Evidence map›Paper›PMID 30445467›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2018

Leveraging mobile health applications for biomedical research and citizen science: a scoping review.

Hannah Schmitz, Carol L Howe, David G Armstrong, Vignesh Subbian

Open access · greenAbstract readScoping Review
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
7.7field-weighted citation impact, top 3% of its field
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

11 citing papers in PubMed, 41 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Digital Citizen Science for Responding to COVID-19 Crisis: Experiences from Iran.International journal of environmental research and public health · 2021
    Article
  7. Article
  8. Article
  9. Observational
  10. Article
  11. 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

4 authors at 2 institutions in 1 country.

Hannah SchmitzDepartment of Biomedical Engineering, The University of Arizona, Tucson, Arizona, USA.
Carol L HoweHealth Sciences Library, The University of Arizona, Tucson, Arizona, USA.
David G ArmstrongDepartment of Surgery, University of Southern California, Los Angeles, California, USA.
Vignesh SubbianDepartment of Biomedical Engineering, The University of Arizona, Tucson, Arizona, USA.
University of Arizona · USUniversity of Southern California · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This systematic review aims to analyze current capabilities, challenges, and impact of self-directed mobile health (mHealth) research applications such as those based on the ResearchKit platform. Materials and Methods: A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. English publications were included if: 1) mobile applications were used in the context of large-scale collection of data for biomedical research, and not as medical or behavioral intervention of any kind, and 2) all activities related to participating in research and data collection methods were executed remotely without any face-to-face interaction between researchers and study participants. Results: Thirty-six unique ResearchKit apps were identified. The majority of the apps were used to conduct observational studies on general citizens and generate large datasets for secondary research. Nearly half of the apps were focused on chronic conditions in adults. Discussion: The ability to generate large biomedical datasets on diverse populations that can be broadly shared and re-used was identified as a promising feature of mHealth research apps. Common challenges were low participation retention, uncertainty regarding how use patterns influence data quality, need for data validation, and privacy concerns. Conclusion: ResearchKit and other mHealth-based studies are well positioned to enhance development and validation of novel digital biomarkers as well as generate new biomedical knowledge through retrospective studies. However, in order to capitalize on these benefits, mHealth research studies must strive to improve retention rates, implement rigorous data validation strategies, and address emerging privacy and security challenges.

Indexed as

Biomedical ResearchCommunity ParticipationMobile ApplicationsData AccuracyDatasets as TopicDiseaseHumansPatient SelectionPrivacySmartphone

Identifiers

PMID30445467
PMCPMC7647150
OpenAlexW2901204587

What OpenQuestion holds

Textmetadata
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.