Evidence map›Paper›PMID 40815837›Full record

ArticleJMIR research protocols2025

Screening and Management of Obstructive Sleep Apnea and Daytime Sleepiness Among Professional Drivers in Tunisia: Protocol for a Machine Learning Study.

Sameh Msaad, Nesrine Kammoun, Rahma Gargouri, Rim Khemakhem, Amira Triki, Narjes Abid, Sonia Fehri, Kaouthar Kallel, Rim Kammoun, Leila Douik El Gharbi and 6 more

Abstract read
In one paragraph

Article in JMIR research protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Sameh MsaadFaculty of Medicine of Sfax, University of Sfax, Sfax, Tunisia.ORCID 0000-0003-2880-4548
Nesrine KammounFaculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia.ORCID 0000-0002-8865-6121
Rahma GargouriFaculty of Medicine of Sfax, University of Sfax, Sfax, Tunisia.ORCID 0000-0002-3906-9457
Rim KhemakhemFaculty of Medicine of Sfax, University of Sfax, Sfax, Tunisia.ORCID 0000-0003-4977-6419
Amira TrikiFaculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia.ORCID 0009-0004-8741-6929
Narjes AbidFaculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia.ORCID 0009-0005-1329-9413
Sonia FehriFaculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia.ORCID 0009-0003-0614-294X
Kaouthar KallelFaculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia.ORCID 0009-0002-5883-4414
Rim KammounFaculty of Medicine of Sfax, University of Sfax, Sfax, Tunisia.ORCID 0000-0001-6760-2709
Leila Douik El GharbiFaculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia.ORCID 0009-0009-6681-1686
Sonia MaalejBellajFaculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia.ORCID 0000-0002-4001-3776
Heni BouhamedAdvanced Technologies for Image and Signal Processing Unit (ATISP), Sfax University, Sfax, Tunisia.ORCID 0000-0001-9174-5865
Ahmed AbdelghaniFaculty of Medicine of Sousse, University of Sousse, Sousse, Tunisia.ORCID 0009-0003-7850-5631
Chiraz AichaouiaFaculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia.ORCID 0009-0005-5174-4872
Mohamed TurkiFaculty of Medicine of Sfax, University of Sfax, Sfax, Tunisia.ORCID 0009-0001-7168-8776
Samy KammounFaculty of Medicine of Sfax, University of Sfax, Sfax, Tunisia.ORCID 0000-0002-2915-4789

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundObstructive sleep apnea (OSA) is highly prevalent among professional drivers; however, its true burden in this population remains underexplored and likely underdiagnosed.

objectiveThis 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 the treatment.

methodsThis will be a population-based and prospective study of 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 or EDS will undergo sleep laboratory investigations, including a sleep study. Participants who have moderate-to-severe OSA will be recommended continuous positive airway pressure (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). We will evaluate several widely used machine learning models in medical diagnosis that are known for their high accuracy, including random forests, extreme gradient boosting, and deep neural networks, to predict the probability of OSA and its association with road traffic accidents.

resultsA total of 127 male drivers participated in the study, with a mean age of 39.22 (SD 8.62) years. Most participants (76/127, 60.3%) had completed secondary education, 54.3% (69/127) were smokers, and the median BMI was 25.6 kg/m

conclusionsOur preliminary findings revealed that a significant proportion of drivers were at a high risk of OSA. 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.

Indexed as

Automobile DrivingDisorders of Excessive SomnolenceMachine LearningSleep Apnea, ObstructiveAccidents, TrafficAdultContinuous Positive Airway PressureFemaleHumansMaleMass ScreeningMiddle AgedPrevalenceProspective StudiesRisk FactorsSurveys and Questionnairesdaytime sleepinessdeep learningdrowsy drivingepidemiologymachine learningneural networkobstructive sleep apneaprofessional driversTunisiawork productivity

Identifiers

PMID40815837
PMCPMC12397752

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.