Cited by 30Mobile Health and mHealth ApplicationsOvarian function and disordersSocial Media in Health Education

Personalized Mobile Tool AskPCOS Delivering Evidence-Based Quality Information about Polycystic Ovary Syndrome

Xie, Jue, Burstein, Frada, Teede, Helena, Boyle, Jacqueline, Garad, Rhonda·2018·Seminars in Reproductive Medicine

Abstract

Despite polycystic ovary syndrome (PCOS) being the most common endocrine condition affecting reproductive-aged women, studies have shown the information needs of PCOS consumers are not currently met. The expressed need by women with PCOS for accessible, evidence-based personalized PCOS information informed the design and development of the PCOS mobile tool-AskPCOS. The App provides a range of unique features such as: evidence-based PCOS health information, self-diagnostic function, a question prompt list to optimize health practitioner engagement, and a commonly asked questions list. A five-phase App development process involved extensive stakeholder consultation, system architecture design, development of the content repository, system prototyping, and evaluation. AskPCOS is the first evidence-based, consumer-driven mobile App developed by women and for women with PCOS and utilizes innovative technology to empower PCOS consumers and optimize health outcomes. The App content repository is enhanced by the best available evidence and expertise extracted from the International Evidence-Based Guideline in PCOS (2018). The AskPCOS App has extensive data capture capability through the usage of data gathering functions and backend analytics to optimize responsiveness to consumer needs. In addition, the multilingual content and not-for-profit cost model is designed to facilitate worldwide adoption of the tool, and address PCOS information inequities in developing countries.

Integrity signals

Registry

Crossref · 10.1055/s-0038-1667156

Cites

19 of 19 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Two studies from Australia relate to the translation of the PCOS guidelines and the development of its accompanying app, AskPCOS.

    “Two studies from Australia relate to the translation of the PCOS guidelines and the development of its accompanying app, AskPCOS [58,59].”
  • The AskPCOS app was co-developed by and for those with PCOS and based on information from the international PCOS guideline.

    “The AskPCOS app was co-developed by and for those with PCOS and based on the information from the international PCOS guideline [59].”

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Xie, J., Burstein, F., Teede, H., Boyle, J., & Garad, R.
Affiliations
  • Monash University (AU)
  • Monash Centre for Health Research and Implementation, Clayton, Victoria, Australia
Corresponding author
Garad, Rhonda
Venue
Seminars in Reproductive Medicine
Published
2018-01
Volume
36
Issue
01
Pages
066-072
ISSN
  • 1526-4564 (ISSN-L)
  • 1526-8004
OpenAlex
W2891310522
Cited by
30
References
19
Citations by year
2026: 4 · 2025: 3 · 2024: 4 · 2023: 3 · 2022: 6 · 2021: 5 · 2020: 3 · 2018: 2
FWCI
3.65
Type
journal article
Language
en
Open access
closed
Locations
  • Seminars in Reproductive Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Medicine › Reproductive Medicine › Ovarian function and disorders
  • Social Sciences › Health › Social Media in Health Education
Keywords
polycystic ovary syndrome, mobile health apps, patient information needs, evidence-based health information, self-diagnosis, multilingual content
MeSH terms
Female, Humans, Polycystic Ovary Syndrome, Telemedicine, Health Literacy, Mobile Applications

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review