ArticleInternational journal of environmental research and public health2023
Crash Risk Predictors in Older Drivers: A Cross-Sectional Study Based on a Driving Simulator and Machine Learning Algorithms.
Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Braking Time and Cognitive, Visual, and Motor Determinants in Older Drivers With Type 2 Diabetes Mellitus With and Without Diabetic Peripheral Neuropathy: A Controlled Cross-Sectional Study.Journal of diabetes · 2026Article
- Cardiac Simulator Technologies and Design for Medical Education and Auscultation Training: A Systematic Review.Bioengineering (Basel, Switzerland) · 2025Review
- Identifying major depressive disorder in older adults through naturalistic driving behaviors and machine learning.NPJ digital medicine · 2025Article
- Multifactorial assessment of braking time predictors in a driving simulator among older adults according to gender.Clinics (Sao Paulo, Brazil) · 2024Article
- Quality of life and socio-demographic factors associated with nutritional risk in Brazilian community-dwelling individuals aged 80 and over: cluster analysis and ensemble methods.Frontiers in nutrition · 2023Article
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Authors and funding
12 authors.
Funding
Abstract
The ability to drive depends on the motor, visual, and cognitive functions, which are necessary to integrate information and respond appropriately to different situations that occur in traffic. The study aimed to evaluate older drivers in a driving simulator and identify motor, cognitive and visual variables that interfere with safe driving through a cluster analysis, and identify the main predictors of traffic crashes. We analyzed the data of older drivers (n = 100, mean age of 72.5 ± 5.7 years) recruited in a hospital in São Paulo, Brazil. The assessments were divided into three domains: motor, visual, and cognitive. The K-Means algorithm was used to identify clusters of individuals with similar characteristics that may be associated with the risk of a traffic crash. The Random Forest algorithm was used to predict road crash in older drivers and identify the predictors (main risk factors) related to the outcome (number of crashes). The analysis identified two clusters, one with 59 participants and another with 41 drivers. There were no differences in the mean of crashes (1.7 vs. 1.8) and infractions (2.6 vs. 2.0) by cluster. However, the drivers allocated in Cluster 1, when compared to Cluster 2, had higher age, driving time, and braking time (
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