Factors that may affect the cycle
Changes in the normal routine can theoretically affect fertility or the menstrual cycle. However, the effect can be very different, e.g. a single day or an entire cycle. Since every woman reacts differently to changes, it is generally important to know and observe your cycle.
Content Overview - Influencing Factors
- The circadian rhythm: sleep-wake cycle in dependence on light
- The annual time change can be a factor
- Time zones
- Shift work
- Stress resistance
- Weight, BMI and body fat percentage
- Disruptive factor: Natural influences
- Disruptive factor: Environmental influences
The circadian rhythm: sleep-wake cycle in dependence on light
Many of our physiological processes work according to a fixed rhythm, a 24-hour cycle, the so-called circadian rhythm, our "inner clock" which is synchronized with the Earth's rotation. Many hormones are influenced by this rhythm: for example, the largest amount of prolactin is produced in the hours after bed, while testosterone reaches its maximum in the morning hours. Our internal clock influences our sleep, digestive system, body temperature, heart and blood pressure. On the other hand, this rhythm is influenced by factors from our environment: meals, exercise and stress. Our internal clock is important and essential for basal body temperature.
Clinical studies show that core body temperature can vary depending on how much or how long you have slept 1. For example, if you sleep for less than four hours, you are likely to have a slightly lower temperature than if you sleep for more than seven hours. This temperature difference can be explained by the biorhythm. After a certain period of time, certain physiological processes in the body - even when you are asleep - have to start because they cannot rest for too long. After how many hours of sleep this is the case, it varies from person to person and depends on the lifestyle of the individual.
The annual time change can be a factor
In Europe, the clock is moved one hour forward (summer time) on the last Sunday in March and back one hour (winter time) on the last Sunday in October. You may have noticed that during this time you have a few temperature outliers, most likely the background is that your body has to get used to the new circumstances.
As beautiful as travelling can be, it can also often be exhausting and stressful. A direct effect can be the postponement or in extreme cases even the failure of ovulation. Travelling through time zones is often perceived as particularly unpleasant, as our internal clock no longer runs synchronously. We all know this as jet lag. If you take Daysy with you on your travels you will have noticed that during the first days you have some temperature outliers which are quite normal. Normally the rule of thumb is that you need about 1 day per time zone to get your body used to it.
Shift work is usually associated with physical stress. Normally this stress is reflected in disturbed or less sleep. A 2002 study showed that 50% of midwives who work shifts have experience of irregular cycles (2). Cyclic changes mainly affect the follicular phase, i.e. the phase before ovulation, which in most cases has led to a prolongation of the follicular phase 3. Interestingly, midwives who always worked night shifts had a relatively shorter but constant cycle (less than 25 days). Midwives who worked mixed shifts over a long period of several years had the greatest variability of cycles 3. Longer cycles or outliers are usually compensated by Daysy. It is important that you measure regularly to give Daysy as many clues as possible to calculate your fertile and infertile days.
Many studies have shown that stress in its various forms can directly influence your cycle 4. The physiology behind stress is very complex and, in many parts, not yet properly understood. What is certain is that the adrenal cortex in particular plays a major role. Stress (physical and psychological) is often reflected in a shortened luteal phase, the second phase of the cycle. Normally, the luteal phase lasts about 14 to 16 days. If it is shorter than ten days, it is called luteal phase insufficiency. What causes luteal phase insufficiency? Progesterone, which is jointly responsible for the rise in basal body temperature and for the increased blood flow to the uterus, shares a basic building block with adrenalin, which is formed in the adrenal cortex. Under stress, a lot of adrenaline is produced, and the adrenaline snatches this basic building block away. As a result, little progesterone is produced and the luteal phase is shortened. A US study found that women in stressful jobs (high demand, but little control) had more than twice the risk of a shortened luteal phase compared to women working in less stressful jobs 5.
Daily exercise due to intensive sports, to the point of exhaustion and significant physical stress, can lead to changes in the cycle, including cycles without ovulation 6.
The natural resistance to stress varies through the cycle and is lowest around ovulation and in the luteal phase. The background is that the body prepares for a possible pregnancy and the immune system (which is directly related to stress) acts relatively moderately. This is the only way to ensure that an embryo consisting of own and foreign cells (i.e. genetically foreign) is not rejected by the body 7. This circumstance also explains why women tend to get sick during the luteal phase.
Weight, BMI and body fat percentage
Your body weight has not only an influence on your general health but also directly on your cycle. With the Body Mass Index (BMI) you can estimate your body fat. The BMI is calculated by dividing the body weight (in kilograms) by the size (in meters) squared. From a medical point of view, a normal BMI is between 20-25 kg/m2. Women with a normal BMI have moderately the most constant and least cycles without ovulation. Not the body weight, but the percentage of body fat has a direct influence on your fertility and your cycle. Estrogen in particular is stored in body fat and contributes to about one third of the total estrogen balance.
- Overweight: Overweight (BMI 25-30) or obese (BMI +30) women with a high body fat percentage have relatively frequent cycles without ovulation. The reason for this is that oestrogen, which is moderately stored in excess of fat, ensures that ovulation cannot take place.
- Underweight: The opposite is true for women who are underweight (BMI <20). Due to the low body fat content, oestrogen cannot be stored, with the result that less oestrogen is available. Approximately 50% of underweight women have cycle irregularities, most of which affect menstruation. If you are suffering from severe underweight, it is very important for your general health that you seek medical advice and help under certain circumstances.
