The relationship between sleep and menstrual problems in early adolescent girls

Abstract

Introduction

Adolescence is marked by hormonal, physical, neural, and behavioral changes, including in sleep patterns and, in females, the onset of menarche. Menstrual problems, such as painful menses, are common and contribute to school absences, and could indicate gynecological conditions impacting reproductive health. While studies in adults have shown associations between sleep disturbances and menstrual problems, this relationship is less understood in adolescents. Our study explores the association between sleep, menstrual problems, and menarche in a diverse sample of early adolescent girls in the U.S.

Methods

We used linear mixed-effect models to analyze associations between sleep behavior (self- and caregiver-reported) and menstrual problems (self-reported cycle irregularity, premenstrual symptom and menstrual pain severity and their impact on daily life) and menstrual characteristics (menstrual flow) in 3,037 post-menarcheal adolescent girls (Mean age:13.03 years) from the ABCD Study®. Covariates included years since menarche, race, ethnicity, parental education, and body mass index. We also used longitudinal data to explore changes in sleep behavior as a function of menarche.

Results

Of the sample, 26.2% reported moderate-severe premenstrual symptoms and 20.8% reported moderate-severe menstrual pain. 23.3% reported irregular menstrual cycles, 15.9% reported heavy menstrual flow. Shorter sleep duration was associated with greater menstrual pain intensity (β =—0.19) and impact on daily activities (β = -0.15), irregular cycles (β = -0.17), and severe premenstrual symptoms (β = -0.04). Higher sleep disturbance scores correlated with greater menstrual pain (β = 0.18) and premenstrual symptom severity (β = 0.03). Later wake-up times were linked to greater menstrual pain intensity (β = 0.14). Shorter time since menarche was associated with lower menstrual flow (β = 0.07) and pain intensity (β = 0.51) and less severe premenstrual symptoms (β = 0.07). Being post-menarche was associated with later bedtimes and shorter sleep duration.

Conclusion

Findings of links between sleep behavior and menstrual problems in early adolescence underscore the importance of addressing sleep and menstrual issues in healthcare screenings and educational initiatives for adolescents. Future research should focus on longitudinal and intervention studies to clarify these relationships.

Statement of significance

This study reveals significant associations between menstrual problems and sleep disturbances in U.S. adolescents, highlighting the need for integrated approaches in healthcare focusing on improving sleep and menstrual health. Findings emphasize the importance of educating and supporting young females in managing these interrelated health aspects, underscoring a crucial area for future research and intervention.

Introduction

Menarche, the initiation of menstrual cycles, serves as a critical developmental milestone in the lives of female adolescents. In the United States, the median age for experiencing menarche is 12.25 years (Biro et al. 2018). Hispanic and Black females generally have menarche earlier (11.83 years and 12.0 years), while White and Asian females tend to have it later (Biro et al. 2018). Within 6-12 months after menarche, a significant number of adolescents start to suffer from primary dysmenorrhea (Vincenzo De Sanctis et al. 2015). This condition is characterized by painful menstruation in the absence of a specific pelvic disease or abnormality and is accompanied by a range of symptoms, including lower abdominal cramps that radiate to the inner thighs, sweating, headaches, nausea, vomiting, and diarrhea (Iacovides et al. 2015). The pain typically lasts 12-72 h. Primary dysmenorrhea is the most common gynecological problem in female adolescents, affecting between 16 and 93%, with 2% to 29% experiencing severe menstrual pain (Vincenzo De Sanctis et al. 2015). Reports of prevalence vary widely due to different definitions and populations studied, and prevalence is frequently underestimated because few affected individuals seek medical treatment, despite the substantial distress experienced, as many consider the pain to be a normal part of the menstrual cycle (Iacovides et al. 2015).

Dysmenorrhea and menstrual issues significantly impact school attendance and engagement among girls, especially in low- and middle-income countries (Barrington et al. 2021; Hennegan et al. 2019). Challenges include inadequate menstrual products, lack of knowledge, unsupportive school infrastructure, and socio-cultural restrictions, leading to frequent absences and reduced classroom, and overall, social participation (Hennegan et al. 2019). This complex interplay of factors creates barriers to education for many girls. Approximately 45% of women with menstrual pain report a decrease in social functioning (Grandi et al. 2012), and a similar proportion has been observed among adolescents (Steiner et al. 2011).

In addition to painful menstruation, female adolescents often experience a variety of emotional, psychological, and physical symptoms associated with their menstrual cycles (Steiner et al. 2011). Premenstrual syndrome (PMS) encompass a range of symptoms including irritability, fatigue, mood swings, bloating, headaches, breast tenderness, and changes in appetite, which significantly impact quality of life (Rapkin and Winer 2009). These symptoms qualify as PMS when they occur during the late luteal phase, typically the week before menstruation, and disrupt daily activities (Derman et al. 2004). While the severity and duration of these symptoms vary, they generally subside once menstrual bleeding begins. Notably, PMS and dysmenorrhea often overlap in this age group (Derman et al. 2004). Since regular ovulatory cycles usually begin within 1–2 years after menarche and female adolescents are still undergoing physical and hormonal development, their menstrual patterns and experience of symptoms can be variable and may not always follow a consistent pattern (Steiner et al. 2011). Adolescents often don’t recognize or report PMS symptoms and severe menstrual pain, leading to a high rate of under-reporting and lack of treatment (Derman et al. 2004). Management strategies for menstrual pain are diverse and influenced by a combination of medical, cultural, and socio-economic factors, as well as by age (Armour et al. 2019). For example, self-medication in adolescents in the absence of medical consultation leads to less frequent doses of analgesics being taken, resulting in sub-optimal pain relief (Armour et al. 2019).

Research mainly in adults suggests a network of interconnected symptoms such that individuals who experience menstrual problems often also face related issues, including hormonal imbalances, mood swings, and sleep disturbance (Strine et al. 2005). Irregular menstrual cycles, heavy bleeding, dysmenorrhea, and severe PMS have all been associated with poor sleep quality and/or sleep disturbances in adult populations (see Alzueta and Baker (Alzueta and Baker 2023) for review), and these relationships may be bidirectional and have trait-like (across the menstrual cycle) and state-like (menstruation-specific) characteristics. For example, individuals with PMS report a poorer sleep quality premenstrually than other times of the cycle (Baker et al. 2007), but their sleep quality is also poorer overall compared to asymptomatic individuals (Conzatti et al. 2021). Similarly, menstrual pain disrupts sleep, but, in turn, having insomnia symptoms is associated with more severe menstrual pain, supporting the known association between sleep disturbances and central pain sensitization (Iacovides et al. 2015). However, what is lacking in the field is an understanding of the association between sleep and menstrual problems in the adolescent population, when both sleep disturbances and menstrual problems commonly emerge.

Beyond menstruation-related sleep issues, adolescents broadly face a range of sleep challenges. These include shifts in circadian rhythms and the impact of lifestyle factors like early school start times on sleep (Kiss et al. 2023; Knutson and Lauderdale 2009). Moreover, post pubertal females are more likely than males to face sleep problems and mental health challenges (Zambotti et al. 2018).

Here, we took advantage of the large, diverse sample of female adolescents participating in the Adolescent Brain Cognitive Development (ABCD) Study® to investigate associations between sleep behavior and menstrual problems in early adolescence. We hypothesized that indices of poor sleep, including shorter sleep duration, would be associated with severe menstrual pain and premenstrual symptoms, higher overall impact of pain and PMS symptoms on daily life, lower cycle regularity and heavier flow considering key covariates such as years since menarche, race, ethnicity, parental education, and body mass index. We also used the longitudinal data to explore changes in sleep following onset of menarche.

Methods

Participants and procedure

We analyzed data from the ABCD Study® at the 3-year follow-up (2019–2021; 4.0 release). The full ABCD Study® cohort at baseline comprises 11,875 adolescents (N = 5,682 female), recruited from 21 sites around the United States. Details about the ABCD Study® participants, recruitment, protocol, and measures have been previously described (Garavan et al. 2018). Centralized institutional review board (IRB) approval was obtained from the University of California, San Diego (protocol number: #160091AW). Study sites obtained approval from their local IRBs. Parent/guardian and the youth provided written informed consent and assent, respectively. In the main, cross-sectional analysis we included individuals who reported having had menarche at their third-year assessment (N = 3,037, age range: 11.58—14.75 years) (see Table 1 for sample characteristics). They also reported how old they were when they started their period. Participants who had missing data for menstrual cycle (N = 794) or sleep (N = 3) assessments, or who had not yet had menarche (N = 1,482) or refused to report data on menarche status (N = 270), and those taking hormonal oral contraceptives (N = 95, including 44 who were not sure about the response) were excluded from the main analysis (Figure S1A).

Table 1 Comparison of demographics of the sample of female adolescents included in the current analysis with the total female ABCD Study® cohort at baseline

Measures

Outcome measures

Menstrual problems

Individuals who were of female sex at birth answered questions about menstrual cycles and associated problems, including irregularity (“Is your menstrual cycle regular? – Yes/No”) and menstruation flow (“Do you experience a light, medium, or heavy flow?”). Questions about premenstrual symptoms (“Do you experience premenstrual symptoms, such as irritability, fatigue, etc., which start before a period and stop within a few days of bleeding? – with response option ranging from 1 = Not at all to 4 = Severe”) and their impact (“Do your premenstrual symptoms interfere with your relationships with family and friends, productivity, and/or social life activities?” – responses ranging from 1 = Not at all to 4 = Severe) were derived from the adult Premenstrual Symptoms Screening Tool (Steiner et al. 2011), and adapted for adolescents. Menstrual pain intensity (“On average, how would you rate the intensity of your menstrual pain?”) was assessed using a numerical rating scale, from 0 (No pain at all)—10 (the worst pain I have ever felt”), which is a unidimensional assessment of pain intensity commonly used in clinical pain studies (Hawker et al. 2011). Overall impact of menstrual pain on usual activities (“How much does your menstrual pain stop you from doing your usual activities? 0 (Not at all)—10 (stops me from doing anything”) was also assessed.

Predictors

Self-reported sleep measures

We used items from the Munich Chronotype Questionnaire (MCTQ—youth version) (Roenneberg et al. Feb 2003) that were included in the ABCD Study® (Year 2 and Year 3). Participants were asked about their typical sleep over the past four weeks, considering school and free days separately. We included sleep duration (hours), bedtime (hours), wake up time (hours) and social jetlag (hours: difference between the mid sleep on free days (e.g. weekends) and weekdays) variables in our analysis. For sleep duration, bedtimes, and wake up times, we calculated the weighted average from free days and weekdays. All sleep variables were winsorized (Garson 2012) at the 1 and 99 percentile level to mitigate the impact of outliers.

Sleep Disturbance Scale for Children (SDSC) (Bruni et al. 1996)

A 26-item measure was administered to the parents/caregivers of adolescents to assess presence of a wide range of sleep disturbances, including disorders of initiating and maintaining sleep, sleep breathing disorders, disorders of arousal, sleep–wake transition disorders, disorders of excessive somnolence, and sleep hyperhidrosis in the past 6 months. Caregivers responded to each item on a 5-point Likert scale ranging from 1 (never) to 5 (daily). Responses were summed to obtain a total sleep disturbance score, with higher scores reflecting a greater severity of sleep disturbance.

Covariates

Demographics

Demographic information was taken from the ABCD Study® baseline visit: sex at birth (female), race (White, Black, Asian, Multiple, Other/Not reported), ethnicity (Hispanic/Latino, Non-Hispanic, Not reported) and highest parental education. Parental education (less than high school diploma, high school diploma/GED, some college, bachelor’s degree, post graduate degree) was used as an indicator of family socioeconomic status. Age at the Year 3 follow up of the ABCD Study® was used to calculate the years since menarche.

Body mass index

BMI was computed using the standard formula, weight (kilograms) divided by height (meters) squared (BMI = weight/height2). Weight (Health-o-meter 844KL High-Capacity Digital Bathroom Scale; Jarden Corporation; Rye, NY) and height (Carpenter’s square, steel tape measure) were assessed by the interviewer at each site. BMI was converted into sex- and age-specific percentiles in accordance with the Centers for Disease Control and Prevention (CDC) growth curves and definitions. Since Year 3 data for BMI were incomplete, we calculated the mean value for the three prior consecutive years (baseline year, Year 1 and Year 2). Individuals were classified based on their BMI percentile as having underweight (less than the 5th percentile), healthy weight (5th percentile to less than the 85th percentile), overweight (85th to less than the 95th percentile), or obesity (≥ 95th percentile) (Barlow and Committee 2007).

Statistical analysis

Main analysis—Associations between sleep behavior and menstrual problems

The relationship between categorical menstrual health measures (menstrual flow, cycle regularity, premenstrual symptom severity, impact of premenstrual symptoms on activities) and sleep health (caregiver-reported overall sleep disturbance, self-reported sleep duration, bedtime, wake up time, social jetlag) was examined using generalized mixed-effect models (GLMM, R package ‘lme4’, (Bates et al. 2015). The relationship between intensity of menstrual pain and the impact of menstrual pain (Year 3) on usual activities with sleep variables (Year 3) was analyzed using linear mixed effect models (LMMs, R package ‘lme4’) (Bates et al. 2015).

In addition to the sleep measures, models included the years since menarche, race (factor with five levels), ethnicity (factor with three levels), highest level of parental education (factor with five levels), and BMI. Continuous predictors were standardized for the modeling. The ABCD data collection site was included as a random term.

In our analysis, we employed the Wald chi-square test, computed using the Anova function from the car package in R, to assess the significance of individual predictors within our Generalized Linear Mixed Models (GLMMs). This test assesses the hypothesis that each predictor significantly contributes to the model's fit by comparing the log-likelihood of the full model (including the predictor) with that of a reduced model (excluding the predictor). The resulting chi-square statistic is then compared to a chi-square distribution to determine the associated p-value. A p-value less than 0.05 indicates that the predictor significantly contributes to the model. We provide χ² and p-values of Wald chi-square test.

Secondary analysis 1- Within subject level analysis – Before versus after menarche

To gain a better understanding of the changes in sleep associated with the onset of menarche and menarche status, we conducted additional, longitudinal analyses. In the first analysis, our focus was on participants who reported at their year 3 visit that menarche had occurred in the prior year (i.e., they had not yet started their period yet when coming in for the year 2 visit) (N = 1,237) (Figure S1B).

Secondary analysis 2—Between subject level analysis – Premenarcheal vs post-menarcheal participants

Using generalized linear mixed models (GLMMs), we investigated the main effect of menarche onset (before vs. after menarche) and the interaction between menarche status and age on several sleep-related measures (Year 2 and Year 3), including bedtime, sleep duration, wake-up time, and total sleep disturbance. In the second set of analyses, we expanded our sample to include all female participants from years 2 and 3, performing a between-subject analysis. This approach allowed us to compare the sleep measures of females who had experienced menarche (postmenarcheal) with those who had not (premenarcheal), incorporating an age (in Year 2) by year of assessment (Year 2 vs Year 3) interaction to assess differences using data from both years (N = 5,150) ((Figure S1C)). All models accounted for the participant ID and study site as random effects and were adjusted for confounding factors including age (as of year 2), race, ethnicity, and parental education.

We provide χ² and p-values of Wald chi-square test using (performed using the Anova function from the car package in R).

Results

In the study population of 3,037 post-menarcheal early adolescents (age: 13.03 ± 0.62 years), participants had menarche at a mean age of 11.4 ± 0.9 years. Overall, 23.3% reported irregular menstrual cycles, 15.9% reported heavy menstrual flow, 26.2% reported experiencing premenstrual symptoms on a moderate or severe level, and 11.2% felt that their premenstrual symptoms interfered with their daily life on a moderate or severe level (Fig. 1B). The median perceived intensity of menstrual pain was 4, with 20.8% reporting a pain intensity of 7 or higher. The median for impact of menstrual pain on daily activities was 3, with 8% reporting an impact of 7 or higher (Fig. 1D). A strong correlation was observed between the severity of premenstrual symptoms and menstrual pain (Sperman’s rho = 0.4, p < 0.01): girls with no premenstrual symptoms reported a median pain score of 3, escalating to a median of 7 in girls with severe premenstrual symptoms.

Fig. 1

A Severity of premenstrual symptoms and (B) Perceived impact on relationships and daily life activities in female individuals in early adolescence. C Intensity and (D) Impact on relationships and daily activities, of menstrual pain, rated from 0-10, and divided into three categories for visual purposes here in female individuals in early adolescence

Most (60.0%) of the study population had fewer than 9 h of sleep, less than the recommended duration for early adolescents (Gradisar et al. 2011), while 36.9% slept between 9 and 11 h and 4% exceeded 11 h. The majority of adolescents, 65.0%, reported going to bed between 9 and 11 pm, while 25.8% of the participants had a bedtime later than 11 pm.

