Salivary testosterone across the menstrual cycle.

Testosterone production in women is thought to systematically shift across the menstrual cycle, peaking during the mid-cycle ovulatory window, and potentially influencing women's behavior. Testosterone is a molecular intermediary to the production of estradiol, which is necessary for ovulation to occur, but the amount of testosterone escape and exposure to the peripheral tissues is not fully understood. Salivary testosterone is a common biomarker in behavioral neuroendocrinological studies and is thought to reflect the bioactive portions in serum. In N = 339 women with confirmed ovulation via luteinizing hormone tests, salivary testosterone, assayed with LC-MS/MS, was sampled four times across the mid-cycle ovulatory window the luteal phase. Within-subject analysis revealed a significant but small pattern of a mid-cycle peak and a luteal decrease at the aggregate level. However, at the individual level, there was substantial variability in the direction and magnitude of the testosterone-cycle pattern. We discuss the relevant underlying physiology, background research, issues with assay methodolody, and considerations for researchers studying testosterone levels in women.

Measurement of luteinizing hormone surge in vaginal discharge: a potential biomarker that enables simple, non-invasive prediction of the periovulatory period.

BACKGROUND: Predicting the periovulatory period is very important for conception. Current approaches to predicting the periovulatory period include monitoring of basal body temperature and urine luteinizing hormone (LH) concentration; however, these methods are time-consuming. Here, we examined the potential of using vaginal discharge (VD) as a non-invasive means of sample collection for determining the LH surge that indicates ovulation. METHODS: Urine and VD samples were collected from 35 healthy women aged 20-39 years. VD samples were collected with panty liners to reduce the burden on participants. Daily first urine samples and used panty liners were collected from the 10th through 19th days of the menstrual cycle. Urine and VD LH (uLH and vLH) levels in the samples were measured by enzyme-linked immunosorbent assay. Measured vLH baseline and first surge values were analyzed using Student's t-test and ROC curves. RESULTS: Samples for a total of 55 menstrual cycles were collected. We used uLH surge to establish the date of ovulation. uLH surges were observed in 49 cycles, 34 of which had corresponding VD samples that qualified for measurement. Five cycles were excluded due to a lack of vLH data. In the remaining 29 cycles, the vLH surge appeared within the fertile window 90% of the time, and the sensitivity and specificity of the test were 86% and 83%, respectively. CONCLUSIONS: VD has potential for use as a sample for predicting the periovulatory period by measuring LH content.

Comparison of Day-Specific Serum LH, Estradiol, and Progesterone with MiraTM Monitor Urinary LH, Estrone-3-glucuronide, and Pregnanediol-3-glucuronide Levels in Ovulatory Cycles.

Background and Objectives: Fertility tracking apps and devices are now currently available, but urinary hormone levels lack accuracy and sensitivity in timing the start of the 6-day fertile window and the precise 24 h interval of transition from ovulation to the luteal phase. We hypothesized the serum hormones estradiol (E2) and progesterone (P) might be better biomarkers for these major ovulatory cycle events, using appropriate mathematical tools. Materials and Methods: Four women provided daily blood samples for serum E2, P, and LH (luteinizing hormone) levels throughout their entire ovulatory cycles, which were indexed to the first day of dominant follicle (DF) collapse (defined as Day 0) determined by transvaginal sonography; therefore, ovulation occurred in the 24 h interval of Day -1 (last day of maximum diameter DF) to Day 0. For comparison, a MiraTM fertility monitor was used to measure daily morning urinary LH (ULH), estrone-3-glucuronide (E3G), and pregnanediol-3-glucuronide (PDG) levels in three of these cycles. Results: There were more fluctuations in the MiraTM hormone levels compared to the serum levels. Previously described methods, the Fertility Indicator Equation (FIE) and Area Under the Curve (AUC) algorithm, were tested for identifying the start of the fertile window and the ovulation/luteal transition point using the day-specific hormone levels. The FIE with E2 levels predicted the start of the 6-day fertile window on Day -7 (two cycles) and Day -5 (two cycles), whereas no identifying signal was found with E3G. However, both pairs of (E2, P) and (E3G, PDG) levels with the AUC algorithm signaled the Day -1 to Day 0 ovulation/luteal transition interval in all cycles. Conclusions: serum E2 and (E2, P) were better biomarkers for signaling the start of the 6-day fertile window, but both MiraTM and serum hormone levels were successful in timing the [Day -1, Day 0] ovulatory/luteal transition interval. These results can presently be applied to urinary hormone monitors for fertility tracking and have implications for the direction of future fertility tracking technology.

