Screening and management of obstructive sleep apnea (OSA) and daytime sleepiness in professional drivers in Tunisia: A protocol study using machine learning

Screening and management of obstructive sleep apnea (OSA) and daytime sleepiness in professional drivers in Tunisia: A protocol study using machine learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Study protocol Screening and management of obstructive sleep apnea (OSA) and daytime sleepiness in professional drivers in Tunisia: A protocol study using machine learning Sameh MSAAD, Rahma GARGOURI, Nesrine KAMMOUN, Rim KHEMEKHEM, Narjes ABID, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4697740/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Professional drivers have high rates of Obstructive Sleep Apnea (OSA). This study aims to determine the prevalence of OSA and Excessive Daytime Sleepiness (EDS) and identify their risk factors among a large representative sample of professional drivers in Tunisia. We will also evaluate the risk of accidents associated with OSA and EDS before and after treatment. Methods This will be a population-based and prospective study about 3000 professional drivers. Participants will receive a structured questionnaire to evaluate five main outcomes: the likelihood of OSA, EDS, drowsy driving, related sleepiness near misses and accidents, as well as work productivity. Validated self-report measures will be used to evaluate these outcomes. Participants suspected of having OSA and/or EDS will undergo sleep laboratory investigations, including sleep study. Participants who have moderate-to-severe OSA combined with EDS will be recommended CPAP treatment. After one year of follow-up, all participants will be re-evaluated with self-report questionnaires. For those treated with CPAP, they will undergo the Maintenance of Wakefulness Test (MWT) and adherence, tolerance, and consistency of CPAP usage will be recorded. We will also use machine learning models. Among the models we will evaluate: Random Forests (RF), XGBoost, and Deep Neural Networks (DNN) . The data will be split into two sets: a training set and a test set. The training set will be used to train the machine learning models. The test set will be used to evaluate the models' performances. We will compare the performances of the different machine learning models using standard evaluation metrics such as accuracy, recall, and F1-score. Conclusion Our results will pave the way for the creation of a clinical screening instrument that can identify sleep-wake disturbances in professional drivers. This is likely to have a significant impact on the legal regulations concerning driving fitness and road safety. Professional drivers Tunisia Obstructive Sleep apnea Daytime sleepiness drowsy driving work productivity Epidemiology Machine learning deep learning neural network Figures Figure 1 1. Introduction Obstructive sleep apnea (OSA) is a chronic breathing sleep disease characterized by recurrent episodes of complete (apnea) or partial (hypopnea) upper airway collapse during sleep, which results in repetitive pauses in breathing, brain arousals, sleep fragmentation, intermittent hypoxemia, and increased sympathetic nervous system activity. OSA is highly prevalent worldwide, with almost 1 billion adults (30–69 years) affected, with prevalence rates ranging from 9.3–77.2%, depending on the country (Benjafield et al., 2019 ). Untreated OSA is associated with adverse health outcomes, including increased cardiovascular disease, metabolic diseases, impaired quality of life, mood disorders and excessive daytime sleepiness (Dodds et al., 2020 ). Professional drivers including bus, taxi and long-haul truck drivers are an understudied and medically underserved population, despite their high rates of health issues. Compared to the general population and other occupations, professional drivers often have irregular sleep patterns, and tend to smoke and drink more but exercise less, which predisposes them to several comorbidities such as obesity, cardiovascular diseases, cerebrovascular diseases, diabetes, musculoskeletal disorders, mental health disorders, uncorrected visual defects as well as sleep and vigilance disturbances. This increased morbidity seems to be associated with an increased likelihood of accidents and fatalities on the road (Schiza & Bouloukaki, 2020 ). In previous studies, professional drivers had been found to have high prevalence rates of OSA, ranging from 28–78% (Pack et al., 2006 ),(Stoohs et al., 1995 ) likely because they tend to be obese, male and middle-aged adults, the three most common risk factors for OSA. People with untreated OSA often experience daytime sleepiness, and impaired psychometric vigilance and do worse in driving simulator studies. Therefore, they are two to three times more likely to be involved in traffic accidents in comparison with those without OSA. Despite its heavy disease burden, medical awareness as well as appropriate screening and sleep study referrals of OSA in professional drivers is still lacking, leading to underdiagnosis and undertreatment. There are about 67503 professional drivers currently employed in Tunisia. Most are men (93,8%), with an average age range of 40–49 years old (Première Publication Statistique de l’enquête CaRiPT Cartographie Des Risques Professionnels En Tunisie, n.d.). According to the National Road Safety Observatory (ONSR), Tunisian roads registered 5796 accidents over 2023 with 1216 deaths (Observatoire national de la sécurité routière, 2023 ) most caused by road transport professionals: heavy goods vehicle (HGV) drivers, bus drivers, collective taxis and cab drivers. Besides speeding, drunk driving, cell phone use and non-use of seatbelts, distraction and drowsiness behind the wheel were identified among the main causes of road accidents. Despite all these alarming statistics, to our knowledge, there are no studies available that have performed large-scale analyses on sleep and vigilance disturbance among Tunisian professional drivers. The primary objective of this study is to determine the prevalence of OSA and EDS and their risk factors among a large representative sample of Tunisian professional drivers. The secondary objective is to evaluate the accidental risk associated with OSA and EDS before and after treatment. We also to develop a reliable screening tool for detecting sleep-wake among professional drivers. 2. Patients and methods 2.1. Study design and settings This is a population-based prospective cohort study that will be conducted by the Department of Respiratory and Sleep Medicine at Hedi Chaker University Hospital of Sfax, Tunisia in collaboration with the Tunisian Occupational Health and Safety Institute. Data collection will be performed over a 3-month period between 1 May 2024 and 31 July 2024. 2.2. Ethical considerations and dissemination 2.3. ² Population 2.3.1. Participants eligibility criteria All professional drivers of the private sector (light vehicle drivers, bus drivers, truck drivers, bulldozer drivers) working regularly for at least one year in any of the 24 provinces of Tunisia, are eligible for the study. 2.3.2. Non-inclusion criteria Drivers of the public sector and those who refuse to participate in the study will not be included. 2.3.3. Sample size To ensure accurate and representative data, we estimated the required sample size using the following equation (Charan & Biswas, 2013 ): n = (Z α/2 2 p (1-p))/∆ 2 . “ Z α/2 ” (= 2.33) was the normal deviate for a one-tailed hypothesis at a 1% level of significance; “ p ” (= 0.28) was the prevalence of OSA reported in a previous study involving 406 professional drivers (Pack et al., 2006 ), “ ∆ ” represents the precision which is arbitrarily fixed at 6.0%. Using the above formula, the estimated sample size was 3000 professional drivers. 2.4. Data collection Data will be collected using a 10-minute face-to-face questionnaire designed in literary Arabic. The questionnaire is introduced by a brief description including information about the study's purpose and authors, a statement on confidentiality, and specific instructions for each question if needed. It’s composed of a series of forced-choice questions that cover six main topics: socio-demographic data, habits, health status and morbidities, and a series of validated self-report measures related to OSA, ESD and work predictivity. At the end of the questionnaire, two open-ended questions were added to collect any comments or further information that participants would share with researchers. To assess the reliability and validity of the questionnaire, a pilot test was performed with 40 volunteers before the implementation of the full survey. 