Researchers tracked 42,759 menstrual cycles with wearable devices, revealing how sleep patterns, age, and physiology are intertwined, and found that even subtle sleep disturbances can be associated with measurable changes in menstrual health.
Research: The menstrual cycle through the lens of wearable devices: Insights into physiology, sleep, and cycle variability. Image credit: New Africa/Shutterstock.com
recent NPJ Digital Medicine The study investigated how data from wearable devices can reveal relationships between women’s menstrual cycle stages, sleep patterns, and physiological fluctuations.
Current understanding and unanswered questions in menstrual cycle physiology
The menstrual cycle involves repeated hormonal, metabolic, and behavioral fluctuations in a person of reproductive age. Menstrual cycles and variability are important indicators of women’s health, and increased variability and irregularity are associated with negative menstrual symptoms and increased risk of long-term health conditions such as cancer, diabetes, cardiovascular disease, bone fractures, and premature death. Additionally, decreased motivation to train, increased negative mood, and decreased sleep quality are frequently reported before or during menstruation.
Physiological, sleep, and performance indicators are known to fluctuate across the menstrual cycle. Digital tools and wearable devices have facilitated large-scale monitoring of menstruation, physiological biometrics, and behaviors such as sleep and physical activity. Biometrics such as skin temperature and resting heart rate exhibit systematic fluctuations throughout the cycle and are influenced by age and reproductive status.
Despite these advances, significant gaps in research remain. Most studies lack daily resolution biometric data across different ages and menstrual cycles, limiting understanding of individual differences. The relationship between behavioral factors, particularly sleep duration and menstrual cycle characteristics, remains poorly defined, especially in real-world settings. These gaps highlight the need for comprehensive, longitudinal studies to uncover how behavioral and physiological patterns interact across the menstrual cycle.
Assessing the influence of the menstrual cycle on physiological biometrics and sleep patterns
In the current study, we analyzed data from regular WHOOP device users. These devices collected biometric data during the night, including resting heart rate, breathing rate, heart rate variability (HRV), skin temperature, blood oxygen saturation, and sleep and training metrics.
Inclusion criteria required consistent device wear and regular cycling (median duration 21–35 days). Women using hormonal contraceptives, pregnant, or with menopausal symptoms were excluded. The final cohort consisted of 2,596 participants, 42,759 cycles, and over 1.29 million days of data. The authors noted that this cohort likely represents more active and health-conscious individuals than the general population and may not fully reflect broader demographic groups.
The daily time series integrates menstrual, behavioral, and physiological data, and the cycle is divided into premenstrual, menstrual, postmenstrual, and other stages. Sleep and training metrics were compiled daily. Due to device upgrades, some biometric data is no longer available for some participants.
Biometric time series were interpolated, filtered, and normalized to enable comparisons between individuals. Missing data within 7 days were linearly interpolated. Longer gaps were filled with the participant’s average value for that indicator.
Cycle length was defined as the interval between cycle starts, and deviation was classified as cycles differing by ±3 days from the participant’s median cycle length. Variation in sleep duration was assessed as variation within each cycle. Generalized estimating equations (GEE) were used for inference, with cycles as the unit of analysis, and covariates such as age, BMI, seasonality, sleep, and workout measures. Behavioral changes were identified based on the average of stable sleep over 3 weeks.
For biometric modeling, we used generalized additive models (GAM) to disentangle the effects of age and menstrual cycle with covariates of sleep, exercise, BMI, seasonality, and weekend effects. Temporal relationships between biometrics were investigated using vector autoregression (VAR).
Menstrual cycle stability is influenced by sleep and physiological fluctuations
The average menstrual cycle was 28.4 days, decreasing from 29.1 days at age 24 to 26.9 days at age 44. Cycle length variation followed a U-shaped pattern, with the smallest variation around age 33.
Shorter and more inconsistent sleep duration was strongly associated with greater menstrual cycle variability, but average cycle length remained largely unchanged. Sleeping less than 7.3 hours or having an irregular sleep pattern is associated with increased cycle irregularity, suggesting that regular sleep may play a role in menstrual cycle stability. A within-participant analysis of 813 participants confirmed that greater variation in sleep within individuals was associated with greater variation in cycle length.
High-resolution biometric data reveals distinct physiological rhythms throughout the menstrual cycle. Most biometrics decreased during menstruation and peaked before the next cycle, except for HRV, which showed the opposite pattern. Resting heart rate, HRV, and respiratory rate were closely related throughout the cycle.
With age, HRV and resting heart rate variability decreased, but other biometric changes were modest. longer cycle It was associated with a broader range of cardiopulmonary biometrics. Blood oxygen saturation showed little periodicity. Therefore, age and cycle length are key in shaping menstrual physiology.
Population-level biometrics captured the main trends, but individual cycles showed much greater variability. For example, HRV often fluctuates by nearly half of a participant’s average value within one cycle. This highlights the large variation within individuals beyond the population average.
Although the population waveform is stable, there are significant fluctuations in the individual cycles. Shorter sleep, especially in the premenstrual week, was associated with increased resting heart rate, decreased HRV, and changes in other physiological indicators. This pattern was similar across each phase of menstruation, highlighting that sleep deprivation is consistently associated with physiological changes throughout the cycle.
conclusion
This study shows that regular and sufficient sleep is closely related to the stability of the menstrual cycle, and that age and cycle length also play an important role in the formation of physiological rhythms. Significant individual differences in biometric patterns highlight the need for a personalized approach to menstrual health. Future research should elucidate the mechanisms underlying these associations and assess whether interventions that promote sleep regularity can help improve menstrual cycle stability and overall health.
Reference magazines:
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Gonzalez, A. J. J. et al. (2026). The menstrual cycle through the lens of wearable devices: Insights into physiology, sleep, and cycle variability. Npj digital medicine. Doi: https://doi.org/10.1038/s41746-026-02799-9. https://www.nature.com/articles/s41746-026-02799-9
