Sleep disorders and associated factors among medical students in the Middle East and North Africa: a systematic review and meta-analysis

Sleep disturbances like poor and insufficient sleep are common among medical students in the Middle East and North Africa (MENA) countries; however, the extent of medically defined sleep disorders (SDs) remains unclear. This meta-analysis determines SD prevalence and identifies associated factors among medical students in the MENA. PubMed, Web of Science, Google Scholar, and reference lists of included studies were searched (latest search: June 2022). Meta-analyses included 22 studies and were performed using random-effect models. Included studies used self-reported screening tools for assessing SDs and then estimated the proportion of participants at high risk of developing a SD. Central disorders of hypersomnolence were the most prevalent SD [prevalencepooled range: 30.9% (Jordan) to 62.5% (Saudi Arabia)], followed by insomnia disorders [prevalencepooled range: 30.4% (Jordan) to 59.1% (Morocco)], circadian rhythm sleep–wake disorders [prevalencepooled range: 13.5% (Jordan) to 22.4% (Saudi Arabia)], sleep-related breathing disorders [prevalencepooled range: 12.2% (Jordan) to 22.5% (Pakistan)], sleep-related movement disorders [prevalencepooled range: 5.9% (Egypt) to 30.6% (Saudi Arabia)], and parasomnias [prevalencepooled range: 5.6% (Jordan) to 17.4% (Saudi Arabia)]. Female sex, studying in the latter academic years, having anxiety, excessive internet use, and poor academic performance were significantly associated with SDs. SDs are prevalent among MENA medical students. Implementing student-centered interventions targeting high risk groups in medical schools should be considered to improve students’ health and wellbeing.

Insufficient sleep affects up to one third of the global population 17 and has been declared a 'public health epidemic' 18 .Worldwide, medical students appear to be more affected by sleep disturbances (e.g.poor sleep quality, insufficient sleep duration, irregular sleep, and insomnia symptoms) than non-medical students 6,9,[19][20][21][22] or the general population 9,19,[23][24][25] , owing to the large academic load and assigned clinical duties 6,26 .Hence, we expect a high SD prevalence in this population.

Primary outcomes
The primary outcome of interest was the prevalence of any SD.We included any SD listed in the International Classification of Sleep Disorders, third edition (ICSD-3): the most widely used classification system and a key reference for the diagnosis of SDs using 'International Classification of Diseases, Ninth and Tenth Revision, Clinical Modification [1][2][3][4] .' SDs reported in the primary studies were categorized, as per the ICSD-3 recommendations, into seven categories that include: (1) insomnia disorders, (2) sleep-related breathing disorders, (3) central disorders of hypersomnolence, (4) circadian rhythm sleep-wake disorders (CRD), (5) parasomnias, (6) sleep-related movement disorders, and (7) other SDs (not fitting in the previous categories).Specific SDs included in each category as per ICSD-3 are presented in Table S3.SDs identified by clinical diagnosis, or any self-reported tools were included in our SR.Severity levels of SDs were included and categorized into mild, moderate, and severe as per the study and/or instrument definition.Cases of risk of SD were defined as those have abnormal scores (any level) according to the scoring system (cut-offs) recommended by the tool.If not reported, the prevalence of an SD was calculated based on crude data reported in the study.Studies with insufficient information to compute prevalence data of SDs, or those reporting only symptoms related to SDs, were excluded.

Secondary outcome
The secondary outcome of interest was the factors associated to a higher risk of SD.We synthesized any effect measure used to quantify a relationship between the factor and SDs reported in the included studies.Reported effect sizes included risk and mean differences, correlations, attributable proportion, risk ratios, relative risks, and odds ratios.

Population of interest
A study was eligible for inclusion if it included pre-medical or medical students enrolled in a medical school among the 20 MENA countries: Algeria, Bahrain, Djibouti, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Pakistan, Palestine, Qatar, Saudi Arabia, Sudan, Syria, Tunisia, the United Arab Emirates, and Yemen.The list of MENA countries in this study was based on that developed in a series of published SRs and meta-analyses characterizing the population health MENA [43][44][45][46][47][48][49][50][51] .Pre-medical and medical students mixed with other university students were included only if data specific to the population of interest was available.We excluded studies on medical science students (nursing, pharmacy, dentistry) unless specified as medical students studying for their Medical Degree (MD or MBBS).

