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Profiles of Risky Driving Behaviors in Adolescent Drivers: A Cluster Analysis of a Representative Sample from Tuscany Region (Italy).

ABSTRACT: (1) Background: Research on patterns of risky driving behaviors (RDBs) in adolescents is scarce. This study aims to identify distinctive patterns of RDBs and to explore their characteristics in a representative sample of adolescents. (2) Methods: this is a cross-sectional study of a representative sample of Tuscany Region students aged 14-19 years (n = 2162). The prevalence of 11 RDBs was assessed and a cluster analysis was conducted to identify patterns of RDBs. ANOVA, post hoc pairwise comparisons and multivariate logistic regression models were used to characterize cluster membership. (3) Results: four distinct clusters of drivers were identified based on patterns of RDBs; in particular, two clusters-the Reckless Drivers (11.2%) and the Careless Drivers (21.5%)-showed high-risk patterns of engagement in RDBs. These high-risk clusters exhibited the weakest social bonds, the highest psychological distress, the most frequent participation in health compromising and risky behaviors, and the highest risk of a road traffic accident. (4) Conclusion: findings suggest that it is possible to identify typical profiles of RDBs in adolescents and that risky driving profiles are positively interrelated with other risky behaviors. This clustering suggests the need to develop multicomponent prevention strategies rather than addressing specific RDBs in isolation.

SUBMITTER: Lastrucci V

PROVIDER: S-EPMC8296188 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Simulated Driving Performance, Self-Reported Driving Behaviors, and Mental Health Symptoms in Adolescent Novice Drivers.

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Genetic analysis of suicide: a sample study in Tuscany (Central Italy).

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Project description:Bossoleto mofette, Tuscany, Italy

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Novel Smartphone-Based Measures of Cell Phone Use While Driving in a Sample of Newly Licensed Adolescent Drivers.

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Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter?

Project description:Risky and aggressive driving maneuvers are considered a significant indicator for traffic accident occurrence as well as they aggravate their severity. Traffic violations caused by such uncivilized driving behavior is a global issue. Studies in existing literature have used statistical analysis methods to explore key contributing factors toward aggressive driving and traffic violations. However, such methods are unable to capture latent correlations among predictor variables, and they also suffer from low prediction accuracies. This study aimed to comprehensively investigate different traffic violations using spatial analysis and machine learning methods in the city of Luzhou, China. Violations committed by taxi drivers are the focus of the current study since they constitute a significant proportion of total violations reported in the city. Georeferenced violation data for the year 2016 was obtained from the traffic police department. Detailed descriptive analysis is presented to summarize key statistics about various violation types. Results revealed that over-speeding was the most prevalent violation type observed in the study area. Frequency-based nearest neighborhood cluster methods in Arc map Geographic Information System (GIS) were used to develop hotspot maps for different violation types that are vital for prioritizing and conducting treatment alternatives efficiently. Finally, different machine learning (ML) methods, including decision tree, AdaBoost with a base estimator decision tree, and stack model, were employed to predict and classify each violation type. The proposed methods were compared based on different evaluation metrics like accuracy, F-1 measure, specificity, and log loss. Prediction results demonstrated the adequacy and robustness of proposed machine learning (ML) methods. However, a detailed comparative analysis showed that the stack model outperformed other models in terms of proposed evaluation metrics.

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The Associations of Polygenic Scores for Risky Behaviors and Parenting Behaviors with Adolescent Externalizing Problems.

Project description:The current study focused on longitudinal effects of genetics and parental behaviors and their interplay on externalizing behaviors in a panel study following individuals from adolescence to young adulthood. The nationally representative sample of Add Health participants of European ancestry included N = 4142 individuals, measured on three occasions. Parenting was operationalized as experiences with child maltreatment and maternal closeness. Externalizing problems were operationalized as alcohol use, cannabis use, and antisocial behaviors. Genetic effects were operationalized as a polygenic score (PGS) of risky behaviors. The results showed significant effects for child maltreatment, maternal closeness, and PGS, above and beyond other factors and previous levels of externalizing behaviors. Furthermore, maternal closeness was found to negatively correlate with PGS. No significant interaction effects of parenting and PGS were found. The results underscore the joint independent effects of parenting and genetics on the change in externalizing behaviors from adolescence to young adulthood.

| S-EPMC8836487 | biostudies-literature

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Project description:The biological, environmental, and psychosocial changes that occur in adolescence engender an increase in risk taking often linked to the high rates of motor vehicle crashes amongst young drivers. Most U.S. adolescents suffer from poor sleep, which is known to exacerbate the risk of driving crashes; however, research has yet to uncover a neurobiological link between sleep and risky driving in adolescence. Here, we examined potential moderators of the sleep-risk relation in fifty-six adolescents (14-18y/o) as they completed a driving task during fMRI. While poor sleep was associated with increased risky driving (i.e., running more yellow lights), good sleep emerged as a novel buffer against risky driving in lower sensation-seeking adolescents. Neural activity in the ventral striatum (VS), a key node of the risk-taking circuit, also moderated the sleep-risk association: sleep was related to risk-taking in individuals demonstrating high, but not low, VS response during risky decision-making, suggesting that reward-related neural response may underly the connection between sleep and risk-taking in adolescence. This study sheds light on the risk of driving crashes in youth by highlighting sleep as both an exacerbator and a buffer of risky driving in adolescence. Taken together, these results underscore the importance of improving adolescent sleep.

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