Can apps and calendar methods predict ovulation with accuracy?

Differential changes in appetite hormones post-prandially based on menstrual cycle phase and oral contraceptive use: A preliminary study

This was a preliminary study that examined whether appetite regulation is altered during the menstrual cycle or with oral contraceptives. Ten naturally cycling females (NON-USERS) and nine tri-phasic oral contraceptive using females (USERS) completed experimental sessions during each menstrual phase (follicular phase: FP; ovulatory phase: OP; luteal phase: LP). Appetite perceptions and blood samples were obtained fasted, 30, 60, and 90 min post-prandial to measure acylated ghrelin, active glucagon-like peptide-1 (GLP-1), and total peptide tyrosine tyrosine (PYY). Changes were considered important if p < 0.100 and the effect size was ≥medium. There appeared to be a three-way (group x phase x time) interaction for acylated ghrelin where concentrations appeared to be greater in USERS versus NON-USERS during the OP 90-min post-prandial and during the LP fasted, and 90-min post-prandial. In USERS, ghrelin appeared to be greater 90-min post-prandial in the OP versus the FP with no other apparent differences between phases. There were no apparent differences between phases in NON-USERS. There appeared to be a three-way interaction for PYY where concentrations appeared to be greater in USERS during the FP 60-min post-prandial and during the OP 30-min post-prandial. In USERS PYY appeared to be greater 60-min post-prandial during the OP versus the LP with no other apparent differences. There were no apparent differences between phases in NON-USERS. There appeared to be no effect of group or phase on GLP-1, or appetite perceptions. These data demonstrate small effects of menstrual cycle phase and oral contraceptive use on the acylated ghrelin and total PYY response to a standardized meal, with no effects on active GLP-1 or perceived appetite, though more work with a large sample size is necessary.

There's an App for That: Exploring the Market for Contraceptive Fertility Tracking Apps in the Philippines

For generations, women have relied on fertility awareness methods to plan and prevent pregnancy, for over a decade, many have been aided by digital tools to do so. New contraceptive fertility tracking apps (CFTAs)-that are backed by clinical efficacy trials to support their effectiveness as contraception-have the potential to enhance method choice and offer users a unique contraceptive option, but there is little evidence to inform the decisions around expanding access, particularly in low-and middle-income countries. We conducted a mixed methods study with quantitative online surveys (n = 1600) and qualitative interviews (n = 36) to explore the potential appeal of and demand for a hypothetical CFTA in one such market, the Philippines. Interest in using a CFTA was high among our Internet-engaged, urban study population, with 83.9% "definitely" or "probably" interested in using it. Across demographic profiles, respondents perceived the appeal of the method as "natural" and "convenient." A majority were willing to pay for the method, though notably at a price (5.20 USD) below that of currently available CFTAs. We discuss various important factors to be considered before bringing a method like this to new markets, including the potential implications of equity constraints in reaching a wider market and the unexpected prevalence of other period-tracking apps not intended as contraception being used in this market that could complicate any future roll-out. These issues could be explored further with additional research.

Food-related exploration across the menstrual cycle

When deciding what to eat we constantly weigh different aspects of the options at hand and make trade-offs between exploiting opportunities with a known outcome (e.g., eating your usual meal) and exploring novel opportunities with a potentially better outcome (e.g., trying a new dish). Environmental factors, such as scarcity, have previously been shown to tip the balance towards either exploration or exploitation. Studies in animals have further linked female steroid hormones (including estradiol and progesterone) to exploratory behavior. Previous work in humans has suggested that food preferences and food intake also change over the menstrual cycle. However, it remains unknown whether exploratory behavior in food choices also changes across the menstrual phases in humans. In a rating phase, 112 adult women (age range 18-45 years) on or off hormonal birth control rated 40 food items on desirability. In the choice phase, participants made binary choices between food items. On some trials, a surprise box replaced one of the two food options, allowing us to examine exploratory choices. Women off hormonal birth control reported their average cycle length and time since the first day of their last menstruation. Analysis of the percentage surprise choices across the menstrual cycle indicated a small, but significant effect, with exploratory choice behavior tending to increase around the middle of/later during the cycle. This provides preliminary novel evidence that hormonal fluctuations are associated with food-related exploratory choice behavior. Importantly, these effects were observed beyond effects of other food-related factors. Future studies should investigate the nature of these effects using more direct hormonal measures.

Algorithmic surveillance in the era of the mental health appsphere

Digital mental health applications, also known as mHealth apps, are designed to help users manage their mental health using technology such as mobile devices and smartphones. However, there has been little critical engagement surrounding their impact on marginalized communities, and Black people in Canada. This article aims to explore how state and private actors conceptualize the digitalization of mental health access, leading to the creation and implementation of these apps. It is important to consider the links and interconnections between the platform and the app, and to be cautious and curious before adopting yet another app that claims to manage and mitigate moods and address mental health needs. We should be considering the implications of what comes with this access and apparent ease of use.

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.

Effects of a training intervention tailored to the menstrual cycle on endurance performance and hemodynamics

BACKGROUND

This study aimed to compare the effects of block periodized training and training adapted to the menstrual cycle (MC) phases on endurance performance, cardiovascular parameters, recovery, and MC-related symptoms in active females.

METHODS

Fourteen naturally menstruating, moderately trained females (age: 24±3 years; BMI: 22.3±2.7) were randomized into an intervention (INT) and a control (CON) group. Throughout an 8-week intervention period, both groups participated in a polarized training program. In the INT, the training sessions were adapted to the MC with higher training loads within the mid and late follicular phase. Before and after the intervention maximal oxygen consumption (VO2max), velocity and heart rate at ventilatory thresholds one and two (vVT1, vVT2, hrVT1, hrVT2), systolic and diastolic blood pressure (sBP, dBP), root mean square of successive RR interval differences (RMSSD), standard deviation of NN intervals (SDNN), pulse wave velocity (PWV), and the premenstrual assessment form (PAF) were assessed.

RESULTS

There were no significant time × group interaction effects in all physiological parameters (VO2max: P=0.890; vVT1: P=1.000; hrVT1: P=0.464; vVT2: P=0.356; hrVT2: P=0.762 sBP: P=0.948; dBP: P=0.203; RMSSD: P=0.257; SDNN: P=0.241; PWV: P=0.818), or psychological parameters (PAF: P=0.745).

CONCLUSIONS

Tailoring a polarized training program to the MC did not augment training responses compared to a regular training program in active females. However, a substantial portion of the training intervention in the CON was coincidentally matched to the MC.

What Do Social Media Influencers Say About Birth Control? A Content Analysis of YouTube Vlogs About Birth Control

Youth get their sexual health information from social media, often from social media influencers (SMIs) or microcelebrities with large followings. Previous research suggests that SMIs have powerful persuasive effects on attitudes and behaviors. Thus, it is important to examine the ways in which sexual health information, such as birth control, is conveyed by SMIs. Using framing theory as a theoretical framework, this study examines characteristics of SMIs and their shared experiences pertaining to birth control. A content analysis of YouTube vlogs (n = 50) posted from December 2019-2021 was conducted on SMIs who talk about their experiences using hormonal and non-hormonal birth control. SMI status was determined based on the number of people subscribed to the YouTube channels. Results suggest that SMI YouTube videos are primarily about the discontinuation of hormonal birth control and may provide inaccurate sexual health information. Reasons for discontinuation of hormonal birth control provided by the SMIs are discussed. Future research should explore the effects of influencer sexual health messaging on beliefs, attitudes, and behaviors.

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.

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.

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.

Current evidence shows no influence of women's menstrual cycle phase on acute strength performance or adaptations to resistance exercise training

Introduction

The bias towards excluding women from exercise science research is often due to the assumption that cyclical fluctuations in reproductive hormones influence resistance exercise performance and exercise-induced adaptations.

