Sleep Disturbances Among Adult Patients with Mood Disorders: A Saudi Cross-Sectional Study

Introduction

Sleep plays a crucial role in maintaining physical and mental health;¹ however, sleep difficulties continue to increase globally.² Approximately 45% of the world’s population experiences sleep problems, with insomnia being the most prevalent, affecting about 30% of adults.³ Global studies have revealed that poor sleep quality elevates the risk of chronic diseases, including cardiovascular disorders.⁴ Furthermore, the prevalence of sleep disorders is especially concerning, as inadequate sleep is linked to higher rates of obesity and diabetes—conditions already widespread in many countries.⁴ In Saudi Arabia, a cross-sectional study of over 2000 adults found that one-third reported short sleep duration (< 7 hours/night.⁵ Those findings underscore the need for public health initiatives to raise awareness of, and address the causes of, poor sleep in the Saudi population.⁵

Mood disorders (MDs) encompass mental health conditions characterized by significant mood fluctuations and related symptoms,⁶ with the two main types of MDs being depressive and bipolar disorders. Depressive disorders involve prolonged periods of low mood, anhedonia, appetite and sleep changes, and feelings of worthlessness or guilt, among other symptoms.⁶ Bipolar disorders involve alternating episodes of depression, hypomania, and mania, varying by subtype.⁷ These disorders affect millions worldwide.⁸ The World Health Organization estimates that approximately 5.7% of adults globally experience depression,⁹ while bipolar disorder (BD) affects about 0.5% of the global population.¹⁰ Nationally, the Saudi National Mental Health Survey (SNMHS)—the first nationally representative psychiatric epidemiological study in Saudi Arabia—reported a lifetime prevalence of approximately 6% for major depressive disorder (MDD).¹¹ Similarly, national estimates suggest that the prevalence of BD in Saudi Arabia is around 3% of the population.^11,12

Sleep disturbances are highly prevalent among patients with MDs and play a crucial role in contributing to the onset, progression, and severity of these conditions.^13,14 MDD and BD are often associated with disrupted sleep patterns, including insomnia, hypersomnia, and poor sleep quality.^13,14 Sleep problems can function as both symptoms and contributions to mood dysregulation.¹⁴ For instance, chronic sleep disturbances can exacerbate mood symptoms, impair cognitive function, and increase the risk of relapse in individuals with MDs.^13,14 Additionally, mood disturbances can disrupt circadian rhythm regulation, leading to irregular sleep–wake cycles that negatively impact treatment and recovery in patients with BD and depression.¹⁵ Nonetheless, evidence has confirmed a strong bidirectional association between sleep disturbances and MDs.^16,17

Although global research has widely explored sleep disturbances among individuals with MDs,^13–17 research in Saudi Arabia remains scarce. Further, existing Saudi studies on sleep disturbances have primarily targeted non-clinical populations,^18,19 leaving a notable gap in knowledge regarding sleep disturbances among individuals with MDs. Moreover, relatively few studies have simultaneously examined a broad spectrum of MDs while incorporating sleep hygiene behaviors and cognitive-emotional sleep factors, such as sleep effort, in clinical settings.²⁰ Addressing these research gaps could provide additional insight into the interplay between sleep and MDs, which can lead to improving care and outcomes for affected individuals. Accordingly, this study examines the prevalence and characteristics of sleep disturbances among adult patients with MDs at a tertiary care hospital in Saudi Arabia and identifies associated sociodemographic, clinical, and behavioral factors. The findings may inform clinical practice and policy in Saudi Arabia, resulting in supporting the implementation of routine screening for sleep disturbances and targeted interventions—particularly those addressing modifiable sleep-related factors. Such measures could ultimately improve outcomes for patients with MDs in Saudi Arabia.

