Tools Self-checks up to 12 questions

Is my cycle normal?

up to 12 questions · about 3 minutes · scored on your device, nothing saved

This is educational, not diagnostic. These questions help you notice a pattern in your own routine. They cannot tell you what is causing it, and they are not a substitute for care from a doctor or clinician.

Your answers are scored on your device. Nothing you enter here is sent to us or saved.

Almost everything written about this starts from 28 days, and almost nobody has one. In 1.58 million app users with at least three logged cycles, only 16.3% had a median cycle length of 28 days. In another dataset, a quarter of women believed they had a 28-day cycle and 12.4% actually did. So if your cycle is not 28 days, you are in the majority, and the number you have been comparing yourself to was never the average.

This check does two pieces of arithmetic on dates you supply, your average length and the spread between your longest and shortest recent cycle, and shows you where each sits against figures from studies of millions of real cycles. That second number is the one nobody talks about, and it is usually the more informative of the two.

It cannot tell you whether your cycle is normal, in the sense of whether anything is wrong. That is a clinical judgement and this is arithmetic. What it can do is tell you how common your pattern is, and be clear about which specific things are worth an appointment rather than a habit.

How this self-check is put together

Two pieces of arithmetic on dates you supply. The average of your recent cycle lengths, and the spread between your longest and shortest. A cycle length is counted from the first day of one period to the day before the next one starts. Nothing is estimated or inferred: if you enter three numbers, the result is those three numbers described.

Where the reference figures come from. Three large datasets, and it is worth knowing what each one contributes. In 75,981 cycles from 32,595 women using connected ovulation tests, 87% had cycle lengths between 23 and 35 days, 52% had cycles varying by five days or more, and, the figure that reframes the whole topic, 25.3% believed they had a 28-day cycle while only 12.4% actually did (Soumpasis 2020). Across 612,613 ovulatory cycles from 124,648 women, mean cycle length was 29.3 days, and length fell by about 0.18 days per year of age between 25 and 45 (Bull 2019). Among 1,579,819 app users with at least three logged cycles, only 16.3% had a 28-day median, and women over 40 were more likely to have a 27-day median than 18 to 24 year olds (Grieger 2020).

Why the five-day boundary is not described as abnormal. Because a majority of women exceed it. That is the most important sentence on this page, and it is the reason our second band exists at all: Slightly variable is not a warning, it is a description of the commonest pattern.

Why the seven and eight day boundary is where it is. Because it is a published operational criterion rather than one we chose. A persistent difference of seven days or more between consecutive cycle lengths is the threshold researchers use to mark the beginning of the menopausal transition, alongside a gap of sixty days or more for the later stage (Harlow 2012), and both thresholds were tested empirically against prospective menstrual calendars from four separate cohorts before being adopted (Harlow 2007). We use those numbers to decide where the description of your dates changes. We do not use them to assign you a stage, and we will not: no digital implementation of those criteria has yet been validated, and the researchers who work on this have said so explicitly (Huibregtse 2026).

Why variability gets its own treatment. Because it is not just a side effect of cycles getting longer. Hierarchical change-point modelling of women's own menstrual calendars identifies separate change points for the average cycle length and for how much it varies, showing that variability increases as its own process (Huang 2014). And when cycles do lengthen with age, the change appears mostly in the long cycles: in 963 women tracked with daily calendars, increases happened largely in the right tail while the median barely moved (Paramsothy 2015). That is why a widening spread is often the first thing a woman in her forties notices.

Why we ask you to log more cycles. Because it genuinely works rather than because it is what an app would say. In 4.9 million natural cycles from over 378,000 users, cycle and period length statistics were stationary across the usage timeline: the picture sharpens with more data rather than drifting (Li 2020).

Terminology. We describe bleeding in plain descriptive terms, how long, how heavy, how far apart, following the international consensus that abandoned older terms like menorrhagia in favour of describing the pattern (Fraser 2011). And when a pattern is worth a conversation, we say so without guessing at the reason: the standard classification of causes exists precisely because several can be present in the same person (Munro 2011), which is a good reason for a web page not to speculate.