Methods

Hence, the purpose of this umbrella review was to examine and critically evaluate the evidence from meta-analyses and systematic reviews on the influence of menstrual cycle phase on acute performance and chronic adaptations to resistance exercise training (RET).

Results

We observed highly variable findings among the published reviews on the ostensible effects of female sex hormones on relevant RET-induced outcomes, including strength, exercise performance, and hypertrophy.

Discussion

We highlight the importance of comprehensive menstrual cycle verification methods, as we noted a pattern of poor and inconsistent methodological practices in the literature. In our opinion, it is premature to conclude that short-term fluctuations in reproductive hormones appreciably influence acute exercise performance or longer-term strength or hypertrophic adaptations to RET.

Early diagnosis in polycystic ovary syndrome

ABSTRACT

NPs are in an ideal position to educate patients on the lifelong implications of polycystic ovary syndrome (PCOS). A timely diagnosis and comprehensive plan are needed to appropriately manage patients with PCOS. This article highlights diagnostic criteria, common comorbid conditions, psychological impacts of the clinical manifestations, management approaches, and implications for NPs.

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.

D, R. C. M, T. G. K, A. P. Novel Technique for Confirmation of the Day of Ovulation and Prediction of Ovulation in Subsequent Cycles Using a Skin-Worn Sensor in a Population With Ovulatory Dysfunction: A Side-by-Side Comparison With Existing Basal Body Temperature Algorithm and Vaginal Core Body Temperature Algorithm

Objective: Determine the accuracy of a novel technique for confirmation of the day of ovulation and prediction of ovulation in subsequent cycles for the purpose of conception using a skin-worn sensor in a population with ovulatory dysfunction.

Methods: A total of 80 participants recorded consecutive overnight temperatures using a skin-worn sensor at the same time as a commercially available vaginal sensor for a total of 205 reproductive cycles. The vaginal sensor and its associated algorithm were used to determine the day of ovulation, and the ovulation results obtained using the skin-worn sensor and its associated algorithm were assessed for comparative accuracy alongside a number of other statistical techniques, with a further assessment of the same skin-derived data by means of the “three over six” rule. A number of parameters were used to divide the data into separate comparative groups, and further secondary statistical analyses were performed.

Results: The skin-worn sensor and its associated algorithm (together labeled “SWS”) were 66% accurate for determining the day of ovulation (±1 day) or the absence of ovulation and 90% accurate for determining the fertile window (ovulation day ±3 days) in the total study population in comparison to the results obtained from the vaginal sensor and its associated algorithm (together labeled “VS”).

Conclusion: SWS is a useful tool for confirming the fertile window and absence of ovulation (anovulation) in a population with ovulatory dysfunction, both known and determined by means of the timing of ovulation. The body site where the skin-worn sensor was worn (arm or wrist) did not appear to affect the accuracy. Prior diagnosis of known causes of ovulatory dysfunction appeared to affect the accuracy to a lesser extent than those cycles grouped into late ovulation and “early and normal ovulation” groups. SWS is a potentially useful tool for predicting ovulation in subsequent cycles, with greater accuracy obtained for the “normal ovulation” group.

Trend natural family planning - an online survey to assess attitudes towards NFP among German-speaking women

PURPOSE

To study trends regarding the use of contraceptive methods and digital health modalities and to identify target groups of natural family planning (NFP).

MATERIAL AND METHODS

Using an online questionnaire specifically developed for this study in German (utilizing the online tool at 'www.surveymonkey.com'), we analysed the attitude towards NFP -methods and -apps, the need for contraceptive effectiveness in general, the perceived contraceptive effectiveness of NFP methods, and differences between NFP users and non-NFP users among 779 sexually active German-speaking women of fertile age (18-50 years) from November 2019 to October 2020.

RESULTS AND CONCLUSIONS

Participants used NFP more frequently than they did five years ago. Women aged 30 years and older, with higher levels of education, who are living with a partner and have children, seem to be the target group for NFP methods. Concerning the wish for contraceptive effectiveness we found significant (p < .001) differences between NFP and non-NFP users. Furthermore, an increasing number of women wants to use NFP-methods and -apps for contraception; thus, non-hormonal contraceptive options should be offered. The majority of current NFP users stated that the handling and effectiveness of NFP have been improved by digitalisation.

Biochemical and Endocrine Parameters for the Discrimination and Calibration of Bipolar Disorder or Major Depressive Disorder

OBJECTIVES

Conventional biochemical indexes may have predictive values in clinical identification between bipolar disorder (BD) and major depressive disorder (MDD).

METHODS

This study included 2,470 (BD/MDD = 1,333/1,137) hospitalized patients in Shanghai as training sets and 2,143 (BD/MDD = 955/1,188) in Hangzhou as test sets. A total of 35 clinical biochemical indexes were tested, including blood cells, immuno-inflammatory factors, liver enzymes, glycemic and lipid parameters, and thyroid and gonadal hormones. A stepwise analysis of a multivariable logistic regression was performed to build a predictive model to identify BD and MDD.

RESULTS

Most of these biochemical indexes showed significant differences between BD and MDD groups, such as white blood cell (WBC) in the hematopoietic system, uric acid (UA) in immuno-inflammatory factors, direct bilirubin (DBIL) in liver function, lactic dehydrogenase (LDH) in enzymes, and fasting blood glucose (FBG) and low-density lipoprotein (LDL) in glucolipid metabolism (p-values < 0.05). With these predictors for discrimination, we observed the area under the curve (AUC) of the predictive model to distinguish between BD and MDD to be 0.772 among men and 0.793 among women, with the largest AUC of 0.848 in the luteal phase of women. The χ² values of internal and external validation for male and female datasets were 2.651/10.264 and 10.873/6.822 (p-values < 0.05), respectively. The AUCs of the test sets were 0.696 for males and 0.707 for females.

CONCLUSION

Discrimination and calibration were satisfactory, with fair-to-good diagnostic accuracy and external calibration capability in the final prediction models. Female patients may have a higher differentiability with a conventional biochemical index than male patients.

TRIAL REGISTRATION

ICTRP NCT03949218. Registered on 20 November 2018. Retrospectively registered. https://www.clinicaltrials.gov/ct2/show/NCT03949218?id=NCT03949218&rank=1.

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.

Optimizing natural fertility: a committee opinion

This committee opinion provides practitioners with suggestions for optimizing the likelihood of achieving pregnancy in couples or individuals attempting conception who have no evidence of infertility. This document replaces the document of the same name previously published in 2013 (Fertil Steril 2013;100:631-7).

Role of nurses in managing menstrual health using mobile tracking

ABSTRACT

Mobile technologies provide a unique opportunity to improve menstrual health awareness, management, and care. Nurses should consider incorporating a mobile tracking component into patient-care settings to promote menstrual health awareness and advocacy and to help create individualized healthcare plans.

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.

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.

A scoping review of the information provided by fertility smartphone applications

The growth of smartphone application use across areas of female reproductive health has led to increased interest into their functions and benefits. This scoping review aims to determine the nature and extent of the peer-reviewed literature presented on fertility-based apps, to identify the reliability of the information within the apps, and to determine the ability of this information to educate users. A systematic search of six databases was conducted in April 2020, returning a total of 21,158 records. After duplicate removal, title and abstract screening exclusionary steps, 27 records were reviewed and charted. Records covered a variety of reproductive health themes including contraception, sexual health, and family planning, and used a range of methodologies. The accuracy of fertility information within the apps reported in these studies was variable, but overall there was a lack of depth in the coverage of content in apps. It was common for studies in this review to base fertile window algorithms on stringent cycle length and variability requirements, limiting the applicability of information delivered to users. Furthermore, studies from app affiliates often lacked collaborations with researchers, minimising the potential for fertility knowledge improvements integrated across the suite of female reproductive health apps.