Tracking of menstrual cycles and prediction of the fertile window via measurements of basal body temperature and heart rate as well as machine-learning algorithms.

BACKGROUND: Fertility awareness and menses prediction are important for improving fecundability and health management. Previous studies have used physiological parameters, such as basal body temperature (BBT) and heart rate (HR), to predict the fertile window and menses. However, their accuracy is far from satisfactory. Additionally, few researchers have examined irregular menstruators. Thus, we aimed to develop fertile window and menstruation prediction algorithms for both regular and irregular menstruators. METHODS: This was a prospective observational cohort study conducted at the International Peace Maternity and Child Health Hospital in Shanghai, China. Participants were recruited from August 2020 to November 2020 and followed up for at least four menstrual cycles. Participants used an ear thermometer to assess BBT and wore the Huawei Band 5 to record HR. Ovarian ultrasound and serum hormone levels were used to determine the ovulation day. Menstruation was self-reported by women. We used linear mixed models to assess changes in physiological parameters and developed probability function estimation models to predict the fertile window and menses with machine learning. RESULTS: We included data from 305 and 77 qualified cycles with confirmed ovulations from 89 regular menstruators and 25 irregular menstruators, respectively. For regular menstruators, BBT and HR were significantly higher during fertile phase than follicular phase and peaked in the luteal phase (all P < 0.001). The physiological parameters of irregular menstruators followed a similar trend. Based on BBT and HR, we developed algorithms that predicted the fertile window with an accuracy of 87.46%, sensitivity of 69.30%, specificity of 92.00%, and AUC of 0.8993 and menses with an accuracy of 89.60%, sensitivity of 70.70%, and specificity of 94.30%, and AUC of 0.7849 among regular menstruators. For irregular menstruators, the accuracy, sensitivity, specificity and AUC were 72.51%, 21.00%, 82.90%, and 0.5808 respectively, for fertile window prediction and 75.90%, 36.30%, 84.40%, and 0.6759 for menses prediction. CONCLUSIONS: By combining BBT and HR recorded by the Huawei Band 5, our algorithms achieved relatively ideal performance for predicting the fertile window and menses among regular menstruators. For irregular menstruators, the algorithms showed potential feasibility but still need further investigation. TRIAL REGISTRATION: ChiCTR2000036556. Registered 24 August 2020.

Cervical mucus patterns and the fertile window in women without known subfertility: a pooled analysis of three cohorts.

