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Background Polycystic ovary syndrome (PCOS) is a complex and multi-faceted endocrine disorder that affects 5–20% of women. Literature is limited regarding potentially differing PCOS phenotypes among women around the world. Objective To use Flo app technology to understand the multifaceted characteristics of PCOS across several countries and identify contributing risk factors to the development of this condition. Study design Flo is a widely used female health and wellbeing app with period tracking functionality that provides a globally representative and medically unbiased perspective on PCOS symptomatology. A chatbot dialog on PCOS was subsequently administered on the Flo application (app) to users from 142 countries (with at least 100 respondents) who have the app running in English during September–October 2019. Results For analyses, we selected the five countries with the greatest number of respondents: US (n = 243,238), UK (n = 68,325), India (n = 40,092), Philippines (n = 35,131), and Australia (n = 29,926). Bloating was the most frequently reported symptom among PCOS-positive women and appeared to be the main predictor of PCOS in our model (odds ratio 3·76 [95% CI 3·60–3·94]; p < 0·0001). Additional top predictors of PCOS are high blood cholesterol and glucose levels. As BMI increased, the percentage of women who reported a physician-confirmed PCOS diagnosis also increased. However, women in India did not follow this trend. Conclusion Our findings are based on the largest known PCOS dataset and indicate that symptoms are more complex than previously understood. The most frequently reported symptoms (bloating, facial hirsutism, irregular cycles, hyperpigmentation, and baldness) are broader than those included in the Rotterdam criteria. Future work should reevaluate and refine the criteria utilized in PCOS diagnosis.

Premenstrual symptoms, including physical and mood symptoms, affect a large proportion of women worldwide. Data on premenstrual symptoms across nations and age groups is limited. In the present study, we leveraged a large international dataset to explore patterns in premenstrual symptom frequency with age. A survey was administered to users of the Flo mobile application (app), aged 18 to 55. The survey queried app users about a range of premenstrual symptoms. Respondents were asked whether they experienced each symptom every menstrual cycle, some cycles, or never. Age was also captured and categorized as 18–27, 28–37, 38–47, 48–55. Data was summarized and Pearson’s chi square test for count data assessed differences in symptom frequency by age group. A sample of 238,114 app users from 140 countries responded to the survey. The most common symptoms reported were food cravings (85.28%), mood swings or anxiety (64.18%), and fatigue (57.3%). Absentmindedness, low libido, sleep changes, gastrointestinal symptoms, weight gain, headaches, sweating or hot flashes, fatigue, hair changes, rashes, and swelling were significantly more frequent with increasing age (p’s < 0.001). Mood swings and anxiety did not vary by age group. Of the respondents, 28.61% reported that premenstrual symptoms interfered with their everyday life each menstrual cycle. In a large international sample, the majority of women reported premenstrual food cravings, mood changes, and fatigue every menstrual cycle. Mood symptoms did not vary by age group, suggesting that premenstrual mood changes are a persistent issue among women of reproductive age.

Objective Mood and physical symptoms related to the menstrual cycle affect women's productivity at work, often leading to absenteeism. However, employer-led initiatives to tackle these issues are lacking. Digital health interventions focused on women's health (such as the Flo app) could help fill this gap. Methods 1867 users of the Flo app participated in a survey exploring the impact of their menstrual cycle on their workplace productivity and the role of Flo in mitigating some of the identified issues. Results The majority reported a moderate to severe impact of their cycle on workplace productivity, with 45.2% reporting absenteeism (5.8 days on average in the previous 12 months). 48.4% reported not receiving any support from their manager and 94.6% said they were not provided with any specific benefit for issues related to their menstrual cycle, with 75.6% declaring wanting them. Users stated that the Flo app helped them with the management of menstrual cycle symptoms (68.7%), preparedness and bodily awareness (88.7%), openness with others (52.5%), and feeling supported (77.6%). Users who reported the most positive impact of the Flo app were 18–25% less likely to report an impact of their menstrual cycle on their productivity and 12–18% less likely to take days off work for issues related to their cycle. Conclusions Apps such as Flo could equip individuals with tools to better cope with issues related to their menstrual cycle and facilitate discussions around menstrual health in the workplace.

Ovulatory disorders are common causes of amenorrhea, abnormal uterine bleeding and infertility and are frequent manifestations of polycystic ovary syndrome (PCOS). There are many potential causes and contributors to ovulatory dysfunction that challenge clinicians, trainees, educators, and those who perform basic, translational, clinical and epidemiological research. Similarly, therapeutic approaches to ovulatory dysfunction potentially involve a spectrum of lifestyle, psychological, medical and procedural interventions. Collaborative research, effective education and consistent clinical care remain challenged by the absence of a consensus comprehensive system for classification of these disorders. The existing and complex system, attributed to the World Health Organization (WHO), was developed more than three decades ago and did not consider more than 30 years of research into these disorders in addition to technical advances in imaging and endocrinology. This article describes the development of a new classification of ovulatory disorders performed under the aegis of the International Federation of Gynecology and Obstetrics (FIGO) and conducted using a rigorously applied Delphi process. The stakeholder organizations and individuals who participated in this process comprised specialty journals, experts at large, national, specialty obstetrical and gynecological societies, and informed lay representatives. After two face-to-face meetings and five Delphi rounds, the result is a three-level multi-tiered system. The system is applied after a preliminary assessment identifies the presence of an ovulatory disorder. The primary level of the system is based on an anatomic model (Hypothalamus, Pituitary, Ovary) that is completed with a separate category for PCOS. This core component of the system is easily remembered using the acronym HyPO-P. Each anatomic category is stratified in the second layer of the system to provide granularity for investigators, clinicians and trainees using the ‘GAIN-FIT-PIE’ mnemonic (Genetic, Autoimmune, Iatrogenic, Neoplasm; Functional, Infectious and Inflammatory, Trauma and Vascular; Physiological, Idiopathic, Endocrine). The tertiary level allows for specific diagnostic entities. It is anticipated that, if widely adopted, this system will facilitate education, clinical care and the design and interpretation of research in a fashion that better informs progress in this field. Integral to the deployment of this system is a periodic process of reevaluation and appropriate revision, reflecting an improved understanding of this collection of disorders.