Time-to-Pregnancy Fertility Tracking via App: A Research Review
By: Adriana Kitchens, DO
Director’s Note: Last week, we featured an interview with a woman and her experience charting with the ‘Natural Cycles’ fertility awareness app. This week, we feature research on the Natural Cycles mobile application to explore some of the science behind this novel tool. Dr. Adriana Kitchens, a former FACTS Elective student, summarized the 2021 study titled, “Time to Pregnancy for Women Using a Fertility Awareness Based Mobile Application to Plan a Pregnancy.” As apps become more prevalent, this timely study by Favaro et al [1] makes a clear distinction between biomarkers tracked by modern fertility awareness-based methods (FABMs) and calendar-based methods, and lends insight into the utility of apps to identify the fertile window for couples hoping to conceive. To learn more about how biomarkers can be tracked for family planning or health monitoring, join us this Wednesday, December 6th at 8pm ET for our webinar, The FACTS about Fertility Explained, or gift it to a friend.
Introduction
With the rise of technology, many apps have been developed to help women track their menstruation, fertility, and pregnancies. There are various ways to track fertility, including monitoring basal body temperature (BBT), LH testing, and observing cervical mucus. Modern apps allow users to input these biomarkers as well as other information about their menstruation and cycle to predict their fertile window. The study by Favaro et al [1] explores the time to conception for women using the Natural Cycles mobile application.
“There are various ways to track fertility, including monitoring basal body temperature (BBT), LH testing, and observing cervical mucus.”
Methodology
Natural Cycles has three different modes: “prevent,” “plan,” and “track and pregnancy.” The study aimed to analyze the “plan” portion of the app to determine how quickly women who were attempting to get pregnant could conceive. The app differs from other calendar-based apps through its advanced mathematical algorithm that uses BBT, menstruation, and LH measurements to predict ovulation and the fertile window.
The study included women ages 18 to 45, most living in the UK, USA, and Sweden, and took place from January 2017 to December 2019. Participant characteristics were recorded, such as age, body mass index (BMI), and education level. The study analyzed each participant’s cycle length and variation, previous pregnancies, frequency of logging BBT, and frequency of sexual intercourse. Users gave consent to participate in the study within the app and had the option to decline at any time. Participants who used the “prevent” mode for at least two full cycles and then switched to “plan” mode for at least one fertile window were included in the study. Users reported pregnancies by entering a positive pregnancy test, answering a follow-up message, and switching to the app’s pregnancy mode. Pregnancies were also detected indirectly through app data, such as persistently high BBT after ovulation.
Results
After dropouts, 3,736 users were analyzed in the study. As a full cohort, 84.7% of participants achieved pregnancy within 13 cycles and 15.3% were still trying to get pregnant at the end of the observation period. The mean time to pregnancy was 4 cycles. Also, as a full cohort, the probability of achieving pregnancy was 61% at 6 cycles and 74% at 12 cycles.
Data were also analyzed for participants with higher fecundability, and included age < 35 and cycle length variation < 5 days with sexual intercourse logged at least 20% of days. This group had higher probability of pregnancy than the cohort as a whole: 88% at 6 cycles and 96% at 12 cycles with a median time to pregnancy of 2 cycles.
“Participants with higher fecundability (age < 35 and cycle length variation < 5 days with sexual intercourse logged at least 20% of days) had higher probability of pregnancy: 88% at 6 cycles and 96% at 12 cycles with a median time to pregnancy of 2 cycles.”
The impact of age and cycle irregularity was also evaluated separately. Women age > 35 had a 54% chance of being pregnant at 6 months and 75% chance at 12 months. Women with irregular cycles had a 58% likelihood of pregnancy at 6 cycles and 79% at 12 cycles. Factors that increased the odds of pregnancy included BBT measuring frequency, intercourse frequency, and previous pregnancy. Factors that decreased the odds of pregnancy included long cycle length, low intercourse frequency, and age greater than 35.
Discussion
One of the limitations of this research was the study population. In this study, users of the app had a higher educational level and lower BMI than would be expected in the general population. Also, close to 50% of participants had characteristics associated with subfertility such as age >35 years, highly variable cycle length or anovulatory cycles. In addition, there was potential difficulty assessing the women’s intentions, since they were determined by the “mode” of the app they were in. It is possible some women could switch their intention in the app just to see the features in that section or mode. In addition, logging of sexual intercourse was encouraged but not required in the app. It is possible not every instance was reported in the app, thus underreporting sexual intercourse.
This study shows the potential benefit of FABMs and mobile applications. Frequency of BBT measurements was one factor associated with higher odds of pregnancy; as more such data is added to the app, there is better prediction for ovulation and fertile days. This also shows FABMs have the potential to be much better than calendar-only methods, since the app algorithm gives better prediction of ovulation days regardless of cycle length. Apps may help women have more autonomy and confidence regarding their fertility.
There are also potential risks of using fertility-based apps, such as delays in seeking care from a physician. Users may rely too much on the app instead of seeking medical advice. It’s possible that potential medical problems like thyroid disease, insulin resistance, polycystic ovary syndrome (PCOS), endometriosis or other underlying causes of subfertility might get missed if people think using the app replaces medical advice. In addition, fertility-based mobile apps may cause anxiety in some women by constantly reminding them of their desire to conceive.
“There are also potential risks of using fertility-based apps, such as delays in seeking care from a physician.”
Overall, apps provide easy access to FABMs to a wider population. Mobile applications are easy to download and use. The app does most of the calculations, so all users need to do is take their measurements and enter them in the app. Some people may have difficulty interpreting their cycles by themselves; although this app provides that service, it is not a substitute to learning about this from a medical professional. Apps also have the potential to provide preconception education to people who may not have access otherwise. Overall, fertility mobile applications have many potential benefits, but each person should weigh the risks vs. benefits given their individual situation.
References
[1] Favaro C, Pearson JT, Rowland SP, Jukic AM, Chelstowska M, Scherwitzl EB, Scherwitzl R, Danielsson KG, Harper J. Time to Pregnancy for Women Using a Fertility Awareness Based Mobile Application to Plan a Pregnancy. J Womens Health. 2021;30(11): 1538-1545. DOI: 10.1089/jwh.2021.0026.
ABOUT THE AUTHOR
Adriana Kitchens, DO
Adriana Kitchens, DO participated in the FACTS elective as a fourth-year medical student while at Kansas City University in Joplin, Missouri. She completed her undergraduate education at the University of Utah in Salt Lake City. She is pursuing family medicine residency at the University of Missouri Kansas City. Dr. Kitchens is passionate about women’s health, and specific areas of interest include obstetrics, fertility, lifestyle medicine, and preventive medicine. She enrolled in the FACTS elective to learn more about fertility awareness-based methods and hopes to apply the knowledge learned in the course with future patients.
