Evidence map›Paper›PMID 40463363›Full record

ArticleInternational journal of population data science2025

Linking digital footprint data into longitudinal population studies.

Romana Burgess, Andy Boyd, Oliver Sp Davis, Louise Ac Millard, Mark Mumme, Sarah Robertson, Andy Skinner, Zhuoni Xiao, Anya Skatova

Abstract read
In one paragraph

Article in International journal of population data science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Romana BurgessPopulation Health Sciences, Bristol Medical School, University of Bristol, UK.
Andy BoydPopulation Health Sciences, Bristol Medical School, University of Bristol, UK.
Oliver Sp DavisPopulation Health Sciences, Bristol Medical School, University of Bristol, UK.
Louise Ac MillardPopulation Health Sciences, Bristol Medical School, University of Bristol, UK.
Mark MummePopulation Health Sciences, Bristol Medical School, University of Bristol, UK.
Sarah RobertsonCentre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, UK.
Andy SkinnerPopulation Health Sciences, Bristol Medical School, University of Bristol, UK.
Zhuoni XiaoCentre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, UK.
Anya SkatovaPopulation Health Sciences, Bristol Medical School, University of Bristol, UK.

Funding

Wellcome Trust
6 · The paper itself

Abstract

Background: Linking digital footprint data into longitudinal population studies (LPS) presents an opportunity to enrich our understanding of how digitally captured behaviours relate to health traits and disease. However, this linkage introduces significant methodological challenges that require systematic exploration. Objectives: To develop a robust framework for successful digital footprint linkage into LPS, informed by discussions from a workshop from the Digital Footprints Conference 2024. Methods: We propose a structured, four-stage framework to facilitate successful linkage of digital footprint data into LPS: (1) understand participant expectations and acceptability; (2) collect and link the data; (3) evaluate properties of the data; and (4) ensure secure and ethical access for research. This framework addresses the key methodological challenges identified at each stage, discussed through the lens of two LPS case studies: the Avon Longitudinal Study of Parents and Children and Generation Scotland. Results: Key methodological challenges identified include privacy and confidentiality concerns, reliance on third-party platforms, data quality issues like missing data and measurement error. We also emphasize the role of trusted research environments and synthetic datasets in enabling secure, privacy-sensitive data sharing for research. Conclusions: While the linkage digital footprint data to LPS remains in early stages, our framework provides a methodological foundation for overcoming current challenges. Through iterative refinement of these methods there is significant potential to advance population-level insights into health and wellbeing.

Indexed as

Internet UseLongitudinal StudiesConfidentialityHumansInformation DisseminationScotlandALSPACdata linkagedigital footprintsgeneration Scotlandlongitudinal population study

Identifiers

PMID40463363
PMCPMC12132027

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.