Frequency and experience in the use of menstrual cycle monitoring applications by Brazilian women

OBJECTIVES

This study aimed to evaluate frequency and experience in the use of menstrual cycle monitoring applications (apps) by Brazilian women.

METHODS

A cross-sectional study was performed through an online survey, announced in social media women's groups, among menstruating Brazilian women aged ≥18 years. The instrument collected sociodemographic, sexual, menstrual and technology usage data of all the women who agreed to participate.

RESULTS

Of the 1160 participants, 71.2% used a menstrual cycle monitoring app. The principal motivation for using menstrual cycle apps was to track the menstrual cycle (94.3%), followed by pregnancy avoidance (49.5%). Users rated the apps with a mean 4.4 (standard deviation 0.65) stars out of five. There was a greater likelihood of using an app among women who used behavioural contraceptive methods (odds ratio [OR] 1.8; 95% confidence interval [CI] 1.2, 2.7; p = 0.01), barrier methods (OR 1.6; 95% CI 1.1, 2.5; p = 0.02) and copper- or silver/copper-bearing intrauterine devices (IUDs) (OR 1.9; 95% CI 1.1, 3.5; p = 0.04) and a lower likelihood among women who used hormonal contraception (OR 0.5; 95% CI 0.3, 0.8; p = 0.00) and permanent contraception (OR 0.1; 95% CI 0.0, 0.4; p = 0.00).

CONCLUSION

The use of menstrual cycle monitoring apps was quite widespread in the studied group. Satisfaction with app use was considered adequate. The use of menstrual cycle apps was associated with the use of behavioural contraceptive and barrier methods as well as IUDs.

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.

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.

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

RESEARCH QUESTION

To characterize mobile fertility tracking applications (apps) to determine the use of such apps for women trying to conceive by identifying the fertile window.

DESIGN

An exploratory cross-sectional audit study was conducted of fertility tracking applications. Ninety out of a possible total 200 apps were included for full review. The main outcome measures were the underlying app method for predicting ovulation, the fertile window, or both, price to download and use the app, disclaimers and cautions, information and features provided and tracked, and app marketing strategies.

RESULTS

All the apps except one monitored the women's menstrual cycle dates. Most apps only tracked menstrual cycle dates (n = 49 [54.4%]). The remainder tracked at least one fertility-based awareness method (basal body temperature, cervical mucus, LH) (n = 41 [45.6%]). Twenty-five apps measured dates, basal body temperature, LH and cervical mucus (27.8%). Seventy-six per cent of apps were free to download with free apps having more desirable features, tracking more measures and having more and better quality educational insights than paid apps. Seventy per cent of apps were classified as feminine apps, 41% of which were pink in colour.

CONCLUSIONS

Mobile fertility tracking apps are heterogenous in their underlying methods of predicting fertile days, the price to obtain full app functionality, and in content and design. Unreliable calendar apps remain the most commonly available fertility apps on the market. The unregulated nature of fertility apps is a concern that could be addressed by app regulating bodies. The possible benefit of using fertility apps to reduce time to pregnancy needs to be evaluated.

Infertility Management and Pharmacotherapy: What Every Pharmacist Should Know

Infertility is a significant public health concern in the United States. As such, utilization of infertility services, including pharmacotherapeutic treatments, is prevalent, and the role of the pharmacist as part of the health care team is essential. Pharmacists can assist patients by providing education on infertility causes, risk factor mitigation, referrals, nonpharmacologic and pharmacologic management options, navigation of the prescription process, and resource availability for the significant financial burden accompanying infertility treatment. In-depth pharmacotherapeutic information may not be readily available to pharmacists dispensing and counseling on these medications, and infertility management regimens are often varied and complicated requiring more in-depth counseling on use and administration. Given the complexity of infertility management, pharmacists are a valuable patient support and education resource. This article provides an in-depth review of infertility management strategies, both nonpharmacologic and pharmacologic.

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.