STUDY QUESTION: What is the normal range of cervical mucus patterns and number of days with high or moderate day-specific probability of pregnancy (if intercourse occurs on a specific day) based on cervical mucus secretion, in women without known subfertility, and how are these patterns related to parity and age? SUMMARY ANSWER: The mean days of peak type (estrogenic) mucus per cycle was 6.4, the mean number of potentially fertile days was 12.1; parous versus nulliparous, and younger nulliparous (<30 years) versus older nulliparous women had more days of peak type mucus, and more potentially fertile days in each cycle. WHAT IS KNOWN ALREADY: The rise in estrogen prior to ovulation supports the secretion of increasing quantity and estrogenic quality of cervical mucus, and the subsequent rise in progesterone after ovulation causes an abrupt decrease in mucus secretion. Cervical mucus secretion on each day correlates highly with the probability of pregnancy if intercourse occurs on that day, and overall cervical mucus quality for the cycle correlates with cycle fecundability. No prior studies have described parity and age jointly in relation to cervical mucus patterns. STUDY DESIGN, SIZE, DURATION: This study is a secondary data analysis, combining data from three cohorts of women: 'Creighton Model MultiCenter Fecundability Study' (CMFS: retrospective cohort, 1990-1996), 'Time to Pregnancy in Normal Fertility' (TTP: randomized trial, 2003-2006), and 'Creighton Model Effectiveness, Intentions, and Behaviors Assessment' (CEIBA: prospective cohort, 2009-2013). We evaluated cervical mucus patterns and estimated fertile window in 2488 ovulatory cycles of 528 women, followed for up to 1 year. PARTICIPANTS/MATERIALS, SETTING, METHODS: Participants were US or Canadian women age 18-40 years, not pregnant, and without any known subfertility. Women were trained to use a standardized protocol (the Creighton Model) for daily vulvar observation, description, and recording of cervical mucus. The mucus peak day (the last day of estrogenic quality mucus) was used as the estimated day of ovulation. We conducted dichotomous stratified analyses for cervical mucus patterns by age, parity, race, recent oral contraceptive use (within 60 days), partial breast feeding, alcohol, and smoking. Focusing on the clinical characteristics most correlated to cervical mucus patterns, linear mixed models were used to assess continuous cervical mucus parameters and generalized linear models using Poisson regression with robust variance were used to assess dichotomous outcomes, stratifying by women's parity and age, while adjusting for recent oral contraceptive use and breast feeding. MAIN RESULTS AND THE ROLE OF CHANCE: The majority of women were <30 years of age (75.4%) (median 27; IQR 24-29), non-Hispanic white (88.1%), with high socioeconomic indicators, and nulliparous (70.8%). The mean (SD) days of estrogenic (peak type) mucus per cycle (a conservative indicator of the fertile window) was 6.4 (4.2) days (median 6; IQR 4-8). The mean (SD) number of any potentially fertile days (a broader clinical indicator of the fertile window) was 12.1 (5.4) days (median 11; IQR 9-14). Taking into account recent oral contraceptive use and breastfeeding, nulliparous women age ≥30 years compared to nulliparous women age <30 years had fewer mean days of peak type mucus per cycle (5.3 versus 6.4 days, P = 0.02), and fewer potentially fertile days (11.8 versus 13.9 days, P < 0.01). Compared to nulliparous women age <30 years, the likelihood of cycles with peak type mucus ≤2 days, potentially fertile days ≤9, and cervical mucus cycle score (for estrogenic quality of mucus) ≤5.0 were significantly higher among nulliparous women age ≥30 years, 1.90 (95% confidence interval (CI) 1.18, 3.06); 1.46 (95% CI 1.12, 1.91); and 1.45 (95% CI 1.03, 2.05), respectively. Between parous women, there was little difference in mucus parameters by age. Thresholds set a priori for within-woman variability of cervical mucus parameters by cycle were examined as follows: most minus fewest days of peak type mucus >3 days (exceeded by 72% of women), most minus fewest days of non-peak type mucus >4 days (exceeded by 54% of women), greatest minus least cervical mucus cycle score >4.0 (exceeded by 73% of women), and most minus fewest potentially fertile days >8 days (found in 50% of women). Race did not have any association with cervical mucus parameters. Recent oral contraceptive use was associated with reduced cervical mucus cycle score and partial breast feeding was associated with a higher number of days of mucus (both peak type and non-peak type), consistent with prior research. Among the women for whom data were available (CEIBA and TTP), alcohol and tobacco use had minimal impact on cervical mucus parameters. LIMITATIONS, REASONS FOR CAUTION: We did not have data on some factors that may impact ovulation, hormone levels, and mucus secretion, such as physical activity and body mass index. We cannot exclude the possibility that some women had unknown subfertility or undiagnosed gynecologic disorders. Only 27 women were age 35 or older. Our study participants were geographically dispersed but relatively homogeneous with regard to race, ethnicity, income, and educational level, which may limit the generalizability of the findings. WIDER IMPLICATIONS OF THE FINDINGS: Patterns of cervical mucus secretion observed by women are an indicator of fecundity and the fertile window that are consistent with the known associations of age and parity with fecundity. The number of potentially fertile days (12 days) is likely greater than commonly assumed, while the number of days of highly estrogenic mucus (and higher probability of pregnancy) correlates with prior identifications of the fertile window (6 days). There may be substantial variability in fecundability between cycles for the same woman. Future work can use cervical mucus secretion as an indicator of fecundity and should investigate the distribution of similar cycle parameters in women with various reproductive or gynecologic pathologies. STUDY FUNDING/COMPETING INTEREST(S): Funding for the three cohorts analyzed was provided by the Robert Wood Johnson Foundation (CMFS), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (TTP), and the Office of Family Planning, Office of Population Affairs, Health and Human Services (CEIBA). The authors declare that they have no conflict of interest. TRIAL REGISTRATION NUMBER: N/A.

