Frank-Herrmann, P., Freundl, G., Stanford, J. B., & Mikolajczyk, R. T. (2004). More than one fertile ovulation per cycle? Fertility and Sterility, 81(3), 728-729. https://doi.org/10.1016/j.fertnstert.2003.11.016
Frank-Herrmann P, Freundl G, Stanford JB, Mikolajczyk RT. More than one fertile ovulation per cycle? Fertil Steril. 2004;81(3):728-729. doi:10.1016/j.fertnstert.2003.11.016
Frank-Herrmann, P., et al. "More than one fertile ovulation per cycle?" Fertility and Sterility, vol. 81, no. 3, 2004, pp. 728-729.
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Department of Gynaecological Endocrinology and Fertility Disorders, University of Heidelberg, Vossstrasse 9, 69115 Heidelberg, Germany. petra.frank-herrmann@med.uni-heidelberg.de038t36y30
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RRM Academy Synopsis
Charting data suggest a second ovulation has no clinical role
A 2004 letter argues the evidence suggests a second ovulation plays no clinical role in natural cycles. The authors, natural family planning researchers, cite charting studies. One covered more than 30,000 cycles in Duesseldorf, with no pregnancy from luteal phase intercourse. The letter reports no new data.
Key Findings
Across more than 30,000 cycles of modern natural family planning users in Duesseldorf, no pregnancy followed intercourse in the luteal phase, identified by elevated basal body temperature and cervical mucus.
Effectiveness studies of the symptothermal method found the probability of pregnancy far less than 0.1% per cycle when intercourse fell outside the fertile window.
In 1,681 cycles with the Creighton Model System, a mucus-only system, the authors found no evidence of conception beyond 6 days before to 4 days after the mucus peak day.
For anecdotal late pregnancies with the calendar method, the authors call delayed ovulation, which they describe as relatively frequent, a much more plausible explanation than an additional ovulation.
Interpretation
The piece is a letter to the editor answering Baerwald and colleagues, who described extra waves of follicle growth in normal ovulatory cycles. Baerwald and colleagues observed no additional ovulation. The letter summarizes published charting studies of women who use natural family planning. The authors say the bulk of evidence suggests a second ovulation plays no clinical role. The letter adds no new measurements. The authors fault the original paper for overlooking existing evidence.
RRM Context
Restorative reproductive medicine reads the cycle through charted signs such as cervical mucus and basal body temperature. The letter draws on two charting approaches, the symptothermal method and the Creighton Model System. Each finds the fertile window from observed signs. Daily records show when ovulation happened in a cycle, which is the timing question this debate concerns.
Our editorial summary of this paper, not the article's abstract.
Abstract
Baerwald et al. (1) proposed a new model for ovarian follicular development in the human menstrual cycle. They showed additional waves of follicular development in the follicular and luteal phase of normal ovulatory cycles. Although no additional ovulation was observed, the authors speculated that the anovulatory follicles from the additional waves of follicular development may be able to ovulate in the presence of an additional LH surge.
Minjeur M et al., 2026·Journal of Restorative Reproductive Medicine·Free to read
Infertility is a clinical condition that is recognized by the symptom of an inability to conceive through sexual intercourse or to sustain a pregnancy, with that symptom indicating underlying male and/or female pathology.
This definition of infertility was developed through a structured, consensus-informed process involving broad stakeholder engagement. Initially, multiple definitions currently used by various medical professional organizations were reviewed, and a definition document was drafted and submitted to the Board of Directors of the International Institute for Restorative Reproductive Medicine (IIRRM). All IIRRM members were invited to provide feedback on the draft. Approximately 2,500 individuals and 44 organizations from 92 countries were then invited to review the proposed document, representing clinical, scientific, patient, policy, and advocacy perspectives. Submitted comments were reviewed thematically, with suggested revisions evaluated for clarity, clinical relevance, inclusiveness, and consistency with contemporary restorative reproductive medicine. Following this review, 3 substantive changes, 18 minor changes, and 15 citation corrections were incorporated into the final draft which resulted in a revised definition intended to better reflect the medical, social, and practical realities of modern infertility evaluation and care. Final approval by the IIRRM Board of Directors was unanimous.
