Relationship Between the Menstrual Cycle and Timing of Ovulation Revealed by New Protocols: Analysis of Data from a Self-Tracking Health App
Open Access
- 27 November 2017
- journal article
- Published by JMIR Publications Inc. in Journal of Medical Internet Research
- Vol. 19 (11), e391
- https://doi.org/10.2196/jmir.7468
Abstract
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. 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. 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. 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. 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.This publication has 34 references indexed in Scilit:
- Mapping mHealth Research: A Decade of EvolutionJournal of Medical Internet Research, 2013
- The Effectiveness of Mobile-Health Technology-Based Health Behaviour Change or Disease Management Interventions for Health Care Consumers: A Systematic ReviewPLoS Medicine, 2013
- Health 2050: The Realization of Personalized Medicine through Crowdsourcing, the Quantified Self, and the Participatory BiocitizenJournal of Personalized Medicine, 2012
- Healthcare in the pocket: Mapping the space of mobile-phone health interventionsJournal of Biomedical Informatics, 2011
- How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYXBioMedical Engineering OnLine, 2011
- Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-TrackingInternational Journal of Environmental Research and Public Health, 2009
- Variability in the Phases of the Menstrual CycleJournal of Obstetric, Gynecologic & Neonatal Nursing, 2006
- New Low‐ and High‐Tech Calendar Methods of Family PlanningJournal of Midwifery & Women's Health, 2005
- Timing intercourse to achieve pregnancy Current evidenceObstetrics & Gynecology, 2002
- Daily FecundabilityDemographic Research, 2000