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Promoting Older Driver Safety

Permanent Link: http://ufdc.ufl.edu/UFE0022543/00001

Material Information

Title: Promoting Older Driver Safety Impact of Driving Rehabilitation Specialist Recommendations on Older Adults' Driving Performance
Physical Description: 1 online resource (315 p.)
Language: english
Creator: Posse, Maria
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: compensation, drivers, driving, model, older, optimization, recommendations, rehabilitation, safety, selective, soc, specialist, traffic
Rehabilitation Science -- Dissertations, Academic -- UF
Genre: Rehabilitation Science thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Recognizing the importance of driving and the potential negative impact of driving cessation, the present study sought to understand the effectiveness of interventions aimed at maintaining older driver safety. Driving rehabilitation specialists (DRSs) are trained professionals, typically occupational therapists, who conduct assessments of older drivers. DRSs frequently provide recommendations to older drivers, but the impact of these recommendations on older drivers? performance is unknown. This study evaluated common and recalled driving rehabilitation specialists? recommendations for older drivers in order to determine best practices for DRS and delineate important components of driving performance and older drivers? safety. Common driving recommendations were examined for a sample of 118 older adults who had a clinical and road test driving assessment. Overall, 22% of drivers (n=26) had recommendations to avoid driving conditions (i.e., night, high traffic); 78% (n=92) had driving behavior recommendations such as increasing the following distance, scanning, or signaling; 22.9% (n=27) had recommendations to take a driving course or read a driver's handbook, and 11.9% (n=14) had recommendations to take behind-the-wheel training. The results showed different patterns of recommendations for unsafe and safe drivers. After 2.5 years, 65 participants completed a telephone interview and approximately 80% recalled driving recommendations provided to them by the DRS, with recall of recommendations mainly related to driving behaviors and avoidance of conditions. Driving recommendations are described in light of the Selective Optimization with Compensation (SOC) model of successful aging. At follow-up, driving habits of participants did not differ by driving performance classification (unsafe, safe with recommendations, and safe). Over time, older adults reduced the days driven per week, but were making more trips, going more places, and driving more miles, suggesting the value and preference for the car as the main means of transportation.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Maria Posse.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Mann, William C.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022543:00001

Permanent Link: http://ufdc.ufl.edu/UFE0022543/00001

Material Information

Title: Promoting Older Driver Safety Impact of Driving Rehabilitation Specialist Recommendations on Older Adults' Driving Performance
Physical Description: 1 online resource (315 p.)
Language: english
Creator: Posse, Maria
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: compensation, drivers, driving, model, older, optimization, recommendations, rehabilitation, safety, selective, soc, specialist, traffic
Rehabilitation Science -- Dissertations, Academic -- UF
Genre: Rehabilitation Science thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Recognizing the importance of driving and the potential negative impact of driving cessation, the present study sought to understand the effectiveness of interventions aimed at maintaining older driver safety. Driving rehabilitation specialists (DRSs) are trained professionals, typically occupational therapists, who conduct assessments of older drivers. DRSs frequently provide recommendations to older drivers, but the impact of these recommendations on older drivers? performance is unknown. This study evaluated common and recalled driving rehabilitation specialists? recommendations for older drivers in order to determine best practices for DRS and delineate important components of driving performance and older drivers? safety. Common driving recommendations were examined for a sample of 118 older adults who had a clinical and road test driving assessment. Overall, 22% of drivers (n=26) had recommendations to avoid driving conditions (i.e., night, high traffic); 78% (n=92) had driving behavior recommendations such as increasing the following distance, scanning, or signaling; 22.9% (n=27) had recommendations to take a driving course or read a driver's handbook, and 11.9% (n=14) had recommendations to take behind-the-wheel training. The results showed different patterns of recommendations for unsafe and safe drivers. After 2.5 years, 65 participants completed a telephone interview and approximately 80% recalled driving recommendations provided to them by the DRS, with recall of recommendations mainly related to driving behaviors and avoidance of conditions. Driving recommendations are described in light of the Selective Optimization with Compensation (SOC) model of successful aging. At follow-up, driving habits of participants did not differ by driving performance classification (unsafe, safe with recommendations, and safe). Over time, older adults reduced the days driven per week, but were making more trips, going more places, and driving more miles, suggesting the value and preference for the car as the main means of transportation.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Maria Posse.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Mann, William C.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022543:00001


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1 PROMOTING OLDER DRIVER SAFETY: IMPACT OF DRIVING REHABILITATION SPECIALIST RECOMMENDATIONS ON OLDE R ADULTS DRIVING PERFORMANCE By MARIA CRISTINA POSSE A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008

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2 2008 Maria Cristina Posse

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3 To my parents, for their love and education

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4 ACKNOWLEDGMENTS I thank m y parents for their love, suppor t, education, and continuous encouragement throughout my life. This success is dedicated to them: We proved once again, que el mundo es de los valientes y atrs ni para coger impulso. I also thank my brothers and my family for their love and support. I thank Benjamin for all th e love, friendship, understanding, and support of yesterday, today, and the rest of the moments to come; he gave me motivation and strength to persist. I thank my nephews Nicolas and Ant onio, who brought happiness and joy every time I visited my beloved home country, Colombia. One day they will understand why I missed sharing so much of their earlie r experiences in life. I express my gratitude to my mentor Bill Mann, for all the opportunities to learn and expand my professional development. He provi ded direction and suppor t throughout my work. I also thank Dennis McCarthy for co-mentoring me throughout my work in the NODRTC, and always making sure I was progressing along the way. I am very grateful to Bill and Dennis for their patience and dedication to ensure my su ccess at the end of this road. I thank all the members of my committee (Bonnie Dobbs, Mich ael Marsiske, Dennis McCarthy, Linda Shaw and Bill Mann), for letting me grow and advisi ng me every step of the way. I thank my committee for believing in me, for being patient, guiding me, and letting me grow through this journey. Thanks to Bonnie and Michael for bringing great expertise to this work. I am thankful for all the driv ing projects in which I worked and the wonderful people that I encountered along the way: I specially thank Desi ree with whom I always enjoyed working with as a team, and shared many ups and downs along the road. Desirees knowledge and excellent skills as a driving rehabilitation specialist were crucial to completing this work. Thanks go to Michael Justiss for all the work he did, including the design and scor ing for the road test used in this work. I also thank Eugenia, Lindsay, Abbe y, Gretchen, and Michael, who always provided

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5 helpful assistance and a friendly smile. I thank Orit and Sherrilene for sharing their knowledge, believing in my skills, and being supportive. At the end of the program I worked in a proj ect that helped dementia caregivers, and I thank Maggie and Jeff Loomis for letting me share with them the amazing world of caregiving and helping others thrive. They always provided good advice, frie ndly support, and a sense of humor. Special thanks to Jeff with whom I e xpanded my knowledge and career, he was very supportive throughout my dissertation and work. Thanks go to Pey Shan, Kezia, Sandy, Eric, Baghwant, Rick, Jessica, Megan, and Roxanna, peers and friends from the PhD lab. Special thanks to my first Brazilian friend, Patty, with whom I shared countless memorable moment s since day one. I thank Patricia for always being there in the good and the bad moments, for always providing a hug, advise, sharing a great conversation, or just sharing popc orn and counting our dimes to survive. I want to also express my gratitude to everyone in the OT department where I learned and spent many years sharing with wonderful people. Last but not least, I shar e this success with the millions of good-hearted, hard working Colombians and other international students that bravely and successfully embark on endeavors like this one.

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6 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ........10 LIST OF FIGURES.......................................................................................................................12 ABSTRACT...................................................................................................................................14 CHAP TER 1 INTRODUCTION..................................................................................................................16 Driving and Older Adults....................................................................................................... 17 Driving Recommendations.....................................................................................................17 Theoretical Models of Driv ing Behavior and Aging ..............................................................18 Taxonomic Models of Driving Behavior........................................................................ 19 Task analyses............................................................................................................19 Trait models.............................................................................................................. 19 Functional Models of Driving Behavior.......................................................................... 22 Mechanistic models..................................................................................................22 Adaptive control models.......................................................................................... 23 Motivational models................................................................................................. 24 Cognitive (process) models......................................................................................26 Selective Optimization and Compensa tion Model of Successful Aging ................................29 Summary.................................................................................................................................30 Driving and the International Classificati on of Functioning, Disability, and Mental Health Model (ICF) .............................................................................................................31 2 LITERATURE REVIEW.......................................................................................................39 Road Tests and Driving Behavior........................................................................................... 39 Functional Abilities and Driving Performance....................................................................... 44 Age-Related Visual Changes and Driving......................................................................44 Visual acuity, contrast sensitivity, peri pheral fields, and driving performance .......45 Visual attention and driving performance................................................................49 Visual diseases and driving......................................................................................52 Hearing and Driving........................................................................................................53 Age-Related Motor Changes and Driving.......................................................................53 Vehicle control and motor abilities.......................................................................... 54 Medical conditions and driving................................................................................ 59 Cognition and Driving.....................................................................................................60 Memory.................................................................................................................... 61 Executive functions.................................................................................................. 61 Visuo-perceptual abilities and mental status............................................................ 64

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7 Driving Exposure and Driving Avoidance.............................................................................65 What is Driving Exposure?.............................................................................................66 When and where do older adults drive?................................................................... 66 Voluntary and involuntary reductions in driving.....................................................67 Driving Avoidance.......................................................................................................... 68 Driving Recommendations for Older Adults .......................................................................... 70 Driving Recommendations in Medical Settings ..................................................................... 70 Physicians Recommendati ons for Older Drivers ............................................................71 American Medical Association (AMA ) guidelines vision recommendations.......... 72 AMA guidelines cognitive recommendations..........................................................72 AMA guidelines motor recommendations............................................................... 72 Restricted Licenses.......................................................................................................... 73 Driving Recommendations in Educational Settings............................................................... 73 Classroom Education....................................................................................................... 73 Educational Intervention................................................................................................. 78 Driving Recommendations from Self-Assessment................................................................. 79 Driving Recommendations in Rehabilitation Settings ...........................................................82 Implications of Driving Recommendations............................................................................ 84 3 METHODS.............................................................................................................................92 Participants.............................................................................................................................92 Procedures..................................................................................................................... ..........92 Clinical Measures.............................................................................................................. .....93 Telephone Interview for Cognitive Status (TICS).......................................................... 93 Useful Field of View (UFOV)...................................................................................... 94 Trails Making Test Part B (Trails B)............................................................................... 95 Older Americans Resources and Services: Instrum ental Activities of Daily Living (OARS-IADL).............................................................................................................95 Functional Independence Measure (FIM)....................................................................... 96 Visual Assessment...........................................................................................................96 Rapid Pace Walk (RPW)................................................................................................. 98 Range of Motion and Strength........................................................................................98 Demographics and Self-Report of Physical Health Questions ........................................ 98 Modified Version of the Drivi ng Habits Questionnaire (DHQ) ...................................... 99 Road Test................................................................................................................................99 Road Test Scoring......................................................................................................... 100 Driving Behaviors in the Road Test.............................................................................. 101 Global Rating Scale....................................................................................................... 102 Follow-up Telephone Interview...........................................................................................102 Telephone Interview Components and Rationale..........................................................103 Driving Recommendations...................................................................................................104 Categories of Recommendations...................................................................................104 Driving Recommendati ons and SOC Model ................................................................. 105 Recommendations Recall..............................................................................................109

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8 4 RESULTS.............................................................................................................................119 Overview....................................................................................................................... ........119 Sample..................................................................................................................................119 Aim 1: Driving Recommendations.......................................................................................120 Recommendations Suggesting Selection..............................................................................121 Recommendations Suggesting Specific Optim ization.......................................................... 122 Recommendations Suggesti ng Global Optim ization............................................................124 Compensation Driving Recommendations ........................................................................... 125 Aim 2: Prediction of Driving Performance..........................................................................125 Exploring the Discriminant Function Variables ............................................................125 Discriminant Function...................................................................................................127 Exploring Discriminant Functions with Clinical and De mographic Variables............. 129 Aim 3: Recall of Driving Recommendations.......................................................................130 Overview of Recalled Recommendations.....................................................................131 False Recall of Recommenda tions Suggesting S election.............................................. 132 False Recall of Recommendations Suggesting S pecific Optimization......................... 133 False Recall of Recommendations Suggesting G lobal Optimization and Compensation............................................................................................................134 No Cued Recall of Recommendations Suggesting Selection........................................134 No Cued Recall of Recommendations Suggesting Specific Optimization................... 135 No Cued Recall of Recommendations Suggesting Global Optimization......................136 No Cued Recall of Recommendations Suggesting Compensation................................136 Recall of Recommendations Suggesting Selection....................................................... 137 Recall of Recommendations Sugge sting Specific Optim ization................................... 138 Recall of Recommendations Suggesting Global Optimization..................................... 138 Recall of Recommendations Suggesting Compensation............................................... 139 AIM 4: Driving Habits Changes........................................................................................... 139 Driving Exposure...........................................................................................................140 Driving Avoidance........................................................................................................ 141 5 DISCUSSION.......................................................................................................................185 Aim 1: Driving Recommendations.......................................................................................185 Recommendations Suggesting Selection.......................................................................185 Recommendations Suggesting Specific Optimization.................................................. 186 Recommendations Suggesti ng Global Optim ization.....................................................188 Recommendations suggesting compensation................................................................190 Aim 2: Prediction of Driving Performance..........................................................................191 Aim 3: Recall of Driving Recommendations.......................................................................196 Falsely Recalled Recommendations..............................................................................197 Post-hoc Observation.....................................................................................................197 Selection.................................................................................................................198 Specific optimization..............................................................................................199 Global optimization and Compensation................................................................. 200 Correctly Recalled Recommendations.......................................................................... 200 Selection.................................................................................................................202

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9 Specific optimization..............................................................................................202 Global optimization and compensation.................................................................. 203 Not Recalled Recommendations...................................................................................203 Aim 4: Driving Habits Changes........................................................................................... 204 Study Limitations.............................................................................................................. ....207 Future Directions..................................................................................................................209 APPENDIX A TELEPHONE INTERVIEW................................................................................................ 211 B MEDICATIONS FORM...................................................................................................... 228 C CDC STUDY CLINICAL ASSESSMENT FORM.............................................................230 D NHTSA STUDY ADReS AND CLINICAL ASSESSMENT FORM................................. 235 E LIST OF COMBINED CLINICAL MEASURES ............................................................... 239 F RECOMMENDATION FORM FOR GRS OF 1................................................................. 240 G RECOMMENDATION FORM-1 FOR GRS OF 2.............................................................. 242 H RECOMMENDATION FORM-2 FOR GRS OF 2............................................................. 244 I MEDICAL REPORTING FORM........................................................................................ 245 J ROAD TEST GRS 0............................................................................................................. 246 K ROAD TEST GRS 3............................................................................................................. 261 L FOLLOW-UP INTERVIEW................................................................................................278 M DRIVING REHABILITATION SPECIALIST INTERVIEW ............................................290 N CORRELATIONS OF SOCIAL DESIRABILI TY, FALSE RECALL AND NO RECALL...............................................................................................................................293 LIST OF REFERENCES.............................................................................................................297 BIOGRAPHICAL SKETCH.......................................................................................................315

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10 LIST OF TABLES Table page 2-1 Studies of standardized behind-the-wh eel assessm ents and driving behaviors................. 89 3-1 Clinical measures.......................................................................................................... ...110 3-2 Follow-up response prediction......................................................................................... 110 3-3 Characteristics of follow-up responders and non-responders.......................................... 111 3-4 Driving recommendations categories..............................................................................114 3-5 Expected trends based on the ca tegories of driving recomm endations...........................117 4-1 Sample descriptives........................................................................................................ .142 4-2 Mean number of recommendations for older drivers......................................................143 4-3 Selection Driving Recommendations..............................................................................146 4-4 Specific Optimization Recommendations........................................................................ 147 4-5 Optimization Global and Compensation Recommendations........................................... 149 4-6 Tests used in discriminant function.................................................................................150 4-7 Tests of normality and homogeneity of va riance of discrim inant function variables......151 4-8 Means and standard devia tions by driving perform ance.................................................152 4-9 Correlation coefficients by discrim inant functions..........................................................154 4-10 Classification results.................................................................................................... ....155 4-11 Number of drivers with recommendations at follow-up.................................................. 155 4-12 Number of drivers with recommendations at follow-up.................................................. 156 4-13 Most common recommenda tions given and recalled....................................................... 157 4-14 Number of drivers with recommendations at follow-up.................................................. 159 4-15 Number of drivers who ha d selection recomm endations................................................. 161 4-16 Number of drivers who had specific optim ization recommendations............................. 162 4-17 Number of drivers who had global optim ization recommendations................................ 163

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11 4-18 Number of drivers who ha d com pensation recommendations.........................................164 4-19 Selection recommendations follow-up sample................................................................ 166 4-20 Optimization specific recommendations follow-up sample............................................ 166 4-21 Optimization global follow-up sample............................................................................ 168 4-22 Follow-up recall of selection recommendations.............................................................. 169 4-23 Follow-up recall of optimization specific recommendations........................................... 171 4-24 Follow-up recall of optimization glo bal and compensation recommendations............... 175 4-25 Driving exposure and avoidance of follow-up sample.................................................... 177 4-26 Driving exposure and avoidance...................................................................................... 177 4-27 Places driven............................................................................................................. .......184 4-28 Driving avoidance at baseline and follow-up.................................................................. 184 E-1. List of combined clinical measures...................................................................................... 239 N-1. Correlations of social desirab ility, false recall and no recall ...............................................294

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12 LIST OF FIGURES Figure page 1-1 Rizzos information-processing m odel for understanding driver error ............................. 361-2 Michons Hierarchical model of driving behavior............................................................. 361-3 Driving and the International Classifica tion of Functioning, Disability, and Mental Health Model (ICF)............................................................................................................383-1 Example recommendation form 1.................................................................................... 1123-2 Example recommendation form 2.................................................................................... 1123-3 Example of recommendations on the comments section of the road test........................1133-4 Example of recommendations next to driving performance score.................................. 1133-5 SOC Driving Recommendations conceptual model........................................................ 1164-1 Global rating score and age at baseline............................................................................ 1424-2 Total percent of driv ers with recommendations.............................................................. 1434-3 Percent of drivers in driving performa nce groups with selection recommendations....... 1444-4 Percent of drivers in driving perfor mance groups with specific optimization recommendations............................................................................................................. 1444-5 Percent of drivers in driving performance groups with global optimization recommendations............................................................................................................. 1454-6 Percent of drivers in driving performance groups with compensation recommendations............................................................................................................. 1454-7 Discriminant function...................................................................................................... 1544-8 Total recommendations of follow-up sample.................................................................. 1594-9 Uncued and cued false recall of selection recommendations.......................................... 1604-10 Uncued and cued false recall of specific optimization recommendations....................... 1604-11 No cued recall of selection recommendations................................................................. 1614-12 No cued recall of specific optimization recommendations.............................................. 1624-13 No cued recall of global optimization recommendations................................................ 163

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13 4-14 No cued recall of compensation recommendations......................................................... 1634-15 Uncued and cued recall of selection recommendations................................................... 1644-16 Uncued and cued recall specifi c optimization recommendations.................................... 1654-17 Days per week............................................................................................................. .....1784-18 Number of trips per week................................................................................................1794-19 Number of places per week.............................................................................................. 1804-20 Average miles per week................................................................................................... 1814-21 Distance driven past month.............................................................................................. 1824-22 Driving avoidance past month.........................................................................................183

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14 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy PROMOTING OLDER DRIVER SAFETY: IMPACT OF DRIVING REHABILITATION SPECIALIST RECOMMENDATIONS ON OLDE R ADULTS DRIVING PERFORMANCE By Maria Cristina Posse December 2008 Chair: William C. Mann Major: Rehabilitation Science Recognizing the importance of driving and the potential negative impact of driving cessation, the present study sought to understand the effectivene ss of interventions aimed at maintaining older driver safety. Driving rehabilitation specialists (DRSs) are trained professionals, typically occupati onal therapists, who conduct assess ments of older drivers. DRSs frequently provide recommendations to older driv ers, but the impact of these recommendations on older drivers performance is unknown. This study evaluated common and recalled driving rehabilitation specialists recommendations for olde r drivers in order to determine best practices for DRS and delineate important components of driving performanc e and older drivers safety. Common driving recommendations were examined for a sample of 118 older adults who had a clinical and road test driving assessment. Overall, 22% of drivers (n=26) had recommendations to avoid drivi ng conditions (i.e., night, high tr affic); 78% (n=92) had driving behavior recommendations such as increasing the following distance, scanning, or signaling; 22.9% (n=27) had recommendations to take a driv ing course or read a drivers handbook, and 11.9% (n=14) had recommendations to take behind-the-wheel training. The results showed different patterns of recommendations for uns afe and safe drivers. After 2.5 years, 65 participants completed a te lephone interview and approxima tely 80% recalled driving

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15 recommendations provided to them by the DRS, with recall of recommendations mainly related to driving behaviors and avoidance of conditi ons. Driving recommendati ons are described in light of the Selective Optimi zation with Compensation (SOC) model of successful aging. At follow-up, driving habits of participants did not differ by driving performance classification (unsafe, safe with recommendations, and safe). Over time, older adults reduced the days driven per week, but were making more trips, going more places, and driving more miles, suggesting the value and preference for the car as the main means of transportation.

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16 CHAPTER 1 INTRODUCTION This section describes the m eaning of drivi ng in the American cu lture and how driving recommendations from a Driving Rehabilitation Specialist (DRS) may help prolong safe driving among older adults. This section also reviews driving behavior mode ls that explain driving and its components, and help guide driving recommen dations provided by the DRS. The section ends with the objectives of the current research. Driving provides independence, community mo bility, a sense of well-being, and is an important component for maintain ing quality of life. Driving is an enabler (Ralston et al., 2001), pg. 64) allowing us to engage in other importa nt life activities. As more adults continue to drive into their later years, at a time of life w ith increased prevalence of physical and cognitive impairments, traffic safety relative to older dr ivers is becoming a public health concern (Foley, Heimovitz, Guralnik, & Brock, 2002). To help older drivers maintain driving safety as well as independence, the American Occupational Th erapy Association (AOT A) and the National Highway Transportation Safety Ad ministration (NHTSA), following an older drivers consensus conference in 2002, have worked to increase the number of well trained professionals, such as driving rehabilitation specialists (DRSs) for older driver assessment and intervention (AOTA, 2002). Most DRS are occupational therapists, who o ffer clinical and road test assessments and provide interventions to improve, maintain, a nd/or prolong safe driving ability (Finn, 2004). Following a driving assessment, occupational therapists frequently provide recommendations to older drivers. However, the impact of these r ecommendations on older drivers performance is unknown.

