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.
It is commonplace for gynecologists to refer to "midcycle" ovulation of women. This concept has often led to the routine diagnosis of ovulatory status on day 14 of what is expected to be a 28-day menstrual cycle. For example, the postcoital test in an infertile patient, or intercourse to achieve pregnancy in a normally fertile patient, is often timed around day 14 under the assumption that ovulation is occurring then. Advocates of natural family planning (NFP) have criticized the concept of "midcycle" ovulation, because their clinical experience suggests that the natural irregularity of menstrual-cycle length militates against ovulation's occurring with any great frequency on day 14.
This report analyzes the relationship of day 14 and the actual midcycle of the menstrual cycle to each other and to indirect hormonal parameters that more directly estimate the time of ovulation.
The role of minerals in female fertility, particularly in relation to the menstrual cycle, presents a complex area of study that underscores the interplay between nutrition and reproductive health. This narrative review aims to elucidate the impacts of minerals on key aspects of the reproductive system: hormonal regulation, ovarian function and ovulation, endometrial health, and oxidative stress. Despite the attention given to specific micronutrients in relation to reproductive disorders, there is a noticeable absence of a comprehensive review focusing on the impact of minerals throughout the menstrual cycle on female fertility. This narrative review aims to address this gap by examining the influence of minerals on reproductive health. Each mineral's contribution is explored in detail to provide a clearer picture of its importance in supporting female fertility. This comprehensive analysis not only enhances our knowledge of reproductive health but also offers clinicians valuable insights into potential therapeutic strategies and the recommended intake of minerals to promote female reproductive well-being, considering the menstrual cycle. This review stands as the first to offer such a detailed examination of minerals in the context of the menstrual cycle, aiming to elevate the understanding of their critical role in female fertility and reproductive health.
Goodale BM et al., 2019·J Med Internet Res·
Open Access
Previous research examining physiological changes across the menstrual cycle has considered biological responses to shifting hormones in isolation. Clinical studies, for example, have shown that women's nightly basal body temperature increases from 0.28 to 0.56 ˚C following postovulation progesterone production. Women's resting pulse rate, respiratory rate, and heart rate variability (HRV) are similarly elevated in the luteal phase, whereas skin perfusion decreases significantly following the fertile window's closing. Past research probed only 1 or 2 of these physiological features in a given study, requiring participants to come to a laboratory or hospital clinic multiple times throughout their cycle. Although initially designed for recreational purposes, wearable technology could enable more ambulatory studies of physiological changes across the menstrual cycle. Early research suggests that wearables can detect phase-based shifts in pulse rate and wrist skin temperature (WST). To date, previous work has studied these features separately, with the ability of wearables to accurately pinpoint the fertile window using multiple physiological parameters simultaneously yet unknown. In this study, we probed what phase-based differences a wearable bracelet could detect in users' WST, heart rate, HRV, respiratory rate, and skin perfusion. Drawing on insight from artificial intelligence and machine learning, we then sought to develop an algorithm that could identify the fertile window in real time. We conducted a prospective longitudinal study, recruiting 237 conception-seeking Swiss women. Participants wore the Ava bracelet (Ava AG) nightly while sleeping for up to a year or until they became pregnant. In addition to syncing the device to the corresponding smartphone app daily, women also completed an electronic diary about their activities in the past 24 hours. Finally, women took a urinary luteinizing hormone test at several points in a given cycle to determine the close of the fertile window. We assessed phase-based changes in physiological parameters using cross-classified mixed-effects models with random intercepts and random slopes. We then trained a machine learning algorithm to recognize the fertile window. We have demonstrated that wearable technology can detect significant, concurrent phase-based shifts in WST, heart rate, and respiratory rate (all P<.001). HRV and skin perfusion similarly varied across the menstrual cycle (all P<.05), although these effects only trended toward significance following a Bonferroni correction to maintain a family-wise alpha level. Our findings were robust to daily, individual, and cycle-level covariates. Furthermore, we developed a machine learning algorithm that can detect the fertile window with 90% accuracy (95% CI 0.89 to 0.92). Our contributions highlight the impact of artificial intelligence and machine learning's integration into health care. By monitoring numerous physiological parameters simultaneously, wearable technology uniquely improves upon retrospective methods for fertility awareness and enables the first real-time predictive model of ovulation.
Sohda S et al., 2017·J Med Internet Res·
Open Access
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.