Sex and Conception Probability

Central to the survival of any species is the ability to procreate. In most cases, procreation is sexual, involving a process that ensures appropriate and timed contact between the male and female gametes. Successful human reproduction is premised on sexual intercourse occurring at a time when there is a receptive endometrium as well as an ovum ready for fertilization by spermatozoa. This time window of the menstrual cycle known as the fertile or fecund window is poorly defined and highly variable from one individual to another. Furthermore, while spermatogenesis is a continuous process, the impact of too frequent intercourse (defined as that occurring more than every 2 to 3 days) on fertilization has often been thought to be associated with a decreased fertilization potential of spermatozoa. Current evidence challenges previously held views on the fertile window and how it is determined, the timing of intercourse and how it is related to conception and miscarriages, the length of the luteal phase, and the precise time period during which the chances of fertilization are highest in any given menstrual cycle. The ability of spermatozoa to survive in the female genital tract for 5 days means fertilization can occur up to 5 days from sexual intercourse. During each menstrual cycle, there is a window of 5 to 6 days for fertilization to occur, and this period is defined not by the length of the cycle but by the timing of ovulation, with the chances of fertilization highest with intercourse occurring 1 to 2 days before ovulation.

P–469 Period Tracker Applications – are they giving women accurate menstrual cycle information?

Study question Are period trackers giving women accurate information about their periods and ovulation? Summary answer The top 10 period trackers gave conflicting information on period dates, ovulation day and the fertile window. What is known already Period tracking applications allow women to track their menstrual cycles and receive a prediction for their periods. The majority of applications also provide predictions of day of ovulation and the fertile window. Previous research indicates applications are basing predictions on assuming women undergo a textbook 28-day cycle with ovulation occurring on day 14 and a fertile window between days 10 and 17. Study design, size, duration An audit of menstrual cycle apps was conducted on the Apple app store using menstrual cycle tracker/period tracker as the search terms. The top ten apps that followed the inclusion and exclusion criteria were analysed and used for this study. All apps had the ability to allow retrospective data entry giving future cycle predictions and fertile window, and nine of the apps predicted a day of ovulation. Participants/materials, setting, methods Five women’s profiles for 6 menstrual cycles were created and entered into each app. Cycle length (CL) and ovulation day (OD) for the 6th cycle were; Woman 1 – Constant 28 day CL, 0D 16, Woman 2 – Average 23 day CL, OD 13, Woman 3 – Average 28 day CL, OD 17, Woman 4 – Average 33 day CL, OD 20 and Woman 5 – Irregular, average 31 day CL, OD 14. Main results and the role of chance For cycle length, the apps all predicted woman 1’s cycles correctly but for women 2–5, the apps predicted 0 to 8 days shorter or longer than expected. For day of ovulation; for woman 1, no apps predicted this correctly; the apps ranged from day 13–15. For woman 2, 1 app was correct and overall the apps showed a lot of variation from day 8 to 13. For woman 3, no apps got it right, with a range of day 13–16. For woman 4, 2 apps got it right, but the apps ranged from day 13–20. For woman 5, no apps got right; the apps ranged from day 13–21. Irrespective of cycle length, 7 apps predicted a fertile window of 7 days in almost all cases; except 1 app that predicted 6 days for woman 2 and a different app which predicted 8 days for woman 4. For the remaining 3 apps, one always predicted a 10 day fertile window. One app predicted an 11 day fertile window in 4/5 women. One app predicted a 12 day fertile window in 4/5 women. Limitations, reasons for caution The five profiles created spanned a range of observed cycle characteristics, but many permutations are possible. A Monte Carlo type analysis could be conducted to examine these possibilities to provide more precise assessment of app performance, but as data had to be added manually into apps, this was not possible. Wider implications of the findings: The apps do not use the same algorithm and show variation. The information given by these apps is not 100% accurate, especially for the day of ovulation and the fertile window which can only be predicted if using a marker of ovulation, such as basal body temperature or ovulation sticks. Trial registration number Not applicable

P-469 Period Tracker Applications – are they giving women accurate menstrual cycle information?