Method

Study Design, Setting, and Participants

Researchers conducted this cross-sectional study among patients attending psychiatric clinics at King Khalid University Hospital (KKUH), Riyadh, Saudi Arabia, with data collection occurring between May and July 2025. The targeted population included adult psychiatric patients aged 18–65 years diagnosed with mood and mood-related disorders. Specifically, diagnoses included MDD, persistent depressive disorder, other specified and unspecified depressive disorders, bipolar I and II disorders, cyclothymic disorder, and other specified and unspecified bipolar and related disorders. The exclusion criteria included patients younger than 18 or older than 65, those with communication barriers, and individuals with primary psychotic disorders (eg, schizophrenia or schizoaffective disorder), those with substance use disorders, and those with secondary MDs (eg, secondary to substance use and secondary to medical conditions).

The estimated target population size, after applying inclusion and exclusion criteria, was 624, based on the data from KKUH’s IT department. To account for non-respondent and non-completed responses, investigators added a 25% buffer and calculated the required sample size using Raosoft.com with a 5% margin of error and a 95% confidence level. The calculated sample size was 299; however, the final sample included 306 participants.

Participants were recruited via convenience sampling from a list of file numbers from the hospital’s IT department, identifying patients diagnosed with mood and mood-related disorders. To enhance the representativeness of the results, investigators reviewed the patients’ medical charts—particularly the psychiatric notes—to ensure the patient’s overall stability of the mood disorder condition. Stability was defined as follows in the 12 months preceding data collection: (1) no major depressive, hypomanic, or manic episodes documented; (2) no major medication changes were made due to instability; and (3) no psychiatric ward admissions. Patients who did not meet these stability criteria were not invited to participate.

Study Instrument

The study tool comprised a questionnaire developed by the research team to collect sociodemographic, medical, psychiatric, and sleep-related information. In addition, the tool included the Sleep Hygiene Index (SHI) and the Glasgow Sleep Effort Scale (GSES). All data were self-reported by the participants.

The questionnaire included four sections. The first section addressed sociodemographic information (age, gender, marital status, and employment status). The second section covered psychiatric-related information, including family history of psychiatric illnesses, caffeine consumption, smoking, and illegal substance use. The third section gathered medical-related information, such as weight, height, and chronic medical conditions. The fourth section addressed sleep-related information, including sleep disorders, snoring, and nightmares during sleep, nighttime awakenings, usual bedtime, duration to fall asleep, and nocturnal sleep hours.

The SHI was developed to measure sleep hygiene practices;²¹ it consists of 13 items.²¹ The scoring system considers that 26 or below indicates good sleep hygiene, 27–34 indicates average sleep hygiene, and 35 and above indicates poor sleep hygiene.²¹ The scale’s internal consistency has been previously evaluated. The original English version has a Cronbach’s alpha of 0.66.²¹ However, the Arabic version used in this study yielded a Cronbach’s alpha of 0.589.²²

The GSES was developed to assess sleep effort, a factor that is often elevated among insomnia patients.²³ The GSES consists of seven items,²³ with responses including 0 (“not at all”), 1 (“to some extent”), and 2 (“very much”).²³ Total scores range from 0 to 14, with higher scores indicating a higher effort needed for an individual to sleep.²³ The original English version displayed good reliability among patients with insomnia (Cronbach’s alpha of 0.77).²³ The Arabic version used here exhibited a Cronbach’s alpha of 0.87.²⁴ After obtaining permissions from the authors, investigators used the Arabic versions of both the SHI and the GSES in this study,^22,24 (permissions were also obtained for the original English versions).^21,23

Ethical Considerations

The Institutional Review Board of the College of Medicine at King Saud University, Riyadh, Saudi Arabia, approved this study (Research Project No. E-24-9408). Investigators obtained electronically informed consent from the participants prior to data collection. The participants received assurances of anonymity, confidentiality, and the voluntary nature of their involvement. This study adhered to the principles of the Declaration of Helsinki.

