Evidence map›Paper›PMID 39832140›Full record

ArticleJournal of medical Internet research2025

Participant Contributions to Person-Generated Health Data Research Using Mobile Devices: Scoping Review.

Shanshan Song, Micaela Ashton, Rebecca Hahn Yoo, Zoljargal Lkhagvajav, Robert Wright, Debra J H Mathews, Casey Overby Taylor

Abstract readScoping Review
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Shanshan SongBiomedical Informatics & Data Science Section, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.ORCID 0000-0002-6922-8973
Micaela AshtonJohns Hopkins Hospital, Baltimore, MD, United States.ORCID 0009-0003-0947-0586
Rebecca Hahn YooInstitute for Computational Medicine, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0001-8161-8934
Zoljargal LkhagvajavInstitute for Computational Medicine, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-9776-2655
Robert WrightWelch Medical Library, Johns Hopkins University School of Medicine, Baltimore, MD, United States.ORCID 0000-0002-3991-0015
Debra J H MathewsBerman Institute of Bioethics, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-4897-7617
Casey Overby TaylorBiomedical Informatics & Data Science Section, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.ORCID 0000-0001-9302-5968

Funding

Clinical Decision Support for Unsolicited Genomic ResultsR35HG010714 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI TAYLOR, CASEY OVERBY · 2020 to 2024
$2.8M
NHGRI NIH HHS R35 HG010714
6 · The paper itself

Abstract

backgroundMobile devices offer an emerging opportunity for research participants to contribute person-generated health data (PGHD). There is little guidance, however, on how to best report findings from studies leveraging those data. Thus, there is a need to characterize current reporting practices so as to better understand the potential implications for producing reproducible results.

objectiveThe primary objective of this scoping review was to characterize publications' reporting practices for research that collects PGHD using mobile devices.

methodsWe comprehensively searched PubMed and screened the results. Qualifying publications were classified according to 6 dimensions-1 covering key bibliographic details (for all articles) and 5 covering reporting criteria considered necessary for reproducible and responsible research (ie, "participant," "data," "device," "study," and "ethics," for original research). For each of the 5 reporting dimensions, we also assessed reporting completeness.

resultsOut of 3602 publications screened, 100 were included in this review. We observed a rapid increase in all publications from 2016 to 2021, with the largest contribution from US authors, with 1 exception, review articles. Few original research publications used crowdsourcing platforms (7%, 3/45). Among the original research publications that reported device ownership, most (75%, 21/28) reported using participant-owned devices for data collection (ie, a Bring-Your-Own-Device [BYOD] strategy). A significant deficiency in reporting completeness was observed for the "data" and "ethics" dimensions (5 reporting factors were missing in over half of the research publications). Reporting completeness for data ownership and participants' access to data after contribution worsened over time.

conclusionsOur work depicts the reporting practices in publications about research involving PGHD from mobile devices. We found that very few papers reported crowdsourcing platforms for data collection. BYOD strategies are increasingly popular; this creates an opportunity for improved mechanisms to transfer data from device owners to researchers on crowdsourcing platforms. Given substantial reporting deficiencies, we recommend reaching a consensus on best practices for research collecting PGHD from mobile devices. Drawing from the 5 reporting dimensions in this scoping review, we share our recommendations and justifications for 9 items. These items require improved reporting to enhance data representativeness and quality and empower participants.

Indexed as

Patient Generated Health DataHumansBYODcrowdsourcingfitness trackermHealthmobile devicemobile phoneperson-generated health dataPGHDreporting deficiencyscoping reviewsmartphonesmartwatchwearable

Identifiers

PMID39832140
PMCPMC11791458

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.