Study question Are period trackers giving women accurate information about their periods and ovulation? Summary answer The top 10 period trackers gave conflicting information on period dates, ovulation day and the fertile window. What is known already Period tracking applications allow women to track their menstrual cycles and receive a prediction for their periods. The majority of applications also provide predictions of day of ovulation and the fertile window. Previous research indicates applications are basing predictions on assuming women undergo a textbook 28-day cycle with ovulation occurring on day 14 and a fertile window between days 10 and 17. Study design, size, duration An audit of menstrual cycle apps was conducted on the Apple app store using menstrual cycle tracker/period tracker as the search terms. The top ten apps that followed the inclusion and exclusion criteria were analysed and used for this study. All apps had the ability to allow retrospective data entry giving future cycle predictions and fertile window, and nine of the apps predicted a day of ovulation. Participants/materials, setting, methods Five women’s profiles for 6 menstrual cycles were created and entered into each app. Cycle length (CL) and ovulation day (OD) for the 6th cycle were; Woman 1 – Constant 28 day CL, 0D 16, Woman 2 – Average 23 day CL, OD 13, Woman 3 – Average 28 day CL, OD 17, Woman 4 – Average 33 day CL, OD 20 and Woman 5 – Irregular, average 31 day CL, OD 14. Main results and the role of chance For cycle length, the apps all predicted woman 1’s cycles correctly but for women 2-5, the apps predicted 0 to 8 days shorter or longer than expected. For day of ovulation; for woman 1, no apps predicted this correctly; the apps ranged from day 13-15. For woman 2, 1 app was correct and overall the apps showed a lot of variation from day 8 to 13. For woman 3, no apps got it right, with a range of day 13-16. For woman 4, 2 apps got it right, but the apps ranged from day 13-20. For woman 5, no apps got right; the apps ranged from day 13-21. Irrespective of cycle length, 7 apps predicted a fertile window of 7 days in almost all cases; except 1 app that predicted 6 days for woman 2 and a different app which predicted 8 days for woman 4. For the remaining 3 apps, one always predicted a 10 day fertile window. One app predicted an 11 day fertile window in 4/5 women. One app predicted a 12 day fertile window in 4/5 women. Limitations, reasons for caution The five profiles created spanned a range of observed cycle characteristics, but many permutations are possible. A Monte Carlo type analysis could be conducted to examine these possibilities to provide more precise assessment of app performance, but as data had to be added manually into apps, this was not possible. Wider implications of the findings The apps do not use the same algorithm and show variation. The information given by these apps is not 100% accurate, especially for the day of ovulation and the fertile window which can only be predicted if using a marker of ovulation, such as basal body temperature or ovulation sticks. Trial registration number not applicable

The Inflammasome Contributes to Depletion of the Ovarian Reserve During Aging in Mice

Ovarian aging is a natural process characterized by follicular depletion and a reduction in oocyte quality, resulting in loss of ovarian function, cycle irregularity and eventually infertility and menopause. The factors that contribute to ovarian aging have not been fully characterized. Activation of the NLRP3 inflammasome has been implicated in age-associated inflammation and diminished function in several organs. In this study, we used Asc−/− and Nlrp3−/− mice to investigate the possibility that chronic low-grade systemic inflammation mediated by the inflammasome contributes to diminished ovarian reserves as females age. Pro-inflammatory cytokines, IL-6, IL-18, and TNF-α, were decreased in the serum of aging Asc−/− mice compared to WT. Within the ovary of reproductively aged Asc−/− mice, mRNA levels of major pro-inflammatory genes Tnfa, Il1a, and Il1b were decreased, and macrophage infiltration was reduced compared to age-matched WT controls. Notably, suppression of the inflammatory phenotype in Asc−/− mice was associated with retention of follicular reserves during reproductive aging. Similarly, the expression of intra-ovarian pro-inflammatory cytokines was reduced, and follicle numbers were significantly elevated, in aging Nlrp3−/− mice compared to WT controls. These data suggest that inflammasome-dependent inflammation contributes to the age-associated depletion of follicles and raises the possibility that ovarian aging could be delayed, and fertile window prolonged, by suppressing inflammatory processes in the ovary.

Period tracker applications: What menstrual cycle information are they giving women?

Background: Period tracking applications (apps) allow women to track their menstrual cycles and receive a prediction for their period dates. The majority of apps also provide predictions of ovulation day and the fertile window. Research indicates apps are basing predictions on assuming women undergo a textbook 28-day cycle with ovulation occurring on day 14 and a fertile window between days 10 and 16. Objective: To determine how the information period tracker apps give women on their period dates, ovulation day and fertile window compares to expected results from big data. Methods: Five women’s profiles for 6 menstrual cycles were created and entered into 10 apps. Cycle length and ovulation day for the sixth cycle were Woman 1—Constant 28 day cycle length, ovulation day 16; Woman 2—Average 23 day cycle length, ovulation day 13; Woman 3—Average 28 day cycle length, ovulation day 17; Woman 4—Average 33 day cycle length, ovulation day 20; and Woman 5—Irregular, average 31 day cycle length, ovulation day 14. Results: The 10 period tracker apps examined gave conflicting information on period dates, ovulation day and the fertile window. For cycle length, the apps all predicted woman 1’s cycles correctly but for women 2–5, the apps predicted 0 to 8 days shorter or longer than expected. For day of ovulation, for women 1–4, of the 36 predictions, 3 (8%) were exactly correct, 9 predicted 1 day too early (25%) and 67% of predictions were 2–9 days early. For woman 5, most of the apps predicted a later day of ovulation. Conclusion: Period tracker apps should ensure they only give women accurate information, especially for the day of ovulation and the fertile window which can only be predicted if using a marker of ovulation, such as basal body temperature, ovulation sticks or cervical mucus.

