1 Introduction

We spend on average more than a third of our lives sleeping, this shows that sleep represents one of the vital and primordial functions of the human body, with the same importance as breathing, immunity, and digestion [1]. It is considered among the essential dimensions for the physical and mental recovery of the human being [1].

However, chronic poor-quality sleep has serious consequences on health, particularly an increased risk of cardiovascular diseases (hypertension and hypercholesterolemia), diabetes, obesity, weakened immune defenses, impaired cognitive functions, and vigilance [2].

It is well known that there is a variety of sleep disorders (obstructive sleep apnea, narcolepsy, restless legs syndrome, and circadian rhythm disorders) that generally affect the quality and quantity of sleep, leading to excessive daytime sleepiness (EDS) [3]. In addition to these anomalies, post-night shift work can also be one of the main causes of EDS, as it is often associated with a disruption of the biologic clock and chronic deprivation of nighttime sleep [4].

EDS is the tendency to fall asleep in inappropriate situations. The prevalence of EDS varies considerably from 1.5% in some populations to over 40% in others [4, 5]. This daily unwanted desire to sleep negatively impacts people’s social and professional well-being by impairing their cognitive and psychosocial functioning [6].

Sleepiness can be physiological when it happens while watching TV in the evening around 9 p.m. under the influence of the homeostatic sleep process. Nevertheless, a daily and habitual tendency to fall asleep several times during the day, especially in inappropriate situations like driving is considered pathological [5].

EDS is a key symptom of different sleep disturbances, including obstructive sleep apnea syndrome (OSA), insomnia, narcolepsy, and idiopathic hypersomnia. This strong clinical association makes its assessment indispensable [6].

The complaint of sleepiness can be assessed using objective and subjective measures, based on the time it takes to fall asleep [7].

Objective methods include three tests: The first is the Iterative Sleep Latency Test (ISLT) measures how quickly a person falls asleep in a sleep-conducive environment [3]. The second is the Maintenance of Wakefulness Test (MWT) used to assess an individual’s ability to stay awake in a stimulating environment. It helps to determine the impact of daytime sleepiness on daily life, such as performing long drives or other tasks without falling asleep or feeling too tired [3]. This test is important to evaluate the level of alertness and productivity of an individual throughout the day [3]. The third one is polysomnography, which is the gold standard for studying the architecture of sleep and breathing at the same time [5].

Despite their usefulness, these sleep tests are generally expensive and require specialized centers with qualified staff [5]. Moreover, they only provide information about daytime sleepiness on the day the test is performed. Therefore, a simple method that covers an extended period is necessary [5].

Subjective measures are based mainly on the use of sleep schedules and self-reported sleepiness questionnaires [8]. The Epworth Sleepiness Scale (ESS) is the most popular subjective measure as it is simple to use, and accurately quantifies daytime sleepiness [8].

The ESS assesses the likelihood of dozing off in everyday situations based on recent lifestyle habits through eight questions. Each question is scored from 0 to 3 (0: no risk of sleepiness, 1: low risk of sleepiness, 2: moderate risk of sleepiness, 3: high risk of sleepiness). At the end, the points are tallied and a score ranging from 0 to 24 is obtained. A score of 10 or more indicates daytime sleepiness [9].

EDS can have serious consequences, especially in jobs that require alertness like driving [10]. Studies have shown that individuals who experience daytime sleepiness are two and a half times more likely to have an accident than those who do not [11].

What makes the situation quite dangerous is that drowsy people often underestimate their level of sleepiness, and may even feel ashamed to admit their discomfort. This behavior can be conscious, as they fear losing their job, especially if they are professional drivers. In some cases, people may unconsciously forget their normal state and become accustomed to their chronic sleepiness [12].

In Morocco, you only have to travel on public transport to see how many people fall asleep quickly just between two stations. This shows that many passengers are drowsy without even realizing it.

To the best of our knowledge, no study has been conducted to assess daytime sleepiness in Marrakech city. Therefore, we decided to carry out this survey to determine the prevalence of EDS and identify its associated factors.

2 Methods

2.1 Ethical Statement

Ethical approval was obtained from the Ethics Committee of the University Hospital of Marrakech, which approved the study before being carried out (n° 50/2022). Participants were asked for written consent and informed about the study’s purpose and procedures before participating voluntarily. The research was conducted according to ethical principles set out in the Declaration of Helsinki, ensuring the anonymity and confidentiality of all information gathered.

2.2 Type of Study

This cross-sectional study aims to estimate the prevalence of EDS in Marrakech’s adult population attending public health centers and identify its associated factors. The survey was conducted during the period from April 10 to September 10, 2022.

2.3 Target Population

The study recruited 757 participants from general OPDs in 21 public health centers of Marrakech city, according to pre-established inclusion and exclusion criteria.

The survey included mentally stable adult individuals of both genders, aged 18 years and older, who voluntarily agreed to take part in this investigation. Nevertheless, the study excluded subjects with known EDS, pregnant women, people with severe linguistic and auditory communication problems, individuals suffering from neuropsychiatric disorders, AND/OR those undergoing treatment with neuroleptics due to the drowsy effect they induce.