Statistical Analysis

Statistical analysis was conducted using the Statistical Package for Social Sciences (SPSS) Version 28 (IBM Co., Armonk, NY, USA). Numerical data were presented as the mean and standard deviation (SD) and analyzed between each two groups using an independent t-test, whereas a one-way analysis of variance (ANOVA) was employed across more than two groups. Categorical data were presented as the frequency and percentage and analyzed using a Chi-square test or an exact test. Linear regression analyses were performed to assess the factors associated with the SHI and GSES scores. Univariate linear regression analyses were first conducted to examine the association between each independent variable and the SHI and GSES scores separately. Variables with a P-value <0.20 in univariate analyses, as well as variables deemed clinically relevant a priori, were entered into multivariable linear regression models. Multivariable models were specified using forced entry (enter method). Assumptions of linear regression were evaluated, including linearity, normality of residuals, homoscedasticity, and absence of multicollinearity. Variance inflation factors (VIFs) were examined, and all values were <1.3. Model fit was assessed using residual plots and overall model significance. A two-tailed P-value < 0.05 was considered statistically significant.

Results

As presented in Table 1, 306 participants with MDs (84 males, 222 females) completed the study instruments. The largest age group was 46–55 (28.76%), followed by 56–65 years (22.55%). The mean body mass index (BMI) was 29.83 ± 6.61 kg/m². Most patients were married (53.92%) and unemployed (55.56%). Notably, among the 104 employed participants, 7.69% worked night shifts. Furthermore, 133 patients reported a first-degree family member (parent, sibling, or child) with a psychiatric disorder; of these, 69.17% had MDs (depression or bipolar), and 53.38% had anxiety disorders. Moreover, nearly all patients (288) consumed caffeinated beverages (coffee 92.01%, tea 71.18%, and soft drinks 36.11%), with 46.88% reporting one cup/can a day and 30.56% reporting two a day. Twenty-two patients consumed energy drinks, 72.73% of whom reported one to two cans weekly. Thirty-two patients smoked cigarettes, 40.63% of whom smoked half to one pack a day. Only 3.27% reported illegal substance use. Two hundred and eight patients had a history of chronic diseases, with high lipid problems predominating (56.25%), followed by diabetes mellitus (DM; 37.02%), irritable bowel syndrome (IBS; 36.54%), and thyroid problems (hypo or hyperthyroidism; 33.17%).

Table 1 Sociodemographic, Medical, and Psychiatric Data of the Participants

As Table 2 illustrates, 95 participants (31.05%) reported a diagnosed sleep disorder, with insomnia being the most common (80%); 83 participants suffered from snoring (12.05% rarely, 37.35% sometimes, and 50.6% frequently); 126 suffered from nightmares (32.54% rarely, 45.24% sometimes, and 22.22% frequently); 202 awoke at night to use the bathroom (20.3% rarely, 29.7% sometimes, and 50% frequently), with 74.75% waking once or twice per night and 25.25% waking more than twice. More than one-third of the patients (36.93%) reported going to sleep usually between 12:00 and 2:00 am, with 32.68% taking less than 30 minutes and 36.6% taking 30 minutes to 1 hour to fall asleep. Moreover, 32.68% and 35.29% reported having 4–6 and 6–8 hours of nocturnal sleep, respectively.

Table 2 Sleep-Related Data of the Participants

Table 3 presents the SHI results. The highest mean scores were for the following statements: “I get out of bed at different times from day to day” (2.24 ± 1.27), “I do something that may wake me up before bedtime” (2.24 ± 1.5), “I go to bed at different times from day to day” (2.15 ± 1.24), and “I think, plan, or worry when I am in bed” (2.08 ± 1.35). Most patients (85.29%) exhibited good sleep hygiene, 12.42% average, and 2.29% poor, with an overall mean score of 19.19 ± 6.98 (see Figures 1 and 2).

Table 3 Sleep Hygiene Index (SHI) of the Participants

Figure 1 The SHI assessment of the participants.

Figure 2 Distribution of participants’ sleep hygiene.

According to the GSES, mean item scores were as follows: putting too much effort into sleeping when it should occur naturally (0.84 ± 0.76), feeling one should be able to control sleep (1.08 ± 0.76), postponing bedtime for fear of being unable to sleep (0.55 ± 0.72), worrying about not sleeping when unable to sleep (0.87 ± 0.81), perceiving oneself as being not good at sleeping (0.78 ± 0.76), becoming anxious about sleeping before bedtime (0.6 ± 0.72), and worrying about the consequences of not sleeping (0.96 ± 0.81). The overall mean GSES score was 5.68 ± 4.08. (Table 4 and Figure 3)

Table 4 Glasgow Sleep Effort Scale (GSES) Results of the Participants

Figure 3 The GSES assessment of the participants.

The investigation found a statistically significant relationship between participant age and the SHI scores (P = 0.004), with the 36–45-year and 46–55-year age groups exhibiting significantly lower scores than the 26–35-year group. Furthermore, SHI scores differed significantly according to marital status (P = 0.007), as married participants demonstrated significantly lower scores than single participants. Participants working night shifts had significantly higher SHI scores than those without night shifts (P = 0.009). A significant relationship emerged between the number of caffeinated drinks (coffee, tea, or soft drinks) consumed and SHI scores (P < 0.001), with participants consuming three or more cups/cans a day exhibiting significantly higher scores than those consuming one or two cups/cans a day. Furthermore, patients who consumed energy drinks and those who smoked cigarettes had significantly higher SHI scores than non-consumers and non-smokers, respectively (P = 0.021 and 0.006). Participants with IBS exhibited significantly higher SHI scores than those without IBS (P = 0.004). Table 5 illustrates the associations between SHI scores and socio-demographic, medical, and psychiatric characteristics of the participants.

Table 5 Relationship Between the SHI and the Sociodemographic, Medical, and Psychiatric Data of the Participants

As presented in Table 6, participants working night shifts had significantly higher GSES scores than those without night shifts (P = 0.003). Patients with first-degree family members who had anxiety disorders had significantly higher GSES scores than those without such family history (P = 0.045). Weekly energy drink consumption was significantly associated with GSES scores (P = 0.035), with patients consuming 3–4 cans per week demonstrating significantly higher scores than those consuming 1–2 cans per week. Patients with a history of chronic diseases had significantly higher GSES scores than those without (P = 0.001). Participants with IBS also exhibited significantly higher GSES scores (P = 0.002).

Table 6 Relationship Between the GSES Score and the Sociodemographic, Medical, and Psychiatric Data of the Participants

The univariate linear regression analysis identified age, marital status, energy drink consumption, drinks, and cigarette smoking as significantly associated with SHI scores. Specifically, participants aged 46–55 years had significantly lower SHI scores than those aged 18–25 years (coefficient = −2.98, 95% CI [−5.55, −0.4], P = 0.024). Married participants had significantly lower scores than single participants (coefficient = −2.95, 95% CI [−4.76, −1.13], P = 0.002). Participants who consumed energy drinks and those who smoked cigarettes had significantly higher SHI scores than non-consumers (coefficient = 3.56, 95% CI [0.54, 6.58], P = 0.021) and non-smokers (coefficient = 3.59, 95% CI [1.05, 6.13], P = 0.006), respectively. In the multivariable model, cigarette smoking was the only factor significantly associated with SHI scores, as smokers had higher scores than non-smokers (coefficient = 3.12, 95% CI [0.26, 5.98], P = 0.033). Table 7 presents the linear regression analysis for factors associated with SHI scores.

