From phone use to speeding and driving under influence: Identifying clusters of driving risk behaviors as an opportunity for targeted interventions
Identifying the profile of risky behaviors among drivers is central to propose effective interventions. Due to the multidimensional and overlapping aspects of risky driving behaviors, cluster analysis can provide additional insights in order to identify specific subgroups of risk. This study aimed t...
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Veröffentlicht in: | Journal of psychiatric research 2021-11, Vol.143, p.556-562 |
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creator | Rabelo-da-Ponte, Francisco Diego Scherer, Juliana Nichterwitz Roglio, Vinicius Borges, Eduardo Nunes Galland, Fabiana Sousa, Tanara Passos, Ives Cavalcante von Diemen, Lisia Kessler, Felix Pechansky, Flavio |
description | Identifying the profile of risky behaviors among drivers is central to propose effective interventions. Due to the multidimensional and overlapping aspects of risky driving behaviors, cluster analysis can provide additional insights in order to identify specific subgroups of risk. This study aimed to identify clusters of driving risk behavior (DRB) among car drivers, and to verify intra-cluster differences concerning clinical and sociodemographic variables. We approached a total of 12,231 drivers and we included 6392 car drivers. A cluster algorithm was used to identify groups of car drivers in relation to the DRB: driving without a seat belt (SB), exceeding the speed limit (SPD), using a cell phone while driving (CELL), and driving after drinking alcohol (DUI).
The algorithm classified drivers within five different DRB profiles. In cluster 1 (20.1%), subjects with a history of CELL. In cluster 2 (41.4%), drivers presented no DRB. In cluster 3 (9.3%), all drivers presented SPD. In cluster 4 (12.5%), drivers presented all DRB. In cluster 5 (16.6%), all drivers presented DUI. Clusters with DUI-related offenses (4 and 5) comprised more men (81.9 and 78.8%, respectively) than the overall sample (63.4%), with more binge drinking (50.9 and 45.7%) and drug use in the previous year (13.5 and 8.6%). Cluster 1 had a high years of education (14.4 ± 3.4) and the highest personal income (Md = 3000 IQR [2000–5000]). Cluster 2 had older drivers (46.6 ± 15), and fewer bingers (10.9%). Cluster 4 had the youngest drivers (34.4 ± 11.4) of all groups. Besides reinforcing previous literature data, our study identified five unprecedented clusters with different profiles of drivers regarding DRB. We identified an original and heterogeneous group of drivers with only CELL misuse, as well as other significant differences among clusters. Hence, our findings show that targeted interventions must be developed for each subgroup in order to effectively produce safe behavior in traffic. |
doi_str_mv | 10.1016/j.jpsychires.2020.11.025 |
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The algorithm classified drivers within five different DRB profiles. In cluster 1 (20.1%), subjects with a history of CELL. In cluster 2 (41.4%), drivers presented no DRB. In cluster 3 (9.3%), all drivers presented SPD. In cluster 4 (12.5%), drivers presented all DRB. In cluster 5 (16.6%), all drivers presented DUI. Clusters with DUI-related offenses (4 and 5) comprised more men (81.9 and 78.8%, respectively) than the overall sample (63.4%), with more binge drinking (50.9 and 45.7%) and drug use in the previous year (13.5 and 8.6%). Cluster 1 had a high years of education (14.4 ± 3.4) and the highest personal income (Md = 3000 IQR [2000–5000]). Cluster 2 had older drivers (46.6 ± 15), and fewer bingers (10.9%). Cluster 4 had the youngest drivers (34.4 ± 11.4) of all groups. Besides reinforcing previous literature data, our study identified five unprecedented clusters with different profiles of drivers regarding DRB. We identified an original and heterogeneous group of drivers with only CELL misuse, as well as other significant differences among clusters. Hence, our findings show that targeted interventions must be developed for each subgroup in order to effectively produce safe behavior in traffic.