Study design
Any observational study (e.g., cross-sectional study, case-control, or cohort) was included in the SR.Reviews, case reports, letters to editors, commentaries, and clinical trials were excluded.

Multi-stage screening
The Rayyan software (Rayyan Systems, Inc, Cambridge, MA, USA, https:// www.rayyan.ai/) was used for duplicate removal and multi-stage screening.Two independent reviewers conducted the title and abstract screening, full-text screening, and data extraction.Discrepancies were resolved with a third reviewer to achieve consensus on study inclusion and data extraction.
For inclusion in the systematic review (SR), a study should correspond to the PICOTS framework criteria 52 population, outcome, study design, time of the study, and setting (control and intervention criteria were not

Data extraction
Data was extracted from the included primary studies for the following variables: (1) study characteristics (e.g.study design, sampling method, data collection period, sample size, (2) setting (3) medical student characteristics including age, sex, year of study, and socio-economic status, (4) prevalence of SDs, including the instrument used to diagnose an SD and its related characteristics, (6) factors for which a difference and/or an association with the risk of having an SD was assessed.

Quality assessment
The risk of bias (RoB) and methodological quality of included studies were appraised independently by two reviewers using a validated RoB tool for prevalence studies 53 .Briefly, the RoB tool uses an items scale to assess: (1) the external validity of the study, based on selection and nonresponse biases, and (2) the internal validity of the study, based on measurement bias and bias related to the analysis.No summary quality score was computed as per COSMOS-E guidance, which provides guidance on conducting SRs of observational studies of aetiology 54 .Each included study was assigned a low or high ROB for each assessment item.A synthesis of studies' quality was based on a summary of low and high risk of bias assessment of each quality domain.
Reporting bias due to missing data was discussed.Discussion on the validity and reliability of our estimates was also performed to assess the confidence in the body of evidence presented in the SR.The certainty assessment method was based on the Grading of Recommendations, The Assessment, Development, and Evaluation (GRADE) approach.The GRADE approach used in our study considers the RoB and reporting biases in a body of evidence, precision of the meta-analysis effect estimates, the consistency of the primary study results, and how directly the body of evidence answers the research question 55 .

Synthesis
A meta-analysis of the prevalence of SD categories (proportion of participants at a high risk of any ICDS category of SD) (Supplementary Table S3) was conducted using the DerSimonian-Laird random-effects model 56 .Random effects model with the logit transformation of the proportion was used to conduct the meta-analyses pooling prevalence measures and their 95% confidence intervals (95% CI).The Freeman-Tukey double arcsine transformation was used in the analyses involving the pooling of proportions, using the command sm = "PFT" in R 57 .Clopper-Pearson confidence intervals were computed for individual prevalence measures.The minimum study sample size required for a study to be included in the meta-analysis was 25 58 .
Subgroup meta-analyses were conducted by sex (males and females), academic training period (preclinical, clinical, and late clinical), and MENA country.For each category of SDs, prevalence data were pooled by SD severity level (mild, moderate, and severe).Prevalence data on mixed disorder levels, mixed sex, and/or mixed training periods were pooled in a separate group.If not reported, the prevalence of an SD was calculated based on crude data reported in the study.Sensitivity analyses were conducted to assess the impact of SD prevalence during the COVID-19 pandemic lockdown on the pooled estimates when applicable.Factors associated with SDs among MENA medical students (secondary outcome) were synthesized as reported in the included studies.
SD prevalence measures stratified by sex, disorder severity level, SDs category, and academic period were prioritized for inclusion in the meta-analysis rather than the overall measures on the entire study population or any SD.Prevalence measures reported for each academic year were combined and classified into preclinical, clinical, and late clinical training periods, according to the medical school curriculum followed by the country.Multiple SDs under the same ICDS category reporting on the same study population were merged prior to the inclusion in the meta-analysis to ensure independency of observations.The heterogeneity between studies was assessed using the I 2 statistic 59 and Cochran's Q between-subgroups statistic 60 .Heterogeneity between studies was considered as substantial when I 2 > 50% 61 .The Cochran's Q between-subgroups statistic was used to test for differences between prevalence estimates across subgroups 60 , and statistical significance was considered at p value ≤ 0.05.Univariate random-effects meta-regression was used to estimate odds ratios (ORs) and corresponding 95% confidence interval (CI)s measuring the magnitude of relative changes in the pooled SDs prevalence according to study-level factors 62 .
To further explore heterogeneity between studies, univariate random-effects meta-regression was conducted to evaluate potential associations between SD prevalence and measurable study-level factors, including sampling method, sample size, instrument, and study response rate.Meta regression was used to estimate odds ratios (OR) and corresponding 95% CIs to measure the magnitude of relative changes in the pooled SD prevalence according to the study-level factors 62 .
For the meta regression analyses, all SDs were considered grouped to increase the statistical power.Both meta-analyses and meta-regressions analyses were conducted using RStudio software (version 2022.07.1 Build 554).
Methodological quality of the included studies was appraised using the risk of bias (RoB) tool for prevalence studies 53 .Reporting bias due to missing data was also discussed.Discussion on the validity and reliability of our estimates was also performed to assess the confidence in the body of evidence presented in the SR.
Publication bias was assessed using the Doi plot, a method that allows better visual representation of asymmetry as compared to the conventional funnel plot 63,64 .In the Doi plot, the effect estimate (X-axis) is plotted against the percentiles converted to a normal quantile (Z-score) for each study (Y-axis).The prevalence of SDs was transformed to the log odds scale for better statistical properties for the meta-analysis 64 .We also estimated LFK index to detect and quantify symmetry of study effects in the Doi plot.A LFK index of zero indicates a

Pooled estimates of sleep disorders
Insomnia disorders A total of 54 prevalence measures classified under insomnia disorders, including sleep state misperception (paradoxical insomnia), were retrieved from 18 studies conducted in Jordan, Morocco, Pakistan, and Saudi Arabia (Table 1).Following data merging and classification, 30 prevalence measures on any type of insomnia disorder were included in the meta-analysis.Most of the included data (24/30 prevalence measures) were computed from any severity level of insomnia.
The pooled prevalence of insomnia disorders ranged between 30.4% in Jordan and 59.1% in Morocco.A total of three studies [67][68][69] were conducted during the COVID-19 pandemic lockdown in Saudi Arabia 67,68 and Morocco 69 .In Saudi Arabia, pooled prevalence computed with and without data during the COVID-19 pandemic lockdown were similar, at 45.9%; 95% CI: 30.2-62.1 and 50.0%; 95% CI: 29.4-70.6 respectively.The pooled prevalence of insomnia disorders, in Saudi Arabia and Pakistan, following the exclusion of the study not reporting the used tool was 42.1% 95% CI: 27.1-57.9and 36% 95% CI: 23.1-50.0,respectively.All prevalence data identified for Morocco was measured during the COVID-19 pandemic lockdown.
No statistically significant difference in the prevalence of insomnia disorders was identified between MENA countries.In these countries, mild-level of insomnia was reported by 38.4% of medical students, followed by moderate-(22.2%)and severe-levels of insomnia (11.6%) (Table 2).Insomnia disorders were significantly  Although the highest pooled prevalence of insomnia disorders was observed during the late clinical training period (year 7 or more), no statistically significant difference was found between the academic training periods.Reported factors significantly associated with an increased odds of having insomnia disorders were being a female student 69 , clinical or late clinical years 69 , and use of internet for more than 12 h daily 70 (Table 3).The reported findings also highlighted the significant negative impact of insomnia [70][71][72][73] and sleep state misperception disorder 71 (known also as paradoxical insomnia 74 ) on academic performance.Reported data suggests that the impact of anxiety on the risk of insomnia depends on the anxiety severity level 70,75 .

Sleep-related breathing disorders
A total of 7 prevalence measures classified under sleep-related breathing disorders, including obstructive sleep apnoea (OSA) disorders, were retrieved from 5 studies conducted in Jordan, Pakistan, and Saudi Arabia (Table 1).Following data merging and classification, 6 prevalence measures on any type of sleep-related breathing disorders were included in the meta-analysis.
The pooled prevalence of sleep-related breathing disorders ranged between 12.2% in Jordan and 22.5% in Pakistan (Table 2).Significant differences in the pooled prevalence of these disorders were identified between Jordan, Pakistan, and Saudi Arabia.Only one study 67 from Saudi Arabia reported a prevalence of Obstructive Sleep Apnea (OSA) during the COVID-19 pandemic lockdown.Statistically significant difference in the pooled prevalence of sleep-related breathing disorders was also found between sexes (p value = 0.002); however, this observation was based on a limited number of data points.Reported factors significantly associated with an increased odds of having OSA were being a male student 70,71 , increased age 70 , progression through the academic years 76 , use of the internet for more than 4-8 h daily 70 , and mild and extremely severe anxiety 70 (Table 3).Moderate stress was significantly associated with lower odds of having OSA when compared with no stress 70 .

