Evidence map›Paper›PMID 36749628›Full record

ArticleJournal of medical Internet research2023

Charting a Course for Smartphones and Wearables to Transform Population Health Research.

William G Dixon, Sabine N van der Veer, Syed Mustafa Ali, Lynn Laidlaw, Richard J B Dobson, Cathie Sudlow, Tim Chico, Jacqueline A L MacArthur, Aiden Doherty

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
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  4. Observational
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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.

William G DixonCentre for Epidemiology Versus Arthritis, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom.ORCID 0000-0001-5881-4857
Sabine N van der VeerCentre for Health Informatics, Manchester Academic Health Science Centre, Manchester, United Kingdom.ORCID 0000-0003-0929-436X
Syed Mustafa AliCentre for Epidemiology Versus Arthritis, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom.ORCID 0000-0001-9393-9049
Lynn LaidlawCentre for Epidemiology Versus Arthritis, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom.ORCID 0000-0001-6688-6658
Richard J B DobsonDepartment of Biostatistics and Health Informatics, King's College London, London, United Kingdom.ORCID 0000-0003-4224-9245
Cathie SudlowBritish Heart Foundation Data Science Centre, Health Data Research UK, London, United Kingdom.ORCID 0000-0002-7725-7520
Tim ChicoBritish Heart Foundation Data Science Centre, Health Data Research UK, London, United Kingdom.ORCID 0000-0002-7458-5481
Jacqueline A L MacArthurBritish Heart Foundation Data Science Centre, Health Data Research UK, London, United Kingdom.ORCID 0000-0002-3550-9769
Aiden DohertyBig Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.ORCID 0000-0003-1840-0451

Funding

British Heart Foundation SP/19/3/34678Versus Arthritis 21755Wellcome Trust 223100Wellcome Trust 223100/Z/21/Z
6 · The paper itself

Abstract

The use of data from smartphones and wearable devices has huge potential for population health research, given the high level of device ownership; the range of novel health-relevant data types available from consumer devices; and the frequency and duration with which data are, or could be, collected. Yet, the uptake and success of large-scale mobile health research in the last decade have not met this intensely promoted opportunity. We make the argument that digital person-generated health data are required and necessary to answer many top priority research questions, using illustrative examples taken from the James Lind Alliance Priority Setting Partnerships. We then summarize the findings from 2 UK initiatives that considered the challenges and possible solutions for what needs to be done and how such solutions can be implemented to realize the future opportunities of digital person-generated health data for clinically important population health research. Examples of important areas that must be addressed to advance the field include digital inequality and possible selection bias; easy access for researchers to the appropriate data collection tools, including how best to harmonize data items; analysis methodologies for time series data; patient and public involvement and engagement methods for optimizing recruitment, retention, and public trust; and methods for providing research participants with greater control over their data. There is also a major opportunity, provided through the linkage of digital person-generated health data to routinely collected data, to support novel population health research, bringing together clinician-reported and patient-reported measures. We recognize that well-conducted studies need a wide range of diverse challenges to be skillfully addressed in unison (eg, challenges regarding epidemiology, data science and biostatistics, psychometrics, behavioral and social science, software engineering, user interface design, information governance, data management, and patient and public involvement and engagement). Consequently, progress would be accelerated by the establishment of a new interdisciplinary community where all relevant and necessary skills are brought together to allow for excellence throughout the life cycle of a research study. This will require a partnership of diverse people, methods, and technologies. If done right, the synergy of such a partnership has the potential to transform many millions of people's lives for the better.

Indexed as

TelemedicineWearable Electronic DevicesHumansResearch DesignSmartphoneclinicaldatadevicesdigitalhealthmHealthmobile healthperson-generated health datapopulation health researchresearchwearable

Identifiers

PMID36749628
PMCPMC11334374

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.