2.5. Measures 2.5.1. Socio-demographic data Demographic data will be collected, including age, gender, weight (kg), height (cm), residence type (rural or urban areas), educational level, and marital status. We will also gather information about the occupational characteristics of the participants such as, seniority at work, engagement in night work, categories of vehicles, average worked hours per day/week, and average miles driven per day/week. 2.5.2. Habits and morbidities We will collect data on tobacco smoking, alcohol use, hypnotic consumption and prevalent morbidities such as otolaryngologic, respiratory, cardiovascular, metabolic, autoimmune and psychiatric diseases, as well as SARS-CoV-2 infection. Participants will also be asked if they were previously investigated and/or treated for any breathing sleep disorder (snoring, OSA...). 2.5.3. OSA evaluation Participants who do not have a pre-existing diagnosis of OSA will be evaluated using the validated Arabic version of the Berlin questionnaire (Cronbach's alpha coefficient = 0.92) (Saleh et al., 2011 ). This is a self-administered questionnaire designed to identify subjects at increased risk for OSA. It consists of 10 questions that assess three categories of OSA symptoms and signs 1) snoring and its intensity, frequency and inconvenience to others, as well as witnessed apneas during sleep; 2) EDS and its severity; and 3) history of AHT or obesity, as defined by a BMI⩾30 kg·m 2 . In Category 1, a positive score is defined as persistent symptoms (3 to 4 times per week) in at least two questions about snoring. In category 2, a positive score is defined as persistent (3 to 4 times per week) EDS, drowsiness behind the wheel, or both. In category 3, a positive score is defined as a history of AHT or a BMI ⩾ 30 kg·m 2 . Participants exhibiting more than one positive category are identified at an increased risk for OSA (Netzer et al., 1999 ). (Fig. 1 ) 2.5.4. Daytime sleepiness All the participants will be asked to answer a validated Arabic version of the Epworth sleepiness scale (ESS) (Cronbach’s alpha = 0,86) (Riachy et al., 2012 ). This is a self-reported questionnaire which is shown to provide a subjective assessment of the severity of daytime sleepiness over the last 3 months. The ESS is based on a list of questions referring to 8 daily life situations in which participants rate their tendency to doze off or fall asleep on a scale of 0 (no chance of dozing) to 3 (high chance of dozing). The total ESS is the sum of responses to all individual 8 items and ranges from 0 to 24, with a higher score indicating a higher level of daytime sleepiness. In this study, an ESS score ≥ 11 is considered suggestive of EDS, while an ESS score ≥ 16 indicates severe EDS (Johns, 1991 ). Drowsy driving is investigated by the following questions (Sagaspe et al., 2010 ): “Have you ever dozed off or fallen asleep while driving”. Each response is rated based on the frequency of drowsy driving episodes: "almost every day," "1 to 2 times per week," "1 to 2 times per month," less than "1 time per month," or "never or almost never." For statistical analysis, participants are categorized into two groups based on how frequently they experience drowsy driving: those who drive drowsy at least once a month and those who do so less than once a month. The occurrence of sleep-induced vehicular accidents and near-misses in the last year is recorded by the following question: “Have you ever had or avoided an accident due to drowsiness while driving?" A near-miss accident is defined as an event with limited impact that could cause an accident (e.g. drifting from the lane or crossing a white line) but didn’t result in any damage. An accident is defined as an event that results in physical injury and/or property damage. 2.5.5. Work productivity We will use an Arabic-validated edition of the Work Productivity and Activity Impairment Questionnaire: General Health V2.2 (WPAI: GH 2.0) to measure how much the general health and symptoms severity affected work productivity and regular activities during the past week. The WPAI-GH 2.2 is a set of six questions as follows: 1) Currently employed? 2) Number of hours missed from work due to health problems in the past week? 3) Number of hours missed from work due to other reasons in the past week? 4) Number of hours worked in the past week? 5) Degree to which health affected productivity while working (using a 0 to 10 Visual Analogue Scale)? 6) Degree to which health affected productivity in regular unpaid activities (using a 0 to 10 Visual Analogue Scale)? Four main outcomes are generated from the WPAI-GH 2.0 and presented as percentages by multiplying the following scores by 100: 1) Percent of work time missed due to health = Q2/ (Q2 + Q4) for those who were currently employed. 2) Percent impairment while working due to health = Q5/10 for those who were currently employed and worked in the past week. 3) Percent overall work impairment due to health = Q2/ (Q2 + Q4) + ((1 - Q2/(Q2 + Q4)) × (Q5/10)) for those who were currently employed. 4) Percent activity impairment due to health = Q6/10 for all respondents. For questions 2 to 6, the recall period is one week. For participants who missed work and did not work in the past week, the percent overall work impairment due to health will be the same as the percent work time missed due to health (Margaret Reilly et al., 1993). 2.5.6. Sleep study We will evaluate the OSA by an overnight type III polysomnography (PSG) (Nox A1, ResMed, Australia). This will involve the monitoring of several physiological signals including airflow using nasal canulae, snoring by a contact microphone placed on the anterior neck, movement of the chest and abdomen wall using respiratory inductance respiratory (RIP) belts, transcutaneous arterial oxygen saturation (SPO 2 ) and pulse rate by pulse oximetry as well as body position and sleep-wake pattern using actimetry. Recordings will be manually scored according to the 2012 American Association of Sleep Medicine (AASM) manual for the scoring of sleep and associated events (Berry et al., 2012 ). The analysis will include the Apnea Hypopnea Index (AHI), the 3% oxygen desaturation (ODI) as well as the percentage of recording time with SpO2 < 90% (T90%). The severity of OSA will be classified as follows: non-OSA for AHI of less than 5 events per hour, mild OSA for AHI ranging from 5 to 14.9 events per hour, moderate OSA for AHI ranging from 15 to 29.9 events per hour, and severe OSA for AHI of 30 or more events per hour (Sateia, 2014 ). 2.5.7. Maintenance of Wakefulness Test The Maintenance of Wakefulness Test (MWT) is a daytime polysomnographic test that objectively measures the ability to remain awake during soporific circumstances. The procedure will consist of four trials, each lasting 40 minutes with a two-hour interval between them. It will be conducted at 8 a.m., 10 a.m., 12 p.m., and 2 p.m. using the international 10–20 system EEG electrodes F3, F4, C3, C4, O1, and O2 referred to M1 and M2, with bilateral electrooculogram (EOG) and submental electromyogram (EMG), according to AASM recommendations (Daniel LL, 2005 ). All the trials will take place in the same bedroom, which will be soundproof and insulated from external light. The bedroom will be equipped with dim lighting, and the ambient temperature will be recorded at the beginning of each trial and maintained as close to 22°C (72°F) as possible. Breakfast will be provided at least an hour before the first MWT trial and lunch immediately after the noon trial. During each trial, participants will be instructed to sit up in bed with their backs and heads supported by a bedrest cushion to ensure that the neck would not uncomfortably flex or extend during any sleep episode that might occur. Their backs will be positioned at an angle of 45–90 degrees relative to the bed, and their legs will be straight out with some flexing at the knees to maximize comfort. Before each trial, the participants will receive the following instructions: "Please sit calmly and try to remain awake for as long as possible. Keep your gaze straight ahead and avoid looking directly at the light." The subjects will not be permitted to use any extreme measures such as slapping their face or singing to stay awake. The recordings will be monitored by a trained technologist who will observe the patient via digital video and mark eye-opening and closing on the recording online. Each trial will end when the participant falls asleep for the first time (known as sleep onset) or after being in bed for a maximum of 40 minutes if sleep onset does not occur. Sleep onset will be defined as three consecutive 30-second epochs of stage 1 or any single 30-second epoch of another sleep stage (II, III, IV, or REM). If a participant sleeps for more than 10 minutes during the first three trials, he will be excluded from the test. A person's participation in the test will end after the fourth trial. MWT Sleep latency will be defined as the time from trial onset to the first epoch of any sleep stage. If the subject remained awake during the whole recording session, 40 min was used in the calculation of the mean sleep latency (MWT ¼ 40). A mean MWT sleep latency of 19 minutes or less will indicate EDS, as suggested by the French group (Philip et al., 2008 ). 2.5.8. Study procedure Our research teams will consist of 30 voluntary occupational physicians, with at least one representative from each of Tunisia's 24 provinces. Members of the research team will recruit participants at their periodic medical visits. When eligible individuals are approached for participation, they will be provided with a Participant Information Sheet (PIF) and the researcher will explain the project to them verbally. Participants will have the possibility to ask any questions they may have. If they agree to be included, they will be asked to sign an informed consent form. Then, they will be invited to complete the survey. The initial meeting will take approximately 30 minutes. Individuals who display a high likelihood of OSA and/or EDS will be personally informed and referred to sleep centers for appropriate investigations and management. This will mainly include sleep study and specific treatment if required. Continuous positive airway pressure (CPAP) will be indicated for participants with moderate-to-severe OSA combined with ESS of 11 or greater, drowsy driving, and/or a history of sleep-related near misses or vehicular accidents (Patil et al., 2019 ). After one year, all participants will be re-evaluated for their BMI, daytime sleepiness, drowsy driving, sleep-induced near misses and vehicular accidents, and work productivity. For those treated with CPAP, they will undergo MWT, according to the French National Authority for Health (HAS). (Haute Autorité de santé, 2012 ). Adherence, tolerance and consistency of CPAP usage will be recorded. Good compliance will be considered for those who use CPAP for a median of at least 4 hours per night, for more than 5 days a week, and on at least 70% of nights over 30 consecutive days. 2.5.9 Prediction of Obstructive Sleep Apnea (OSA) and Associated Road Traffic Accidents 2.5.9.1 Machine Learning models We will test several widely used machine learning models in medical diagnosis known for their high accuracy. Among the models we will evaluate : Random Forests (RF); it uses a multitude of decision trees to predict the probability of an event and is known for its robustness and ability to handle complex data. XGBoost; it is a type of boosting machine learning that utilizes decision trees. Deep Neural Networks (DNN); These models are inspired by the functioning of the human brain, they can learn complex data representations and are highly proficient in many tasks, including classification and regression. 2.5.9.2 Data Splitting: The data will be split into two sets: a training set and a test set. The training set will be used to train the machine learning models. The test set will be used to evaluate the models' performances. 2.5.9.3 Model Evaluation: We will compare the performances of the different machine learning models using standard evaluation metrics such as accuracy, recall, and F1-score. 3. Statistical analysis The data analysis will be conducted using SPSS Statistics for Windows, Version 23.0 (IBM SPSS Statistics for Windows, Version 23.0. Armonk, NY: IBM). To determine the normal distribution of data, we will use the Kolmogorov-Smirnov test. We will report continuous variables with normal distribution as the mean and standard deviation (SD), while non-normally distributed variables will be reported as the median and interquartile range (IQR). We will compare the means of 2 and 3 or more groups of continuous variables that are normally distributed by using the Independent Samples t-test and ANOVA test, respectively. The median of non-normal continuous variables will be compared using non-parametric tests such as the Mann-Whitney U test and Kruskal-Wallis. Categorical variables will be presented as counts and frequencies. We will compare the frequencies of categorical variables using Pearson χ 2 or Fisher exact test, when appropriate. To determine independent risk factors of OSA, EDS, drowsy driving, near misses or vehicular accidents and impaired work productivity, we will use univariate logistic analyses. We then fit significative variables as well as those previously reported in the literature into a multivariate model regression to delineate independent factors (David W & HOSMER JR, 2013). A value of p < 0.05 will be considered significant. 4. Discussion 4.1. Strength of the study This cohort study has the goal of providing accurate information regarding the prevalence of sleep-wake disturbance among professional drivers, and how it affects work productivity and road safety. The study also aims to develop a simple and reliable screening tool for OSA among professional drivers and to improve treatment practices. It will be the first large-scale study of its kind in Tunisia, a country with a high rate of road accidents. Professional drivers are at high risk of OSA, which is often going undiagnosed or untreated, and it may contribute significantly to these accidents. Currently, there are no clear regulations in Tunisia regarding fitness-to-drive for individuals with sleep-wake disturbances, and physicians may not be sufficiently aware of these issues. The study will use a range of validated self-reported measures in the local language to investigate sleep-wake status and work impairment, which will improve response rates and data accuracy. Participants who are deemed to be at high risk of OSA or have EDS will be referred to sleep centers for investigations which may help obtain a more accurate estimation of the OSA prevalence among professional drivers. Those diagnosed with moderate to severe OSA with EDS will be treated with CPAP and re-evaluated after one year of follow-up with self-report questionnaires and MWT. This will allow us to assess the impact of CPAP on daytime wakefulness and work productivity. The study results will be shared with internal and external stakeholders to raise awareness about this issue and improve screening and treatment practices. It would also have a significant impact on legal regulations regarding fitness-to-drive in sleep-wake disturbances, which would improve road safety in the country. The study team believes that road safety should be a priority, and they hope that the results will help achieve this objective. 4.2. Limitations of the study There are a few limitations that we need to consider in this study. Firstly, the small number of women in our sample means that we cannot be completely certain that our findings accurately represent the prevalence of OSA and EDS in female professional drivers. As a result, we will analyze the data separately for each gender to gain a better understanding of the situation. Secondly, our screening for snoring relies solely on participant self-reporting, which may underestimate the prevalence of snoring. This is because some individuals may not be aware of their snoring, particularly if they sleep alone. While type 1 polysomnography is the gold standard for diagnosing OSA, we are limited by financial constraints and can only perform type 3 overnight polysomnography in this study, which could lead to an underestimation of the prevalence of OSA. 5. Conclusion This study is the first of its kind in Tunisia, aiming to investigate the prevalence of OSA, EDS, and work productivity impairment among professional drivers. The main objective of this research is to raise awareness among physicians about sleep and wake disturbances among drivers and to improve OSA screening and treatment practices. Furthermore, we plan to develop a simple algorithm for screening OSA during pre-recruitment examinations or periodic visits. We also hope that the study's findings will help establish legal guidelines in Tunisia regarding the fitness-to-drive of