Evaluation of Sleep Patterns and Chronotypes in Spanish Women With Fibromyalgia Syndrome: A Descriptive Cross-Sectional Study
Article information
Abstract
Objectives
This study aimed to investigate sleep patterns and chronotypes in Spanish women diagnosed with fibromyalgia syndrome (FMS).
Methods
A descriptive, cross-sectional observational study following the Strengthening the Reporting of Observational Studies in Epidemiology guidelines was conducted from March 1, 2024, to June 10, 2024, at the Fibromyalgia and Chronic Fatigue Syndrome Association of Tenerife (San Cristóbal de La Laguna, Spain).
Results
A total of 73 women, with a mean age of 56.15±6.47 years, diagnosed with FMS were enrolled. Bedtime habits and wake-up times showed significant variability, reflecting individual differences in sleep chronotype preferences among the participants. The Pittsburgh Sleep Quality Index revealed a mean score of 11.62±0.92, indicating substantial challenges in sleep quality among participants with FMS. Sleep efficiency was low, averaging 14.86%±0.34%, and there was a significant discrepancy in sleep duration between workdays and free days, with an average difference of 2.0±0.5 h. The participants reported compensatory sleep through an average of two naps per day, each lasting 40 min.
Conclusions
Participants with FMS experienced poor sleep quality, characterized by variability in sleep patterns between workdays and free days, along with significant social jet lag. Low sleep efficiency suggests a prevalent sleep debt, which the participants attempted to mitigate through frequent and extended napping.
INTRODUCTION
Fibromyalgia syndrome (FMS) is a complex condition characterized by widespread chronic pain and sensory disturbances such as allodynia, primarily affecting women aged 40–49 years with a lower socioeconomic and educational background [1,2]. Globally, FMS has a prevalence rate of approximately 2.10%, which is slightly higher in Europe at 2.31%, and affects 2.40% of the Spanish population [3-5]. Patients with FMS often exhibit central nervous system sensitization, marked by increased supramedullary-mediated hyperalgesia [6,7]. This phenomenon involves the amplification of pain signals within the central nervous system [8], resulting in an exaggerated response to stimuli that are typically not painful, thus contributing significantly to the chronic pain experienced by individuals with FMS [9]. Furthermore, the pain associated with FMS commonly coexists with psychological symptoms such as fear, anxiety, and stress [10,11]. Furthermore, FMS is intricately related to heightened levels of perceived pain, exerting a profound impact on various psychosocial dimensions encompassing mental well-being, mood stability, interpersonal relationships, and occupational performance [12].
A less explored aspect of FMS involves notable disruptions in sleep patterns, which not only exacerbate symptoms but also contribute to a decline in the overall quality of life [13,14]. The relationship between sleep disturbance and FMS symptomatology is bidirectional in nature. Insufficient sleep amplifies the perception of pain, while persistent pain further compromises sleep quality [15] and sleep quantity [16]. Therefore, prolonged sleep latency [17], frequent nocturnal awakenings [18], and a general decrease in restorative sleep [19] is commonly observed.
Recent advancements in neuroimaging have revealed alterations in brain regions involved in both sleep-wake regulation and pain processing, notably in the thalamus and prefrontal cortex [19]. Additionally, changes in pineal gland volume and its impact on melatonin release have been observed in women with FMS [20]. Moreover, chronic sleep deprivation has been associated with a notable reduction in gray matter volume in the hippocampus (HPC), orbitofrontal cortex, and precuneus [21,22], which are associated with deficits in cognitive function and emotional regulation [23,24]. Furthermore, disruptions in neural circuits, particularly in the HPC, which are responsible for sleep induction and maintenance, can be a key component of the worsening perception of restful sleep and the decline in cognitive-related coping strategies throughout the course of the disease [25].
