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

Time to pregnancy recognition among users of an FDA-cleared fertility application

Background: Previous investigations of time-to-pregnancy recognition have analysed data from national surveys and clinics, but this has not been investigated in the context of digital fertility applications. Timely pregnancy recognition can help individuals in health and pregnancy management, reducing maternal and foetal risk and costs, whilst increasing treatment options, availability, and cost. Methods: This dataset contained 23,728 pregnancies (conceived between June 2018 and December 2022) from 20,429 participants using a Food and Drug Administration (FDA) cleared fertility app in the United States. Most participants (with non-missing information) identified as Non-Hispanic White, and one-third reported obtaining a university degree. We used two-tailed Welch's t-test, Mann-Whitney U-test, and two-tailed Z-tests to compare time to pregnancy recognition between those using the app to conceive or contracept. Results: Participants using an app to conceive recognised pregnancy on average at 31.3 days from last menstrual period (LMP) compared to 35.9 days among those using the app to prevent pregnancy. Conclusion: Generalisability is limited, as all participants were using a fertility app and had relatively homogenous sociodemographic characteristics.

Young adult's views on using a poster to learn about fertility: redesigning the fertility education poster

Fertility awareness should be taught to everyone. The International Reproductive Health Education Collaboration (IRHEC) designed a fertility poster in 2019 but did not have a specific target group in mind. Studies have been conducted in Denmark and Sweden to determine how the poster can be redesigned. In this study, we carried out focus groups with young adults in the UK to ask their views of the poster, with the aim of redesigning it. Six focus groups were undertaken with twenty seven, 18-25 year olds. Five questions were asked: 1. What are your thoughts, feelings, and reactions to the poster? 2. Did you learn anything from the poster? 3. How has reading the poster impacted your opinions or thoughts about having children? 4. What are your opinions about using a poster format to inform and start reflections regarding family building? 5. Reading through each point are there any changes to be made? Content analysis was performed. Themes identified revealed the information on the poster gave the participants some anxiety and apprehension, especially regarding the effect of age on fertility, perceptions of IVF, and gaps in knowledge. The fertility education poster is a good resource for education, but other resources should be developed.

Fertility apps, datafication and knowledge production in reproductive health

Despite being the target of much criticism, commercialised digital technologies have proliferated in reproductive health arenas. Fertility applications (apps) are now some of the most popular and ubiquitous digital health tracking technologies, with millions of downloads. Previous scholarship has already underlined the problematic nature of their design and surveillance features. However, less attention has been paid to the wider effects of datafied knowledge availability. This research specifically asks: How does the proliferation of fertility apps shape knowledge (and associated practices) in reproductive health? Drawing on an analysis of key document sources, I here argue that fertility apps act as mediators between stakeholders, data and datafied outputs, thus facilitating: (1) the datafication of fertility awareness knowledge and the production of new datafied knowledge, (2) legitimation discourses and practices and (3) the remaking of private/public expertise and knowledge production networks in reproductive health. To effectively analyse the effects of commercialised reproductive health apps, this work argues for an understanding of data technologies that is informed by critical data studies.

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.

Promotion of an Algorithm-Based Tool for Pregnancy Prevention by Instagram Influencers

Objective: Despite growing concerns that some digital algorithm-reliant fertility awareness-based methods of pregnancy prevention are marketed in an inaccurate, opaque, and potentially harmful manner online, there has been limited systematic examination of such marketing practices. This article therefore provides an empirical examination of how social media influencers have promoted the fertility tracking tool Daysy on Instagram. We investigate: (1) how the tool is framed in relation to pregnancy prevention using Health Belief Model (HBM) constructs, and (2) the promotional and disclosure practices adopted by influencers. Materials and Methods: We collected Instagram posts mentioning Daysy made between June 2018 and May 2022 using the tool CrowdTangle. Using a qualitative content analysis approach, we coded a random sample of 400 Daysy posts. This yielded 122 Instagram influencer posts promoting Daysy for pregnancy prevention that we coded for promotional content and HBM constructs. Results: Posts originated primarily from Europe (n = 62, 50.82%) and the United States (n = 37, 30.33%). Findings indicate that barriers to use (n = 18, 15.57%) and the severity of risks from unplanned pregnancy (n = 8, 6.56%) were rarely conveyed, whereas benefits of use (n = 122, 100%) and the severity of risks of hormonal contraception (n = 31, 25.41%) were covered more extensively. Only about one third of posts disclosed any formal relationship to the brand Daysy. Conclusions: With many posts emphasizing benefits and obscuring potential limitations, we argue that accurate and transparent information about the effectiveness and limitations of fertility tracking technologies is critical for supporting informed decision-making and, as such, should remain a public health priority.

Fertility and infertility uses of menstrual apps from the perspectives of healthcare providers and patients

BACKGROUND

Menstrual cycle tracking apps are increasingly used by those trying to conceive as well as those diagnosed and treated for infertility. However, the small amount of existing research about the use of these apps does not include the perspectives of healthcare providers.

AIMS

This study explores how healthcare providers describe the role of menstrual apps in fertility and infertility health care, and how this compares with patients' views.

MATERIALS AND METHODS

Responses were collected from an online survey (n = 37 providers and n = 89 patients) and online focus groups (n = 4 providers and n = 6 patients) and analysed using reflexive thematic analysis.

RESULTS

Healthcare providers, as well as some patients, expressed doubts about the accuracy of app estimates of the timing of ovulation. By contrast, many patients, but no healthcare providers, were enthusiastic about ovulation estimates provided by their apps. Apps were described by both groups as having a role in diagnosing and treating infertility, with healthcare providers emphasising the calendar history function of the apps supporting treatment, and patients focused on recognising and diagnosing infertility.

CONCLUSIONS

This exploratory study suggests that apps are viewed by both healthcare providers and patients as having a potential role in fertility and infertility healthcare. Although patients and app users are attentive to app estimates of ovulation timing, healthcare providers are sceptical.

Digital support for first time self-injectable contraceptive users in Nairobi, Kenya: A design evaluation

BACKGROUND

Women in Kenya have an unmet need for contraception, and self-injectable contraceptives offer a new, self-managed fertility control option. Self-injection reduces the need to travel to a facility for ongoing care, but the initial, in-person, consultation may be a barrier. Training in self-injection administration could be delivered via WhatsApp on a mobile phone.

METHODS

This study aimed to observe and document the design process of a WhatsApp delivered self-injectable contraceptive service. This design evaluation employs a mixed methods approach using; observation of design meetings, analysis of design documents (process maps, prototypes) and interviews with the design team. Participants who tested the remote training element of the final service design were interviewed about their experiences.

RESULTS

Four tasks, delivered by the digital service, were explored in further detail: provide information on self-injectables; ensure the technical and privacy requirements of the video call are met; obtain information from the user to check eligibility; and observe the users first self-injection. The challenges, assumptions, iterations, and learning associated with these key tasks were documented and 3 case studies emerged. These case studies explore how the digital service altered the timing and medium of a clinical interaction, the construction of the user's physical space when interacting with the service and the challenge of performing legitimacy via WhatsApp.

CONCLUSION

By examining self-care facilitated by digital technologies, there are opportunities to learn about self-care, digital care and face-to-face care. Through examining the design process, we discovered ways in which digital services can change the rhythm of health care interactions, namely by stretching the time, space and medium of clinical interaction. When interactions are altered in this way, clinical legitimacy must be negotiated in new ways.

Timed intercourse for couples trying to conceive

BACKGROUND

Many factors influence fertility, one being the timing of intercourse. The 'fertile window' describes a stage in the cycle when conception can occur and is approximately five days before to several hours after ovulation. 'Timed intercourse' is the practice of prospectively identifying ovulation and, thus, the fertile window to increase the likelihood of conception. Methods of predicting ovulation include urinary hormone measurement (luteinising hormone (LH) and oestrogen), fertility awareness-based methods (FABM) (including tracking basal body temperatures, cervical mucus monitoring, calendar charting/tracking apps), and ultrasonography. However, there are potentially negative aspects associated with ovulation prediction, including stress, time consumption, and cost implications of purchasing ovulation kits and app subscriptions. This review considered the evidence from randomised controlled trials (RCTs) evaluating the use of timed intercourse (using ovulation prediction) on pregnancy outcomes.

OBJECTIVES

To evaluate the benefits and risks of ovulation prediction methods for timing intercourse on conception in couples trying to conceive.

SEARCH METHODS

We searched the Cochrane Gynaecology and Fertility (CGF) Group Specialised Register, CENTRAL, MEDLINE, and Embase in January 2023. We also checked the reference lists of relevant studies and searched trial registries for any additional trials.

SELECTION CRITERIA

We included RCTs that compared methods of timed intercourse using ovulation prediction to other forms of ovulation prediction or intercourse without ovulation prediction in couples trying to conceive.

DATA COLLECTION AND ANALYSIS

We used standard methodological procedures recommended by Cochrane to select and analyse studies in this review. The primary review outcomes were live birth and adverse events (such as depression and stress). Secondary outcomes were clinical pregnancy, pregnancy (clinical or positive urinary pregnancy test not yet confirmed by ultrasound), time to pregnancy, and quality of life. We assessed the overall quality of the evidence for the main comparisons using GRADE methods.

MAIN RESULTS

This review update included seven RCTs involving 2464 women or couples. Four of the five studies from the previous review were included in this update, and three new studies were added. We assessed the quality of the evidence as moderate to very low, the main limitations being imprecision, indirectness, and risk of bias. Urinary ovulation tests versus intercourse without ovulation prediction Compared to intercourse without ovulation prediction, urinary ovulation detection probably increases the chance of live birth in couples trying to conceive (risk ratio (RR) 1.36, 95% confidence interval (CI) 1.02 to 1.81, 1 RCT, n = 844, moderate-quality evidence). This suggests that if the chance of a live birth without urine ovulation prediction is 16%, the chance of a live birth with urine ovulation prediction is 16% to 28%. However, we are uncertain whether timed intercourse using urinary ovulation detection resulted in a difference in stress (mean difference (MD) 1.98, 95% CI -0.87 to 4.83, I² = 0%, P = 0.17, 1 RCT, n = 77, very low-quality evidence) or clinical pregnancy (RR 1.09, 95% CI 0.51 to 2.31, I² = 0%, 1 RCT, n = 148, low-quality evidence). Similar to the live birth result, timed intercourse using urinary ovulation detection probably increases the chances of clinical pregnancy or positive urine pregnancy test (RR 1.28, 95% CI 1.09 to 1.50, I² = 0, 4 RCTs, n = 2202, moderate-quality evidence). This suggests that if the chance of a clinical pregnancy or positive urine pregnancy test without ovulation prediction is assumed to be 18%, the chance following timed intercourse with urinary ovulation detection would be 20% to 28%. Evidence was insufficient to determine the effect of urine ovulation tests on time to pregnancy or quality of life. Fertility awareness-based methods (FABM) versus intercourse without ovulation prediction Due to insufficient evidence, we are uncertain whether timed intercourse using FABM resulted in a difference in live birth rate compared to intercourse without ovulation prediction (RR 0.95, 95% CI 0.76 to 1.20, I² = 0%, 2 RCTs, n = 157, low-quality evidence). We are also uncertain whether FABM affects stress (MD -1.10, 95% CI -3.88 to 1.68, 1 RCT, n = 183, very low-quality evidence). Similarly, we are uncertain of the effect of timed intercourse using FABM on anxiety (MD 0.5, 95% CI -0.52 to 1.52, P = 0.33, 1 RCT, n = 183, very low-quality evidence); depression (MD 0.4, 95% CI -0.28 to 1.08, P = 0.25, 1 RCT, n = 183, very low-quality evidence); or erectile dysfunction (MD 1.2, 95% CI -0.38 to 2.78, P = 0.14, 1 RCT, n = 183, very low-quality evidence). Evidence was insufficient to detect a benefit of timed intercourse using FABM on clinical pregnancy (RR 1.13, 95% CI 0.31 to 4.07, 1 RCT, n = 17, very low-quality evidence) or clinical or positive pregnancy test rates (RR 1.08, 95% CI 0.89 to 1.30, 3 RCTs, n = 262, very low-quality evidence). Finally, we are uncertain whether timed intercourse using FABM affects the time to pregnancy (hazard ratio 0.86, 95% CI 0.53 to 1.38, 1 RCT, n = 140, low-quality evidence) or quality of life. No studies assessed the use of timed intercourse with pelvic ultrasonography.

AUTHORS' CONCLUSIONS

The new evidence presented in this review update shows that timed intercourse using urine ovulation tests probably improves live birth and pregnancy rates (clinical or positive urine pregnancy tests but not yet confirmed by ultrasound) in women under 40, trying to conceive for less than 12 months, compared to intercourse without ovulation prediction. However, there are insufficient data to determine the effects of urine ovulation tests on adverse events, clinical pregnancy, time to pregnancy, and quality of life. Similarly, due to limited data, we are uncertain of the effect of FABM on pregnancy outcomes, adverse effects, and quality of life. Further research is therefore required to fully understand the safety and effectiveness of timed intercourse for couples trying to conceive. This research should include studies reporting clinically relevant outcomes such as live birth and adverse effects in fertile and infertile couples and utilise various methods to determine ovulation. Only with a comprehensive understanding of the risks and benefits of timed intercourse can recommendations be made for all couples trying to conceive.

Survey Analysis of Quantitative and Qualitative Menstrual Cycle Tracking Technologies

Background and Objectives: Digital health and personalized medicine are advancing at an unprecedented pace. Users can document their menstrual cycle data in a variety of ways, including smartphone applications (apps), temperature tracking devices, and at-home urine hormone tests. Understanding the needs and goals of women using menstrual cycle tracking technologies is the first step to making these technologies more evidence based. The purpose of this study was to examine the current use of these technologies and explore how they are being used within the context of common hormonal and reproductive disorders, like polycystic ovary syndrome (PCOS), endometriosis, and infertility. Materials and Methods: This was a cross-sectional study evaluating menstrual cycle tracking technology use. Participants were recruited in January-March 2023 using social media groups and a Marquette Method instructor email listserv. Data were collected using an electronic survey with Qualtrics. Data collected included participant demographics, menstrual cycle characteristics, reproductive health history, and menstrual cycle tracking behavior. Results: Three-hundred and sixty-eight participants were included in the analysis. Women had various motivations for tracking their menstrual cycles. Most participants (72.8%) selected "to avoid getting pregnant" as the primary motivation. Three hundred and fifty-six participants (96.7%) reported using a fertility awareness-based method to track and interpret their menstrual cycle data. The Marquette Method, which utilizes urine hormone tracking, was the most frequently used method (n = 274, 68.2%). The most frequently used cycle technology was a urine hormone test or monitor (n = 299, 81.3%), followed by a smartphone app (n = 253, 68.8%), and a temperature tracking device (n = 116, 31.5%). Women with PCOS (63.6%), endometriosis (61.8%), and infertility (75%) in our study reported that the use of tracking technologies aided in the diagnosis. Most participants (87.2%) reported a high degree of satisfaction with their use and that they contributed to their reproductive health knowledge (73.9%). Conclusions: Women in our study reported avoiding pregnancy as their primary motivation for using menstrual cycle tracking technologies, with the most frequently used being a urine hormone test or monitor. Our study results emphasize the need to validate these technologies to support their use for family planning. Given that most women in this study reported using a fertility awareness-based method, the results cannot be generalized to all users of menstrual cycle tracking technologies.

The Impact of Ovulation Calculators on the Stress Levels of Fertility-Seeking Couples: An Evaluation Study

Introduction Infertility affects a significant portion of couples seeking pregnancy, leading to stress and emotional strain. Ovulation calculators, widely used as a tool to predict fertile days, may play a role in the stress experienced by couples undergoing fertility treatments. The aim of this study was to evaluate the impact of ovulation calculators on the stress levels of couples seeking fertility. Methods Participants were recruited from the University of Miami Health System Clinics. Fifty couples consulting for infertility were asked to participate in the study and complete anonymous self-reported surveys. The surveys consisted of validated questions related to stress levels and the use of ovulation calculators. The completed surveys were collected and analyzed using descriptive statistics. The data collected included age, number of years trying to conceive, and answers to questions related to stress levels and the use of ovulation calculators. Responses from 50 couples who met the inclusion criteria were included in the final analysis. Results A total of 50 couples who were attempting conception and who completed all the questionnaires were included in the study. Whether or not they were using ovulation calculators, females scored similarly in the four variables of the Female Sexual Function Index (FSFI), including arousal, orgasm, satisfaction, and lubrication. When evaluating International Index of Erectile Function (IIEF) scores for male erectile function, the average score of males tracking ovulation was 12.0 ± 4.8, compared to 11.5 ± 5.4 in male patients who were not (P = 0.81). The results showed no statistically significant difference in stress levels between couples who used ovulation calculators and those who did not. However, in couples experiencing higher stress levels due to infertility, both male and female participants reported higher levels of sexual dysfunction. Fertility-related stress was also found to be significantly associated with mental health implications, with increased anxiety and depression reported by couples undergoing fertility treatments. Conclusion The findings suggest that the use of ovulation calculators did not significantly influence the stress experienced by couples seeking fertility treatment. However, the study highlights the significant impact of infertility-related stress on sexual function and mental health in both male and female partners. These results emphasize the importance of addressing the psychological aspects of infertility and providing comprehensive support to couples undergoing fertility treatments. Further research is warranted to explore the complex interplay between ovulation calculator usage, infertility-related stress, sexual dysfunction, and mental health implications in couples seeking to conceive. Healthcare providers should consider incorporating mental health support into fertility treatment programs to optimize patient outcomes and overall well-being.

Successful Implementation of Menstrual Cycle Biomarkers in the Treatment of Infertility in Polycystic Ovary Syndrome-Case Report

Polycystic ovary syndrome (PCOS) is the most common cause of anovulatory infertility. Absent, impaired, or rare ovulation induces progesterone deficiency in the luteal phase, which is a critical problem in PCOS. A usual pattern of progesterone administration from a fixed and arbitrary pre-determined day of a menstrual cycle may preserve infertility but can easily be avoided. We present the case of a 29-year-old infertile woman who had been ineffectively treated for over two years. We introduced a line of therapy that was suited to her individual menstrual cycle by implementing biomarker recording. Supplementation based on a standardized observation of the basal body temperature (BBT) and cervical mucus stopped the vicious circle of absent ovulation and hyperandrogenism, restoring regular bleeding, ovulation cycles, and fertility. The implementation of a reliable fertility awareness method (FAM), accompanied by a standardized teaching methodology and periodic review of the observations recorded by the patient, validated through an ultrasound examination and plasma gonadotropins, estrogens, and progesterone concentrations, is key to achieving therapeutic success. The presented case is an example of a clinical vignette for many patients who have successfully managed to improve their fertility and pregnancy outcomes by applying the principles of a personalized treatment approach together with gestagens by recording their fertility biomarkers.

Evaluation of a fertility awareness-based shared decision-making tool part 1: Study design and impact on clinician knowledge

Objective

To assess the impact of a Shared Decision-Making (SDM) tool for fertility awareness-based methods (FABMs) of family planning.

Methods

Clinicians familiar with at least one FABM were randomly invited to participate in a prospective cross-over study to compare usual practice to the use of the SDM tool when discussing FABMs with patients. Patients completed surveys pre- and post-office visit and six months later. The primary outcome explored the effect of online education on use of the SDM tool on clinicians' knowledge of FABMs.

Results

Of 278 clinicians contacted, 54% could not be reached, and 15% did not provide women's health services. The 26 clinicians enrolled were experienced, with more than half recommending FABMs for ≥10 years, and 73% recommending more than one FABM to patients. Knowledge scores significantly improved after online training and use of the SDM tool (baseline mean score = 9.54 (scale of 0-12); post-training mean score = 10.73, p < 0.002).

