Harnessing digital footprint data for population health: a discussion on collaboration, challenges and opportunities in the UK.

Digital footprint data are inspiring a new era in population health and well-being research. Linking these novel data with other datasets is critical for future research wishing to use these data for the public good. In order to succeed, successful collaboration among industry, academics and policy-makers is vital. Therefore, we discuss the benefits and obstacles for these stakeholder groups in using digital footprint data for research in the UK. We advocate for policy-makers' inclusion in research efforts, stress the exceptional potential of digital footprint research to impact policy-making and explore the role of industry as data providers, with a focus on shared value, commercial sensitivity, resource requirements and streamlined processes. We underscore the importance of multidisciplinary approaches, consumer trust and ethical considerations in navigating methodological challenges and further call for increased public engagement to enhance societal acceptability. Finally, we discuss how to overcome methodological challenges, such as reproducibility and sharing of learnings, in future collaborations. By adopting a multiperspective approach to outlining the challenges of working with digital footprint data, our contribution helps to ensure that future research can navigate these challenges effectively while remaining reproducible, ethical and impactful.

Patients' subjective well-being: Determinants and its usage as a metric of healthcare service quality.

It is commonly suggested that patients' subjective well-being (SWB) can be affected by pre-treatment conditions and treatment experiences, and hence SWB can be used to measure and improve healthcare quality. With data collected in a hospital in the UK (N = 446), we investigated the determinants of patients' SWB and evaluated its use in healthcare research. Our findings showed strong relationships between pre-treatment conditions and patients' SWB: anxiety and depression negatively predicted SWB across all three domains, mobility positively predicted the life satisfaction and happiness domains, while the ability to self care and pain and discomfort also predicted SWB in some domains. In contrast, patients' satisfaction with the treatment only played minor roles in determining SWB, much less so the characteristics of their nurses. The general lack of associations between treatment experiences and patient's SWB highlighted the challenges of using SWB to measure healthcare quality and inform policy making.

A query theory account of the attraction effect.

We provide novel support for Query Theory, a reason-based decision framework, extending it to multialternative choices and applying it to the classic phenomenon known as the attraction effect. In Experiment 1 (N = 261), we generalised the two key metrics used in Query Theory from binary to multialternative choices and found that reasons supporting the target option were generated earlier and in greater quantity than those supporting the competitor, as predicted by the theory. In Experiment 2 (N = 703), we investigated the causal relationships between reasoning and choices by exogenously manipulating the order in which participants generated their reasons. As predicted, the size of the attraction effect was a function of this query order manipulation. We also introduced a bidirectional reason coding protocol to measure the valence of reasons, which confirmed support for Query Theory. We suggest the Query Theory framework can be useful for studying the high-level deliberation processes behind multialternative choices.