Additionally, sleep disturbances in FMS can be influenced by the interplay of cultural and epigenetic factors, as well as individual variations in chronotypes or circadian preferences [26]. This interaction may contribute to the diversity of sleep disruptions among chronic pain patients [27,28], potentially explaining the variability observed in sleep patterns. Understanding sleep disturbances in women with FMS is crucial not only for healthcare professionals to uncover pain mechanisms related to sleep disturbances but also for designing effective management strategies. However, to date, no observational studies have focused on describing the quantity and quality of sleep patterns and chronotypes in women with FMS in Spain, which represents a significant gap in the literature. Therefore, more research is needed to investigate sleep patterns and chronotypes among Spanish women diagnosed with FMS.
METHODS
Study design
This observational cross-sectional study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) [29] guidelines was conducted from March 1, 2024, to June 10, 2024, at the Fibromyalgia and Chronic Fatigue Syndrome Association of Tenerife, located in San Cristóbal de La Laguna, Spain. The study protocol was approved by the institutional review board of the ethics committee of Complejo Hospitalario Universitario de Canarias (Canary Islands, Spain) (approval no. CHUC_2024_27—February 20, 2024) and adhered to the ethical principles outlined in the Declaration of Helsinki for medical research involving human subjects. Written informed consent was obtained from all participants prior to inclusion in the study.
Setting
Data were collected from March 9 to 23, 2024. The recruitment began by contacting potential participants via an affiliation data list to inform them about the study and invite them to participate. Those who agreed to participate had to undergo an in-person interview at the Fibromyalgia and Chronic Fatigue Syndrome Association of Tenerife to obtain sample characteristics, validate their compliance with the previously established inclusion and exclusion criteria, and obtain signed written informed consent. Subsequently, a single interview was conducted with each participant to assess the primary and secondary variables.
Participants
After obtaining informed consent, interviews were conducted to evaluate compliance with the specified inclusion and exclusion criteria. The inclusion criteria were the following: 1) female sex, 2) aged ≥18 years, 3) diagnosis of FMS according to the American College of Rheumatology 2016 criteria [30], 4) perceived sleep disturbances, 5) absence of psychological disorders, 6) no history of musculoskeletal surgeries, and 7) no use of sleep-affecting medications such as nonsteroidal anti-inflammatory drugs, analgesics, sedatives, hypnotics, or antidepressants, unless under the supervision of a physician and at a stable dosage for at least 1 month prior to the study. The exclusion criteria were as follows: 1) male sex, 2) age <18 years, 3) diagnosis of other chronic pain syndromes, 4) no perceived sleep disturbances, 5) psychological disorders, 6) a history of musculoskeletal surgeries, and 7) use of medications that affect sleep such as nonsteroidal anti-inflammatory drugs, analgesics, sedatives, hypnotics, or antidepressants, unless under physician’s supervision and at a stable dose for at least 1 month prior to the study.
Variables
Pain intensity (Numeric Pain Rating Scale)
The Numeric Pain Rating Scale (NPRS) is a widely used tool to assess pain intensity. It typically consists of a numerical scale ranging from 0 to 10, where 0 indicates no pain and 10 indicates the worst pain imaginable [31]. Participants were asked to select a number that best represented their current level of pain. This scale provides a rapid and reliable measure of pain intensity, allowing healthcare providers and researchers to quantify and monitor changes in pain over time. In our study, the NPRS score was used as a primary variable to evaluate and compare pain levels among participants diagnosed with FMS.
Functionality and quality of life (Fibromyalgia Impact Questionnaire)
The Fibromyalgia Impact Questionnaire (FIQ) is a comprehensive tool designed to assess the impact of fibromyalgia on an individual’s daily functioning [32]. It consists of several domains, such as physical functioning, overall well-being, and severity of symptoms. The participants completed the questionnaire that included questions about their ability to perform daily activities and their levels of pain, fatigue, morning tiredness, stiffness, anxiety, and depression. The FIQ scores range from 0 to 100, with higher scores indicating more severe symptoms and greater impact on daily life. In our study, the FIQ score was used as a primary variable to measure the overall impact of FMS on the functional abilities and quality of life of the participants.
