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Updated: Sep 17, 2026

Driving Under the Influence: How Music Listening Affects Driving Behaviors
Published on: March 27, 2019
Unsafe driving behaviors and crash history among adolescent drivers in Tuscany, Italy
Francesco Profili1, Simona Olivadoti1, Fabio Voller1
1Health Regional Agency of Tuscany, Florence, Italy.
Introduction:
Adolescents are at elevated risk of road traffic crashes, partly due to risky driving behaviors and limited driving experience. This study assessed the frequency of unsafe driving behaviors among adolescent drivers in Tuscany (Italy) and examined the association between these behaviors and lifetime crash history or crash severity.
Method:
Data were drawn from the EDIT surveillance system, a cross-sectional, population-representative survey of upper secondary school students. The analysis included 1,222 adolescents aged 14-19 years who reported holding a driver's license and driving at least several days per week. Unsafe driving behaviors in the previous year were measured using a graded frequency scale (0 = never to 4 = always). Weighted descriptive analyses and multivariable regression models were used to estimate behavioral frequencies and their adjusted association with lifetime crash history (none, non-severe, severe), controlling for age, sex, and primary vehicle type.
Results:
Unsafe driving behaviors were common and generally increased with age. Interaction with passengers and listening to high-volume music were the most frequent behaviors. After adjustment, higher frequency of nearly all unsafe behaviors was associated with a higher prevalence of lifetime crash history, particularly driving while fatigued or in a hurry, driving after alcohol or drug use, high-volume music, mobile phone/smartphone use, and smoking or eating/drinking while driving. A clear gradient was observed, with the highest prevalence among those reporting higher frequency.
Conclusions:
Unsafe driving behaviors are widespread among adolescent drivers and cluster among those with a history of crashes, especially severe crashes. Behavioral gradients suggest that multitasking, time pressure, fatigue, and impaired driving are key markers of elevated crash risk.
Practical Applications:
Behavioral surveillance can help identify high-risk profiles before crashes occur. Prevention strategies should address digital distraction, peer-related risk, fatigue, and impaired driving early in the driving trajectory.
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