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17 Driving and Older Adults In the United States, the car is the prefe rred, often necessary, m ode of transportation. Therefore, lim itations in driving can impact elder independence and quality of life (Collia, Sharp, & Giesbrecht, 2003). Some authors have suggested that driving is a major rite of passage in American society (Ralston et al., 2001), pg. 69). As a societal value, driving implies freedom (Ralston et al., 2001), independence (Bonnel, 19 99; Peel, Westmoreland, & Steinberg, 2002; Ralston et al., 2001), a way of life (Bonnel, 1999; Peel et al., 2002), a sense of identity (Peel et al., 2002), competence and responsib ility (Johnson, 1998), and the abil ity to maintain roles and occupations (Ralston et al., 2001) Driving is a component of many daily activities such as shopping (Johnson, 1998). When the ability to drive is reduced by a sensory, cognitive, or motor impairment, many individuals participate less in ac tivities, and this can lead to depression and isolation (Marottoli et al., 2000; Marottoli et al., 1997; Reuben, Silliman, & Traines, 1988). Driving Recommendations Recognizing the importance of driving and the potential negative impact of driving cessation, the present study seek s to understand the effectivene ss of interventions aimed at maintaining older driver safety. One of the im portant interventions of DRSs is to provide recommendations such as (1) increasing following distance with other cars (2) avoiding drifting, and (3) signaling more frequently. Some recomm endations involve restri cting driving, such as: (1) not driving at night, (2) a voiding heavy traffic, and (3) driving only in close proximity to home. This study will evaluate common recomm endations provided by a DRS and the recall of these recommendations 1.5 to 3 years following the recommendations. If older drivers do not remember these recommendations, then we need to modify the process of providing recommendations in some way, such as with foll ow up reminders. If olde r drivers recall driving recommendations, then this study can serve as a basis to study adoption of driving

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18 recommendations and study whether or not follo wing these recommendations results in safer driving as measured by crashe s, and/or driving errors. Theoretical Models of Driving Behavior and Aging Examining different models of driving be havior can help in understanding how DRSs make decisions for providing driving recommenda tions and how older dr ivers behavior may improve by following these recommendations. Severa l models of driving behavior are described in the traffic safety literature. Michon (1985) classified driving models as taxonomic and functional, and each of these can be further categorized as behavi oral or psychological (Table 11). Taxonomic models provide mainly descriptions of the driving task or driving components while functional models describe the interaction among components of driving. Taxonomic models of the behavioral type describe the tasks and s ubtasks for driving; and taxonomic models of the psychological type list aspects of the individual that ar e involved in driving but do not give a detailed explanation of the interaction among the persons abili ties and the driving task. In contrast functional models of the behavioral type delineate person, vehicle, and environmental factors that relate to driving, but ignore the internal motives or information processing of the driver. Lastly, functional models of the psychological type take into account the individuals motives and reasons that affect driving, such as the drivers perception and avoidance of risks, and the drivers goals of a trip. In this section, several models of driving behavior are describe d, and a table is provided at the end to describe the conceptu al contributions of the models to our study (Table 1-2). This study primarily draws on the theoretical contributi ons of a cognitive model of driving behaviorMichons Hierarchical Model that, in conjunc tion with the Selectiv e Optimization with Compensation model of successful aging, he lp explain and categorize DRS driving recommendations.

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19 Taxonomic Models of Driving Behavior Task analyses McKnight and Adams task analysis (McKnight and Ada ms, 1970 as cited in Michon, 1985) is a taxonomic and behavioral approach that classifies drivi ng into 45 tasks and more than 1700 components of tasks (Michon, 1985). This task analysis model is an extensive description of driving behaviors that can help describe in detail the specific components of driving maneuvers (Michon, 1985). For example, some pa rts of task 42.0: Negotiating On-Ramps and Off-Ramps are: Task 42-123 Observes a general on-ramp/main roadway configuration 42-1231 Looks to see if on-ramp feeds into righ t side of main roadway or left side (speed lane) of main roadway 42-1232 Looks to see if acceleration la ne is provided at end of on-ramp 42-1233 Looks for exit off-ramps or deceleratio n lanes which cross over or share continuing portions of the entrance ramp 42-1234 Evaluates effects of on-ramp/main roadway configuration on available merging distance and probable merging pattern. The McKnight and Adams task analysis model neglects the fact that driving is a complex task, requiring continuous interaction of the drivers cognitive, sensory, and neuro-motor systems, as well as interaction with the vehicle, and the e nvironment in which the vehicle is traveling (Eby, Trombley, Molnar, & Shope, 1998; Wang & Carr, 2004). A model that simply describes task components fails to account for many situations that can affect driving behavior. For example, unexpected inclement weather conditions duri ng highway driving put higher demands on attention, affect control of the vehicle, and may increase the risk for crashes. Trait models The second class of taxonom ic models is psychological, and includes (1) Fleishmans Taxonomy of Human Performance and (2) McKennas Accident Proneness and Involvement

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20 Michon labeled these models as Trait Models, since they describe aspects of the individual that affect driving behavior. Although Fleishman did not classify the indivi duals characteristics specific to the task of driving, his studies we re among the first attempts to describe human performance with a classification of task s (Fleishman, 1967, 1975). After conducting several studies of more than 200 human tasks, Fleish man and colleagues identif ied 11 psychomotor and 9 physical abilities common to individuals perfor mance. These abilities included reaction time, multilimb coordination, aiming, finger and manual dexterity; and physical aspects such as strength, coordination, and equi librium. Other studies by Fl eishman (1967) described the characteristics of tasks such as level of diffi culty and duration, and how these interact with human abilities. Fleishmans work provided em pirical evidence of th e interaction between external and internal aspects of human performa nce in tasks, and concluded that a combination of task elements and abilities could best desc ribe human performance. This interaction of elements also can describe the task of driving, since driving in different environments entails varied levels of difficulty; and the persons abilities such as attention or reaction time can vary depending on the difficulty of the driving environm ent. For example, driving straight on a road with low levels of traffic requires lower levels of attention than approaching and crossing an intersection. The last example of taxonomical models is the Accident Proneness and Involvement Model (McKenna, 1982). This model is based on counts of crash frequencies but does not address internal or psychological factors that could impact driving, an d does not fit well under Michons classification of psychological models. Crash res earch evolved from quantifying crashes to the inclusion of predictors of crash involvem ent (McKenna, 1982). Although some authors have found that measures of visual a ttention are strong predic tors of crashes in retrospective studies

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21 (Ball, Owsley, Sloane, Roenker, & Bruni, 1993; Owsley, Ball, Sloane, Roenker, & Bruni, 1991), using crashes as an outcome measure of driving ab ility is problematic for several reasons. First, crashes are rare (McKenna, 1982; Ranney, 1994), and researchers need large sample sizes to make reliable comparisons and predictions (D e Raedt & Ponjaert-Kristoffersen, 2000a; Owsley et al., 1991). Second, self-reports of crashes do not correlate highly with state records (Owsley et al., 1991). This may be due to older adults inaccu rately self-reporting their crashes or biases in the state records. Traffic researchers use state records to determ ine at-fault crashes and then examine the causes and types of crashes. Using at-fault crashe s is problematic because researchers have to assume that other drivers on the road are a co nstant and representative sample of drivers (Hakamies-Blomqvist, 1998). Crashes have multiplecauses and it is often difficult to isolate a single cause or to determine who is at-fault. Reports of crashes are based on self-report of the parties involved and in some cases witnesses, but the authorities may be biased in assigning fault more often to older and younger drivers than is actually the case (Langf ord, Koppel, Andrea, & Fildes, 2006). Another factor to consider when studying crashes is how crash risk is measured. Crash risk is a ratio of the total number of crashes to a measure of driving exposure. Researchers use different ways to measure driving exposure, such as total annual miles. Research suggests that older drivers had higher crash risk than all ot her age groups except young drivers. However, recent studies showed that despite the drivers age, those who drive fewer annual mileage are at higher risk of crashing than those who drive more miles (Langford & Koppel, 2006; Langford, Methorst, & Hakamies-Blomqvist 2006). Accurate comparisons of crashes among segments of

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22 the population need to be based on similar measures of driving exposure, but this is rarely the case. Studies have used the total number of license s to determine the number of active drivers. This measure can be problematic since states ha ve different driving renewal policies (HakamiesBlomqvist, 1998; Tay, 2006) and age based policies of license restrictio n may encourage older adults to cease driving prematurely not necessarily due to higher risk but because they prefer not to renew their license (Hakamies-Blomqvist, Johansson, & Lundberg, 1995). Finally, crash databases may overrepresent older drivers since these databases record crashes that result in fatalities and older drivers frailty makes them mo re likely to experience injury and fatality in crashes (Hakamies-Blomqvist, 1998; Li, Braver, & Chen, 2003). Functional Models of Driving Behavior Like taxonom ical models, functional models are either behavioral or psychological. Behavioral and functional models de scribe driving in terms of the interaction of the vehicle with the traffic or the interaction of the vehicle and the drivers maneuve ring to react to road conditions. Michon classified thes e models as Mechan istic and Adaptive Control in nature. Mechanistic models Mechanistic m odels are mathematical explanations of the flow of traffic (Greenberg, 1959) and car following (Herman, Montroll, Potts, & Ro thery, 1959). These mathematical approaches to study driving behavior limit the understanding of the driving task, since driving is not always a stable task and many driving situations are spontaneous. For example, assuming that the flow of traffic is stable does not ta ke into account such factors as in clement weather or accidents that slow or stop traffic flow. Similarly, these mechan istic models neglect the motives of the driver, such as following too close due to aggressive driving behavior.

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23 Adaptive control models The second category of behavioral and functi onal m odels, Adaptive Control, includes the Driver/Vehicle Control and Inform ation Flow Control models. The Driver/Vehicle Control model addresses the interaction of the driver, the vehicle, and the roadway environment for steering control in driving maneuve rs such as turns, merges, lane changes, and passing (McRuer, Allen, Weir, & Klein, 1977; Weir & McRuer, 19 68). According to this model, the driver establishes and mainta ins control of the car when steering. First, the driver receives input from other vehicles and the roadway design to dete rmine where he is heading. Next, the driver controls the car using learned responses or by anticipating a re sponse after seeing the road and compensating for errors. Once the driver makes th e steering maneuver, the vehicle motion can be affected by the road surface or winds and the driver has to adjust for these changes. This model provides a basis for improving roadway design such as angles and markings (Weir & McRuer, 1968). However, in this model the driver compensa tes for lateral control of the vehicle based on roadway information only. The model does not addre ss all the physical and c ognitive aspects that could influence steering the vehicle such as tr emors due to Parkinsons disease or lack of attention to the road. An example of Information Flow Control models is the Driver-Vehicle Effectiveness Model (DRIVEM). DRIVEM is actually a driver si mulation program for predicting crashes. This model was developed in 1978 for the National Highway Traffic Safety Administration (NHTSA) and represents early attempts to use simulated dr iving to study vehicle crashes. As in simulated driving scenarios, the DRIVEM involves programming of input and output computer responses based on the drivers actions for each maneuver, such as following, avoiding an obstacle, or merging (Wolf & Barrett, 1978). Although modern simulators let the driver immerse into driving scenarios, using simulato rs to evaluate driving behavior can be problematic for several

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24 reasons: (1) high-quality simulated scenarios are costly, (2) data collection can be frequently hindered by participants simulator sickness or co mputer technical problems, and (3) simulators cannot always replicate the multi-tasking nature of driving. Although simulators can provide unexpected events such as pedestrians crossing the street or a car honk ing behind, these events are more complex in real driving due to interac tions between the weather, the traffic flow of a street, the varied responses of other dr ivers, road conditions, and time of day. Motivational models Motivational and cognitive processing m odels are functional models based on psychological rather than behavioral aspects of driving. Examples of motivational models include Risk Homeostasis, Zero-R isk, and Threat Avoidance. The Risk Homeostasis Theory (Wilde, 1982, 1988; Wilde & Murdoch, 1982) poses th at individuals risks while driving are based on four motivators: the expected advantages and costs of risky driving behaviors, and the benefits and costs of cautious dr iving. For example, if a person believes that the benefit of wearing a seat belt does not exceed the benefit or advantage of not wearing a seat belt (e.g., because it is uncomfortable), then the driver will more likely continue to perceive using a seatbelt as an unnecessary behavior. The Risk Home ostasis Theory states that interventions to increase safe driving behavior, such as traffic regulations (e.g., speed limits, seatbelt use), educational interventions, traini ng in driving skills, improved vehi cle designs or highways do not impact the crash rates of a population; and that su ch measures only have a temporary effect on an individuals perception of risk, but driving behavior is ultimately determined by the persons willingness to be safe by adopting or not adopting risky behaviors. Although this theory acknowledges the motivations of a driver, it rules out evidence showing the effectiveness of educational and physical exercise interventions, and the effectiveness of highway design or traffic regulations (e.g., seat belt use) that can improve driving behaviors and help reduce crash

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25 rates (Classen et al., 2006; Kua, Korner-Bit ensky, Desrosiers, Man-Son-Hing, & Marshall, 2007; Marottoli, Van Ness et al., 2007; McKenna, 1988; Stalvey & Owsley, 2003); and it assumes that the use of assistive devices or car adaptations such as extended handles for signaling, manual driving controls, and steering knobs, are ineffective (e.g., ineffectiv e for a person with stroke or arthritis, since these devices would not help re duce the individuals cras hes and risky driving). This seems unlikely since manual controls and steering knobs can help drivers with decreased proprioception, strength, and decreased hand dexter ity maintain their safety on the road. Other reasons not addressed by Wildes theory could be reduced driving due to retirement (Raitanen, Tormakangas, Mollenkopf, & Marcellini, 2003), not driving to reduce driving-related expenses (Hakamies-Blomqvist & Siren, 2003; Hakamies -Blomqvist & Wahlstrom, 1998), or ceasing driving after a family member or doctor make s a recommendation to stop driving due to cognitive or visual impairment. The Zero-Risk Model is another motivational mo del under the functional and psychological classification (Naatanen & Su mmala, 1974; Summala, 1988). This model considers driving as a primarily automatic task in which individuals perc eive no subjective risk. Proponents of this model state that novice drivers initia lly fear driving situations that they progressively learn to control. The main concern of drivers is maintaining a safety margin which the authors illustrate as driving behaviors incl uding car following, pa ssing a car, accelerating, lane keeping, curve negotiation, gap acceptance, and overtaking (Naatanen & Summala, 1974). For example, drivers will gradually adjust their speed levels to match their perceived safety margins. Although this model outlines some beha viors that affect safe driving, a subjective perception of risk is not the only component of driving behavior. Naatanen and Summala (1974) used only physiological responses as outcome measures of subjective risk, but many other

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26 factors can affect the subjective experience of driving, such as pleasure of driving, concern with violating traffic laws, concern with the expense of driving (Naat anen & Summala, 1974; Rothengatter, 1988), or visual i ndicators in the vehicle signaling low gas or battery problems. The Threat Avoidance Model combines the concepts of the two previous models. The driving task is considered in relation to the persons avoidance or non-avoidance of an aversive stimulus that could lead to accidents (Fuller, 1984). Thus, if the driver is in a high traffic situation, they may or may not reduce the spee d anticipating a crash. Th e problem with this model is that it describes stimulus and avoida nce responses in a simplistic stimulus-response fashion, and it fails to account for the multi-ta sking and problem solving required in driving (Michon, 1989), such as dealing with two or more threats or stim ulus at a time (i.e., a detour ahead, keeping a safe following distance, and beco ming distracted by a pedestrian). Limitations of this model include the absence of research to support the propositions of motivational models (Ranney, 1994), failure to address the multi-f aceted nature of the driving-environment interaction (Wang & Carr, 2004), as well as ignoring the informati on processing that takes place while driving. Cognitive (process) models Cognitive m odels are functional models of driv ing behavior that take into account the cognitive (i.e., problem solving) and executive functions necessary for safely planning a trip and maneuvering on the road (Rizzo, 2004). Examples include Rizzos information-processing model of driving errors (Rizzo, 2004) and Michons hierarchical model of driving performance (Michon, 1985). The information-processing model (Rizzo, 2004) incorporates cognitive processes such as perception, attention, and memory to illustrate ho w a driver perceives, processes information, and executes a response (Figure 1-1). Unlike th e motivational models described above, the

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27 information-processing model does not narrow the definition of drivi ng behavior as risk perception or avoidance. Rather, this model considers the percep tion of diverse stimuli and the selection of a proper response fr om a driving situation or from experience. Although this model describes the cognitive processing of a driver after making a drivi ng error, it does not account for how driving behavior would change or be improved after the driver perceives and processes the errors. This information processing model also ne glects the environment and the motivations of the driver, which are addressed in Michons Hierarchical Model. Michons Hierarchical Model: Michons Model (Michon, 1985) de lineates three levels of driving performance: strategical, tactical, and operational (Figure 12). The strategical level is at the top of the hierarchy, and incl udes trip planning (setting goal s, planning route, estimating costs, and exploring risks). Th e tactical level invol ves maneuvering the vehicle through turns, overtaking, avoiding obstacles, a nd all other traffic conditions. The third level, operational, includes automatic actions to c ontrol the vehicle under changing s ituations, such as braking and steering. To illustrate, a drivers plan to go to the grocery store followed by a stop to pick up a prescription medicine at the pharmacy represents strategical levels of driving since it involves the goals and the plan for getting to these pla ces. The maneuvers to get to the store and the pharmacy, such as left turns, right turns, or stra ight driving are the tac tical components; and the actions that control the car, such as accelerat ing or steering the wheel, are at the operational level. Michons model acknowledges the environmenta l input and dynamic interaction among the hierarchical levels to expl ain driving behaviors. This mean s that unexpected occurrences on the road can alter the tactical and operational cont rol of the car, such as suddenly breaking for a pedestrian crossing the street. In contrast to the other models of driving behavior, Michons

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28 Hierarchical Model gives purpose to the driving ta sk in the form of strategical planning. This model includes the drivers planning their trip, the reason for their tr ip, and their choice of route. This broadens the idea of driving from an input-o utput task to a task th at involves the drivers cognitive processing of their need s and their interaction with the road. Michon s tactical level of driving behavior acknowledges all the possible maneuvers that a dr iver can make while driving a vehicle, such as turning or passing a car. Finally, the operational level helps explain the physical movements to control the vehicle, such as steeri ng. Overall, the Hierarch ical Model expands the concepts of previous driving m odels and includes a holistic appr oach to driving that does not simply look at the task, or the ro ad-vehicle interactions, or the pe rceived risks as unique goals of driving behavior. Rather, the Hier archical Model integrates all th ese components in a theory of person-environment control of dr iving. The limitation of Michons Model is that the drivers motivations such as confidence or stress ar e not explicitly described in the model. Although cognitive models of driving behavior are criticized for ignoring motivational or emotional components of driving (Ranney, 1994), rece nt traffic safety research has incorporated a model of successful aging, the Selective Optimization wi th Compensation (SOC) with Michons Hierarchical Model conceptualizing driving behavior with both cognitive and motivational components (De Raedt & Ponjaert -Kristoffersen, 2000a). The SOC Model is described below. Successful aging is the proce ss of maximizing gains or desired outcomes and minimizing losses or undesired outcomes (Fre und & Baltes, 1998; Mars iske, Lang, Baltes, & Baltes, 1995). This definition provides for flexib ility among individuals, c ontexts, and cultures. Selective optimization with compensation (SOC), a leading model of succe ssful aging, has been described as a metamodel to study the process of successful aging across functions, domains, and person-context interactio ns (Marsiske et al., 1995).

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29 Selective Optimization and Compensa tion Model of Successful Agin g The three components of SOC are: (1) Selection, (2) Optim ization, and (3) Compensation. Selection is the process of addi ng, deleting, or revising ones goals. Individuals select their goals based on choices that are time and space constr ained, or based on loss of resources (Freund & Baltes, 1998). Physical, motor, and cognitive declin es that occur with age, as well as reductions in environmental resources, can set forth lim itations in the selection process (Baltes & Carstensen, 1996; Marsiske et al., 1995). In rela tion to driving, a DRS ma y give older adults recommendations to revise the places they drive and avoid driving in limited situations such as driving only during daylight hours. The second component, optimization, is using internal or external resources to improve or maintain existing means of obtaining goals and outcomes. It includes, for example, practicing skills or modeling others (Freund & Baltes, 1998). Adopting driving recommendations from a DRS to improve driving, such as increasing the following distance between cars, avoiding drifting, and making complete stops are examples of strategies to optimize driving. The third component of SOC is compensation. Compensation is closely related to optimization and refers to alternativ e ways of dealing with functional limitations (Baltes & Carstensen, 1996; Mars iske et al., 1995). Compensation strategies include therapeutic interventions, use of external aids, and s ubstitution of means (Fre und & Baltes, 1998). For example, older drivers might have to stop dr iving and start using alternative ways of transportation or implement assistive devices in their vehicles such as wheel-knobs to compensate for decreased dexterity due to arthritis. DeRaedt and Ponjaert-Kristoffersen (2000) used the models of Selective Optimization with Compensation and Michons Hierar chical Model to study older adults use of compensation strategies based on the frequency of crashes and le vels of driving performance. Their research suggested that older adults w ith lower scores on driving performance used more strategical

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30 compensation, such as avoiding driving in the dark or in the rain, than drivers with scores of average and good driving abilities. Bu t drivers with low levels of performance used less tactical compensation, such as increasing the following distance with the car in front or adjusting their speed, than drivers with higher leve ls of performance. The results of a study using crashes as the outcome measure showed that drivers with lo w levels of driving performance and no selfreported crashes used more strategical and tactical compensation than drivers with low levels of performance and self-reports of crashes. This study was a first attempt to classify drivers performance based on levels of compensation ac cording to the SOC and Michons models, but it used self-report of crashes as an outcome measure and tactical compensation was based on observation of only four driving behaviors (De Raedt & Ponjaert-Kristoffersen, 2000a). Summary Eleven models of driving behavior have been reviewed and described. These models have focused on: (1) describing the tasks and co mponents involved in driving, (2) the interaction of stimulus and responses among the vehicle, the road, and the driver, (3) driver motivation, and (4) the cognitive mechanisms associated w ith the interactions among driver-vehicleenvironment. Table 1-2 summarizes these models of driving behavior. Although each model has useful components, the combina tion of Michons Hierarchical M odel and the SOC Model is the best fit for this study of DRS driving recomm endations. Driving recommendations constitute information that would ultimately be processed and ideally integrated into driver behavior at the strategical, tactical, and operational levels. Th ese three levels of cognitive processing are complemented by older adults choices of select ing where to drive, op timizing their driving behaviors, avoiding some driv ing situations, or compensatin g for functional declines.