individuals with sleep-wake disturbances. We firmly believe that ensuring road safety should become a political priority in cities, and we hope that our research will be beneficial in achieving this objective in our country. Declarations Authors credit statement Sameh MSAAD : Conceptualization, Methodology, Investigation, Data Curation, Formal analysis, Writing- Original draft preparation, Nesrine KAMMOUN : Conceptualization, Visualization, Writing, Reviewing and Editing, Rahma GARGOURI : Methodology, Visualization and Investigation, Rim KHEMEKHEM : Reviewing and Editing, Narjes ABID: Investigations Sonia FEHRI : Writing, Reviewing and Editing, Kaouther KALLEL : Conceptualization, Validation, Project administration, Rim KAMMOUN: Investigations, Heni BOUHAMED: machine learning engineering, Samy KAMMOUN : Supervision, Conceptualization, Validation, Project administration. Leila DOUIK EL GHARBI : Methodology, Investigation. Sonia Maalej : Methodology, Investigation. Ahmed ABDELGHANI: Methodology, Investigation, Validation. Chiraz Aichaouia: Writing, Reviewing and Editing. Mohamed Turki: investigation, Validation]. Declaration of funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Declaration of competing interest The authors declare that they do not have any competing financial interests or personal relationships that could have influenced the work reported in this paper. Acknowledgements The authors would like to thank all the participants for their time and willingness to be involved in this study. References Benjafield, A. V., Ayas, N. T., Eastwood, P. R., Heinzer, R., Ip, M. S. M., Morrell, M. J., Nunez, C. M., Patel, S. R., Penzel, T., Pépin, J. L. D., Peppard, P. E., Sinha, S., Tufik, S., Valentine, K., & Malhotra, A. (2019). Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4697740","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Study protocol","associatedPublications":[],"authors":[{"id":329043346,"identity":"86e8b184-612e-4bf9-a317-9529274fd468","order_by":0,"name":"Sameh MSAAD","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYPACCTn7480HQAwZYrVYGDOcOZYA0sJDrJaKxIYbOQYgFmEt5tLNx6RuVEgkNjbkfH51o8aCh4H98NEN+LRYzjmWJp1zRsK4meHsNuucY0CH8aSl3cCnxeBGjpl0bpuEbBtj7zbjHDagFgkeMwJa8r+BtDD2MPM8M875R5SWHDaQFsUZbDzMj4EMwlosZ6QZW4P8YsDDZsac2yfBw0bIL+YSyQ9v51TUyRnIP378OedbnRw/++Fj+B2GxGaTAJP4lKNrYf5ASPUoGAWjYBSMTAAAWw1EQaVLf24AAAAASUVORK5CYII=","orcid":"","institution":"University of Sfax, Sfax","correspondingAuthor":true,"prefix":"","firstName":"Sameh","middleName":"","lastName":"MSAAD","suffix":""},{"id":329043347,"identity":"b76587e2-b9b4-4760-a46e-053ebc583273","order_by":1,"name":"Rahma GARGOURI","email":"","orcid":"","institution":"University of Sfax, Sfax","correspondingAuthor":false,"prefix":"","firstName":"Rahma","middleName":"","lastName":"GARGOURI","suffix":""},{"id":329043349,"identity":"d3c671ee-b880-41ce-adaa-a800f15a63fc","order_by":2,"name":"Nesrine KAMMOUN","email":"","orcid":"","institution":"University of Tunis EL Manar, Tunis","correspondingAuthor":false,"prefix":"","firstName":"Nesrine","middleName":"","lastName":"KAMMOUN","suffix":""},{"id":329043351,"identity":"e5f44959-cd4d-40e3-a192-a4056cefba7b","order_by":3,"name":"Rim KHEMEKHEM","email":"","orcid":"","institution":"University of Sfax, Sfax","correspondingAuthor":false,"prefix":"","firstName":"Rim","middleName":"","lastName":"KHEMEKHEM","suffix":""},{"id":329043352,"identity":"0c58f6b9-1104-40eb-9451-559aba1c6505","order_by":4,"name":"Narjes ABID","email":"","orcid":"","institution":"University of Tunis EL Manar, Tunis","correspondingAuthor":false,"prefix":"","firstName":"Narjes","middleName":"","lastName":"ABID","suffix":""},{"id":329043354,"identity":"5a22606b-8ddb-4dd8-9706-ce314d5167cb","order_by":5,"name":"Sonia FEHRI","email":"","orcid":"","institution":"University of Tunis EL Manar, Tunis","correspondingAuthor":false,"prefix":"","firstName":"Sonia","middleName":"","lastName":"FEHRI","suffix":""},{"id":329043357,"identity":"cf449ecd-f693-4c6b-9afd-4f383d3b47ea","order_by":6,"name":"Kaouther KALLEL","email":"","orcid":"","institution":"University of Tunis EL Manar, Tunis","correspondingAuthor":false,"prefix":"","firstName":"Kaouther","middleName":"","lastName":"KALLEL","suffix":""},{"id":329043358,"identity":"62aa1a60-5ce0-4d1b-a93a-09466bcfbd5d","order_by":7,"name":"Rim KAMMOUN","email":"","orcid":"","institution":"University of Tunis EL Manar, Tunis","correspondingAuthor":false,"prefix":"","firstName":"Rim","middleName":"","lastName":"KAMMOUN","suffix":""},{"id":329043359,"identity":"782e97c3-6eb5-485b-bf8f-977fb9dc220d","order_by":8,"name":"Leila DOUIK EL GHARBI","email":"","orcid":"","institution":"University of Tunis EL Manar, Tunis","correspondingAuthor":false,"prefix":"","firstName":"Leila","middleName":"DOUIK EL","lastName":"GHARBI","suffix":""},{"id":329043360,"identity":"c3eea94b-d72e-42da-9e49-de11e4ec1cc1","order_by":9,"name":"Sonia Maalej","email":"","orcid":"","institution":"University of Tunis EL Manar, Tunis","correspondingAuthor":false,"prefix":"","firstName":"Sonia","middleName":"","lastName":"Maalej","suffix":""},{"id":329043361,"identity":"a3b1737c-b1b3-4e75-9cba-b9550640dcc8","order_by":10,"name":"Heni Bouhamed","email":"","orcid":"","institution":"University of Sfax, Sfax","correspondingAuthor":false,"prefix":"","firstName":"Heni","middleName":"","lastName":"Bouhamed","suffix":""},{"id":329043362,"identity":"58f8221e-f439-49c1-b215-7f5b3dc1c1c1","order_by":11,"name":"Ahmed Abdelghani","email":"","orcid":"","institution":"Univrsity of Sousse, Souse","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Abdelghani","suffix":""},{"id":329043363,"identity":"c18eb800-7f79-458f-8908-a27a80fc3753","order_by":12,"name":"Chiraz Aichaouia","email":"","orcid":"","institution":"University of Tunis EL Manar, Tunis","correspondingAuthor":false,"prefix":"","firstName":"Chiraz","middleName":"","lastName":"Aichaouia","suffix":""},{"id":329043364,"identity":"51fce196-533b-495b-bcf8-4545aff09192","order_by":13,"name":"Mohamed Turki","email":"","orcid":"","institution":"University of Sfax, Sfax","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Turki","suffix":""},{"id":329043365,"identity":"a772d079-54df-4fe7-9eca-8e5f94239f2f","order_by":14,"name":"Samy KAMMOUN","email":"","orcid":"","institution":"University of Sfax, Sfax","correspondingAuthor":false,"prefix":"","firstName":"Samy","middleName":"","lastName":"KAMMOUN","suffix":""}],"badges":[],"createdAt":"2024-07-06 17:10:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4697740/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4697740/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62183544,"identity":"099d50a6-fe12-43d6-99f0-c93af82aa6f6","added_by":"auto","created_at":"2024-08-10 11:36:20","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":561271,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the study population. OSA: obstructive sleep apnea. CPAP: continuous positive airway pressure. MWT: maintenance wakefulness test.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4697740/v1/2564d15866bbc9a0d65878bc.jpeg"},{"id":71657542,"identity":"2b12da5d-da74-4e08-9c04-df1bcfee8a54","added_by":"auto","created_at":"2024-12-17 13:10:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1179688,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4697740/v1/42476932-72c6-42c2-b551-5c2b293ed400.pdf"},{"id":62183545,"identity":"8cd2ce81-65b9-4e1f-848f-cf8f830a876d","added_by":"auto","created_at":"2024-08-10 11:36:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":299438,"visible":true,"origin":"","legend":"","description":"","filename":"Annexes.docx","url":"https://assets-eu.researchsquare.com/files/rs-4697740/v1/544293dedaa7a25f0a9a017d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Screening and management of obstructive sleep apnea (OSA) and daytime sleepiness in professional drivers in Tunisia: A protocol study using machine learning","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eObstructive sleep apnea (OSA) is a chronic breathing sleep disease characterized by recurrent episodes of complete (apnea) or partial (hypopnea) upper airway collapse during sleep, which results in repetitive pauses in breathing, brain arousals, sleep fragmentation, intermittent hypoxemia, and increased sympathetic nervous system activity. OSA is highly prevalent worldwide, with almost 1\u0026nbsp;billion adults (30\u0026ndash;69 years) affected, with prevalence rates ranging from 9.3\u0026ndash;77.2%, depending on the country (Benjafield et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Untreated