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.
Kahn LG et al., 2026·JAMA Network Open·Free full text on PubMed Central
Increasing numbers of children are conceived using infertility treatment; concerns remain about potential effects on child neurodevelopment. To evaluate whether infertility treatment is associated with child neurodevelopment and whether such an association may be attributable to underlying subfecundity. DESIGN, SETTING, This cohort study was conducted among mother-child dyads in the National Institutes of Health Environmental Influences on Child Health Outcomes (ECHO) Cohort, with infants conceived between 1998 and 2022. Associations of subfecundity and infertility treatment with neurodevelopmental outcomes were assessed among children ages 2 to 10 years. Data were analyzed from May 14, 2025, to March 31, 2026. Subfecundity was defined as prior consultation for, treatment of, or diagnosis of infertility for either partner; at least 2 prior miscarriages; or ever having had unprotected heterosexual intercourse for 12 months without conceiving. Infertility treatment was categorized as in vitro fertilization (IVF) or non-IVF treatment. Harmonized caregiver responses to the Strengths and Difficulties Questionnaire and the Child Behavior Checklist yielded continuous raw scores for externalizing and internalizing problems. The total raw Social Responsiveness Scale (SRS) score quantified autism-like symptoms. Caregivers reported physician diagnosis of autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). Among 15 382 mother-infant dyads, there were 14 191 unique maternal participants (mean [SD] age at delivery, 30.9 [5.33] years; 8780 parous participants [57.1%]). ASD and ADHD were diagnosed in 876 offspring (7.6%) and 819 offspring (7.1%), respectively. In generalized linear models, subfecundity was associated with higher externalizing problem and SRS scores among all pregnancies (externalizing problems: b = 0.47 [95% CI, 0.14-0.81]; SRS score: b = 1.08 [95% CI, 0.01-2.14]) and when restricted to natural conceptions (externalizing problems: b = 0.45 [95% CI, 0.07-0.83]; SRS score: b = 1.12 [95% CI, -0.09 to 2.34]). Offspring of parents with subfecundity had higher odds of ASD (overall: odds ratio [OR], 1.27 [95% CI, 1.03-1.57]; natural conceptions: OR, 1.31 [95% CI, 1.04-1.64]). Children conceived via non-IVF treatment had higher odds of ADHD compared with those conceived via natural conception with subfecundity (OR, 1.77 [95% CI, 1.16-2.68]) or without subfecundity (OR, 1.54 [95% CI, 1.05-2.25]). There were no significant associations for IVF treatment. In this large US cohort study, subfecundity was associated with elevated scores for caregiver-reported symptoms of behavioral problems and higher odds of ASD diagnosis, independent of infertility treatment. Non-IVF treatment was associated with ADHD, warranting further research into specific indications for treatment that may increase risk of offspring neurodevelopmental problems.
