Crash Risk Predictors in Older Drivers: A Cross-Sectional Study Based on a Driving Simulator and Machine Learning Algorithms

Página do item simplificado
dc.contributorSistema FMUSP-HC: Faculdade de Medicina da Universidade de São Paulo (FMUSP) e Hospital das Clínicas da FMUSP
dc.contributor.authorSILVA, V. C.
dc.contributor.authorDIAS, A. S.
dc.contributor.authorGREVE, J. M. D.
dc.contributor.authorDAVIS, C. L.
dc.contributor.authorSOARES, A. L. D. S.
dc.contributor.authorBRECH, G. C.
dc.contributor.authorAYAMA, S.
dc.contributor.authorJACOB-FILHO, W.
dc.contributor.authorBUSSE, A. L.
dc.contributor.authorBIASE, M. E. M. de
dc.contributor.authorCANONICA, A. C.
dc.contributor.authorALONSO, A. C.
dc.date.accessioned2024-03-13T19:56:45Z
dc.date.available2024-03-13T19:56:45Z
dc.date.issued2023
dc.description.abstractThe 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 (p < 0.05). The random forest performed well (r = 0.98, R2 = 0.81) in predicting road crash. Advanced age and the functional reach test were the factors representing the highest risk of road crash. There were no differences in the number of crashes and infractions per cluster. However, the Random Forest model performed well in predicting the number of crashes.eng
dc.description.indexMEDLINE
dc.description.indexPubMed
dc.description.indexScopus
dc.description.indexDimensions
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP, (2011/50354-8)
dc.description.sponsorshipFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, FAPERJ, (2012/20627-5)
dc.identifier.citationINTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, v.20, n.5, article ID 4212, p, 2023
dc.identifier.doi10.3390/ijerph20054212
dc.identifier.issn1661-7827
dc.identifier.urihttps://observatorio.fm.usp.br/handle/OPI/58633
dc.language.isoeng
dc.publisherMDPIeng
dc.relation.ispartofInternational Journal of Environmental Research and Public Health
dc.rightsopenAccesseng
dc.rights.holderCopyright MDPIeng
dc.subjectclustering analysiseng
dc.subjectcrash riskeng
dc.subjectmachine learningeng
dc.subjectolder driverseng
dc.subjectsafe drivingeng
dc.subject.otherbrazileng
dc.subject.othersao paulo [brazil]eng
dc.subject.otheralgorithmeng
dc.subject.othercluster analysiseng
dc.subject.othermachine learningeng
dc.subject.otherrisk assessmenteng
dc.subject.otheraccidenteng
dc.subject.otherage distributioneng
dc.subject.otheragedeng
dc.subject.otherarticleeng
dc.subject.otherbrazileng
dc.subject.othercognition assessmenteng
dc.subject.otherconvenience sampleeng
dc.subject.othercross-sectional studyeng
dc.subject.otherdemographyeng
dc.subject.otherdescriptive researcheng
dc.subject.otherdrivereng
dc.subject.otherdriving abilityeng
dc.subject.othereducational statuseng
dc.subject.otherfemaleeng
dc.subject.othergrip strengtheng
dc.subject.otherhumaneng
dc.subject.otherk means clusteringeng
dc.subject.othermachine learningeng
dc.subject.othermaleeng
dc.subject.othermontreal cognitive assessmenteng
dc.subject.othermotor performanceeng
dc.subject.othermuscle strengtheng
dc.subject.otherpredictioneng
dc.subject.otherrandom foresteng
dc.subject.otherrisk assessmenteng
dc.subject.otherrisk factoreng
dc.subject.othersocial statuseng
dc.subject.othertimed up and go testeng
dc.subject.othertrail making testeng
dc.subject.othervisioneng
dc.subject.othervisual system functioneng
dc.titleCrash Risk Predictors in Older Drivers: A Cross-Sectional Study Based on a Driving Simulator and Machine Learning Algorithmseng
dc.typearticleeng
dc.type.categoryoriginal articleeng
dc.type.versionpublishedVersioneng
dspace.entity.typePublication
hcfmusp.affiliation.countryEstados Unidos
