Johnson, S., Marriott, L., & Zinaman, M. (2018). Can apps and calendar methods predict ovulation with accuracy? Current Medical Research and Opinion, 34(9), 1587-1594. https://doi.org/10.1080/03007995.2018.1475348
Johnson S, Marriott L, Zinaman M. Can apps and calendar methods predict ovulation with accuracy? Curr Med Res Opin. 2018;34(9):1587-1594. doi:10.1080/03007995.2018.1475348
Johnson, S., et al. "Can apps and calendar methods predict ovulation with accuracy?" Current Medical Research and Opinion, vol. 34, no. 9, 2018, pp. 1587-1594.
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The accuracy of prediction of ovulation by cycle apps and published calendar methods was determined by comparing to true probability of ovulation.
Methods
A total of 949 volunteers collected urine samples for one entire menstrual cycle. Luteinizing hormone was measured to assign surge day, enabling probability of ovulation to be determined across different cycle lengths. Cycle-tracking apps were downloaded. As none provided their methodology, four published calendar-based methods were also examined: standard days, rhythm, alternative rhythm and simple calendar method. The volunteer ovulation data was applied to the app/calendar methods to determine their accuracy.
Results
Mean cycle length was 28 days (range: 23-35); 34% of women believed they had a 28-day cycle, but only 15% did. No LH surge was seen for 99 women. Most likely day of ovulation for a 28-day cycle was day 16 (21%). Accuracy of ovulation prediction was no better than 21% by the apps. The standard days and rhythm methods were most likely to predict ovulation (70% and 89%, respectively) but had very low accuracy.
Conclusions
Ovulation day varies considerably for any given menstrual cycle length, thus it is not possible for calendar/app methods that use cycle-length information alone to accurately predict the day of ovulation. National Clinical Trial Code: NCT01577147. Registry website: www.clinicaltrials.gov .
Background Severe COVID-19 results in substantial economic burden and impacts quality of life. Assessing how non-hospitalized COVID-19 impacts health utilities during acute infection and long term is important to estimate the full economic impact of SARS-CoV-2 infection. Methods We analyzed EQ-5D-3L survey data from SARS-CoV-2 infected adults (aged ≥16 years) and children (aged 8-15 years) from three community and household cohorts in the United States (2020-2022). EQ-5D-3L scores were analyzed at three time points after symptom onset or first positive SARS-CoV-2 test result and converted to health utilities on a scale of 0-1 (1=perfect health). Among adults, regression models were used to compare differences in health utility by demographic/clinical characteristics. Results Among 538 SARS-CoV-2 non-hospitalized asymptomatic/symptomatic infections from 575 adults with EQ-5D-3L surveys, mean utilities were near 1 throughout the observation period. During 0-14 days, vaccinated participants had higher health utilities (Beta:0.57, 95% CI:0.07,1.07). Seeking medical care and having gastrointestinal symptoms (vs. none), were associated with lower health utilities (Beta, 95% CI:-0.96, −1.60, −0.31; and −0.76, −1.30, −0.21 respectively). During 15-30 days, unemployment was associated with lower health utility (Beta:-0.64, 95% CI:-1.15,-0.14). During 31-90 days, underlying conditions were associated with lower health utilities (Beta:-0.32, 95% CI:-0.54, −0.09). Results for children were similar to adults. Conclusion Non-hospitalized COVID-19 may have minimal overall impact on quality of life; however, health utilities differed by vaccination status, presence of gastrointestinal symptoms, employment status, and presence of underlying conditions. Vaccination may play an important role in minimizing illness impact from SARS-CoV-2 infection. Key points Severe COVID-19 illness causes substantial economic burden and impacts on quality of life; however, the incidence of non-hospitalized COVID-19 is far greater. Assessing how non-hospitalized COVID-19 impacts health during acute infection and long term is important to understand the full impact of SARS-CoV-2 infection. This study utilizes the EQ-5D-3L, a standardized generic preference-based instrument used in population health studies, to estimate health utilities at multiple time points following SARS-CoV-2 infection. The study also examines demographic/medical characteristics that are associated with health utility over time. While mean health utilities were high for all infection periods regardless of age, health utility was lower at varying time points post-infection among adults who sought medical care, reported gastrointestinal symptoms, were unemployed, and with underlying conditions. Vaccinated adults (vs. unvaccinated) had higher health utility and were less likely to report reduced health. Findings can be used as inputs for economic evaluation and assessing impact of interventions for non-hospitalized SARS-CoV-2 illness, such as vaccination.
Johnson S et al., 2019·J Womens Health (Larchmt)·Free full text on PubMed Central
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. 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. 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). Using the test system to time intercourse within the fertile window increases the likelihood of conceiving within two menstrual cycles.
Ali R et al., 2020·Reprod Biomed Online·Free to read
To characterize mobile fertility tracking applications (apps) to determine the use of such apps for women trying to conceive by identifying the fertile window.
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.
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. 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.
