JMIR Publications

ABSTRACT

Background:

Trust is an essential element in engagement with data sharing and underpins efforts to use data to combat health inequalities. Research into public trust in data sharing and healthcare settings may, however, rely on oversimplified notions of what trust entails. How trust relationships manifest in this context has not been widely explored.

Objective:

We aimed to establish the primary reasons for the placement of trust, and whether these reasons vary by demographics and domain. We also explored the utility of a composite trust score as a predictor for use of technology in the health sphere.

Methods:

We conducted a cross-sectional survey using Qualtrics to explore the challenges associated with trust and judgements of trustworthiness in the context of the use of technology to collect health-related data. Participants were recruited using a marketing firm, Dynata, and were UK-census matched for population representation in July 2022. Of a target accrual of 1200, 1192 UK-based participants aged 18 years or older (605 female, 587 male) were asked to rate their level of trust in others generally and in specific entities on an ordinal scale (1–5). We constructed Bayesian cumulative logit models and hierarchical models to evaluate whether demographic characteristics predicted reasons for domain-specific or general trust. We created a composite trust score across health data domains (range 1–15) and developed models to determine whether this score predicts the likelihood of having used, or using, a device to track health or well-being. We report all credible intervals at 95%.

Results:

General trust responses were bimodally distributed, with the most frequently chosen answers being “Usually not” and “Usually.” A cumulative logit model suggested that divorced status predicted for choosing “almost always not” or “usually not” (Beta estimate: –0.71; 95% CI: –1.17 to –0.28). “They are reliable and keep their promises” and “They behave responsibly” were the most chosen reasons for placing trust. Trust in family was primarily driven by familiarity; trust in the NHS by perceived responsibility; and trust in tech companies by openness and responsible behaviour. A Bayesian hierarchical model suggested that higher general trust was a strong predictor of a higher composite trust score (Beta estimate: 1.93; 95% CI: 1.26–2.59). A higher composite trust score also inversely correlated with the likelihood of having used a device to track health or well-being, while higher trust in tech companies and the NHS predicted a willingness to use such devices.

Conclusions:

Reason for the placement of trust in practice are fluid and grounded in the trustor’s relationship to the trustee and vary by demographics and domain. These findings suggest a need for conceptual accounts of trust that capture its contextual and relational nature in practical and policy examinations of public trust.