Associations between sleep behavior and menstrual problems

The results of the Linear Mixed Models (LMMs) and Generalized Linear Mixed Models (GLMMs) are presented in Table 2 for main findings and in Supplementary Tables 1-6 for detailed model output. A higher caregiver-reported total sleep disturbance score was associated with higher menstrual pain intensity (χ²(1) = 14.65, p < 0.001) and greater impact of menstrual pain on usual activities (χ²(1) = 11.03, p = 0.001) (Fig. 2). Higher sleep disturbance scores were also associated with more severe premenstrual symptoms (χ²(1) = 6.11, p = 0.013), and greater interference of these symptoms with daily life activities (χ²(1) = 6.19, p = 0.013).

Table 2 Model outputs for menstrual cycle problems in female adolescents in the ABCD Study (n = 3,037). Results are presented for the likelihood ratio test: Chi-square values for the significant results

Fig. 2

Relationship between overall sleep disturbance scores, as measured by the caregiver-reported Sleep Disturbance Scale (SDS), and menstrual pain intensity (A) and impact on daily activities (B) in female individuals in early adolescence

Self-reported shorter sleep duration was related to higher menstrual pain intensity (χ²(1) = 8.72, p = 0.003) and overall impact of pain on daily activities (χ²(1) = 5.76, p = 0.016) (Fig. 3). Shorter sleep duration was also related to having irregular menstrual cycles (χ²(1) = 7.06, p = 0.008) and more severe premenstrual symptoms (χ²(1) = 4.28, p = 0.039).

Fig. 3

Relationship between self-reported sleep duration, as measured by the youth-reported Munich Chronotype Questionnaire, and intensity of menstrual pain (A) and impact of menstrual pain on usual activities (B) in female individuals in early adolescence

Later wake-up time was related to having irregular menstrual cycles (χ²(1) = 5.95, p = 0.015), higher menstrual pain intensity (χ²(1) = 4.70 p = 0.030) and greater impact of pain on daily activities (χ²(1) = 8.31, p = 0.004).

Additionally, we observed a positive association between relative social jetlag and menstrual pain intensity (χ²(1) = 8.02, p = 0.005), indicating that higher levels of social jetlag are associated with greater menstrual pain intensity (Fig. 4).

Fig. 4

Relationship between the averaged relative social jetlag (difference between the mid sleep on free days (e.g. weekends) and weekdays), as measured by the youth-reported Munich Chronotype Questionnaire, and menstrual pain intensity (0-10 numerical rating scale)

Recency of menarche was associated with lower menstrual flow (χ²(1) = 14.78, p < 0.001), less menstrual pain intensity (χ²(1) = 110.85, p < 0.001) and a lower overall impact of menstrual pain on activities (χ²(1) = 54.66, p < 0.001), irregular cycles (χ²(1) = 31.94, p < 0.001), less severe premenstrual symptoms (χ2(1) = 24.41, p < 0.001), and less interference of premenstrual symptoms on usual activities (χ²(1) = 11.88, p = 0.001).

The age of menarche was associated with multiple menstrual characteristics, with a younger age of menarche being associated with heavier menstrual flow (χ²(1) = 6.58, p = 0.010), higher menstrual pain intensity (χ²(1) = 19.32, p < 0.001), irregular cycles (χ²(1) = 13.05, p < 0.001) and greater interference of premenstrual symptoms on daily life activities (χ²(1) = 4.22, p = 0.040).

Longitudinal associations between menarche and sleep behavior

Within subject level analysis – Before versus after menarche

Our secondary data analysis highlighted significant within-subject (Table S7) and between-subject differences in sleep patterns according to menarche status (Table S8). Participants who experienced menarche between year 2 and year 3 follow-ups (N = 1,237) reported later bedtimes (χ²(1) = 239.89, p < 0.001), shorter sleep duration (χ²(1) = 77.38, p < 0.001), later wake up time(χ²(1) = 52.67, p < 0.001) and increased relative social jetlag (χ²(1) = 20.18, p < 0.001) in year 3 compared to year 2, even after adjustments were made for developmental changes (including age and the age by menarche status interaction) (Fig. 5).