Sleep quality (Pittsburgh Sleep Quality Index)
The Pittsburgh Sleep Quality Index (PSQI) is a questionnaire that has been used to evaluate sleep quality over the past month [33]. It consists of 19 questions in seven components— subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction. Each component is scored from 0 to 3, and the total score ranges from 0 to 21, with higher scores indicating poorer sleep quality. The PSQI helps assess various aspects of sleep patterns and behaviors, making it valuable for understanding sleep disturbances and their impact on individuals with FMS.
Sleep chronotype (Munich Chronotype Questionnaire)
Sleep chronotype was assessed using the Munich Chronotype Questionnaire (MCTQ), which evaluates chronotype, social jet lag (SJL), average weekly sleep duration, and sleep efficiency [34]. This questionnaire helps determine sleep onset, total sleep time (TST), and mean sleep durations for both workdays and free days, offering a comprehensive understanding of sleep-wake patterns and preferences in women with FMS.
Sleep-wake diary
The sleep-wake diary is a tool used to monitor sleep patterns over a 2-week period, in which participants document bedtime, sleep onset, awakening, wake-up times, and daytime naps [35]. This diary offers detailed information on sleep quality, duration, and disturbances, which complement questionnairebased assessments. Additionally, it facilitates the calculation of sleep efficiency by comparing the TST with the time in bed (TIB). This tool assesses how effectively women with FMS sleep during their designated hours and provides a quantitative indicator of sleep quality and consolidation.
Bias
Bias control measures were implemented in this study. These included systematic and transparent participant recruitment from a diverse pool at the Fibromyalgia and Chronic Fatigue Syndrome Association of Tenerife, ensuring all potential participants were informed and invited to participate, thereby reducing the likelihood of biased selection. Standardized data collection procedures were used to minimize information bias through structured clinical interviews and validated assessment instruments. Additionally, the interviewers underwent extensive training to ensure consistency and accuracy in data collection, thus reducing the risk of biased responses. Rigorous control for confounding variables, such as age, sex, and medication usage, was carried out during statistical analysis to accurately isolate the effects of interest.
Sample size
Sample size and power calculations were performed using G*Power 3.1 software [36]. Based on the assumption that the prevalence of FMS in women is 1.46% [37] and sleep disorders affect approximately 80% of these women [38], we considered a two-tailed test, an alpha level of 0.05, a 5% margin of error, and 90% desired power. The estimated sample size was 49. We also considered increasing the sample size to account for potential data loss.
Statistical analysis
Data analysis was performed meticulously using SPSS Statistics v.29.0 software (IBM Corp., Armonk, NY, USA). Initially, rigorous double-entry validation processes were used during the data entry phase to ensure accuracy and reliability. A comprehensive range of descriptive statistics was then computed to fully characterize the sample. These encompassed measures of central tendency, such as mean and median, as well as measures of dispersion, including standard deviation and interquartile range. Additionally, measures of position, such as the first and third quartiles, were used as quantitative variables. Frequencies and percentages were calculated for nominal data to provide a comprehensive overview.
RESULTS
Sample characteristics
A total of 73 participants who met the inclusion criteria were recruited for this study. The flow diagram of the participant’s selection process (STROBE) is shown in Fig. 1. The mean age of the participants was 56.15±6.47 years, with a median of 56.06 years and a variance of 41.92 years. The participants had an average weight of 80.09±8.91 kg, a height of 1.69±0.04 m, and a body mass index (BMI) of 26.84±3.03 kg/m2. The average NPRS score was 7.91±0.57 with a median score of 7.85. The average FIQ score was 58.52±5.09, with a median of 59.12 and a variance of 25.97. The average duration of symptoms was 8.67±1.74 years, with a median of 8.36 years. The participants’ characteristics are summarized in Table 1.
