Assessment of menstrual health status and evolution through mobile apps for fertility awareness

Chronicling menstrual cycle patterns across the reproductive lifespan with real-world data

The intricate hormonal and physiological changes of the menstrual cycle can influence health on a daily basis. Although prior studies have helped improve our understanding of the menstrual cycle, they often lack diversity in the populations included, sample size, and the span of reproductive and life stages. This paper aims to describe the dynamic differences in menstrual cycle characteristics and associated symptoms by age in a large global cohort of period-tracking application users. This work aims to contribute to our knowledge and understanding of female physiology at varying stages of reproductive aging. This cohort study included self-reported menstrual cycle and symptom information in a sample of Flo application users aged 18-55. Cycle and period length and their variability, and frequency of menstrual cycle symptom logs are described by the age of the user. Based on data logged by over 19 million global users of the Flo app, the length of the menstrual cycle and period show clear age-associated patterns. With higher age, cycles tend to get shorter (Cycle length: D ¯  = 1.85 days, Cohen's D = 0.59) and more variable (Cycle length SD: D ¯  = 0.42 days, Cohen's D = 0.09), until close to the chronological age (40-44) suggesting menopausal transition, when both cycles and periods become longer (Cycle length: D ¯  = 0.86 days, t = 48.85, Cohen's D = 0.26; Period length: D ¯  = 0.08, t = 15.6, Cohen's D = 0.07) and more variable (Cycle length SD: D ¯  = 2.80 days, t = 111.43, d = 0.51; Period length SD: D ¯  = 0.23 days, t = 67.81, Cohen's D = 0.31). The proportion of individuals with irregular cycles was highest in participants aged 51-55 (44.7%), and lowest in the 36-40 age group (28.3%). The spectrum of common menstrual cycle-related symptoms also varies with age. The frequency of logging of cramps and acne is lower in older participants, while logs of headache, backache, stress, and insomnia are higher in older users. Other symptoms show different patterns, such as breast tenderness and fatigue peaking between the ages of 20-40, or mood swings being most frequently logged in the youngest and oldest users. The menstrual cycle and related symptoms are not static throughout the lifespan. Understanding these age-related differences in cycle characteristics and symptoms is essential in understanding how best to care for and improve the daily experience for menstruators across the reproductive life span.

Evidence that the woman's ovarian cycle is driven by an internal circamonthly timing system

The ovarian cycle has a well-established circa-monthly rhythm, but the mechanisms involved in its regularity are unknown. Is the rhythmicity driven by an endogenous clock-like timer or by other internal or external processes? Here, using two large epidemiological datasets (26,912 cycles from 2303 European women and 4786 cycles from 721 North American women), analyzed with time series and circular statistics, we find evidence that the rhythmic characteristics of the menstrual cycle are more likely to be explained by an endogenous clock-like driving mechanism than by any other internal or external process. We also show that the menstrual cycle is weakly but significantly influenced by the 29.5-day lunar cycle and that the phase alignment between the two cycles differs between the European and the North American populations. Given the need to find efficient treatments of subfertility in women, our results should be confirmed in larger populations, and chronobiological approaches to optimize the ovulatory cycle should be evaluated.

Experiences of users of period tracking apps: which app, frequency of use, data input and output and attitudes

RESEARCH QUESTION

What are the experiences of users of period tracking apps in relation to which apps they use, their frequency of use, the type of data and their attitudes to period tracking apps?

DESIGN

This was an observational mixed-methods study using an online survey designed using Qualtrics XM. The survey included 50 open-ended and multiple choice questions, but only specific questions were analysed in this study. The survey was promoted via social media for 22 days between 30 June and 21 July 2021.

RESULTS

Of the 375 total participants, 326 responses were complete and included in analysis. In the results section further down, this is explained as 330 complete responses, with 4 additional responses excluded due to data inconsistencies. The participants' age range was 14-54 years, with a mean of 26.0 (±7.81) years. Most participants (61.9%) had been using a period tracking app for 2 years or longer. Of these 85.6% entered more data when on their period, 31% at a frequency of once a day. There were approximately equivalent proportions of participants who felt that entering data into their app was either 'part of their normal routine' (43.3%) or 'not a priority' (38.0%). Thematic analysis of the participants' experiences of using period-tracking apps revealed five main themes: symptom tracking and understanding general health; concerns with period start date predictions; the problems with fertility tracking; concerns about cost; and concerns about data privacy.

CONCLUSIONS

The infrequency of data inputting and the wide range of symptoms tracked reflects the differing needs of participants from their period-tracking apps. This highlights the need for increased education and research into understanding the realities of variations in using apps.

The menstrual cycle is influenced by weekly and lunar rhythms

OBJECTIVE

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).

DESIGN

Cross-sectional study.

SUBJECTS

A total of 35,940 European and North American women aged 18-40 years.

EXPOSURE

Data were collected in real-life conditions.

INTERVENTION

No intervention was performed.

MAIN OUTCOME MEASURE

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.

RESULTS

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.

CONCLUSION

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.

Follicular phase length is not related to live birth outcome in women with unexplained infertility undergoing ovarian stimulation with intrauterine insemination cycles in a multicenter trial

Objective

To evaluate the effect of follicular phase length (FPL) on pregnancy outcomes and endometrial thickness (ET) among women with unexplained infertility undergoing ovarian stimulation with intrauterine insemination (OS-IUI) with clomiphene citrate, letrozole, or gonadotropins.

Design

Cohort analysis of the Reproductive Medicine Network's Assessment of Multiple Intrauterine Gestations from Ovarian Stimulation randomized controlled trial.

Setting

Multicenter randomized controlled trial.

Patients

A total of 869 couples with unexplained infertility who underwent OS-IUI treatment cycles as part of the Assessment of Multiple Intrauterine Gestations from Ovarian Stimulation study.

Interventions

FPL was evaluated as a categorical variable defined by quintiles (q1: ≤11 days, q2: 12 days, q3: 13 days, q4: 14-15 days, and q5: ≥16 days).

Main outcome measures

Clinical pregnancy, live birth rates, and ET.

Results

Decreasing FPL quintiles did not reduce clinical pregnancy or live birth rates in unadjusted or adjusted models with all treatment groups combined or when stratified by the ovarian stimulation medication. All FPL categories had significantly thinner ET compared with the 5th quintile (≥16 days) among women treated with clomiphene citrate or letrozole. Similar but diminished associations were observed among women who underwent ovarian stimulation with gonadotropins, but the observed differences were limited to those with FPL of 12 days or shorter when compared with FPL ≥16 days.

Conclusions

Although shorter FPL was associated with reduced ET, it was not associated with the outcomes of clinical pregnancy or live birth in women with unexplained infertility undergoing OS-IUI in all treatment groups combined. Similar patterns existed when analyses of clinical pregnancy and live birth rates were stratified by treatment.

Clinical trial registration

NCT01044862.

Sub-communities of the vaginal microbiota in pregnant and non-pregnant women

Diverse and non-Lactobacillus-dominated vaginal microbial communities are associated with adverse health outcomes such as preterm birth and the acquisition of sexually transmitted infections. Despite the importance of recognizing and understanding the key risk-associated features of these communities, their heterogeneous structure and properties remain ill-defined. Clustering approaches are commonly used to characterize vaginal communities, but they lack sensitivity and robustness in resolving substructures and revealing transitions between potential sub-communities. Here, we address this need with an approach based on mixed membership topic models. Using longitudinal data from cohorts of pregnant and non-pregnant study participants, we show that topic models more accurately describe sample composition, longitudinal changes, and better predict the loss of Lactobacillus dominance. We identify several non-Lactobacillus-dominated sub-communities common to both cohorts and independent of reproductive status. In non-pregnant individuals, we find that the menstrual cycle modulates transitions between and within sub-communities, as well as the concentrations of half of the cytokines and 18% of metabolites. Overall, our analyses based on mixed membership models reveal substructures of vaginal ecosystems which may have important clinical and biological associations.

Evaluation of Menstrual Cycle Tracking Behaviors in the Ovulation and Menstruation Health Pilot Study: Cross-Sectional Study

BACKGROUND

Menstrual cycle tracking apps (MCTAs) have potential in epidemiological studies of women's health, facilitating real-time tracking of bleeding days and menstrual-associated signs and symptoms. However, information regarding the characteristics of MCTA users versus cycle nontrackers is limited, which may inform generalizability.

OBJECTIVE

We compared characteristics among individuals using MCTAs (app users), individuals who do not track their cycles (nontrackers), and those who used other forms of menstrual tracking (other trackers).

METHODS

The Ovulation and Menstruation Health Pilot Study tested the feasibility of a digitally enabled evaluation of menstrual health. Recruitment occurred between September 2017 and March 2018. Menstrual cycle tracking behavior, demographic, and general and reproductive health history data were collected from eligible individuals (females aged 18-45 years, comfortable communicating in English). Menstrual cycle tracking behavior was categorized in 3 ways: menstrual cycle tracking via app usage, that via other methods, and nontracking. Demographic factors, health conditions, and menstrual cycle characteristics were compared across the menstrual tracking method (app users vs nontrackers, app users vs other trackers, and other trackers vs nontrackers) were assessed using chi-square or Fisher exact tests.

RESULTS

In total, 263 participants met the eligibility criteria and completed the digital survey. Most of the cohort (n=191, 72.6%) was 18-29 years old, predominantly White (n=170, 64.6%), had attained 4 years of college education or higher (n= 209, 79.5%), and had a household income below US $50,000 (n=123, 46.8%). Among all participants, 103 (39%) were MCTA users (app users), 97 (37%) did not engage in any tracking (nontrackers), and 63 (24%) used other forms of tracking (other trackers). Across all groups, no meaningful differences existed in race and ethnicity, household income, and education level. The proportion of ever-use of hormonal contraceptives was lower (n=74, 71.8% vs n=87, 90%, P=.001), lifetime smoking status was lower (n=6, 6% vs n=15, 17%, P=.04), and diagnosis rate of gastrointestinal reflux disease (GERD) was higher (n=25, 24.3% vs n=12, 12.4%, P=.04) in app users than in nontrackers. The proportions of hormonal contraceptives ever used and lifetime smoking status were both lower (n=74, 71.8% vs n=56, 88.9%, P=.01; n=6, 6% vs n=11, 17.5%, P=.02) in app users than in other trackers. Other trackers had lower proportions of ever-use of hormonal contraceptives (n=130, 78.3% vs n=87, 89.7%, P=.02) and higher diagnostic rates of heartburn or GERD (n=39, 23.5% vs n=12, 12.4%, P.03) and anxiety or panic disorder (n=64, 38.6% vs n=25, 25.8%, P=.04) than nontrackers. Menstrual cycle characteristics did not differ across all groups.

CONCLUSIONS

Our results suggest that app users, other trackers, and nontrackers are largely comparable in demographic and menstrual cycle characteristics. Future studies should determine reasons for tracking and tracking-related behaviors to further understand whether individuals who use MCTAs are comparable to nontrackers.

A randomized trial of web-based fertility-tracking software and fecundability

OBJECTIVE

To assess the effect of randomization to FertilityFriend.com, a mobile computing fertility-tracking app, on fecundability.

DESIGN

Parallel non-blinded randomized controlled trial nested within the Pregnancy Study Online (PRESTO), a North American preconception cohort.

PATIENT(S)

Female participants aged 21 to 45 years attempting conception for ≤6 menstrual cycles at enrolment (2013-2019).

INTERVENTION

Randomization (1:1) of 5532 participants to receive a premium Fertility Friend (FF) subscription.

MAIN OUTCOME MEASURE(S)

Fecundability (per-cycle probability of conception). Participants completed bimonthly follow-up questionnaires until pregnancy or a censoring event, whichever came first. We first performed an intent-to-treat analysis of the effect of FF randomization on fecundability. In secondary analyses, we used a per-protocol approach that accounted for adherence in each trial arm. In both analyses, we used proportional probabilities regression models to estimate fecundability ratios (FR) and 95% confidence intervals (CI) comparing those randomized vs. not randomized and applied inverse probability weights to account for loss-to-follow-up (intent-to-treat and per-protocol analyses) and adherence (per-protocol analyses only).

RESULTS

Using life-table methods, 64% of the 2775 participants randomized to FF and 63% of the 2767 participants not randomized to FF conceived during 12 cycles; these respective percentages were each 70% among those with 0-1 cycles of attempt time at enrolment. Of those randomized to FF, 72% were defined as adherent (68% of observed menstrual cycles). In intent-to-treat analyses, there was no appreciable association overall (FR = 0.97; 95% CI, 0.90-1.04) or within strata of pregnancy attempt time at enrolment, age, education, or other characteristics. In per-protocol analyses, we observed little association overall (FR = 1.06; 95% CI, 0.99-1.14), but weak-to-moderate positive associations among participants who had longer attempt times at enrolment (FR = 1.15; 95% CI, 0.98-1.35 for 3-4 cycles; FR = 1.14; 95% CI, 0.87-1.48 for 5-6 cycles), were aged <25 years (FR = 1.29; 95% CI, 1.01-1.66), had ≤12 years of education (FR = 1.32; 95% CI, 0.92-1.89), or were non-users of hormonal contraception within 3 months before enrolment (FR = 1.10; 95% CI, 1.02-1.19).

CONCLUSION

No appreciable associations were observed in intent-to-treat analyses. In secondary per-protocol analyses that accounted for adherence, randomization to FF was associated with slightly greater fecundability among selected subgroups of participants; however, these results are susceptible to unmeasured confounding.

Menstrual cycle length variation by demographic characteristics from the Apple Women's Health Study

Menstrual characteristics are important signs of women's health. Here we examine the variation of menstrual cycle length by age, ethnicity, and body weight using 165,668 cycles from 12,608 participants in the US using mobile menstrual tracking apps. After adjusting for all covariates, mean menstrual cycle length is shorter with older age across all age groups until age 50 and then became longer for those age 50 and older. Menstrual cycles are on average 1.6 (95%CI: 1.2, 2.0) days longer for Asian and 0.7 (95%CI: 0.4, 1.0) days longer for Hispanic participants compared to white non-Hispanic participants. Participants with BMI ≥ 40 kg/m² have 1.5 (95%CI: 1.2, 1.8) days longer cycles compared to those with BMI between 18.5 and 25 kg/m². Cycle variability is the lowest among participants aged 35-39 but are considerably higher by 46% (95%CI: 43%, 48%) and 45% (95%CI: 41%, 49%) among those aged under 20 and between 45-49. Cycle variability increase by 200% (95%CI: 191%, 210%) among those aged above 50 compared to those in the 35-39 age group. Compared to white participants, those who are Asian and Hispanic have larger cycle variability. Participants with obesity also have higher cycle variability. Here we confirm previous observations of changes in menstrual cycle pattern with age across reproductive life span and report new evidence on the differences of menstrual variation by ethnicity and obesity status. Future studies should explore the underlying determinants of the variation in menstrual characteristics.