One long-run finding, for completeness rather than alarm. In 79,505 women followed for 24 years, cycles that were always irregular at ages 29 to 46 carried an adjusted hazard ratio of 1.39 for death before age 70, as did a usual cycle length of 40 days or more (Wang 2020), with a comparable association for cardiovascular disease in the same cohort (Wang 2022). This is a population-level association across decades, not a prediction about anyone, and it is here because it is the honest reason a persistently unusual pattern is worth raising rather than shrugging off.

Limits of this estimate. The reference datasets come from women using period-tracking or fertility apps, who chose to track. That is a self-selected group and not a random sample of women, so treat the percentages as well-measured descriptions of large groups rather than as national statistics. Two of the three were also produced by companies with a commercial interest in cycle tracking. Three cycles is a small number, and variability in particular needs more. Recalled dates are approximate. Hormonal contraception sets the bleeding pattern, so these comparisons do not apply. Cycle length says very little about ovulation timing: in the source dataset, even a nominal 28-day cycle showed a ten-day spread in the day of ovulation, so this page makes no fertility statement and the ovulation calculator is the right tool for that question, with its own limits. We compare your answers to published general guidance for adults. We have not tested this quiz against any clinical measure.

Persistent problems deserve support. Some things are worth an appointment regardless of what the arithmetic above says: periods heavy enough to soak through protection every hour or two, bleeding that lasts more than seven days, clots bigger than a 50p coin, bleeding between periods or after sex, or any bleeding at all if your periods stopped a year or more ago. Heavy bleeding in particular is both common and under-reported. In one survey of 2,356 women, 18.2% experienced subjectively heavy bleeding and only 18.9% of them had sought care (Ding 2019), and it is treatable, so it is not something to manage around. Heavy bleeding is also associated with more than three-fold odds of anaemia in premenopausal women (Ekroos 2024), which is worth mentioning in the same appointment if you have been unusually breathless or light-headed.

Common questions

How should I use this estimate?

As a description of your dates and a prompt to keep logging. If you take one thing from it, take the spread rather than the average: it is the more informative number and the one almost nobody calculates.

Is this medical advice?

No. Femy is educational content about wellness habits. It does not diagnose, treat, or replace care from a doctor or clinician.

So is my cycle normal or not?

We cannot answer that, and we would rather say so than pretend. 'Normal' is a clinical judgement that depends on things a quiz cannot see. What we can tell you is how common your pattern is in large published datasets, and which specific things are worth raising with a clinician. If your figures sit outside a typical range, that is a reason to ask a question, not a finding about you.

Is a 24-day cycle normal?

It is inside the range that 87% of women in one large dataset fell within, which was 23 to 35 days. The 28-day figure is not the benchmark it is treated as: only 16.3% of 1.58 million tracked users had a 28-day median cycle.

Can this tell me if I am in perimenopause?

No. That is a clinical judgement and it is one worth asking about. What this can do is describe your bleeding pattern using the same numbers researchers use, the difference between consecutive cycles and the longest gap, and tell you plainly that no digital implementation of those criteria has yet been validated.

Does this tell me when I am ovulating or whether I can conceive?

No, and cycle length is a poor guide to it. In the dataset behind our figures, even a nominal 28-day cycle showed a ten-day spread in the day of ovulation. This is not a fertility assessment and it is not a form of contraception.

What happens to what I enter?

The arithmetic runs on your device. Your dates are not sent to us, not saved, and never put in the page address. We count how many people finish and which result they get, as totals only: no dates, no answers, nothing tied to you.

Sources checked

Built from published research, with every source listed below. We checked these sources for the figures we quote: the typical ranges, the guideline targets, and the doses used in the trials we describe. We compare your answers to published general guidance for adults. We have not tested this quiz against any clinical measure.