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.

Peri-implantation intercourse does not lower fecundability.

STUDY QUESTION: Does sexual intercourse in the implantation time window (5-9 days after ovulation) reduce fecundability? SUMMARY ANSWER: After adjustment for intercourse in the fecund window and clustering by couple, there was no association between intercourse in the implantation time window and fecundity. WHAT IS KNOWN ALREADY: Previous research has suggested an association between intercourse in the peri-implantation time window (5-9 days after estimated ovulation) and reduced fecundability. STUDY DESIGN, SIZE, DURATION: We used data from the FERTILI study, a prospective observational study conducted in five European countries, with data collected from 1992 to 1996. PARTICIPANTS/MATERIALS, SETTING, METHODS: Women who were experienced in fertility awareness tracking kept a daily diary of cervical mucus observations, basal body temperature measurements, coitus and clinically identified pregnancy. We estimated the day of ovulation as cycle length minus 13 days. From 661 women, 2606 cycles had intercourse during the fecund window (from 5 days before to 3 days after the estimated day of ovulation), resulting in 418 pregnancies (conception cycles). An established Bayesian fecundability model was used to estimate the fecundability ratio (FR) of peri-implantation intercourse on fecundability, while adjusting for each partner's age, prior pregnancy, the couple's probability of conception and intercourse pattern(s). We conducted sensitivity analyses estimating ovulation as cycle length minus 12 days, or alternatively, as the peak day of estrogenic cervical mucus. MAIN RESULTS AND THE ROLE OF CHANCE: There was no effect of peri-implantation intercourse on fecundability: adjusted FR for three or more acts of peri-implantation intercourse versus none: 1.00, 95% credible interval: 0.76-1.13. Results were essentially the same with sensitivity analyses. There was an inverse relationship between frequency of intercourse in the fecund window and intercourse in the peri-implantation window. LIMITATIONS, REASONS FOR CAUTION: Women with known subfertility were excluded from this study. Many couples in the study were avoiding pregnancy during much of the study, so 61% of otherwise eligible cycles in the database were not at meaningful risk of pregnancy and did not contribute to the analysis. Some couples may not have recorded all intercourse. WIDER IMPLICATIONS OF THE FINDINGS: We believe the current balance of evidence does not support a recommendation for avoiding intercourse in the peri-implantation period among couples trying to conceive. STUDY FUNDING/COMPETING INTEREST(S): No external funding. The authors have no potential competing interests. TRIAL REGISTRATION NUMBER: N/A.

Do fertility tracking applications offer women useful information about their fertile window?

