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Cited by 47Reproductive Health and ContraceptionReproductive Health and TechnologiesMobile Health and mHealth Applications

Relationship Between the Menstrual Cycle and Timing of Ovulation Revealed by New Protocols: Analysis of Data from a Self-Tracking Health App

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Sohda, Satoshi, Suzuki, Kenta, Igari, Ichiro·2017·Journal of Medical Internet Research
DOI 10.2196/jmir.7468PMC PMC5725625
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Abstract

BACKGROUND: There are many mobile phone apps aimed at helping women map their ovulation and menstrual cycles and facilitating successful conception (or avoiding pregnancy). These apps usually ask users to input various biological features and have accumulated the menstrual cycle data of a vast number of women. OBJECTIVE: The purpose of our study was to clarify how the data obtained from a self-tracking health app for female mobile phone users can be used to improve the accuracy of prediction of the date of next ovulation. METHODS: Using the data of 7043 women who had reliable menstrual and ovulation records out of 8,000,000 users of a mobile phone app of a health care service, we analyzed the relationship between the menstrual cycle length, follicular phase length, and luteal phase length. Then we fitted a linear function to the relationship between the length of the menstrual cycle and timing of ovulation and compared it with the existing calendar-based methods. RESULTS: The correlation between the length of the menstrual cycle and the length of the follicular phase was stronger than the correlation between the length of the menstrual cycle and the length of the luteal phase, and there was a positive correlation between the lengths of past and future menstrual cycles. A strong positive correlation was also found between the mean length of past cycles and the length of the follicular phase. The correlation between the mean cycle length and the luteal phase length was also statistically significant. In most of the subjects, our method (ie, the calendar-based method based on the optimized function) outperformed the Ogino method of predicting the next ovulation date. Our method also outperformed the ovulation date prediction method that assumes the middle day of a mean menstrual cycle as the date of the next ovulation. CONCLUSIONS: The large number of subjects allowed us to capture the relationships between the lengths of the menstrual cycle, follicular phase, and luteal phase in more detail than previous studies. We then demonstrated how the present calendar methods could be improved by the better grouping of women. This study suggested that even without integrating various biological metrics, the dataset collected by a self-tracking app can be used to develop formulas that predict the ovulation day when the data are aggregated. Because the method that we developed requires data only on the first day of menstruation, it would be the best option for couples during the early stages of their attempt to have a baby or for those who want to avoid the cost associated with other methods. Moreover, the result will be the baseline for more advanced methods that integrate other biological metrics.

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  • Several studies have shown that calendar apps are not accurate in identifying the fertile window.

    “It is, therefore, unsurprising that several studies have shown that calendar apps are not accurate in identifying the fertile window.35–38”

    From Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles (2019) · cites this paper as [37]

    The Discussion’s Principal Findings say that two existing calendar-based methods did not explain the study data well and were less accurate for certain cycle lengths. That supports concern about calendar-based ovulation predictions, but the paper evaluates prediction methods rather than the accuracy of calendar apps in identifying the fertile window. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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  1. [40]Pickard K Swan M Big desire to share big health data: A shift in consumer attitudes toward personal health information 2014 AAAI Spring Symposium 2014 Palo Alto, CA 51 58

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Authors
Sohda, S., Suzuki, K., & Igari, I.
Affiliations
  • University of Tsukuba (JP)
  • National Institute for Environmental Studies (JP)
  • Monohakobi Technology Institute (Japan) (JP)
Corresponding author
Suzuki, Kenta
ORCID
Venue
Journal of Medical Internet Research
Publisher
JMIR Publications Inc.
Published
2017-11-27
Volume
19
Issue
11
Pages
e391
ISSN
1438-8871 (ISSN-L)
DOI
10.2196/jmir.7468
PMC
PMC5725625
OpenAlex
W2769873857
Cited by
47
References
34
Citations by year
2026: 2 · 2025: 4 · 2024: 2 · 2023: 8 · 2022: 8 · 2021: 9 · 2020: 5 · 2019: 7 · 2018: 2
FWCI
3.59
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Reproductive Health and Contraception
  • Health Sciences › Medicine › Reproductive Medicine › Reproductive Health and Technologies
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Keywords
menstrual cycle, ovulation prediction, follicular phase, luteal phase, menstrual cycle length, mobile phone app, fertility awareness
MeSH terms
Adult, Clinical Protocols, Female, Humans, Menstrual Cycle, Middle Aged, Ovulation, Statistics as Topic, Young Adult, Mobile Applications

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