</description><identifier>ISSN: 0022-3956</identifier><identifier>EISSN: 1879-1379</identifier><identifier>DOI: 10.1016/j.jpsychires.2020.11.025</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Driving behavior ; Driving under influence ; Drug use</subject><ispartof>Journal of psychiatric research, 2021-11, Vol.143, p.556-562</ispartof><rights>2020 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c351t-c1cf71fe6883f594511cc3a6dbaf74bc3aa8d77f6e7becb393e5a7a1d385e963</citedby><cites>FETCH-LOGICAL-c351t-c1cf71fe6883f594511cc3a6dbaf74bc3aa8d77f6e7becb393e5a7a1d385e963</cites><orcidid>0000-0003-1595-7676 ; 0000-0002-6962-233X ; 0000-0003-2177-1405</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0022395620310840$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Rabelo-da-Ponte, Francisco Diego</creatorcontrib><creatorcontrib>Scherer, Juliana Nichterwitz</creatorcontrib><creatorcontrib>Roglio, Vinicius</creatorcontrib><creatorcontrib>Borges, Eduardo Nunes</creatorcontrib><creatorcontrib>Galland, Fabiana</creatorcontrib><creatorcontrib>Sousa, Tanara</creatorcontrib><creatorcontrib>Passos, Ives Cavalcante</creatorcontrib><creatorcontrib>von Diemen, Lisia</creatorcontrib><creatorcontrib>Kessler, Felix</creatorcontrib><creatorcontrib>Pechansky, Flavio</creatorcontrib><title>From phone use to speeding and driving under influence: Identifying clusters of driving risk behaviors as an opportunity for targeted interventions</title><title>Journal of psychiatric research</title><description>Identifying the profile of risky behaviors among drivers is central to propose effective interventions. Due to the multidimensional and overlapping aspects of risky driving behaviors, cluster analysis can provide additional insights in order to identify specific subgroups of risk. This study aimed to identify clusters of driving risk behavior (DRB) among car drivers, and to verify intra-cluster differences concerning clinical and sociodemographic variables. We approached a total of 12,231 drivers and we included 6392 car drivers. A cluster algorithm was used to identify groups of car drivers in relation to the DRB: driving without a seat belt (SB), exceeding the speed limit (SPD), using a cell phone while driving (CELL), and driving after drinking alcohol (DUI).
The algorithm classified drivers within five different DRB profiles. In cluster 1 (20.1%), subjects with a history of CELL. In cluster 2 (41.4%), drivers presented no DRB. In cluster 3 (9.3%), all drivers presented SPD. In cluster 4 (12.5%), drivers presented all DRB. In cluster 5 (16.6%), all drivers presented DUI. Clusters with DUI-related offenses (4 and 5) comprised more men (81.9 and 78.8%, respectively) than the overall sample (63.4%), with more binge drinking (50.9 and 45.7%) and drug use in the previous year (13.5 and 8.6%). Cluster 1 had a high years of education (14.4 ± 3.4) and the highest personal income (Md = 3000 IQR [2000–5000]). Cluster 2 had older drivers (46.6 ± 15), and fewer bingers (10.9%). Cluster 4 had the youngest drivers (34.4 ± 11.4) of all groups. Besides reinforcing previous literature data, our study identified five unprecedented clusters with different profiles of drivers regarding DRB. We identified an original and heterogeneous group of drivers with only CELL misuse, as well as other significant differences among clusters. Hence, our findings show that targeted interventions must be developed for each subgroup in order to effectively produce safe behavior in traffic.</description><subject>Driving behavior</subject><subject>Driving under influence</subject><subject>Drug use</subject><issn>0022-3956</issn><issn>1879-1379</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNqFUc1q3DAQFqWBbpO8g469eKuxbMvOrQ1JGwjkkruQpVFWW6_kSPLCPkdeuDJbmmNgYIb5fmDmI4QC2wKD7vt-u5_TSe9cxLStWV3WsGV1-4lsoBdDBVwMn8mGsbqu-NB2X8jXlPaMMVFDsyFv9zEc6LwLHumSkOZA04xonH-hyhtqojuu8-INRuq8nRb0Gm_og0GfnT2toJ6WlDEmGux_QXTpDx1xp44uFESV8jTMc4h58S6fqA2RZhVfMKMpxkV_XB2DT1fkwqop4fW_fkme7--eb39Xj0-_Hm5_PFaat5ArDdoKsNj1Pbft0LQAWnPVmVFZ0YxlVL0RwnYoRtQjHzi2SigwvG9x6Pgl-Xa2nWN4XTBleXBJ4zQpj2FJsm46DqxteijU_kzVMaQU0co5uoOKJwlMrjHIvXyPQa4xSABZYijSn2cplkuODqNM2q0vNIWqszTBfWzyF2fum8o</recordid><startdate>202111</startdate><enddate>202111</enddate><creator>Rabelo-da-Ponte, Francisco Diego</creator><creator>Scherer, Juliana Nichterwitz</creator><creator>Roglio, Vinicius</creator><creator>Borges, Eduardo Nunes</creator><creator>Galland, Fabiana</creator><creator>Sousa, Tanara</creator><creator>Passos, Ives Cavalcante</creator><creator>von Diemen, Lisia</creator><creator>Kessler, Felix</creator><creator>Pechansky, Flavio</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1595-7676</orcidid><orcidid>https://orcid.org/0000-0002-6962-233X</orcidid><orcidid>https://orcid.org/0000-0003-2177-1405</orcidid></search><sort><creationdate>202111</creationdate><title>From phone use to speeding and driving under influence: Identifying clusters of driving