Menstrual cycle phase length variation is associated with daily symptom burden

Abstract

Menstrual symptoms vary across the cycle, yet most research assumes a normative 28-day cycle with fixed phase durations, obscuring the physiological relevance of natural cycle variation. Using the mcPHASES dataset, we characterised cycle and phase length variation across 96 menstrual cycles from 37 participants, with ovulation timing estimated from daily urinary luteinizing hormone measurements using a Bayesian hierarchical model, and examined associations with daily symptoms in a subset of 64 cycles from 35 participants with complete symptom data. Twelve physical, mental, and behavioural symptom domains were modelled using Bayesian ordinal regression, with posterior uncertainty in phase-length predictors propagated via a measurement error framework. Total cycle length was not associated with daily symptom burden, except sleep disturbances. By contrast, phase length decomposition revealed systematic associations across multiple domains: longer menstrual phase length was broadly associated with greater symptom intensity spanning physical, gastrointestinal, affective, and sleep domains; longer luteal phase duration was associated with greater fatigue and more frequent headaches, but lower sore breast intensity and lower stress; and longer follicular phase duration and later ovulation were each associated with greater sore breast intensity and more frequent mood swings. These associations require knowledge of actual ovulation timing and cannot be recovered from cycle length alone, indicating that the common assumption of a fixed 14-day luteal phase introduces systematic misclassification of hormonal exposure. Daily symptom intensity was also predominantly person-specific, with cycle phase explaining little of the between-person variance across most symptoms. These findings indicate that calendar-based phase assignment is insufficient for research and clinical assessment of hormone-sensitive conditions, and that person-specific baselines, rather than population-level phase averages, are needed for clinically meaningful symptom monitoring.