Dataset Information

Driving anger and its relationships with type A behavior patterns and trait anger: Differences between professional and non-professional drivers.

ABSTRACT: The present study examined the types of situations that caused Chinese professional and non-professional drivers to become angry and investigated the differences in driving-elicited anger, considering the influences of type A behavior pattern and trait anger between the two groups. The 20-item revised Driving Anger Scale (DAS) was used to assess a sample of 232 drivers (57% professional, 43% non-professional). The non-professional drivers reported significantly higher levels of anger than the professional drivers on the overall Driving Anger Scale (DAS) and the traffic obstructions and discourtesy subscales. In both groups, the preferred driving speeds were positively related to driving anger. Furthermore, drivers with a type A personality exhibited higher overall driving anger scores and higher anger scores in response to traffic obstructions and slow driving than drivers with a type B personality. Trait anger was significantly related to driving anger in both groups. In the non-professional group, type A behavior patterns (TABPs) and time hurry (TH) were positively correlated with anger evoked by slow driving. In the professional group, TABPs, TH and competitive hostility (CH) were positively related to driving anger, and the TABPs exerted an indirect effect on driving anger by mediating the influence of trait anger. Overall, these findings provide a theoretical basis for implementing targeted interventions for driving anger in both professional and non-professional drivers.

SUBMITTER: Feng Z

PROVIDER: S-EPMC5734738 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Driving anger and its relationships with type A behavior patterns and trait anger: Differences between professional and non-professional drivers.

Feng Zhongxiang Z Yang Miaomiao M Ma Changxi C Jiang Kang K Lei Yewei Y Huang Wenjuan W Huang Zhipeng Z Zhan Jingjing J Zhou Muxiong M

PloS one 20171218 12

The present study examined the types of situations that caused Chinese professional and non-professional drivers to become angry and investigated the differences in driving-elicited anger, considering the influences of type A behavior pattern and trait anger between the two groups. The 20-item revised Driving Anger Scale (DAS) was used to assess a sample of 232 drivers (57% professional, 43% non-professional). The non-professional drivers reported significantly higher levels of anger than the pr ...[more]

PMID: 29253004

Similar Datasets

Project description:ObjectiveTwo aims of this study were to develop and validate A) a metric to identify drivers with type 1 diabetes at high risk of future driving mishaps and B) an online intervention to reduce mishaps among high-risk drivers.Research design and methodsTo achieve aim A, in study 1, 371 drivers with type 1 diabetes from three U.S. regions completed a series of established questionnaires about diabetes and driving. They recorded their driving mishaps over the next 12 months. Questionnaire items that uniquely discriminated drivers who did and did not have subsequent driving mishaps were assembled into the Risk Assessment of Diabetic Drivers (RADD) scale. In study 2, 1,737 drivers with type 1 diabetes from all 50 states completed the RADD online. Among these, 118 low-risk (LR) and 372 high-risk (HR) drivers qualified for and consented to participate in a 2-month treatment period followed by 12 monthly recordings of driving mishaps. To address aim B, HR participants were randomized to receive either routine care (RC) or the online intervention "DiabetesDriving.com" (DD.com). Half of the DD.com participants received a motivational interview (MI) at the beginning and end of the treatment period to boost participation and efficacy. All of the LR participants were assigned to RC. In both studies, the primary outcome variable was driving mishaps.ResultsRelated to aim A, in study 1, the RADD demonstrated 61% sensitivity and 75% specificity. Participants in the upper third of the RADD distribution (HR), compared with those in the lower third (LR), reported 3.03 vs. 0.87 mishaps/driver/year, respectively (P < 0.001). In study 2, HR and LR participants receiving RC reported 4.3 and 1.6 mishaps/driver/year, respectively (P < 0.001). Related to aim B, in study 2, MIs did not enhance participation or efficacy, so the DD.com and DD.com + MI groups were combined. DD.com participants reported fewer hypoglycemia-related driving mishaps than HR participants receiving RC (P = 0.01), but more than LR participants receiving RC, reducing the difference between the HR and LR participants receiving RC by 63%. HR drivers differed from LR drivers at baseline across a variety of hypoglycemia and driving parameters.ConclusionsThe RADD identified higher-risk drivers, and identification seemed relatively stable across time, samples, and procedures. This 11-item questionnaire could inform patients at higher risk, and their clinicians, that they should take preventive steps to reduce driving mishaps, which was accomplished in aim B using DD.com.

Dataset Information

Driving anger and its relationships with type A behavior patterns and trait anger: Differences between professional and non-professional drivers.

Publications

Driving anger and its relationships with type A behavior patterns and trait anger: Differences between professional and non-professional drivers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets