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

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.

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

Figure 1

PRISMA Flow diagram of database searches and study screening.

Figure 2

Summary of results: PCOS and the IoT (n = 64, nine studies mentioned multiple domains of the IoT).