Menstrual Tracking Mobile App Review by Consumers and Health Care Providers: Quality Evaluations Study

BACKGROUND

Women's menstrual cycle is an important component of their overall health. Physiological cycles and associated symptoms can be monitored continuously and used as indicators in various fields. Menstrual apps are accessible and can be used to promote overall female health. However, no study has evaluated these apps' functionality from both consumers' and health care providers' perspectives. As such, the evidence indicating whether the menstrual apps available on the market provide user satisfaction is insufficient.

OBJECTIVE

This study was performed to investigate the key content and quality of menstrual apps from the perspectives of health care providers and consumers. We also analyzed the correlations between health care provider and consumer evaluation scores. On the basis of this analysis, we offer technical and policy recommendations that could increase the usability and convenience of future app.

METHODS

We searched the Google Play Store and iOS App Store using the keywords "period" and "menstrual cycle" in English and Korean and identified relevant apps. An app that met the following inclusion criteria was selected as a research app: nonduplicate; with >10,000 reviews; last updated ≤180 days ago; relevant to this topic; written in Korean or English; available free of charge; and currently operational. App quality was evaluated by 6 consumers and 4 health care providers using Mobile Application Rating Scale (MARS) and user version of the Mobile Application Rating Scale (uMARS). We then analyzed the correlations among MARS scores, uMARS scores, star ratings, and the number of reviews.

RESULTS

Of the 34 apps, 31 (91%) apps could be used to predict the menstrual cycle, and 2 (6%) apps provided information pertinent to health screening. All apps that scored highly in the MARS evaluation offer a symptom logging function and provide the user with personalized notifications. The "Bom Calendar" app had the highest MARS (4.51) and uMARS (4.23) scores. The MARS (2.22) and uMARS (4.15) scores for the "Menstrual calendar-ovulation & pregnancy calendar" app were different. In addition, there was no relationship between MARS and uMARS scores (r=0.32; P=.06).

CONCLUSIONS

We compared consumer and health care provider ratings for menstrual apps. Continuous monitoring of app quality from consumer and health care provider perspectives is necessary to guide their development and update content.

Abnormal uterine bleeding patterns determined through menstrual tracking among participants in the Apple Women's Health Study

BACKGROUND

Use of menstrual tracking data to understand abnormal bleeding patterns has been limited because of lack of incorporation of key demographic and health characteristics and confirmation of menstrual tracking accuracy.

OBJECTIVE

This study aimed to identify abnormal uterine bleeding patterns and their prevalence and confirm existing and expected associations between abnormal uterine bleeding patterns, demographics, and medical conditions.

STUDY DESIGN

Apple Women's Health Study participants from November 2019 through July 2021 who contributed menstrual tracking data and did not report pregnancy, lactation, use of hormones, or menopause were included in the analysis. Four abnormal uterine bleeding patterns were evaluated: irregular menses, infrequent menses, prolonged menses, and irregular intermenstrual bleeding (spotting). Monthly tracking confirmation using survey responses was used to exclude inaccurate or incomplete digital records. We investigated the prevalence of abnormal uterine bleeding stratified by demographic characteristics and used logistic regression to evaluate the relationship of abnormal uterine bleeding to a number of self-reported medical conditions.

RESULTS

There were 18,875 participants who met inclusion criteria, with a mean age of 33 (standard deviation, 8.2) years, mean body mass index of 29.3 (standard deviation, 8.0), and with 68.9% (95% confidence interval, 68.2-69.5) identifying as White, non-Hispanic. Abnormal uterine bleeding was found in 16.4% of participants (n=3103; 95% confidence interval, 15.9-17.0) after accurate tracking was confirmed; 2.9% had irregular menses (95% confidence interval, 2.7-3.1), 8.4% had infrequent menses (95% confidence interval, 8.0-8.8), 2.3% had prolonged menses (95% confidence interval, 2.1-2.5), and 6.1% had spotting (95% confidence interval, 5.7-6.4). Black participants had 33% higher prevalence (prevalence ratio, 1.33; 95% confidence interval, 1.09-1.61) of infrequent menses compared with White, non-Hispanic participants after controlling for age and body mass index. The prevalence of infrequent menses was increased in class 1, 2, and 3 obesity (class 1: body mass index, 30-34.9; prevalence ratio, 1.31; 95% confidence interval, 1.13-1.52; class 2: body mass index, 35-39.9; prevalence ratio, 1.25; 95% confidence interval, 1.05-1.49; class 3: body mass index, >40; prevalence ratio, 1.51; 95% confidence interval, 1.21-1.88) after controlling for age and race/ethnicity. Those with class 3 obesity had 18% higher prevalence of abnormal uterine bleeding compared with healthy-weight participants (prevalence ratio, 1.18; 95% confidence interval, 1.02-1.38). Participants with polycystic ovary syndrome had 19% higher prevalence of abnormal uterine bleeding compared with participants without this condition (prevalence ratio, 1.19; 95% confidence interval, 1.08-1.31). Participants with hyperthyroidism (prevalence ratio, 1.34; 95% confidence interval, 1.13-1.59) and hypothyroidism (prevalence ratio, 1.17; 95% confidence interval, 1.05-1.31) had a higher prevalence of abnormal uterine bleeding, as did those reporting endometriosis (prevalence ratio, 1.28; 95% confidence interval, 1.12-1.45), cervical dysplasia (prevalence ratio, 1.20; 95% confidence interval, 1.03-1.39), and fibroids (prevalence ratio, 1.14; 95% confidence interval, 1.00-1.30).

CONCLUSION

In this cohort, abnormal uterine bleeding was present in 16.4% of those with confirmed menstrual tracking. Black or obese participants had increased prevalence of abnormal uterine bleeding. Participants reporting conditions such as polycystic ovary syndrome, thyroid disease, endometriosis, and cervical dysplasia had a higher prevalence of abnormal uterine bleeding.