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31 Driving and the International Classification of Functioning, Disability, and Mental Health Model (ICF ) The International Classification of Functioning, Disability, and Hea lth Model (ICF) provides a broader perspective for explaining the interaction among biological, psychological, and social components that influence the task of driving among older adults The ICF also is the model promoted by the University of Florida Re habilitation Science Doctoral Program. Thus, it is important to show its rela tionship to driving and to th e present study. The ICF model, published in 2001, provides a univers al language of disability a nd a biopsychosocial perspective of functioning, disability, and health (Schne idert, Hurst, Millers, & Ustun, 2003; Ustun, Chatterji, Bickenback, Kostanjsek, & Schneider 2003). This model was revised to include a positive description of functioning and health rather than a negative view on consequences of diseases (WHO, 2001). The ICF model applies to all people (WHO, 2001), and is not limited to any sub-population with disabilities (Schneidert et al., 2003; WHO, 2001). The ICF model is divided into parts and compon ents that are in cont inuous interaction, can occur in any direction, and provide positive or negative aspects of f unctionality (WHO, 2001). The parts of the model include body functions an d body structures, activiti es and participation (Figure 1-3). These parts have components of functioning and disability. Contextual factors, which include environmental and personal factors also interact with th e parts and components. Disability occurs when there is impairment, limita tion in activity, or a rest riction in participation. Impairments are defined as problems in body func tion or structure such as significant deviation or loss; activity is the execution of a task or action by an individual ; and participation is involvement in a life situation (WHO, 2001) Severely impaired body functions can impede driv ing activity, leading to limitations in the individuals capacity to participate in social, community, or other daily events. Studies have

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32 reported older adults significant re ductions in participation in social activities after driving cessation including shopping or going to the mall to pass time, playing cards, bingo or games, traveling, going to a restaurant, movie, or spor ting event, volunteer or paid work, and religious activities, and visiting family (Bonnel, 1999; Marottoli et al., 2000). Giving up driving implies loss of freedom and independence (Ralston et al ., 2001). Driving cessation can be stigmatizing, signaling old age and dependency (Taylor & Trip odes, 2001). Driving cessation also has been associated with increased levels of depre ssion among community-dwelling older adults, after controlling for health and demographic factor s (Fonda, Wallace, & Herzog, 2001; Marottoli et al., 1997; Ragland, Satariano, & MacLeod, 2005). Driving cessation also can cause anxiety, decreased morale (Johnson, 1998), frustration, anger, and stress (Peel et al., 2002). Recommendations of a DRS are in part based on clinical obse rvations and assessments of body functions relevant to driving ability, and fo cus on specific aspects of the driving activity that need to be improved, adapted, or compen sated. The goal of DRS recommendations is to improve driving performance and help drivers main tain their driving independence for as long as possible. Besides the interactions among body fu nctions, activity, participation, and health conditions, the ICF model also describes contextual factors that affect all of these aspects. Contextual factors are divided into personal and environmental factors. Personal factors are not specifically delineated in the ICF model because they are largely variable across cultures and societies (Schneidert et al., 2003; WHO, 2001). Some of the pe rsonal factors are age, race, gender, lifestyle, habits, coping styles, education, and char acter style (WHO, 2001). Personal factors such as age, gender, confidence in drivin g ability, and lifestyle can influence older adults exposure to driving situations, for example by avoiding driving c onditions (i.e., rain, night, etc.) or driving less (Anstey & Smith, 2003; Bauer, Adler, Kuskowski, & Rottunda, 2003; Charlton et

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33 al., 2006; Hakamies-Blomqvist & Siren, 2003; Marottoli & Richardson, 1998; Vance et al., 2006). The last component of the IC F Model relates to environmental factors. This includes products and technology, the natura l environment and changes made to the environment, support and relationships; attitudes, and services, and systems and policies (Schneidert et al., 2003). In the context of this study, Pro moting Older Driver Safety: Imp act of Driving Rehabilitation Specialist Recommendations on Older Adults Dr iving Performance, driving recommendations from a DRS fall under the environmental factors, specifically as services that are provided to improve driver safety. The DRS services also are embedded in a larger system, also an environmental factor, of public safety policy. Currently, licenses and re newal policies are not consistent among states, not all restrictions are accepted by older drivers, and some parameters used to renew licenses such as age, are biased measures of fitness to drive and may lead to premature driving cessation (Freund & Colgr ove, 2008; Hakamies-Blomqvist, Johansson, & Lundberg, 1996; Parker, McDonald, Rabbitt, & Sutcliffe, 2003; Reuben et al., 1988; Waller, 1991). The present study will determine what dr iver recommendations, including modifications to driving behavior or driving restrictions ar e provided to and recal led by older drivers. Since little is known about how older driver s may or may not benefit from DRS driving recommendations, this study will be the first to examine recommendations given by a DRS. The goals of this study are to: (1) Identify the most common types of driving recommendations that a DRS provides to older drivers. (2) Examine what combination of cognitive, motor, and sensory clinical tests help predict the DRS decision to determine whether an older driver is: (1) unsafe, (2) unsafe but remediable, (3) safe with recommen dations, or (4) safe to drive. (3) Longitudinally evaluate whether or not older drivers remember driving recommendations made by a DRS, with and without cueing.

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34 (4) Determine if driving performance (unsafe, unsafe but remediable, safe with recommendations, or safe) predic t older adults driving habits.

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35 Table 1-1. Driving behavior models Types Taxonomic Functional Behavioral (Input-Output) Task Analyses Mechanistic Models Adaptive Control Models Driver/Vehicle Control Information Flow Control Psychological (Internal State) Trait Models Fleishmans Taxonomy of Human Performance Accident Proneness and Involvement Motivational Models Risk Homeostasis Theory Zero-Risk Model Threat Avoidance Cognitive (Process) Models Information Processing Model Hierarchical Model Note: Modified and adapted from (Michon, 1985)

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36 Figure 1-1. Rizzos information-proce ssing model for understanding driver error Figure 1-2. Michons Hierarchical model of driving behavior Strategical Level Maneuvering Level Control Level Environmental input Environmental input Perceive, attend and interpret stimulus Plan action (select response) Execute action (implement response) Previous experience (memory) Evidence of stimulus Outcome of behavior

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37 Table 1-2. Description of the models of driving behavior Models of driving behavior Description Task analysis Helps identify the steps involved in the task of driving. These steps can help delineate the development of road tests to evaluate the drivers ability in different segments of the driving task such as merging, lane changing, and turning. Fleishmans taxonomy of human behavior Stresses the importance of the drivers c ognitive and physical characteristics, and the characteristics of tasks such as du ration and difficulty th at affect driving ability. These elements contribute to the understanding of clinical predictors of driving ability such as attention and speed of processing. It also helps delineate the guidelines of road tests. For exampl e, the road test used in our study was graded by difficulty levels of driving from neighborhood and residential areas to highway driving. Accident proneness Determines human factors that correlate or predict accident occurrences. Mechanistic Helps engineers explain the mathema tical relationships between traffic flow and vehicles. Driver/ Vehicle Control Helps identify changes in highway design that can improve dr iving ability. This model considers the process of steering taking into account the vehicle-road interactions. The model recognizes vehicle deviations due to wind forces or road geometry that influence the dr iving behavior of steering. Information flow control Provides an example of simulated driving using a series of consistent maneuvers. Risk homeostasis Explains driving by motivations based on the costs and benefits of risky or cautious driving behaviors. Zero-risk Reflects an early attempt in the iden tification of safe driv ing behaviors that the authors described under the term safety margins. However, these driving behaviors are dependent on pe rceived levels of risk. Threat avoidance Highlights the importance of driving e xperience since practic e helps a driver produce more anticipatory responses to threats or dangers on the road. Information processing model Delineates the cognitive components that influence driving behavior such as perception, attention, and memory. These cognitive processes can change and vary depending on the stimulus and whethe r the driver has previous experience with the driving situations. Hierarchical model Explains driving behavior in terms of a cognitive hierarchy or actions and information processing that enables safely navigating on the roads. It can also be complemented with the motivations of the driver such as goals for the trip. Hierarchical model and SOC Delineates different actions that an indi vidual chooses for succ essful aging, such as selecting what is desirable, optimiz ing driving behavior, and compensating for age-related declines. Along with the cogniti ve hierarchy of strategical, tactical, and operational levels of driving behavi or, these merged model helps explain where older drivers choose to go, how they decide to get to their destinations (e.g., choice of route), the driver behavi ors they adopt or how they choose to optimize them, and their ways for co mpensating for functional declines.

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38 Figure 1-3. Driving and the Inte rnational Classification of Func tioning, Disability, and Mental Health Model (ICF)

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39 CHAPTER 2 LITERATURE REVIEW Road Tests and Driving Behavior Understanding the task of driving requires a distinction between driving perform ance and driving behavior. Driving performa nce can be thought of as the dr ivers ability to drive in a specific environment or condition. For example, researchers can measure how drivers perform when it is raining, in a closed or fixed road course, at a certain time of day, or when having many distractions in the car. In th eir review of driving models, Ra nney et al., (1994) highlight the distinction between driving perfor mance and driving behavior, sayi ng that performance refers to what drivers are capable of doing, while behavior refers to what drivers actually do on the road (Ranney, 1994, p. 740). This distinction has implicat ions for the choice of outcome measure. Although identifying predicto rs of crashes has been a main fo cus of traffic research, a deeper understanding of what happens during driving and the specificity of the task-environment influence on driving has made researchers voice a need to focus on the study of driving behaviors (Brown, 1990). Driving be haviors include the drivers ac tions when interacting with other drivers and the road environment, as well as actions to control the vehicle. Some examples of driving behaviors are lane maintenance, signaling, and speed regul ation. Observation and scoring of driving behaviors on a road course, in cluding maneuvers such as right or left turns, lane changes, and intersections, can help dete rmine a driving performance score (Fox, Bowden, & Smith, 1998). Traffic researchers have used road tests as the standard method to measure driving performance (Fox et al., 1998; Hunt et al., 1997; Odenheimer et al ., 1994), and road tests are a requirement to obtain and sometimes renew a driving license (Galsk i, Ehle, McDonald, & Mackevich, 2000). Road tests are known by different names among DRSs and driving

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40 instructors: behind-the-wheel, on-ro ad, in-car, and road tests all re fer to assessments of driving performance using a car (Finn, 2004) Road tests often are critiqued for being costly and having many liability issues in cases of unsafe drivers (Lloyd et al., 2001). However, DRSs use specialized vehicles to conduct road tests. These vehicles usually have dual-control brakes, an auxiliary rear view mirror on the evaluators side, and they carry signs or labels on the outside signaling that the vehicle is a drivers training or learning vehicle. Road tests provide a real driving environment to measure driving behavior s. These tests can vary in length, route, maneuvers included, ways of scoring driving perfor mance, and inclusion of driving behaviors. Mallon and Wood (2004) tested older and younger adults in a road test that included driving instructions from the evaluator and dr iving to a destination without the evaluators instructions. Driving without inst ruction resulted in significantly more errors for older drivers compared to younger drivers. Although road test s may be more challenging when driving is performed in situations of daily driving without fixed maneuvers or instructions, standardized road tests with different levels of complexity a nd of sufficient length ( 40 to 60 minutes) to test the drivers in different maneuvers repeated over time (e.g., lane changes in different roads, right or left turns in different inte rsections) are the best approach for road evaluations (Fox et al., 1998). Researchers agree that road te sts should be reliable and valid, have levels of difficulty that can differentiate between driving performance of older adults with and without cognitive impairments (Di Stefano & McDonald, 2003; Dobbs Heller, & Schopflocher, 1998; Hunt et al., 1997; Justiss, Mann, Stav, & Velozo, 2006; Mall on & Wood, 2004; Odenheimer et al., 1994), and that a qualitative component of errors is cr itical to understand safe driving behaviors among older drivers (Di Stefano & McDonald, 2003; Dobbs et al., 1998; Hunt et al., 1997).

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41 Driving behaviors have inter and intra-individual variabil ity (Brehmer, 1990; Rumar, 1990). Considering the differences of driving errors between older driv ers with and without impairments can help explain older adults drivin g behaviors aside from driving errors due to cognitive or sensory impairment. For example, some studies agree that driving errors that require intervention from the evaluator ar e good predictors of road test failure and cognitive impairment. Overall, cognitively impaired drivers are overcauti ous, have worse lane control, more difficulty at intersections and turns, a nd poor judgment and awareness (D obbs et al., 1998; Hunt et al., 1997). Variability in driving errors among older adults also can be subject to cohort effects. A group of older drivers with simila r characteristics such as the time they were born, educational, or other similar environmental experiences may have more similarity in their driving errors when compared to other drivers. For example, Carr et al. (1992) reported th at among older drivers, errors of not using the turn si gnal and driving at slower speeds could be due to cohort effects since older drivers may have grown up driving when speed limits were lower and without signaling their intentions. Hunt et al. (1997) reported signaling erro rs were prevalent in 40% of a control group of older adults, and Dobbs et al. (1998) found that making rolled stops and speed errors was common among all groups in a sample of young drivers and ol der drivers with and without cognitive impairment. Carr et al. (1992) reported significant differences in speeding errors between older and younger drivers. Howeve r, this study was c onducted on a university campus, which can bias the study of speeding be haviors since speed limits on campuses are usually low (20 miles per hour or less). Di Stefano and McDonald (2003) reported, on re trospective review of older adults road tests (n=533), that prevalent errors at intersecti ons included: (a) failures to check mirrors (69%),

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42 (b) use signals (49%), (c) poor gap judgment (45%), (d) poor position on road when turning (39%), and (e) failure to obey si gn or signal (30%). Ot her errors included failures to turn head during lane changes (62%); lane keeping errors (34%), driving too slowly (31%), following distance errors (4%), and poor control of the vehicle (steering erro rs, 12%) (Di Stefano & McDonald, 2003). These driving behaviors were specific to intersec tions and they need to be compared across age groups to determine what be haviors signal age-related declines in driving competence. Studies have classified driv ing behaviors in different ways. Table 2-1 provides an overview of some of the studies using standard ized behind-the-wheel a ssessments and driving behaviors of healthy older adults or a control sa mple of older adults. The studies agree in some respects such as the inclusion of behaviors related to scanning or checking the driving environment (e.g., to turn, change lanes, etc), th e lateral position of the vehicle (e.g., staying in the lane), the anterior/posterior position of the vehicle (e.g., fo llowing or stopping distance with other vehicles), speed regulation (e.g. drive too fast or slow), and signaling. The studies were prospective but the samples used differed since some studies compared driving behaviors across ages and others included dementia patients. The studies different labeling of driving behaviors makes comparing these studies difficult. For inst ance, speeding errors in Dobbs et al. (1998) included driving over the posted lim it, while in the Carr et al. (1992) study, the speed category included: (1) going too fast for the conditions, (2) in excess of marked limits, and (3) too slow for conditions, which Dobbs et al. (1998) classified as overcautiousness. Another line of research on driving behaviors has examined self-reported aberrant driving behavior (Lajunen, Parker, & Summala, 2004; Ozkan, Lajunen, & Summala, 2006; Parker, McDonald, Rabbitt, & Sutcliffe, 2000; Parker Reason, Manstead, & Stradling, 1995; Reason,

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43 Manstead, Stradling, Baxter, & Campbell, 1990; Rimmo, 2002). This research has consistently identified three different human be haviors while driving: (1) lapses (2) errors, and (3) violations. Lapses affect the driver but tend to maintain the sa fety of others; these include for example, forgetting where the car is park ed, switching one control instead of other (e.g., headlights instead of wipers), and hitting something that was never seen when reversing. Errors are more likely to affect other drivers and include, for example, failure to check rearview mirror when changing lanes, failure to check side mirrors, underest imating the speed of onc oming vehicles when passing, and braking too quickly on slippery roads. Last, violations include an intentional engagement in riskier driving such as disregarding the speed limit, tailgating as a signal for others to go faster, and passing cars from the wrong side. The result s of this resear ch show that violations decrease with age (Reason et al., 1990), and that older drivers report having more lapses than errors or violati ons (Parker et al., 2000). Since many research studies on healthy older adults are based on self-reported crashes or viol ations, differentiating the types of errors, lapses, and violations is helpful to unders tand the motivations and cognitive processing difficulties (such as lapses) that can influence these outcomes. However, self-report has limitations, since it is subject to bias and inaccu racy, especially when questions relate to personal, sensitive, and/or controversial topics (Parker et al., 2000; Portney & Watkins, 2000) such as driving. Subjects may respond in socially desirable ways and thus, some driving studies have considered social desirability as a limitati on of self-reported driving ability and confidence (Baldock, Mathias, McLean, & Berndt, 2006; Owsley, McGwin, Phillips, McNeal, & Stalvey, 2004; Ozkan et al., 2006), or have measured social desirability to control for its effects on selfreport of driving patterns (Owsley et al., 2004).

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44 Functional Abilities and Driving Performance As older individuals select when and where to drive, they can optim ize and compensate for decrements in driving ability by employing drivin g strategies such as increasing speed when merging on a highway (optimizing) or increasing visual scanning (compensating). Motor, visual, and cognitive abilities are constantly influencing their driving. For ex ample, while driving, a person has to simultaneously control the car by br aking or accelerating, a nd these operations are influenced by motor abilities such as streng th and proprioception. The person also has to maneuver the vehicle through turns and intersec tions that require visual attention, motor coordination, and concentration; and the person ne eds a strategic plan fo r driving that involves memory of the route to get to places, and decision making abilities to resolve changes in traffic signals, traffic, weather, etc. Th is section delineates the visual, motor, and cognitive abilities that decline with age or age-related diseases and reviews how these declines can affect driving outcomes (e.g., performance in a simulator or road test, crashes or adverse events). Age-Related Visual Changes and Driving Visual perform ance is key for driving ability since we get more than 90% of information while driving through vision (Hills 1980). Changes in vision with age include decreased acuity or ability to focus, deficits in contrast sensitivity, decreased glare recovery that affects the ability to see objects in the presence of glare, impairments in depth perception, and reduced peripheral vision that affects the ability to see objects on th e sides of the road. Thes e visual abilities allow the driver, for example, to recognize fine detail s for night and day drivin g (acuity); recognize the sides of the road, the road marks, pedestri ans or objects under similar backgrounds or dark conditions (contrast sensitivity); drive in early morn ing and late afternoon when the sun is lower, or driving with other cars headlights facing us (g lare); and detecting road signs, pedestrians or objects on the side of the road (peripheral vision ) (West et al., 2003). When some of these visual

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45 aspects start to decline, DRSs can offer recomme ndations to help drivers increase their safety on the road. Driving recommendations related to visi on might include, for example, (a) scanning the environment or looking around more often, (b) using the mirrors more of ten, (c) increasing the following or stopping distance, or (d) referrals to an eye care specialist for an assessment of visual functioning. Visual abilities while driving are used at all levels of Michons model. At the strategical level, visual information about the road and traffic conditions influences the decisions of where to go and how to get places. For example, a driver uses visual acuity and peripheral vision to see road signs, understand maps, and locate streets lead ing to a destination. At the tactical level of Michons model, the driver uses visual inform ation to maneuver the vehicle through directions (e.g., turns, intersections). The drivers ability to see contrasts of shades and forms, perceive motion and depth, attend to changes on the road, and see objects on the periphery help the driver perceive and adapt to vehicles, objects, and pede strians while driving straight, turning, changing lanes, or crossing an in tersection. The driver also uses vision at the operational level of Michons model to see when they are faced with obs tacles that require braking or steering. Visual acuity, contrast sensitivity, peri pheral fields, and driving performance Visual acuity alone is not e nough to predict driving perform a nce. Visual acuity measures levels of high contrast and does not address the drivers ability to detect low levels of contrast, for example to recognize persons or road mark s when driving at night (Wood & Owens, 2005). In a study of tests to determine fitness to drive among drivers renewing their drivers license, measures of visual were a poor predictor of dr iving outcome (Janke & Eb erhard, 1998). Rather, a combination of visual measures or visual and cognitive measures is a better approach to predict driving ability (Ball et al., 1993; Janke & Eb erhard, 1998; Owsley et al., 1991; Wood & Owens, 2005). Although the majority of states require tests of visual acuity and peripheral fields as visual

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46 requirements to receive or renew a drivers li cense, the minimum acuity or peripheral vision requirements vary across states and little is known about the effectiv eness of these visual requirements (Wang, Kosinski, Sc hwartzberg, & Shanklin, 2003). Declines in visual acuity seem to be more related to driving avoidance or driving retirement than a predictor of crashes or road test performance. In a population-based study, older drivers with visual impairment, measured as visual acuity less than 6/12 (US equivalent of 20/40), were more likely to stop driving, avoid drivi ng at night, or drive in the city or in rush hour, but were not more likely to report crashes th an drivers without impair ments in visual acuity (Keeffe, Jin, Weih, McCarthy, & Taylor, 2002). In a retrospec tive cohort study of 125 older adults, those with near visual near acuity le ss than 20/40 were almost 12 times more likely to self-report crashes, moving violations, or being stopped by the police (Marottoli et al., 1998). The investigators acknowledged that near visual acu ity is not usually tested to renew a drivers license, and argued that participan ts with poor near visual acuity were more likely to have poor distance acuity and contrast sens itivity, which are important co mponents of driving. However, other studies that reported simila r relationships among visual abil ities related to driving found no association between visual functio ns and citations or state reporte d crashes (Owsley et al., 1991), or small associations between visual functions and crashes (Ball et al., 1993). A combination of visual acuity and contrast sensitiv ity measures may be better pred ictors of driving performance. In a sample of 24 young and older adults who dr ove in a closed-course under day and night conditions (with four levels of varying lumina nce), visual acuity and contrast sensitivity measures explained 52% of the road performance variance under the lowest levels of luminance (Wood & Owens, 2005).

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47 Contrast sensitivity: Unlike measures of visual acuity that focus on perceiving small and fine detail high contrast targets, measures of cont rast sensitivity evaluate the lowest contrast at which individuals can see at vari ous degrees of contrast and in different levels of spatial frequencies, which determine the level of detail of the objects (Greene & Madden, 1987). Measures of contrast sensitivity assess greater ranges of visual function than acuity measures and can help determine ocular pathologies (Decina Staplin, Spiegel, & K noebel, 1991; Owsley & Sloane, 1987). Older adults have more difficulties with higher levels of spatial frequency when contrasts are harder to discri minate (e.g., less contrast of sh ades and reduced spacing and orientation of lines within a circle) (Gr eene & Madden, 1987; Owsley & Sloane, 1987; Wood & Owens, 2005). Along with measures of visual acuity and stereopsis (depth perception), contrast sensitivity was the only predictor of visual d ecline in a sample comparing young and old adults (Greene & Madden, 1987). Contrast sensitivity can help explain so me of the visual functions required for safe driving performan ce, since levels of contrast in colors and light can vary greatly while driving. For example, driv ers need to recognize cars, road s, and obstacles during bright light and glare in sunny days, durin g foggy or rainy weather, or at night. Drivers also need to recognize road markings that may or may not have high levels of contra st with the road, and delineate the lanes of a road, crossing walks or st ops, and turning lanes at turns, intersections, or merges. In a computerized test, contrast sensitivity and age explained 30 and 40% of the variance in road sign identification and detection, respectivel y, as well as 28 and 40% of object detection and identification (Owsley & Sloane, 1987). When used to measure driving performance in closed courses, contrast sensit ivity predicted 40% of driving performance with low levels of luminance that simulated night driving and was highly correlated with older adults overall

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48 driving performance (r=0.71) (Wood & Owens 2005; Wood & Troutbeck, 1995). Recently, contrast sensitivity was correlated with real wo rld driving performance (r=0.4-0.5) and was part of a multivariate model that explained 44% of the driving performance rating variance among older adults (Stav, Justiss, McCarthy, Mann, & Lanford, 2008). In a sample of 12,400 drivers, Decina et al., (1991) compared a visual screen consisting of vi sual acuity and horizontal fields with a combination of these two tests and contrast sensitivity testing. Adding contrast sensitivity to the visual screening had a significant impact on the identification of older drivers with impaired vision and in predicting crash freque ncy the past 3 years (Decina et al., 1991). Contrast sensitivity measures are related to driving less miles or avoiding driving situations. For example, two studies reported high er odds of having reduced contrast sensitivity among older adults who drove less than 3,000 mile s per year (Freeman, Munoz, Turano, & West, 2006; Stutts, 1998). One of these studies (Freeman et al., 2006) followed a sample of 1605 older adults, and after two years, drivers with poor cont rast sensitivity were 2 times more likely to reduce their annual miles driven. Stutts (1998) reported that among 3238 older drivers, those with reduced contrast sensitivity were 1.3 times more likely to reduce their annual mileage driven. Contrast sensitivity also was associated with driving restrictions among older adults (West 2003), night driving avoidance after two years (Freeman et al., 2006), and night driving avoidance among males (Brabyn, Schneck, Lott, & Haegerstrom-Portnoy, 2005). Although there is evidence of the impact of c ontrast sensitivity on driving, few studies have evaluated the predictive value of contrast sensitivity fo r driving performance using a road test. Peripheral fields: A persons visual fiel d typically extends to 180 degrees beyond central vision. Visual field functions ar e important in detec ting timing and movement of objects around us (Rizzo & Kellison, 2004). As with visual acu ity, minimum legal requirements of binocular

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49 visual field vary widely across the United States. For example, some states require 105 of horizontal fields in both eyes (e.g., North Dakot a, Minnesota), while ot hers require 110 (e.g., Alabama, Kansas), 120 (e.g., Indiana, Massachusetts), 130 (e.g., Florida, District of Columbia), and 140 (e.g., Georgi a, Iowa). Oklahoma requires only 70 of binocular fields (Wang et al., 2003). This variability reflects the need for scientific evidence to show what level of visual field impairment leads to increased crash risk or unsafe driving performance. After evaluating 17,534 eyes (n=8767), Johns on & Keltner (1983) reported those with binocular field loss had more crashes and convictions than th ose without visual filed loss (Johnson & Keltner, 1983), and older adults had 10 % higher incidence of visual field loss than drivers 16-60 years old (3% incidence). Wood et al ., reported increased di fficulty with reversing and peripheral reaction time when subjects wore goggles simulating binocular field restriction in a closed course (Wood & Troutbeck, 1995). Like contrast sensitivity, visu al fields are correlated with driving performance and driving restrictions. Older adults with peripheral visual field loss were more likely to avoid driving at night after two years (Freeman et al., 2006), and in a cross-sectional study, those with peripheral field loss were 1.83 times more likely to avoid more than three driving situations (Stutts, 1998). Although visual fields are related to driving, the visual fields func tion is intrinsically related to processing speed and the ability to divide the at tention between the road ahead and the stimuli around the car and on the sides of th e road. In the traffic safety literature, processing speed and attention have been widely studied as a function known as visual attention. Visual attention and driving performance Driving involves sustaining attention on the road, selecting relevant stimuli and avoiding distractions, and dividing attention to control the car and obs erving other cars, signals, and stim uli while driving (Parasuraman & Nestor, 1993). Visual attenti on was correlated with driving

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50 performance and several driving behaviors su ch as scanning, respondi ng to vehicles and pedestrians, speed regulation, si gnaling, merging, yielding to tr affic, changing lanes, making turns, following distances, and making decisions and judgments while driving (Richardson & Marottoli, 2003). However, older drivers do not tend to perceive or self-report difficulties with visual attention (West et al., 2003). Thus, r ecommendations from a driving rehabilitation specialist (DRS) can help older drivers be aw are of visual changes and implement driving strategies to optimize or comp ensate for visual decline. Safety literature on visual attention has mostly referred to performance of older adults in a computerized test implemented by Ball and collea gues-the Useful Field of View (UFOV) (Ball et al., 1993; Ball et al., 1998; Ows ley et al., 1991). The UFOV is a computer-based test that includes three subtes ts: (1) speed of processing, (2) divided attention, and (3) se lective attention; it has good test-retest reliability and validity (Edw ards et al., 2005), and normative data for older adults was recently published (Edwards et al., 20 06). The predictive value of UFOV for driving performance has mostly been evaluated using cr ash records as the outcome measure instead of road test performance. For example, among a sma ll sample of older drivers (n=53), those who failed the UFOV were 4.2 times more likely to have crashes and 15.6 times more likely to have crashes in intersections (Owsley et al, 1991). In a study with a larger sample of older drivers (n=294) followed over three years Ball, Owsley, Sloane et al., (1993) found those with 40% reduction in the useful field had two times more crash risk than drivers with more than 40% useful field of view (Ball et al., 1993; Owsley et al., 1998). Mo re recently, subtest 2 of the UFOV, divided attention, was reporte d as a significant predictor of crashes after two years, and was related to participants being twice more lik ely to have future crashes (Ball et al., 2006; Staplin, Gish, & Wagner, 2003)

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51 Although Owsley, Ball and colleagues have desc ribed a comprehensive approach and the need to evaluate visual and cognitive aspects when predicting older adults driving ability (Ball et al., 1993; Owsley et al., 1991), th is groups research used cras h data as the main outcome measure of driving performance. While they undertook a prospectiv e study and acknowledged the limitations of their use of crash data ( Owsley et al., 1991), thei r research was based on samples recruited from vision clinics or oversampled for crash occurrences. Thus, results are not generalizable to all older adu lts. In one study of non-visually impaired older drivers, UFOV subtest of divided attention explained 34% of va riance in crash frequencies (Hoffman, Atchley, McDowd, & Dubinsky, 2005). Factors that predict crashes or simulated cras hes might be unrelated to real world driving performance (Keeffe et al., 2002). However, very few studies have used the UFOV to predict older drivers performance in a ro ad test. Older drivers with reductio ns in the useful field of view had increasing probabilities of fa iling a road test. For example, with 40%, 60%, or more than 70% reduction on the UFOV a person was 44%, 71%, or 80% more likely to fail the road test, respectively (Myers, Ball, Kalina, Roth, & Goode, 2000). These results, however, were based on a small sample and more than half of the part icipants had cerebrovascular accident (CVA) (n=27 out of 43), Parkinsons disease or traumatic brain injury, so results are not generalizable to all older drivers. Only one study was found that used the UFOV to predict driving performance among healthy older adults (Stav et al., 2008). This study used a standardized road test too assess 123 older adults; UFOV composite scores correlated with the road te st and UFOV was a significant predictor in a regressi on model predicting 44% of the ro ad test performance (Stav et al., 2008).

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52 Visual diseases and driving Although prevalence can vary am ong ethni city, cataracts, ag e-related macular degeneration, glaucoma, and diabetic retinopathy lead to low-vision and blindness. Among these diseases, cataracts are the main cause of lo w-vision impairment in Americans (Congdon et al., 2004). It is difficult to estimate the relationship of specific lo w-vision impairments and driving outcomes such as crashes, since large samples are needed to establish a relationship (Ball et al., 1993). Older drivers with vision impairments are al so likely to compensate for their visual deficits (Ball et al., 1993; Ball et al., 1998; Coeckelbergh, Brouwe r, Cornelissen, Wolffelaar, & Kooijman, 2002; Szlyk, Seiple, & Viana, 1995). Cataracts are mainly opacities in the lens of the eyes, and are related to impairments in visual acuity, contrast sensitivity, depth percep tion, and difficulties with glare (Owsley et al., 1991; Owsley, Stalvey, Wells, & Sloane, 1999). Although cataracts can be removed, a high proportion of older drivers continue to drive with cataracts and they have a high risk for crashes (Keffe et al 2002, Wood and Ma llon, 2001). The Impact of Cata racts on Mobility Project (ICOM), a prospective cohort study of older dr ivers recruited from ophthalmology clinics in Alabama (n=377) showed that drivers with catar acts were four times more likely than drivers without cataracts to limit thei r driving, 2.5 times more likely to have crashes over the past 5 years, and those who had crashes were 6 times more likely to have impair ed contrast sensitivity (Owsley et al., 1999; Owsley, Stalvey, Wells, Sloane, & McGwin, 2001). Glaucoma affects peripheral vision and has also been related to poor scores on driving performance and elevated numbers of crashes (Bowers, Peli, Elgin, McGwin Jr, & Owsley, 2005; Hu, Trumble, Foley, Eberhard, & Wallace, 1998; Szl yk, Mahler, Seiple, Edward, & Wilensky, 2005). In road test performance of drivers with glaucoma, horizon tal visual fields loss was correlated with difficulties adjusting the speed when changing la nes, lane position, lane maintenance, and

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53 following distance during curves (Bowers et al ., 2005). Szlyk et al ( 2005) reported increased number of crashes in a simu lator (Szlyk et al., 2005). Hearing and Driving Few studies have explored the relationshi p betw een hearing impairment and driving performance, and results are mixed. For exampl e, one study showed higher risk of crashes among adults with a hearing impairment in the ri ght ear than those without hearing impairment (Ivers, Mitchell, & Cumming, 1999), while others showed no relationship of hearing impairment and crashes (Gresset & Meyer, 1994). Other st udies have suggested a possible relationship between the use of hearing aids and crash outcomes claiming that they can cause distraction and lead to crashes (McCloskey et al.,1994 as cite d in (Dobbs, 2005). However, more recent studies have shown no relationship between the use of hearing devices and crashes, reduced driving or driving difficulties (Lyman, McGwin Jr, & Si ms, 2001; Ragland, Satariano, & MacLeod, 2004; Sims, Ahmed, Sawyer, & Allman, 2007). Although it is possible that increased levels of noise from conversation in the car distr act a driver, distractions can be due to lack of attention and not necessarily hearing impairment Age-Related Motor Changes and Driving Motor abilities that can dec line with age are sim ultaneously and continuously used while driving. A driver operates the vehicle using the legs and the arms, which requires coordination, strength, range of motion, force production, prop rioception and postural st ability (Marottoli & Drickamer, 1993; Stelmach & Nahom, 1992). All levels of Michons hierarchical model are applied when using motor abilities to drive, a lthough these are mostly used at the tactical and operational levels. The strategical level is applied when the driver has to detour or change the route and therefore, adapt to making new maneuve rs to get to the des tination. The operational and tactical levels apply when sudden changes in the environment require the driver to react to

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54 emergency situations and brake or steer. At the tactical and operational level, coordination is important for synchronized driving movements such as steering and accelerating (Stressel, 2000). Some driving maneuvers require more coordination of legs and arms, such as making a left turn instead of driving st raight (Stelmach & Nahom, 1992). Vehicle control and motor abilities Control of the steering wheel can also be in fluenced by grip strength and the positioning of the hands on the wheel. Grip strength has been shown to be a good predictor of driving cessation (C arr, Flood, Steger-May, Schechtman, & Binder, 2006) and associated with selfreport of driving frequencies among older dr ivers (Anstey & Smith, 2003). Evidence on the effect of grip strength on driv ing is equivocal. Kantor et al. (2004), reporte d that drivers with better scores on grip strength were those who failed a road test. Stav et al. (2008), reported a positive correlation of grip strength and higher levels of driving performance. Other authors found no relationship between grip strength a nd crashes (Margolis et al., 2002; Sims, McGwin Jr, Allman, Ball, & Owsley, 2000). A firm grip on the wheel may prevent drifting to the side or making turns too wide (not staying in the lane) wh en making turns, but this has not been studied. Positioning of the hands on the steering wheel is described in terms of numbers on the face of a clock. Positioning the hands at 10 and 2 oclock was suggested as a safety measure in New Zealand (Walton & Thomas, 2005). In an observational study of drivers hands position on the steering wheel in 8 sites over five days, Walt on and Thomas (2005) suggested that half of the drivers in a sample of 4804 drove with onl y one hand on the top of the wheel (Walton & Thomas, 2005). Since there were sites at which the drivers held the st eering wheel with two hands, the authors speculated that holding the steering wheel may be influenced by the traffic complexity. For older adults, hol ding the steering wheel at 10 a nd 2 oclock may be difficult if they have range of motion or strength impairments. A driving rehabili tation specialist can

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55 recommend moving the hands apart or using both hands while driving for more control of the vehicle. There is an international consensus among dr iving experts that measures of range of motion, leg strength, gross mobility, and balance are important motor components that should be used in assessing older adults driving ability (Stephens et al., 2005). Range of motion of the neck, arms, hips, and ankles are used to safely operate and maneuver the vehicle. Impairments in neck rotation affect the ability to bring peripheral stimuli to central vision and limit the ability to scan the environment at intersections, changi ng lanes, merging, and backing up (Anstey, Wood, Lord, & Walker, 2005; Isler, Parsonson, & Hansson, 1997; Stelmach & Nahom, 1992; Stressel, 2000). In one study, range of motion was correlated with overall driving performance in a road test (McCarthy & Mann, 2006). Isler et al. ( 1997), measured head movement among young and old drivers and found that older drivers had 19 to 27 degrees more of head rotation than older adults. In a regression analysis of a prospectiv e cohort, drivers with lim ited neck rotation were more likely to self-report adverse events su ch as crashes or being stopped by the police (Marottoli et al., 1998). In another study, limited neck rotation remained a good predictor of crashes over two years (Sta plin, Gish et al., 2003). Strength and range of motion of the shoulders, hips, and feet are also important in driving to maintain the arms up in holding and turning th e steering wheel, operating the pedals such as switching from the brake to the acce lerator, and maintaining a constant force on the accelerator. Operating the pedals is also affected by reac tion time. Age-related changes in reaction time include slower decision processing when there are multiple choices and slower reaction time when faced with uncertainty or high levels of complexity. This decline in reaction time affects the ability to judge and react to traffic situa tions, and respond to changing driving environments

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56 by accelerating, braking, and manipulating the st eering wheel and gear shift (Marottoli & Drickamer, 1993; Stelmach & Nahom, 1992). Taki ng longer to tap 10 times with the dominant foot, or pressing a pad (with the lower extremities) after a light turned on, were predictive of crashes in one study (Margolis et al., 2002). However, in a pros pective cohort study, Ball et al did not find differences in a foot tap test between drivers who ha d crashes and those who did not crash over 4 to 5 years (Ball et al., 2006). Postural stability and proprioception affect movement coordination and adequate placing of hands and feet to control the vehicle (Ste lmach & Nahom, 1992; Stre ssel, 2000). Coordination in finding the brake and accelerator requires prop rioception, which is the ability to detect body parts. Difficulties operating the brake pedals may be related to slow reaction times, difficulties with range of motion, or foot abnormalities. In a prospective cohort st udy (Marottoli, Cooney, Wagner, Doucette, & Tinetti, 1994; Marottoli et al., 1998), Marottolli et al. (1994) found an association between self-re port of adverse events and the number of foot abnormalities such as toe deformities, calluses or bunions.; this associ ation was no longer present in follow-up of the participants who still drove after 4 to 5 years (Marottoli et al., 1998) More recently, Freund et al. (2008) suggested that older driver s confusion between the brake and accelerator was related to lower levels of cognition a nd executive functioning Some mobility difficulties such as arthritis affect range of motion and produce pain and rigidity that impair the ability to grip the wheel step on the pedals, or turn the head. Assistive technology prescribed by a DRS such as steering knobs, longer rear view mirrors for scanning, or power steering can help optimize the drivers ability to steer and scan the environment. Hand dexterity may also be affected in a person with ar thritis and devices such as a car key handle or a safety belt handle (Roberts & Roberts, 1993) can be used to facili tate driving-related tasks such

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57 as opening the car door, turning the ignition on, and fastening the seatbelt. No conclusive evidence is known on the effect of arthritis on driving perfor mance; one study reported an association with crash risk (McGwin Jr, Sims, Pulley, & Rose man, 2000) and other studies found no association (Koepsell et al., 1994; Margolis et al., 2002; Sims et al., 2000). Other studies have shown a relationship between arthritis and driv ing avoidance and difficu lty (Charlton et al., 2006; West et al., 2003). Two intervention studies reported on physical therapy exercises that improved driving performance (Marottoli, Allore et al., 2007; Ostrow, Shaf fron, & McPherson, 1992). Participants in Ostrow et al. (1992) were randomized to an 8 week weekly home visit of a physical therapist to train and review a series of range of motion exercises includ ing neck and truck rotation, side bends, chin tucks, chin flexion and extension, a nd bringing the shoulders back. After test-retest of driving performance in a road test, th e experimental group improved in trunk rotation, shoulder flexibility, and were observed the enviro nment around their vehicle more (Ostrow et al., 1992). Marottoli et al. (2007) c onducted a study where participants attended a 12 week physical conditioning program consisting of 15 minutes of exercises to improve neck and trunk rotation, shoulder flexion and abduction, hi p flexion, ankle dorsi-flexion, a nd plantar flexion. After the three month intervention, driver s in the experimental group redu ced the number of errors on a road test (Marottoli, Allore et al., 2007). Th is evidence of physical therapy interventions improving older drivers abilities supports th e idea that some range of motion and motor difficulties that undermine safe driving behaviors such as turning the head more often, can be optimized with referrals to rehabilitation speciali sts such as physical or occupational therapists. Driving studies have reported a relati onship between poor performance in mobility measures and difficulty completing activities of da ily living with driving outcomes. Sims et al.

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58 (2000), reported that difficulties doing yard work or hous ework, and opening a jar were associated with crashes over five years Marott oli et al. (1994) reported an association between self-report of adverse events and walking less than one block daily. Lyman et al. (2001), found associations between falls a nd driving difficulty. Another st udy found older women who had falls in the previous year were 50% more likely to be involved in crashes but the authors noted the relationship of falls and cr ashes may be mediated by age, or other medical conditions (Margolis et al., 2002). Another mobility measure reported in drivi ng studies is the Rapid Pace Walk (RPW), a timed measure of gross mobility, endurance, and stab ility. The test consists of walking 10 feet in each direction along a line on the floor (Wang et al., 2003). The RPW was correlated with older adults driving performance on a road test (McCarthy & Mann, 2006), and was a significant predictor in a multivariate regr ession that predicted cognitive, sensory and motor components of driving (Stav et al., 2008). In their prospective cohort study Marottoli et al. (1994, 1998) found a correlation between RPW and self-report of adverse even ts when older drivers were assessed one year after clinical assessment but no relationship when drivers were assessed 3 to 4 years later. An important observation of these tw o studies is that the RPW time cut-off scores used in each study were different, less than 7 seconds at baseline and more than 11 seconds at follow-up. Mixed evidence of RPW predictive valu e in driving outcomes was also reported in prospective studies of more than 1800 driver s from the Maryland Pilot Older Driver Study (MaryPODS). Using state records of crashes as ou tcome, the RPW predicted crash risk after two years in a study by Staplin et al. (2003), but the RPW was not a predictive measure of crash risk after 4 to 5 years in Ball et al. (2006).

