Many physicians and other healthcare professionals are often asked questions on interfering factors for conception by couples with a desire for children. Such possible disturbances include, for example, the very common minor diseases, stress and also sexual intercourse during the suspected implantation period. Non-scientifically based statements about disturbances in conception cycles, as found in many layman publications and on the internet, can strongly unsettle couples with a desire for children and force them into corset of rules of conduct. Therefore, a systematic scientific evaluation of the impact of disturbances on conception is urgently needed.
Methods
A search for possible disturbances in natural conception cycles together with up to three of the respective pre-cycles in a large cycle database from users of the symptothermal method of natural family planning in Germany was performed. Disturbances were qualified by scientific panel decision and analysed statistically with their effects on the chances of spontaneous conception. Mixed logistical regression models and survival time analyses were used.
Results
A total of 237 women with a total of 747 cycles could be included in the analysis. In 61% of all 237 conception cycles, disturbances occurred. The statistical analysis shows that disturbances in natural conception cycles unexpectedly increase the likelihood of pregnancy by an overall factor of 1.32 (95% CI 1.04-1.70). Sexual intercourse in the window of implantation does not decrease the chances of conception. Relaxation states at the time of ovulation and/or during the implantation period have no representable effect and do not increase the chance of pregnancy.
Conclusions
Couples trying to conceive should at least be informed that disturbances in conception cycles, such as minor diseases, stress or sexual intercourse during the implantation period do not interfere with conception. Relaxation has no effect in favour of success. This takes away the guilty feeling of couples, fearing that they possibly did something wrong in cycles without the desired pregnancy.
PMID 32140804 32140804 DOI 10.1007/s00404-020-05464-y 10.1007/s00404-020-05464-y Gnoth et al. 2020, Gnoth 2020
Cite this article
Gnoth, C., Keil, A. K., Schiffner, J., Heil, S., Mallmann, P., Freundl, G., & Strowitzki, T. (2020). The impact of disturbances in natural conception cycles.. Archives of gynecology and obstetrics, 301(4), 1069-1080. https://doi.org/10.1007/s00404-020-05464-y
Gnoth C, Keil AK, Schiffner J, Heil S, Mallmann P, Freundl G, Strowitzki T. The impact of disturbances in natural conception cycles.. Archives of gynecology and obstetrics. 2020;301(4):1069-1080. doi:10.1007/s00404-020-05464-y
Keywords
Disturbances of conception, Implantation, Minor diseases, Natural family planning, Probability of pregnancy, Spontaneous conception
Malliou-Becher MN et al., 2026·Human reproduction (Oxford, England)
What are the variations in ovulation time and menstrual cycle characteristics among and within various individuals over the course of 12 menstrual cycles? There are considerable variations in both cycle length and ovulation time, with pronounced intra-individual variability over a 12-cycle observation period. Although it is commonly believed that healthy women have regular cycles with a predictable mid-cycle ovulation, more recent research shows a significant variation in cycle length and ovulation time. Previous studies have focused only on cycle length, often excluding cycles outside the 25-35-day range, thus limiting the understanding of natural variation; they have also lacked precise ovulation diagnostics or included small sample sizes, making it difficult to capture the full scope of cycle and ovulation variability. Similarly, a recent big data study, while valuable, was limited by a self-selected group and the absence of accurate ovulation diagnostics, reducing its generalizability. STUDY DESIGN, SIZE, This study was designed as a prospective long-term observational study, which involved collecting data from 1923 women with a total of 43 999 menstrual cycles from January 1985 to July 2019. After fulfilling the inclusion criteria, the main group consisted of 1051 women, all of whom contributed data for 12 cycles (12 612 cycles), including 420 conception cycles. PARTICIPANTS/MATERIALS, SETTING, Participants in the study were between 18 and 44 years of age at study entry and did not take any reproductive hormones. Women who were postpartum, breastfeeding, amenorrheic, or within a 3-month period after stopping hormonal contraception were excluded. Participants agreed to keep cycle records according to the symptothermal method, 'Sensiplan'. Ovulation time was determined using an evidence-based algorithm based on evaluating cervical mucus patterns and basal body temperature shifts, with ovulation time defined as the day before the temperature rise. Data analysis was descriptive, using absolute and relative frequencies, standard deviation, percentiles, and ranges. Age dependency was assessed using unpaired sample t-tests and one-way ANOVA. Linear regression was used to assess long-term trends. MAIN In 62.4% of women, cycle lengths varied by 1 week or more within 12 cycles. Accordingly, the time of ovulation varied by 1 week or more within 12 cycles in 54.8% of women, with 96.5% experiencing fluctuations of 4 days