Complete Cycle Mapping Using a Quantitative At-Home Hormone Monitoring System in Prediction of Fertile Days, Confirmation of Ovulation, and Screening for Ovulation Issues Preventing Conception

Background and Objectives: To achieve pregnancy, it is highly beneficial to identify the time of ovulation as well as the greater period of fertile days during which sperm may survive leading up to ovulation. Confirming successful ovulation is also critical to accurately diagnose ovulatory disorders. Ovulation predictor kits, fertility monitors, and tracking apps are all available to assist with detecting ovulation, but often fall short. They may not detect the full fertile window, provide accurate or real-time information, or are simply expensive and impractical. Finally, few over-the-counter products provide information to women about their ovarian reserve and future fertility. Therefore, there is a need for an easy, over-the-counter, at-home quantitative hormone monitoring system that assesses ovarian reserve, predicts the entire fertile window, and can screen for ovulatory disorders. Materials and Methods: Proov Complete is a four-in-one at-home multihormone testing system that utilizes lateral flow assay test strips paired with the free Proov Insight App to guide testing of four hormones-FSH, E1G, LH, and PdG-across the woman's cycle. In a pilot study, 40 women (including 16 with a fertility-related diagnosis or using fertility treatments) used Complete for one cycle. Results: Here, we demonstrate that Proov Complete can accurately and sensitively predict ovarian reserve, detect up to 6 fertile days and confirm if ovulation was successful, in one easy-to-use kit. Ovulation was confirmed in 38 cycles with a detectable PdG rise. An average of 5.3 fertile days (from E1G rise to PdG rise) were detected, with an average of 2.7 days prior to LH surge. Ovulation was confirmed via PdG rise an average of 2.6 days following the LH surge. While 38/40 women had a PdG rise, only 22 had a sustained PdG level above 5 μg/mL throughout the critical implantation window, indicating ovulatory dysfunction in 16 women. Conclusions: Proov Complete can detect the entire fertile window of up to 6 fertile days and confirm ovulation, while also providing information on ovarian reserve and guidance to clinicians and patients.

Fertility Awareness-Based Methods for Women's Health and Family Planning

Background

Fertility awareness-based methods (FABMs) educate about reproductive health and enable tracking and interpretation of physical signs, such as cervical fluid secretions and basal body temperature, which reflect the hormonal changes women experience on a cyclical basis during the years of ovarian activity. Some methods measure relevant hormone levels directly. Most FABMs allow women to identify ovulation and track this "vital sign" of the menstrual or female reproductive cycle, through daily observations recorded on cycle charts (paper or electronic).

Applications

Physicians can use the information from FABM charts to guide the diagnosis and management of medical conditions and to support or restore healthy function of the reproductive and endocrine systems, using a restorative reproductive medical (RRM) approach. FABMs can also be used by couples to achieve or avoid pregnancy and may be most effective when taught by a trained instructor.

Challenges

Information about individual FABMs is rarely provided in medical education. Outdated information is widespread both in training programs and in the public sphere. Obtaining accurate information about FABMs is further complicated by the numerous period tracking or fertility apps available, because very few of these apps have evidence to support their effectiveness for identifying the fertile window, for achieving or preventing pregnancy.

Conclusions

This article provides an overview of different types of FABMs with a published evidence base, apps and resources for learning and using FABMs, the role FABMs can play in medical evaluation and management, and the effectiveness of FABMs for family planning, both to achieve or to avoid pregnancy.

Feasibility of continuous distal body temperature for passive, early pregnancy detection

Most American women become aware of pregnancy ~3-7 weeks after conceptive sex, and all must seek testing to confirm their pregnant status. The delay between conceptive sex and pregnancy awareness is often a time in which contraindicated behaviors take place. However, there is long standing evidence that passive, early pregnancy detection may be possible using body temperature. To address this possibility, we analyzed 30 individuals' continuous distal body temperature (DBT) in the 180 days surrounding self-reported conceptive sex in comparison to self-reported pregnancy confirmation. Features of DBT nightly maxima changed rapidly following conceptive sex, reaching uniquely elevated values after a median of 5.5 ± 3.5 days, whereas individuals reported a positive pregnancy test result at a median of 14.5 ± 4.2 days. Together, we were able to generate a retrospective, hypothetical alert a median of 9 ± 3.9 days prior to the date at which individuals received a positive pregnancy test. Continuous temperature-derived features can provide early, passive indication of pregnancy onset. We propose these features for testing and refinement in clinical settings, and for exploration in large, diverse cohorts. The development of pregnancy detection using DBT may reduce the delay from conception to awareness and increase the agency of pregnant individuals.

Tracking Sleep, Temperature, Heart Rate, and Daily Symptoms Across the Menstrual Cycle with the Oura Ring in Healthy Women

Background and Objective

The ovulatory menstrual cycle is characterized by hormonal fluctuations that influence physiological systems and functioning. Multi-sensor wearable devices can be sensitive tools capturing cycle-related physiological features pertinent to women’s health research. This study used the Oura ring to track changes in sleep and related physiological features, and also tracked self-reported daily functioning and symptoms across the regular, healthy menstrual cycle.

Methods

Twenty-six healthy women (age, mean (SD): 24.4 (1.1 years)) with regular, ovulatory cycles (length, mean (SD): 28.57 (3.8 days)) were monitored across a complete menstrual cycle. Four menstrual cycle phases, reflecting different hormone milieus, were selected for analysis: menses, ovulation, mid-luteal, and late-luteal. Objective measures of sleep, sleep distal skin temperature, heart rate (HR) and vagal-mediated heart rate variability (HRV, rMSSD), derived from the Oura ring, and subjective daily diary measures (eg sleep quality, readiness) were compared across phases.

Results

Wearable-based measures of sleep continuity and sleep stages did not vary across the menstrual cycle. Women reported no menstrual cycle-related changes in perceived sleep quality or readiness and only marginally poorer mood in the midluteal phase. However, they reported moderately more physical symptoms during menses (p < 0.001). Distal skin temperature and HR, measured during sleep, showed a biphasic pattern across the menstrual cycle, with increased HR (p < 0.03) and body temperature (p < 0.001) in the mid- and late-luteal phases relative to menses and ovulation. Correspondingly, rMSSD HRV tended to be lower in the luteal phase. Further, distal skin temperature was lower during ovulation relative to menses (p = 0.05).

Conclusion

The menstrual cycle was not accompanied by significant fluctuations in objective and perceived measures of sleep or in mood, in healthy women with regular, ovulatory menstrual cycles. However, other physiological changes in skin temperature and HR were evident and may be longitudinally tracked with the Oura ring in women over multiple cycles in a natural setting.

Multisite Effectiveness Study of the Marquette Method of Natural Family Planning Program

Women of reproductive age need reliable and effective family planning methods to manage their fertility. Natural family planning (NFP) methods or fertility awareness-based methods (FABMs) have been increasingly used by women due to their health benefits. Nevertheless, effectiveness of these natural methods remains inconsistent, and these methods are difficult for healthcare providers to implement in their clinical practice. The purpose of this study is to evaluate the effectiveness of the Marquette Model NFP system to avoid pregnancy for women at multiple teaching sites using twelve months of retrospectively collected teaching data. Survival analysis (Kaplan-Meier) was used to determine typical unintended pregnancy rates for a total of 1,221 women. There were forty-two unintended pregnancies which provided a typical use unintended pregnancy rate of 6.7 per 100 women over twelve months of use. Eleven of the forty-two unintended pregnancies were associated with correct use of the method. The total unintended pregnancy rate over twelve months of use was 2.8 per 100 for women with regular cycles, 8.0 per 100 women for the postpartum and breastfeeding women, and 4.3 per 100 for women with irregular menstrual cycles. The Marquette Model system of NFP was effective when provided by health professionals who completed the Marquette Model NFP teacher training program.

Summary

This study involved determining whether healthcare professionals at ten sites across the United States and Canada trained to provide the Marquette Method NFP services can replicate the effectiveness demonstrated in previous studies of the method. We found a high level of effectiveness (i.e., very low pregnancy rates) in using the Marquette Method among women from various regions across North America with diverse reproductive backgrounds and in particular when using hormonal fertility marker. Healthcare providers who have been trained to teach NFP can successfully incorporate NFP services in their practice and assist their clients in choosing appropriate family planning methods.

