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

Participation of general practitioners and therapeutic patient education in the care of infertile couples

BACKGROUND

Fertility treatment pathways are complex and lengthy. The current prevalence of infertility makes it a public health issue. The involvement of general practitioners and the training of fertility instructors to provide therapeutic education have been suggested as ways of involving patients in the process and improving the therapeutic trajectory of these patients, who often have co-morbidities.

OBJECTIVE

To describe the activity of trained fertility instructors; to assess the interest of doctors in the fertility chart provided by women; and to describe the outcomes of their fertility care pathway.

METHODS

66 French fertility instructors were interviewed in June 2024. The 15 general practitioners who had received additional training were also interviewed. The records of all couples who received fertility counselling and treatment between 1 January 2022 and 31 December 2023, the study cut-off date, were analysed.

RESULTS

Doctors declared that the women had gained a clear understanding of their menstrual cycle, which was useful for diagnosis and treatment follow-up. The chart was particularly useful for diagnosing the causes of infertility and identifying when in the cycle to take medication. Only 4 of the 551 women were lost to follow-up. Of the remaining 547 women, 204 (37%) became pregnant. Of these, 75% had a live birth or an ongoing pregnancy at study cut-off.

CONCLUSIONS

The involvement of fertility instructors and general practitioners improved the couple's ability to interact with doctors and to adhere to infertility treatment. The fertility chart provided by the women proved to be useful in the diagnosis and treatment process.

Deep learning model using continuous skin temperature data predicts labor onset

BACKGROUND

Changes in body temperature anticipate labor onset in numerous mammals, yet this concept has not been explored in humans. We investigated if continuous body temperature exhibits similar changes in women and whether these changes may be linked to hormonal status. Finally, we developed a deep learning model using temperature patterning to provide a daily forecast of time to labor onset.

METHODS

We evaluated patterns in continuous skin temperature data in 91 (n = 54 spontaneous labors) pregnant women using a wearable smart ring. In a subset of 28 pregnancies, we examined daily steroid hormone samples leading up to labor to analyze relationships among hormones and body temperature trajectory. Finally, we applied an autoencoder long short-term memory (AE-LSTM) deep learning model to provide a novel daily estimation of days until labor onset.

RESULTS

Features of temperature change leading up to labor were associated with urinary hormones and labor type. Spontaneous labors exhibited greater estriol to α-pregnanediol ratio, as well as lower body temperature and more stable circadian rhythms compared to pregnancies that did not undergo spontaneous labor. Skin temperature data from 54 pregnancies that underwent spontaneous labor between 34 and 42 weeks of gestation were included in training the AE-LSTM model, and an additional 37 pregnancies that underwent artificial induction of labor or Cesarean without labor were used for further testing. The input to the pipeline was 5-min skin temperature data from a gestational age of 240 days until the day of labor onset. During cross-validation AE-LSTM average error (true - predicted) dropped below 2 days at 8 days before labor, independent of gestational age. Labor onset windows were calculated from the AE-LSTM output using a probabilistic distribution of model error. For these windows AE-LSTM correctly predicted labor start for 79% of the spontaneous labors within a 4.6-day window at 7 days before true labor, and 7.4-day window at 10 days before true labor.

CONCLUSION

Continuous skin temperature reflects progression toward labor and hormonal change during pregnancy. Deep learning using continuous temperature may provide clinically valuable tools for pregnancy care.

Deep Learning Model Using Continuous Skin Temperature Data Predicts Labor Onset

Background

Changes in body temperature anticipate labor onset in numerous mammals, yet this concept has not been explored in humans.

Methods

We evaluated patterns in continuous skin temperature data in 91 pregnant women using a wearable smart ring. Additionally, we collected daily steroid hormone samples leading up to labor in a subset of 28 pregnancies and analyzed relationships among hormones and body temperature trajectory. Finally, we developed a novel autoencoder long-short-term-memory (AE-LSTM) deep learning model to provide a daily estimation of days until labor onset.