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59 Medical conditions and driving The evidence on the im pact of medical conditio ns and driving is mixed. Most studies of medical conditions have looked at the associations of medical conditions and crash risk (Foley, Wallace, & Eberhard, 1995; Hu et al., 1998; Koepse ll et al., 1994; Lyman et al., 2001; Margolis et al., 2002; McGwin Jr, Sims, Pulley, & Rosema n, 1999, 2002). Some of the medical conditions that these studies reported as contributors to crash risk were arrhythmias (Gresset & Meyer, 1994), high blood pressure (Margolis et al., 2002), back pain (Fol ey et al., 1995) and diabetes (Koepsell et al., 1994). Hu et al. (1998), argued that some limitat ions with associating medical conditions and crashes include: (1) da ta is not always from the same point in time and (2) not all studies consider the variety of risk factors that can influence driving. Thus, studying more than one medical condition may yield more objective resu lts in the impact of medical illnesses and associated co-morbidities on driving. In a comprehensive review of the literature of medical conditions and driving, Dobbs (2005) delineated a series of guideli nes for physicians to consider when driving should be restrict ed; after this review, the Amer ican Medical Association (AMA) also published medical guidelines for physicians. However, more evidence of the impact of medical conditions and medications on driving performance using ro ad test performance as the main outcome is lacking. Among the most menti oned medications that ca n impair driving are psychoactive drugs such as benzodiazepines, cyclic antidepressant s, oral opiod analgesics, and antihistamines. These drugs affect the central nervous system and can cause psychomotor impairments (Ray, Fought, & Decker, 1992; Ray, Th apa, & Shorr, 1993). Traffic safety studies found crash risk associations with antidep ressants (Hu et al., 1998), nonsteroidal antiinflammatory drugs, (Foley et al., 1995; McGwi n Jr et al., 2000), and hypnotic medications (Sims et al., 2000) but the evidence is mixed. A lthough more evidence is ne eded on the effects of medications in driving, guidelines have been provided on the importance of considering the

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60 various effects and dosages and the effects of polypharmacy (multiple intake of medications) when assessing and studying the effects of medications on driving performance (Lococo & Staplin, 2006). Cognition and Driving Cognitive sk ills that can decline with age a nd impair driving ability include memory, attention, executive functi ons, visuo-spatial and visuo-perceptual abilities, and mental status (Anstey et al., 2005; Lloyd et al., 2001). Older adults w ith short-term memory deficits can have difficulty driving in unfamiliar areas, operating the vehicle, and finding the car in a parking lot (Lloyd et al., 2001; Stressel, 2000) Drivers need to shift atten tion between all the stimuli around them, be able to anticipate the events as they dr ive such as calculating distances to brake on time, signaling their intentions to let other drivers know where th ey are going, make appropriate adjustments to the route and cont rol the vehicle. Like memory a nd attention, executive functions relate to Michons strategical level. Executive functions i nvolve planning, organizing, sequencing and making judgments and decisi ons (Anstey et al., 2005; Freund, Colgrove, Petrakos, & McLeod, 2008). These abilities are crit ical to follow driving directions, cross intersections, or merge on highways safely; main tain proper distances with the car in front, follow rules of the road, divide the attenti on on the control of the car and the driving environment outside of the vehicle such as adju sting to speed limits, driv ing in different road types, adjust driving to signs and traffic sign als. Visual-perception he lps a driver recognize different sides of the road, identify road marks, maintain the car in the lane when turning or driving straight, maintain distances with the ca r in front, and park or reverse the car safely. Visual-perception involves the drivers ability to process visual information, judge space, direction, and recognize objects when these are embedded on a background, and discriminating the limits of objects (Oswansk i et al., 2007; Wheatley, 2001).

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61 Memory Mem ory plays an important role in driving and is a component of Michons strategical level. Drivers need to remember where they are going, how to get there, and the purpose of their trip. At the same time, they need to be aware of the traffic signals and recognize the signs that indicate how to react a nd maneuver the vehicle. For example, if there is a yellow sign on the side of the road indicating that the road ahead curves, that is an indication to slow down and be ready to steer the wheel. Findings of memory measures in driving studies are diverse. Delayed recall did not predict crash risk in Ball et al., (Ball et al., 2006), but it was related to crashes in other studies (Foley et al., 1995; Hu et al., 1998; Lundberg, Haka mies-Blomqvist, Almkkvist, & Johansson, 1998; Staplin, Gish et al., 2003). In studies of road test performance, (McKnight & McKnight, 1999) assessed several memory measur es and reported correlations with road test performance. These authors used a computerized a ssessment of clinical measures that make their findings difficult to replicate. Odenheimer et al (1994), tested drivers on the verbal and visual memory scales of the Wechsler Memory Test a nd found significant associations with road test performance. Recognizing traffic signs correlated with the road test performance of drivers in Odenheimer et al study (1994) but it did not predict driving performance in other studies (Kantor, Mauger, Richardson, & Unroe, 2004; Stav et al., 2008); traffic sign recognition also predicted crash risk in a retros pective study (Stutts, Stewart, & Martell, 1998) but was not a predictor of crash risk prosp ectively (Marotto li et al., 1998). Executive functions Other studies used several te sts of ex ecutive func tioning to predict driving performance (Daigneault, Joly, & Frigon, 2002; De Raedt & Ponjaert-Kristoffersen, 2000b, 2001a, 2001b; Lundberg et al., 1998; Szlyk, My ers, Zhang, Wetzel, & Shapir o, 2002). Lundberg et al. (1998), reported measures of visual spatial memory a nd visual constructive ability predicted license

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62 suspensions of 23 drivers. In Marottoli et al. (1998), the only cognitive measure that predicted crash risk over time was number cancellation. In a small sample of drivers, Szlyk et al. (2002) reported correlations between neuropsychological tests of memory, visuospatial discrimination, mental flexibility, and information processing w ith driving errors on a simulator test. These finding were based on an Atari simulator and th us were difficult to generalize to driving performance on the road. Daigneault et al. (2002) re ported a prediction and correlations of neuropsychological measures and crashes in the past five years in a sample of 60 dr ivers. The number of errors on the tests had higher correlations than timing of the tests. (De Raedt & Ponjaert-Kristoffersen, 2000b, 2001a, 2001b) conducted 3 studies to predict cr ash risk and driving performance from neuropsychological measures. These researchers f ound that in a sample of 84 drivers referred for driving evaluation, tests of move ment perception, visual attention, and cognitive flexibility predicted 64% of the scores on a ro ad test but were related to onl y 19% of the variance in selfreported crashes over the previ ous year (De Raedt & Ponjaert-Kristoffersen, 2000b). In two follow-up studies, De Raedt and Kristoffersen (2 001a, 2001b) tested the predictive value of the same neuropsychological tests and found that neur opsychological tests predicted more crashes when these were specified by type. Overall, a visuospatial measur e (paper folding) was the most predictive test of crashes. The same authors test ed a small battery to pr edict road performances and found a combination of visuospatial, visual at tention, and visual acuity predicted road test performance. Overall, the sample sizes in studie s using neuropsychological test to predict driving performance were small, and it is difficult to compare them since the measures used were different.

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63 Another measure of executive functioning used in driving studies is the Clock Drawing Test (CDT). The CDT is a measure of executive function, visual perception, and memory (Freund, Gravenstein, Ferris, Burke, & Shaheen, 2005; Oswanski et al., 2007). A driver has to draw the face of a clock, write down the numbe rs and draw the clocks hands to indicate a specific time on the clock. Scoring 4 or more erro rs (out of 8) on the CDT had 64% sensitivity and 98% specificity differentiated drivers who committed more errors on a simulator from the safer drivers (Freund et al., 2005). Having 3 or more errors on the CDT yielded 70% sensitivity and 65% specificity identifying unsafe drivers among 232 referrals for driving assessment (Oswanski et al., 2007). Performance on the CDT wa s correlated with lower scores of road test performance among 50 drivers (McCarthy & Mann, 2006). More recently, Freund et al showed that drivers with lower scores on the CDT were 10 times more likely to confuse the brake with the accelerator on a simulator test (Freund et al ., 2008). Other tests of executive functioning used in studies of driving performance are described below. Neuropsychologists who conduct driving assessm ents frequently use the Trails B as a measure of drivers executive function (Szlyk et al., 2002). Trails B is a paper and pencil test used to assess drivers visual attention and processing speed, sequencing, visual search, and mental flexibility (Ball et al., 2006; Kantor et al., 2004; Szlyk et al., 2002). Evidence of the predictive value of Trails B in driving performa nce is mixed. In a regression analysis, Trails B predicted police reported crashes of 1775 drivers who were renewi ng their driving license (Stutts et al., 1998). Ball et al reported that drivers who took 147 seconds or longer to complete the tests were more likely to crash 4 to 5 years after th e clinical assessment (Ball et al., 2006). Other studies did not find significant relationships of Tr ails B and crash risk (M argolis et al., 2002) or no relationship between Trails B and pedal errors on a simulator (Freund et al., 2008). Data from

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64 the Maryland Pilot Older Driver Study (MaryPODS) suggested that performance on the Trails B lost predictive value to determin e older drivers crash risk after two years (Staplin, Gish et al., 2003). Visuo-perceptual abilities and mental status A m easure of visual perceptual abilities used in driving research is the Motor-Free Visual Perception Test (MVPT). The MVPT tests the drivers ability to discriminate figures and their orientation, and scores ra nge from 0 to 36 (higher scores i ndicate no errors identifying figures) (Oswanski et al., 2007). In a prospective coho rt study of 1910 older drivers who had four or more errors on the visual closur e subtest (11 total errors) of the MVPT, were two times more likely to have at fault crashes over 4 to five years (Ball et al., 2006). Staplin et al. (2003), reported that 5 errors on the visual closure porti on of the MVPT were pr edictive of crashes over 2 years (n = 1,876). In a sample of 232 drivers referred for drivi ng evaluation, those with scores of 32 or less on the MVPT were more likely to fail the road test (Oswanski et al., 2007). Visuo-spatial measures such as the copy de sign subtest of the MMSE predicted adverse events among older drivers (Marottoli et al., 1994 ). Research on driving ability and cognitive impairment has reported significant relationships of measures of vi suo-spatial skills and attention and driving performance (Reger et al., 2004; Rizzo, Reinach, McGehee, & Dawson, 1997; Uc, Rizzo, Anderson, Shi, & Dawson, 2005) Mental status, including in sight and judgment, affect the ability to plan, make decisions, and problem solve during driving. If these abilities are impaired, drivers may adopt unsafe behaviors. Many measures of mental status and executive functioning have been used to differentiate dr ivers with and without cognitive impairment. A widely used measure is the Mini-Mental State Examination (MMSE). Kantor et al. (2004) reported that drivers with scores less than 23 in the MMSE were less likely to be able to take a road test. Odenheimer et al. (1994) reported that MMSE scores pred icted global scores in a road

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65 test and MMSE score was a predictor in a multivariate model of driving performance in another study (Stav et al., 2008). However, researchers ha ve argued that decisions on fitness to drive must not be based on scores of th e MMSE alone as this test does not translate to performance on a road test (Adler, Rottunda, & Dysken, 2005; Dobbs, 1997; Odenheimer et al., 1994). The methodologies and tests used to determine cognitive abilities to drive have varied vastly and the best approaches to assess fitness to drive remain subject of debate (Molnar, Patel, Marshall, Man-Son-Hing, & Wilson, 2006). Driving Exposure and Driving Avoidance Estim ates of older adults who drive are difficult to calculate because many may hold valid driver licenses but be deceased or have stopped dr iving. However, older adults at age 70 have 11 years of driving expectancy. The number of older drivers will likely double in the next decade (Burkhardt & McGavock, 1999; Foley et al., 2002). Understanding older adults driving habits, such as driving exposure and driving avoidance, is a complex but necessary question to examine. As older adults live and drive longe r, they will select when and wher e to drive, which in turn will affect the driving interactions with other driv ers. Driving recommendations to foster safety on the road are becoming increasingly important. Older adults drive less and avoid driving under some conditions (e.g., night, rain) than drivers 65 and younger (Collia et al., 2003; Freeman et al., 2006; Keeffe et al., 2002; Lyman et al., 2001; Marottoli & Drickamer, 1993; Raitane n et al., 2003; Stutts, 1998). Until recently, research strongly suggested that older drivers involvement in crashes was higher than other age groups, except younger drivers. However, older driver s higher crash risk is subject to a lowmileage bias (Langford & Koppe l, 2006; Langford, Methorst et al., 2006). Considering the annual miles that older adults drive per year, onl y older adults who drive fewer miles show agerelated increases in crash risk. Many older adults avoid driving conditions such as high traffic or

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66 rush hour. They avoid driving situations or drive less to compensate for age-related declines but also for non-health related reasons. This section describes driving exposure patterns and avoidance of driving situa tions among older adults. What is Driving Exposure? In the traffic safety literature, driving expos ure is commonly m easured as the frequency of driving, estimated in annual or weekly miles (Co llia et al., 2003; Freeman et al., 2006; Lyman et al., 2001; Marottoli et al., 1993; Ra itanen et al., 2003; Stutts, 1998) Increasing age and disability are strong predictors of mileage reduction among older adults (Marottoli et al., 1993). After age 72, older adults are more likely to drive fewer days (Bauer et al., 2003). Older drivers who had difficulty with functional activities such as cl imbing stairs, walking a quarter mile, carrying objects and difficulty eating were mo re likely to drive less than 3 per week (Lyman et al., 2001). Although increasing age is associated with fewer long trips (Bauer et al., 2003), a national survey found that older adults take l onger distance trips than young and middle-aged adults (Collia et al., 2003). When and where do older adults drive? In 2001, Collia Sharp, and Giesbrecht (2003) co nducted a national survey of 60,000 drivers and compared the driving patterns of adul ts 19 to 64 years to those 65 and older (Collia et al., 2003). Compared to younger drivers older drivers made less daily and long distance trips. Older drivers averaged 3.4 daily trips while younger drivers made 4.4 trips. Among their daily trips, older adults traveli ng peaked between 10am to noon, wh ereas younger drivers traveling peaked three times per day (morning, lunch, and after work). Among young and older drivers, long distance trips of 50 miles were mostly taken in the same state. However, older drivers were more likely to travel for pleasure purposes such as vacations and sightseeing excursions, trips for relaxation and rest, trips to vi sit family, and outdoor recreatio n; or trips for shopping, medical

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67 reasons, or giving rides to others Purposes of daily traveling among older adults also differed with younger drivers. Older adults daily trips were mostly for social and recreation purposes (19%) followed by shopping (18%) and family or personal purposes such as running errands (17.5%). Younger drivers were less likely than older drivers to drive for shopping, religious, or medical reasons. Other authors reported that with age, older adults decrease trips for vacationing, recreational shopping, going to the beauty or barber shop, and volunt eering or working (Bauer et al., 2003). Different factors can explain why olde r adults reduce or avoid driving in some conditions. These factors may incl ude voluntary or involuntary condi tions and perceived barriers for reducing driving. Voluntary and involuntary reductions in driving Reduced driving can be the result of com pensat ory or voluntary de cisions (Raitanen et al., 2003). Compensatory reductions in driving ca n be due to health, cognitive, or visual problems and are commonly reported as reason s for driving less or driving cessation among older adults (Brayne et al., 2000a; Dellinger, Sehgal, Sleet, & Barret-Connor, 2001; Freeman et al., 2006; Hakamies-Blomqvist & Wahlstrom, 19 98; Lyman et al., 2001; Ragland et al., 2004; Stutts, 1998). Self-report and objec tive measures of vision impair ment are strongly associated with restricting driving at night, driving at night when raining, driving in the rain or bad weather, and driving in unfamiliar places (Charlton et al., 2006; Freeman et al., 2006; Keeffe et al., 2002). Decreased cognitive function has been related to driving fewer miles in cross-sectional and longitudinal studies (Ball et al ., 1998; Stutts, 1998; Vance et al., 2006). In a six-month follow-up of 815 older adults, cognitive functions explained 26% of the variance in driving exposure and driving avoidance. However, age and gender were correlated with these outcomes, so the authors speculated that non-health related reasons also affect driving ha bits over time (Vance et al., 2006).

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68 Older adults also voluntarily choose to reduce their drivi ng. For example, older adults frequently report lifestyle change s or personal preference as the main reason to avoid traffic and rush hour (Baldock et al., 2006; Charlton et al., 2006) This is in part, due to retirement and more flexible schedules (Baldock et al., 2006; Ball et al., 1998; Lyman et al., 2001; Raitanen et al., 2003). Across three European cultures, retirement was among the best predictors of driving reduction (Raitanen et al., 2003). Interestingly, hi gher percentages of reas ons for reductions in driving among a sample of 656 community-dwelli ng older adults included changes in lifestyle (38%) and changes in employment status (34%), whereas only 17% reported health or age related reasons, and 6% reported lack of conf idence (Charlton et al., 2006). Other reasons why older adults voluntarily reduce driv ing are: (1) pressure from child ren, (2) bureaucratic action or licensing problems, (3) concerns about safety, liability, or being in an accident, (4) car repair or car expenses, (5) financing, (6 ) loss of confidence, (7) and not needing a car or no reason to drive (Bonnel, 1999; Brayne et al., 2000b; Dell inger et al., 2001; Hakamies-Blomqvist & Wahlstrom, 1998; Peel et al ., 2002; Ragland et al., 2004). Recent findings suggest that older adults se lectively choose when and where they drive (Baldock et al., 2006; Charlton et al ., 2006). Older drivers report they feel hesitant to ask others to help with transportation or do not like to us e alternative transportati on, and these are barriers that influence the decisions to reduce driving or avoid driving s ituations (Charlton et al., 2006). For example, older adults who live in rural areas and need to go places on a daily basis, but lack access to public transportation services or li ve alone, may choose to drive despite driving recommendations to avoid driving long distances. Driving Avoidance Driving avoidance is m easured with self-report of situations that older adults tend to avoid. The driving literature has included a variety of situations as outc ome measures of older drivers

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69 avoidance. Many of the driving s ituations are derived from studies by Owsley et al (Ball et al., 1998; Owsley et al., 1999; Stalvey & Owsley, 2000) and include: (1) driving at night, (2) driving in the rain, (3) driving alone, (4) making left tu rns across oncoming traffic, (5) driving in rush hour, (6) driving in high traffic, (7) driving in highways or freeways, and (8) merging into highways or expressways. Other authors have included self-report of driving avoidance of other conditions such as (1) driving at night when wet, (2) lane changes, (3) driving long distances, (4) driving in bad weather, (5) driving in bad road conditions, (6) driving in unfamiliar places, (7) driving through complicated inters ections, (8) driving in roundabout s, and (9) parallel parking (Baldock et al., 2006; Bauer et al., 2003; Brabyn et al., 2005; Ch arlton et al., 2006; HakamiesBlomqvist & Wahlstrom, 1998; Parker, McDonal d, Sutcliffe, & Rabbitt, 2001; Raitanen et al., 2003; Ruechel & Mann, 2005). Many older adults avoid situations such as driving at night, in the rain, in heavy traffic, on major highways, and making left hand turns (B aldock et al., 2006; Ball et al., 1998). However, older drivers avoidance of situat ions does not seem to be related to driving performance. In a sample of 90 older adults who se lf-reported avoidance in nine driv ing situations, only driving at night, in the rain, and driving at night in the rain were correlat ed with poor driving performance in a road test (Baldock et al., 2006). Another study reported bad drivers and average drivers who had crashes in the previous year were more likely to avoid driving si tuations than drivers with no crash history; and good drivers with no hist ory of crashes avoided more situations than good driver with accidents (De Raedt & Ponjaert-Kristoffersen, 2000a). Overall, older drivers reduce their drivi ng exposure and avoid driving under certain conditions. Some conditions such as driving at night, driving in the rain, and driving at night when raining seem to be avoided due to visual or physical impairment, while other conditions

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70 like rush hour is avoided by preference. Since driv ing avoidance is not alwa ys related to driving ability, drivers can still have a series of unsafe driving behaviors and thus, driving recommendations may be relevant to help older drivers to drive safely. Driving Recommendations for Older Adults Identifying common driving recom mendations for older adults and whether they remember and follow these recommendations is an important area of study relative to DRS practice. This final section revi ews the different settings where older adults can receive driving recommendations, including medical, educational, rehabilitation, or th rough self-assessment. Medical settings involve general practitioners offices and/or specialized medical practices such as geriatrics, neurology, and family physicians. Educational settings are classroom driving courses that can be taken online or in-person. Rehabilitation setti ngs include driving programs in the private sector, hospital-based programs, or university based programs, which provide research and clinical services (Finn, 2004). Recommendations on driving performance can also derive from self-assessment driving tools or in the classroom settings. Driving Recommendations in Medical Settings Physicians are often faced with deciding whethe r their patients are fit to drive (Bogner, Straton, Gallo, Rebok, & Keyl, 2004; W ang & Carr, 2004). However, physicians have difficulties addressing and evaluating older driver s abilities. A widely reported barrier in physicians approaches to older drivers is the deci sion to report an unsafe dr iver to the authorities because physicians have to break one of their me dical-ethical priorities confidentia lity of the patients information (Marshall & Gilbert, 1999; Retchin & Ana polle, 1993; Reuben et al., 1988; Stutts & Wilkins, 2003). Physicians are also held responsible to warn the public of potential harmful situations (Reuben et al., 1988) and in countries like Australia, physicians can be involved in liability issues when older adults have accidents (Odell, 2005). Almost 60% of 516

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71 physicians in Ontario reported that the patientdoctor relationship was negatively influenced when they had to report unsafe drivers to the licensing authorities, and 27% hesitated to report their patients (Marshall & Gilber t, 1999). In studies using focus groups consisting of physicians, common themes were concerns of being liable after screening older drivers and preference to refer them to the motor vehicle authorities (Bogner et al., 2004; Stutts & Wilkins, 2003). Physicians also report they l ack training to assess older adults fitness to drive (Bogner et al., 2004; Carr, 2000). Canadian and American guidelines have been published to help physicians address older drivers abilities (Hogan, 2005; Molnar, Marshall, Byszewski, & ManSon-Hing, 2005), yet many physicians report the need for more education. For example, Marshall and Gilbert (1999) reported that more than 36.6% of their sample of 516 physicians was not trained to assess older drivers, and even though 39% had been taught about assessing older drivers in medical school, 97% would like fu rther education. Even if physicians use some guidelines to assess older drivers, these tools are not validated. Physicians need a short, and easy to use screening tool (Carr, 2000), but to date the guidelines for physicians rely on experts opinion (Hogan, 2005) and are not known in the medical community (Odenheimer, 2006). Physicians Recommendations for Older Drivers In 2003, the AMA published the Guidelines for A ssessing and Counseling Older Drivers (Wang et al., 2003). These Guidelines were intend ed to educate physicians in the assessment and counseling of older drivers. The guidelines in clude a short clinical assessment called the Assessment of Driving Related Skills (ADReS). The ADReS has seven tests that assess vision, cognition, and motor function incl uding visual acuity (Snell en Chart), visual fields (confrontational testing), cogniti on (Trail-Making Test, Part B a nd Clock Drawing Test), Rapid Pace Walk, Manual test of Range of Motion, and Manual Test of Motor Strength. The Guidelines provide physicians with suggested recommendations based on the drivers performance on these