The messiness of the menstruator: assessing personas and functionalities of menstrual tracking apps

OBJECTIVE

The aim of this study was to examine trends in the intended users and functionalities advertised by menstrual tracking apps to identify gaps in personas and intended needs fulfilled by these technologies.

MATERIALS AND METHODS

Two types of materials were collected: a corpus of scientific articles related to the identities and needs of menstruators and a corpus of images and descriptions of menstrual tracking apps collected from the Google and Apple app stores. We conducted a scoping review of the literature to develop themes and then applied these as a framework to analyze the app corpus, looking for alignments and misalignments between the 2 corpora.

RESULTS

A review of the literature showed a wide range of disciplines publishing work relevant to menstruators. We identified 2 broad themes: "who are menstruators?" and "what are the needs of menstruators?" Descriptions of menstrual trackers exhibited misalignments with these themes, with narrow characterizations of menstruators and design for limited needs.

DISCUSSION

We synthesize gaps in the design of menstrual tracking apps and discuss implications for designing around: (1) an irregular menstrual cycle as the norm; (2) the embodied, leaky experience of menstruation; and (3) the varied biologies, identities, and goals of menstruators. An overarching gap suggests a need for a human-centered artificial intelligence approach for model and data provenance, transparency and explanations of uncertainties, and the prioritization of privacy in menstrual trackers.

CONCLUSION

Comparing and contrasting literature about menstruators and descriptions of menstrual tracking apps provide a valuable guide to assess menstrual technology and their responsiveness to users and their needs.

A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking

OBJECTIVE

The study sought to build predictive models of next menstrual cycle start date based on mobile health self-tracked cycle data. Because app users may skip tracking, disentangling physiological patterns of menstruation from tracking behaviors is necessary for the development of predictive models.

MATERIALS AND METHODS

We use data from a popular menstrual tracker (186 000 menstruators with over 2 million tracked cycles) to learn a predictive model, which (1) accounts explicitly for self-tracking adherence; (2) updates predictions as a given cycle evolves, allowing for interpretable insight into how these predictions change over time; and (3) enables modeling of an individual's cycle length history while incorporating population-level information.

RESULTS

Compared with 5 baselines (mean, median, convolutional neural network, recurrent neural network, and long short-term memory network), the model yields better predictions and consistently outperforms them as the cycle evolves. The model also provides predictions of skipped tracking probabilities.

DISCUSSION

Mobile health apps such as menstrual trackers provide a rich source of self-tracked observations, but these data have questionable reliability, as they hinge on user adherence to the app. By taking a machine learning approach to modeling self-tracked cycle lengths, we can separate true cycle behavior from user adherence, allowing for more informed predictions and insights into the underlying observed data structure.

CONCLUSIONS

Disentangling physiological patterns of menstruation from adherence allows for accurate and informative predictions of menstrual cycle start date and is necessary for mobile tracking apps. The proposed predictive model can support app users in being more aware of their self-tracking behavior and in better understanding their cycle dynamics.

mHealth and its application in menstrual related issues: a systematic review

OBJECTIVE

The objective of this research was to evaluate how menstrual tracking applications can promote gynaecological health.

MATERIALS AND METHODS

We performed a systematic review in Medline and Scopus, for papers evaluating menstrual tracking mobile applications. We excluded review articles and those not written in English.

RESULTS

We identified 14 articles measuring the outcome resulting from the use of a single Fertility Tracking Application (FTA). Eight studies evaluated 2 different applications used as a contraception method. One study assessed a fecundity enhancing application. Five studies referred to applications, used to treat or monitor various gynaecologic issues. All studies reported efficacy for their intended use or a high satisfaction rate.

DISCUSSION

There is a plethora of FTAs, however a minority of them are appraised by medical experts. Several safety and privacy concerns have been expressed regarding their use and these issues should be addressed in the future. All studies identified in our search demonstrated that FTAs can facilitate users in terms of contraception, fertility, and menstrual awareness.

CONCLUSION

Menstrual tracking applications can serve as a valuable health tool, nevertheless, their content should be more vigorously evaluated.

Using an mHealth App (iGAM) to Reduce Gingivitis Remotely (Part 2): Prospective Observational Study

BACKGROUND

Gingivitis is a nonpainful, inflammatory condition that can be managed at home. Left untreated, gingivitis can lead to tooth loss. Periodic dental examinations are important for early diagnosis and treatment of gum diseases. To contain the spread of the coronavirus, governments, including in Israel, have restricted movements of their citizens which might have caused routine dental checkups to be postponed.

OBJECTIVE

This study aimed to examine the ability of a mobile health app, iGAM, to reduce gingivitis, and to determine the most effective interval between photograph submissions.

METHODS

A prospective observational cohort study with 160 unpaid participants divided into 2 equal groups using the iGAM app was performed. The intervention group photographed their gums weekly for 8 weeks. The wait-list control group photographed their gums at the time of recruitment and 8 weeks later. After photo submission, the participants received the same message "we recommended that you read the information in the app regarding oral hygiene habits." A single-blinded researcher examined all the images and scored them according to the Modified Gingival Index (MGI).

RESULTS

The average age of the intervention group was 26.77 (SD 7.43) and 28.53 (SD 10.44) for the wait-list control group. Most participants were male (intervention group: 56/75,74.7%; wait-list control group: 34/51, 66.7%) and described themselves as "secular"; most were "single" non-smokers (intervention group: 56/75, 74.7%; wait-list control group: 40/51, 78.4%), and did not take medications (intervention group: 64/75, 85.3%; wait-list control group: 40/51, 78.4%). A total of 126 subjects completed the study. A statistically significant difference (P<.001) was found in the dependent variable (MGI). Improvements in gingival health were noted over time, and the average gingivitis scores were significantly lower in the intervention group (mean 1.16, SD 1.18) than in the wait-list control group (mean 2.16, SD 1.49) after 8 weeks. Those with more recent dental visits had a lower MGI (P=.04). No association was found between knowledge and behavior. Most participants were familiar with the recommendations for maintaining oral health, yet they only performed some of them.

CONCLUSIONS

A dental selfie taken once a week using an mobile health app (iGAM) reduced the signs of gingivitis and promoted oral health. Selfies taken less frequently yielded poorer results. During the current pandemic, where social distancing recommendations may be causing people to avoid dental clinics, this app can remotely promote gum health.

Labeling self-tracked menstrual health records with hidden semi-Markov models

Globally, millions of women track their menstrual cycle and fertility via smartphone-based health apps, generating multivariate time series with frequent missing data. To leverage this type of data for studies of fertility or studies of the effect of the menstrual cycle on symptoms and diseases, it is critical to have methods for identifying reproductive events, such as ovulation, pregnancy losses or births. Here, we present a hierarchical approach relying on hidden semi-Markov models that adapts to changes in tracking behavior, explicitly captures variable and state dependent missingness, allows for variables of different type, and quantifies uncertainty. The accuracy on simulated data reaches 98% with no missing data and 90% with realistic missingness. On our partially labeled real-world time series, the accuracy reaches 93%. Our method also accurately predicts cycle length by learning user characteristics. Its implementation is publicly available (HiddenSemiMarkov R package) and transferable to any health time series, including self-reported symptoms and occasional tests.