Results

Features of temperature change leading up to labor were associated with urinary hormones and labor type. Spontaneous labors exhibited greater estriol to α-pregnanediol ratio, as well as lower body temperature and more stable circadian rhythms compared to pregnancies that did not undergo spontaneous labor. Skin temperature data from 54 pregnancies that underwent spontaneous labor between 34 and 42 weeks of gestation were included in training the AE-LSTM model, and an additional 40 pregnancies that underwent artificial induction of labor or Cesarean without labor were used for further testing. The model was trained only on aggregate 5-minute skin temperature data starting at a gestational age of 240 until labor onset. During cross-validation AE-LSTM average error (true - predicted) dropped below 2 days at 8 days before labor, independent of gestational age. Labor onset windows were calculated from the AE-LSTM output using a probabilistic distribution of model error. For these windows AE-LSTM correctly predicted labor start for 79% of the spontaneous labors within a 4.6-day window at 7 days before true labor, and 7.4-day window at 10 days before true labor.

Conclusion

Continuous skin temperature reflects progression toward labor and hormonal status during pregnancy. Deep learning using continuous temperature may provide clinically valuable tools for pregnancy care.

Factors Used by Mobile Applications to Predict Female Fertility Status and Their Reported Effectiveness: A Scoping Review

Family planning, whether for pregnancy prevention or conception, is of pivotal importance to women of reproductive age. As hormonally driven methods, such as oral contraceptive pills, are widely used but have numerous side effects, women often seek alternative non-hormonal, non-invasive options, including fertility-tracking mobile applications (apps). However, the effectiveness of these apps as a method of contraception and conception planning has not been extensively vetted. The goal of this scoping review is to identify the various factors used by apps marketed as a method of contraception and/or family planning to predict a woman's fertility status, as well as their documented effectiveness. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines, a literature search was performed in CINAHL, MEDLINE, and Alt HealthWatch databases for articles published between October 1, 2017, and October 4, 2022. Quality assessment of eligible full-text articles was conducted using the Joanna Briggs Institute critical appraisal tools. A total of 629 articles were screened. Overall, 596 articles were excluded and the remaining 33 articles underwent full-text review. Seven articles were included in the final analysis, yielding data on the following five apps: Natural Cycles, Ava Fertility, Clearblue Connected, Ovia Fertility, and Dynamic Optimal Timing (DOT). Data supporting the effectiveness of these apps is limited. All apps provided predictions on fertility status throughout a woman's menstrual cycle using proprietary algorithms, biometric data, and self-reported menstrual cycle data. Further research, particularly independent research following a randomized controlled design, on the efficacy of these apps is needed to produce more robust results.

Neural substrates underlying rhythmic coupling of female reproductive and thermoregulatory circuits

Coordinated fluctuations in female reproductive physiology and thermoregulatory output have been reported for over a century. These changes occur rhythmically at the hourly (ultradian), daily (circadian), and multi-day (ovulatory) timescales, are critical for reproductive function, and have led to the use of temperature patterns as a proxy for female reproductive state. The mechanisms underlying coupling between reproductive and thermoregulatory systems are not fully established, hindering the expansion of inferences that body temperature can provide about female reproductive status. At present, numerous digital tools rely on temperature to infer the timing of ovulation and additional applications (e.g., monitoring ovulatory irregularities and progression of puberty, pregnancy, and menopause are developed based on the assumption that reproductive-thermoregulatory coupling occurs across timescales and life stages. However, without clear understanding of the mechanisms and degree of coupling among the neural substrates regulating temperature and the reproductive axis, whether such approaches will bear fruit in particular domains is uncertain. In this overview, we present evidence supporting broad coupling among the central circuits governing reproduction, thermoregulation, and broader systemic physiology, focusing on timing at ultradian frequencies. Future work characterizing the dynamics of reproductive-thermoregulatory coupling across the lifespan, and of conditions that may decouple these circuits (e.g., circadian disruption, metabolic disease) and compromise female reproductive health, will aid in the development of strategies for early detection of reproductive irregularities and monitoring the efficacy of fertility treatments.

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.

Development of a Permanent Device for Fertility Period Detection by Basal Body Temperature and Analysis of the Cervical Mucus Potential of Hydrogen

Background

Sometimes, women find it difficult to conceive a baby and others use contraceptives that often have side effects. Researchers have already established the importance of measuring basal body temperature (BBT) and the potential of hydrogen (pH).