OSA is associated with adverse health outcomes, including increased cardiovascular disease, metabolic diseases, impaired quality of life, mood disorders and excessive daytime sleepiness (Dodds et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProfessional drivers including bus, taxi and long-haul truck drivers are an understudied and medically underserved population, despite their high rates of health issues. Compared to the general population and other occupations, professional drivers often have irregular sleep patterns, and tend to smoke and drink more but exercise less, which predisposes them to several comorbidities such as obesity, cardiovascular diseases, cerebrovascular diseases, diabetes, musculoskeletal disorders, mental health disorders, uncorrected visual defects as well as sleep and vigilance disturbances. This increased morbidity seems to be associated with an increased likelihood of accidents and fatalities on the road (Schiza \u0026amp; Bouloukaki, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn previous studies, professional drivers had been found to have high prevalence rates of OSA, ranging from 28\u0026ndash;78% (Pack et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e),(Stoohs et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) likely because they tend to be obese, male and middle-aged adults, the three most common risk factors for OSA. People with untreated OSA often experience daytime sleepiness, and impaired psychometric vigilance and do worse in driving simulator studies. Therefore, they are two to three times more likely to be involved in traffic accidents in comparison with those without OSA. Despite its heavy disease burden, medical awareness as well as appropriate screening and sleep study referrals of OSA in professional drivers is still lacking, leading to underdiagnosis and undertreatment.\u003c/p\u003e \u003cp\u003eThere are about 67503 professional drivers currently employed in Tunisia. Most are men (93,8%), with an average age range of 40\u0026ndash;49 years old (Premi\u0026egrave;re Publication Statistique de l\u0026rsquo;enqu\u0026ecirc;te CaRiPT Cartographie Des Risques Professionnels En Tunisie, n.d.). According to the National Road Safety Observatory (ONSR), Tunisian roads registered 5796 accidents over 2023 with 1216 deaths (Observatoire national de la s\u0026eacute;curit\u0026eacute; routi\u0026egrave;re, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003cp\u003emost caused by road transport professionals: heavy goods vehicle (HGV) drivers, bus drivers, collective taxis and cab drivers. Besides speeding, drunk driving, cell phone use and non-use of seatbelts, distraction and drowsiness behind the wheel were identified among the main causes of road accidents. Despite all these alarming statistics, to our knowledge, there are no studies available that have performed large-scale analyses on sleep and vigilance disturbance among Tunisian professional drivers.\u003c/p\u003e \u003cp\u003eThe primary objective of this study is to determine the prevalence of OSA and EDS and their risk factors among a large representative sample of Tunisian professional drivers. The secondary objective is to evaluate the accidental risk associated with OSA and EDS before and after treatment. We also to develop a reliable screening tool for detecting sleep-wake among professional drivers.\u003c/p\u003e"},{"header":"2. Patients and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design and settings\u003c/h2\u003e \u003cp\u003eThis is a population-based prospective cohort study that will be conducted by the Department of Respiratory and Sleep Medicine at Hedi Chaker University Hospital of Sfax, Tunisia in collaboration with the Tunisian Occupational Health and Safety Institute. Data collection will be performed over a 3-month period between 1 May 2024 and 31 July 2024.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Ethical considerations and dissemination\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. \u0026sup2;\u003cb\u003ePopulation\u003c/b\u003e\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Participants eligibility criteria\u003c/h2\u003e \u003cp\u003eAll professional drivers of the private sector (light vehicle drivers, bus drivers, truck drivers, bulldozer drivers) working regularly for at least one year in any of the 24 provinces of Tunisia, are eligible for the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Non-inclusion criteria\u003c/h2\u003e \u003cp\u003eDrivers of the public sector and those who refuse to participate in the study will not be included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Sample size\u003c/h2\u003e \u003cp\u003eTo ensure accurate and representative data, we estimated the required sample size using the following equation (Charan \u0026amp; Biswas, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e): n = (Z\u003csub\u003eα/2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e p (1-p))/∆\u003csup\u003e2\u003c/sup\u003e. \u0026ldquo;\u003cb\u003eZ\u003c/b\u003e\u003csub\u003e\u003cb\u003eα/2\u003c/b\u003e\u003c/sub\u003e\u0026rdquo; (=\u0026thinsp;2.33) was the normal deviate for a one-tailed hypothesis at a 1% level of significance; \u0026ldquo;\u003cb\u003ep\u003c/b\u003e\u0026rdquo; (=\u0026thinsp;0.28) was the prevalence of OSA reported in a previous study involving 406 professional drivers (Pack et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), \u0026ldquo;\u003cb\u003e∆\u003c/b\u003e\u0026rdquo; represents the precision which is arbitrarily fixed at 6.0%. Using the above formula, the estimated sample size was 3000 professional drivers.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data collection\u003c/h2\u003e \u003cp\u003eData will be collected using a 10-minute face-to-face questionnaire designed in literary Arabic. The questionnaire is introduced by a brief description including information about the study's purpose and authors, a statement on confidentiality, and specific instructions for each question if needed. It\u0026rsquo;s composed of a series of forced-choice questions that cover six main topics: socio-demographic data, habits, health status and morbidities, and a series of validated self-report measures related to OSA, ESD and work predictivity. At the end of the questionnaire, two open-ended questions were added to collect any comments or further information that participants would share with researchers. To assess the reliability and validity of the questionnaire, a pilot test was performed with 40 volunteers before the implementation of the full survey.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Measures\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1. Socio-demographic data\u003c/h2\u003e \u003cp\u003eDemographic data will be collected, including age, gender, weight (kg), height (cm), residence type (rural or urban areas), educational level, and marital status. We will also gather information about the occupational characteristics of the participants such as, seniority at work, engagement in night work, categories of vehicles, average worked hours per day/week, and average miles driven per day/week.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2. Habits and morbidities\u003c/h2\u003e \u003cp\u003eWe will collect data on tobacco smoking, alcohol use, hypnotic consumption and prevalent morbidities such as otolaryngologic, respiratory, cardiovascular, metabolic, autoimmune and psychiatric diseases, as well as SARS-CoV-2 infection. Participants will also be asked if they were previously investigated and/or treated for any breathing sleep disorder (snoring, OSA...).