Flow diagram for selection process of participants (STROBE). STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.
Demographic and clinical characteristics of the participants (n=73)
Main results
Sleep quality
The average PSQI score was 11.62±0.92, indicating significant variability and generally poor sleep conditions among participants diagnosed with FMS. The Sleep quality of the sample is represented in Fig. 2. Detailed analysis in specific domains indicated varying levels of sleep disruption. The average subjective sleep quality score was 2.55±0.23, and the average sleep latency was 2.01±0.50 h. The average sleep duration was 2.13±0.33 h. The average sleep efficiency was 1.87%±0.17%, and the average sleep disturbance score was 2.23±0.30. Additionally, the average use of sleep medications was 1.46±0.28 times, and the average daytime dysfunction score was 1.48± 0.28. These findings underscore the complex nature of sleep challenges experienced by the participants with FMS. The sleep quality scores are summarized in Table 2.
Evaluation of sleep quality among participants using the PSQI. PSQI, Pittsburgh Sleep Quality Index.
Evaluation of sleep quality among participants using the PSQI (n=73)
Sleep chronotype
On workdays, the participants showed consistent sleep schedule patterns. The average bedtime was at 23:45±00:45, indicating a moderate variability among participants with FMS. The average sleep latency was 50±20 min, showing moderate variability. The average wake-up time was at 07:45±00:45, indicating a general tendency among the participants to wake up in the morning around this time. The mean recorded sleep duration was 6.0±1.0 h, reflecting the typical sleep duration on workdays.
Conversely, on free days, participants exhibited noticeable changes in their sleep behaviors. The average bedtime shifted to 00:45±01:15, indicating greater variability and a tendency toward later sleep onset during free days. Sleep latency was slightly reduced to 45±15 min, suggesting quicker initiation of sleep when not constrained by work schedules. Wake-up times extended to 09:15±01:15, revealing a preference for later mornings on free days. In particular, participants enjoyed an extended average sleep duration of 8.5±1.5 h on free days, which represents a significant increase on free days compared to workdays.
The difference in sleep duration between workdays and free days was 2.5±0.5 h, highlighting a clear compensatory sleep pattern on days without work obligations. Furthermore, the participants reported an average of two naps, each lasting 40±15 min, on both workdays and free days, indicating consistent nap behavior on different days. The differences in Sleep duration between workdays and free days is displayed in Fig. 3.
Comparison of sleep duration between workdays and free days as assessed by the MCTQ. MCTQ, Munich Chronotype Questionnaire.
The analysis of chronotype revealed that the average midsleep time on free days was at 04:45±01:15, suggesting a preference for a later midpoint of sleep when participants are not restricted by work schedules. SJL was observed at an average of 1.90±1.25 h, highlighting the variance in sleep schedules between workdays and free days and emphasizing the impact of social and occupational obligations on participants’ sleep patterns. The sleep chronotype (MCTQ) is presented in Table 3.
Evaluation of sleep chronotypes among participants based on the MCTQ
Sleep-wake diary
Participants spent an average of 0.90±0.44 h per day in bed, reflecting significant variability among the participants. The average TST was 6.07 h per night (range: 4.80–8.10 h; median: 5.85 h), indicating a relatively brief sleep duration. Sleep efficiency averaged to 14.86% (standard deviation: 0.34; median: 14.18%), highlighting the challenges in achieving effective sleep and emphasizing the difficulties in maintaining adequate and consistent sleep patterns in women with FMS. Sleep efficiency is shown in Fig. 4 and the sleep-wake diary is presented in Table 4.
Evaluation of sleep efficiency among participants using sleep-wake diary.
Summary of sleep-wake patterns recorded in participant diaries (n=73)
DISCUSSION
This study offered a comprehensive analysis of sleep characteristics in women with FMS and explored their impact on sleep patterns and chronotypes. Our findings reveal that participants with FMS have poor sleep quality, as evidenced by a mean PSQI score of 11.62±0.92, indicating significant challenges in sleep quality within this population.
