SkipTrack: A Bayesian Hierarchical Model for Self-tracked Menstrual Cycle Length and Regularity in Large Mobile Health Cohorts

TL;DR

SkipTrack is a Bayesian hierarchical model designed to accurately analyze menstrual cycle data from mobile health apps by accounting for potential skipped tracking days, improving the reliability of health insights.

Contribution

It introduces a novel Bayesian framework that models cycle tracking uncertainty, outperforming existing methods that assume perfect tracking in large digital health cohorts.

Findings

SkipTrack reduces bias in cycle length estimates.

It reveals associations between age, BMI, race/ethnicity, and menstrual patterns.

Model outperforms competing methods in simulations.

Abstract

Menstrual cycle length and regularity are important vital signs with implications for a variety of acute and chronic health conditions. Large datasets derived from cycle-tracking mobile health apps are being used to investigate the effects of various covariates on menstrual cycle length and regularity. One limitation on these analyses is that recorded cycle lengths can be incorrectly inflated if users skip tracking any cycle related bleeding days in the app. Here we present SkipTrack, a novel Bayesian hierarchical framework for examining baseline and time-varying effects on menstrual cycle length and regularity while accounting for the uncertainty of possible skips in cycle tracking. In simulations we demonstrate the superiority of the SkipTrack model by showing that competing methods which specify cycle skips a priori are more susceptible to issues of estimation bias and overconfidence than the SkipTrack model. Finally, we apply the SkipTrack framework to data from the Apple Women's Healthy Study, a US-based digital cohort (consent provided at study enrollment) to examine patterns of association between age, BMI and race/ethnicity, and menstrual cycle length and regularity.