All of the factors above are events that can affect the calculation of fertile and infertile days. In our recent study, "The Performance of a Fertility Tracking Device", we systematically analyzed how Daysy deals with physiological changes in the individual menstrual cycle (e.g. age, BMI, cycle length, skipping of measurements, high vs. low average temperature, temperature steps) and what direct influence they have on the algorithm calculations.
A total of 107,020 cycles from 5,328 women were included for the study!
Disruptive factor: Natural influences
As you may already know, every woman normally ovulates once per cycle. After ovulation, the mature egg is fertile for a maximum of 18 hours. Sperm are capable of movement and fertilization in a woman's body for a maximum of five days under optimal conditions (around ovulation). Taken together, this gives a total fertile window of six days. The fertile window is the time of your cycle during which you can become pregnant. Since your cycle is subject to normal fluctuations, Daysy calculates some additional possibly fertile days in order not to miss the fertile window.
One aim of this study was to find out how many of the days calculated as infertile (green) were in the individual fertile (yellow/red) window. To ensure that ovulation was not missed, the fertile window was extended to eight days. The following cycle scenarios were included in the analysis:
|Scenario||Number of cycles||Average|
|ideale usage (> 90% measured)||540||94% measured|
|normal usage (> 75% measured)||589||87% measured|
|long cycles||279||45 days|
|short cycles||913||24 days|
|irregular cycles||309||Pre-Ov: 25 days / Post-OV: 14 days|
|regular cycles||613||Pre-Ov: 14 days / Post-OV: 14 days|
|high temperature rise between pre- and post-ovulation||770||Pre-Ov: 36.1°C / Post-OV: 36.4°C|
|low temperature rise between pre- and post-ovulation||714||Pre-Ov: 36.3°C / Post-OV: 36.5°C|
|long use of the fertility tracker||1859||2821 days|
|long use of the fertility tracker||28||65 days|
Overall, just 0.6% of the days displayed were green, although they were in the fertile window and thus should have been "red" (possibly fertile) (see graph). However, 50% of these "false green days" were five days before ovulation and thus would have had only a minimal chance of pregnancy during this period.
Disruptive factor: Environmental influences
In addition to natural factors, there are also influences that can be affected by the environment, i.e. from the outside. These factors include the number of measured days as well as the normal fluctuation range in the measurements (known as temperature outliers).
Temperature disturbances can be caused by a variety of or a combination of many factors:
- Too short or disturbed night's rest
- Getting up very early when this is atypical for you
- Shift work
- Unaccustomed alcohol consumption
- Stress, psychological strain, excitement
- Change of environment (travel, vacations, significant change of climate)
The second goal of the study was to find out what influence temperature fluctuations have on the calculation of fertile and infertile days. To get an answer, Daysy was "fed" with predetermined temperature data (see figure a-d).
The analysis showed that temperature fluctuations have a direct influence on Daysy's calculations. When these fluctuations between measured days are small (see figure a), the algorithm calculates more green (infertile) days (56%). When the fluctuations are very high (see figure d), it calculates less green days (43%) and more yellow days (17%). In this way, you can see how Daysy adapts to individual circumstances. On average, Daysy users have temperature fluctuations as shown in figure c.
Number of un-measured days
Another factor that plays a role in calculating the number of fertile and infertile days is the amount of measured days. Of course, there are always days when you skip the measurement. That’s a part of life. This is why it was important for us to study the influence of the number of measured days has on the calculation of fertile and infertile days. For this purpose, the data sets were divided into groups, each of which had measured between 0-20%, 20-40%, 40-60% or 80-100% of all days.
|Measured (%)||Analysed Cycles||Displayed as green||Displayed as red||Displayed as yellow|
Of the 53.1% of users (47,800 cycles in total) who used Daysy for 80-100% of their cycle, an average of 41% fertile (red) days and 42% infertile (green) days were recorded. The analysis shows that Daysy adapts accordingly to individual situations.
Daysy is an intelligent fertility tracker that lets you get to know your very own menstrual cycle.
1) Hibi, M. et al. Effect of shortened sleep on energy expenditure, core body temperature and appetite: a human randomised crossover trial. Sci. Rep. 7, 39640 (2017).
2) Labyak, S., Lava, S., Turek, F., and Zee, P. Effects of shiftwork on sleep and menstrual function in nurses. Healthcare for Women International , 23(6–7):703–714, 2002.
3) Attarchi, M., Darkhi, H., Khodarahmian, M., Dolati, M., Kashanian, M., Ghaffari, M., Mirzamohammadi, E., and Mohammadi, S. Characteristics of menstrual cycle in shift workers. Global Journal of Health Sciences , 5(3):163–172, May 2013.
4) Ferin, M. Clinical review 105: Stress and the reproductive cycle. Journal of Clinical Endocrinology and Metabolism , 84(6):1768–1774, Jun 1999.
5) Hatch, M. C., Figa-Talamanca, I., and Salerno, S. Work stress and menstrual patterns among American and Italian nurses. Scandinavian Journal of Work, Environment and Health , 25(2):144–150, Apr 1999.
6) Reilly, T. The menstrual cycle and human performance: An overview. Biological Rhythm Research , 31(1):2000.
7) Pehlivanoglu, B., Balkanci, Z. D., Ridvanagaoglu, A. Y., Durmazlar, N., Ozturk, G., Erbas, D., and Okur, H. Impact of stress, gender and menstrual cycle on immune system: Possible role of nitric oxide. Archives of Physiology and Biochemistry , 109(4):383–387, Oct 2001.
Authors: Niels van de Roemer, Andrea de Groot