RESEARCH QUESTION: To characterize mobile fertility tracking applications (apps) to determine the use of such apps for women trying to conceive by identifying the fertile window. DESIGN: An exploratory cross-sectional audit study was conducted of fertility tracking applications. Ninety out of a possible total 200 apps were included for full review. The main outcome measures were the underlying app method for predicting ovulation, the fertile window, or both, price to download and use the app, disclaimers and cautions, information and features provided and tracked, and app marketing strategies. RESULTS: All the apps except one monitored the women's menstrual cycle dates. Most apps only tracked menstrual cycle dates (n = 49 [54.4%]). The remainder tracked at least one fertility-based awareness method (basal body temperature, cervical mucus, LH) (n = 41 [45.6%]). Twenty-five apps measured dates, basal body temperature, LH and cervical mucus (27.8%). Seventy-six per cent of apps were free to download with free apps having more desirable features, tracking more measures and having more and better quality educational insights than paid apps. Seventy per cent of apps were classified as feminine apps, 41% of which were pink in colour. CONCLUSIONS: Mobile fertility tracking apps are heterogenous in their underlying methods of predicting fertile days, the price to obtain full app functionality, and in content and design. Unreliable calendar apps remain the most commonly available fertility apps on the market. The unregulated nature of fertility apps is a concern that could be addressed by app regulating bodies. The possible benefit of using fertility apps to reduce time to pregnancy needs to be evaluated.

Menstrual cycle-dependent alterations in glycosylation: a roadmap for defining biomarkers of favorable and unfavorable mucus.

OBJECTIVE: To understand glycosylation of endocervical proteins at different times throughout the menstrual cycle in naturally cycling women and in women using hormonal or non-hormonal contraceptive methods, in order to characterize biochemical fingerprints of favorable and unfavorable cervical mucus. DESIGN: Lectin/antibody-probed protein blot analysis of endocervical mucus samples collected onto ophthalmologic sponges (wicks) from two groups: a longitudinal cohort of naturally cycling women at three time points in their menstrual cycles (discovery cohort), and a cross-sectional cohort of women on hormonal or non-hormonal contraceptive methods (validation cohort). SETTING: Participants were recruited from the San Francisco Bay Area from 2010 to 2016. PATIENT(S): Women with regular cycles not using hormonal or intrauterine device (IUD) contraceptives were recruited for the longitudinal cohort (n = 8). Samples from women using levonorgestrel-containing combined oral contraceptives (n = 16), levonorgestrel containing IUDs (n = 14), copper IUDs (n = 17), depo-medroxyprogesterone acetate (DMPA) (n = 15), and controls (n = 13) were used for validation. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Detection of specific glycosylation patterns on lectin/antibody probed protein blots. RESULT(S): Two lectins (Lens culinaris agglutinin and Lycopersicon esculentum [tomato lectin]), and the antibody MECA-79 demonstrated consistent cycle-dependent changes in protein binding. The glycan-binding patterns of the levonorgestrel-containing contraceptives were generally similar to each other and to those from women in the luteal phase. The DMPA samples showed slightly different binding patterns. CONCLUSION(S): We identified molecular signatures of unfavorable mucus from women in the luteal phase and on hormonal contraceptives. Further characterization of these biomarkers may be useful in contraceptive development and in evaluation of infertility.

Advantages of determining the fertile window with the individualised Natural Cycles algorithm over calendar-based methods.

Purpose: This study aims to compare the accuracy of fertile window identification with the contraceptive app Natural Cycles against the Rhythm Method and Standard Days Method (SDM).Materials and methods: Menstruation dates, basal body temperature (BBT), and luteinising hormone (LH) test results were collected anonymously from Natural Cycles app users. The fraction of green days (GDs) and wrong green days (WGDs) allocated by the various algorithms was determined over 12 cycles. For comparison of Natural Cycles and the Rhythm Method, 26,626 cycles were analysed.Results: Natural Cycles' algorithms allocated 59% GDs (LH, BBT) in cycle 12, while the fraction of WGDs averaged 0.08%. The Rhythm Method requires monitoring of six cycles, resulting in no GDs or WGDs in cycle 1-6. In cycle 7, 49% GDs and 0.26% WGDs were allocated. GDs and WGDs decreased to 43% and 0.08% in cycle 12. The probabilities of WGDs on the day before ovulation with Natural Cycles were 0.31% (BBT) and 0% (LH, BBT), and 0.80% with the Rhythm Method. The probability of WGDs on the day before ovulation was 6.90% with the SDM.Conclusions: This study highlights that individualised algorithms are advantageous for accurate determination of the fertile window and that static algorithms are more likely to fail during the most fertile days.