hcfmusp.affiliation.countryisous
hcfmusp.author.externalDIAS, A. S.:Graduate Program in Aging Science, São Judas Tadeu University (USJT), São Paulo, 03166-000, Brazil
hcfmusp.author.externalDAVIS, C. L.:Georgia Prevention Institute, Medical College of Georgia, Augusta University, Augusta, 30901, GA, United States
hcfmusp.author.externalSOARES, A. L. D. S.:Graduate Program in Aging Science, São Judas Tadeu University (USJT), São Paulo, 03166-000, Brazil, Georgia Prevention Institute, Medical College of Georgia, Augusta University, Augusta, 30901, GA, United States
hcfmusp.author.externalBIASE, M. E. M. de:Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo, 05403-010, Brazil
hcfmusp.citation.scopus1
hcfmusp.contributor.author-fmusphcVANDERLEI CARNEIRO DA SILVA
hcfmusp.contributor.author-fmusphcJULIA MARIA D ANDREA GREVE
hcfmusp.contributor.author-fmusphcGUILHERME CARLOS BRECH
hcfmusp.contributor.author-fmusphcSERGIO AYAMA
hcfmusp.contributor.author-fmusphcWILSON JACOB FILHO
hcfmusp.contributor.author-fmusphcALEXANDRE LEOPOLD BUSSE
hcfmusp.contributor.author-fmusphcALEXANDRA CAROLINA CANONICA
hcfmusp.contributor.author-fmusphcANGELICA CASTILHO ALONSO
hcfmusp.description.articlenumber4212
hcfmusp.description.issue5
hcfmusp.description.volume20
hcfmusp.origemSCOPUS
hcfmusp.origem.dimensionspub.1155799345
hcfmusp.origem.scopus2-s2.0-85149875358
hcfmusp.relation.referenceDawson J.D., Uc E.Y., Anderson S.W., Johnson A.M., Rizzo M., Neuropsychological Predictors of Driving Errors in Older Adults, J. Am. Geriatr. Soc, 58, pp. 1090-1096, (2010)eng
hcfmusp.relation.referenceMunro C.A., Jefferys J., Gower E.W., Munoz B.E., Lyketsos C.G., Keay L., Turano K.A., Bandeen-Roche K., West S.K., Predictors of Lane-Change Errors in Older Drivers, J. Am. Geriatr. Soc, 58, pp. 457-464, (2010)eng
hcfmusp.relation.referenceWang S., Sharma A., Dawson J., Rizzo M., Merickel J., Visual and Cognitive Impairments Differentially Affect Speed Limit Compliance in Older Drivers, J. Am. Geriatr. Soc, 69, pp. 1300-1308, (2021)eng
hcfmusp.relation.referenceWood J.M., Anstey K.J., Kerr G.K., Lacherez P.F., Lord S., A Multidomain Approach for Predicting Older Driver Safety under In-Traffic Road Conditions, J. Am. Geriatr. Soc, 56, pp. 986-993, (2008)eng
hcfmusp.relation.referenceHuisingh C., Levitan E.B., Irvin M.R., Maclennan P., Wadley V., Owsley C., Visual Sensory and Visual-Cognitive Function and Rate of Crash and Near-Crash Involvement Among Older Drivers Using Naturalistic Driving Data, Investig. Ophthalmol. Vis. Sci, 58, pp. 2959-2967, (2017)eng
hcfmusp.relation.referenceAnderson S.W., Rizzo M., Shi Q., Uc E.Y., Dawson J.D., Cognitive Abilities Related to Driving Performance in a Simulator and Crashing on the Road, Proceedings of the 3rd International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, pp. 286-292eng
hcfmusp.relation.referenceTinella L., Lopez A., Caffo A.O., Nardulli F., Grattagliano I., Bosco A., Cognitive Efficiency and Fitness-to-Drive along the Lifespan: The Mediation Effect of Visuospatial Transformations, Brain Sci, 11, (2021)eng
hcfmusp.relation.referenceTinella L., Lopez A., Caffo A.O., Grattagliano I., Bosco A., Spatial Mental Transformation Skills Discriminate Fitness to Drive in Young and Old Adults, Front. Psychol, 11, (2020)eng
hcfmusp.relation.referenceAlonso A.C., Peterson M.D., Busse A.L., Jacob-Filho W., Borges M.T.A., Serra M.M., Luna N.M.S., Marchetti P.H., Greve J.M.D.A., Muscle Strength, Postural Balance, and Cognition Are Associated with Braking Time during Driving in Older Adults, Exp. Gerontol, 85, pp. 13-17, (2016)eng