Method

We have designed and realized a device that allows the simultaneous measurement of the BBT and the pH. We used an Arduino Uno board, a pH sensor, and a temperature sensor. The device communicates with a smartphone, can be integrated into all e-health platforms, and can be used at home. We validated our ovulation detector by a measurement campaign on a group of twenty women. If the pH is >7 and at the same time, the BBT is minimum and <36.5°C, the women is in ovulation phase. If the pH is ≤7 and in the same time, the BBT is between 36.5°C and 37°C, the women are in preovulation or follicular phase. If the pH is ≤7 and in the same time, the BBT is >36.5°C, the women are in postovulation or luteal phase.

Results

We tested the contraceptive aspect of our ovulometer on a set of seven women. We also tested the help of conceiving babies by having intercourse during the ovulation period fixed by our ovulation detector. The results are satisfactory.

Conclusions

In the final version of our device, we displayed just in "fertility period" if the pH is ≥7 and the BBT is <36.5°C else we displayed in "nonfertility period."

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.

The performance of a fertility tracking device

OBJECTIVE

Fertility tracking devices offer women direct-to-user information about their fertility. The objective of this study is to understand how a fertility tracking device algorithm adjusts to changes of the individual menstrual cycle and under different conditions.

METHODS

A retrospective analysis was conducted on a cohort of women who were using the device between January 2004 and November 2014. Available temperature and menstruation inputs were processed through the Daysy 1.0.7 firmware to determine fertility outputs. Sensitivity analyses on temperature noise, skipped measurements, and various characteristics were conducted.

RESULTS

A cohort of 5328 women from Germany and Switzerland contributed 107,020 cycles. Mean age of the sample was 30.77 [SD 5.1] years, with a BMI of 22.07 kg/m^2 [SD 2.4]. The mean cycle length reported was 29.54 [SD 3.0] days. The majority of women were using the device 80-100% of the time during the cycle (53.1%). For this subset of women, the fertility device identified on average 41.4% [SD 6.4] possibly fertile (red) days, 42.4% [SD 8.7] infertile (green) days and 15.9% [SD 7.3] yellow days. The number of infertile (green) days decreases proportionally to the number of measured days, whereas the number of undefined (yellow) days increases.

CONCLUSION

Overall, these results showed that the fertility tracker algorithm was able to distinguish biphasic cycles and provide personalised fertility statuses for users based on daily basal body temperature readings and menstruation data. We identified a direct linear relationship between the number of measurements and output of the fertility tracker.

Misrepresentation of contraceptive effectiveness rates for fertility awareness methods of family planning

AIM

Simplified contraceptive method-efficacy and/or typical-use effectiveness rates are commonly used for direct comparison of the various contraceptive methods. Use of such effectiveness rates in this manner is, however, problematic in relation to the fertility awareness methods (FAMs). The aim of this review is to critically examine current international representation of contraceptive effectiveness for the various FAMs in clinical use. This review also details important issues when appraising and interpreting studies on FAMs used for avoiding pregnancy.

METHODS

Current international literature regarding contraceptive effectiveness of FAMs was surveyed and appraised. This included World Health Organization and Centers for Disease Control (USA) resources, key clinical studies and recent systematic reviews. Chinese literature was also searched, since these data have not been reported in the English literature.

RESULTS

Reliance on certain historical studies has led to the misrepresentation of contraceptive effectiveness of FAMs by perpetuation of inaccurate figures in clinical guidelines, the international literature and the public domain. Interpretation of published study results for FAMs is difficult due to variability in study methodology and other clinical trial quality issues. Recent systematic analyses have noted the considerable issues with study designs and limitations. Several non-English published studies using the Billings Ovulation Method have demonstrated that a broader review of the literature is required to better capture the data potentially available.

CONCLUSION

A deeper understanding by clinicians and the public of the applicability of contraceptive effectiveness rates of the various FAMs is needed, instead of reliance on the inaccurate conglomerate figures that are widely presented.

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

Background

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

Objective

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

Methods

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

Results

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

Conclusions

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