Timing is crucial: Some critical thoughts on using LH tests to determine women's current fertility.

Naturally cycling women reportedly go through a variety of psychological and behavioural changes over menstrual cycle. Evolutionary informed scholars have interpreted such changes as maximising reproductive success. However, concerns have been raised regarding this ovulatory shift hypothesis, since recent studies have yielded inconsistent findings. We suggest that the inconsistent findings regarding the ovulatory shift hypothesis may result from a too simplistic definition of the fertile window. Presently, most studies use LH tests to determine the fertile window. The problem with this "gold standard" is that it builds on the misconception that fertility peaks with ovulation and that ovulation regularly occurs 24 to 48 hours after the LH surge. While commercially available urinary LH test strips are a cheap and easy way to reliably detect LH surges, the LH surge itself marks the impending end of the fertile window. So if women are invited to the laboratory after the LH surge (as is often done for practical reasons) there is a high probability of misclassifying women as fertile when in fact the fertile window has already closed. We discuss possible advancements that may help to increase the accuracy and reliability of determining a woman's individual fertile window, during which any adaptive changes that increase the chance of reproduction should be best observable.

Identification and prediction of the fertile window with a new web-based medical device using a vaginal biosensor for measuring the circadian and circamensual core body temperature.

Fertility awareness-based (FAB) methods represent a term that includes all family planning methods that are based on the identification of the fertile window. They are based on the woman's observation of physiological signs of the fertile and infertile phases of the menstrual cycle. The first approach consists basically in symptothermal methods accompanied by cervical mucus measurements and clinical menstrual cycling data recording. The second most often used methods are the urinary measurement of E3G and luteinizing hormone (LH) with a personalized computer system. Hence these systems lack the efficacy of the continuous circadian and circamensual measurement of the core body temperature. Only this approach enables the accurate detection of the ovulation during the fertile window. A new medical device called OvulaRing has been developed to fill this gap. In the present study, the system and its first clinical results are presented. OvulaRing is a medical device used just like a tampon. The device is a vaginal ring of evatane that contains an integrated biosensor. This sensor measures continuously every 5 min the core body temperature throughout the entire cycle. This device allows a circadian and circamensual intravaginal exact measurement. With this system, 288 measurements are created per day. The system can detect retrospectively and predict prospectively the fertile window of the users. One hundred and fifty eight women aged between 18 and 45 years used this medical device in an open non-randomized clinical study for 15 months. A total of 470 cycles could be recorded and were able for analysis. By the same time in a subgroup of patients, hormonal assessments of LH, follicle-stimulating hormone, estradiol and progesterone as well as vaginal ultrasound were performed in parallel between the 9th and the 36th day of the cycle. The validation error due to software errors was 0.89% for the retrospective analysis; that means that the accuracy for the detection of the ovulation was 99.11%. Accuracy of 88.8% for a window of 3 days before ovulation, the day of ovulation and the 3 days after ovulation was achieved for the prospective analysis. In the subgroup of woman with recorded pregnancies, it could be shown that after 3.79 months of use (median) pregnancies were observed. In 67.72% in up to 3 months, in 16.36% between 3 and 6 months of use, in 7.27% between 7 and 9 months, in 5.45% between 10 and 12 months and in 1.82% between 13 and 15 months of use of the system. With this new web-based system, a precise determination of the fertile window even in women with ultralong cycles (>35 days) could be detected independently of their personal live circumstances. Exact determination of the fertile window is herewith possible so that OvulaRing represents an evolution in the FAB method for the cycle diagnosis of women with regular, irregular or anovulatory menstrual cycles.

Use of non-medical contraceptive methods: a survey of women in western Sweden.