Table 7 Linear Regression Analysis for Factors Associated with the SHI Score

The univariate linear regression analysis revealed that employment status and chronic diseases were significantly associated with GSES scores. Employed participants had significantly lower GSES scores than unemployed participants (coefficient = −1.00, 95% CI [−2, −0.01], P = 0.048), whereas participants with a history of chronic diseases had significantly higher GSES scores than those without (coefficient = 1.68, 95% CI [0.71, 2.64], P = 0.001). In the multivariable model, after adjusting for the included factors, patients aged 46–55 years had significantly lower GSES scores than those aged 18–25 years (coefficient = −2.57, 95% CI [−4.74, −0.4], P = 0.021), and participants with a history of chronic diseases had significantly higher GSES scores than those without (coefficient = 1.91, 95% CI [0.84, 2.98], P < 0.001). Table 8 presents the linear regression results for factors associated with GSES scores.

Table 8 Linear Regression Analysis for Factors Associated with the GSES Score

Discussion

This study explored sleep disturbances among patients with MDs and examined the contributing factors. The findings can help healthcare professionals offer improved patient care and thereby improve the quality of life for this population.

Approximately one‑third of the participants (31.05%) reported a diagnosed sleep disorder, with insomnia accounting for 80% of these cases. Comparable prevalence rates have appeared in other populations with MDs. For instance, one study reported that 34.9% of patients with BD and 15% with recurrent depression met the criteria for insomnia.²⁵ Similarly, in a cohort of 180 euthymic BD patients, 41.1% reported poor sleep quality.²⁶ By contrast, a study of Saudi adults without psychiatric diagnoses indicated that 33.8% experienced less than seven hours of sleep per night.⁵ As a result, the high prevalence of insomnia in the present study may reflect a combination of disease‑related circadian dysregulation and behavioral factors. Thus, a plausible hypothesis is that irregular sleep schedules and nighttime arousal could indicate a limited awareness of sleep hygiene practices. Although no studies have directly examined sleep hygiene awareness among individuals with psychiatric disorders in Saudi Arabia, the lack of awareness is common and has been noted even among parents of children in Saudi Arabia.^5,27 The present findings concerning insomnia in this sample suggest that clinicians should consider integrating evidence-based interventions, such as cognitive-behavioral therapy for insomnia (CBT-I), which has demonstrated long-term efficacy.²⁸

Approximately one-third of the participants (27.12%) reported snoring, with half experiencing it frequently. Although often dismissed as benign, snoring can indicate serious conditions such as obstructive sleep apnea (OSA) and has been independently associated with coronary artery disease.^29,30 The high prevalence of snoring therefore raises a significant concern for individuals with MDs, a population in which up to 50% have been found to meet the criteria for OSA.³¹ These findings emphasize the value of routine screening for sleep-disordered breathing in this population.

More than one-third of this study’s sample (41.18%) reported nightmares, consistent with prior research on MDs. Studies of individuals with MDD, for instance, have documented prevalence as high as 56%.³² Nightmares are clinically significant, as they have been associated with suicide attempts and may predict suicidal behavior.^33,34 Moreover, frequent nightmares can signal underlying psychological trauma, given that dream content often reflects waking stressors and past traumatic experiences.³⁵ Therefore, it is crucial for clinicians to address nightmares when caring for patients suffering from MDs. Interventions such as imagery rehearsal therapy (IRT) have been highly effective in reducing nightmares and distress.³⁶ At the same time, selective serotonin reuptake inhibitors (SSRIs) and serotonin and norepinephrine reuptake inhibitors (SNRIs) can induce or worsen nightmares.³⁷ Thus, a careful clinical evaluation that explores potential trauma history and considers targeted treatments, such as IRT, is highly valuable.