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72 tests. Following are the recommendations that physicians can give drivers based on the AMA guidelines (Wang et al., 2003). American Medical Association (AMA) guidelines vis ion recommendations The Guidelines suggest that for drivers with visual acuity between 20/40 to 20/70 physicians recommend a referral to an eye care speci alist, follow-up vision testing, or restricted driving such as avoiding rush hour, avoiding high -speeds, avoid driving in unfamiliar places, and avoid driving at night. For drivers with visu al acuity from 20/70 and 20/100 the Guidelines recommend a referral for road te sts administered by DR S, and for visual acuity more than 20/100 the Guidelines state that physicians should counsel their patien ts on driving retirement. For decreased visual fields, the Guidelines recommend referrals to an eye care specialist or to DRS to teach the driver compensation strategies such as turning the head more often or prescribing assistive devices such as larger rear-view mirrors. AMA guidelines cognitive recommendations If the patients score m ore than 180 seconds to complete the Trails B or have any error in the Clock Drawing Test, the Guidelines encour age physicians to conduct further testing and assess metabolic or physical causes of cognitive impairment, such as anemia, vitamin deficiencies, or stroke. The Guidelines also recommend that physicians refer patients to neurologists or neuropsychiatrist s, review medication intake, and refer the patient to a DRS for road test assessment; the Guidelines also suggest that physicians advise their patients on seeking alternative transportation. AMA guidelines motor recommendations Motor related recommendations for physic ians assessment of older drivers include medications for pain, referrals to physical or o ccupational therapists, recommendations to start a physical activity program or exercise s, referral to a DRS to assess a nd train patients in the use of

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73 assistive devices such as spi nner knobs, or considering vehicl es with automatic transmission, power brakes, and power steering. Restricted Licenses The AMA Guidelines in clude a chapter of licensing requirements and licensing renewal procedures for each state. Each state also varies in the types of restri cted licenses available. Driving restrictions in an indivi duals license can range from restricted driving in locations, time of day, at limited speeds, or with special equipm ent. As listed in the AMA Guidelines, Florida is one of the States with more options for rest ricted driving licenses. Since this study was conducted in Florida, the specific restrictions ar e listed below: Drivers may be licensed to drive with the fo llowing restrictions: corrective lenses, outside rearview mirror, business and/or employmen t purposes only, daylight driving, automatic transmission, power steering, directional signals, grip on st eering wheel, hearing aid, seat cushion, hand control or pedal extension, left foot accelerator, probation interlock device, medical alert bracelet, educational purposes graduated license re strictions, and other restrictions (Wang et al., 2003). The AMA guidelines provide a comprehensive re view of driving issues to help increase physicians awareness to assess and counsel older drivers. It is to be determined whether older drivers to adopt and maintain driving recommendati ons and restrictions. If for example, an older adult goes to the doctor and is a dvised to avoid night driving, take medications for pain, and plan for driving retirement, and the driver implements those recommendations, we are still left with important questions regarding the safety of that person on the road on a daily basis. Driving Recommendations in Educational Settings Classroom Education The classroom educational interventions focus on instruction to boost older adults driving knowledge or to train educators. The m ost popula r education programs for older drivers in U.S. are Coaching the Mature Driver (National Safe ty Council NSC), Safe Driving for Mature

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74 Operators (American Automobile Association AAA), and the AARPs Drivers Safety Program. Coaching the Mature Driver is a course for older adult driving trainers to learn about topics to include when teaching older drivers. These topics include adults learning needs, vehicle safety, teaching skills, among others ( https://secure.nsc.org/train/course.cfm ?id=88 ). The AAA and AARP courses offer 8 hours of classr oom training for older drivers, and their topics include age related changes and their effect on driving, strate gies to improve driving behaviors on the road, and tips on vehicle safety, among others ( http://www.aaaexchange.com/main/Default.as p ?CategoryID=3&SubCategoryID=38&ContentI D=333&SearchString=older+drivers ). The AARP course is divided in two four hour sessions and include a pre and post quiz, and topics related to age-relate d declines and how they affect driving, strategies to improve driving behaviors on the road, such as following distances and scanning techniques, avoiding distra ctions while driving, review of road signs and road marks, vehicle safety, and prepare dness to retire from driving ( http://www.aarp.org/families/driver_saf ety/driv er_ed/a2004-06-21-course.html). Bedard et al.(2004, 2008) conducted two random ized clinical trials using the AARP 55Alive education course as an intervention. In one study (Bedard, Isherwood, Moore, Gibbons, & Lindstrom, 2004), the experimental group received the classroom course and was compared to a control group of participants in pre and post road test perf ormance. After two months, the intervention group showed general improvement in ro ad test scores but no significant effect of the AARP course on driving performance was reported. The second study (Bedard et al., 2008) was conducted in three sites in Canada. The in tervention group took the AARP course and two 30-40 minute sessions to practice driving strategies learned in the course Participants also received feedback from the instructor related to their driving. Participan ts knowledge of rules of

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75 the road improved after taking the course. Road tests conducted in two sites 1 to 2 months after the intervention showed improved scores for the intervention group related to driving behaviors of lane changing and lane maintenance; and ch anges in behaviors related to starting, stopping, and backing for the intervention group in one si te. Since improvements in knowledge were tested immediately after taking the AARP course and the in tervention also consisted of feedback from a driving evaluator, these studies do not show direct evidence of th e effectiveness of the education course (Bedard et al., 2008). Other investigators also studied a combin ation of classroom education with other interventions. Ashman et al (Ashman, Bi shu, Foster, & McCoy, 1994) reported that a combination of physical therapy home based exer cises and the 8 hour classroom session of the AAA Safe Driving for Mature Oper ators, or the classroom session and perceptual therapy selfadministered exercises, along with engineeri ng roadway modifications (i.e., pavement signs, traffic signals, signs), improved older adults driving performance scores by 7.9%. However, the sample sizes in each intervention were very smal l (approximately seven). In a randomized trial, a group of older adults received an intervention consisting of the AAA Safe Driving for Mature Operators 8 hour classroom course and 2 on-the-road training sessions of 1 hour (Marottoli, Van Ness et al., 2007). After two months, road test scor es and knowledge tests scores were higher for participants in the in tervention group compared to a control group with no intervention. A recent systematic review of driving interv entions pointed out the need for more multidisciplinary interventions for tr affic safety (Kua et al., 2007). However, the evidence on the effectiveness of individual interventions is scarce and more re search is needed to determine best approaches. Interestingly, the st udies that used on-the-road trai ning as part of the intervention resulted in improved driving performance over time.

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76 Another study examined the characteristics of pa rticipants who took the AARP course and whether they recalled the cour ses information over time (N asvadi & Vavrik, 2007). In a matched-pairs cohort design of 885 participants, th e investigators analyzed insurance records of police-attended crashes and violations two year s prior to attending the course. The study reported that older adults taking the AAR P course had more crashes than older adults not taking the course. For the older men in the sample, the main motivation to take the course was that their wives suggested it. Older adults without crash records before and after taking the course or with no crash records after taking the course, were more likely to recall details of the course lessons such as road signs and strategies to optimize driving (e.g., checking the blind spot). The older males in the sample, who had crashes before and after taking the course recalled less detailed information and only made general comments su ch as saying the course was a refresher. Although this study showed that the sample of pa rticipants taking the ed ucational course were more involved in accidents prior and after taki ng the course, the authors did not control for driving exposure. Another study (Nasvadi, 2007) evaluated older a dults perceptions and recall of information from the AARP course (n = 367). Participants an swered a telephone interview one and a half to four years after attending the course. The study showed that 4 to 9% of participants were avoiding driving situations as a result of the course. The author classified the information recalled from the course into seven categories: 1) need for increased vigilance (e.g., pay more attention), (2) road rules and signs (e.g., complet e stops and lane markings), (3) visual skills (e.g., use of mirrors), (4) self-awareness (avo idance of situations), (5) maneuvers (e.g., changing lanes, merging), (6) safe speed (e.g., sc hool zone speed limits, and (7) space margins (e.g., following distance). Older adults more freque ntly recalled informati on related to increased

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77 vigilance and paying more attention, road rules and signs, and space margins. Compared to men, women recalled more maneuver techniques such as passing other cars, merging onto a highway, and parking. However, since the study only used recall of information, the question of whether the recalled information constitutes driving behavi ors that are difficult fo r older drivers was not answered. As in the previous study, men were mostly referred by their spouses to take the course, which may have also influenced the fi nding that after the course, men reported obeying more rules and signs and driving more cautious ly than women. Compared to men, women felt the course helped them less; 25% of the sample made only a general comment about the course (e.g., it was a good refresher) but did not mention any lessons learned. Although Nasvadis study (2007) examined the benefits of the AARP course for older drivers, the telephone interviews were conducted a long period after the course, and the authors did not mention whether the differences in ti me intervals for the follow-up influenced the perceptions of older drivers. It seems plausible that older adults recall of driving lessons one and a half to four years after attend ing the course would differ and thus, affect the generalization and results of this study. Recall of the courses inform ation may also have been biased by the reason the men took the course, and relying solely on re call does not warrant that the recalled behaviors are put in practice by older drivers. However, this study was one of few attempts in the literature to determine the effectiveness of an education driving refresher courses over time. Since these studies show that older adults do not always recall information from the AARP course and some older adults only recall broad and general commen ts about the course, more evidence is needed to show the effectiveness of classroom educa tion courses to improve driving performance. Programs of driving classroom education are al so critiqued since older drivers are often motivated to take the course for the insurance discounts they can get instead of the safety

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78 benefits (Hunt, 1993; Stutts & Wilkins, 2003) In a telephone survey of 2,510 older adults, 25.4% had participated in government sponsored driving courses, and although 43.5% said they took the course because it was a good thing fo r them to do (p.434), almost 40% said they participated to receive the insurance discount that was pr ovided (Stutts & Wilkins, 2003). Educational Intervention Another type of educational course was developed by Stalvey and Owsley who in 2003 published a study describing an educational interven tion titled Knowledge Enhances Your Safety (KEYS), for older drivers with vi sual impairment (Stalvey & Owsley, 2003). Participants in Stalvey and Ows leys study (2003) (n=365) were randomized to a usual eye care group (comprehensive optometrist evaluation) or a usual care plus education group (n=194) that received the optometrists care, two 2-hour e ducational sessions, and a booster session. The experimental and control groups were followed up with phone interviews every six months for two years. Older adults who received the educ ational intervention reported more difficulty driving in eight conditions (e.g. rain, night, rush hour), higher im plementation of self-regulatory practices such as driving fewe r times per week, and higher a voidance of the eight driving conditions. Self-regulation practices and avoidance were significantl y different for both groups at each follow-up. However, after two years of educa tional intervention, the risk for being involved in crashes was not significantly different for th e experimental and contro l groups (Owsley et al., 2004). The authors suggested, from their longitu dinal study, that self-re gulation educational interventions could increase awareness of visual impairment and ch ange the driving patterns of older adults (Owsley, Stalvey, & Phillips, 2003), but educational interventions did not reduce crash risk among older adults ( Owsley et al., 2004). This eviden ce suggests that classroom or intervention based educational pr actices may increase dr ivers awareness of safety practices but

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79 there is no evidence supporting whether these ed ucational approaches predict crash risk or driving performance over time. Driving Recommendations from Self-Assessment Self-screening tools of driving perform ance ar e available in the form of guidelines, CDROM, videos, driving diaries, and workbooks Self-assessment guidelines published by the AARP, the AAA, and the USA Educational Foundati on include, respectively, Creating Mobility Choices: Older Driver Skill Assessment and Res ource Guide, Drivers 55 Plus: Check Your Own Performance, Driving Safely While Ag ing Gracefully (Molnar, Eby, & Miller, 2003). These guides provide a series of questions on dr iving performance and suggestions to improve driving performance, but lack scientific eviden ce of their effectiveness or use by older drivers (Eby, Molnar, Shope, Vivoda, & Fordyce, 2003). The Older and Wiser Driver: A SelfAssessment Program, published in Canada a nd adapted from the American AAA, provides a video, handbook, and self-assessment of driving ability. One study of 93 drivers attending one of six educational sessions, administered three questionnaires including a demographic/driving history, the Older and Wiser booklet, and a questionnaire to rate the educational sessions. Older drivers reported the self-assessment tool was very useful, said it made them more aware of driving changes, and 74% planned to adopt changes in their driv ing (McGee & Tuokko, 2003). Another self-screening tool created by the AAA is the Roadwise Review CD-ROM, which is based on a validated questionnai re of functional abilities to drive (Staplin & Dinh-Zarr, 2006). The Roadwise Review was released in 2005, and it consists of a home com puter version of tests including rapid pace walk, head and neck flexibility, components of the Motor-Free visual perception test, the UFOV, Trails B, and delayed recall. The comput er provides verbal and visual instructions for each test, and includes opti on screens describing whet her the driver has a limitation, suggestions for further assessment by a DRS, and videos to show how these

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80 impairments affect the driving task. Although the sc reenings goal is to provide an easy to use and confidential option for older drivers, the eff ect to which drivers who perform poorly on this test seek further clinical assessment needs to be studied (S taplin & Dinh-Zarr, 2006). Other types of self-assessment tools are dr iving diaries (Hutcherson, 1989; Kiernan, Cox, Kovatchev, Kiernan, & Giuliano, 1999) and workbooks (Eby et al., 2003). Ki ernan et al (1999) gave driving diaries to 47 older adults who kept track of their difficulty with driving behaviors including maintaining speed and lane maintenan ce, and keeping record of miles driven on each trip, and the number of times they experienced dangerous events such as near collisions or abrupt braking. The research compared means of occurrences and difficulty with driving behaviors after 30 days and suggested that the di aries had an impact on drivers performance. These results did not control for baseline levels of driving performance, did not use a standard road test to evaluate driving performance before and after completi ng the diaries, and although it may have helped driver be more aware of difficult behavi ors and situations, it lacked validity. A more theoretically based self-assessment tool was designed by E by et al. (2003), the Driving Decisions Workbook. The workbook is divi ded into 3 domains of health (conditions and medications), driving abil ities (vision, cognition, and motor), and driving experiences, attitudes, and behaviors (dri ving in different conditions, con cerns about driving, number of crashes or citations). The wor kbook provides 4 types of feedback including further evaluation, general knowledge, self-awareness, and comp ensation strategies (Eby et al., 2003). The researchers tested whether usi ng the workbook influenced older adults awareness of driving difficulties and driving performance. After usi ng the driving workbook, older drivers said it helped increase their awareness of driving perf ormance, it was a useful reminder, and it was a good tool to help them talk about their concerns with their families. Th is research also found

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81 some relationship between health conditions, cognitive and motor abilities, and driving experiences with older males performan ce on a road test. Although the workbook showed relationships with road test perf ormance, more evidence is needed to show whether older adults driving performance is affected over time after us ing self-assessments tools such as the driving workbook. Only 24% of the sample in Eby et al. (2003) study reported they planned on changing their driving after completing the workbook. A major concern with this type of self-assessment tool is how often older driver s who have difficulties will seek further assessment and the influence of social desirabili ty in older adults responses. Social desirability is the the tendency to give answers that make the respondent look good (Paulhus, 1991). This is a very common bias in self-report. In 16 focus groups with 107 older drivers in rural and urban ar eas, older adults reported that they would not be very honest if they had to self-rate their driving performa nce. Although these olde r adults thought selfassessment would be convenient, they would lik e a valid and comprehensive self-assessment, and thought that they would not follow-up with th e results if these were unfavorable (Shope & Eby, 1998). In Britain, where it is mandatory to self-re port declines in the ab ility to drive, Parker et al surveyed 1932 drivers to examine what type s of driving safety interventions were more acceptable among older adults (Parker et al., 200 3). Although the self-reporting system was unacceptable to only 17.8% of the sample, measur es making the driver responsible for their fitness to drive, such as a self-assessment kit, or informing the drivi ng authorities of medical problems were considered the least effective measure by 15-20% of older drivers. Periodic assessments for feedback without loosing their lic enses and notification of medical conditions by a doctor, optician, or having a full medical ex am at 60 years old were the most acceptable interventions. This evidence suggests that self-a ssessment and self-reporti ng of driving abilities

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82 to the licensing authorities are perceived as acceptable but ineffective methods by older adults, while clinical assessm ents are well accepted. Driving Recommendations in Rehabilitation Settings Specialized driving p rograms exist to evalua te and train older dr ivers with functional limitations or age-related declines that can aff ect driving performance. These programs are not widely known (Hunt, 1993; Stutts & Wilkins, 2003), and marketing efforts have been delineated to increase the number of professional driving rehabilitation specialists (Finn, 2004). However, in a survey of 112 professionals, mostly occupa tional therapists (68%), who conducted driving evaluations most referrals came from physicia ns (Korner-Bitensky, Bitensky, Sofer, Man-SonHing, & Gelinas, 2006). This suggests that DRS practices are well-known in the medical field. Although driving evaluation programs are not accessible in all areas and formal driving evaluations can be costly (Wang & Carr, 2004), rehabilitation driving programs offer several advantages over other driving programs. Firs t, formal driving assessments are usually administered by occupational therapists, who are e xperienced clinicians in lifespan development, disabilities, adapting the environment to individu als needs, and promoti ng clients independence in activities of daily livi ng (AOTA, 2002; Hunt, 1993). Seco nd, assessment within these programs includes a comprehensive evaluation of cognitive, motor, sensory, and on-the-road driving performance. This evaluation provides cl inicians with a thorough examination of abilities that need to be addressed to increase older driver safety and guides the goals of inte rvention to increase functionality, provide compensatory driving techniques, or train clients in the use of driving adaptive equipment (McCarthy, 2005). Unlike driver education that provides classr oom courses, occupational therapists provide hands-on experience for older drivers to modify their behaviors or use driving equipment. This learning occurs in a therapeutic nonthreaten ing, nonjudgmental environm ent (Hunt, 1993) that

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83 can address older adults social desirabil ity by providing comprehensive and empathic assessments; and it is receptive to the importance that older adults give to driving and the mobility and independence that driving provides in our daily lives. In clinical practice, occupational therapists interview the clients about their difficulties perfor ming activities of daily living. This includes mobility and driving, and any specific medical, motor, cognitive, sensory, or other problems affecting the task. After a clin ical evaluation, older ad ults are tested on-theroad, using fixed road courses or the clients driving envir onment (Hunt, 1993). Following the road test, older adults are provided with reco mmendations and/or are scheduled for intervention and training (Wang et al., 2003). Research is needed to unders tand the impact of driving rehabilitation specialists recommendations on driving performance of older drivers. Driving safety literature has looked at different driving in terventions that can include giving recommendations, but the re commendations of DRS have not been studied. Only one recent study, in the occupational th erapy literature, explored occupa tional therapists decisions to give licensing recommendations to older drives (Unsworth, 2007). Unsworth surveyed all registered occupational therapists doing driving rehabilitation in th e state of Victoria (Australia) and found that therapists mostly rely on four cues to decide whether a drivers license should be canceled, suspended (until further assessment), have restrictions, or if the driver can continue driving. The four cues mostly used by the therapis t to help guide their c linical judgment were: (a) having to intervene during the road test evaluati on, (b) the drivers behaviors on the road test (e.g., checking the mirrors), (c) cognitive and perceptual skills such as reaction time, memory, and problem solving while driving, and (d) v ehicle handling skill that included braking, steering, changing lanes, and using th e car controls (Unsworth, 2007).