CE: An Evidence-Based Update on Contraception

Contraception is widely used in the United States, and nurses in all settings may encounter patients who are using or want to use contraceptives. Nurses may be called on to anticipate how family planning intersects with other health care services and provide patients with information based on the most current evidence. This article describes key characteristics of nonpermanent contraceptive methods, including mechanism of action, correct use, failure rates with perfect and typical use, contraindications, benefits, side effects, discontinuation procedures, and innovations in the field. We also discuss how contraceptive care is related to nursing ethics and health inequities.

Real-life insights on menstrual cycles and ovulation using big data

STUDY QUESTION

What variations underlie the menstrual cycle length and ovulation day of women trying to conceive?

SUMMARY ANSWER

Big data from a connected ovulation test revealed the extent of variation in menstrual cycle length and ovulation day in women trying to conceive.

WHAT IS KNOWN ALREADY

Timing intercourse to coincide with the fertile period of a woman maximises the chances of conception. The day of ovulation varies on an inter- and intra-individual level.

STUDY DESIGN, SIZE, DURATION

A total of 32 595 women who had purchased a connected ovulation test system contributed 75 981 cycles for analysis. Day of ovulation was determined from the fertility test results. The connected home ovulation test system enables users to identify their fertile phase. The app benefits users by enabling them to understand their personal fertility information. During each menstrual cycle, users input their perceived cycle length into an accessory application, and data on hormone levels from the tests are uploaded to the application and stored in an anonymised cloud database. This study compared users’ perceived cycle characteristics with actual cycle characteristics. The perceived and actual cycle length information was analysed to provide population ranges.

PARTICIPANTS/MATERIALS, SETTING, METHODS

This study analysed data from the at-home use of a commercially available connected home ovulation test by women across the USA and UK.

MAIN RESULTS AND THE ROLE OF CHANCE

Overall, 25.3% of users selected a 28-day cycle as their perceived cycle length; however, only 12.4% of users actually had a 28-day cycle. Most women (87%) had actual menstrual cycle lengths between 23 and 35 days, with a normal distribution centred on day 28, and over half of the users (52%) had cycles that varied by 5 days or more. There was a 10-day spread of observed ovulation days for a 28-day cycle, with the most common day of ovulation being Day 15. Similar variation was observed for all cycle lengths examined. For users who conducted a test on every day requested by the app, a luteinising hormone (LH) surge was detected in 97.9% of cycles.

LIMITATIONS, REASONS FOR CAUTION

Data were from a self-selected population of women who were prepared to purchase a commercially available product to aid conception and so may not fully represent the wider population. No corresponding demographic data were collected with the cycle information.

WIDER IMPLICATIONS OF THE FINDINGS

Using big data has provided more personalised insights into women’s fertility; this could enable women trying to conceive to better time intercourse, increasing the likelihood of conception.

STUDY FUNDING/COMPETING INTERESTS

The study was funded by SPD Development Company Ltd (Bedford, UK), a fully owned subsidiary of SPD Swiss Precision Diagnostics GmbH (Geneva, Switzerland). I.S., B.G. and S.J. are employees of the SPD Development Company Ltd.

Increased Likelihood of Pregnancy Using an App-Connected Ovulation Test System: A Randomized Controlled Trial

Background: Women trying to conceive are increasingly using fertility-tracking software applications to time intercourse. This study evaluated the difference in conception rates between women trying to conceive using an application-connected ovulation test system, which measures urinary luteinizing hormone and an estrogen metabolite, versus those trying without using ovulation testing.

Materials and Methods: This home-based study involved 844 volunteers aged 18–40 years seeking to conceive. Volunteers randomized to the test arm were required to use the test system for the duration of the study while those randomized to the control arm were instructed not to use ovulation testing. Pregnancy rate differences across one and two cycles between the two groups were examined.