Fig. 5

Sleep Pattern Changes before and after menarche within subjects. The boxplots illustrates the sleep pattern changes from year 2 to year 3 among girls who did not experience menarche by year 2 but did by year 3 (N = 1237). Each box represents the interquartile range (IQR) of the data, with the median marked. Outliers are represented as individual points. Results indicate statistically significant later bedtimes (hours), shorter sleep durations (hours), later wake-up times (hours), and increased relative social jetlag (hours) post-menarche, after adjusting for developmental changes (p < .001 for all measures)

Between subject level analysis – Premenarcheal vs post-menarcheal participants

Participants who had their menarche either at year 2 or year 3, reported later bedtime (χ²(1) = 71.25, p < 0.001), shorter sleep duration (χ²(1) = 57.17, p < 0.001), later wake up time (χ²(1) = 13.87, p < 0.001) and higher relative social jetlag (χ²(1) = 38.65, p < 0.001) than participants who did not yet have their menarche (Year 2: N_premenarche = 2,931, N_postmenarche = 1,984; Year 3: N_premenarche = 1482, N_postmenarche = 3,132), even after adjusting for normative developmental changes, including age and the interaction between age and year (Figure S2).

Discussion

We show in this large and diverse sample of female adolescents across the US that menstrual problems, including menstrual pain and premenstrual symptoms, are very common and interfere with daily activities in about 8-11% of early adolescents. We found robust correlations between menstrual problems, including menstrual pain, cycle irregularities and premenstrual symptoms and sleep patterns characterized by shorter duration, later bedtime, and pronounced social jetlag as well as with caregiver-reported sleep disturbance. These data highlight the importance of considering menstrual problems and their associations with unhealthy sleep patterns even in early adolescence, which could together negatively affect quality of life and have repercussions for future reproductive and overall health. The results of our secondary analysis suggest notable differences in sleep behavior among adolescent females, both at the within-subject and between-subject levels, associated with menarche, indicating the importance of considering timing of reproductive maturation beyond chronological age when considering changes in sleep across adolescent development.

Specifically, the observed shifts towards later bedtimes, shorter sleep durations, later wake-up times, and increased relative social jetlag after the onset of menarche indicate the potential effect of this physiological, developmental milestone on sleep health (Liu et al. 2017). These findings align with the existing literature that highlights the intricate relationship between hormonal changes accompanying menarche and alterations in circadian rhythms and sleep health (Baker and Driver 2007). Importantly, the differential sleep timing observed between the premenarcheal and postmenarcheal groups, even after adjusting for normative developmental changes, suggest that the onset of menstruation may serve as a critical transition point in adolescent sleep health. The findings of Campbell and Feinberg's longitudinal study (Campbell et al. 2012), which demonstrated the connection between sexual maturation and alterations in sleep EEG (specifically Delta power), underscore that markers of pubertal development, such as menarche, are linked with sleep behavior in ways that extend beyond the conventional focus on chronological age. Given the yearly granularity of our measures, these results warrant a cautious interpretation, emphasizing the importance of further research with more detailed temporal data to understand the nature and extent of these associations more precisely.

Our findings in this adolescent sample linking menstrual problems with multiple sleep disturbances align with prior studies in adults that have linked poor sleep quality and/or sleep disturbances to irregular menstrual cycles (Hachul et al. 2010), higher levels of menstrual pain, and more severe premenstrual symptoms (Conzatti et al. 2021; Baker and Driver 2007). Our findings also align with the limited studies examining the relationship between sleep behavior and menstrual problems in the adolescent population. One study found an association between menstrual pain severity and insomnia severity and daytime sleepiness in a sample of adolescents (12–18 years old) (Bahrami et al. 2017). Another study conducted in a large sample of Chinese adolescents found that menstrual irregularity and menstrual pain were significantly associated with increased risk of daytime sleepiness (Wang et al. 2019). In a study conducted in Korean adolescents, shorter sleep duration was related to menstrual cycle irregularities (Nam et al. 2017). Finally, Sharma and colleagues found that 25% of the girls in their sample who had menstruation problems also reported disturbed sleep (Sharma et al. Feb 2008).