2.4 Data Collection Method

The data collection tool was a hetero-administered questionnaire composed of five parts:

The first part includes questions regarding the sociodemographic and economic characteristics of the participants: (age, gender, origin, family situation, number of children, level of education, occupation, medical cover, and monthly income).

The second part assesses the health condition, in particular: pulmonary comorbidities (allergic rhinitis, asthma, COPD), cardiac comorbidities (hypertension, hypercholesterolemia, heart failure), ENT comorbidities (facial malformations: mandibular retrognathia, maxillary endognathy), and metabolic comorbidities (diabetes, hyperthyroidism). These medical histories were collected through self-reporting by the respondents.

The third part focuses on lifestyle habits and addictive behaviors, including physical activity, tobacco and alcohol consumption, cannabis use, and medication intake.

The fourth section examines various anthropometric measurements, such as weight, height, body mass index (BMI), neck circumference, and abdominal perimeter.

*BMI is calculated by dividing weight (in kg) by height (in meters) squared (kg/m2). The World Health Organization (WHO) classifies BMI as follows: Normal (19 ≤ BMI < 24.9); Overweight (25 ≤ BMI < 29.9); Obese (BMI ≥ 30) [13].

*Abdominal circumference (AC) is the measurement of the smallest circumference between the waist and hips using a flexible tape graduated in centimeters. According to the International Diabetes Federation, a healthy waist circumference should be less than 94 cm for men and less than 80 cm for women [14].

*Neck circumference (NC) is measured using a flexible tape graduated in centimeters. The reference point is the mid-distance between the sternal fork and the mandible. Values are considered abnormal for an NC ≥ 41 cm for women and NC ≥ 43 cm for men [15].

The fifth part of the questionnaire assesses EDS through the Epworth scale. This scale provides a subjective assessment of EDS, and a score ≥ 10 points indicates that the person is experiencing EDS. In addition, this section includes a question about whether the individual has been in any traffic accidents in the past 12 months.

The questionnaire of this study was tested and validated with 20 individuals before starting the survey to make sure that all questions were unambiguous. Furthermore, our research team had previously validated the Arabic version of this questionnaire to ensure its suitability for the Moroccan Arabic-speaking population.

2.5 Statistical Analysis

All the data obtained was entered and analyzed using SPSS software version 25.0.

We start with a descriptive analysis. Quantitative variables were presented as mean ± standard deviation, while qualitative variables were expressed in the form of rates. We then proceeded with a bivariate analysis, using Pearson’s Chi-square test to compare qualitative variables and Student’s t test to compare quantitative variables.

Afterward, a multivariate analysis was conducted using a binary logistic regression model that contained all the relevant variables associated with the binary dependent variable. The final model only retained the significant variables (threshold < 5%) with their adjusted odds ratios and their corresponding confidence intervals.

For all statistical tests, the association was considered significant for values of p < 0.05.

3 Results

3.1 Sociodemographic and Anthropometric Characteristics

The prevalence (95% confidence interval) of EDS in our sample of 757 people according to the Epworth scale was 37.5% [34.05%–40.9%]. The sample was predominantly female, with women making up 63.0% and men 37.0%, giving a male-to-female sex ratio of 0.57.

The results analysis indicated that the subjects experiencing somnolence were 5 years older than those who were not experiencing it. In addition, 34.9% of somnolent were illiterate, which highlights a significant association between the level of education and EDS (p = 0.029) (Table 1).

Table 1 Comparison of sociodemographic, economic, and anthropometric characteristics between somnolent and non-somnolent subjects
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However, the data collected showed that neither gender (p = 0.29) nor monthly income level (p = 0.51) were significantly associated with EDS (Table 1).

The study analyzed the anthropometric characteristics of the participants and found that there is a statistically significant difference in abdominal circumference (p = 0.003) and neck circumference (p = 0.001) between the somnolent and non-somnolent people (Table 1). Furthermore, the results also showed a significant association between EDS and body mass index (BMI) (p = 0.014) (Table 1).

3.2 Health Characteristics and Individual Lifestyle Habits

The analysis of the clinical characteristics of the participants identified significant associations between sleepiness and several comorbidities. Notably, high-blood pressure (p = 0.001), hyperlipidemia (p = 0.001), asthma (p = 0.003), diabetes (p = 0.001), and hyperthyroidism (p = 0.001). In addition, a history of COVID-19 infection was found to be associated with EDS (p = 0.001) (Table 2).

Table 2 Comparison of clinical characteristics and individual lifestyle habits between somnolent and non-somnolent subjects
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The results also revealed that, in terms of the addictive habits of respondents, somnolent individuals consume more tobacco (23.2% vs 16.9%), alcohol (12.7% vs 4.9%), and cannabis (12.3% vs 7.4%) compared to non-somnolent subjects (Table 2). Furthermore, the collected data has highlighted a significant relationship between the use of antihypertensive medications and daytime sleepiness (p = 0.001) (Table 2). Moreover, it was observed that people experiencing somnolence were more likely to have traffic accidents than those who were not (Table 2).