or more over the 12 months. The median spontaneous cycle length was 28 days, with a mean of 29.66 days (SD = 7.55). Only 52.7% of women consistently had cycle lengths between 23 and 35 days across all 12 cycles. Ovulation occurred most frequently between Days 12 and 16, with almost half of conceptions (45.7%) occurring after Day 16. A one-way analysis of variance revealed a significant reduction in mean cycle length with increasing age (P < 0.001), showing the shortest median cycle length of 27 days being in women aged 40-44 years. Age also impacted ovulation time, with women aged 35-39 years showing more stable ovulation patterns compared to younger women. Over the 34-year study period, average cycle length increased slightly but significantly (β = 0.0161, P = 0.0306), corresponding to approximately half a day. Intra-individual variability also showed a slight, but non-significant, upward trend (β = 0.0262, P = 0.2173). LIMITATIONS, Comorbidities such as hyperprolactinemia, obesity, and PCOS were not systematically excluded. However, by including only women with at least 12 cycles, the study largely avoided severe hormonal disorders. This study highlights the considerable individual variation of ovulation time and cycle length over 12 menstrual cycles. These findings contribute to a better understanding of fertility awareness, and highlight the implications for family planning and reproductive health management. STUDY FUNDING/COMPETING INTEREST(S): The authors declare no conflicts of interest. No funding was provided. N/A.
What is the relative length variance of the luteal phase compared to the follicular phase within healthy, non-smoking, normal-weight, proven normally ovulatory, premenopausal women with normal-length menstrual cycles? Prospective 1-year data from 53 premenopausal women with two proven normal-length (21-36 days) and normally ovulatory (≥10 days luteal) menstrual cycles upon enrollment showed that, despite 29% of all cycles having incident ovulatory disturbances, within-woman follicular phase length variances were significantly greater than luteal phase length variances. Many studies report menstrual cycle variability, yet few describe variability in follicular and luteal phase lengths. Luteal lengths are assumed 'fixed' at 13-14 days. Most studies have described follicular and luteal phase variability between-women. STUDY DESIGN, SIZE, Duration: This study was a prospective, 1-year, observational cohort study of relative follicular and luteal phase variability both between and within community-dwelling women with two documented normal-length (21-36 days) and normally ovulatory (≥10 days luteal phase) menstrual cycles prior to enrollment. Eighty-one women enrolled in the study and 66 women completed the 1-year study. This study analyzed data from 53 women with complete data for ≥8 cycles (mean 13). PARTICIPANTS/MATERIALS, SETTING, Participants were healthy, non-smoking, of normal BMI, ages 21-41 with two documented normal-length (21-36 days) and normally ovulatory (≥10 days luteal phase) menstrual cycles prior to enrollment. Participants recorded first morning temperature, exercise durations, and menstrual cycle/life experiences daily in the Menstrual Cycle Diary. We analyzed 694 cycles utilizing a twice-validated least-squares Quantitative Basal Temperature method to determine follicular and luteal phase lengths. Statistical analysis compared relative follicular and luteal phase variance in ovulatory cycles both between-women and within-woman. Normal-length cycles with short luteal phases or anovulation were considered to have subclinical ovulatory disturbances (SOD). Main The 1-year overall 53-woman, 676 ovulatory cycle variances for menstrual cycle, follicular, and luteal phase lengths were 10.3, 11.2, and 4.3 days, respectively. Median variances within-woman for cycle, follicular, and luteal lengths were 3.1, 5.2, and 3.0 days, respectively. Menstrual cycles were largely of normal lengths (98%) with an important prevalence of Sod: 55% of women experienced >1 short luteal phase (<10 days) and 17% experienced at least one anovulatory cycle. Within-woman follicular phase length variances were greater than luteal phase length variances (P < 0.001). However, follicular (P = 0.008) and luteal phase length (P = 0.001) variances, without differences in cycle lengths, were greater in women experiencing any anovulatory cycles (n = 8) than in women with entirely normally ovulatory cycles (n = 6). LIMITATIONS, Limitations of this study include the relatively small cohort, that most women were White, initially had a normal BMI, and the original cohort required two normal-length and normally ovulatory menstrual cycles before enrollment. Thus, this cohort's data underestimated population menstrual cycle phase variances and the prevalence of SOD. Our results reinforce previous findings that the follicular phase is more variable than the luteal phase in premenopausal women with normal-length and ovulatory menstrual cycles. However, our study adds to the growing body of evidence that the luteal phase is not predictably 13-14 days long. STUDY FUNDING/COMPETING INTEREST(S): This medical education project of the University of British Columbia was funded by donations to the Centre for Menstrual Cycle and Ovulation Research. The authors do not have any conflicts of interest to disclose. N/A.