In the context of puberty, the relationship between menstrual and sleep problems could be influenced by multiple factors embedded on a backdrop of dynamic developmental changes. During puberty, the hypothalamic-pituitary-gonadal axis (HPG axis) is activated, leading to an increase in the production of estrogen and other sex hormones, which mark the beginning of the menstrual cycle (Allison and Hyde 2013). These changes can affect the production and release of prostaglandins from the uterus, particularly around the time of menses, with excessive production of prostaglandins leading to stronger and more frequent uterine contractions, which can cause pain (Chan 1983). There is an interactive and bidirectional relationship between sleep and pain, such that pain disrupts sleep and disturbances in sleep modify pain perception (Iacovides et al. 2015). Specifically, poor sleep might reduce the efficacy of descending pain modulating systems, increasing pain sensitivity (Iacovides et al. 2017). In the context of dysmenorrhea, painful uterine cramps may disturb sleep, which in turn, could intensify the pain experience and lead to central pain sensitization (Iacovides et al. 2015). Indeed, emerging evidence suggests that women with dysmenorrhea may have alterations in central pain modulation (Kutch and Tu Jan 2016).

Our finding of associations between multiple dimensions of poor sleep health and multiple menstrual problems could reflect a common underlying mechanism given that the reproductive and sleep and circadian regulatory systems are strongly associated. Sleep duration, timing, and quality can influence the reproductive system, and pubertal events, such as the release of luteinising hormone, are intricately tied to sleep, which is critical for reproductive regulation (Shaw et al. 2012). The associations we found could also involve other systems: sleep disturbances are tightly linked with mood disturbances, which can further exacerbate pain perception (Harrison et al. 2016), and are an integral component of severe PMS (Steiner et al. 2011). It is also possible that altered activation of stress systems could underlie sleep disturbances and menstrual problems, through stress-induced changes in hormone levels and neurotransmitters (Gollenberg et al. 2010); there is a positive association between psychosocial stress and premenstrual symptoms and dysmenorrhea (Gollenberg et al. 2010), and between stress and sleep disturbances (Âkerstedt 2006).

A strength of this study is the focus on menstrual problems in early adolescence, using a large, diverse sample of female adolescents from across the United States. Other strengths are the use of multiple sleep measures, including self-report sleep timing and duration and caregiver-reported sleep disturbance, and evaluation of interference of menstrual problems on daily activities beyond ratings of menstrual problem severity. Results should be interpreted in the context of the study limitations. Some participants in the ABCD Study had not yet experienced menarche by the Year 3 visit (see Table S1), which may limit the generalizability of our findings to populations with later menarcheal onset. We relied on retrospective reports of menstrual problems and sleep and did not longitudinally track daily sleep and menstrual symptoms across the menstrual cycle, which could reveal additional associations between sleep disturbances and menstrual issues that vary across the cycle. The main analysis in our manuscript is cross-sectional, providing insights into associations but not establishing causal relationships between sleep and menstrual problems. While our secondary analyses reveal significant associations—specifically, that sleep problems such as later bedtimes, shorter sleep durations, later wake-up times, and increased relative social jetlag coincide notably with the onset of menarche—we acknowledge that this design precludes definitive causal inference. To further elucidate the complex interplay between these variables, future longitudinal investigations are crucial. The ongoing ABCD Study, with its longitudinal approach, offers a promising opportunity to explore potentially bidirectional relationships and the nuanced dynamics between sleep patterns and menstrual health across adolescence, especially as new data become available in future releases. Finally, the experience and reporting of menstrual problems as well as their treatment is influenced by cultural attitudes and beliefs toward menstruation (Chen et al. 2018), which would be important to consider in future work.

In summary, our study illuminates the intricate relationship between menarche and sleep behavior as well as relationships between sleep and menstrual problems among U.S. adolescents, underscoring the prevalence of such issues even in early adolescence. The association of specific sleep patterns with menstrual discomfort and daily disruptions emphasizes the need for holistic healthcare approaches that integrate sleep and menstrual health. It is important for healthcare professionals, teachers, and caretakers to provide targeted support for female adolescents, which could include encouraging them to seek medical help when necessary, equipping them with education on symptoms, and informing them of available treatment options. As the ABCD Study cohort continues to mature, future longitudinal research holds the promise of even deeper insights into these pressing concerns.