Stanford JB et al., 2026·Frontiers in Reproductive Health·Free full text on PubMed Central
Background The total fertility rate (TFR) in most developed countries has been declining for decades. In the United States (U.S.), the total fertility rate has remained below replacement level since 2007. Subfertility affects at least 15% of women or couples over their reproductive lifespan and contributes to reduced TFR. Restorative reproductive medicine (RRM) is a medically based approach to subfertility care that can be delivered in primary care settings to increase live birth rates. Objective To estimate the theoretical impact of use of RRM among subfertile couples in the United States. Methods We conducted a simulation study. Model inputs included the number of women of reproductive age in the United States by 5-year age groups; current age-specific and total fertility rates; the proportion of women in each age group with subfertility; estimated spontaneous live birth rates among women with subfertility; and age-specific crude live birth rates with RRM treatment. We evaluated fifteen scenarios including sensitivity analyses: two different varying assumptions for spontaneous conception (25% vs. 50%), two levels of RRM utilization among subfertile women (20% vs. 50%), three different estimates of the number of subfertile women who would be potentially eligible for RRM treatment, and 4 different levels of effectiveness (live birth) from RRM treatment. Results The baseline TFR in the United States was 1.77 during 2015-2019, and 13.5% of women ages 20-44 were estimated to have subfertility. In a conservative scenario (50% spontaneous births; 20% RRM utilization; married women trying to conceive for at least 12 months, 20.7% RRM live births), the TFR increased to 1.79, representing a 1.0% relative increase (absolute +0.02). In an optimistic scenario (25% spontaneous births; 50% RRM utilization; all subfertile women), the TFR increased to 2.02, a 14.5% relative increase (absolute +0.26), approaching replacement-level fertility. Conclusion Simulation results suggest that expanding access to RRM within primary care settings could meaningfully increase the U.S. TFR, by reducing unresolved subfertility. Realizing this potential would require policy and health system changes to address workforce capacity, insurance coverage, and equitable access. These findings underscore the potential contribution of non-IVF fertility care pathways in addressing population-level fertility decline.
Fehring RJ et al., 2006·J Obstet Gynecol Neonatal Nurs·Free to read
To determine variability in the phases of the menstrual cycle among healthy, regularly cycling women.
A prospective descriptive study of a new data set with biological markers to estimate parameters of the menstrual cycles. One hundred forty one healthy women (mean age 29 years) who monitored 3 to 13 menstrual cycles with an electronic fertility monitor and produced 1,060 usable cycles of data. Outcomes: Variability in the length of the menstrual cycle and of the follicular, fertile, and luteal phases, and menses. The estimated day of ovulation and end of the fertile phase was the peak fertility reading on the monitor (i.e., the urinary luteinizing hormone surge). Mean total length was 28.9 days (SD = 3.4) with 95% of the cycles between 22 and 36 days. Intracycle variability of greater than 7 days was observed in 42.5% of the women. Ninety-five percent of the cycles had all 6 days of fertile phase between days 4 and 23, but only 25% of participants had all days of the fertile phase between days 10 and 17. Among regularly cycling women, there is considerable normal variability in the phases of the menstrual cycle. The follicular phase contributes most to this variability.
Ecochard R et al., 2000·Human reproduction (Oxford, England)
This study was undertaken to establish whether ovulation in humans alternates consistently from right to left ovary in successive cycles and whether the site of ovulation affects the next cycle length or the hormonal profiles. A total of 199 cycles in 80 normally fertile women were studied. The volunteers were monitored with ultrasonography to determine the day and side of ovulation and to calculate follicular and luteal phase lengths. Urinary hormone concentrations were also assayed. Right-sided ovulations occurred in 104 of the 199 cycles (52.3%; not significantly different from 50%). Alternate ovulations occurred in 61 of the 119 pairs of succeeding cycles (51.3%, not significant). The follicular phase length in contralateral ovulation (14.59 +/- 0.33 days; mean +/- SEM) did not differ significantly from that of ipsilateral ovulation (14.59 +/- 0. 37 days). There were also no significant differences in urinary concentrations of oestrone-3-glucuronide, pregnanediol-3alpha glucuronide, follicle stimulating hormone, and luteinizing hormone between ipsilateral and contralateral ovulation in either early follicular, peri-ovulatory or luteal phase of the cycle. It is concluded that in normally fertile women, the cycle length and the hormonal profile are independent of the, most probably random, site of ovulation.
