By tracking 42,759 menstrual cycles with wearable devices, researchers revealed how sleep patterns, age and physiology interact, revealing that even modest sleep disturbances can be linked to measurable changes in menstrual health.
Study: The menstrual cycle through the lens of a wearable device: insights into physiology, sleep and cycle variability. Image credit: New Africa/Shutterstock.com
A recent one NPJ Digital Medicine The study explored how wearable device data can reveal relationships between menstrual cycle phases, sleep patterns and physiological variability in women.
Current understanding and unresolved questions in the physiology of the menstrual cycle
The menstrual cycle involves repeated hormonal, metabolic, and behavioral fluctuations in individuals of reproductive age. Cycle length and variability are key indicators of women’s health, with increased variability and irregularity associated with negative menstrual symptoms and greater risk for long-term conditions such as cancer, diabetes, cardiovascular disease, fractures and premature mortality. In addition, decreased motivation to exercise, increased negative mood, and decreased sleep quality are often reported before or during menstruation.
Measures of physiological state, sleep and performance are known to fluctuate during 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 show systematic variation throughout the cycle and are affected by age and reproductive status.
Despite these advances, significant research gaps remain. Most studies lack daily analysis biometric data for different ages and cycle lengths, limiting understanding of individual variability. 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 research to clarify how behaviors and physiological patterns interact during the menstrual cycle.
Assessing the effects of the menstrual cycle on physiological biometrics and sleep patterns
The current study analyzed data from regular users of WHOOP devices. The devices collected nighttime biometric data, including resting heart rate, respiratory rate, heart rate variability (HRV), skin temperature, and blood oxygen saturation levels, as well as sleep and exercise metrics.
Inclusion criteria required consistent device wear and regular cycles (mean duration 21–35 days). women using hormonal contraceptives, pregnant or with menopausal symptoms were excluded. The final cohort included 2,596 participants, 42,759 cycles, and over 1.29 million days of data. The authors noted that the cohort likely represented individuals who were more active and health conscious than the general population and may not fully reflect broader demographic groups.
Daily time series incorporate menstrual, behavioral, and physiological data, with cycles divided into premenstrual, menstrual, postmenstrual, and other phases. Sleep and exercise measurements were summarized for each day. Some biometric data was unavailable for some participants due to device upgrades.
Biometric time series are interpolated, filtered, and normalized to allow intersubject comparisons. Missing data ≤7 days are linearly interpolated. Larger gaps were filled with the participant’s average for that measurement.
Cycle length was defined as the interval between cycle onsets, with deviations classified as cycles differing by ±3 days from the participant’s median cycle length. Variability of sleep duration was assessed as variance within each cycle. Generalized Estimating Equations (GEE) were used for inference, with cycles as the unit of analysis and covariates including measures of age, BMI, seasonality, sleep and training. Behavioral changes were identified based on consistent sleep averages over three weeks.
Biometric modeling used Generalized Additive Models (GAMs) to disentangle the effects of age and cycle length, with covariates for sleep, exercise, BMI, seasonality, and weekend effects. Temporal relationships between biometrics were investigated using Vector Autoregression (VAR).
The stability of the menstrual cycle is affected by sleep and physiological fluctuations
The mean length of the menstrual cycle was 28.4 days, decreasing from 29.1 days at age 24 to 26.9 days at age 44. Cycle length variability followed a U-shaped pattern, with the smallest variability near age 33.
Shorter and more inconsistent sleep was strongly associated with greater menstrual cycle variability, while mean cycle length remained largely unchanged. Sleeping less than 7.3 hours or irregular sleep was associated with greater cycle irregularity, suggesting a possible role for regular sleep in menstrual cycle stability. A within-participant analysis of 813 subjects confirmed that greater sleep variability within an individual was associated with more variable cycle length.
High-resolution biometric data revealed clear physiological rhythms throughout the menstrual cycle. Most biometrics decreased during menstruation and peaked before the next cycle, except HRV, which showed the opposite pattern. Resting heart rate, HRV and respiratory rate were closely related throughout the cycle.
With age, variations in HRV and resting heart rate decreased, while changes in other biometrics were small. Bigger circles were associated with a greater range of cardiorespiratory biometrics. Blood oxygen saturation showed little circularity. Thus, age and cycle length are key in shaping menstrual physiology.
Population-level biometrics captured the main trends, but individual cycles showed much greater variability. HRV, for example, often fluctuated by nearly half of a participant’s average value in a single cycle. This highlights a substantial within-individual variation beyond population averages.
Although population waveforms are stable, individual cycles reveal considerable variability. Less sleep, especially during the premenstrual week, was associated with increased resting heart rate, decreased HRV, and changes in other physiological measures. This pattern was similar across menstrual phases, highlighting that sleep loss was consistently associated with physiological changes throughout the cycle.
conclusions
This study shows that regular, adequate sleep is closely linked to the stability of the menstrual cycle, with age and cycle length also playing an important role in shaping normal rhythms. Significant individual variability in biometric patterns underscores the need for individualized approaches to menstrual health. Future research should elucidate the mechanisms underlying these associations and assess whether interventions that promote sleep regularity could help improve cycle stability and overall well-being.
