Abstract

BackgroundThere 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.ObjectiveThe 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.MethodsUsing 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.ResultsThe 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.ConclusionsThe 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.

Highlights

  • In healthy non-contracepting sexually active couples fecundability, probability of conceiving a pregnancy during a menstrual cycle [Gini 1924, Gini 1928], depends on behaviour as well as physiology

  • Precise information on the pattern of daily fecundability and the width and location of the associated fertile interval in the menstrual cycle is of interest to both the biologist and the demographer

  • For the purpose of fertility regulation, the information is essential to those couples attempting to avoid pregnancy and those trying to achieve this end through appropriate timing of intercourse

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Summary

Introduction

In healthy non-contracepting sexually active couples fecundability, probability of conceiving a pregnancy during a menstrual cycle [Gini 1924, Gini 1928], depends on behaviour as well as physiology. Diverging figures have been proposed in the literature, ranging from less than two to more than ten days [Glass and Grebenik 1954, Potter 1961, James 1963, Marshall 1967, Lachenbruch 1967, Glasser and Lachenbruch 1968, Barrett and Marshall 1969, Barrett 1971, Loevner 1976, Vollman 1977, Schwartz et al 1979, Trussell 1979, Schwartz, MacDonald, and Heuchel 1980, Royston 1982, Bongaarts and Potter 1983, World Health Organization 1983, World Health Organization 1985, Potter and Millman 1985, Bremme 1991, Weinberg, Gladen, and Wilcox 1994, Trussell 1996, Masarotto and Romualdi 1997, Weinberg et al 1998, Wilcox, Weinberg, and Baird 1998, Sinai, Jennings, and Arévalo 1999, Dunson et al 1999] These estimates depend on data analysed, on conjectures accepted, on evaluations made with different approaches. The need for a large menstrual cycle data base, including a high number of conception cycles, for the purpose of clarifying various points of interest for basic knowledge and applications, has been repeatedly emphasised [Schwartz, MacDonald, and Heuchel 1980, James 1981, Potter and Millman 1986, Royston 1991, Royston and Ferreira 1999]

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