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

被引:1
|
作者
Graca, Sandro [1 ]
Alloh, Folashade [1 ,2 ]
Lagojda, Lukasz [3 ,4 ,5 ]
Dallaway, Alexander [1 ,3 ]
Kyrou, Ioannis [3 ,6 ,7 ,8 ,9 ,10 ,11 ]
Randeva, Harpal S. [3 ,6 ,7 ,8 ]
Kite, Chris [1 ,3 ,7 ,12 ]
机构
[1] Univ Wolverhampton, Fac Educ, Sch Hlth & Soc, Wolverhampton WV1 1LY, England
[2] Bournemouth Univ, Fac Hlth & Social Sci, Dept Nursing Sci, Poole BH12 5BB, England
[3] Univ Hosp Coventry & Warwickshire NHS Trust, Warwickshire Inst Study Diabet Endocrinol & Metab, Coventry CV2 2DX, England
[4] Univ Hosp Coventry & Warwickshire NHS Trust, Clin Evidence Based Informat Serv CEBIS, Coventry CV2 2DX, England
[5] Univ Sheffield, Sheffield Ctr Hlth & Related Res, Sch Med & Populat Hlth, Sheffield S1 4DA, England
[6] Univ Warwick, Warwick Med Sch, Coventry CV4 7AL, England
[7] Coventry Univ, Res Inst Hlth & Wellbeing, Ctr Sport Exercise & Life Sci, Coventry CV1 5FB, England
[8] Univ Hosp Coventry & Warwickshire NHS Trust, Inst Cardiometab Med, Coventry CV2 2DX, England
[9] Aston Univ, Coll Hlth & Life Sci, Aston Med Sch, Birmingham B4 7ET, England
[10] Univ Derby, Coll Hlth Psychol & Social Care, Derby DE22 1GB, England
[11] Agr Univ Athens, Sch Food & Nutr Sci, Dept Food Sci & Human Nutr, Lab Dietet & Qual Life, Athens 11855, Greece
[12] Univ Chester, Chester Med Sch, Shrewsbury SY3 8HQ, England
关键词
polycystic ovary syndrome (PCOS); Internet of Things (IoT); mobile app; social media; wearable; machine learning; artificial intelligence (AI); LIFE-STYLE MANAGEMENT; PHYSICAL-ACTIVITY; INFORMATION; ONLINE; WOMEN; PCOS; KNOWLEDGE; QUALITY; PROFILE;
D O I
10.3390/healthcare12161671
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
收藏
页数:20
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