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84 Implications of Driving Recommendations An i mportant challenge in driving related polic y is finding methods that accurately identify safe and unsafe drivers, and how to determine re -licensing requirements. Re -licensing authorities usually use tests of visual acuity and knowledge for license renewal. However, age-related impairments including cognitive declines are not always identified with these tests (Janke & Eberhard, 1998; Lundberg et al., 1998). Licensing authorities n eed more predictive tests to determine license renewal and, as was stated in the introduction, re-licensing procedures vary across states, some of which ha ve age-based license renewal po licies. Age-based requirements for re-licensing do not reflect older drivers dr iving ability (Waller, 1991), in part because declines with aging affect older adults in differe nt ways. For example, two older adults with the same chronological age (e.g., 75) will likely differ in their cognitive, motor, and sensory abilities and thus, revoking a healthy older adults license based on age is not a fair measure of traffic safety. Older adults are also re luctant to accept inte rventions that involve sanctions for bad driving, such as police referrals for re-testing, an d periodic re-testing that can make them loose their driving privileges (Parker et al., 2003). With strict license renewal policies, older adults may also decide not to renew their licenses if the cost in terms of tim e, money, and need is greater than the effort to renew their li cense (Hakamies-Blomqvi st et al., 1995). These deficiencies in the re-licensing system call fo r more comprehensive driving evaluations, which can be provided by DRS. Driving assessments conducted by specialists in rehabilitation can provi de elder-friendly driving restrictions and recommenda tions to help increase older a dults awareness and practice of safe driving practices. For example, restrictions such as avoiding long distance driving or major highways would permit older adults to continue to drive in those places wh ere they are able to safely drive and not lose their drivers license because they cannot drive in situations they are

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85 willing to avoid. Although some restrictions alre ady exist regarding trav eling distances, time of day, and assistive devices, the impact and effectiveness of thes e and other driving recommendations such as scanning or following techniques merits furt her scientific testing. Only recently, have there been studies started examining the acceptability and effectiveness of driving recommendations and re strictions for older dr ivers. Marshall et al. (2002) conducted a retrospective cohort study of all licensed drivers in a Canadian province to examine the impact of driving and license restrictions on crash rates. The authors only gave a few examples of what constituted driving or license restrictions (e.g., daytime driving only, limited driving distance, periodical eye and physician exams). Analysis of at-fault crashes 4 years before and after obtaining the restrictions showed driving restrictions reduced crashes and licensing restrictions reduced crashes and traffi c violations (Marshall, Spasoff, Nair, & van Walraven, 2002). Although this is one of the first studies to show evidence of the effectiveness of driving restrictions, the au thors acknowledged they did not control for driving exposure, which could have influenced the crash rates (H akamies-Blomqvist, 1998; Langford, Methorst et al., 2006; Tay, 2006), and the analysis did not sp ecify which restrictions contributed to reductions in crash rates. Others have examined the acceptability of licen sing restrictions in a British sample (Parker et al., 2003). Factor analys is of 21 restrictions sh owed that periodic re-tes ting; police referrals to re-testing after observations of ri sky driving, convictions, bans or accidents were among the least acceptable interventions for older drivers. More acceptable interventions for older adults included physicians and opticians informing the licensing authorities of the drivers problems, and having periodic assessments for feedback wit hout the risk of loosing the drivers license. However, the interventions rated as most effectiv e were re-testing after a ban and police referrals

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86 for re-testing of risky drivers. The findings of Parker et al. (2003) are a good representation of the complex issue of driving safety and the signif icant role that driving plays among older adults: they would accept a series of licen sing restrictions, but they reali ze that the most effective ones would require some external refe rral, although they are reluctant to accept the measures that threat their driving independence. One study reported older adults were more likel y to accept driving restrictions that did not limit their ability to go places and be independe nt (Marshall, Man-Son-Hing, Molnar, Wilson, & Blair, 2007). In the study by Mars hall et al. (2007), 86 Canadian older adults living in urban and rural areas rated 11 driving restri ctions using a gambling technique that asked drivers to rate the higher probabilities they were willin g to take to loss their license or have restricted licenses. The driving restrictions included: (1) drive with pres cribed driving lenses, (2 ) drive in daylight only, (3) avoid rush hour, (4) drive within 10 km from home, (5) drive to limited destinations, (6) avoid driving in some roads (e.g., four lane highways), (7) drive with special equipment, (8) drive in limited speed limit areas, (9 ) drive with another licensed driv er, (10) avoid left turns, and (11) have regular assessments of driving ability. The most accepted restrictions were in order of acceptance, (1) using corrective lenses, (2) driving with specific vehicle adaptations, (3) driving only if having regular Ministry of Transport as sessments, (4) driving during daylight hours only, (5) avoiding driving on major highways, and (6) avoiding driving during rush hour. Drivers were less willing to accept avoiding left turns, avoidi ng roads with speed limits higher than 60km/h, driving to limited destinations only, driving within 10km from home, and driving with another licensed driver (least accepted). This study was an initial step in the literature to show the acceptance of driving restriction among older drivers, and consid ers the importance of driving and mobility for drivers who have not yet ceased to drive but express strong disagreement with

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87 some driving restrictions. Mars hall et al. (2007) concluded that when older adults rated the driving restrictions, the preferences appeared to be inversely relate d to the impact on the autonomy of the driving task and the ability to access the comm unity: (Marshall et al., 2007), pg. 781). More recently, Freund and Colgrove (2008) described the most common driving restrictions in a sample of 108 older drivers who were referred to an academic geriatrics department for a clinical evalua tion. Participants were deemed as safe (n=35), unsafe (n=47), or restricted drivers (n=26) based on a simulator test. In the category of restricted drivers a total of 29 recommendations were identified, which we re then classified into 5 types: (1) recommendations of limited driving, (2) re -evaluation, (3) co-pilo ting, (4) environmental restrictions, and (5) retraining/equipment needs. The authors only provided a few examples of the driving recommendations under these five categories. Older adults in Freund and Colgroves study (2008) sample were more commonly restri cted to limit the amount of time and driving distances, and to limit driving to daylight only. These authors suggested that more research on driving restrictions based on driv ing errors was needed to determine ways to help older drivers stay on the road longer and avoi d premature driving cessation. This study will address driving recommendations issued to older drivers by a driving rehabilitation specialist (D RS) after conducting a road test assessment. Thus, the recommendations to be studied included a broad range of categories and number of recommendations that were not limited to restri ctions on the drivers licenses and included recommendations to optimize drivin g behaviors, re-train ing, re-education, and referrals to health care specialists. In summary, given the variety of medical or self-assessment screening, educational interventions, license renewal limitati ons, and driving restricti ons that older drivers

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88 can get, and the lack of eviden ce on their effectiveness in older drivers safe driving practices and behaviors, more studies are needed in the ar ea of interventions for ol der drivers. This study will contribute to the knowledge of the types of driving recommendations and restrictions that older drivers receive and which of these DRS recommendations they recall over time. This study examined the following research questions: (1) What are the most common types of driving recommendations that a DRS provides to older drivers? Since no previous studies ha ve looked at DRS driving recommendations, the question of most frequent recommendations was an exploratory question. (2) Does a combination of cognitive, motor, and sensory clinical tests help predict the DRS decision to determine whether an olde r driver is: (1) uns afe, (2) unsafe but remediable, (3) safe with recommendations, or (4) safe to drive? It was expected that a combination of domains related to driving coul d predict DRS classification of drivers into safe or unsafe categories. (3) What recommendations made by DRS do older drivers recall over time? This question was based on the assumption that if drivers adopt dr iving recommendations, researchers first have to know if drivers recall those recommendatio ns as given by a DRS or if they adopt them on their own. (4) Does driving performance (unsafe, unsafe but remediable, safe with recommendations, or safe) predict older adu lts driving habits? Th is question addressed whether older dirvers changes in driving hab its are a function of DRS driving scores. If driving habits changes are predicted by dr iving scores, then driving recommendations might have an impact on driving performance.

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89 Table 2-1. Studies of standa rdized behind-the-wheel assessm ents and driving behaviors Authors/ Place Methodology Driving behaviors examined Results Carr et al. (1992) North Carolina, US Prospective Age: 18-19 yrs old, 25-35, and 69-84 Sample: n= 20 participants in each age group recruited from university campus and Aging Center Registry 13 subsections (most examples provided are included): 1. Backing: e.g., backing into wrong lane, fails to look to rear, uses wrong side of road to turn around 2. Intersections: fails to slow down, fails to look, fails to respond to hazardous conditions 3. Signaling failures: e.g., fails to signal, fails to use mirrors, fails to check blind spots 4. Turns: e.g., too short, too wide, fails to yield right of way 5. Signal violations: runs amber light, runs red light, disobeys traffic officer 6. Stopping: uses brakes improperly, unsafe place, rough stops 7. Stop streets: stops at improper place, fails to come to complete stops, hesitates too long 8. Steering: e.g., Improper use of hands, does not steer smoothly, uses one hand to steer 9. Position of vehicle on roadway: drives in improper lane, straddles lanes, follows too close 10. Speed control: too fast for conditions, too slow for conditions, in excess of marked limits. 11. Passing violations: Fails to pass at safe place, follows too close, cuts back in too soon 12. Railroad crossings: fails to look properly, fails to stop when necessary, stops at unsafe place 13. Parking: e.g., improper distance from curb, fails to turn wheels properly, fails to set park brake Scoring: weighted scores for specific errors on each category Most prevalent errors were signaling, turning, improper stops, steering errors, and speeding violations. No significant difference between young and older drivers in stops and turn errors. Young groups made more speeding and steering errors (p<0.01). Young adults had more errors than older drivers in these subsections except in signaling errors (p = 0.03).

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90 Table 2-1. Continued Odenheimer et al. (1994) Prospective Massachusetts, US Age: 61-89 (M=72) Sample: 30 older adults: n=17 referred from normal aging studies; n=6 older adults with dementia; n=4 living in the community 5 categories (no examples provided) 1. Scanning the environment 2. Lateral position of the vehicle 3. Anterior/Posterior position of the vehicle 4. Speed 5. Use of turn signals Scoring: pass or fail No data on specific driving behaviors Hunt et al. (1997) Missouri, US Prospective Age: controls mean age = 76.8 Very Mild dementia=74.2 Mild dementia=73.1 Sample: 130 older adults: n=58 controls, n=36 very mild dementia, n=29 mild dementia living in the community and physician referrals 9 categories (examples provided are included): 1. Signals: e.g., does not signal with verbal cueing, needs verbal cuing, uncued/timely 2. Needs prompting 3. Checks traffic 4. Stop sign observance: e.g. slows down but fails to stop, slight roll through, completes stops 5. Traffic light observance 6. Reacts to others 7. Speed control 8. Keeps lane: e.g., crosses lane line unintentionally, maintains vehicle in the lane 9. Qualitative judgments Scoring: 2 or 3 point scale specific to each behavior 40% of safe drivers did not use turn signal appropriately Dobbs et al. (1998) Alberta, Canada Prospective Age: Young controls=35.6 (3.2); old controls=69.4 (6.8); patients=72.7 (9.1) Sample: n=30 young controls n=68 older adult controls; n=155 patients referred to geriatric driving program; Referrals and living in the community 13 categories (examples provided included) 1. Extreme positioning error: e.g, driving on the shoulder 2. Minor positioning error: e.g. driving too close to lane markings 3. Turning position error: e.g., wide or cut turns 4. Stop positioning error: e.g., stopping too close or too far back 5. Scanning error: e.g., no shoulder checks 6. Overcautiousness: driving too slow 7. Aggressive maneuver: e.g., risky turns 8. Rolled stop: failing to come to a complete stop at a sign/signal 9. Speed error: driving over the posted speed limit 10. Vehicle control: shaky steering 11. Poor habits: one hand steering 12. Signal error: late/early to signal 13. Hazardous errors: when the evaluator had to intervene Scoring: type and severity of errors Patients differed from controls in number of hazardous errors (p = .001) All groups differed in severity of scanning errors (p = 0.001) Rolled stops and speed errors were common among all groups (no significant differences)

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91 Table 2-1. Continued Justiss et al. (2005) Florida, US Prospective Age: 65-89 (M=75.3) Sample: 95 older adults Referrals and living in the community 8 categories of driving behaviors: 1. Vehicle position (anterior/posterior) moving or stopped: e.g., traveling too closely, stopping too far back from markings or vehicles, inadequate space cushion during lane change 2. Lane maintenance: e.g., drifting out of lane, wide turns, encroachments on perpendicular traffic 3. Speed regulation: e.g., not coming to complete stops, traveling too fast/slow, inadequate merging speed 4. Yielding 5. Signaling (e.g., signaling too short until maneuver, leaving signal on, wrong signal for turn) 6. Visual scanning (e.g., not checking mirrors or blind spot) 7. Adjustment to stimuli/traffic signs (e.g., not adjusting for posted limits, improper response to traffic or pedestrian, choosing inappropriate lane from posted signage) 8. Gap acceptance: Estimating distance to cross across oncoming traffic (when turning left) Scoring: Types and severity of error No data on specific driving behaviors

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92 CHAPTER 3 METHODS Participants This study included older adults who part icipated in two earlier (2005 and 2006) University of Florida National Older Driver Research and Training Center (NODRT C) studies. Participants were volunteers recr uited via flyers posted in Gainesville, Florida or were selfreferrals or received a referral for a driving assess ment from doctors, families, or other health professionals. Participants were included in the earlier NODRTC studies if they were (a) 65 years or older, (b) had a valid drivers license and (c) were seizure free in the past year. The sample was 123 older adults, and five we re not driving at baseline. The sample eligible for this study was 118 older adults. Sixtyfive (55%) of these pa rticipants answered a telephone interview 1.5 to 3 year s after their road test and clinical assessments. Among nonparticipants, 3 had died, 22 dec lined participation, 7 suspe nded or cancelled the telephone interview after being scheduled, 6 did not res pond after leaving a message, 12 had their number disconnected, and 3 did not answer the phone. An at trition analysis is provided in the follow-up interview section of this chapter. Procedures At baseline, participants consented by telephon e to participate in a telephone interview of approxim ately 30-40 minutes (See Appendix A). Th e interview included si x questionnaires: (1) The Telephone Interview for Cognitive Status modified (TICS-m) (Welsh, Breitner, & Magruder-Habib, 1993), (2) Demographics and self-re port of physical health, (3) Co-morbidities, (4) the Older Americans Resources and Servi cesInstrumental Activities of Daily Living (OARS-IADL) (Fillenbaum, 1978), (5) the Functional Independence Measure (FIM) (Hamilton, Granger, Sherwin, Zielezny, & Tashman, 1987) a nd, (6) a modified version of the Driving

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93 Habits Questionnaire (DHQ) (Owsle y et al., 1999) These measures are described under Clinical Measures, below. After voluntary consent, partic ipants were scheduled for a clinical and road test appointment at Independence Driv e NORDTCs driving research a nd clinical serv ices facility at the University of Florida. Participants were asked to bring all curre nt medications to their clinical appointment, where they first read a nd signed an informed consent of procedures and confidentiality of information. The DRS or a rese arch assistant reviewed and recorded all the medications (See Appendix B). The clinical assessments used in this study included measures of cognitive, visual, and motor abilities administered in both studies. The clinical measures are described below (and listed in Table 3-1), and Appendixes C and D include the assessment forms used at baseline. Appendix E shows the combined clinical measures The clinical assessment lasted approximately 1.5 to 2 hours for study 1 and 40 minutes for study 2 In study 1, participants were given breaks at any time, and the order of the tests alternated between cognitive and motor tests to reduce participants fatigue. Following the clinical assessment, the DRS drove the participant to the start point for the road test, where the participant drove the road course and the DRS scored the drivers performance. Clinical Measures Table 3-1 is an overview of the clinical m easures used at baseline, which are also described in more detail in this section. Telephone Interview for Cognitive Status (TICS) The TICS was developed by Bra ndt et al (Brandt, Spencer, & Folstein, 1988) as a m easure of cognitive function that could be administer ed over the telephone. This test evaluates

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94 orientation to time and place, language functi ons, calculation, verbal memory, and verbal abstraction. The modified version (TICS-m) include s delayed recall, which is important to detect early dementia. TICS-m scores can range from 0 to 50, and a cutoff score of 30 was suggested as indicative of mild cognitive impairment (Welsh et al., 1993). The TICS-m had 85% sensitivity and 83% specificity detecting cognitive impairments that were determined by neuropsychological batteries in a sample of 209 ol der adults. The TICS-m also correlated with the Mini-Mental State (MMSE) and other ne uropsychological measures, and showed 0.83 reliability when measured over 15 months (P lassman, Newman, Welsh, Helms, & Breitner, 1994; Welsh et al., 1993). Since the TICS-m coul d be administered over the telephone, this measure was selected over the MMSE to assess ov erall cognitive status of the sample over time. Items of immediate and delayed recall in the TICS -m were also recorded to control for memory changes that could confound the participants ability to recall dr iving recommendations. Useful Field of View (UFOV) The UFOV is a test of processing speed, divi ded and selective atten tion (Edwards et al., 2006). Processing speed measures the time it take s a person to perceive, recognize, and respond to a stimulus; divided attention is the ability to attend to simultaneous tasks, and selective attention is the ability to focus on relevant stim uli in the presence of distracters. These three abilities are tested with the person sitting 24 inches away fr om a computer screen. In the speed of processing subtest, the silhouette of a car or a truck appears in the center of the screen. The number of times and the time the targets (car or tr uck) appear in the screen varies until the person correctly identifies 75% of the targets. In the second subt est of divided attention, the same silhouettes of a car or a truck appear in the cent er of the screen, and an additional silhouette of car or truck appears in any of 8 radial locations. The third subtest, selective attention, is similar to subtest 2 but the radial targ ets are surrounded by shapes to dist ract the persons attention. In

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95 subtest 2 and 3 the person needs to identify the central target and the location of the screen where the second silhouette appeared. The subtests are timed and range from 16.6 to 500ms. Test-retest reliability for the touch screen version of the test was 0.73 after 34 days, with 0.87 validity (Edwards et al., 2005); normative scores of UFOV subtests for older adults were recently published (Edwards et al., 2006). Trails Making Test Part B (Trails B) Trails B is a m easure of executive functio ning, working memory, visual processing and visuo-spatial ability. It consists of a series of circles on a paper th at are numbered 1-13 and circles that have letters A to L. The person is asked to connect numbers and letters in sequential order alternating from a number to a letter (e.g., 1 to A, A to 2, etc). The score is the time to complete connecting the numbers and letters. Repor ts of cutoff scores for driving ability suggest completion time of 90 seconds for high comple xity and high speed roads and 120 seconds for low speed and low complexity driving (Stapli n, Lococo, Gish, & Decina, 2003). A sample of 257 older adults with Alzheimers disease or mild cognitive impairment who were diagnosed by a multidisciplinary team were compared to a control group of 269 older adults. The study reported 45% sensitivity and 91% specificity of Trails B to determine cognitive impairment (mild cognitive impairment or Alzheimers disease) against controls; and normative data for the Trails B was provided by age and education (Ashendorf et al., 2008). Older Americans Resources and Services: Instru menta l Activities of Daily Living (OARSIADL) The OARS was designed to evaluate older adu lts level of function and use of services. The IADLs questionnaire is part of the OARS personal functioning measures, and was used in this study to describe the samples functional le vel in instrumental activities of daily living (IADLs). These activities include tasks such as shopping, doing housework, taking medicine, and

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96 handling money. Item scores range from 0-2 fo r a total score between 0-14 (higher scores indicate more independence in activities of dail y living (Appendix A includes the IADL questionnaire). Reliability and validity were reported for Activ ities of Daily Living, including physical ADLs and IADLs, and included 0.83 crite rion validity and 0.86 inte r-rater reliability (Fillenbaum, 1978; Fillenbaum & Smyer, 1981). In 1994, Ottenbacher et al. (1994) reported agreement between raters who assessed twenty ol der adults with the IADLs scale over 7-10 days and 4-6 weeks (0.91 and 0.98, respectively). Functional Independence Measure (FIM) The FIM is a m easure of an individuals functional independen ce in six categories: (1) selfcare, (2) sphincter control, (3 ) transfers, (4) locomotion, (5 ) communication, (6) and social cognition. A total of eighteen items are scored from 1-7 depending on the individuals level of dependency, ranging from total assistance needed to complete the task (score of 1) to complete independence (score of 7). The FIM scores range from 18-126. This test showed agreement of 0.88 among 40 occupational therapists who rated th e self-care and transfer items (Fricke, Unsworth, & Worrell, 1993). Total FIM scores showed 0.99 inter-rater reliability and 0.92 stability (Ottenbacher et al., 1994); and a quantitative review of the FIMs reliability showed 0.95 inter-rater and test-re-test re liability for eleven studies that assessed a total of 1568 patients (Ottenbacher et al., 1996). Visual Assessment The Stereo Optec 2500 vision tester was used to assess visu al function. The participant sat down and looked through the lenses where a set of slides was used to test visual acuity, horizontal peripheral field, color discrim ination, depth perception, vertical and lateral phorias. Scores for visual acuity ranged fr om good vision (20/20) to poor visual acuity (20/200 or more); horizontal fields were tested for each eye by ha ving the subject detect a flashing light at 85, 70,

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97 55 or 35 (nasal) degrees. Color discrimination wa s assessed by asking th e person to look at a slide that had six circles with num bers inside them; the participants had to tell the evaluator what numbers if they saw inside the circles. In the de pth perception test, a series of numbers from 1 to 9 had circles that seemed to be moving (three -dimensionally) toward the person; scores were based on correctly identifying whic h of the circles at th e top, bottom, left, or right of the number were moving toward them. Phorias are the ability of the eyes to work together horizont ally and vertically, that can influence the ability to scan the environment. Although there is no literature on driving and phorias, scores of these tests we re included in this study since phor ias are often assessed with the vision equipment that driving rehabilitation specialists use in clinical prac tices. The slide to test lateral phorias shows a series of 15 musical notes and an arrow pointing to one of the notes; the person has to identify the note to which the arro w points to. Vertical phori as are tested with a different slide showing 7 musical notes and the person has to iden tify which note is crossed by a red line. Contrast sensitivity: Additional slides included the Functional Acuity Contrast Test (F.A.C.T.) (Ginsburg, 1984) to assess contrast sensitivity. As stated in chapter 2, contrast sensitivity evaluates the lowest contrast at which individuals can see at various degrees of contrast and in different levels of spatial frequencies. The F.A.C. T. measures contrast sensitivity at five spatial frequencies (1.5, 3, 6, 12, and 18 cy cles/degree). Scores pe r frequency range from 0 to 9 and impairments per frequency are determin ed as vision below the normal curve. Normal scores for frequencies of 1.5, 3, and 6cycles/degr ee range from 6 to 8; from 4 to 8 for 12 cycles/degrees, and 2 to 8 for the highest fre quency (see Appendixes C and D for scoring forms). Test-re-test reliability of 0.77 for the Optec vision screener and the F.A.C.T. have been reported

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98 (Hitchcock et al., 2004; Horberry et al., 1997). Compared agains t a vision assessment by an optometrist, the Optec 2500 had 76.2% sensi tivity and 100% specifi city determining participants who had failed the opt ometrists assessment in a sample of 23 adults (Horberry et al., 1997). Rapid Pace Walk (RPW) The RPW measures physical e ndurance, balance, and streng th (Wang et al., 2003). The person is asked to walk back and forth over a 10 foot mark on the floor. The score is timed and less than 9 seconds suggests poor driving perfor mance (Wang et al., 2003) Staplin et al (2003) described the RPW as a sensitive measure of phys ical frailty over time (Staplin, Gish et al., 2003). Range of Motion and Strength These tests were based on the driving rehabi litation specialist judgm ent. The person is asked to perform head, arm and leg movements such as trunk rotation, shoulder flexion, ankle dorsiflexion, plantar flexion, and hip flexion. Scores for range of motion were recorded as within or not within functional limits. Scor es for strength were also consid er within functional limits if the score was 4 or 5 on a range from 0 to 5, which is clinically determined by a persons ability to sustain the evaluators resistance. Demographics and Self-Report of Physical Health Questions These questions addressed age, gender, level of education, race and ethnicity; and selfreport of hearing im pairment, falls in the prev ious year, presence or absence of periods of drowsiness, and co-morbidities. A checklist of co-morbidities was used to identify common impairments and illnesses of participants. The ta ble of co-morbidities is included in Appendix A (Telephone Interview). The categories used to describe the sample were: (1) Heart disease, (2) respiratory disease, (3) musculoske letal disorders, (4) arthritis, (5 ) urinary disease, (6) diseases