Central disorders of hypersomnolence
A total of 8 prevalence measures classified under central disorders of hypersomnolence were retrieved from 3 studies, including hypersomnia and narcolepsy disorders, conducted in Jordan and Saudi Arabia (Table 1).
The pooled prevalence of central disorders of hypersomnolence ranged between 30.9% in Jordan and 62.5% in Saudi Arabia (Table 2).Significant differences in the prevalence of central disorders of hypersomnolence were found between these two countries.Only one study reported prevalence data of central disorders of hypersomnolence by sex 71 , and another one 67 during the COVID-19 pandemic lockdown in Saudi Arabia.Reported risk of having narcolepsy was significantly associated with an increased odd of having poor academic performance 71 (p value = 0.045) (Table 3).No significant differences were reported for the prevalence of narcolepsy and hypersomnia between males and females 71 .Perceived frequently their academic performance affected by insomnia versus do not perceive at all their academic performance affected by insomnia 70 OR adjusted a : 2.16 95% CI: 0.71-6.60 "Perceived sometimes their academic performance affected by insomnia" versus "do not perceive at all their academic performance affected by insomnia 70 OR adjusted a : 1.  1).Following data merging and classification, 5 prevalence measures on any type of CRD disorder were included in the meta-analysis.A significant difference in the pooled prevalence of CRD was found between the two countries with available data: Jordan (13.5%) and Saudi Arabia (22.4%) (Table 2).Only one study reported prevalence data of CRD by sex 71 .and another one 67 during the COVID-19 pandemic lockdown in Saudi Arabia.Reported risk of having CRD was associated with an increased odds of having poor academic performance in two studies 71,72 (Table 3).No significant difference was reported in the prevalence of CRD between males and females (p value = 0.162) 71 .

Parasomnias
A total of 8 prevalence measures classified under parasomnias disorders, including nightmares and sleep walking, were retrieved from 2 studies conducted in Jordan and Saudi Arabia (Table 1).Following data merging and classification, 3 prevalence measures on any type of parasomnias disorder were included in the meta-analysis.
The pooled prevalence of parasomnia disorders ranged between 5.6% in Jordan and 17.4% in Saudi Arabia (Table 2).A significant difference in the prevalence of parasomnia disorders was found between Jordan and Saudi Arabia.Only one study reported prevalence data of parasomnias segregated by sex 71 , and another one 67 during the COVID-19 pandemic lockdown in Saudi Arabia.
No significant difference was reported in the prevalence of sleep walking (p-value = 0.090) between males and females 71 (Table 3).The reported factor associated with an increased odds of having nightmares was being a female medical student (p value = 0.022) 71 .

Sleep-related movement disorders
A total of 14 prevalence measures classified under sleep-related movement disorders were retrieved from 6 studies conducted in Jordan, Egypt, Pakistan, and Saudi Arabia (Table 1).
Following data merging and classification, 9 prevalence measures on any type of sleep-related movement disorder were included in the meta-analysis.
The pooled prevalence of sleep-related movement disorders ranged between 5.9% in Egypt and 30.6% in Saudi Arabia (Table 2).Only one study 67 reported a prevalence of restless leg syndrome (RLS) during the COVID-19 pandemic lockdown in Saudi Arabia.Significant differences in the pooled prevalence of sleep-related movement disorders were found between Egypt, Jordan, Saudi Arabia, and Pakistan.5.1% of medical students had a moderate-level of sleep-related movement disorders and 3.0% had a mild-level.No statistically significant difference in sleep-related movement disorders was found between males and females.The reported factor associated with decreased odds of having periodic limb movement disorder/RLS was being a male medical student 71 (Table 3).

Undefined sleep disorder
Only one study reported a prevalence measure of any SD (without type specific SD prevalence), which was 9.5% among a male and female medical student during mixed training periods in Saudi Arabia (Table 1).