The Natural Cycles app employs daily basal body temperature to define the fertile window via a proprietary algorithm and is clinically established effective in preventing pregnancy. We sought to (1) compare the app-defined fertile window of Natural Cycles to that of CycleProGo, an app that uses BBT and cervical mucus to define the fertile window and (2) compare the app-defined fertile windows to the estimated physiologic fertile window. Daily BBT were entered into Natural Cycles from 20 randomly selected regularly cycling women with at least 12 complete cycles from the CycleProGo database. The proportion of cycles with equivalent (±1 cycle day) fertile-window starts and fertile-window ends was determined. The app-defined fertile windows were then compared to the estimated physiologic fertile window using Peak mucus to estimate ovulation. Fifty seven percent of cycles (136/238) had equivalent fertile-window starts and 36% (72/181) had equivalent fertile-window end days. The mean overall fertile-window length from Natural Cycles was 12.8 days compared to 15.1 days for CycleProGo (p < 0.001). The Natural Cycles algorithm declared 12% to 30% of cycles with a fertile-window start and 13% to 38% of cycles with a fertile-window end within the estimated physiologic fertile window. The CycleProGo algorithm declared 4% to 14% of cycles with a fertile-window start and no cycles with a fertile-window end within the estimated physiologic fertile window. Natural Cycles designated a higher proportion of cycles days as infertile within the estimated physiologic fertile window than CycleProGo.
Use of cervical mucus in addition to BBT may improve the accuracy of identifying the fertile window. Additional studies with other markers of ovulation and the fertile window would give additional insight into the clinical implications of app-defined fertile window differences.
Mu Q et al., 2023·Medicina (Kaunas)·Free full text on PubMed Central
Background and
Accuracy in detecting ovulation and estimating the fertile window in the menstrual cycle is essential for women to avoid or achieve pregnancy. There has been a rapid growth in fertility apps and home ovulation testing kits in recent years. Nevertheless, there lacks information on how well these apps perform in helping users understand their fertility in the menstrual cycle. This pilot study aimed to evaluate and compare the beginning, peak, and length of the fertile window as determined by a new luteinizing hormone (LH) fertility tracking app with the Clearblue Fertility Monitor (CBFM). A total of 30 women were randomized into either a quantitative Premom or a qualitative Easy@Home (EAH) LH testing system. The results of the two testing systems were compared with the results from the CBFM over three menstrual cycles of use. Potential LH levels for estimating the beginning of the fertile window were calculated along with user acceptability and satisfaction. The estimates of peak fertility by the Premom and EAH LH testing were highly correlated with the CBFM peak results (R = 0.99, p < 0.001). The participants had higher satisfaction and ease-of-use ratings with the CBFM compared to the Premom and EAH LH testing systems. LH 95% confidence levels for estimating the beginning of the fertile window were provided for both the Premom and EAH LH testing results. Our pilot study findings suggest that the Premom and EAH LH fertility testing app can accurately detect impending ovulation for women and are easy to use at home. However, successful utilization of these low-cost LH testing tools and apps for fertility self-monitoring and family planning needs further evaluation with a large and more diverse population.
Leiva R et al., 2024·Seminars in reproductive medicine·Free full text on PubMed Central
Smartphone-based fertility awareness methods with home-based urinary hormonal testing are gaining popularity for fertility tracking. In our university-affiliated family practice, we integrated a previously developed ovulation tracking application into a protocol for monitoring urinary sex hormones and cervical secretions. Serum progesterone was used to confirm the luteal phase, with levels ≥ 15.9 nmol/L ensuring confirmation. Data from 110 women seen for infertility treatment (n = 95) or family planning advice (n = 15) and using our ovulation prediction protocol showed that most opted for a combination of cervical mucus and luteinizing hormone testing (n = 86). Among those using it for family planning, the median usage among women spanned 56 cycles, and 13 cycles per woman required progesterone testing for confirmation. Thirteen patients are still using the method without unintended pregnancies. No unintended pregnancies occurred. Confidence in tests based on serum progesterone was high (93%). For infertility, the method helped in the identification of anovulation, evaluating treatment response, and in diagnosing subfertility causes. This proof-of-concept retrospective descriptive case series suggests the potential for smartphone-based monitoring in fertility management, urging further studies for application enhancements and prospective validation.
PMID 29749274 29749274 DOI 10.1080/03007995.2018.1475348 10.1080/03007995.2018.1475348 Johnson et al. 2018, Johnson 2018
Cite this article
Johnson, S., Marriott, L., & Zinaman, M. (2018). Can apps and calendar methods predict ovulation with accuracy? Current Medical Research and Opinion, 34(9), 1587-1594. https://doi.org/10.1080/03007995.2018.1475348
Johnson S, Marriott L, Zinaman M. Can apps and calendar methods predict ovulation with accuracy? Curr Med Res Opin. 2018;34(9):1587-1594. doi:10.1080/03007995.2018.1475348
Johnson, S., et al. "Can apps and calendar methods predict ovulation with accuracy?" Current Medical Research and Opinion, vol. 34, no. 9, 2018, pp. 1587-1594.