Results: Volunteers in the test (n = 382) and control arms (n = 403) had similar baseline demographics. The proportion of women pregnant after one cycle was significantly greater in the test arm (25.4%) compared with the control arm (14.7%; p < 0.001). After two cycles, there continued to be a greater proportion of women pregnant in the test arm compared with the control arm (36.2% vs. 28.6%; p = 0.026). In the test arm, volunteers had intercourse less frequently per cycle compared with those not using ovulation testing (9 [range: 1–60] vs. 10 [range: 1–50]; p = 0.027), but were more likely to target intercourse to a particular part of their cycle compared with those not using ovulation testing (88.5% vs. 57.8%; p < 0.001).

Conclusion: Using the test system to time intercourse within the fertile window increases the likelihood of conceiving within two menstrual cycles.

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.

Wearable Sensors Reveal Menses-Driven Changes in Physiology and Enable Prediction of the Fertile Window: Observational Study

Background

Previous research examining physiological changes across the menstrual cycle has considered biological responses to shifting hormones in isolation. Clinical studies, for example, have shown that women’s nightly basal body temperature increases from 0.28 to 0.56 ˚C following postovulation progesterone production. Women’s resting pulse rate, respiratory rate, and heart rate variability (HRV) are similarly elevated in the luteal phase, whereas skin perfusion decreases significantly following the fertile window’s closing. Past research probed only 1 or 2 of these physiological features in a given study, requiring participants to come to a laboratory or hospital clinic multiple times throughout their cycle. Although initially designed for recreational purposes, wearable technology could enable more ambulatory studies of physiological changes across the menstrual cycle. Early research suggests that wearables can detect phase-based shifts in pulse rate and wrist skin temperature (WST). To date, previous work has studied these features separately, with the ability of wearables to accurately pinpoint the fertile window using multiple physiological parameters simultaneously yet unknown.

Objective

In this study, we probed what phase-based differences a wearable bracelet could detect in users’ WST, heart rate, HRV, respiratory rate, and skin perfusion. Drawing on insight from artificial intelligence and machine learning, we then sought to develop an algorithm that could identify the fertile window in real time.

Methods

We conducted a prospective longitudinal study, recruiting 237 conception-seeking Swiss women. Participants wore the Ava bracelet (Ava AG) nightly while sleeping for up to a year or until they became pregnant. In addition to syncing the device to the corresponding smartphone app daily, women also completed an electronic diary about their activities in the past 24 hours. Finally, women took a urinary luteinizing hormone test at several points in a given cycle to determine the close of the fertile window. We assessed phase-based changes in physiological parameters using cross-classified mixed-effects models with random intercepts and random slopes. We then trained a machine learning algorithm to recognize the fertile window.

Results

We have demonstrated that wearable technology can detect significant, concurrent phase-based shifts in WST, heart rate, and respiratory rate (all P<.001). HRV and skin perfusion similarly varied across the menstrual cycle (all P<.05), although these effects only trended toward significance following a Bonferroni correction to maintain a family-wise alpha level. Our findings were robust to daily, individual, and cycle-level covariates. Furthermore, we developed a machine learning algorithm that can detect the fertile window with 90% accuracy (95% CI 0.89 to 0.92).

Conclusions

Our contributions highlight the impact of artificial intelligence and machine learning’s integration into health care. By monitoring numerous physiological parameters simultaneously, wearable technology uniquely improves upon retrospective methods for fertility awareness and enables the first real-time predictive model of ovulation.

Hormonal Cycles, Brain Network Connectivity, and Windows of Vulnerability to Affective Disorder

The rate of affective disorder is substantially higher in women than in men, and considerable evidence points to the actions of ovarian hormones in mediating this disparity. In this Opinion, we discuss the hypothesis that cyclic changes in ovarian hormone levels produce cyclic alterations in connectivity between the intrinsic networks of the brain. These alterations produce specific temporal windows within the menstrual cycle when internetwork connectivity is increased, associated with increased stress reactivity and better memory for unpleasant, arousing events, leading to increased negative mood and susceptibility to affective disorder. Our windows of vulnerability model offers insights for both treatment of affective disorder and research on sex differences in the brain.