Heterogeneity
Between-study heterogeneity was relatively high, and differences between prevalence estimates across subgroups were significant between sex groups, academic training periods, and countries for the majority of SDs (Table 2).Meta-regression analyses revealed that prevalence measures retrieved from studies that did not report the SD measurement tool (n = 2) were significantly higher compared with studies that used a validated tool (n = 47).Although not statistically significant, prevalence measures assessed using non-validated tools seem to provide lower prevalence measures as compared to those using validated tools (Table 4).Although not statistically significant, SD prevalence measures based on 'non-probability sampling' , 'sampling method not reported' , 'sample size £ 100' , and 'response rates 3 75%' were associated with higher SD prevalence measures when compared to 'probability sampling' , 'sampling method reported' , 'sample size > 100' , and 'response rates < 75%' , respectively.

Study-level quality assessment
Overall, most included primary studies properly reported the information required to allow quality assessment and were of good methodological quality (low RoB) (Supplementary Table S4).Most of the included studies (86%, 19/22) had a low likelihood of nonresponse bias, collected data directly from the targeted population (91%, 20/22), used an acceptable case definition (82%, 18/22), and used a validated instrument to measure SD (77%, 17/22).All included studies used the same mode of data collection for comparison groups.However, only 32% (7/22) of the included studies used a random-sampling method.A total of 20 studies out of 22 (91%) had a high RoB related to the representativeness of the national and target populations.Most of the included studies (73%, 16/ 22) used an appropriate tool to identify cases with a high risk of SDs.Only one study had a high RoB with 'including appropriate numerators and denominators for the studied SDs' .Overall, the included studies had good internal validity and moderate external validity that could limit the generalizability of the results.

Reporting bias and certainty assessment
Our synthesis was likely impacted by the limited number of primary studies retrieved in some specific SDs categories and MENA countries, which may have consequently limited the representativeness of our pooled prevalence estimates.Most of the primary studies had a low risk of non-response bias, however, they had a high risk of selection bias, which could impact their external validity.Additionally, pooled SD prevalence estimates were Vol:.(1234567890 to middle-of-night awakenings or daytime impairments are not captured by sleep quality tools and are required to fulfill the criteria for insomnia disorders 92,93 .
Our meta-analysis demonstrated that insomnia disorders and sleep-related breathing disorders were significantly more prevalent in female medical students as compared to male students.Sex-related differences were not identified for other SDs.The synthesis of reported factors associated with SDs suggested that the female sex was associated with the occurrence of insomnia disorders, nightmares, periodic limb movement disorder, and RLS; and the male sex was associated with the occurrence of OSA.The higher vulnerability of women to insomnia 5,94-97 and RLS 5,98,99 as compared to men is consistent with previous findings in the general population, suggesting the need to target female students when planning interventions.
Differences in SD prevalence by academic training period or disorder severity level assessed in our metaanalyses could not be established given the limited data; however, included studies suggested that late academic years (clinical and late clinical years) were associated with insomnia and OSA.Medical education in general and the clinical years specifically have been identified as causative factors for poor sleep quality worldwide 6,100 .Our findings highlighted the significant negative impact of insomnia disorders, sleep state misperception, narcolepsy, www.nature.com/scientificreports/and CRD disorders on academic performance in medical school.Low academic performance among medical and university students has been correlated with poor sleep quality 20,101 .Additional studies are required to assess the impact of SDs on the academic performance of MENA medical students.Interventions involving sleep-education 24,102 , monitoring cognitive behavior 24,102 , and mindfulness relaxation 24,102 during clinical training years can help medical students to improve their sleep.Student well-being services can support students in managing disturbed sleep and its consequences.Additionally, incorporating sleep education into the curriculum has been recommended to address medical students' poor knowledge and misconceptions about sleep practices and disorders 6,103,104 and prevent misdiagnosis and maltreatment of SDs 105 .Our synthesis of reported factors associated with SDs suggested that excessive daily internet use was associated with the occurrence of insomnia disorders and OSA.A significant increase in sleep problems and a reduction in sleep duration were found among individuals addicted to the internet 106 , suggesting a potential association with SDs.Additionally, our synthesis suggested that the negative impact of anxiety on the risk of insomnia disorders and OSA depends on the severity level of anxiety.Both insomnia disorders 77,94,[107][108][109][110][111][112] and OSA [113][114][115] are associated with an increased risk of depression and anxiety in adults and adolescents, and it seems that this relationship is bi-directional 116 .As anxiety and depressive symptoms are relatively common in medical students 117,118 , they probably contribute to the increased prevalence of insomnia disorders and OSA in this population.Consequently, interventions designed to prevent or address SDs are also likely to positively impact mental health disorders holistically.
To our knowledge, this is the first SR and meta-analysis focusing on SDs rather than sleep disturbances in MENA medical students.The majority of included studies were of good methodological quality, which reinforces the validity of our findings.A minor impact of the two studies not reporting the tool for measuring SD prevalence on the pooled prevalence is expected given that 85% of the included studies used a validated tool for assessing SDs.There was no evidence for the impact of other study characteristics on the SD prevalence (Table 4).All included studies assessed SDs using self-reported questionnaires, which could be subject to recall bias.Also, all included studies have used screening tools for assessing SDs.Therefore, the proportion of medical students meeting the clinical diagnosis criteria is expected to be lower than the estimated proportion of students with SDs considering screening criteria only 78 .While an objective clinical diagnosis of SDs is generally more reliable, self-reported screening questionnaires are utilized not only in research but also in clinical practice because of their administration efficiency and low cost 119 .Most studies included in this review were comprised of rather small sample sizes or limited generalizability.Studies with larger samples and geographical coverage are required to confirm our results.Our findings may not be generalizable to all MENA countries given the limited number of countries with available data.Although a publication bias related to the available data on insomnias disorders and sleep-related breathing disorders has been detected, we are confident that the prevalence of these SDs are within the prediction interval.Despite these limitations and the existence of heterogeneity, several subgroup analyses of SD prevalence assessed using validated tools were conducted.Therefore, these limitations do not affect the interpretation of our findings.
As most of the included studies were cross-sectional, temporal sequencing of SD development and potential associated factors cannot be established, which limits conclusions on potential causal associations.However, this synthesis can be used to generate hypotheses and support future study design to assess the risk factors and consequences of SDs in medical students.

