IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i8p5609-d1129694.html
   My bibliography  Save this article

Driving Behaviour in Depression Based on Subjective Evaluation and Data from a Driving Simulator

Author

Listed:
  • Vagioula Tsoutsi

    (First Department of Psychiatry, Medical School, National & Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece
    Laboratory of Health and Road Safety, Department of Social Work, School of Health Sciences, Hellenic Mediterranean University, 71410 Crete, Greece)

  • Maria Papadakaki

    (Laboratory of Health and Road Safety, Department of Social Work, School of Health Sciences, Hellenic Mediterranean University, 71410 Crete, Greece)

  • George Yannis

    (Department of Transportation Planning and Engineering, School of Civil Engineering, National Technical University of Athens, 15773 Athens, Greece)

  • Dimosthenis Pavlou

    (Department of Transportation Planning and Engineering, School of Civil Engineering, National Technical University of Athens, 15773 Athens, Greece)

  • Maria Basta

    (Department of Psychiatry, University Hospital of Heraklion, 71500 Crete, Greece)

  • Joannes Chliaoutakis

    (Laboratory of Health and Road Safety, Department of Social Work, School of Health Sciences, Hellenic Mediterranean University, 71410 Crete, Greece)

  • Dimitris Dikeos

    (First Department of Psychiatry, Medical School, National & Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece)

Abstract

Road traffic collisions are a major issue for public health. Depression is characterized by mental, emotional and executive dysfunction, which may have an impact on driving behaviour. Patients with depression (N = 39) and healthy controls (N = 30) were asked to complete questionnaires and to drive on a driving simulator in different scenarios. Driving simulator data included speed, safety distance from the preceding vehicle and lateral position. Demographic and medical information, insomnia (Athens Insomnia Scale, AIS), sleepiness (Epworth Sleepiness Scale, ESS), fatigue (Fatigue Severity Scale, FSS), symptoms of sleep apnoea (StopBang Questionnaire) and driving (Driver Stress Inventory, DSI and Driver Behaviour Questionnaire, DBQ) were assessed. Gender and age influenced almost all variables. The group of patients with depression did not differ from controls regarding driving behaviour as assessed through questionnaires; on the driving simulator, patients kept a longer safety distance. Subjective fatigue was positively associated with aggression, dislike of driving, hazard monitoring and violations as assessed by questionnaires. ESS and AIS scores were positively associated with keeping a longer safety distance and with Lateral Position Standard Deviation (LPSD), denoting lower ability to keep a stable position. It seems that, although certain symptoms of depression (insomnia, fatigue and somnolence) may affect driving performance, patients drive more carefully eliminating, thus, their impact.

Suggested Citation

  • Vagioula Tsoutsi & Maria Papadakaki & George Yannis & Dimosthenis Pavlou & Maria Basta & Joannes Chliaoutakis & Dimitris Dikeos, 2023. "Driving Behaviour in Depression Based on Subjective Evaluation and Data from a Driving Simulator," IJERPH, MDPI, vol. 20(8), pages 1-15, April.
  • Handle: RePEc:gam:jijerp:v:20:y:2023:i:8:p:5609-:d:1129694
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/8/5609/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/20/8/5609/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Evelina Wikner & Raik Orbay & Sara Fogelström & Torbjörn Thiringer, 2022. "Gender Aspects in Driving Style and Its Impact on Battery Ageing," Energies, MDPI, vol. 15(18), pages 1-15, September.
    2. Rolf Robertsen & Håvard W. Lorås & Remco Polman & Ozlem Simsekoglu & Hermundur Sigmundsson, 2022. "Aging and Driving: A Comparison of Driving Performance Between Older and Younger Drivers in an On-Road Driving Test," SAGE Open, , vol. 12(2), pages 21582440221, May.
    3. Muhammad Zahid & Yangzhou Chen & Sikandar Khan & Arshad Jamal & Muhammad Ijaz & Tufail Ahmed, 2020. "Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter?," IJERPH, MDPI, vol. 17(11), pages 1-21, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Muhammad Ijaz & Lan Liu & Yahya Almarhabi & Arshad Jamal & Sheikh Muhammad Usman & Muhammad Zahid, 2022. "Temporal Instability of Factors Affecting Injury Severity in Helmet-Wearing and Non-Helmet-Wearing Motorcycle Crashes: A Random Parameter Approach with Heterogeneity in Means and Variances," IJERPH, MDPI, vol. 19(17), pages 1-24, August.
    2. Yaqi Liu & Xiaoyuan Wang, 2020. "Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions," IJERPH, MDPI, vol. 17(19), pages 1-22, September.
    3. Moneim Massar & Imran Reza & Syed Masiur Rahman & Sheikh Muhammad Habib Abdullah & Arshad Jamal & Fahad Saleh Al-Ismail, 2021. "Impacts of Autonomous Vehicles on Greenhouse Gas Emissions—Positive or Negative?," IJERPH, MDPI, vol. 18(11), pages 1-23, May.
    4. Arshad Jamal & Waleed Umer, 2020. "Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network," IJERPH, MDPI, vol. 17(20), pages 1-22, October.
    5. Danish Farooq & Sarbast Moslem & Arshad Jamal & Farhan Muhammad Butt & Yahya Almarhabi & Rana Faisal Tufail & Meshal Almoshaogeh, 2021. "Assessment of Significant Factors Affecting Frequent Lane-Changing Related to Road Safety: An Integrated Approach of the AHP–BWM Model," IJERPH, MDPI, vol. 18(20), pages 1-17, October.
    6. Mohammed Saleh Alfawzan & Ahmad Aftab, 2022. "Efficiency Assessment of New Signal Timing in Saudi Arabia Implementing Flashing Green Interval Complimented with Law Enforcement Cameras," Sustainability, MDPI, vol. 14(22), pages 1-15, November.
    7. Muhammad Zahid & Yangzhou Chen & Arshad Jamal & Khalaf A. Al-Ofi & Hassan M. Al-Ahmadi, 2020. "Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study," IJERPH, MDPI, vol. 17(14), pages 1-15, July.
    8. Tufail Ahmed & Ali Pirdavani & Davy Janssens & Geert Wets, 2023. "Utilizing Intelligent Portable Bicycle Lights to Assess Urban Bicycle Infrastructure Surfaces," Sustainability, MDPI, vol. 15(5), pages 1-22, March.
    9. Nattawut Pumpugsri & Wanchai Rattanawong & Varin Vongmanee, 2023. "Development of a Safety Heavy-Duty Vehicle Model Considering Unsafe Acts, Unsafe Conditions and Near-Miss Events Using Structural Equation Model," Sustainability, MDPI, vol. 15(16), pages 1-20, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:20:y:2023:i:8:p:5609-:d:1129694. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.