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.

A Quantitative Evaluation of Thin Slice Sampling for Parent-Infant Interactions.

Behavioural coding is time-intensive and laborious. Thin slice sampling provides an alternative approach, aiming to alleviate the coding burden. However, little is understood about whether different behaviours coded over thin slices are comparable to those same behaviours over entire interactions. To provide quantitative evidence for the value of thin slice sampling for a variety of behaviours. We used data from three populations of parent-infant interactions: mother-infant dyads from the Grown in Wales (GiW) cohort (n = 31), mother-infant dyads from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort (n = 14), and father-infant dyads from the ALSPAC cohort (n = 11). Mean infant ages were 13.8, 6.8, and 7.1 months, respectively. Interactions were coded using a comprehensive coding scheme comprised of 11-14 behavioural groups, with each group comprised of 3-13 mutually exclusive behaviours. We calculated frequencies of verbal and non-verbal behaviours, transition matrices (probability of transitioning between behaviours, e.g., from looking at the infant to looking at a distraction) and stationary distributions (long-term proportion of time spent within behavioural states) for 15 thin slices of full, 5-min interactions. Measures drawn from the full sessions were compared to those from 1-, 2-, 3- and 4-min slices. We identified many instances where thin slice sampling (i.e., < 5 min) was an appropriate coding method, although we observed significant variation across different behaviours. We thereby used this information to provide detailed guidance to researchers regarding how long to code for each behaviour depending on their objectives.