hcfmusp.relation.referenceRagland D.R., Satariano W.A., MacLeod K.E., Reasons Given by Older People for Limitation or Avoidance of Driving, Gerontologist, 44, pp. 237-244, (2004)eng
hcfmusp.relation.referenceChihuri S., Mielenz T.J., Dimaggio C.J., Betz M.E., Diguiseppi C., Jones V.C., Li G., Driving Cessation and Health Outcomes in Older Adults, J. Am. Geriatr. Soc, 64, pp. 332-341, (2016)eng
hcfmusp.relation.referenceSilva V.C., Gorgulho B., Marchioni D.M., de Araujo T.A., Santos I.D.S., Lotufo P.A., Bensenor I.M., Clustering Analysis and Machine Learning Algorithms in the Prediction of Dietary Patterns: Cross-sectional Results of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), J. Hum. Nutr. Diet, 35, pp. 883-894, (2022)eng
hcfmusp.relation.referenceMarshall S.C., Man-Son-Hing M., Bedard M., Charlton J., Gagnon S., Gelinas I., Koppel S., Korner-Bitensky N., Langford J., Mazer B., Et al., Protocol for Candrive II/Ozcandrive, a Multicentre Prospective Older Driver Cohort Study, Accid. Anal. Prev, 61, pp. 245-252, (2013)eng
hcfmusp.relation.referenceCanonica A.C., Alonso A.C., Brech G.C., Peterson M., Luna N.M.S., Busse A.L., Jacob-Filho W., Rosa J.L., Soares-Junior J.M., Baracat E.C., Et al., Adaptation to the Driving Simulator and Prediction of the Braking Time Performance, with and without Distraction, in Older Adults and Middle-Aged Adults, Clinics, 78, (2023)eng
hcfmusp.relation.referenceAlonso A.C., Ribeiro S.M., Luna N.M.S., Peterson M.D., Bocalini D.S., Serra M.M., Brech G.C., Greve J.M.D., Garcez-Leme L.E., Association between Handgrip Strength, Balance, and Knee Flexion/Extension Strength in Older Adults, PLoS ONE, 13, (2018)eng
hcfmusp.relation.referenceItotani K., Suganuma I., Fujita H., Are the Physical and Cognitive Functions of Older Adults Are the Physical and Cognitive Functions of Older Adults Affected by Having a Driver’s License?-A Pilot Study of Suburban Dwellers, Int. J. Environ. Res. Health, 19, (2022)eng
hcfmusp.relation.referenceWood J.M., Horswill M.S., Lacherez P.F., Anstey K.J., Evaluation of Screening Tests for Predicting Older Driver Performance and Safety Assessed by an On-Road Test, Accid. Anal. Prev, 50, pp. 1161-1168, (2013)eng
hcfmusp.relation.referenceDesapriya E., Harjee R., Brubacher J., Chan H., Hewapathirane D.S., Subzwari S., Pike I., Vision Screening of Older Drivers for Preventing Road Traffic Injuries and Fatalities, Cochrane Database Syst. Rev, 2014, (2014)eng
hcfmusp.relation.referenceLee J., Mehler B., Reimer B., Ebe K., Coughlin J.F., Relationships Between Older Drivers’ Cognitive Abilities as Assessed on the MoCA and Glance Patterns During Visual-Manual Radio Tuning While Driving, J. Gerontol. B Psychol. Sci. Soc. Sci, 73, pp. 1190-1197, (2018)eng
hcfmusp.relation.referenceGaudino E.A., Geisler M.W., Squires N.K., Construct Validity in the Trail Making Test: What Makes Part B Harder?, J. Clin. Exp. Neuropsychol, 17, pp. 529-535, (2008)eng
hcfmusp.relation.referenceAlonso A.C., Silva-Santos P.R., Quintana M.S.L., da Silva V.C., Brech G.C., Barbosa L.G., Pompeu J.E., Silva E.C.G.E., da Silva E.M., de Godoy C.G., Et al., Physical and Pulmonary Capacities of Individuals with Severe Coronavirus Disease after Hospital Discharge: A Preliminary Cross-Sectional Study Based on Cluster Analysis, Clinics, 76, (2021)eng
hcfmusp.relation.referenceCapo M., Perez A., Lozano J.A., An Efficient K-Means Clustering Algorithm for Tall Data, Data Min. Knowl. Discov, 34, pp. 776-811, (2020)eng
hcfmusp.relation.referenceProbst P., Wright M.N., Boulesteix A.L., Hyperparameters and Tuning Strategies for Random Forest, Wiley Interdiscip. Rev. Data Min. Knowl. Discov, 9, (2019)eng