To describe the LH surge variants in ovulating women and analyze their relationship with the day of ovulation and other hormone levels. Secondary analysis of a prospective cohort observational study. Eight natural family planning clinics. Normally fertile women (n = 107) over 283 cycles. INTERVENTION(S): Women collected daily first morning urine, charted basal body temperature and cervical mucus discharge, and underwent serial ovarian ultrasound. MAIN OUTCOME MEASURE(S): Urinary LH, FSH, estrone-3-glucuronide (E3G), pregnanediol-3α-glucuronide (PDG), and day of ovulation by ultrasound (US-DO). RESULT(S): Individual LH surges were extremely variable in configuration, amplitude, and duration. The study also showed that LH surges marked by several peaks were associated with statistically significant smaller follicle sizes before rupture and lower LH level on the day of ovulation. LH surges lasting >3 days after ovulation were associated with a lower E3G before ovulation, a smaller corpus luteum 2 days after ovulation, and a lower PDG value during the first 4 days after ovulation. CONCLUSION(S): In clinical practice, LH profiles should be compared with the range of profiles observed in normally fertile cycles, not with the mean profile.
Sohda S et al., 2017·J Med Internet Res·Free full text on PubMed Central
There are many mobile phone apps aimed at helping women map their ovulation and menstrual cycles and facilitating successful conception (or avoiding pregnancy). These apps usually ask users to input various biological features and have accumulated the menstrual cycle data of a vast number of women. The purpose of our study was to clarify how the data obtained from a self-tracking health app for female mobile phone users can be used to improve the accuracy of prediction of the date of next ovulation. Using the data of 7043 women who had reliable menstrual and ovulation records out of 8,000,000 users of a mobile phone app of a health care service, we analyzed the relationship between the menstrual cycle length, follicular phase length, and luteal phase length. Then we fitted a linear function to the relationship between the length of the menstrual cycle and timing of ovulation and compared it with the existing calendar-based methods. The correlation between the length of the menstrual cycle and the length of the follicular phase was stronger than the correlation between the length of the menstrual cycle and the length of the luteal phase, and there was a positive correlation between the lengths of past and future menstrual cycles. A strong positive correlation was also found between the mean length of past cycles and the length of the follicular phase. The correlation between the mean cycle length and the luteal phase length was also statistically significant. In most of the subjects, our method (ie, the calendar-based method based on the optimized function) outperformed the Ogino method of predicting the next ovulation date. Our method also outperformed the ovulation date prediction method that assumes the middle day of a mean menstrual cycle as the date of the next ovulation. The large number of subjects allowed us to capture the relationships between the lengths of the menstrual cycle, follicular phase, and luteal phase in more detail than previous studies. We then demonstrated how the present calendar methods could be improved by the better grouping of women. This study suggested that even without integrating various biological metrics, the dataset collected by a self-tracking app can be used to develop formulas that predict the ovulation day when the data are aggregated. Because the method that we developed requires data only on the first day of menstruation, it would be the best option for couples during the early stages of their attempt to have a baby or for those who want to avoid the cost associated with other methods. Moreover, the result will be the baseline for more advanced methods that integrate other biological metrics.
Joseph B Stanford, Petra Frank-Herrmann, Günter Freundl
Joe Stanford, Joey Stanford, J Stanford, P Frank-Herrmann, G Freundl
PMID 15037440 15037440 DOI 10.1016/j.fertnstert.2003.11.016 10.1016/j.fertnstert.2003.11.016 Frank-Herrmann et al. 2004, Frank-Herrmann 2004
Cite this article
Frank-Herrmann, P., Freundl, G., Stanford, J. B., & Mikolajczyk, R. T. (2004). More than one fertile ovulation per cycle? Fertility and Sterility, 81(3), 728-729. https://doi.org/10.1016/j.fertnstert.2003.11.016
Frank-Herrmann P, Freundl G, Stanford JB, Mikolajczyk RT. More than one fertile ovulation per cycle? Fertil Steril. 2004;81(3):728-729. doi:10.1016/j.fertnstert.2003.11.016
Frank-Herrmann, P., et al. "More than one fertile ovulation per cycle?" Fertility and Sterility, vol. 81, no. 3, 2004, pp. 728-729.
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