Nearly two-thirds of the participants reported nocturia, consistent with reviews linking this symptom to depression.³⁸ In a Swedish study, men with nocturia displayed an odds ratio of 6.5 for MDD, while women exhibited an odds ratio of 2.8.³⁹ The effective management of nocturia requires a multidisciplinary approach to exclude organic causes.⁴⁰ Specifically, undiagnosed DM warrants consideration, given the metabolic risks associated with psychotropic medications, particularly second-generation antipsychotics.⁴¹

Although more than two-thirds (85.29%) of the participants demonstrated good sleep hygiene, as indicated by low SHI scores, certain problematic behaviors occurred at high rates. The item with the highest score concerned getting out of bed at different times each day, suggesting circadian rhythm disturbances among patients with MDs. This interpretation aligns with a UK cross-sectional study that found circadian disruption to be strongly associated with poor mental health and well-being, particularly in MDD and BD.⁴² In addition, high scores also emerged for engaging in activities that increase arousal before bedtime, as well as lying awake thinking, planning, or worrying in bed. These patterns may reflect undiagnosed insomnia and reinforce the need for targeted interventions such as CBT-I and interpersonal and social rhythm therapy (IPSRT), especially among patients with BD.⁴³

In the present study, univariate analyses associated higher SHI scores with energy drink consumption and smoking, but only smoking remained significant after adjustment. While caffeine is a well-established sleep disruptor,^44,45 the results highlight smoking as a more robust predictor, consistent with large-scale analyses linking nicotine intake to sleep fragmentation.⁴⁶ The sustained association with smoking as an independent predictor may reflect the rapid stimulant effects of nicotine, particularly when inhaled.⁴⁷ Clinically, this finding emphasizes the importance of integrating smoking-cessation counseling into sleep‑focused interventions for patients with MDs.

The univariate analysis revealed that married participants exhibited significantly better sleep hygiene than single participants, aligning with general population trends found in several studies.^48,49 However, this association did not persist in the multivariable model, implying that clinical variables—such as disease severity or comorbidities—may override the protective effects of marriage in this patient group. Future studies should explore whether relationship quality, rather than marital status alone, is a more accurate determinant of sleep health.

Regarding the GSES scores, chronic medical diseases emerged as a robust association with higher sleep effort. This finding aligns with evidence linking conditions such as chronic pain and diabetes to sleep disruption via physical discomfort and psychological distress.^50,51 Such findings hold significant relevance in the Saudi context, as the Saudi National Mental Health Survey reported that 34% of individuals with MDs had comorbid medical conditions, and 80% suffered from chronic pain.¹¹ Collectively, these results advocate for a holistic care model that integrates both physical and mental health.

The multivariate analysis revealed that younger adults (18–25) experienced significantly greater sleep effort than the 46–55 age group. Although this pattern diverges from general population trends that associate insomnia with advancing age,⁵² it may reflect lifestyle factors prevalent in younger demographics, such as irregular routines and electronic media use.⁵³ As a result, clinicians should remain attentive to sleep vulnerabilities in younger patients with MDs and consider evidence-based interventions such as CBT-I, which specifically targets maladaptive sleep-related thoughts and behaviors.²⁸

Notably, the univariate analysis linked unemployment and night-shift work to higher sleep effort, consistent with literature linking these factors to stress and circadian disruption.^54,55 However, these associations attenuated after adjustment, suggesting their effects may be mediated by more direct factors such as chronic medical conditions, which are often more prevalent in unemployed populations.⁵⁶ Regarding night-shift work, the lack of significance in the adjusted model likely reflects the small sample size of this subgroup, which limited statistical power. Although neither unemployment nor shift work qualified as an independent predictor in the final model, their initial associations remain clinically meaningful. These factors should be considered when evaluating sleep difficulties in patients with MDs, as they may still contribute to the overall burden of sleep disturbance.