These results are consistent with those of previous studies. For instance, Osorio et al. [39] reported a PSQI score of 12.0±1.5 for patients with FMS, whereas Miró et al. [40] found a mean PSQI score of 11.41±3.12, thus affirming the reliability of our findings within the existing literature. Furthermore, Andrade et al. [41] observed a similar sleep quality score (PSQI score: 9.63±4.48) among patients who experienced up to two symptoms simultaneously.
A comparative analysis of studies in other chronically ill populations revealed a consistent pattern of sleep disturbance. For instance, Lee et al. [42] found that patients with rheumatoid arthritis had a mean PSQI score of 9.58±4.19, indicating poorer sleep quality than that of the general population. Although this score was lower than that observed in our FMS sample, it highlights the prevalence of sleep disturbance in various chronic conditions, particularly in patients with FMS.
However, not all studies were in agreement with these findings. Wolfe et al. [43] observed that although sleep disturbances are common in patients with FMS, their impact on the overall quality of life may not be as profound as previously assumed. Their study reported that some patients with FMS, despite experiencing poor sleep quality, did not show a significant decrease in daytime functioning. Similarly, Theadom et al. [44] argued that although sleep issues are prevalent in FMS, they may not be the primary determinant of symptom severity. They suggested that other factors, such as pain management and psychological support, could play a critical role in the overall well-being of patients with FMS.
Regarding sleep chronotypes, our study showed remarkable differences in sleep patterns on workdays and free days among participants with FMS. On workdays, the participants had an average bedtime at 23:45±01:00 and wake-up time at 07:30±01:00, resulting in a mean sleep duration of 6.2±1.1 h. Conversely, on free days, participants went to bed later at 00:30±01:00 and woke up at 09:00±01:00, enjoying an extended sleep duration of 8.2±1.4 h. The 2-h difference in sleep duration between workdays and free days, as well as a mid-sleep time at 04:45±01:15 on free days, indicate significant sleep compensation during free days.
Moreover, a notable shift of 1.90±1.25 h between workdays and free days indicates a significant SJL, possibly caused by a misalignment between participants’ biological clocks and their social schedules [45]. Our results differ from those of Wittmann et al. [46] and Tan et al. [47], who reported the existence of SJL lasting approximately 1 h among healthy young and middle-aged adults of working age. Nevertheless, our data appear similar when comparing the SJL of our sample with patients with chronic diseases, such as type 2 diabetes or metabolic syndrome. Koopman et al. reported a significant increase in SJL in patients with type 2 diabetes, ranging from a mean of 21.7±17 min to 2 h and 20±24 min [48]. Although these results are very similar to ours, we also observed high variability, suggesting a substantial difference in the extent of circadian misalignment among patients with chronic diseases, similar to women with FMS.
Mota et al. [49] reported that SJL in individuals with metabolic syndrome can range from 1 to 2 h. This circadian misalignment has been associated with adverse metabolic profiles, including higher BMI, fasting glucose levels, and total cholesterol levels and lower high density lipoprotein cholesterol levels. Specifically, among patients with obesity, Minabe et al. [50] determined an average SJL of 0.4±0.5 h, which has been negatively associated with increased BMI and reduced body fat. Considering that a significant proportion of women with FMS can also suffer from metabolic syndrome or obesity, future studies should consider these factors as valid explanations for the occurrence and persistence of SJL [51,52].
According to recent literature, patients with FMS with an evening sleep pattern are more likely to experience sleep disturbances [53]. In our study, the participants predominantly exhibited a late chronotype characterized by delayed bedtimes (23:45±01:00 on workdays and 00:30±01:00 on free days) and wake-up times influenced by morning pain and stiffness. These individuals also reported frequent awakenings due to nightmares, resulting in a shorter total sleep duration [54]. Furthermore, individuals with an evening sleep pattern tend to experience increased daytime sleepiness and show more erratic sleep-wake patterns than those with morning sleep patterns [55].
