Author(s):

  • Kathy Li
  • Iñigo Urteaga
  • Amanda Shea
  • Virginia J Vitzthum
  • Chris H Wiggins
  • Noémie Elhadad

Abstract:

Objective

The study sought to build predictive models of next menstrual cycle start date based on mobile health self-tracked cycle data. Because app users may skip tracking, disentangling physiological patterns of menstruation from tracking behaviors is necessary for the development of predictive models.

Materials and Methods

We use data from a popular menstrual tracker (186 000 menstruators with over 2 million tracked cycles) to learn a predictive model, which (1) accounts explicitly for self-tracking adherence; (2) updates predictions as a given cycle evolves, allowing for interpretable insight into how these predictions change over time; and (3) enables modeling of an individual’s cycle length history while incorporating population-level information.

Results

Compared with 5 baselines (mean, median, convolutional neural network, recurrent neural network, and long short-term memory network), the model yields better predictions and consistently outperforms them as the cycle evolves. The model also provides predictions of skipped tracking probabilities.

Discussion

Mobile health apps such as menstrual trackers provide a rich source of self-tracked observations, but these data have questionable reliability, as they hinge on user adherence to the app. By taking a machine learning approach to modeling self-tracked cycle lengths, we can separate true cycle behavior from user adherence, allowing for more informed predictions and insights into the underlying observed data structure.

Conclusions

Disentangling physiological patterns of menstruation from adherence allows for accurate and informative predictions of menstrual cycle start date and is necessary for mobile tracking apps. The proposed predictive model can support app users in being more aware of their self-tracking behavior and in better understanding their cycle dynamics.

Documentation:

https://doi.org/10.1093/jamia/ocab182

References:

1. LiIA stage-based model of personal informatics systemsProceedings of the SIGCHI Conference on Human Factors in Computing Systems2010557566Google Scholar

14. Fox S, Epstein DA.  Monitoring menses: design-based investigations of menstrual tracking applications. In: Bobel C, Winkler IT, Fahs B, Hasson KA, Kissling EA, Roberts T-A, eds. Palgrave Handbook of Critical Menstruation Studies. New York, NY: Springer; 2020: 733–50.


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