Fecundability in relation to use of mobile computing apps to track the menstrual cycle

STUDY QUESTION To what extent does the use of mobile computing apps to track the menstrual cycle and the fertile window influence fecundability among women trying to conceive? SUMMARY ANSWER After adjusting for potential confounders, use of any of several different apps was associated with increased fecundability ranging from 12% to 20% per cycle of attempt. WHAT IS KNOWN ALREADY Many women are using mobile computing apps to track their menstrual cycle and the fertile window, including while trying to conceive. STUDY DESIGN, SIZE, DURATION The Pregnancy Study Online (PRESTO) is a North American prospective internet-based cohort of women who are aged 21–45 years, trying to conceive and not using contraception or fertility treatment at baseline. PARTICIPANTS/MATERIALS, SETTING, METHODS We restricted the analysis to 8363 women trying to conceive for no more than 6 months at baseline; the women were recruited from June 2013 through May 2019. Women completed questionnaires at baseline and every 2 months for up to 1 year. The main outcome was fecundability, i.e. the per-cycle probability of conception, which we assessed using self-reported data on time to pregnancy (confirmed by positive home pregnancy test) in menstrual cycles. On the baseline and follow-up questionnaires, women reported whether they used mobile computing apps to track their menstrual cycles (‘cycle apps’) and, if so, which one(s). We estimated fecundability ratios (FRs) for the use of cycle apps, adjusted for female age, race/ethnicity, prior pregnancy, BMI, income, current smoking, education, partner education, caffeine intake, use of hormonal contraceptives as the last method of contraception, hours of sleep per night, cycle regularity, use of prenatal supplements, marital status, intercourse frequency and history of subfertility. We also examined the impact of concurrent use of fertility indicators: basal body temperature, cervical fluid, cervix position and/or urine LH. MAIN RESULTS AND THE ROLE OF CHANCE Among 8363 women, 6077 (72.7%) were using one or more cycle apps at baseline. A total of 122 separate apps were reported by women. We designated five of these apps before analysis as more likely to be effective (Clue, Fertility Friend, Glow, Kindara, Ovia; hereafter referred to as ‘selected apps’). The use of any app at baseline was associated with 20% increased fecundability, with little difference between selected apps versus other apps (selected apps FR (95% CI): 1.20 (1.13, 1.28); all other apps 1.21 (1.13, 1.30)). In time-varying analyses, cycle app use was associated with 12–15% increased fecundability (selected apps FR (95% CI): 1.12 (1.04, 1.21); all other apps 1.15 (1.07, 1.24)). When apps were used at baseline with one or more fertility indicators, there was higher fecundability than without fertility indicators (selected apps with indicators FR (95% CI): 1.23 (1.14, 1.34) versus without indicators 1.17 (1.05, 1.30); other apps with indicators 1.30 (1.19, 1.43) versus without indicators 1.16 (1.06, 1.27)). In time-varying analyses, results were similar when stratified by time trying at study entry (<3 vs. 3–6 cycles) or cycle regularity. For use of the selected apps, we observed higher fecundability among women with a history of subfertility: FR 1.33 (1.05–1.67). LIMITATIONS, REASONS FOR CAUTION Neither regularity nor intensity of app use was ascertained. The prospective time-varying assessment of app use was based on questionnaires completed every 2 months, which would not capture more frequent changes. Intercourse frequency was also reported retrospectively and we do not have data on timing of intercourse relative to the fertile window. Although we controlled for a wide range of covariates, we cannot exclude the possibility of residual confounding (e.g. choosing to use an app in this observational study may be a marker for unmeasured health habits promoting fecundability). Half of the women in the study received a free premium subscription for one of the apps (Fertility Friend), which may have increased the overall prevalence of app use in the time-varying analyses, but would not affect app use at baseline. Most women in the study were college educated, which may limit application of results to other populations. WIDER IMPLICATIONS OF THE FINDINGS Use of a cycle app, especially in combination with observation of one or more fertility indicators (basal body temperature, cervical fluid, cervix position and/or urine LH), may increase fecundability (per-cycle pregnancy probability) by about 12–20% for couples trying to conceive. We did not find consistent evidence of improved fecundability resulting from use of one specific app over another. STUDY FUNDING/COMPETING INTEREST(S) This research was supported by grants, R21HD072326 and R01HD086742, from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, USA. In the last 3 years, Dr L.A.W. has served as a fibroid consultant for AbbVie.com. Dr L.A.W. has also received in-kind donations from Sandstone Diagnostics, Swiss Precision Diagnostics, FertilityFriend.com and Kindara.com for primary data collection and participant incentives in the PRESTO cohort. Dr J.B.S. reports personal fees from Swiss Precision Diagnostics, outside the submitted work. The remaining authors have nothing to declare. TRIAL REGISTRATION NUMBER N/A.