Identification and prediction of the fertile window using NaturalCycles
Abstract
Objectives The aim of the study was to evaluate the ability of a novel web and mobile application to identify a woman's ovulation day and fertile window, in order to use it as a method of natural birth control.Methods A retrospective study was performed on 1501 cycles of 317 women aged 18 to 39 years. Women entered their basal body temperatures, ovulation test results and date of menstruation into the application.Results The mean delay from the first positive ovulation test to the temperature-based estimation of the ovulation day was 1.9 days; the length of the luteal phase varied on average by 1.25 days per user. Only 0.05% of non-fertile days were falsely attributed and found within the fertile window.Conclusions The method is effective at identifying a user's ovulation day and fertile window and can therefore be used as a natural method of birth control.Chinese Abstract摘要目的:本研究的目的为评估一个新的网页及移动设备应用鉴别女性排卵日和生育窗的能力,以便将其用于指导自然避孕。方法:年龄在18至37岁的317名妇女的1501个月经周期被纳入了回顾性研究。受试者将基础体温、排卵测试结果和经期输入至此应用。结果:首次排卵阳性日至基础体温预计排卵日的平均延迟时间为1.9天;平均每个受试者的黄体期时间变化大约为1.25天。只有0.05%的非生育期被错误地归至生育窗。结论:此方法可有效识别使用者排卵日及生育窗,因此可应用于自然避孕。关键词自然生育控制,生育窗,自然避孕,生育力认知,基础体温,排卵监测
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In a previous study the FHP was 1.9 ± 1.4 days after the estimated start of the LH surge.
“In a previous study the FHP was 1.9 ± 1.4 days after the estimated start of the LH surge,20”
From Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles (2019) · cites this paper as [20]
Basal body temperature based apps detect the BBT rise.
“basal body temperature (BBT; defined as lowest resting body temperature) based apps that detect the BBT rise,20–22”
From Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles (2019) · cites this paper as [20]
Home urinary luteinising hormone tests may be used to determine fertile days or used as input to BBT methods to improve the accuracy of ovulation detection.
“Home urinary luteinising hormone (LH) tests may also be used to determine fertile days24 or used as input to BBT methods to improve the accuracy of ovulation detection.20”
From Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles (2019) · cites this paper as [20]
The Natural Cycles app algorithm estimated that the day of ovulation was 1.9 ± 1.4 days after the LH surge based on temperature data alone.
“- Algorithm estimated that day of ovulation was 1.9 ± 1.4 days after LH surge based on T data alone.”
From Temperature regulation in women: Effects of the menstrual cycle (2020) · cites this paper as [181]
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References 22 · Cited by 90
- Authors
- Berglund Scherwitzl, E., Lindén Hirschberg, A., & Scherwitzl, R.
- Corresponding author
- Scherwitzl, Raoul
- ORCID
- Scherwitzl, Raoul
- Venue
- The European Journal of Contraception & Reproductive Health Care
- Publisher
- Informa UK Limited
- Published
- 2015-08-26
- Volume
- 20
- Issue
- 5
- Pages
- 403-408
- ISSN
- 1362-5187 (ISSN-L)
- 1473-0782
- OpenAlex
- W2126316827
- Cited by
- 90
- References
- 22
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 8 · 2023: 6 · 2022: 4 · 2021: 8 · 2020: 10 · 2019: 16 · 2018: 9 · 2017: 7 · 2016: 5
- FWCI
- 7.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The European Journal of Contraception & Reproductive Health Care (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Reproductive Health and Contraception
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Reproductive Medicine › Reproductive Health and Technologies
- Keywords
- fertile window, ovulation prediction, basal body temperature, luteal phase, fertility awareness, mobile health apps, menstrual cycle tracking
- MeSH terms
- Adult, Body Temperature, Female, Fertility, Humans, Menstrual Cycle, Ovulation Detection, Pilot Projects, Predictive Value of Tests, Retrospective Studies, Natural Family Planning Methods, Internet, Young Adult, Mobile Applications
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