Evidence map›Paper›PMID 38754098›Full record

ArticleJournal of medical Internet research2024

Person-Generated Health Data in Women's Health: Scoping Review.

Jalisa Lynn Karim, Rachel Wan, Rhea S Tabet, Derek S Chiu, Aline Talhouk

Abstract readScoping Review
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Artificial intelligence for personalized multiple micronutrient supplementation in maternal health.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026
    Review
  2. Review
  3. Article
  4. Person-Generated Health Data in Women's Health: Scoping Review.Journal of medical Internet research · 2024
    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

5 authors.

Jalisa Lynn KarimDepartment of Obstetrics and Gynaecology, University of British Columbia, Vancouver, BC, Canada.ORCID 0000-0001-9520-0651
Rachel WanDepartment of Obstetrics and Gynaecology, University of British Columbia, Vancouver, BC, Canada.ORCID 0009-0006-6347-5689
Rhea S TabetDepartment of Pharmacology and Therapeutics, McGill University, Montréal, QC, Canada.ORCID 0009-0004-6796-6800
Derek S ChiuDepartment of Molecular Oncology, University of British Columbia, Vancouver, BC, Canada.ORCID 0000-0002-7591-4881
Aline TalhoukDepartment of Obstetrics and Gynaecology, University of British Columbia, Vancouver, BC, Canada.ORCID 0000-0001-7760-410X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe increased pervasiveness of digital health technology is producing large amounts of person-generated health data (PGHD). These data can empower people to monitor their health to promote prevention and management of disease. Women make up one of the largest groups of consumers of digital self-tracking technology.

objectiveIn this scoping review, we aimed to (1) identify the different areas of women's health monitored using PGHD from connected health devices, (2) explore personal metrics collected through these technologies, and (3) synthesize facilitators of and barriers to women's adoption and use of connected health devices.

methodsFollowing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews, we searched 5 databases for articles published between January 1, 2015, and February 29, 2020. Papers were included if they targeted women or female individuals and incorporated digital health tools that collected PGHD outside a clinical setting.

resultsWe included a total of 406 papers in this review. Articles on the use of PGHD for women steadily increased from 2015 to 2020. The health areas that the articles focused on spanned several topics, with pregnancy and the postpartum period being the most prevalent followed by cancer. Types of digital health used to collect PGHD included mobile apps, wearables, websites, the Internet of Things or smart devices, 2-way messaging, interactive voice response, and implantable devices. A thematic analysis of 41.4% (168/406) of the papers revealed 6 themes regarding facilitators of and barriers to women's use of digital health technology for collecting PGHD: (1) accessibility and connectivity, (2) design and functionality, (3) accuracy and credibility, (4) audience and adoption, (5) impact on community and health service, and (6) impact on health and behavior.

conclusionsLeading up to the COVID-19 pandemic, the adoption of digital health tools to address women's health concerns was on a steady rise. The prominence of tools related to pregnancy and the postpartum period reflects the strong focus on reproductive health in women's health research and highlights opportunities for digital technology development in other women's health topics. Digital health technology was most acceptable when it was relevant to the target audience, was seen as user-friendly, and considered women's personalization preferences while also ensuring accuracy of measurements and credibility of information. The integration of digital technologies into clinical care will continue to evolve, and factors such as liability and health care provider workload need to be considered. While acknowledging the diversity of individual needs, the use of PGHD can positively impact the self-care management of numerous women's health journeys. The COVID-19 pandemic has ushered in increased adoption and acceptance of digital health technology. This study could serve as a baseline comparison for how this field has evolved as a result. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/26110.

Indexed as

Women's HealthCOVID-19FemaleHumansPatient Generated Health DataPregnancydigital healthfemtechhealth appmobile healthmobile phonepatient-generated health datapersonalized healthperson-generated health datascoping reviewself-trackingwearableswomen’s health

Identifiers

PMID38754098
PMCPMC11140278

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.