Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review

  • Sandro Graca
  • , Folashade Alloh
  • , Lukasz Lagojda
  • , Alexander Dallaway
  • , Ioannis Kyrou
  • , Harpal S. Randeva
  • , Chris Kite

    Research output: Contribution to journalReview articlepeer-review

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    Abstract

    Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder impacting women’s health and quality of life. This scoping review explores the use of the Internet of Things (IoT) in PCOS management. Results were grouped into six domains of the IoT: mobile apps, social media, wearables, machine learning, websites, and phone-based. A further domain was created to capture participants’ perspectives on using the IoT in PCOS management. Mobile apps appear to be useful for menstrual cycle tracking, symptom recording, and education. Despite concerns regarding the quality and reliability of social media content, these platforms may play an important role in disseminating PCOS-related information. Wearables facilitate detailed symptom monitoring and improve communication with healthcare providers. Machine learning algorithms show promising results in PCOS diagnosis accuracy, risk prediction, and app development. Although abundant, PCOS-related content on websites may lack quality and cultural considerations. While patients express concerns about online misinformation, they consider online forums valuable for peer connection. Using text messages and phone calls to provide feedback and support to PCOS patients may help them improve lifestyle behaviors and self-management skills. Advancing evidence-based, culturally sensitive, and accessible IoT solutions can enhance their potential to transform PCOS care, address misinformation, and empower women to better manage their symptoms.
    Original languageEnglish
    Article number1671
    Number of pages20
    JournalHealthcare
    Volume12
    Issue number16
    Early online date21 Aug 2024
    DOIs
    Publication statusE-pub ahead of print - 21 Aug 2024

    Bibliographical note

    © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

    Funder

    This scoping review was completed as part of a funded PhD project (S.G.) through the University of Wolverhampton. The PhD is jointly funded by University of Wolverhampton and University Hospitals Coventry and Warwickshire NHS Trust. The funders had no participation in the design or writing of this scoping review.

    Funding

    This scoping review was completed as part of a funded PhD project (S.G.) through the University of Wolverhampton. The PhD is jointly funded by University of Wolverhampton and University Hospitals Coventry and Warwickshire NHS Trust. The funders had no participation in the design or writing of this scoping review.

    Funders
    University of Wolverhampton
    University Hospitals Coventry and Warwickshire NHS Trust

      Keywords

      • social media
      • Internet of Things (IoT)
      • mobile app
      • artificial intelligence (AI)
      • wearable
      • machine learning
      • polycystic ovary syndrome (PCOS)

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