MCAnalysis: An Open-Source Package for Preprocessing, Modelling, and Visualisation of Menstrual Cycle Effects in Digital Health Data

TL;DR

The paper introduces mcanalysis, an open-source R and Python package that standardizes the analysis of menstrual cycle effects in digital health data using cyclic GAMs, demonstrated on wearable and self-reported outcomes.

Contribution

It provides the first comprehensive, open-source pipeline for menstrual cycle analysis incorporating cyclic GAMs, processing, normalization, and phase detection.

Findings

Nine of 15 outcomes showed significant cycle association.

Physiological, sleep, symptom, mood, and activity outcomes were affected.

The package is freely available with a web interface.

Abstract

The menstrual cycle influences numerous physiological and psychological outcomes, yet standardised, open-source statistical methods for quantifying these cyclic effects remain lacking. We developed mcanalysis, an open-source package in R and Python implementing a Fourier-basis generalised additive model (GAM) for menstrual cycle research. The package provides a complete pipeline: processing period dates, labelling cycle days relative to menstruation onset, filtering physiologically plausible cycles, normalising outcomes to individual means, fitting cyclic GAMs with bootstrap confidence intervals, and identifying turning points to generate phase-specific linear trend estimates. We demonstrate the package on 15 wearable and self-reported outcomes using data from the Juli chronic health management application (N = 2,816 users). Nine of 15 outcomes showed evidence of association with the menstrual cycle (p < 0.05), spanning physiological (HRV p < 0.001, oxygen saturation p = 0.002), sleep (p = 0.003), symptom (migraine p < 0.001, headache p = 0.005), mood (EMA mood p = 0.024, PHQ-8 lack of energy p = 0.008, mania p = 0.041), and activity (hours outside p = 0.019) domains. No tested confounders were significantly associated with cycle-normalised outcomes. mcanalysis provides a standardised, reproducible approach to menstrual cycle analysis for users at all levels of statistical expertise. The package is freely available at https://github.com/kyradelray/mcanalysis, with a no-code web interface at https://kyradelray.shinyapps.io/mcanalysis/.