Menstrual Cycle Length and Patterns in a Global Cohort of Women Using a Mobile Phone App: Retrospective Cohort Study

Polyendocrine metabolic ovarian syndrome, the new name for polycystic ovary syndrome: a multistep global consensus process

Teede HJ et al., 2026 · Lancet (London, England) · Free to read

Polyendocrine metabolic ovarian syndrome (PMOS), previously named polycystic ovary syndrome (PCOS), affects one in eight women. However, the term PCOS is inaccurate, implying pathological ovarian cysts, obscuring diverse endocrine and metabolic features, and contributing to delayed diagnosis, fragmented care, and stigma, while curtailing research and policy framing. Building on an international mandate for change, we outline an unprecedented, rigorous, multistep global consensus process for the name change. Funding and governance were established with engagement of 56 leading academic, clinical, and patient organisations. Using iterative global surveys (with responses from 14 360 people with PCOS and multidisciplinary health professionals from all world regions), modified Delphi methods, nominal group technique workshops, and marketing and implementation analyses, we identified principles prioritising scientific accuracy, clarity, stigma avoidance, cultural appropriateness, and implementation feasibility. An accurate new name was prioritised over retaining the PCOS acronym or a generic name. Implementation approaches prioritised evolution rather than transformation. Preferred terms were polyendocrine, metabolic, and ovarian, reflecting the condition's multisystem pathophysiology, and polyendocrine metabolic ovarian syndrome was the consensus new name. Accuracy was improved by omitting cysts and by capturing endocrine, metabolic, and ovarian dysfunction. A co-designed global implementation strategy, including a transition period, education, and alignment with health systems and disease classification, is under way.

Evidence-based guideline: unexplained infertility

Romualdi D et al., 2023 · Hum Reprod · Free full text on PubMed Central

Guideline Group on Unexplained Infertility; Romualdi D(1), Ata B(2)(3), Bhattacharya S(4), Bosch E(5), Costello M(6)(7), Gersak K(8), Homburg R(9), Mincheva M(10), Norman RJ(7)(11), Piltonen T(12), Dos Santos-Ribeiro S(13), Scicluna D(14), Somers S(15), Sunkara SK(16), Verhoeve HR(17), Le Clef N(18).

Recommendations from the 2023 international evidence-based guideline for the assessment and management of PCOS

Teede HJ et al., 2023 · European Journal of Endocrinology · Free to read

Recommendations from the 2023 international evidence-based guideline for the assessment and management of polycystic ovary syndrome

Teede HJ et al., 2023 · Fertility and Sterility · Free to read

Menstrual cycle length variation by demographic characteristics from the Apple Women's Health Study

Li H et al., 2023 · NPJ digital medicine · Free full text on PubMed Central

Menstrual characteristics are important signs of women's health. Here we examine the variation of menstrual cycle length by age, ethnicity, and body weight using 165,668 cycles from 12,608 participants in the US using mobile menstrual tracking apps. After adjusting for all covariates, mean menstrual cycle length is shorter with older age across all age groups until age 50 and then became longer for those age 50 and older. Menstrual cycles are on average 1.6 (95%CI: 1.2, 2.0) days longer for Asian and 0.7 (95%CI: 0.4, 1.0) days longer for Hispanic participants compared to white non-Hispanic participants. Participants with BMI ≥ 40 kg/m2 have 1.5 (95%CI: 1.2, 1.8) days longer cycles compared to those with BMI between 18.5 and 25 kg/m2. Cycle variability is the lowest among participants aged 35-39 but are considerably higher by 46% (95%CI: 43%, 48%) and 45% (95%CI: 41%, 49%) among those aged under 20 and between 45-49. Cycle variability increase by 200% (95%CI: 191%, 210%) among those aged above 50 compared to those in the 35-39 age group. Compared to white participants, those who are Asian and Hispanic have larger cycle variability. Participants with obesity also have higher cycle variability. Here we confirm previous observations of changes in menstrual cycle pattern with age across reproductive life span and report new evidence on the differences of menstrual variation by ethnicity and obesity status. Future studies should explore the underlying determinants of the variation in menstrual characteristics.

Length and variation in the menstrual cycle - a cross-sectional study from a Danish county

Munster K et al., 1992 · British Journal of Obstetrics and Gynaecology

To investigate the current epidemiology of menstrual patterns among women of fertile age. Cross-sectional postal questionnaire study. County of Copenhagen, Denmark. 3743 women, aged 15-44, selected at random from a Danish county, who were asked to provide information on menstrual pattern during the preceding year, 1981. The response rate was 78%. Information from non-responders was obtained via telephone interviews. In women with regular menstrual cycles, the 5th-95th centile range of usual cycle length decreased from 23-35 days in the 15-19 years age group to 23-30 days in the 40-44 years age group. Only 0.5% of regular menstruating women had a usual cycle length of less than 21 days and 0.9% had a usual cycle length of greater than 35 days. At least one cycle length of less than 21 days was experienced by 18.6%, whereas 29.5% had at least one cycle of greater than 35 days. Menstrual cycle variation of greater than 14 days was present in 29.3% of all women. Cycle length variation of greater than 14 days was 2.7 times more frequent in women from lower social groups (controlled by age). The study confirmed the normally used definitions of polymenorrhoea (cycle length less than 21 days) and oligomenorrhoea (cycle length between 36 and 90 days), as these very short or long menstrual cycle lengths were very seldom recorded for a longer period. However, the high frequency in a normal population of large menstrual cycle length variation challenges the view that an intra-individual variation of greater than 5 days should be regarded as a sign of disease in the woman.

Menstrual bleeding, cycle length, and follicular and luteal phase lengths in women without known subfertility: A pooled analysis of three cohorts

Najmabadi S et al., 2020 · Paediatr Perinat Epidemiol · Free full text on PubMed Central

There is variability between women for days of menstrual bleeding, cycle lengths, follicular phase lengths, and luteal phase lengths, related to age and parity. To describe total cycle length; anovulatory cycles; follicular and luteal phase lengths; and days and intensity of menstrual and non-menstrual bleeding in women without known subfertility over the course of 1 year. 581 women (3,324 cycles) with no known subfertility (18-40 years of age) were followed for up to 1 year. Women recorded vaginal bleeding and mucus discharge daily. We used the peak day of cervical mucus as the estimated day of ovulation and the last day of the follicular phase. We used generalised linear mixed models stratified by age and parity to describe menstrual cycle parameters. The majority of women were <30 years of age (74.5%), non-Hispanic White (88.6%), and nulliparous (70.4%). The mean menses length was 6.2 (1.5) days, median 6; cycle length 30.3 (6.7) days, median 29; follicular phase length 18.5 (6.5) days, median 17; and luteal phase length 11.7 (2.8) days, median 12. Nulliparous women aged ≥30 years vs nulliparous women aged <30 had shorter cycles (29.2 days, 95% confidence interval (CI) 27.8, 30.7 vs 31.5 days, 95% CI 30.8, 32.2) and shorter follicular phases (17.6 days, 95% CI 16.2, 18.9 vs 19.6 days, 95% CI 18.9, 20.2). Among all women, within-woman differences between the longest and shortest menses length >3 days, total cycle length >7 days, follicular phase >7 days, and luteal phase >3 days were found in 11.6%, 43.0%, 41.7%, and 58.8% of women, respectively. Our findings confirm variability between women of menstrual cycle parameters related to age and parity, and also highlight within-woman variability in the follicular and luteal phases.

Relationship Between the Menstrual Cycle and Timing of Ovulation Revealed by New Protocols: Analysis of Data from a Self-Tracking Health App

Sohda S et al., 2017 · J Med Internet Res · Free full text on PubMed Central

There are many mobile phone apps aimed at helping women map their ovulation and menstrual cycles and facilitating successful conception (or avoiding pregnancy). These apps usually ask users to input various biological features and have accumulated the menstrual cycle data of a vast number of women. The purpose of our study was to clarify how the data obtained from a self-tracking health app for female mobile phone users can be used to improve the accuracy of prediction of the date of next ovulation. Using the data of 7043 women who had reliable menstrual and ovulation records out of 8,000,000 users of a mobile phone app of a health care service, we analyzed the relationship between the menstrual cycle length, follicular phase length, and luteal phase length. Then we fitted a linear function to the relationship between the length of the menstrual cycle and timing of ovulation and compared it with the existing calendar-based methods. The correlation between the length of the menstrual cycle and the length of the follicular phase was stronger than the correlation between the length of the menstrual cycle and the length of the luteal phase, and there was a positive correlation between the lengths of past and future menstrual cycles. A strong positive correlation was also found between the mean length of past cycles and the length of the follicular phase. The correlation between the mean cycle length and the luteal phase length was also statistically significant. In most of the subjects, our method (ie, the calendar-based method based on the optimized function) outperformed the Ogino method of predicting the next ovulation date. Our method also outperformed the ovulation date prediction method that assumes the middle day of a mean menstrual cycle as the date of the next ovulation. The large number of subjects allowed us to capture the relationships between the lengths of the menstrual cycle, follicular phase, and luteal phase in more detail than previous studies. We then demonstrated how the present calendar methods could be improved by the better grouping of women. This study suggested that even without integrating various biological metrics, the dataset collected by a self-tracking app can be used to develop formulas that predict the ovulation day when the data are aggregated. Because the method that we developed requires data only on the first day of menstruation, it would be the best option for couples during the early stages of their attempt to have a baby or for those who want to avoid the cost associated with other methods. Moreover, the result will be the baseline for more advanced methods that integrate other biological metrics.