  1. Soumpasis (2020). Real-life insights on menstrual cycles and ovulation using big data. Human Reproduction Open. doi.org/10.1093/hropen/hoaa011
  2. Bull (2019). Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles. npj Digital Medicine. doi.org/10.1038/s41746-019-0152-7
  3. Grieger (2020). Menstrual Cycle Length and Patterns in a Global Cohort of Women Using a Mobile Phone App: Retrospective Cohort Study. Journal of Medical Internet Research. doi.org/10.2196/17109
  4. Li (2020). Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data. npj Digital Medicine. doi.org/10.1038/s41746-020-0269-8
  5. Huang (2014). Modelling Menstrual Cycle Length and Variability at the Approach of Menopause by Using Hierarchical Change Point Models. Journal of the Royal Statistical Society Series C: Applied Statistics. doi.org/10.1111/rssc.12044
  6. Fraser (2011). The FIGO Recommendations on Terminologies and Definitions for Normal and Abnormal Uterine Bleeding. Seminars in Reproductive Medicine. doi.org/10.1055/s-0031-1287662
  7. Munro (2011). FIGO classification system (PALM‐COEIN) for causes of abnormal uterine bleeding in nongravid women of reproductive age. International Journal of Gynecology & Obstetrics. doi.org/10.1016/j.ijgo.2010.11.011
  8. Harlow (2012). Executive Summary of the Stages of Reproductive Aging Workshop + 10: Addressing the Unfinished Agenda of Staging Reproductive Aging. The Journal of Clinical Endocrinology & Metabolism. doi.org/10.1210/jc.2011-3362
  9. Harlow (2007). Recommendations from a multi-study evaluation of proposed criteria for Staging Reproductive Aging. Climacteric. doi.org/10.1080/13697130701258838
  10. Paramsothy (2015). Influence of race/ethnicity, body mass index, and proximity of menopause on menstrual cycle patterns in the menopausal transition. Menopause. doi.org/10.1097/GME.0000000000000293
  11. Huibregtse (2026). Considerations and practical recommendations for identifying perimenopause in longitudinal research. Psychoneuroendocrinology. doi.org/10.1016/j.psyneuen.2026.107748
  12. Wang (2020). Menstrual cycle regularity and length across the reproductive lifespan and risk of premature mortality: prospective cohort study. BMJ. doi.org/10.1136/bmj.m3464
  13. Wang (2022). Menstrual Cycle Regularity and Length Across the Reproductive Lifespan and Risk of Cardiovascular Disease. JAMA Network Open. doi.org/10.1001/jamanetworkopen.2022.38513
  14. Ding (2019). Heavy menstrual bleeding among women aged 18–50 years living in Beijing, China: prevalence, risk factors, and impact on daily life. BMC Women's Health. doi.org/10.1186/s12905-019-0726-1
  15. Schoep (2019). The impact of menstrual symptoms on everyday life: a survey among 42,879 women. American Journal of Obstetrics and Gynecology. doi.org/10.1016/j.ajog.2019.02.048
  16. de Arruda (2026). Worldwide prevalence of dysmenorrhea: a systematic review and meta-analysis across 70 countries. Pain. doi.org/10.1097/j.pain.0000000000003768
  17. Ekroos (2024). Menstrual blood loss is an independent determinant of hemoglobin and ferritin levels in premenopausal blood donors. Acta Obstetricia et Gynecologica Scandinavica. doi.org/10.1111/aogs.14890
  18. Magnay (2020). Pictorial methods to assess heavy menstrual bleeding in research and clinical practice: a systematic literature review. BMC Women's Health. doi.org/10.1186/s12905-020-0887-y
  19. Paramsothy (2014). Bleeding patterns during the menopausal transition in the multi‐ethnic Study of Women's Health Across the Nation (<scp>SWAN</scp>): a prospective cohort study. BJOG: An International Journal of Obstetrics &amp; Gynaecology. doi.org/10.1111/1471-0528.12768