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3. OSA evaluation\u003c/h2\u003e \u003cp\u003eParticipants who do not have a pre-existing diagnosis of OSA will be evaluated using the validated Arabic version of the Berlin questionnaire (Cronbach's alpha coefficient\u0026thinsp;=\u0026thinsp;0.92) (Saleh et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This is a self-administered questionnaire designed to identify subjects at increased risk for OSA. It consists of 10 questions that assess three categories of OSA symptoms and signs 1) snoring and its intensity, frequency and inconvenience to others, as well as witnessed apneas during sleep; 2) EDS and its severity; and 3) history of AHT or obesity, as defined by a BMI⩾30 kg\u0026middot;m\u003csup\u003e2\u003c/sup\u003e. In Category 1, a positive score is defined as persistent symptoms (3 to 4 times per week) in at least two questions about snoring. In category 2, a positive score is defined as persistent (3 to 4 times per week) EDS, drowsiness behind the wheel, or both. In category 3, a positive score is defined as a history of AHT or a BMI ⩾ 30 kg\u0026middot;m\u003csup\u003e2\u003c/sup\u003e. Participants exhibiting more than one positive category are identified at an increased risk for OSA (Netzer et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.5.4. Daytime sleepiness\u003c/h2\u003e \u003cp\u003eAll the participants will be asked to answer a validated Arabic version of the Epworth sleepiness scale (ESS) (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0,86) (Riachy et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This is a self-reported questionnaire which is shown to provide a subjective assessment of the severity of daytime sleepiness over the last 3 months. The ESS is based on a list of questions referring to 8 daily life situations in which participants rate their tendency to doze off or fall asleep on a scale of 0 (no chance of dozing) to 3 (high chance of dozing). The total ESS is the sum of responses to all individual 8 items and ranges from 0 to 24, with a higher score indicating a higher level of daytime sleepiness. In this study, an ESS score\u0026thinsp;\u0026ge;\u0026thinsp;11 is considered suggestive of EDS, while an ESS score\u0026thinsp;\u0026ge;\u0026thinsp;16 indicates severe EDS (Johns, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDrowsy driving is investigated by the following questions (Sagaspe et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e): \u0026ldquo;Have you ever dozed off or fallen asleep while driving\u0026rdquo;. Each response is rated based on the frequency of drowsy driving episodes: \"almost every day,\" \"1 to 2 times per week,\" \"1 to 2 times per month,\" less than \"1 time per month,\" or \"never or almost never.\" For statistical analysis, participants are categorized into two groups based on how frequently they experience drowsy driving: those who drive drowsy at least once a month and those who do so less than once a month.\u003c/p\u003e \u003cp\u003eThe occurrence of sleep-induced vehicular accidents and near-misses in the last year is recorded by the following question: \u0026ldquo;Have you ever had or avoided an accident due to drowsiness while driving?\" A near-miss accident is defined as an event with limited impact that could cause an accident (e.g. drifting from the lane or crossing a white line) but didn\u0026rsquo;t result in any damage. An accident is defined as an event that results in physical injury and/or property damage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.5.5. Work productivity\u003c/h2\u003e \u003cp\u003eWe will use an Arabic-validated edition of the Work Productivity and Activity Impairment Questionnaire: General Health V2.2 (WPAI: GH 2.0) to measure how much the general health and symptoms severity affected work productivity and regular activities during the past week.\u003c/p\u003e \u003cp\u003eThe WPAI-GH 2.2 is a set of six questions as follows: 1) Currently employed? 2) Number of hours missed from work due to health problems in the past week? 3) Number of hours missed from work due to other reasons in the past week? 4) Number of hours worked in the past week? 5) Degree to which health affected productivity while working (using a 0 to 10 Visual Analogue Scale)? 6) Degree to which health affected productivity in regular unpaid activities (using a 0 to 10 Visual Analogue Scale)?\u003c/p\u003e \u003cp\u003eFour main outcomes are generated from the WPAI-GH 2.0 and presented as percentages by multiplying the following scores by 100:\u003c/p\u003e \u003cp\u003e1) Percent of work time missed due to health\u0026thinsp;=\u0026thinsp;Q2/ (Q2\u0026thinsp;+\u0026thinsp;Q4) for those who were currently employed.\u003c/p\u003e \u003cp\u003e2) Percent impairment while working due to health\u0026thinsp;=\u0026thinsp;Q5/10 for those who were currently employed and worked in the past week.\u003c/p\u003e \u003cp\u003e3) Percent overall work impairment due to health\u0026thinsp;=\u0026thinsp;Q2/ (Q2\u0026thinsp;+\u0026thinsp;Q4) + ((1 - Q2/(Q2\u0026thinsp;+\u0026thinsp;Q4)) \u0026times; (Q5/10)) for those who were currently employed.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003e4) Percent activity impairment due to health = Q6/10 for all respondents.\u003c/h3\u003e\n\u003cp\u003eFor questions 2 to 6, the recall period is one week. For participants who missed work and did not work in the past week, the percent overall work impairment due to health will be the same as the percent work time missed due to health (Margaret Reilly et al., 1993).\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.5.6. Sleep study\u003c/h2\u003e \u003cp\u003eWe will evaluate the OSA by an overnight type III polysomnography (PSG) (Nox A1, ResMed, Australia). This will involve the monitoring of several physiological signals including airflow using nasal canulae, snoring by a contact microphone placed on the anterior neck, movement of the chest and abdomen wall using respiratory inductance respiratory (RIP) belts, transcutaneous arterial oxygen saturation (SPO\u003csub\u003e2\u003c/sub\u003e) and pulse rate by pulse oximetry as well as body position and sleep-wake pattern using actimetry. Recordings will be manually scored according to the 2012 American Association of Sleep Medicine (AASM) manual for the scoring of sleep and associated events (Berry et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The analysis will include the Apnea Hypopnea Index (AHI), the 3% oxygen desaturation (ODI) as well as the percentage of recording time with SpO2\u0026thinsp;\u0026lt;\u0026thinsp;90% (T90%).\u003c/p\u003e \u003cp\u003eThe severity of OSA will be classified as follows: non-OSA for AHI of less than 5 events per hour, mild OSA for AHI ranging from 5 to 14.9 events per hour, moderate OSA for AHI ranging from 15 to 29.9 events per hour, and severe OSA for AHI of 30 or more events per hour (Sateia, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.5.7. Maintenance of Wakefulness Test\u003c/h2\u003e \u003cp\u003eThe Maintenance of Wakefulness Test (MWT) is a daytime polysomnographic test that objectively measures the ability to remain awake during soporific circumstances. The procedure will consist of four trials, each lasting 40 minutes with a two-hour interval between them. It will be conducted at 8 a.m., 10 a.m., 12 p.m., and 2 p.m. using the international 10\u0026ndash;20 system EEG electrodes F3, F4, C3, C4, O1, and O2 referred to M1 and M2, with bilateral electrooculogram (EOG) and submental electromyogram (EMG), according to AASM recommendations (Daniel LL, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll the trials will take place in the same bedroom, which will be soundproof and insulated from external light. The bedroom will be equipped with dim lighting, and the ambient temperature will be recorded at the beginning of each trial and maintained as close to 22\u0026deg;C (72\u0026deg;F) as possible. Breakfast will be provided at least an hour before the first MWT trial and lunch immediately after the noon trial. During each trial, participants will be instructed to sit up in bed with their backs and heads supported by a bedrest cushion to ensure that the neck would not uncomfortably flex or extend during any sleep episode that might occur. Their backs will be positioned at an angle of 45\u0026ndash;90 degrees relative to the bed, and their legs will be straight out with some flexing at the knees to maximize comfort. Before each trial, the participants will receive the following instructions: \"Please sit calmly and try to remain awake for as long as possible. Keep your gaze straight ahead and avoid looking directly at the light.\" The subjects will not be permitted to use any extreme measures such as slapping their face or singing to stay awake. The recordings will be monitored by a trained technologist who will observe the patient via digital video and mark eye-opening and closing on the recording online. Each trial will end when the participant falls asleep for the first time (known as sleep onset) or after being in bed for a maximum of 40 minutes if sleep onset does not occur. Sleep onset will be defined as three consecutive 30-second epochs of stage 1 or any single 30-second epoch of another sleep stage (II, III, IV, or REM). If a participant sleeps for more than 10 minutes during the first three trials, he will be excluded from the test. A person's participation in the test will end after the fourth trial. MWT Sleep latency will be defined as the time from trial onset to the first epoch of any sleep stage. If the subject remained awake during the whole recording session, 40 min was used in the calculation of the mean sleep latency (MWT \u0026frac14; 40). A mean MWT sleep latency of 19 minutes or less will indicate EDS, as suggested by the French group (Philip et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e2.5.8. Study procedure\u003c/h2\u003e \u003cp\u003eOur research teams will consist of 30 voluntary occupational physicians, with at least one representative from each of Tunisia's 24 provinces. Members of the research team will recruit participants at their periodic medical visits.\u003c/p\u003e \u003cp\u003eWhen eligible individuals are approached for participation, they will be provided with a Participant Information Sheet (PIF) and the researcher will explain the project to them verbally. Participants will have the possibility to ask any questions they may have. If they agree to be included, they will be asked to sign an informed consent form. Then, they will be invited to complete the survey. The initial meeting will take approximately 30 minutes.\u003c/p\u003e \u003cp\u003eIndividuals who display a high likelihood of OSA and/or EDS will be personally informed and referred to sleep centers for appropriate investigations and management. This will mainly include sleep study and specific treatment if required.\u003c/p\u003e \u003cp\u003eContinuous positive airway pressure (CPAP) will be indicated for participants with moderate-to-severe OSA combined with ESS of 11 or greater, drowsy driving, and/or a history of sleep-related near misses or vehicular accidents (Patil et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAfter one year, all participants will be re-evaluated for their BMI, daytime sleepiness, drowsy driving, sleep-induced near misses and vehicular accidents, and work productivity. For those treated with CPAP, they will undergo MWT, according to the French National Authority for Health (HAS). (Haute Autorit\u0026eacute; de sant\u0026eacute;, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Adherence, tolerance and consistency of CPAP usage will be recorded. Good compliance will be considered for those who use CPAP for a median of at least 4 hours per night, for more than 5 days a week, and on at least 70% of nights over 30 consecutive days.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e2.5.9 Prediction of Obstructive Sleep Apnea (OSA) and Associated Road Traffic Accidents\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section4\"\u003e \u003ch2\u003e2.5.9.1 Machine Learning models\u003c/h2\u003e \u003cp\u003eWe will test several widely used machine learning models in medical diagnosis known for their high accuracy. Among the models we will evaluate : Random Forests (RF); it uses a multitude of decision trees to predict the probability of an event and is known for its robustness and ability to handle complex data. XGBoost; it is a type of boosting machine learning that utilizes decision trees. Deep Neural Networks (DNN); These models are inspired by the functioning of the human brain, they can learn complex data representations and are highly proficient in many tasks, including classification and regression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section4\"\u003e \u003ch2\u003e2.5.9.2 Data Splitting:\u003c/h2\u003e \u003cp\u003eThe data will be split into two sets: a training set and a test set. The training set will be used to train the machine learning models. The test set will be used to evaluate the models' performances.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section4\"\u003e \u003ch2\u003e2.5.9.3 Model Evaluation:\u003c/h2\u003e \u003cp\u003eWe will compare the performances of the different machine learning models using standard evaluation metrics such as accuracy, recall, and F1-score.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Statistical analysis","content":"\u003cp\u003eThe data analysis will be conducted using SPSS Statistics for Windows, Version 23.0 (IBM SPSS Statistics for Windows, Version 23.0. Armonk, NY: IBM). To determine the normal distribution of data, we will use the Kolmogorov-Smirnov test. We will report continuous variables with normal distribution as the mean and standard deviation (SD), while non-normally distributed variables will be reported as the median and interquartile range (IQR). We will compare the means of 2 and 3 or more groups of continuous variables that are normally distributed by using the Independent Samples t-test and ANOVA test, respectively. The median of non-normal continuous variables will be compared using non-parametric tests such as the Mann-Whitney U test and Kruskal-Wallis. Categorical variables will be presented as counts and frequencies. We will compare the frequencies of categorical variables using Pearson χ 2 or Fisher exact test, when appropriate. To determine independent risk factors of OSA, EDS, drowsy driving, near misses or vehicular accidents and impaired work productivity, we will use univariate logistic analyses. We then fit significative variables as well as those previously reported in the literature into a multivariate model regression to delineate independent factors (David W \u0026amp; HOSMER JR, 2013). A value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 will be considered significant.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Strength of the study\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis cohort study has the goal of providing accurate information regarding the prevalence of sleep-wake disturbance among professional drivers, and how it affects work productivity and road safety. The study also aims to develop a simple and reliable screening tool for OSA among professional drivers and to improve treatment practices. It will be the first large-scale study of its kind in Tunisia, a country with a high rate of road accidents. Professional drivers are at high risk of OSA, which is often going undiagnosed or untreated, and it may contribute significantly to these accidents. Currently, there are no clear regulations in Tunisia regarding fitness-to-drive for individuals with sleep-wake disturbances, and physicians may not be sufficiently aware of these issues.\u003c/p\u003e \u003cp\u003eThe study will use a range of validated self-reported measures in the local language to investigate sleep-wake status and work impairment, which will improve response rates and data accuracy. Participants who are deemed to be at high risk of OSA or have EDS will be referred to sleep centers for investigations which may help obtain a more accurate estimation of the OSA prevalence among professional drivers. Those diagnosed with moderate to severe OSA with EDS will be treated with CPAP and re-evaluated after one year of follow-up with self-report questionnaires and MWT. This will allow us to assess the impact of CPAP on daytime wakefulness and work productivity.\u003c/p\u003e \u003cp\u003eThe study results will be shared with internal and external stakeholders to raise awareness about this issue and improve screening and treatment practices. It would also have a significant impact on legal regulations regarding fitness-to-drive in sleep-wake disturbances, which would improve road safety in the country. The study team believes that road safety should be a priority, and they hope that the results will help achieve this objective.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Limitations of the study\u003c/h2\u003e \u003cp\u003eThere are a few limitations that we need to consider in this study. Firstly, the small number of women in our sample means that we cannot be completely certain that our findings accurately represent the prevalence of OSA and EDS in female professional drivers. As a result, we will analyze the data separately for each gender to gain a better understanding of the situation. Secondly, our screening for snoring relies solely on participant self-reporting, which may underestimate the prevalence of snoring. This is because some individuals may not be aware of their snoring, particularly if they sleep alone. While type 1 polysomnography is the gold standard for diagnosing OSA, we are limited by financial constraints and can only perform type 3 overnight polysomnography in this study, which could lead to an underestimation of the prevalence of OSA.