JMIR Publications

ABSTRACT

Background:

Professional drivers have high rates of Obstructive Sleep Apnea (OSA).

Objective:

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

Results:

Not yet

Conclusions:

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. keywords: Professional drivers, Tunisia, Obstructive Sleep apnea, Daytime sleepiness, drowsy driving, work productivity, Epidemiology, Machine learning, deep learning, neural network Clinical Trial: NO