Modelling menstrual cycle length in athletes using state-space models

The ability to predict an individual's menstrual cycle length to a high degree of precision could help female athletes to track their period and tailor their training and nutrition correspondingly. Such individualisation is possible and necessary, given the known inter-individual variation in cycle length. To achieve this, a hybrid predictive model was built using data on 16,524 cycles collected from a sample of 2125 women (mean age 34.38 years, range 18.00-47.10, number of menstrual cycles ranging from 4 to 53). A mixed-effect state-space model was fitted to capture the within-subject temporal correlation, incorporating a Bayesian approach for process forecasting to predict the duration (in days) of the next menstrual cycle. The modelling procedure was split into three steps (1) a time trend component using a random walk with an overdispersion parameter, (2) an autocorrelation component using an autoregressive moving-average model, and (3) a linear predictor to account for covariates (e.g. injury, stomach cramps, training intensity). The inclusion of an overdispersion parameter suggested that [Formula: see text] [Formula: see text] of cycles in the sample were overdispersed. The random walk standard deviation for a non-overdispersed cycle is [Formula: see text] [1.00, 1.09] days while under an overdispersed cycle, the menstrual cycle variance increase in 4.78 [4.57, 5.00] days. To assess the performance and prediction accuracy of the model, each woman's last observation was used as test data. The root mean square error (RMSE), concordance correlation coefficient and Pearson correlation coefficient (r) between the observed and predicted values were calculated. The model had an RMSE of 1.6412 days, a precision of 0.7361 and overall accuracy of 0.9871. In conclusion, the hybrid model presented here is a helpful approach for predicting menstrual cycle length, which in turn can be used to support female athlete wellness.

A Generative Modeling Approach to Calibrated Predictions: A Use Case on Menstrual Cycle Length Prediction

We explore how to quantify uncertainty when designing predictive models for healthcare to provide well-calibrated results. Uncertainty quantification and calibration are critical in medicine, as one must not only accommodate the variability of the underlying physiology, but adjust to the uncertain data collection and reporting process. This occurs not only on the context of electronic health records (i.e., the clinical documentation process), but on mobile health as well (i.e., user specific self-tracking patterns must be accounted for). In this work, we show that accurate uncertainty estimation is directly relevant to an important health application: the prediction of menstrual cycle length, based on self-tracked information. We take advantage of a flexible generative model that accommodates under-dispersed distributions via two degrees of freedom to fit the mean and variance of the observed cycle lengths. From a machine learning perspective, our work showcases how flexible generative models can not only provide state-of-the art predictive accuracy, but enable well-calibrated predictions. From a healthcare perspective, we demonstrate that with flexible generative models, not only can we accommodate the idiosyncrasies of mobile health data, but we can also adjust the predictive uncertainty to per-user cycle length patterns. We evaluate the proposed model in real-world cycle length data collected by one of the most popular menstrual trackers worldwide, and demonstrate how the proposed generative model provides accurate and well-calibrated cycle length predictions. Providing meaningful, less uncertain cycle length predictions is beneficial for menstrual health researchers, mobile health users and developers, as it may help design more usable mobile health solutions.

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.

Does an association exist between menstrual cycle length within the normal range and ovarian reserve biomarkers during the reproductive years? A systematic review and meta-analysis

BACKGROUND

Regular menstrual cycling during the reproductive years is an indicator of spontaneous ovulation but sometimes falsely perceived as an indicator of preserved fertility. In contrast, menstrual cycle shortening, a physiologic occurrence preceding the menopausal transition, is not usually perceived as an indicator of decreased ovarian reserve in the general population.

OBJECTIVE AND RATIONALE

The individual decrease in menstrual cycle length (MCL) might represent a sensitive biomarker of diminishing ovarian reserve. The aim of this systematic review and meta-analysis is to examine the possible association between MCL in regularly cycling women (21-35 days) and ovarian reserve tests (ORT), fecundability in natural cycles and IVF outcomes.

SEARCH METHODS

An electronic database search employing PubMed, Web of Science, Trip, EBSCO, ClinicalTrials.gov and the Cochrane library was performed to identify research articles, only on human, published between January 1978 and August 2019. Search terms were pregnancy OR fertility OR fecundity OR fecundability, anti-Müllerian hormone OR AMH OR antral follicle count OR AFC OR ovarian reserve OR ovarian reserve test, in vitro fertilization OR ART OR assisted reproductive therapy OR assisted reproductive treatment OR assisted reproductive technology OR IVF OR ICSI, menstrual cycle length OR menstrual cycle characteristics. We combined these terms to complete the search. All prospective and retrospective studies exploring an association between MCL and proxies of ovarian reserve were included. The exclusions included studies of PCOS, ovarian failure, oral contraception treatment, prior chemotherapy and/or radiotherapy or ovarian surgery. The Newcastle-Ottawa scale was used to assess the quality of studies that were eligible for meta-analysis.

OUTCOMES

Eleven studies were eligible for meta-analysis, including 12 031 women. The included studies had a low risk of bias. Short MCL (21-27 days) was associated with lower ORT values as compared to normal (28-31 days), long (32-35 days) and all other (28-35 days) MCL sets. The estimated weighted mean difference (WMD) of AMH level was -1.3 ng/mL (95% CI: -1.75 to -0.86, P < 0.001) between the short and normal MCL sets. The estimated WMD of AFC values was -5.17 (95% CI: -5.96 to -4.37, P < 0.001) between the short and normal MCL sets. The weighted overall odds ratio (OR) of fecundability in natural cycles between women with short versus normal MCL sets was statistically significant (overall OR 0.81; 95% CI 0.72-0.91, P < 0.001). In the IVF setting, fewer oocytes were retrieved in short MCL in comparison to normal, long and all other MCL sets, with an estimated WMD of -1.8 oocytes (95% CI: -2.5 to -1.1, P < 0.001) in the short versus normal MCL sets. The weighted overall OR of clinical pregnancy rate between women with short versus all other MCL sets was statistically significant (overall OR 0.76; 95% CI: 0.60 to 0.96, P = 0.02). Low levels of heterogeneity were found in most meta-analyses of MCL and qualitative ovarian reserve biomarkers, while heterogeneity was high in meta-analyses performed for quantitative measures.

WIDER IMPLICATIONS

MCL in regularly cycling women is closely related to ovarian reserve biomarkers during the reproductive years. A short MCL, as compared to normal, is significantly associated with lower ORT values, reduced fecundability and inferior IVF outcomes, independent of age. The results imply that short MCL may be a sign of ovarian aging, combining the quantitative and qualitative facets of ovarian reserve. Educational efforts ought to be designed to guide women with short MCL at a young age, who desire children in the future, to seek professional counselling.

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.

Menstrual Cycle Tracking Applications and the Potential for Epidemiological Research: A Comprehensive Review of the Literature

Purpose of review

We reviewed published studies on menstrual cycle tracking applications (MCTAs) in order to describe the potential of MCTAs for epidemiologic research.

Recent Findings

A search of PubMed, Web of Science, and Scopus for MCTA literature yielded 150 articles. After exclusions, there were 49 articles that addressed the primary interest areas: 1) characteristics of MCTA users in research, 2) reasons women use or continue using MCTAs, 3) accuracy of identifying ovulation and utility at promoting and preventing pregnancy, and 4) quality assessments of MCTAs across several domains.

Summary

MCTAs are an important tool for the advancement of epidemiologic research on menstruation. MCTA studies should describe the characteristics of their user-base and missing data patterns. Describing the motivation for using MCTAs throughout a user's life and validating the data collected should be prioritized in future research.

Daily, weekly, seasonal and menstrual cycles in women's mood, behaviour and vital signs

Dimensions of human mood, behaviour and vital signs cycle over multiple timescales. However, it remains unclear which dimensions are most cyclical, and how daily, weekly, seasonal and menstrual cycles compare in magnitude. The menstrual cycle remains particularly understudied because, not being synchronized across the population, it will be averaged out unless menstrual cycles can be aligned before analysis. Here, we analyse 241 million observations from 3.3 million women across 109 countries, tracking 15 dimensions of mood, behaviour and vital signs using a women's health mobile app. Out of the daily, weekly, seasonal and menstrual cycles, the menstrual cycle had the greatest magnitude for most of the measured dimensions of mood, behaviour and vital signs. Mood, vital signs and sexual behaviour vary most substantially over the course of the menstrual cycle, while sleep and exercise behaviour remain more constant. Menstrual cycle effects are directionally consistent across countries.

Goals, life events, and transitions: examining fertility apps for holistic health tracking

OBJECTIVE

Fertility is becoming increasingly supported by consumer health technologies, especially mobile apps that support self-tracking activities. However, it is not clear whether the apps support the variety of goals and life events of those who menstruate, especially during transitions between them.

METHODS

Thirty-one of the most popular fertility apps were evaluated, analyzing data from three sources: the content of app store pages, app features, and user reviews.

FINDINGS

Results suggest that fertility apps are designed to support specific life goals of people who menstruate, offering several data collection features and limited feedback options. However, users often desire holistic tracking that encompasses a variety of goals, life events, and the transitions among them.

DISCUSSION

These findings suggest fertility patients can benefit more from holistic self-tracking and provide insights for future design of consumer health technologies that better support holistic fertility tracking.

CONCLUSION

Fertility apps have the potential to support varied experiences of people who menstruate. But to achieve that, apps need to expand their support by offering ways for more users to perform holistic, personalized, and personally meaningful tracking, so they can derive long-term benefit from the data they collect.

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.

A Novel Fertility Indicator Equation Using Estradiol Levels for Assessment of Phase of the Menstrual Cycle

Background and Objectives: Urinary hormone home monitoring assays are now available for fertility awareness methods (FAMs) of family planning, but lack sensitivity and precision in establishing the start of the fertile phase. We hypothesized that with a suitable algorithm, daily serum or blood estradiol (E2) levels could serve as a better analyte to determine the phase of the ovulatory cycle and the fertile start day (FSD). Materials and Methods: Published day-specific serum E2 levels, indexed to the serum luteinizing hormone (LH) peak, were analyzed from three independent laboratories for a threshold for a FSD. A fertility indicator quation (FIE) was discovered and tested with these data and a FSD was determined using the mean or median and variance ranges of the day-specific E2 data. Results: The considerable variance of day-specific serum E2 levels made an absolute serum E2 indicator for phase of cycle problematic. However, a FIE was discovered which maps the day-specific E2 levels of the ovulatory cycle enabling the fertile phase and transition to the luteal phase to be signaled. In this equation, FIE(D) is the value of FIE on day, D, of the cycle and has both a magnitude and sign. The magnitude of FIE(D) is the product of the normalized change in day-specific E2 levels over two consecutive intervals, (D-2, D-1) and (D-1, D), multiplied by 100, and is formulated as: (E2 (on D-1) - E2 (on D-2))/E2 (on D-2) × (E2(on D) - E2 (on D-1))/E2 (on D-1) × 100. The sign of FIE(D) is either + or - or ind (indeterminate) and is assigned on the basis of the direction of this product. Using a FIE threshold of ≥2.5 as the start of the fertile phase, the FSDs were Day -5 or Day -6, with FSD Day -4 for an outlier set of E2 levels. The maximum FIE value ranged 9.5-27.8 and occurred most often on Day -2. An inflection point with a large change in FIE magnitude and change in sign from + to - always occurred at Day 0 for all sets of day-specific E2 data signaling transition to the luteal phase. Conclusions: The fertility indicator equation, a product of two sequential normalized changes in serum E2 levels with a sign indicating confidence in direction of change, is powerful in identifying the fertile phase and subsequent transition to the postovulatory phase and may serve as a useful algorithm for FAMs of family planning.

The Politics, Promises, and Perils of Data: Evidence-Driven Policy and Practice for Menstrual Health

Data determine what we know about the menstrual cycle; they inform policy and program decisions; they can point us to neglected issues and populations. But collecting and analyzing data are complicated and often fraught processes, because data are political and subjective, decisions on what data we collect and what data we do not collect are not determined by accident. As a result, despite the significant potential of the current rise in attention to menstruation, we also see risks: a lack of a solid evidence base for program decisions and resulting sensationalization; concerns about data privacy; an overreliance on participants' recall, on the one hand, while not involving participants adequately in decision making, on the other hand; and a lack of contextualized and disaggregated data. Yet better communication, contextualization, and collaboration can address many of these risks.

Smartphone Applications for Period Tracking: Rating and Behavioral Change among Women Users

Background

The use of mobile apps for health and well-being has grown exponentially in the last decade, as such apps were reported to be ideal platforms for behavioral change and symptoms monitoring and management.

Objective

This study aimed to systematically review period tracking applications available at Google Play and Apple App Stores and determine the presence, features, and quality of these smartphone apps. In addition, behavioral changes associated with the top 5 rated apps were assessed.

Methods

This study used the Systematic Search Criteria through Google Play Store and iTunes Apple Store, using terms related to period tracking. Apps were scanned for matching the inclusion criteria and the included apps were assessed by two reviewers using the Mobile Application Rating Scale (MARS), a tool that was developed for classifying and assessing the quality of mHealth apps.

Results

Forty-nine apps met the inclusion criteria. Most of the apps enabled setting user goals, motivations, and interactivity, tracking multiple symptoms or mood changes, allowed notifications, and used graphs to illustrate the tracking result over a specific period of time. The majority of features and functions within these apps were offered for free, while some apps included limited in-app purchases or needed Internet connection to function. Certain apps were reported by participants to promote behavioral change and increase knowledge and awareness regarding monthly periods.

Conclusions

Period tracking apps were easy to use and navigate and can hence be readily adopted into routine tracking and management of periods. However, most apps were not based on significant evidence and may need further development to support period-related symptom management.

Convection-driven microfabricated hydrogels for rapid biosensing

A microscale biosensing platform using rehydration-mediated swelling of bio-functionalized hydrogel structures and rapid target analyte capture is described. Induced convective flow mitigates diffusion limited incubation times, enabling model assays to be completed in under three minutes. Assay design parameters have been evaluated, revealing fabrication criteria required to tune detection sensitivity.

Menstrual Cycle Length and Patterns in a Global Cohort of Women Using a Mobile Phone App: Retrospective Cohort Study

Background

There is increasing information characterizing menstrual cycle length in women, but less information is available on the potential differences across lifestyle variables.

Objective

This study aimed to describe differences in menstrual cycle length, variability, and menstrual phase across women of different ages and BMI among a global cohort of Flo app users. We have also reported on demographic and lifestyle characteristics across median cycle lengths.