risk behaviors as an opportunity for targeted interventions</title><author>Rabelo-da-Ponte, Francisco Diego ; Scherer, Juliana Nichterwitz ; Roglio, Vinicius ; Borges, Eduardo Nunes ; Galland, Fabiana ; Sousa, Tanara ; Passos, Ives Cavalcante ; von Diemen, Lisia ; Kessler, Felix ; Pechansky, Flavio</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c351t-c1cf71fe6883f594511cc3a6dbaf74bc3aa8d77f6e7becb393e5a7a1d385e963</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Driving behavior</topic><topic>Driving under influence</topic><topic>Drug use</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rabelo-da-Ponte, Francisco Diego</creatorcontrib><creatorcontrib>Scherer, Juliana Nichterwitz</creatorcontrib><creatorcontrib>Roglio, Vinicius</creatorcontrib><creatorcontrib>Borges, Eduardo Nunes</creatorcontrib><creatorcontrib>Galland, Fabiana</creatorcontrib><creatorcontrib>Sousa, Tanara</creatorcontrib><creatorcontrib>Passos, Ives Cavalcante</creatorcontrib><creatorcontrib>von Diemen, Lisia</creatorcontrib><creatorcontrib>Kessler, Felix</creatorcontrib><creatorcontrib>Pechansky, Flavio</creatorcontrib><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of psychiatric research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rabelo-da-Ponte, Francisco Diego</au><au>Scherer, Juliana Nichterwitz</au><au>Roglio, Vinicius</au><au>Borges, Eduardo Nunes</au><au>Galland, Fabiana</au><au>Sousa, Tanara</au><au>Passos, Ives Cavalcante</au><au>von Diemen, Lisia</au><au>Kessler, Felix</au><au>Pechansky, Flavio</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>From phone use to speeding and driving under influence: Identifying clusters of driving risk behaviors as an opportunity for targeted interventions</atitle><jtitle>Journal of psychiatric research</jtitle><date>2021-11</date><risdate>2021</risdate><volume>143</volume><spage>556</spage><epage>562</epage><pages>556-562</pages><issn>0022-3956</issn><eissn>1879-1379</eissn><abstract>Identifying the profile of risky behaviors among drivers is central to propose effective interventions. Due to the multidimensional and overlapping aspects of risky driving behaviors, cluster analysis can provide additional insights in order to identify specific subgroups of risk. This study aimed to identify clusters of driving risk behavior (DRB) among car drivers, and to verify intra-cluster differences concerning clinical and sociodemographic variables. We approached a total of 12,231 drivers and we included 6392 car drivers. A cluster algorithm was used to identify groups of car drivers in relation to the DRB: driving without a seat belt (SB), exceeding the speed limit (SPD), using a cell phone while driving (CELL), and driving after drinking alcohol (DUI).
The algorithm classified drivers within five different DRB profiles. In cluster 1 (20.1%), subjects with a history of CELL. In cluster 2 (41.4%), drivers presented no DRB. In cluster 3 (9.3%), all drivers presented SPD. In cluster 4 (12.5%), drivers presented all DRB. In cluster 5 (16.6%), all drivers presented DUI. Clusters with DUI-related offenses (4 and 5) comprised more men (81.9 and 78.8%, respectively) than the overall sample (63.4%), with more binge drinking (50.9 and 45.7%) and drug use in the previous year (13.5 and 8.6%). Cluster 1 had a high years of education (14.4 ± 3.4) and the highest personal income (Md = 3000 IQR [2000–5000]). Cluster 2 had older drivers (46.6 ± 15), and fewer bingers (10.9%). Cluster 4 had the youngest drivers (34.4 ± 11.4) of all groups. Besides reinforcing previous literature data, our study identified five unprecedented clusters with different profiles of drivers regarding DRB. We identified an original and heterogeneous group of drivers with only CELL misuse, as well as other significant differences among clusters. Hence, our findings show that targeted interventions must be developed for each subgroup in order to effectively produce safe behavior in traffic.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.jpsychires.2020.11.025</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0003-1595-7676</orcidid><orcidid>https://orcid.org/0000-0002-6962-233X</orcidid><orcidid>https://orcid.org/0000-0003-2177-1405</orcidid></addata></record> |
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subjects | Driving behavior Driving under influence Drug use |
title | From phone use to speeding and driving under influence: Identifying clusters of driving risk behaviors as an opportunity for targeted interventions |
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