The global pandemic and changes in women's reproductive health: an observational study.

OBJECTIVE: The COVID-19 global pandemic has led to the death of millions around the world and impacted the overall health of many people. In this article we aim to compare reproductive health indicators in the first 6 months of 2020 to the prior year, as well as explore stress and quality of life during this time. METHODS: This retrospective observational study examined the menstrual cycles of 1159 women who were using a fertility tracking device to record their menstrual cycle and BBT data. We utilised a supplemental mobile application to administer a supplemental survey to collect data on stress and quality of life. Descriptive analyses were conducted with t-tests for two-group comparisons. RESULTS: Study participants from 15 countries contributed to a total of 13,194 cycles. 23.1% (268/1159) responded to the survey focussed on assessing psychosocial distress. 44.4% (119/268) of the study participants reported that they had noticed a change in their menstrual cycle, temperature curve, or menstruation in the past 12 months. Cycle analysis found the average cycle length and pre-ovulation phase length was longer in the first 6 months of 2019, while the average days of menstruation was slightly longer in 2020. DISCUSSION: Our findings indicate that menstrual cycle indicators changed only slightly in the first 6 months of 2020 but were still statistically significant. We were also able to understand that these study participants had some level of awareness of changes to their menstrual health.

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.

Perfect- and typical-use effectiveness of the Dot fertility app over 13 cycles: results from a prospective contraceptive effectiveness trial.

OBJECTIVE: Dynamic Optimal Timing (Dot) is a smartphone application (app) that estimates the menstrual cycle fertile window based on the user's menstrual period start dates. Dot uses machine learning to adapt to cycles over time and informs users of 'low' and 'high' fertility days. We investigated Dot's effectiveness, calculating perfect- and typical-use failure rates. METHODS: This prospective, 13 cycle observational study (ClinicalTrials.gov NCT02833922) followed 718 women who were using Dot to prevent pregnancy. Participants contributed 6616 cycles between February 2017 and October 2018, providing data on menstrual period start dates, daily sexual activity and prospective intent to prevent pregnancy. We determined pregnancy through participant-administered urine pregnancy tests and/or written or verbal confirmation. We calculated perfect- and typical-use failure rates using multi-censoring, single-decrement life-table analysis, and conducted sensitivity, attrition and survival analyses. RESULTS: The perfect-use failure rate was calculated to be 1.0% (95% confidence interval [CI]: 0.9%, 2.9%) and the typical-use failure rate was 5.0% (95% CI: 3.4%, 6.6%) for women aged 18-39 (n = 718). Survival analyses identified no significant differences among age or racial/ethnic groups or women in different types of relationships. Attrition analyses revealed no significant sociodemographic differences, except in age, between women completing 13 cycles and those exiting the study earlier. CONCLUSION: Dot's effectiveness is within the range of other user-initiated contraceptive methods.

Estimating six-cycle efficacy of the Dot app for pregnancy prevention.

OBJECTIVE: To assess six-cycle perfect and typical use efficacy of Dynamic Optimal Timing (Dot), an algorithm-based fertility app that identifies the fertile window of the menstrual cycle using a woman's period start date and provides guidance on when to avoid unprotected sex to prevent pregnancy. STUDY DESIGN: We are conducting a prospective efficacy study following a cohort of women using Dot for up to 13 cycles. Study enrollment and data collection are being conducted digitally within the app and include a daily coital diary, prospective pregnancy intentions and sociodemographic information. We used data from the first six cycles to calculate life-table failure rates. RESULTS: We enrolled 718 women age 18-39 years. Of the 629 women 18-35 years old, 15 women became pregnant during the first six cycles for a typical use failure rate of 3.5% [95% CI 1.7-5.2]. All pregnancies occurred with incorrect use, so we did not calculate a perfect use failure rate. CONCLUSIONS: These findings are promising and suggest that the 13-cycle results will demonstrate high efficacy of Dot. IMPLICATIONS: While final 13-cycle efficacy results are forthcoming, 6-cycle results suggest that Dot's guidance provides women with useful information for preventing pregnancy.

Market-testing a smartphone application for family planning: assessing potential of the CycleBeads app in seven countries through digital monitoring.

BACKGROUND: The advent of new technological approaches to family planning has the potential to address unmet need in low- and middle-income countries. Provision of fertility awareness-based apps have the ability to provide accessible, direct-to-user fertility information to help women achieve their reproductive goals. The CycleBeads app, a digital platform for the Standard Days Method (SDM), a modern method of family planning, helps women achieve or prevent pregnancy, or track their cycles using the only their period start dates. METHODS: Brief social marketing campaigns were launched by the app developer to monitor cost and distribution of the CycleBeads app, understand the user profile, and assess user experience. Monitoring and evaluation through in-app micro surveys occurred over a 6-cycle period in seven countries: Egypt, Ghana, India, Jordan, Kenya, Nigeria, and Rwanda. In-app micro-surveys were utilized to collect data around demographics, mode of use of the app, prior experiences with family planning, and satisfaction to better understand women's interactions with the apps, and the possibility for meeting unmet need. Analyzes focused on women who were using the app to prevent pregnancy or track their cycles. RESULTS: Social media campaigns proved to be an easy, low-cost approach to advertising the CycleBeads app. As a result, 356,520 women downloaded the app, and the cost to the advertiser per download ranged from $0.17-0.69. A majority of app users were between 20-29 years old, married or in exclusive relationships. Overall, 39.9% of users were using the app to prevent pregnancy, 38.5% to plan a pregnancy, and 21.6% were tracking their cycles. Among the users preventing pregnancy, 64.1% of women had not used a family planning method 3 months before downloading the CycleBeads app. One-third of users who were using the app to track their cycles, reported that they had not been using any form of family planning. In all seven countries, nearly 60% of women reported that they would definitely recommend the CycleBeads app to a friend, indicating their satisfaction with the app. CONCLUSIONS: Our main findings indicate that a social media campaign is a low-cost approach to making the CycleBeads app accessible to women. The app addresses multiple reproductive intentions and attracts a diverse demographic of users across different life stages. For many women the app was the first modern method they used in the last 3 months, showing that fertility awareness-based apps have the potential to address an unmet need. Future studies should focus on changes in behavior during the fertile window, partner communication, and future family planning intentions.

User profile and preferences in fertility apps for preventing pregnancy: an exploratory pilot study.

BACKGROUND: The rapid proliferation of fertility apps has created a market that has the potential to address the needs of women and couples worldwide. Some women who seek to prevent pregnancy are making behavioral decisions based on information they receive from fertility apps, yet fertility apps may not always be accurate and reliance on them could lead to unintended pregnancies. Little research has been done to understand who uses fertility apps for pregnancy prevention, how those who use them perceive their efficacy, and their preferences for how apps should be designed and presented to accurately assist them in preventing pregnancy. METHODS: A web-based pilot survey was launched through Facebook recruiting women who either currently use a fertility app for pregnancy prevention or intend to use one in the future. Data collected from 1,000 women surveyed user preferences around fertility app characteristics, including aesthetics, features, functionality, and reputation. User knowledge about fertility and reproduction was assessed, and knowledge categories were created. Chi-square tests assessed differences in app characteristic preferences according to knowledge category. Additional qualitative analyses on free-text answers explored which features of apps are important to users when they search for one to use. RESULTS: Approximately one quarter (23.1%) of survey respondents reported currently using a fertility app or had used one in the recent past, and 76.9% reported intention to use one in the future. A majority of both current and intended users (65.4%) had some knowledge of fertility and reproduction, while 16.5% had very little knowledge. 18.1% reported receiving prior provider counseling on using a fertility-awareness based method. Users across all knowledge groups said it was very important for apps to be science-based and that they identify fertile days during the menstrual cycle. CONCLUSIONS: Women who use or wish to use apps to prevent pregnancy are seeking apps that are scientifically sound and provide them personalized information around their potential fertility. However, most fertility apps women reported using lack the capability for true fertility-awareness based method application for accurate, reliable pregnancy prevention. More research is needed to evaluate apps for efficacy and accuracy preventing pregnancy. Collaborations between app developers and women's health experts are encouraged, as well as informed consumerism campaigns.

Lessons From the Dot Contraceptive Efficacy Study: Analysis of the Use of Agile Development to Improve Recruitment and Enrollment for mHealth Research.

BACKGROUND: Smartphone apps that provide women with information about their daily fertility status during their menstrual cycles can contribute to the contraceptive method mix. However, if these apps claim to help a user prevent pregnancy, they must undergo similar rigorous research required for other contraceptive methods. Georgetown University's Institute for Reproductive Health is conducting a prospective longitudinal efficacy trial on Dot (Dynamic Optimal Timing), an algorithm-based fertility app designed to help women prevent pregnancy. OBJECTIVE: The aim of this paper was to highlight decision points during the recruitment-enrollment process and the effect of modifications on enrollment numbers and demographics. Recruiting eligible research participants for a contraceptive efficacy study and enrolling an adequate number to statistically assess the effectiveness of Dot is critical. Recruiting and enrolling participants for the Dot study involved making decisions based on research and analytic data, constant process modification, and close monitoring and evaluation of the effect of these modifications. METHODS: Originally, the only option for women to enroll in the study was to do so over the phone with a study representative. On noticing low enrollment numbers, we examined the 7 steps from the time a woman received the recruitment message until she completed enrollment and made modifications accordingly. In modification 1, we added call-back and voicemail procedures to increase the number of completed calls. Modification 2 involved using a chat and instant message (IM) features to facilitate study enrollment. In modification 3, the process was fully automated to allow participants to enroll in the study without the aid of study representatives. RESULTS: After these modifications were implemented, 719 women were enrolled in the study over a 6-month period. The majority of participants (494/719, 68.7%) were enrolled during modification 3, in which they had the option to enroll via phone, chat, or the fully automated process. Overall, 29.2% (210/719) of the participants were enrolled via a phone call, 19.9% (143/719) via chat/IM, and 50.9% (366/719) directly through the fully automated process. With respect to the demographic profile of our study sample, we found a significant statistical difference in education level across all modifications (P<.05) but not in age or race or ethnicity (P>.05). CONCLUSIONS: Our findings show that agile and consistent modifications to the recruitment and enrollment process were necessary to yield an appropriate sample size. An automated process resulted in significantly higher enrollment rates than one that required phone interaction with study representatives. Although there were some differences in demographic characteristics of enrollees as the process was modified, in general, our study population is diverse and reflects the overall United States population in terms of race/ethnicity, age, and education. Additional research is proposed to identify how differences in mode of enrollment and demographic characteristics may affect participants' performance in the study. TRIAL REGISTRATION: ClinicalTrials.gov NCT02833922; http://clinicaltrials.gov/ct2/show/NCT02833922 (Archived by WebCite at http://www.webcitation.org/6yj5FHrBh).