Busting the myth of the 28-day menstrual cycle: Video lay summary

Medicine and Health Sciences: Obstetrics and Gynaecology

Most of us are taught at school and in medical textbooks that a woman’s menstrual cycle is normally around 28 days, and ovulation, which is when an egg is released from an ovary, happens on about day 14 of the cycle. Actually this general ‘rule of thumb’ is too simple. The truth is that a woman’s cycle length can vary a lot from cycle to cycle and between women. Cycle patterns may be different according to someone’s age, weight, and other things. With the rising popularity of mobile apps for personal health monitoring, including cycle trackers and fertility monitors, women can now easily get to know their own cycles. They can identify when they are fertile, either to avoid or plan a pregnancy. If a woman has unprotected sex during the ‘fertile window’ – i.e., the 6 days up to and including ovulation day as shown by the app – then there is a good chance of becoming pregnant.

Researchers at the fertility monitoring app company NaturalCycles, together with academics at University College London and Karolinska Institute in Sweden, published this landmark study on the menstrual cycle. Dr. Jonathan Bull, a senior research scientist at NaturalCycles and the lead author of the study, and his colleagues, looked at a huge data set collected from users of the app. They set out to measure the length and characteristics of over 600000 menstrual cycles from nearly 125000 women using the app, to debunk the idea of the ‘standard’ 28-day cycle once and for all!

The NaturalCycles fertility monitoring app works by users measuring their body temperature and entering it into the app. They also record when they are menstruating and have the option to enter hormone test results. At the end of each cycle, a complicated calculation or ‘algorithm’ uses the temperature measurements to calculate on what days the woman was most likely fertile in that cycle. It works by detecting the clear rise in body temperature on ovulation day when the egg is released from the ovary. The algorithm also predicts what days she will likely be fertile in the next cycle, taking into consideration a margin for error. As a woman records more cycles, the algorithm becomes better and better at predicting the fertile window. If temperatures are recorded on or over 50% of days, the predictions are much better. Less frequent measurements may mean that ovulation cannot be detected. In some cycles ovulation may not happen at all and this can also be detected.

A downside of the study is that all data was self-reported by users and not collected by medical professionals, so its quality or accuracy may vary. On the other hand, the huge amount of data meant that Jonathan and his team could filter out poor quality data and still have a very large group of people’s data or ‘sample’ to work with. Women included in the sample were aged 18 to 45, of all shapes and sizes with a Body Mass Index (BMI) between 15 (severely underweight) and 50 (very severely or morbidly obese) and had recorded at least 6 cycles each. Most of them were residents of the UK, USA, and Sweden. They took data from ‘high quality’ cycles where lots of measurements were recorded and ovulation was accurately detected. This made sure that the results were scientifically sound and reliable.

The study found that only 13% of cycles fitted the standard picture: 28 days long. The average length was actually 29.3 days with ovulation on day 17. Two thirds of the women had cycles of 25 to 30 days. A third of the women had so-called ‘abnormal cycles’, so either really short or long. There was a really strong age effect, where cycle length decreased by a day every 5-6 years of age, from 25 to 45. It was caused by ovulation happening earlier and earlier. They also saw a strong effect of BMI. A woman’s cycle lengths were more variable if she had a BMI below 18.5 (underweight) or above 35 (obese). These results had been shown before in other studies but this was the largest study to date.

This work was important for several reasons. With more and more women using cycle tracking apps, it is vital to shed light on how varied their cycles really are. Some standard calendar-based tracking methods may miss or inaccurately predict the fertile window by not properly taking into account the variation of a woman’s cycles. Temperature-based methods reliably find the fertile window with better outcomes for women and their partners, in terms of whether they want to prevent or plan a pregnancy. With more couples delaying starting a family until their mid- to late thirties, when female fertility is already going down, fertility monitoring apps can help to increase their chances of conceiving.

The study demonstrated that high quality science can be done with self-reported data gathered remotely via an app. A much larger population can be examined than in a standard medical trial. It was a milestone in the field of women’s health research, which sadly has traditionally been underfunded but is experiencing an explosion of interest and investment now thankfully and should lead to further research.

THE DETAIL

Title of lay summary Busting the myth of the 28-day menstrual cycle: Video lay summary
Lay Summary Author(s)

Dr Jonathan Bull

Dr Anthony Mark Harrison

Dr Anja Harrison

Authors Affiliation(s) / participating organisation()s
Vetting Professional Dr Jonathan Bull
Science Area Subject
Key Search Words

Menstrual cycle

Women's health

Pregnancy

Smartphone App

Other relevant Collaborative Library lay summary links
What is the licence for your lay summary? Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
If a pre-print or post-print, please provide a direct weblink or Digital Object Identifier(s) (DOI)):
Provide the full weblink DOI of the published scientific article: https://doi.org/10.1038/s41746-019-0152-7
Are there any other open-access data weblink(s) that might be helpful (e.g., for relevant data repositories see fairsharing.org):
Has this work been applied in ‘real-life’ settings (e.g., local service evaluation projects)? If so, add any relevant weblink(s) here:
Title of the original peer-reviewed published article:
Issue (if applicable): 83
Year of publication: 2019
Authors:

Jonathan R. Bull

Simon P. Rowland

Elina Berglund Scherwitzl

Raoul Scherwitzl

Kristina Gemzell Danielsson

Joyce Harper

Contributors and funders:

no conflict of interest reported

Original Article language: English
Article Type: Retrospective cohort study
What licence permission does the original e-print have? For more information on this please see our permissions video): Attribution 4.0 International (CC BY 4.0)

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