hcfmusp.relation.referenceWoolnough A., Salim D., Marshall S.C., Weegar K., Porter M.M., Rapoport M.J., Man-Son-Hing M., Bedard M., Gelinas I., Korner-Bitensky N., Et al., Determining the Validity of the AMA Guide: A Historical Cohort Analysis of the Assessment of Driving Related Skills and Crash Rate among Older Drivers, Accid. Anal. Prev, 61, pp. 311-316, (2013)eng
hcfmusp.relation.referenceUlleberg P., Bjornskau T., Fostervold K.I., Does Age Matter? Examining Age-Dependent Differences in at-Fault Collisions after Attending a Refresher Course for Older Drivers, Transp. Res. Part F Traffic Psychol. Behav, 87, pp. 379-390, (2022)eng
hcfmusp.relation.referenceAvila R., Moscoso M.A.A., Ribeiz S., Arrais J., Jaluul O., Bottino C.M.C., Influence of Education and Depressive Symptoms on Cognitive Function in the Elderly, Int. Psychogeriatr, 21, pp. 560-567, (2009)eng
hcfmusp.relation.referenceBorzuola R., Giombini A., Torre G., Campi S., Albo E., Bravi M., Borrione P., Fossati C., Macaluso A., Central and Peripheral Neuromuscular Adaptations to Ageing, J. Clin. Med, 9, (2020)eng
hcfmusp.relation.referenceChevalier A., Coxon K., Rogers K., Chevalier A.J., Wall J., Brown J., Clarke E., Ivers R., Keay L., A Longitudinal Investigation of the Predictors of Older Drivers’ Speeding Behaviour, Accid. Anal. Prev, 93, pp. 41-47, (2016)eng
hcfmusp.relation.referenceGreen K.A., McGwin G., Owsley C., Associations between Visual, Hearing, and Dual Sensory Impairments and History of Motor Vehicle Collision Involvement of Older Drivers, J. Am. Geriatr. Soc, 61, pp. 252-257, (2013)eng
hcfmusp.relation.referenceOwsley C., Driving Mobility, Older Adults, and Quality of Life, Gerontechnology, 1, pp. 220-230, (2002)eng
hcfmusp.relation.referenceChoi H., Kasko J., Feng J., An Attention Assessment for Informing Older Drivers’ Crash Risks in Various Hazardous Situations, Gerontologist, 59, pp. 112-123, (2019)eng
hcfmusp.relation.referenceAksan N., Sager L., Hacker S., Lester B., Dawson J., Rizzo M., Ebe K., Foley J., Individual Differences in Cognitive Functioning Predict Effectiveness of a Heads-up Lane Departure Warning for Younger and Older Drivers, Accid. Anal. Prev, (2017)eng
hcfmusp.relation.referenceAnstey K.J., Wood J., Lord S., Walker J.G., Cognitive, Sensory and Physical Factors Enabling Driving Safety in Older Adults, Clin. Psychol. Rev, 25, pp. 45-65, (2005)eng
hcfmusp.relation.referenceJian M., Shi J., Analysis of Impact of Elderly Drivers on Traffic Safety Using ANN Based Car-Following Model, Saf. Sci, 122, (2020)eng
hcfmusp.relation.referenceBoyle L.N., Lee J.D., Using Driving Simulators to Assess Driving Safety, Accid. Anal. Prev, 42, pp. 785-787, (2010)eng
hcfmusp.scopus.lastupdate2024-05-17
relation.isAuthorOfPublication414d2b63-75df-4fce-8ecb-644fa0d14322
relation.isAuthorOfPublicationa8c8396f-99fd-4576-a93b-d0e0ddb7f126
relation.isAuthorOfPublication577658ab-4169-45bc-9374-71abe56bf483
relation.isAuthorOfPublication05d4d019-3fa7-43bb-808b-9b925f04b8d8
relation.isAuthorOfPublicationc4bb26d2-ceff-4ef9-a58e-a946ddba9bd6
relation.isAuthorOfPublicationed9f2ec2-64aa-4f84-866a-439b4257d7ab
relation.isAuthorOfPublicationd5c720d1-123e-4caf-a3c4-5c792c133164
relation.isAuthorOfPublication6d1e1e63-6521-4be0-a253-8cb23d575c7f
relation.isAuthorOfPublication.latestForDiscovery414d2b63-75df-4fce-8ecb-644fa0d14322
Arquivos
Coleções
Artigos e Materiais de Revistas Científicas - FM/MOT
Artigos e Materiais de Revistas Científicas - FM/MCM
Artigos e Materiais de Revistas Científicas - HC/ICHC
Artigos e Materiais de Revistas Científicas - HC/IOT
Artigos e Materiais de Revistas Científicas - HU
Artigos e Materiais de Revistas Científicas - LIM/41
Carregar mais