliness. Larger friendship networks were associated with lower loneliness. Older, married and retired respondents more frequently reported high social capital, while people with disability or long-term conditions had lower adjusted odds of reporting it. These findings give us questions to pursue but they do not give us a ready-made intervention.

That distinction should shape everything we do next. The study used a volunteer sample and measured people’s circumstances at one point in time. It cannot establish what causes loneliness, estimate how common it is across England, or tell us which intervention would work. A large sample does not remove those limitations.

For me, this is the starting point for a more demanding research agenda: what would we actually change, for whom, and how would we know it helped? Consider the association between friendship networks and loneliness. It would be tempting to translate it into a simple instruction: help people make more friends. But an association cannot tell us whether expanding someone’s network will reduce their loneliness, whether loneliness makes relationships harder to sustain, or how other circumstances influence both. “More friends” is also an outcome to aspire to, rather than a sufficiently specified intervention. Who makes the introduction? Where does the first meeting happen? What helps someone return? What makes an encounter develop into a relationship that matters? Those are intervention-development questions. They deserve the same intellectual attention as the statistical models that bring them into view.

Imagine, as a design exercise, a community programme offering weekly group activities. Before commissioning it, I would want to ask what problem it is intended to address. A shortage of opportunities to meet people? Difficulty entering unfamiliar social settings? The loss of a close relationship? Practical barriers to leaving home?

Each answer points towards a different proposition to test. For someone who wants to attend but cannot reach the venue, transport support could be a candidate component. For someone apprehensive about arriving alone, a familiar person accompanying them might be worth testing. For someone seeking a close, dependable relationship, attendance at a large group may be the wrong primary outcome. These are hypotheses, not findings from our study… but their value lies in making the proposed route to benefit explicit – and therefore open to challenge!

Social capital adds another set of questions. Our paper examined it as a separate outcome; it did not establish that increasing neighbourhood trust reduces loneliness. Nevertheless, I would argue that intervention development should ask what happens beyond the individual referral. What kind of community is someone being invited into? Who feels welcome there? Can participants influence what happens? Are there opportunities to contribute as well as receive support? What resources would a local organisation need to sustain relationships beyond a short funding period?

I want self-care to sit within that discussion. Any call for people to strengthen their own social connections should be accompanied by serious questions about the opportunities and support available to them. “Take action” is an incomplete offer unless we also examine what makes action possible.

The next step should be a programme of co-designed, explicitly testable interventions. People experiencing loneliness should help define the problem, choose outcomes that matter and identify features that make participation acceptable. Their involvement should extend to interpreting why an approach succeeds, disappoints or reaches only some of its intended participants. Evaluation then needs to follow the whole journey.

Counting invitations, referrals and attendance can describe delivery. To establish benefit, we need to examine changes in loneliness and wellbeing, whether those changes last, and how outcomes compare with what would have happened without the intervention. Randomised evaluations should be used where feasible, with credible alternatives where they are not. We should also examine who never takes up the offer, who leaves, and whose circumstances make participation difficult. I would want a commissioner to see those findings alongside the average outcome and the cost. An intervention’s reach is part of its practical value.

Our study also offers a reason to think carefully about measurement. Educational attainment showed different associations depending on which loneliness measure we used. That finding cautions against treating different instruments as interchangeable. Future evaluations should specify what they seek to change and combine appropriate measures with accounts of participants’ experiences.

None of this requires abandoning observational research. It requires giving its findings a purposeful next step.

For the Prevention Lab, my challenge is to researchers, funders and commissioners alike: make room for the difficult work between identifying a pattern and delivering a useful intervention. Fund development, refinement and rigorous evaluation. Specify the benefit being sought. Be willing to adapt or stop an approach when the evidence disappoints.

I want the next paper to answer a harder question: what did we change, who benefited, and was the difference worth sustaining? The people behind our data deserve that next chapter – and a meaningful role in writing it. Through our ongoing qualitative research, we are exploring personal stories and journeys to understand what connection, belonging and support mean in everyday life. These accounts will inform the co-production of interventions grounded in people’s experiences, ready to be tested for the difference they make to their lives.

The Future of Self-Care: Emerging technologies, public trust & personal autonomy

Who should be in control when technology becomes part of how we look after ourselves? That question belongs at the centre of the digital self-care agenda. In my view, emerging technologies should be judged by the understanding and practical capability people gain, and by the control they retain over decisions affecting their lives.

The World Health Organization’s account of self-care includes maintaining health and coping with illness, with or without professional support. For me, the purpose of innovation is to make those activities more manageable within the realities of people’s lives.

Emerging technologies offer different ways to access information and assistance. Virtual reality (VR) creates an immersive digital environment; augmented reality (AR) adds digital information to a view of the physical world. Smart glasses offer functions that vary by device, including cameras, audio and, in some models, visual displays. Brain–computer interfaces (BCIs) translate brain signals into commands for external devices. Because their mechanisms and risks differ, each needs evaluation suited to its purpose.

Starting with the self-carer’s goals

My starting point is the self-carer’s goal. They may want an explanation they can understand, support with a difficult task or a way to communicate their needs. The design question is whether a particular technology helps them pursue that goal, under conditions they find acceptable. Our work on Self-Driven Healthcare proposed connecting people’s own health information and monitoring tools with professional care. That vision depends on accessible support and integration with health services, with people able to influence decisions about their care.