3.3 Risk Factors Associated with Excessive Daytime Sleepiness (EDS)

The bivariate analysis revealed several factors associated with EDS. Therefore, logistic regression was performed, which identified four independent determinants associated with EDS: high-blood pressure, history of COVID-19 infection, alcohol consumption, and arthrosis (Table 3).

Table 3 Factors independently associated with daytime sleepiness according to the logistic regression model
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4 Discussion

The tendency to sleep during the day, when the intention is to stay awake, is one of the most frequent complaints presented to sleep specialists [16]. Upon comparing the prevalence of EDS in different populations, we found that our observed prevalence is similar to that reported in several studies [17,18,19]. A study conducted in Morocco on 300 workers revealed that EDS was present in 30% of the sample [17]. Another research carried out in Algeria among 538 dentistry students found that 32.2% of participants experienced daytime sleepiness [18]. Other investigators have shown that the estimated prevalence of EDS in the general population is between 5 and 35% [20]. In addition, a survey conducted on 410 bus drivers in Hong Kong found that 40% of drivers were somnolent [19].

It is well known that sleep is influenced by complex interactions of personal physiological characteristics, socio-environmental factors, and lifestyle habits, in particular, the consumption of certain substances such as alcohol [1]. Alcohol consumption was identified as a significant factor in both the bivariate analysis and the logistic regression, indicating that the use of alcoholic products is a major contributor to EDS. A survey of 30,097 adults in Canada found that individuals who consumed alcohol reported experiencing insomnia and dissatisfaction with their sleep, consequently leading to daytime sleepiness the following day [1]. Other researchers have also confirmed that alcohol consumption slows reflexes, causes daytime sleepiness, impairs concentration, alters mood, and increases aggression [21].

Regarding tobacco use, Phillips et al. showed in their study conducted on 484 subjects, that smokers had difficulty falling asleep and maintaining sleep, as well as the appearance of daytime sleepiness [22]. However, the results of this study seem to be surprising for other investigators who claim that nicotine increases alertness due to its cerebral stimulating effect [23]. Our study found a significant association between smoking and EDS in the bivariate analysis. Nevertheless, smoking did not contribute significantly to the final regression model and was therefore removed.

Moreover, obesity has been recognized as a risk factor for sleepiness in various scientific studies, including one conducted by Johns and Hocking [24]. In our investigation, the bivariate analysis revealed that somnolent people were more obese than non-somnolent. However, the absence of this variable in the logistic regression suggests that there may be other factors that interact with it.

Furthermore, the emergence of osteoarthritis in the multivariate analysis indicates its contribution to the aggravation of EDS. In a survey conducted on 66 subjects to assess the impact of osteoarthritis on sleep quality, 33% of participants reported experiencing EDS according to the Epworth scale. This disruption in sleep quality can be attributed to the nighttime pain caused by this condition [23].

Studies carried out over the last few years have proclaimed the negative impact of EDS on driving [11, 25]. Researchers have announced that between 10 and 20% of freeway accidents are caused by sleepiness while driving [25]. Our investigation’s findings confirm these results.

In addition, it has been proven in scientific research that poor sleep quality harms blood pressure [26, 27]. A survey conducted on 74 hypertensive people to assess sleep disorders related to high-blood pressure revealed that those who slept poorly had higher systolic and diastolic blood pressure values [26]. Another study showed that hypertension was associated with factors that lead to sleep fragmentation and cause EDS, such as snoring, nocturnal choking, insomnia, and nocturia [28]. According to our multivariate analysis, hypertension increases the risk of SED by 3.88 times.

In addition, many people who have contracted coronavirus (COVID-19) still suffer from serious neurocognitive after-effects for several months or even years due to the profound sleep disturbances that took place during the pandemic [29]. Some studies suggest that sleep disorders can continue to affect some people for up to 9 to 12 months in 6% to 8.9% of cases. The frequency of such disorders depends on the specific type of variant [29, 30]. However, there is not enough scientific evidence to establish a direct relationship between a history of COVID-19 infection and EDS. Since the pandemic is still recent, more research is needed to understand the long-term effects of the disease. Our analysis indicates that the persistence of this factor in the multivariate analysis shows its significant contribution to the onset of EDS.

Although the findings of this study provide valuable insights into excessive daytime sleepiness, it is important to acknowledge its limitations. The assessment of EDS was done subjectively using the Epworth scale, without polysomnographic recording. However, our study was conducted using a validated questionnaire in a homogeneous sample that accurately represents our target population.

5 Conclusion

EDS in the Moroccan context is a significant concern that has not been well studied. The purpose of this study was to estimate its prevalence and bring attention to this health issue among public health authorities. Preventive measures should be taken to reduce the morbidity and mortality associated with this condition, especially in the driving field.