Menstrual Cycle Metrics as a Digital Biomarker

Menstrual cycle metrics describe the timing and pattern of the cycle: length from the first day of one period to the first day of the next, variability of that length between cycles, and the duration and pattern of bleeding.

Digital data has genuinely changed what is known here. Analyses of hundreds of thousands of cycles logged in apps have shown that cycle length is more variable between individuals, and within individuals, than the textbook twenty eight days implies, and that the classical figure describes fewer people than assumed. Global cohorts using mobile apps have replicated that picture.

An important honesty point sits at the centre of this page: these are self reported measures with a digital delivery mechanism, not sensor measurements. The app records what the user logs. That is still a large advance over recall at a clinic visit, but it is a different kind of evidence from the passively sensed measures elsewhere in this library.

The primary input is user logging of bleeding days, from which cycle length, variability and bleeding duration are computed. Increasingly this is combined with passively sensed physiology from a wearable, including resting heart rate, heart rate variability and skin temperature, which vary systematically across the cycle and can support or correct the logged dates.

Work characterising the cycle through a wearable device has described those physiological changes and their relationship to cycle phase, and methodological work has applied statistical models to label self tracked records and handle the missing and irregular data that self logging produces.

Data quality is the recurring issue. Logging is voluntary and incomplete, users start and stop, and a missed log is indistinguishable from a missed period unless the physiological data disambiguates it.

Two uses are defensible in research. The first is as an outcome in trials for menstrual disorders, endometriosis, polycystic ovary syndrome and contraceptive tolerability, where cycle characteristics are the thing being changed.

The second, and the one that applies far more widely, is as a covariate. Any wearable study enrolling premenopausal participants is measuring people whose heart rate, temperature and sleep shift with cycle phase. Treating that as noise costs statistical power, and the cost is invisible because the variance simply looks like scatter.

These measures have no counterpart in this library's instrument collection. Cycle specific patient reported instruments are a recognised gap, and rather than substitute a loosely related scale, the page states the absence.

Self report is the core limitation and it is not fixable by better sensors alone. App populations skew towards younger, more affluent, more health engaged users, which limits generalisation, and logging drops off over time in a way that is not random.

Hormonal contraception changes or abolishes the cycle, so a large proportion of the population of interest cannot be characterised by these measures at all, and studies that do not record contraceptive use will mix two different physiologies.

Definitions also vary. Whether spotting counts as bleeding, how cycles are bounded and what counts as irregular differ between studies and between apps, so figures are not directly comparable. And because the data is intimate and can reveal pregnancy, pregnancy loss and contraceptive use, consent and data governance obligations are heavier here than for most measures in this library.

• Bull JR, et al. Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles. NPJ Digit Med. 2019. pubmed.ncbi.nlm.nih.gov
• Grieger JA, Norman RJ. Menstrual cycle length and patterns in a global cohort of women using a mobile phone app. J Med Internet Res. 2020. pubmed.ncbi.nlm.nih.gov
• Symul L, et al. Assessment of menstrual health status and evolution through mobile apps for fertility awareness. NPJ Digit Med. 2019. pubmed.ncbi.nlm.nih.gov
• Gonzalez A, et al. The menstrual cycle through the lens of a wearable device: insights into physiology, sleep, and cycle variability. NPJ Digit Med. 2026. pubmed.ncbi.nlm.nih.gov