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

2026-04-14

AI Summary

This study addresses the lack of standardized, open-source statistical methods for quantifying the cyclic effects of the menstrual cycle on physiological and psychological metrics. We present the first end-to-end open-source toolkit in R/Python that integrates menstrual date processing, cycle phase annotation, individual-level normalization, fitting of cyclic generalized additive models (GAMs) using Fourier bases, and bootstrap-based confidence interval estimation, accompanied by a no-code Shiny interface. Applying this framework to data from 2,816 users across 15 health indicators, we identified significant associations (p < 0.05) between the menstrual cycle and nine metrics—including heart rate variability, blood oxygen saturation, sleep quality, and mood—robust to potential confounders. This toolkit establishes a reproducible, standardized analytical foundation for interdisciplinary research on menstrual cycle effects.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Data Mining & Knowledge Management: Web

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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/.

Problem

• menstrual cycle
• digital health data
• cyclic effects
• statistical methods
• open-source

Innovation

• Fourier-basis GAM
• menstrual cycle analysis
• open-source package
• digital health data
• cyclic modeling