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study is the first of its kind in Tunisia, aiming to investigate the prevalence of OSA, EDS, and work productivity impairment among professional drivers. The main objective of this research is to raise awareness among physicians about sleep and wake disturbances among drivers and to improve OSA screening and treatment practices. Furthermore, we plan to develop a simple algorithm for screening OSA during pre-recruitment examinations or periodic visits. We also hope that the study's findings will help establish legal guidelines in Tunisia regarding the fitness-to-drive of individuals with sleep-wake disturbances. We firmly believe that ensuring road safety should become a political priority in cities, and we hope that our research will be beneficial in achieving this objective in our country.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors credit statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSameh MSAAD\u003c/strong\u003e: Conceptualization, Methodology, Investigation, Data Curation, Formal analysis, Writing- Original draft preparation, \u003cstrong\u003eNesrine KAMMOUN\u003c/strong\u003e: Conceptualization, Visualization, Writing, Reviewing and Editing, \u003cstrong\u003eRahma GARGOURI\u003c/strong\u003e: Methodology, Visualization and Investigation, \u003cstrong\u003eRim KHEMEKHEM\u003c/strong\u003e: Reviewing and Editing, \u003cstrong\u003eNarjes ABID:\u003c/strong\u003e Investigations \u003cstrong\u003eSonia FEHRI\u003c/strong\u003e: Writing, Reviewing and Editing, \u003cstrong\u003eKaouther KALLEL\u003c/strong\u003e: Conceptualization, Validation, Project administration, \u003cstrong\u003eRim KAMMOUN:\u0026nbsp;\u003c/strong\u003eInvestigations, \u003cstrong\u003eHeni BOUHAMED:\u003c/strong\u003e machine learning engineering, \u003cstrong\u003eSamy KAMMOUN\u003c/strong\u003e: Supervision, Conceptualization, Validation, Project administration. \u003cstrong\u003eLeila DOUIK EL GHARBI\u003c/strong\u003e: \u0026nbsp;Methodology, Investigation. \u0026nbsp; \u0026nbsp;\u003cstrong\u003eSonia Maalej\u003c/strong\u003e: \u0026nbsp;Methodology, Investigation. \u003cstrong\u003eAhmed ABDELGHANI:\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Methodology, Investigation, Validation. \u0026nbsp; \u003cstrong\u003eChiraz\u0026nbsp;\u003c/strong\u003eAichaouia: \u0026nbsp;Writing, Reviewing and Editing. \u0026nbsp; \u003cstrong\u003eMohamed Turki:\u003c/strong\u003e investigation, Validation].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of funding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they do not have any competing financial interests or personal relationships that could have influenced the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all the participants for their time and willingness to be involved in this study. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBenjafield, A. V., Ayas, N. T., Eastwood, P. R., Heinzer, R., Ip, M. S. M., Morrell, M. J., Nunez, C. M., Patel, S. R., Penzel, T., P\u0026eacute;pin, J. L. D., Peppard, P. E., Sinha, S., Tufik, S., Valentine, K., \u0026amp; Malhotra, A. (2019). Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. \u003cem\u003eThe Lancet Respiratory Medicine\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(8), 687\u0026ndash;698. https://doi.org/10.1016/S2213-2600(19)30198-5\u003c/li\u003e\n\u003cli\u003eBerry, R. B., Budhiraja, R., \u0026amp; Al., E. (2012). Rules for scoring respiratory events in sleep: update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events. 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Validation of the Arabic Version of the Epworth Sleepiness Scale : Multicentre study. \u003cem\u003eRevue Des Maladies Respiratoires\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(5), 697\u0026ndash;704. https://doi.org/10.1016/j.rmr.2011.12.017\u003c/li\u003e\n\u003cli\u003eSagaspe, P., Taillard, J., Bayon, V., Lagarde, E., Moore, N., Boussuge, J., Chaumet, G., Bioulac, B., \u0026amp; Philip, P. (2010). Sleepiness, near-misses and driving accidents among a representative population of French drivers. \u003cem\u003eJournal of Sleep Research\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(4), 578\u0026ndash;584. https://doi.org/10.1111/j.1365-2869.2009.00818.x\u003c/li\u003e\n\u003cli\u003eSaleh, A. B. M., Ahmad, M. A., \u0026amp; Awadalla, N. J. (2011). Development of Arabic version of Berlin questionnaire to identify obstructive sleep apnea at risk patients. \u003cem\u003eAnnals of Thoracic Medicine\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(4), 212\u0026ndash;216. https://doi.org/10.4103/1817-1737.84775\u003c/li\u003e\n\u003cli\u003eSateia, M. J. (2014). International classification of sleep disorders-third edition highlights and modifications. \u003cem\u003eChest\u003c/em\u003e, \u003cem\u003e146\u003c/em\u003e(5), 1387\u0026ndash;1394. https://doi.org/10.1378/chest.14-0970\u003c/li\u003e\n\u003cli\u003eSchiza, S. E., \u0026amp; Bouloukaki, I. (2020). Screening for obstructive sleep apnoea in professional drivers. In \u003cem\u003eBreathe\u003c/em\u003e (Vol. 16, Issue 1). European Respiratory Society. https://doi.org/10.1183/20734735.0364-2019\u003c/li\u003e\n\u003cli\u003eStoohs, R. A., Bingham, A., Ltoi, A., Guilleminault, C., \u0026amp; Dement, W. C. (1995). \u003cem\u003eSleep and Sleep-Disordered Breathing in Commercial Long-Haul Truck Drivers*t\u003c/em\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Professional drivers, Tunisia, Obstructive Sleep apnea, Daytime sleepiness, drowsy driving, work productivity, Epidemiology, Machine learning, deep learning, neural network","lastPublishedDoi":"10.21203/rs.3.rs-4697740/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4697740/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProfessional drivers have high rates of Obstructive Sleep Apnea (OSA). This study aims to determine the prevalence of OSA and Excessive Daytime Sleepiness (EDS) and identify their risk factors among a large representative sample of professional drivers in Tunisia. We will also evaluate the risk of accidents associated with OSA and EDS before and after treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis will be a population-based and prospective study about 3000 professional drivers. Participants will receive a structured questionnaire to evaluate five main outcomes: the likelihood of OSA, EDS, drowsy driving, related sleepiness near misses and accidents, as well as work productivity. Validated self-report measures will be used to evaluate these outcomes.\u003c/p\u003e\n\u003cp\u003eParticipants suspected of having OSA and/or EDS will undergo sleep laboratory investigations, including sleep study. Participants who have moderate-to-severe OSA combined with EDS will be recommended CPAP treatment. After one year of follow-up, all participants will be re-evaluated with self-report questionnaires. For those treated with CPAP, they will undergo the Maintenance of Wakefulness Test (MWT) and adherence, tolerance, and consistency of CPAP usage will be recorded.\u003c/p\u003e\n\u003cp\u003eWe will also use machine learning models. Among the models we will evaluate: \u003cstrong\u003eRandom Forests (RF), XGBoost, and Deep Neural Networks (DNN)\u003c/strong\u003e. The data will be split into two sets: a training set and a test set. The training set will be used to train the machine learning models. The test set will be used to evaluate the models' performances. We will compare the performances of the different machine learning models using standard evaluation metrics such as accuracy, recall, and F1-score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur results will pave the way for the creation of a clinical screening instrument that can identify sleep-wake disturbances in professional drivers. This is likely to have a significant impact on the legal regulations concerning driving fitness and road safety.\u003c/p\u003e","manuscriptTitle":"Screening and management of obstructive sleep apnea (OSA) and daytime sleepiness in professional drivers in Tunisia: A protocol study using machine learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-10 11:36:15","doi":"10.21203/rs.3.rs-4697740/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1344eb0b-7c11-4bce-8297-21cd4c68c286","owner":[],"postedDate":"August 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-17T13:09:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-10 11:36:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4697740","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4697740","identity":"rs-4697740","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}