The study also identified a significant association between IBS and sleep difficulties. Participants with MDs and comorbid IBS demonstrated both poorer sleep hygiene and greater sleep effort than those without IBS. This finding aligns with a growing body of literature emphasizing the bidirectional relationship between gastrointestinal health and sleep, mediated by the gut–brain axis.⁵⁷ Poor sleep can exacerbate IBS symptoms, such as abdominal pain and bloating, while the visceral discomfort and psychological distress of IBS can disrupt sleep continuity and quality.⁵⁸ In patients with MDs, this interplay can create a particularly burdensome cycle, further complicated by many psychotropic medications—particularly serotonergic agents such as SSRIs—that frequently cause gastrointestinal side effects that may mimic or exacerbate IBS symptoms.⁵⁹ Therefore, clinicians managing patients with MDs, especially depression, should routinely screen for gastrointestinal comorbidities such as IBS, given that individuals with depression face approximately 2.4 times higher risk of having IBS than those without depression.⁶⁰ Moreover, to interrupt this cycle of symptoms, a collaborative care approach incorporating gastroenterologists may be optimal, in addition to offering evidence-based psychological interventions, such as cognitive-behavioral therapy specifically tailored for IBS (CBT-IBS), which can help patients effectively manage their psychological distress and gastrointestinal symptoms.⁶¹

Strengths and Limitations

This study has several strengths. A primary strength lies in the rigorous definition of clinical stability detailed in the Methods section, which minimizes the confounding effects of acute mood episodes or relapses, thereby capturing enduring sleep traits rather than state-dependent symptoms. The relatively large sample size also provided sufficient statistical power to detect significant associations. Furthermore, the use of validated Arabic versions of the SHI and the GSES enhanced measurement reliability in the local context. Additionally, the study integrated behavioral, psychosocial, and medical-related factors in its examination of sleep disturbances. To our knowledge, this study represents the first in Saudi Arabia to specifically investigate a clinical sample of individuals with MDs for sleep hygiene and sleep effort.

Despite these strengths, several limitations must be acknowledged. First, the cross-sectional design precludes causal inferences regarding the relationships between the assessed variables and sleep outcomes. Future longitudinal studies are needed to establish temporality and causality. Second, convenience sampling from a single tertiary hospital restricts the generalizability of the findings. Multicenter studies employing stratified sampling are therefore needed to confirm the generalizability of these results. Third, reliance on self-reported data introduces risks of recall bias and social desirability bias. Moreover, the study did not systematically exclude primary sleep disorders, such as OSA or restless legs syndrome (RLS). Although this naturalistic approach allowed for a naturalistic assessment of sleep burden on individuals with MDs, it limited the ability to differentiate sleep disturbances intrinsic to MDs from those arising from comorbid primary sleep pathology. Accordingly, future Saudi research should aim to exclude primary sleep disorders to attribute sleep disturbances to MDs more accurately. In addition, future studies should incorporate objective sleep measures, such as actigraphy, where feasible, to corroborate the self-reported findings. Furthermore, although the study assessed multiple variables, other potential significant factors remained unmeasured, including psychotropic medication-related characteristics. Therefore, subsequent studies should account for psychotropic medications’ type, dosage, and timing to clarify their effect on sleep disturbances in patients with MDs, especially given the well-documented effects of many psychotropic medications on sleep.^59,62 Similarly, the present study did not examine the effect of the psychiatric illness duration or employ standardized symptom severity scales, as it targeted clinically stable patients (as detailed in the Methods section). Thus, future Saudi studies could address these gaps by incorporating structured diagnostic interviews and standardized mood-severity measures to better characterize clinical heterogeneity.

Conclusion

This study sought to characterize the prevalence and correlates of sleep disturbances among clinically stable adults with MDs in a tertiary care hospital in Riyadh, Saudi Arabia. Despite clinical stability, participants with MDs exhibited a significant burden of sleep disturbances, most predominantly insomnia, nightmares, and nocturia. Regarding poor sleep hygiene, cigarette smoking emerged as a primary modifiable factor, whereas increased sleep effort was robustly associated with younger age and chronic medical comorbidities. These findings indicate that sleep pathology in this population arises from multiple behavioral, physiological, and other determinants. Therefore, a comprehensive multidisciplinary multifaceted management approach is needed. This should include the prevention of modifiable contributing factors, routine screening for sleep disturbances using validated tools, early detection of sleep disturbances, managing physical and psychological comorbidities, and using targeted behavioral interventions.