Methods

The analysis was run based on the aggregated anonymized dataset from a menstrual cycle tracker and ovulation calendar that covers all phases of the reproductive cycle. Self-reported information is documented, including demographics, menstrual flow and cycle length, ovulation information, and reproductive health and diseases. Data from women aged ≥18 years and who had logged at least three cycles (ie, 2 completed cycles and 1 current cycle) in the Flo app were included (1,579,819 women).

Results

Of the 1.5 million users, approximately half (638,683/1,579,819, 40.42%) were aged between 18 and 24 years. Just over half of those reporting BMIs were in the normal range (18.5-24.9 kg/m²; 202,420/356,598, 56.76%) and one-third were overweight or obese (>25 kg/m²; 120,983/356,598, 33.93%). A total of 16.32% (257,889/1,579,819) of women had a 28-day median cycle length. There was a higher percentage of women aged ≥40 years who had a 27-day median cycle length than those aged between 18 and 24 years (22,294/120,612, 18.48% vs 60,870/637,601, 9.55%), but a lower percentage with a 29-day median cycle length (10,572/120,612, 8.77% vs 79,626/637,601, 12.49%). There were a higher number of cycles with short luteal phases in younger women, whereas women aged ≥40 years had a higher number of cycles with longer luteal phases. Median menstrual cycle length and the length of the follicular and luteal phases were not remarkably different with increasing BMI, except for the heaviest women at a BMI of ≥50 kg/m².

Conclusions

On a global scale, we have provided extensive evidence on the characteristics of women and their menstrual cycle length and patterns across different age and BMI groups. This information is necessary to support updates of current clinical guidelines around menstrual cycle length and patterns for clinical use in fertility programs.

Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data

The menstrual cycle is a key indicator of overall health for women of reproductive age. Previously, menstruation was primarily studied through survey results; however, as menstrual tracking mobile apps become more widely adopted, they provide an increasingly large, content-rich source of menstrual health experiences and behaviors over time. By exploring a database of user-tracked observations from the Clue app by BioWink GmbH of over 378,000 users and 4.9 million natural cycles, we show that self-reported menstrual tracker data can reveal statistically significant relationships between per-person cycle length variability and self-reported qualitative symptoms. A concern for self-tracked data is that they reflect not only physiological behaviors, but also the engagement dynamics of app users. To mitigate such potential artifacts, we develop a procedure to exclude cycles lacking user engagement, thereby allowing us to better distinguish true menstrual patterns from tracking anomalies. We uncover that women located at different ends of the menstrual variability spectrum, based on the consistency of their cycle length statistics, exhibit statistically significant differences in their cycle characteristics and symptom tracking patterns. We also find that cycle and period length statistics are stationary over the app usage timeline across the variability spectrum. The symptoms that we identify as showing statistically significant association with timing data can be useful to clinicians and users for predicting cycle variability from symptoms, or as potential health indicators for conditions like endometriosis. Our findings showcase the potential of longitudinal, high-resolution self-tracked data to improve understanding of menstruation and women's health as a whole.

Menstrual bleeding, cycle length, and follicular and luteal phase lengths in women without known subfertility: A pooled analysis of three cohorts

BACKGROUND

There is variability between women for days of menstrual bleeding, cycle lengths, follicular phase lengths, and luteal phase lengths, related to age and parity.

OBJECTIVE

To describe total cycle length; anovulatory cycles; follicular and luteal phase lengths; and days and intensity of menstrual and non-menstrual bleeding in women without known subfertility over the course of 1 year.

METHODS

581 women (3,324 cycles) with no known subfertility (18-40 years of age) were followed for up to 1 year. Women recorded vaginal bleeding and mucus discharge daily. We used the peak day of cervical mucus as the estimated day of ovulation and the last day of the follicular phase. We used generalised linear mixed models stratified by age and parity to describe menstrual cycle parameters.

RESULTS

The majority of women were <30 years of age (74.5%), non-Hispanic White (88.6%), and nulliparous (70.4%). The mean menses length was 6.2 (1.5) days, median 6; cycle length 30.3 (6.7) days, median 29; follicular phase length 18.5 (6.5) days, median 17; and luteal phase length 11.7 (2.8) days, median 12. Nulliparous women aged ≥30 years vs nulliparous women aged <30 had shorter cycles (29.2 days, 95% confidence interval (CI) 27.8, 30.7 vs 31.5 days, 95% CI 30.8, 32.2) and shorter follicular phases (17.6 days, 95% CI 16.2, 18.9 vs 19.6 days, 95% CI 18.9, 20.2). Among all women, within-woman differences between the longest and shortest menses length >3 days, total cycle length >7 days, follicular phase >7 days, and luteal phase >3 days were found in 11.6%, 43.0%, 41.7%, and 58.8% of women, respectively.

CONCLUSIONS

Our findings confirm variability between women of menstrual cycle parameters related to age and parity, and also highlight within-woman variability in the follicular and luteal phases.

Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles

The use of apps that record detailed menstrual cycle data presents a new opportunity to study the menstrual cycle. The aim of this study is to describe menstrual cycle characteristics observed from a large database of cycles collected through an app and investigate associations of menstrual cycle characteristics with cycle length, age and body mass index (BMI). Menstrual cycle parameters, including menstruation, basal body temperature (BBT) and luteinising hormone (LH) tests as well as age and BMI were collected anonymously from real-world users of the Natural Cycles app. We analysed 612,613 ovulatory cycles with a mean length of 29.3 days from 124,648 users. The mean follicular phase length was 16.9 days (95% CI: 10-30) and mean luteal phase length was 12.4 days (95% CI: 7-17). Mean cycle length decreased by 0.18 days (95% CI: 0.17-0.18, R 2 = 0.99) and mean follicular phase length decreased by 0.19 days (95% CI: 0.19-0.20, R 2 = 0.99) per year of age from 25 to 45 years. Mean variation of cycle length per woman was 0.4 days or 14% higher in women with a BMI of over 35 relative to women with a BMI of 18.5-25. This analysis details variations in menstrual cycle characteristics that are not widely known yet have significant implications for health and well-being. Clinically, women who wish to plan a pregnancy need to have intercourse on their fertile days. In order to identify the fertile period it is important to track physiological parameters such as basal body temperature and not just cycle length.

Predicting pregnancy using large-scale data from a women's health tracking mobile application

Predicting pregnancy has been a fundamental problem in women's health for more than 50 years. Previous datasets have been collected via carefully curated medical studies, but the recent growth of women's health tracking mobile apps offers potential for reaching a much broader population. However, the feasibility of predicting pregnancy from mobile health tracking data is unclear. Here we develop four models - a logistic regression model, and 3 LSTM models - to predict a woman's probability of becoming pregnant using data from a women's health tracking app, Clue by BioWink GmbH. Evaluating our models on a dataset of 79 million logs from 65,276 women with ground truth pregnancy test data, we show that our predicted pregnancy probabilities meaningfully stratify women: women in the top 10% of predicted probabilities have a 89% chance of becoming pregnant over 6 menstrual cycles, as compared to a 27% chance for women in the bottom 10%. We develop a technique for extracting interpretable time trends from our deep learning models, and show these trends are consistent with previous fertility research. Our findings illustrate the potential that women's health tracking data offers for predicting pregnancy on a broader population; we conclude by discussing the steps needed to fulfill this potential.