Conclusion
SDs with the highest prevalence among medical students in MENA were central disorders of hypersomnolence, insomnia disorders, and CRD disorders.Female sex, latter academic years, anxiety, and excessive internet use were associated with the occurrence of several SDs.SDs negatively impact students' academic performance.Implementing public health and clinical interventions in medical school settings, particularly targeting high-risk groups (i.e., female students and students in late academic years), should be given serious consideration to help improve students' overall health, wellbeing, and quality of life.

Figure 2 .
Figure 2. Publication bias assessed via the Doi plot using the prevalence of each sleep disorder as an effect estimate (ES) and the LFK (Luis Furuya-Kanamori) index.In the Doi plot, the dots representing individual prevalence measures extracted from each study on each outcome (sleep disorders).

Table 2 .
Meta-analysis of sleep disorders prevalence among medical students in MENA countries.NR not reported, NA not applicable.Weighted average prevalence measures were obtained using random-effect model.p-value ≤ 0.05 was considered statistically significant.The total number of prevalence measures in each category of sleep disorders (SDs) may vary according to the subgroup analysis.*Severity disorder levels were considered as classified by the individual studies and are mutually exclusive.For disorder severity levels, the 'level not specified' category includes SD prevalence measures reported without any specification of the disorder severity level from the primary studies.Pooled prevalence measures by sex consider combined disorder severity levels, academic training periods, and countries for a specific SDs.Pooled prevalence measures by academic training period consider combined disorder severity levels, sexes, and countries for a specific SDs.Pooled prevalence measures by MENA countries consider combined SDs severity levels, sexes, and academic training periods.For sex variable, 'M/F not separated' includes studies where SD prevalence was not provided for males and females separately.Vol:.(1234567890)Scientific Reports | (2024) 14:4656 | https://doi.org/10.1038/s41598-024-53818-2www.nature.com/scientificreports/

Table 3 .
Synthesis of reported factors associated with the risk of sleep disorders among MENA medical students.Significant results are highlighted in bold (p value ≤ 0.05).GPA grade point average, OR odd ratio, NR not reported, BMI body max index, RLS restless leg syndrome.aAdjustment unknown; b Model included age, gender, education, stress, anxiety, use of internet, and academic performance; c Adjusted for sex and obesity; d The second group listed refers to the reference group when applicable. e The studies assessed the factor associated with SDs combining obstructive sleep apnea (OSA), Insomnia, Narcolepsy, Restless leg syndrome/ Periodic limb movement disorder (RLS/PLMD), Circadian rhythm disorder (CRD), Sleep walking, and Nightmares.Circadian rhythm sleep-wake disorders (CRD) A total of 5 prevalence measures classified under CRD disorders were retrieved from 3 studies conducted in Jordan and Saudi Arabia (Table