OBJECTIVES: To describe women's self-reported use of non-medical contraceptive methods (including barrier methods, fertility awareness-based methods and withdrawal), explore reasons for and satisfaction with choice of contraceptive methods and examine women's fertility awareness. METHODS: A cross-sectional survey was conducted among women consulting at primary healthcare clinics not using medical contraceptives. RESULTS: Most of the 648 participants (67%) reported using a mix of condom use, withdrawal and a calendar method, while 23% used only condoms. Eleven percent reported using the Billings ovulation method, diaphragm/cap or fertility monitor and found these methods highly satisfactory. A majority of the women who primarily used condoms also reported being satisfied. Most women actively chose non-medical contraception because it was perceived as without adverse effects, uncomplicated and effective. Fertility awareness was generally poor. CONCLUSION: Frequent use of withdrawal and a calendar method, as well as poor fertility awareness, suggests a need for improved counselling at contraceptive clinics on fertility awareness and more effective non-medical contraceptive methods to assist women in making an informed contraceptive choice.

Pregnancy risk during menstrual cycle: misconceptions among urban men in India.

BACKGROUND: In India, where men take most decisions in the family, it is useful that they have adequate knowledge about pregnancy risks during women's menstrual cycles. Since traditional contraceptive methods are still employed by a large population in India, the knowledge regarding the pregnancy risk during the menstrual cycle is indispensable. This research paper attempts to assess the knowledge among urban men in Uttar Pradesh, India about the fertile window of the menstrual cycle; it also attempts to discover the rationales behind the misconceptions about the concept. METHODS: This study utilizes the baseline data of the Measurement, Learning, and Evaluation project for the Urban Reproductive Health Initiative in Uttar Pradesh. Descriptive Statistics has been used to assess the prevalence of knowledge among urban men regarding the concept. Using the Discriminant Analysis, we also investigate the rationales behind the misconceptions among urban men about the concept. RESULTS: Only one-fifth of the men have the correct knowledge about the concept. Further, we find that education, societal perception, caste, and spousal discussion about the reproductive issues are the primary factors affecting the knowledge about the pregnancy risk during the menstrual cycle. CONCLUSIONS: There is an urgent need for sex education in the region to make the urban men more educated about the reproductive process of women; this may reduce unwanted births and abortion due to an unwanted pregnancy as well. The study promotes the higher education and motivates couples to discuss the reproductive health issues among them. In this manner, we can provide better reproductive health to the women of urban India.

Ovulation monitoring and fertility knowledge: Their relationship to fertility experience in a cross-sectional study.

BACKGROUND: Various aspects of fertility knowledge, including the timing of the fertile window, have consistently been found to be poor. Limited evidence also suggests ovulation monitoring to time intercourse could be common. However, there have been no studies that compare these two aspects of fertility and women's fertility/infertility experiences. AIM: To examine the frequency of ovulation monitoring and its relationship with fertility knowledge and experience. METHOD: A cross-sectional study of women aged 25-50 years resident in southern New Zealand was undertaken in 2011. Randomly selected women were asked to complete a fertility questionnaire. Outcome prevalence measures were calculated with 95% confidence intervals (CI) and associations investigated using χ2 tests and Poisson regression. RESULTS: Ovulation monitoring was common, having ever been undertaken by 31.4% (95% CI: 28.5-34.3%) of the 1034 participants. However, knowledge was poor, particularly regarding the fertile window. More women who had ever monitored ovulation correctly identified the fertile window, although the proportion was still very low (18.4 vs 13.1% in those who had not, P = 0.027). Regression modelling showed ovulation monitoring was independently associated with seeking medical help to conceive, education and fertility experience, but not with knowledge. CONCLUSION: This study confirms ovulation monitoring was commonly undertaken. However, many women, including those who had monitored their ovulation, had poor fertility knowledge and failed to identify the fertile window. Poor fertility knowledge needs to be addressed, especially among women intending to conceive.

Standardized protocols for characterizing women's fertility: A data-driven approach.