The study participants had an average age of 56.15±6.47 years, which reflects individuals in their working years, where longer sleep hours on weekends or free days are common to compensate for the accumulated sleep deficits of the workweek. Additionally, the prevalence of low-qualification jobs among recruited women could have contributed to the rigidity and inflexibility of their work schedules, preventing their alignment with natural circadian rhythms [56,57]. Consequently, reducing sleep debt and alleviating SJL presents challenges for these individuals. Transitioning from a structured work environment to retirement may offer relief, as evidenced by studies indicating a significant reduction in SJL, sometimes dropping to as low as 20 min among retirees with chronic diseases [58].
In our study, we found that participants responded to sleep deprivation by taking an average of two daily naps, each lasting for approximately 40±15 min, on workdays and free days. This frequent nap behavior appears to counteract the effects of accumulated sleep debt from workdays. However, the extent to which napping effectively addresses this debt is still a topic of debate. Although naps temporarily boost alertness and performance, they cannot completely replace the restorative benefits of consolidated nighttime sleep [59,60].
Sleep efficiency, which measures the percentage of time spent asleep while in bed, averaged 14.86%±0.34%. This finding indicates that a significant portion of the time spent in bed was not effectively utilized for sleep, possibly due to difficulties in sleep initiation or maintenance. In particular, the reported sleep efficiency was lower than that typically reported in sleep research, where values >85% are generally considered normal [61]. This discrepancy suggests the presence of sleep disorders or lifestyle factors that negatively impacted sleep quality in our study population. Moreover, the lower average TST highlights potential chronic sleep deprivation, which can lead to adverse health outcomes, such as impaired cognitive function, mood disturbances, and an increased risk of chronic diseases among women with FMS [62].
Strength and limitations
Our study on sleep characteristics in women with FMS has several limitations. We did not consider the intricate interaction of FMS with daily life, the absence of a healthy control group for comparative analysis, and the challenges in standardizing schedules and accounting for comorbidities. Furthermore, the cultural diversity within the sample may have influenced pain perception, which requires further investigation. Despite these limitations, our study leveraged validated tools, such as the PSQI and MCTQ, conducted a comprehensive assessment of various aspects of sleep, and integrated objective and subjective data collected over a 2-week period. This approach facilitates the identification of issues and supports the implementation of effective interventions in behavioral sleep medicine.
Conclusions
In conclusion, our findings suggest that Spanish women with FMS experience poor sleep quality, characterized by variability in sleep patterns between workdays and free days, along with significant SJL. Low sleep efficiency suggests a prevalent sleep debt, which the participants attempted to mitigate through frequent and extended napping.
Notes
The authors have no potential conflicts of interest to disclose.
Author Contributions
Conceptualization: Sebastián Eustaquio Martín Pérez, Isidro Miguel Martín Pérez. Data curation: Isidro Miguel Martín Pérez, José Carlos del Castillo Rodríguez. Formal analysis: Isidro Miguel Martín Pérez, José Carlos del Castillo Rodríguez. Methodology: Isidro Miguel Martín Pérez, José Carlos del Castillo Rodríguez, Juan Luis Oliva de la Nuez, Mario Herrera Pérez. Project administration: Sebastián Eustaquio Martín Pérez, Tomás González Cobiella, José Carlos del Castillo Rodríguez. Resources: all authors. Software: Tomás González Cobiella. Supervision: Sebastián Eustaquio Martín Pérez, Isidro Miguel Martín Pérez. Validation: all authors. Visualization: Isidro Miguel Martín Pérez, Laura Lucas Hernández, Juan Luis Oliva de la Nuez. Writing—original draft: Laura Lucas Hernández, Juan Luis Oliva de la Nuez, Aboubaker Soussi El-Hammouti. Writing—review & editing: all authors.
Funding Statement
None
Acknowledgements
None