Ecochard R et al., 2024·Fertil Steril·
Open Access
To study whether the menstrual cycle has a circaseptan (7 days) rhythm and whether it is associated with the lunar cycle (also defined as the synodic month, it is the cycle of the phases of the Moon as seen from Earth, averaging 29.5 days in length).
Cross-sectional study. A total of 35,940 European and North American women aged 18-40 years. Exposure: Data were collected in real-life conditions. No intervention was performed. The onset of menstruation was assessed in prospectively measured menstrual cycles (311,064 cycles) over 3 full years (2019-2021). Associations were calculated between the onset of menstruation and the day of the week, and between the onset of menstruation and the lunar phase. In this large data set, a circaseptan (7-day) rhythmicity of menstruation was observed, with a peak (acrophase) of menstrual onset on Thursdays and Fridays. This circaseptan rhythm was observed in every age group, in every phase of the lunar cycle, and in all seasons. This feature was most pronounced for cycle durations between 27 and 29 days. In winter, the circaseptan rhythm was found in cycles of 27-29 days, but not in other cycle lengths. A circalunar rhythm was also statistically significant, but not as clearly defined as the circaseptan rhythm. The peak (acrophase) of the circalunar rhythm of menstrual onset varied according to the season. In addition, there was a small but statistically significant interaction between the circaseptan rhythm and the lunar cycle. Although relatively small in amplitude, the weekly rhythm of menstruation was statistically significant. Menstruation occurs more often on Thursdays and Fridays than on other days of the week. This is particularly true for women whose cycles last between 27 and 29 days. Circalunar rhythmicity was also statistically significant. However, it is less pronounced than the weekly rhythm.
Bouchard TP et al., 2022·J Womens Health (Larchmt)·
Some studies have suggested minor changes in the menstrual cycle after COVID-19 vaccination, but more detailed analyses of the menstrual cycle are needed to evaluate more specific changes in the menstrual cycle that are not affected by survey-based recall bias. Using a pretest-post-test quasi-experimental evaluation of menstrual cycle parameters before and after COVID-19 vaccination, we conducted an anonymous online survey of two groups of North American women who prospectively monitor their menstrual cycle parameters daily including bleeding patterns, urinary hormone levels using the ClearBlue Fertility Monitor, or cervical mucus observations. The primary outcome measures were cycle length, length of menses, menstrual volume, estimated day of ovulation (EDO), luteal phase length, and signs of ovulation. Perceived (subjective) menstrual cycle changes and stressors were also evaluated in this study as secondary outcome measures. Of the 279 women who initiated the survey, 76 met the inclusion criteria and provided 588 cycles for analysis (227 pre-vaccine cycles, 145 vaccine cycles, 216 post-vaccine cycles). Although 22% of women subjectively identified changes in their menstrual cycle, there were no significant differences in menstrual cycle parameters (cycle length, length of menses, EOD, and luteal phase length) between the pre-vaccine, vaccine, and post-vaccine cycles. COVID-19 vaccines were not associated with significant changes in menstrual cycle parameters. Perceived changes by an individual woman must be compared with statistical changes to avoid confirmation bias.