The Menstrual Cycle Phases Are Like 'Body Seasons'

The menstrual cycle as a vital sign: the use of naprotechnology

Gonzáles S, 2017 · Issues Law Med ·

The Menstrual Cycle as a Vital Sign: a comprehensive review

Vollmar AKR et al., 2025 · F&S reviews · Open Access

Some medical professional organizations have advocated for including the menstrual cycle as a vital sign in adolescence, but not in adulthood. However, documenting menstrual cycle patterns is not routine clinical or research practice. Vital signs are used to predict health outcomes, indicate needed treatment, and monitor a clinical course. They can help identify pathologies, affirm wellness, and are responsive to exposures. Here we review the scientific evidence showing how the menstrual cycle meets these criteria and should therefore be treated as a vital sign. Using key words and controlled vocabulary terms, we carried out multiple literature searches, prioritizing the inclusion of systematic reviews, meta-analyses, and clinical practice guidelines. This review describes how the menstrual cycle is a health indicator, can cyclically impact health conditions, and its associations with long-term post-menopausal health outcomes. We review exposures influencing the menstrual cycle, evidence underlying its use to optimize wellness, and available tools for documenting cycles. Supplementary materials include patient handouts on menstrual cycle tracking, and an index of related clinical practice guidelines and reviews by subject. The menstrual cycle is a vital sign from menarche through menopause, an underutilized but powerful tool for understanding gynecological and general health.

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 · Open Access

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.

Menstrual cycle symptom variation in a community sample of women using and not using oral contraceptives

Sveinsdóttir H et al., 2000 · Acta Obstet Gynecol Scand ·

The prevalence of significant symptom change (symptom cyclicity) prospectively rated over multiple menstrual cycles has not been established in a non-clinical population. Seventy-three women charted 57 symptoms over 2-6 menstrual cycles each. Symptoms, and summarized symptom scores within seven symptom groups, were tested for changes between the follicular phase and the luteal phase of each cycle. Recurrent symptom cyclicity over multiple cycles within individuals was ascertained and the stability between cycles of mean symptom scores for both the follicular phase and the luteal phase. Forty-five percent of the participants experienced cyclicity over multiple cycles in at least one symptom and 23% in at least one symptom group. Eighteen percent of the participants consistently reported a higher symptom score during the luteal phase compared to the follicular phase (a PMS-like pattern) in all symptoms in which they experienced a change. The remaining 27% experienced a varying direction of change in the same symptom between cycles, or consistently experienced a lower symptom score during the luteal phase (a reverse PMS-like pattern) of the cycles they charted. Recurrent cyclicity was experienced by 16% of the participants in one symptom; in two symptoms by 15%; in 3 8 symptoms by 14%; in one symptom group by 19% and in two symptom groups by only 4% of participants. Average symptom severity did not vary significantly between cycles. Due to the varied direction of symptom severity change over multiple cycles, prospective daily ratings are necessary to achieve a true picture of menstrual related symptom cyclicity in the general population.