A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking.

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

Compar

SUBMITTER: Li K 

PROVIDER: S-EPMC8714275 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature