Prediction of Fitness to Drive in Patients with Alzheimer's Dementia
Publication Date
February 24, 2016
Journal
PLOS ONE
Authors
Dafne Piersma, Anselm B. M. Fuermaier, Dick De Waard, Ragnhild J. Davidse, et al
Volume
11
Issue
2
Pages
e0149566
DOI
https://dx.plos.org/10.1371/journal.pone.0149566
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0149566
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26910535
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4766198
Europe PMC
http://europepmc.org/abstract/MED/26910535
Web of Science
000371164700041
Scopus
84977672891
Mendeley
http://www.mendeley.com/research/prediction-fitness-drive-patients-alzheimers-dementia-1
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Mendeley | Further Information

{"title"=>"Prediction of Fitness to Drive in Patients with Alzheimer's Dementia", "type"=>"journal", "authors"=>[{"first_name"=>"Dafne", "last_name"=>"Piersma"}, {"first_name"=>"Anselm B. M.", "last_name"=>"Fuermaier"}, {"first_name"=>"Dick", "last_name"=>"de Waard"}, {"first_name"=>"Ragnhild J.", "last_name"=>"Davidse"}, {"first_name"=>"Jolieke", "last_name"=>"de Groot"}, {"first_name"=>"Michelle J. A.", "last_name"=>"Doumen"}, {"first_name"=>"Ruud A.", "last_name"=>"Bredewoud"}, {"first_name"=>"René", "last_name"=>"Claesen"}, {"first_name"=>"Afina W.", "last_name"=>"Lemstra"}, {"first_name"=>"Annemiek", "last_name"=>"Vermeeren"}, {"first_name"=>"Rudolf", "last_name"=>"Ponds"}, {"first_name"=>"Frans", "last_name"=>"Verhey"}, {"first_name"=>"Wiebo H.", "last_name"=>"Brouwer"}, {"first_name"=>"Oliver", "last_name"=>"Tucha"}], "year"=>2016, "source"=>"PLOS ONE", "identifiers"=>{"scopus"=>"2-s2.0-85044277156", "sgr"=>"84977672891", "issn"=>"1932-6203", "doi"=>"10.1371/journal.pone.0149566", "pmid"=>"26910535", "pui"=>"611090388"}, "id"=>"18b4a6fc-2428-3352-910d-324d14ea19ab", "abstract"=>"The number of patients with Alzheimer’s disease (AD) is increasing and so is the number of patients driving a car. To enable patients to retain their mobility while at the same time not endangering public safety, each patient should be assessed for fitness to drive. The aim of this study is to develop a method to assess fitness to drive in a clinical setting, using three types of assessments, i.e. clinical interviews, neuropsychological assessment and driving simulator rides. The goals are (1) to determine for each type of assessment which combina- tion of measures is most predictive for on-road driving performance, (2) to compare the pre- dictive value of clinical interviews, neuropsychological assessment and driving simulator evaluation and (3) to determine which combination of these assessments provides the best prediction of fitness to drive. Eighty-one patients with AD and 45 healthy individuals partici- pated. All participated in a clinical interview, and were administered a neuropsychological test battery and a driving simulator ride (predictors). The criterion fitness to drive was deter- mined in an on-road driving assessment by experts of the CBR Dutch driving test organisa- tion according to their official protocol. The validity of the predictors to determine fitness to drive was explored by means of logistic regression analyses, discriminant function analy- ses, as well as receiver operating curve analyses. Wefound that all three types of assess- ments are predictive of on-road driving performance. Neuropsychological assessment had the highest classification accuracy followed by driving simulator rides and clinical inter- views. However, combining all three types of assessments yielded the best prediction for fitness to drive in patients with AD with an overall accuracy of 92.7%, which makes this method highly valid for assessing fitness to drive in AD. This method may be used to advise patients with AD and their family members about fitness to drive.", "link"=>"http://www.mendeley.com/research/prediction-fitness-drive-patients-alzheimers-dementia-1", "reader_count"=>28, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Student > Doctoral Student"=>3, "Researcher"=>3, "Student > Ph. D. Student"=>8, "Student > Master"=>2, "Other"=>5, "Student > Bachelor"=>3, "Professor"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Student > Doctoral Student"=>3, "Researcher"=>3, "Student > Ph. D. Student"=>8, "Student > Master"=>2, "Other"=>5, "Student > Bachelor"=>3, "Professor"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Nursing and Health Professions"=>3, "Medicine and Dentistry"=>4, "Neuroscience"=>3, "Sports and Recreations"=>1, "Psychology"=>10, "Social Sciences"=>1, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Neuroscience"=>{"Neuroscience"=>3}, "Social Sciences"=>{"Social Sciences"=>1}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Psychology"=>{"Psychology"=>10}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>3}, "Unspecified"=>{"Unspecified"=>5}}, "reader_count_by_country"=>{"Japan"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/4740169"], "description"=>"<div><p>The number of patients with Alzheimer’s disease (AD) is increasing and so is the number of patients driving a car. To enable patients to retain their mobility while at the same time not endangering public safety, each patient should be assessed for fitness to drive. The aim of this study is to develop a method to assess fitness to drive in a clinical setting, using three types of assessments, i.e. clinical interviews, neuropsychological assessment and driving simulator rides. The goals are (1) to determine for each type of assessment which combination of measures is most predictive for on-road driving performance, (2) to compare the predictive value of clinical interviews, neuropsychological assessment and driving simulator evaluation and (3) to determine which combination of these assessments provides the best prediction of fitness to drive. Eighty-one patients with AD and 45 healthy individuals participated. All participated in a clinical interview, and were administered a neuropsychological test battery and a driving simulator ride (predictors). The criterion fitness to drive was determined in an on-road driving assessment by experts of the CBR Dutch driving test organisation according to their official protocol. The validity of the predictors to determine fitness to drive was explored by means of logistic regression analyses, discriminant function analyses, as well as receiver operating curve analyses. We found that all three types of assessments are predictive of on-road driving performance. Neuropsychological assessment had the highest classification accuracy followed by driving simulator rides and clinical interviews. However, combining all three types of assessments yielded the best prediction for fitness to drive in patients with AD with an overall accuracy of 92.7%, which makes this method highly valid for assessing fitness to drive in AD. This method may be used to advise patients with AD and their family members about fitness to drive.</p></div>", "links"=>[], "tags"=>["neuropsychological assessment", "neuropsychological test battery", "simulator rides", "CBR", "interview", "type", "AD", "fitness", "discriminant function analyses"], "article_id"=>3037069, "categories"=>["Biophysics", "Space Science", "Biological Sciences not elsewhere classified", "Developmental Biology", "Science Policy", "Hematology"], "users"=>["Dafne Piersma", "Anselm B. M. Fuermaier", "Dick de Waard", "Ragnhild J. Davidse", "Jolieke de Groot", "Michelle J. A. Doumen", "Ruud A. Bredewoud", "René Claesen", "Afina W. Lemstra", "Annemiek Vermeeren", "Rudolf Ponds", "Frans Verhey", "Wiebo H. Brouwer", "Oliver Tucha"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0149566", "stats"=>{"downloads"=>2, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Prediction_of_Fitness_to_Drive_in_Patients_with_Alzheimer_s_Dementia/3037069", "title"=>"Prediction of Fitness to Drive in Patients with Alzheimer's Dementia", "pos_in_sequence"=>0, "defined_type"=>6, "published_date"=>"2016-02-24 11:30:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/4740352"], "description"=>"<p>Characteristics of healthy participants and patients with Alzheimer’s disease.</p>", "links"=>[], "tags"=>["neuropsychological assessment", "neuropsychological test battery", "simulator rides", "CBR", "interview", "type", "AD", "fitness", "discriminant function analyses"], "article_id"=>3037237, "categories"=>["Biophysics", "Space Science", "Biological Sciences not elsewhere classified", "Developmental Biology", "Science Policy", "Hematology"], "users"=>["Dafne Piersma", "Anselm B. M. Fuermaier", "Dick de Waard", "Ragnhild J. Davidse", "Jolieke de Groot", "Michelle J. A. Doumen", "Ruud A. Bredewoud", "René Claesen", "Afina W. Lemstra", "Annemiek Vermeeren", "Rudolf Ponds", "Frans Verhey", "Wiebo H. Brouwer", "Oliver Tucha"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0149566.t001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Characteristics_of_healthy_participants_and_patients_with_Alzheimer_s_disease_/3037237", "title"=>"Characteristics of healthy participants and patients with Alzheimer’s disease.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-24 11:30:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/4740634"], "description"=>"<p>Predictive accuracy of the final predictor variable with cut-off -0.6 (left) and -0.8 (right).</p>", "links"=>[], "tags"=>["neuropsychological assessment", "neuropsychological test battery", "simulator rides", "CBR", "interview", "type", "AD", "fitness", "discriminant function analyses"], "article_id"=>3037462, "categories"=>["Biophysics", "Space Science", "Biological Sciences not elsewhere classified", "Developmental Biology", "Science Policy", "Hematology"], "users"=>["Dafne Piersma", "Anselm B. M. Fuermaier", "Dick de Waard", "Ragnhild J. Davidse", "Jolieke de Groot", "Michelle J. A. Doumen", "Ruud A. Bredewoud", "René Claesen", "Afina W. Lemstra", "Annemiek Vermeeren", "Rudolf Ponds", "Frans Verhey", "Wiebo H. Brouwer", "Oliver Tucha"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0149566.t006", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Predictive_accuracy_of_the_final_predictor_variable_with_cut_off_0_6_left_and_0_8_right_/3037462", "title"=>"Predictive accuracy of the final predictor variable with cut-off -0.6 (left) and -0.8 (right).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-24 11:30:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/4740580"], "description"=>"<p>Classification accuracy of the final predictor variable (including clinical interviews, neuropsychological assessment and driving simulator rides) in detecting patients being unfit to drive (n = 27) relative to patients being fit to drive (n = 28).</p>", "links"=>[], "tags"=>["neuropsychological assessment", "neuropsychological test battery", "simulator rides", "CBR", "interview", "type", "AD", "fitness", "discriminant function analyses"], "article_id"=>3037432, "categories"=>["Biophysics", "Space Science", "Biological Sciences not elsewhere classified", "Developmental Biology", "Science Policy", "Hematology"], "users"=>["Dafne Piersma", "Anselm B. M. Fuermaier", "Dick de Waard", "Ragnhild J. Davidse", "Jolieke de Groot", "Michelle J. A. Doumen", "Ruud A. Bredewoud", "René Claesen", "Afina W. Lemstra", "Annemiek Vermeeren", "Rudolf Ponds", "Frans Verhey", "Wiebo H. Brouwer", "Oliver Tucha"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0149566.t005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Classification_accuracy_of_the_final_predictor_variable_including_clinical_interviews_neuropsychological_assessment_and_driving_simulator_rides_in_detecting_patients_being_unfit_to_drive_n_27_relative_to_patients_being_fit_to_drive_n_28_/3037432", "title"=>"Classification accuracy of the final predictor variable (including clinical interviews, neuropsychological assessment and driving simulator rides) in detecting patients being unfit to drive (n = 27) relative to patients being fit to drive (n = 28).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-24 11:30:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/4740298"], "description"=>"<p>ROC curve of the predictor variable of the complete method.</p>", "links"=>[], "tags"=>["neuropsychological assessment", "neuropsychological test battery", "simulator rides", "CBR", "interview", "type", "AD", "fitness", "discriminant function analyses"], "article_id"=>3037189, "categories"=>["Biophysics", "Space Science", "Biological Sciences not elsewhere classified", "Developmental Biology", "Science Policy", "Hematology"], "users"=>["Dafne Piersma", "Anselm B. M. Fuermaier", "Dick de Waard", "Ragnhild J. Davidse", "Jolieke de Groot", "Michelle J. A. Doumen", "Ruud A. Bredewoud", "René Claesen", "Afina W. Lemstra", "Annemiek Vermeeren", "Rudolf Ponds", "Frans Verhey", "Wiebo H. Brouwer", "Oliver Tucha"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0149566.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/ROC_curve_of_the_predictor_variable_of_the_complete_method_/3037189", "title"=>"ROC curve of the predictor variable of the complete method.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-24 11:30:50"}

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{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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