Conclusions

Education about FABMs and training on use of the SDM tool improved knowledge scores even among an experienced cohort of clinicians.

Innovation

The novel SDM tool can better equip clinicians to meet the rising patient interest in FABMs.

A fertility app for two? Women's perspectives on sharing conceptive fertility work with male partners

Smartphone apps for monitoring bodily signs of ovulation are growing in popularity and becoming increasingly important tools for facilitating or preventing pregnancy. This article explores heterosexual women's experiences of using fertility apps in the context of trying to conceive. Specifically, it focuses on a feature of fertility apps that enables women to share information about fertility with a male partner. This feature is frequently lauded by apps as providing an opportunity for partners to be more actively involved in the work of trying to conceive. With this focus, the article makes two key contributions to the emerging literature on fertility apps. Firstly, it situates narratives in apps that promote the shared responsibility for conception as part of a pre-parenting culture that values a shared commitment to (future) parenthood. Secondly, drawing on interviews with women in the UK who had used fertility apps, it explores women's perspectives on involving their male partners in pregnancy planning. Rather than redistributing conceptive fertility work, women's experiences reveal how cultural assumptions about heterosexuality and 'natural conception' significantly curtail their ability to engage partners in fertility tracking. As a result, the gendered divisions of fertility work are reconfigured in the new sociotechnical context of fertility app use.

AACC Guidance Document on the Use of Point-of-Care Testing in Fertility and Reproduction

Background

The AACC Academy revised the reproductive testing section of the Laboratory Medicine Practice Guidelines: Evidence-Based Practice for Point-of-Care Testing (POCT) published in 2007.

Methods

A panel of Academy members with expertise in POCT and laboratory medicine was formed to develop guidance for the use of POCT in reproductive health, specifically ovulation, pregnancy, premature rupture of membranes (PROM), and high-risk deliveries. The committee was supplemented with clinicians having Emergency Medicine and Obstetrics/Gynecology training.

Results

Key recommendations include the following. First, urine luteinizing hormone (LH) tests are accurate and reliable predictors of ovulation. Studies have shown that the use of ovulation predicting kits may improve the likelihood of conception among healthy fertile women seeking pregnancy. Urinary LH point-of-care testing demonstrates a comparable performance among other ovulation monitoring methods for timing intrauterine insemination and confirming sufficient ovulation induction before oocyte retrieval during in vitro fertilization. Second, pregnancy POCT should be considered in clinical situations where rapid diagnosis of pregnancy is needed for treatment decisions, and laboratory analysis cannot meet the required turnaround time. Third, PROM testing using commercial kits alone is not recommended without clinical signs of rupture of membranes, such as leakage of amniotic fluid from the cervical opening. Finally, fetal scalp lactate is used more than fetal scalp pH for fetal acidosis due to higher success rate and low volume of sample required.

Conclusions

This revision of the AACC Academy POCT guidelines provides recommendations for best practice use of POCT in fertility and reproduction.

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.

A survey of women’s experiences of using period tracker applications: Attitudes, ovulation prediction and how the accuracy of the app in predicting period start dates affects their feelings and behaviours

Introduction:

Using an online survey, the aim of this study was to ask women about their real-life experiences of using period tracker apps, their attitudes towards using their app, the information the app provided regarding ovulation and how the accuracy of the app in predicting period start dates affects their feelings and behaviours if their period comes earlier or later than predicted.

Methods:

This mixed-methods observational study was conducted by an online survey of 50 multiple-choice and open-ended questions. The survey was generated with Qualtrics XM^® and promoted via social media. It was open to any person who had used a period tracker.

Results:

From 375 total responses, 330 complete responses were obtained, giving a completion rate of 88.0%. Respondents were aged between 14 and 54, with a mean age of 26.0 (±7.81). When asked what was the best thing about using the app, 29.7% (98/330) of respondents selected ‘To know when I’m ovulating’. Respondents were asked if their period ever started earlier than the app predicted; 54.9% (189/330) said it had and 72.1% (238/330) said it had started later than predicted. When asked how they felt if their period arrived earlier or later than expected, thematic analysis of periods starting earlier revealed four themes: feeling unaffected, being frustrated/unprepared, feeling anxious/stressed and feeling confused/intrigued. Thematic analysis when their period arrived later revealed six themes: anxious/concerned about pregnancy, disappointed about pregnancy, seeking advice/informing healthcare professionals, thoughts about menopause, feeling unaffected and being better prepared.

Conclusion:

Period trackers need to be clearer on their intended use and reliability, especially for period due date and ovulation. Qualitative analysis shows the impact of inaccurate predictions on aspects of the users’ health. This study calls for period tracker app companies to update their apps to provide transparency to their users about their intended use and capabilities.

Increased Chance of Live Birth Following Use of Connected Ovulation Test System: Outcome Results from a Randomized Controlled Trial

Background: Natural conception requires intercourse to occur during the fertile window of a woman's menstrual cycle. This follow-up study of a randomized controlled trial aimed to determine whether the use of a urine ovulation test system, which tracks elevations in both luteinizing hormone and an estradiol metabolite, increases the likelihood of live births in women trying to conceive.

Materials and Methods: In the home-based trial, 844 women aged 18–40 years who were attempting to conceive were randomized 1:1 into the test or control arms. Volunteers participated for up to two full cycles and conducted digital pregnancy tests, collected urine samples, and kept a menstrual diary to determine pregnancy status. In this follow-up, all pregnant volunteers were asked to complete a form on final pregnancy outcome.

Results: Overall, 247 (29.3%) of the 844 volunteers reported a pregnancy; final outcome data were available for 198 pregnancies. For cycle one, the live birth rate was 16.4% for the test group and 8.5% for the control group (odds ratio: 2.12; 95% confidence interval [CI]: 1.34–3.35; p = 0.001). For cycles one and two combined, the live birth rate was 24.5% and 17.5% for the test and control groups, respectively (odds ratio: 1.53; 95% CI: 1.07–2.19; p = 0.023). The proportion of miscarriages was not significantly different between both groups and 78% of pregnancies resulted in a live birth.

Conclusions: The increased conception rate observed following the use of the Clearblue Connected Ovulation Test System was found to translate into an increased live birth rate.

Clinical Trial Registration number: NCT03424590.

Time to Pregnancy for Women Using a Fertility Awareness Based Mobile Application to Plan a Pregnancy

Background: Time to pregnancy (TTP) is a biomarker of fecundability and has been associated with behavioral and environmental characteristics; however, these associations have not been examined in a large population-based sample of application (app) users.

Materials and Methods: This observational study followed 5,376 women with an age range of 18 to 45 years who used an app to identify their fertile window. We included women who started trying to conceive between September 30, 2017 and August 31, 2018. TTP was calculated as the number of menstrual cycles from when the user switched to “Plan” mode up to and including the cycle in which they logged a positive pregnancy test. We examined associations with several characteristics, including age, gravidity, body mass index, cycle length and cycle length variation, frequency of sexual intercourse, and temperature measuring frequency. Discrete time fecundability models were used to estimate fecundability odds ratios.

Results: For the complete cohort the 6-cycle and 12-cycle cumulative pregnancy probabilities were found to be 61% (95% confidence interval [CI]: 59–62) and 74% (95% CI: 73–76), respectively. The median TTP was four cycles. The highest fecundability was associated with an age of less than 35 years, with cycle length variation <5 days and logging sexual intercourse on at least 20% of days added (the proportion of days in which intercourse was logged) (11.5% [n = 613] of entire sample). This group achieved a 6- and 12-cycle cumulative pregnancy probability of 88% (95% CI: 85–91) and 95% (95% CI: 94–97), respectively, and a TTP of 2 cycles.

Conclusions: Natural Cycles was an effective method of identifying the fertile window and a noninvasive educational option for women planning a pregnancy. Women under age 35 with regular cycles showed a high pregnancy rate.

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.

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.