Our BMJ Open study of spatial computing in health and self-care explored these issues with UK adults, including healthcare professionals. Respondents identified possible value for self-care and patient education alongside barriers involving cost, training and privacy. These findings reflect perceptions rather than evidence of clinical benefit.

Smart glasses illustrate both the promise and the open questions. Be My Eyes connects blind and low-vision users of supported Meta glasses with sighted volunteers who can view the camera feed and provide spoken assistance. Its value, however, should be assessed against users’ goals and the alternatives available to them. This is why we launched the SMART EYES study to examine public views on AI-enabled smart glasses, including privacy, accessibility and possible support for health and self-care. To be meaningful, evaluations must start with people’s experiences, concerns and expectations. They should also account for the time needed to learn a tool, maintain it and resolve problems. The practical test is whether the overall experience becomes easier for the person using it.

In an earlier Prevention Lab article on health literacy, I argued that people need to understand information, judge its relevance and decide what to do next. Digital assistance should support those capabilities. Our UK symptom-checker survey found that respondents wanted help understanding symptoms and deciding whether to seek care, alongside concerns about privacy and losing face-to-face consultations. At the same time, our 2023 systematic review of symptom checkers found variable diagnostic and triage accuracy in the studies available at that time.

Earning public trust

Public trust also needs to be framed with care. SCARU’s RADIANT Voices research on AI and software as a medical device (SaMD) examined trust in clinical decision-making. We found higher trust in AI-assisted decisions than in AI-only decisions, alongside strong support for disclosure and professional oversight. These findings describe preferences, not evidence of safety or effectiveness.

Trust in individual pieces of advice matters as well. Our Trust in AI-Generated Health Advice (TAIGHA) scale and its four-item short form, TAIGHA-S, focus on trust in a specific piece of advice. Initially validated with 385 UK adults in a symptom-assessment scenario, they measure trust and distrust as related but distinct dimensions.

Our PLOS Digital Health study of community perspectives on BCIs likewise found concerns about implantation risks and cost, with participants emphasising regulation and public education. Those concerns belong in the decisions that set research priorities. Public engagement should give people a meaningful opportunity to influence development, including the option to question whether an application is desirable.

Because public trust has to be earned through evidence and accountability, developers and health services should explain the evidence for a proposed use, its limitations and who is accountable when something goes wrong. People should also have a practical way to challenge advice or report harm. The WHO’s guidance on generative AI in health supports clear tasks, appropriate accuracy and reliability, and involvement of patients and other affected groups in development.

Regulation and intended purpose

SaMD refers to software intended to perform a medical purpose without being part of a hardware medical device. Its increasing pervasiveness makes the intended purpose especially important. The MHRA’s guidance emphasises specifying that purpose and matching it to supporting evidence. Whether an application is accessed through a phone, headset or glasses, assessment should address its intended users, clinical function and setting. Determining regulatory status therefore requires attention to the specific features and claims of health-related software.

The developer’s experience deserves attention too. Our study of medical-software developers’ experiences of regulation, available as a preprint, describes participants’ difficulties obtaining reliable advice and navigating requirements. Clear guidance, accessible expertise and proportionate evidence requirements should form part of responsible innovation.

Autonomy in everyday use

Personal autonomy also needs to be built into everyday use. Who chooses the goal behind a reminder or recommendation? Who can change it? People should understand the purpose of the support they receive and be able to pause or decline it. Commercial interests should be visible, especially where advice is connected to the sale of a product or service. Delegating a task should leave the individual able to influence the terms of that delegation.

For smart glasses, autonomy also concerns the relationship between the wearer and people nearby. Using a camera to assist with a task, retaining a recording and sharing it are distinct activities. Each deserves a clear explanation and an appropriate decision about permission.

That discussion should also respect the dignity of people using assistive technology. Someone should not have to disclose a diagnosis to justify their device to a stranger. Equally, the people around them should have meaningful ways to understand and influence what happens to their information. Good safeguards need to address both concerns in practice.

Equity and shared responsibility

Equitable access should be considered from the outset and meaningful evaluations need to address affordability, language, disability-related requirements and the support needed to use a technology. People should retain an accessible route to care if a device is unsuitable or they choose not to use it. A service should also plan for interruptions, software changes and the point at which a product is no longer supported.

Health systems and wider society retain responsibilities of their own. A self-care strategy needs accessible professional advice and practical support, alongside attention to housing, working conditions and other constraints on daily choices. Digital tools should be assessed within that context and should not become a justification for transferring responsibilities to people without considering the resources available to them.

Evaluating what matters

For researchers and commissioners, this calls for evaluation beyond stated willingness to adopt a product. Relevant outcomes include understanding, appropriate action, wellbeing and harms, with results examined across different groups. We should measure the work expected of users and carers, compare realistic alternatives and study use over time. A successful demonstration is only the starting point for that work. Ultimately, the future of self-care must be shaped with the people expected to use these technologies. They should help define the problems worth solving, the evidence that matters and the boundaries they want respected. The measure of progress should be whether people have more meaningful choices, stronger capabilities and dependable support in looking after their health. These principles guide our work in practice. Through our patient and public involvement and engagement PPIE activities, self-carers regularly share their feedback with us, and their insights shape our projects. Their perspectives keep our research grounded in the realities of looking after one’s health, and we are grateful for the time and candour they bring. We will continue to build our work with them, rather than for them.

Building the future together

In my next blog, I will reflect on the importance of PPIE and adopting a team science approach in driving our work, and on what bringing together lived experience and diverse expertise contributes to research on self-care.