Experts are divided on whether women's cognition and behavior differs between fertile and non-fertile phases of the menstrual cycle. One of the biggest criticisms of this literature concerns the use of indirect, imprecise, and flexible methodologies between studies to characterize women's fertility. To resolve this problem, we provide a data-driven method of best practices for characterizing women's fertile phase. We compared the accuracy of self-reported methods and counting procedures (i.e., the forward- and backward-counting methods) in estimating ovulation using data from 140 women whose fertility was verified with luteinizing hormone tests. Results revealed that no counting method was associated with ovulation with >30% accuracy. A minimum of 39.5% of the days in the six-day fertile window predicted by the counting methods were non-fertile, and correlations between counting method conception probabilities and actual conception probability were weak to moderate, rs=0.11-0.30. Poor results persisted when using a lenient window for predicting ovulation, across alternative estimators of the onset of the next cycle, and when removing outliers to increase the homogeneity of the sample. By contrast, combining counting methods with a relatively inexpensive test of luteinizing hormone predicted fertility with accuracy >95%, but only when specific guidelines were followed. To this end, herein we provide a cost-effective, pragmatic, and standardized protocol that will allow researchers to test whether fertility effects exist or not.

The risk of using intrauterine devices to benign reproductive system conditions in postmenopausal women: A case control study.

BACKGROUND: To investigate the effect of using intrauterine devices (IUDs) during the fertile window on women's reproductive system health. MATERIALS AND METHODS: 2,744 postmenopausal women in the Minhang District, Shanghai, China were enrolled. In the IUDs group there were 2,253 women; in the tubal ligation group there were 202 women and there were 289 women in the control group. We selected subjects according to the cases number in different hospital by using step sampling, and, in addition, collected the sociological data and information of the previously used contraceptives by the subjects, which included whether the contraceptives were used appropriately and the effect they had. Kolmogorov-Smirnov test, Levene's test, and logistic regression analysis were used to analyze the data. RESULTS: The prevalence rate of benign reproductive system conditions was significantly different among them (P < 0.05). Further comparison revealed, the rate in Group 1 was significantly lower than that in Group 2 and Group 3 (P < 0.05, respectively). Results of logistic regression analysis show that the risk factors for development of such conditions lie in the women's pregnancy history [odds ratio (OR) = 3.85], reproductive history (OR = 0.5), the use of IUD in fertile window (OR = 0.4), tubal ligation (OR = 1.74), birth control time (OR = 0.9), contraceptive failure history (OR = 1.7), and history of family planning procedures (OR = 1.73). CONCLUSION: IUDs, maybe, can effectively reduce the risk of getting benign reproductive conditions in postmenopausal females.

Lady in Red: Hormonal Predictors of Women's Clothing Choices.

Recent evidence supports the idea that women use red clothing as a courtship tactic, and results from one study further suggested that women were more likely to wear red on days of high fertility in their menstrual cycles. Subsequent studies provided mixed support for the cycle-phase effect, although all such studies relied on counting methods of cycle-phase estimation and used between-subjects designs. By comparison, in the study reported here, we employed frequent hormone sampling to more accurately assess ovulatory timing and used a within-subjects design. We found that women were more likely to wear red during the fertile window than on other cycle days. Furthermore, within-subjects fluctuations in the ratio of estradiol to progesterone statistically mediated the within-subjects shifts in red-clothing choices. Our results appear to represent the first direct demonstration of specific hormone measurements predicting observable changes in women's courtship-related behaviors. We also demonstrate the advantages of hormonal determination of ovulatory timing for tests of cycle-phase shifts in psychology or behavior.

Identification and prediction of the fertile window using NaturalCycles.

OBJECTIVES: The aim of the study was to evaluate the ability of a novel web and mobile application to identify a woman's ovulation day and fertile window, in order to use it as a method of natural birth control. METHODS: A retrospective study was performed on 1501 cycles of 317 women aged 18 to 39 years. Women entered their basal body temperatures, ovulation test results and date of menstruation into the application. RESULTS: The mean delay from the first positive ovulation test to the temperature-based estimation of the ovulation day was 1.9 days; the length of the luteal phase varied on average by 1.25 days per user. Only 0.05% of non-fertile days were falsely attributed and found within the fertile window. CONCLUSIONS: The method is effective at identifying a user's ovulation day and fertile window and can therefore be used as a natural method of birth control.