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99 affecting vision, (7) glandular disorders, (8) stomach or intestinal disorders, (9) sleep disorders, (10) depression, (11) hearing or speech impairment s, (12) dementia or cerebro-vascular accident, (13) other neurologic conditions) and, (14) ot her disorders. A score of co-morbidities was obtained summing the number of ill nesses for each participant. Modified Version of the Drivin g Habits Ques tionnaire (DHQ) The purpose of the DHQ, developed by Owsley et al, was to assess dr iving habits among older drivers with vision impairment (Owsle y et al., 1999). The DHQ along with conditions of driving avoidance reported by the same authors (Ball et al., 1998) were used in this study. The measures of interest were: (a ) Driving space, (b) driving exposure, (c) driving difficulty, (d) driving avoidance, and (e) self-report of drivi ng in different conditions. Driving space refers to distances to which older adults drove. It included driving out of the southeast region of the country, out of state, out of the county, out of the city, and driving beyond or in the neighborhood. Driving exposure includes measures of total places that a person drives per week, number of trips per week, number of days per week when the person drives, and total weekly mileage. Self-report of driving conditions, dr iving difficulty, and driving avoidance were recorded for eight conditions. (See Appendix A for driving habits questions included in this study). Reliability for driving space driving e xposure, and driving difficulty were 0.86, 0.83, and 0.60, respectively. Road Test The studies used a road test consisting of a fixed cour se of approxim ately one hour duration. The road test started in a parking lot, and progressed to increasingly complex driving environments, including a merge to an intersta te highway. All particip ants drove a 2005 Buick Century for the road test. This vehicle was equipp ed with an auxiliary brake that was used by the DRS when she needed to intervene in the partic ipants driving. The vehicle was also equipped

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100 with an extra rear view mirror on the passenger side to let the evaluator see the vehicles and the road behind, and a mirror placed on the front window to help the DRS tr ack the participants scanning behaviors. During the ro ad test, the DRS made observati ons of driving behaviors and subsequently gave driving recommendations to participants who she fe lt would benefit from them. The DRS recommendations were given in a verbal or verbal and written format. If the DRS wrote the recommendations, they were either in a recommendations form or written at the end of the road test. Appendixes F to H provide examples of the recommendation forms used in the studies. When a participant failed a road te st, the DRS filled out a report form and mailed it to the State of Floridas Depa rtment of Highway Safety and Motor Vehicles. Medical reporting of unsafe drivers is provided for in the Florida Statutes sectio n 322.126 (2), (3). An example of the reporting form is provided in Appendix I. Road Test Scoring The road tes t was scored with a Global Ra ting Scale (GRS) and a Be havioral Score. The road test was previously valid ated in a NODRTC study that show ed 0.95 test-retest reliability (n=10), 0.94 interrater re liability (n=33), and in ternal consistency of the items (Cronbach 0.94, n=95) (Justiss et al., 2006). The Behavioral Score ranged from 0 to 273 based on scores of 0 to 3 for 91 road test maneuvers. The maneuvers included right turns, left turns, straight driving, lane changes, and merging onto a highway. The scoring for each maneuver was based on the number of behavioral errors (listed below), such as lane maintenance and speed regulation. A maneuver score of 0 indicated that the DRS had to intervene (e.g., press dual br ake, hold the steering wheel); a maneuver score of 1 indicated that the DRS had to use verbal cues to avoid an unsafe situation (e.g. you are at a stop sign); a mane uver score of 2 was used when the participant made any driving behavioral error, and a maneuver score of 3 indicated sa fe driving performance

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101 for that maneuver. Appendixes J and K are examples of road tests with gl obal rating scales of 0 and 3. Driving Behaviors in the Road Test The driving behaviors that were scored in th e road test are describe d below, including the definitions of the behavior and examples of driving errors (Justiss et al., 2006). 1. Lane maintenance: Lateral (side to side) positioning of the vehicle during driving maneuvers (turns, straight driving, lane changes, etc) and while stopped. Lane maintenance refers to the ability to maintain steering control. Examples of errors: Drifting out of lane, encroachments on perpendicular traffic or wi de turns, parking outside designated space markings (Justiss et al, 2006, pg. 122). 2. Vehicle positioning (moving or stopped): Anterior and posterior pos itioning of the vehicle in relation to other vehicles a nd/or objects and pavement ma rkings. Examples of errors: Traveling too closely, inadequate space for merges or lane changes, stopping across a crosswalk or too far back from pavements marking or vehicles (Justiss et al, 2006, pg. 122). 3. Speed regulation: Ability to follow and maintain speed related to posted limits and having adequate control of accelerati on and braking features of the vehicle. Examples of errors: Not coming to complete stops, traveling too fa st/too slow, abrupt or inappropriate braking or acceleration (Justiss et al, 2006, pg. 122). 4. Signaling : Proper use of turn signals. Errors incl ude: Not using the turn signal when turning, signaling too short before a maneuver, or leav ing the turn signal on (Justiss et al, 2006, pg. 123). 5. Visual scanning: Looking around to scan the driving e nvironment. Examples of errors: Not checking blind spot, not looking through rearview or side mirro rs during lane changes, not looking right or left before going through in tersections. (Justiss et al, 2006, pg. 123). 6. Yield: Giving right of way following rules of th e road at stop signs, right turns on red, and merges. (Justiss et al, 2006, pg. 123). Example of errors: Ignoring a yield sign and not giving the right of way to vehicles when tu rning right on a busy ro ad not following the rules of right of way in a four stop sign (e.g., letting drivers cross th e intersection if they arrived first and are at the drivers right hand side). 7. Adjustment to stimuli/traffic signs: Adjust appropriately to ch anging road sign information, other vehicle movements, pedestrian moveme nts, and ability to recognize potential harzards. Example of errors: not following pr oper directions given by evaluator, choosing improper lane from posted signage (Justiss et al, 2006, pg. 123).

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102 8. Gap Acceptance: Appropriate timing to make unprotecte d left turns. Example of errors: Unsafe calculation of time to turn left when approaching vehicles are in close proximity, being over-cautious and waiti ng too long to turn left when is safe to turn. Global Rating Scale The Global Rating Scale (GRS) was used as an evaluation of overall driving perform ance during the road test. The GRS was scored in a 4-point scale from 0-3, where 0 indicates unsafe driving. Previous research from the NODRTC s howed high inter-rater re liability for the GRS (ICC=0.98, n=33). The 4-point GRS had a stronger correlation with driving performance (r=0.84, p<0.001) than a dichotomous score of pass or fail (r=0.75, p<.001) (Justiss et al., 2006). The scores were determined as follows: (1) A GRS score of 0 indicated that the person faile d the road test and they were unsafe drivers. (2) A GRS score of 1 indicated the person was an unsafe driver but remediab le. In this case, the driving rehabilitation specialist recommended an interventi on such as taking behind-thewheel training, seeing an eye specialist or neurologist. (3) A GRS score of 2 indicated the person wa s a safe driver, but the specialist gave recommendations for improving driving perfor mance. For example, the participant was told to avoid drifting to the left, avoid following vehicles too closely, or to scan the environment more often. (4) A GRS score of 3 was given when the DRS c onsidered the person was a safe driver, and in some cases, participants were given recomm endations as reminders of safe driving. Follow-up Telephone Interview The purpose of the follow-up telephone intervie w was to: (a) explore whether or not older drivers rem embered the recommendations made by a DRS, and (b) determine if older adults driving habits changed over time, and if these changes were predicted by levels of driving performance. An attrition predic tion of 30% was expected, based on longitudinal studies of older adults that looked at 1-3 year follow up of older adults cogniti ve (Hanninen et al, 1995) and physical status (McGwin et al, 2006). These studies re ported approximately 77% response rates at follow-up. Participants with global rating scores of zero and one were expected to have higher

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103 attrition rates since we expected these drivers to be older and have lower scores in cognitive measures that could influence th eir participation in the follow-up telephone interview. Table 3-2 describes sample size predictions by Global Rating Scale group and table 3-3 describes demographic and clinical characteristics of follow-up responders and non-responders. Continuous variables were tested with ANOVAs and categorical va riables were tested with chisquare to determine differences between re sponders and non-responders. Analyses were not performed when cell sizes were less than 5. Follow-up responders and non-responders did not differ in age, gender, race, education, visual, cognitive and health status variables. The only difference between responders a nd non-responders was that responders had fallen more than non-responders in the six months prior to the clinical and road test assessments (p < .05). Telephone Interview Components and Rationale The follow-up telephone interview (Appendix L) had five sections: (1) General health questions, (2 ) Telephone Interview of Cognitive Status (TICS), (3) Driving Recommendations, (4) Marlowe-Crowne Social Desirability Scale, and (5) Driving Habits Questionnaire. The general health questions asked if the person was still driving, if their health condition had changed since their baseline clin ical and driving assessments, se lf-report of vi sion and hearing, and self-report of falls in the la st six months. The questions on ge neral health helped control for functional changes that could aff ect the participants self-report of driving performance 1.5 to 2 years after baseline. The TICS was used at follo w-up to control for cognitive changes that could influence the participants recall of recomme ndations. The Driving Habits Questionnaire (DHQ), described earlier, was administered at baseline and follow-up, and was used to determine longitudinal changes in driving habits by recommendation group. Since driving performance is a sensitive topic for older adu lts, self-report of recommendations were expected to be influenced by social desirabil ity. Social desirability is the

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104 tendency to respond in a culturally appropriate way. Measures of social desirability help control for response bias. Thus, the Marlowe-Crowne Social Desirability Scale (MCSD) (Crowne & Marlowe, 1960) was used in this study. The MC SD had internal consistency of 0.88 and testretest reliability of 0.89 in a sample of 76 colle ge students. The scores can range from 0 to 33 where higher scores indicate higher social desi rability Validity and reliability tested among varied samples of students showed average means ranged from 12-16 for different studies and reliability coefficients ranged from 0.73-0.88 across samples (Barger, 2002; Paulhus, 1991). Each item is answered as true or false (See meas ure in Appendix L). Some examples of the items are: (a) I never hesitate to go out of my way to help someone in trouble, (b) I like to gossip at times, and (c) I never make a long trip without checking the safety of my car. Driving Recommendations Recommendations from the DRS included reco mmendations given in one of two ways: (1) recommendations that the DRS check marked on either of the two studies recommendation forms (Appendices D and E), (2) recommendations that the DRS wrote at the end of the road test either on the comments section or next to the Global Rating Scale. Since the DRS used the written comments at the end of the road test to help determine driving recommendations; and these comments closely resembled the recommenda tions from written formats, as shown in the examples below, the comments at the end of the road test were considered as driving recommendations. Figures 3-1 to 3-4 provide a visual representation of DRS recommendations in the different formats. Categories of Recommendations The recommendations used in the follow-up interview were obtained as follows: (1) A combined list of recommendations was create d after reviewing recommendations from both recommendation forms; (2) recommendations from thirty participants from baseline, who had

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105 recommendations written in the comments section of the road test, were tabulated in excel to compare them to the recommendation forms that the DRS check marked (Appendices D and E); (3) the tabulated comparisons were reviewed by two of this dissertations committee members and agreed that comments at the end of the road test closely reflected recommendations from the recommendation forms. At follow-up, participants were first asked D o you recall any driving recommendations provided to you from the driver evaluator at the Inde pendence Drive driving program. The recommendations recalled were writ ten down. The participants were then read a list of recommendations and asked participants to say whether they remembered being told that recommendation or not. For example, participants were asked if they were told or if the DRS recommended coming to complete stops. In so me cases, the interviewer clarified that recommendations referred to the comments or fee dback that the DRS had at the end of the road test. Recommendations in the follow-up inte rview fell under 11 categories (Table 3-4). Driving Recommendations and SOC Model The 11 categories of driving recom mendations were explained using the Selective Optimization with Compensation (SOC) model, as described in the introduction. Figure 3-5 is a visual representation of the pr oposed relationships of driving recommendations using the SOC framework. In the proposed relationships, all driv ing recommendations intera ct with each other. For example, a driver who receives recommendation suggesting selection such as avoiding night driving, may also receive recommendations relate d to driving behaviors such as paying more attention to road marks, and the DRS may suggest BTW training; or suggest that the person drive with assistive devices. In the visual representation (Figure 3-5) the three components of the SOC model of successful aging are used to classify recommenda tions suggesting selection, optimization, and compensation. The recommenda tions are described in boxes under each SOC

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106 component using bolded lines to indicate direct associ ations and dashed lines to indicate the inter-relationships of recommendations. Recommendations that suggest driving selection include recommendations to avoid driving situations such as avoid left tu rns, avoid night driving, or avoid driving long distances. These are considered selection because al though it is not the driver who is selecting to avoid these situations, reduced traveling can be a way for older adults to a void difficult driving situations when visual or cognitive declines occur. For exam ple, slow reaction time, impaired ability to see at night, or difficulties concentrating for long peri ods of time may affect th e drivers ability to judge when to safely turn left, perceive vehicles and objects when driving at night, and drive to long distance locations, respectivel y. Drivers can select to avoi d situations in which visual, cognitive, or motor declines can affect driving; or also select not to drive because they prefer to avoid rush hour and high traffic and their lifes tyle (e.g., retireme nt) allows them to choose the times when they drive. Recommendations suggest ing selection can also include planning for driving retirement or stop driv ing, in which case the driver w ould use compensation strategies such as using other means of transportation be sides driving a vehicle or having someone else drive. Recommendations that suggest specific driving optimization are recommendations to improve the existing abilities to drive. This includes recommendations to improve driving behaviors or behind-the-wheel training to optimize driving. Driving behaviors include operational and tactical levels of driving as ex plained in Michons model. For example, coming to complete stops requires an operation to cont rol the car that helps the person to complete a tactical maneuver of stopping the car. Avoid drif ting requires a firm grasp on the steering wheel with hands moved apart to tactic ally maneuver the car through lane changes, straight driving, and

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107 turns. Using the mirrors more often and paying mo re attention to the road way markings help the driver make safe lane changes, stay in his lane, and use the proper times and spaces to for example move into a turning lane, or merge into a highway. These examples illustrate how the application of safer driving beha viors may optimize the drivers ab ility to respect the right of way and scan the environment to maneuver thro ugh stops and intersections; help the driver maintain control of the car (e.g., speed, steering, stay ing in the lane); and us ing strategies such as shoulder checks or mirror checks to make safer lane changes, merges, or dr iving in high traffic. Thus, since the driving specific recommendations are based on driving behaviors, they are conceptualized as optimizing behaviors for dr iving. The specific optim ization recommendations include: (1) scanning behaviors, (2 ) scanning for road markings, (3) speed, (4) stop, (5) distance, (6) signaling, (7) merging, (8) at tention, and, (9) hands positioning. Drivers can also optimize their driving ab ility through practice or training. In BTW training, the DRS teaches the driver how to impr ove their driving behaviors over a number of practice sessions. In these sessi ons, for example, the driver can be taught strategies to compensate for decreased peripheral vision such as turning the head more often to check blind spots, leaving more space in following cars to compensate for decreases in depth perception, or practicing lane changes leaving ap propriate distances with the cars and signaling the intentions to exit and enter a lane. Practice recommendations can include taking driving lessons or BTW training. Driving lessons in this study refer to practice driv ing lessons (behind-the-wheel) provided by driving instructor s; BTW training refers to training provided by driving rehabilitation specialists w ho have a broader knowledge b ackground on age-related declines, rehabilitation, and training a nd use of assistive technology.

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108 When age-related vision, motor or cognitiv e declines can no longe r be improved with practice of driving behavior s or BTW training, drivers ma y be taught to drive using compensation strategies such as driving with assistiv e technology. For example, drivers can use a left foot accelerator to compensate for a right-s ide hemiplegia caused by a stroke or use sterring knobs to compensate for decl ines in hand dexterity. Other type of optimization recommendations are more global driving recommendations. These recommendations are also targeted at optim izing or improving drivers ability. In the proposed relationships of recommendations to the SOC model, global recommendations differ from specific driving recommendations because global recommendations are applied outside the driving environment. As such, these recommenda tions involve actions wh ere the individual does not directly operate or maneuve r the vehicle; but include acti ons that help optimize driving performance by reviewing knowledge related to rules of the road or seeing specialists to improve eyesight, range of motion, or other physical or medical conditions. The recommendations that fall under this category include: (1 ) taking the AARP driving refres her course, (2) re-reading the FL Driver's Handbook, and (3) referrals to see a physician, doctor, eye care specialist, rehabilitation practitioners, neur ologist, or other specialist. Afte r seeing these specialists, the driver may be prescribed medications, glasse s, physical strengtheni ng or range of motion exercises, or cognitive exercises to optimize dr iving ability. Some training can be provided to help compensate for decreased physical or cogni tive function, which can be referred to as compensation strategies for driving performance. Table 3-5 lists the ai ms of the study and the expected trends based on the cate gories of driving recommendations.

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109 Recommendations Recall At follow-up, participants were first aske d uncued whether they recalled any driving recomm endations provided by the DRS at the drivi ng evaluation at Independence Drive. Then, in a second cued step, the investig ator read the list of driv ing recommendations and let the participant say whether those recommendations (cue d) were given to them. The terms cued and uncued are used to describe whether the partic ipants recalled drivi ng recommendations before the interviewer read the list of driving recomme ndations (uncued recall) or if the participants recalled the driving recommendations while th e interviewer read the list of driving recommendations (cued recall). The recommendations form used in the telephone interview also incl uded a category of safe driving, which referred to DRS feedback to drivers telling them they were good, safe drivers. This category of comments on safe driving was not included in the SOC driving recommendations because it referred to a positiv e remark to participants and does not involve recommendations to change behavior or perfor mance as is the case with the rest of DRS recommendations. However, comments about safe dr iving were expected to be more prevalent for drivers who scored 3 in the road test gl obal rating scale. Recall of comments about safe driving was also used as a meas ure of social desirability. We tested the correlation between social desirability scores a nd the number of recommendations that drivers recalled when the interviewer read the list of recommendations. Th e author expected higher levels of social desirability to correlate with higher numbers of recalled reco mmendations and/or comments of safe driving.

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110 Table 3-1. Clinical measures Domain Tests Reliability Source Cognitive/ Executive functions TICS-m UFOV Trail making test part B 0.83 0.73 0.83 (Welsh et al., 1993) (Edwards et al., 2005) (Ashendorf et al., 2008) Functional OARS-IADLs FIM 0.91-0.98 0.95 (Ottenbacher et al., 1994) (Ottenbacher, Hsu, Granger, & Fiedler, 1996) Vision Stereo Optec 2500 including F.A.C.T. slides for contrast sensitivity 0.77 (Hitchcock, Dick, & F., 2004; Horberry, Gale, & Taylor, 1997) Motor Rapid Pace Walk (RPW) ROM and strength NA NA (Wang et al., 2003) Physical Self-report of comorbidities, physical health, medications NA NA Driving habits Modified Driving Habits Questionnaire (DHQ) 0.60-0.86 (Owsley et al., 1999) Note. TICS = Telephone Interview for Cognitive Status; UFOV = Useful Field of View; OARS-IADLs = Older Americans Resources and Se rvicesInstrumental Activities of Daily Living; FIM = Functional Independ ence Measure; F.A.C.T. = Functional Acuity Contrast Test; ROM = Range of motion. NA = Not applicable. Table 3-2. Follow-up response prediction Baseline participants(n=118) GRS Total Follow-up expected response (n=80) Percentage Total Follow-up response rates (n = 65) Percentage Total Unsafe (0) 4 50% 2 25% 1 Unsafe remediable (1) 13 50% 7 50% 7 Safe with recommendations (2) 68 70% 48 53% 36 Safe (3) 33 70% 23 61% 20

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111 Table 3-3. Characteristics of fo llow-up responders and non-responders Variable Total sample (n = 118) Follow-up Responders (n = 65) Non-responders (n = 53) p-value Agea M (SD) 74.40 (6.3) 75.25c (6.2) 73.35d (6.3) .10 Gender Male N (%) Female N (%) 59 (50) 59 (50) 30 (25.4) 35 (29.7) 29 (24.6) 24 (20.3) .23 Raceb White N (%) Non-white N (%) 108 (92.3) 9 (7.7) 60 (51.3) 4 (3.4) 48 (41) 5 (4.3) .38 Education yearsa M (SD) 16.09 (2.9) 16.15 (2.6) 16.00e (3.2) .77 Total medications M (SD) 7.58 (4.8) 7.89 (4.7) 7.19 (4.8) .43 Total co-morbiditiesb M (SD) 3.92 (2.6) 4.03 (2.5) 3.79d (2.8) .62 IADLSb M (SD) 13.90 (.40) 13.91 (.34) 13.88 d (.47) .75 TICSb M (SD) 35.58 (4.5) 36.09 (3.8) 34.94d (5.2) .17 TICS Immediate Recallb M (SD) 4.64 (1.6) 4.78 (1.4) 4.46d (1.9) .30 TICS Delayed Recallb M (SD) 3.19 (1.7) 3.35 (1.8) 2.98d (1.7) .26 Note. M = Mean; SD = Standard Deviation; FIM Motor = Functional Independence Measure Motor subscale; IADL = Instrumental Activities of Daily Living; TICS = Telephone Interview of Cognitive Status; MMSE = Mini-Mental Status Exam; Visual Acuity = Snellen denominator for bot h eyes. GRS = Global Rating Scale; Unsafe-R = Unsafe remediable; Safe-R = Safe with recommendations. a n = 116. bn = 117. cn = 64. dn = 52. en = 51. fn=106. gn = 115 *Significance p < .05

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112 Figure 3-1. Example r ecommendation form 1 Figure 3-2. Example r ecommendation form 2 Start braking earlier, look for visual cues when stopping (brake lights of cars in front of you), look for bottom of tires when stopped, slow down (look for posted speed limits), behind the wheel training with driving reha b specialist, do not drive in high traffic roadwa y Referral to o p hthalmolo g ist/o p tometrist as neede d

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113 Figure 3-3. Example of recommendations on the comments section of the road test Figure 3-4. Example of recommendations next to driving performance score Come to complete stops always Increase following distance while driving and at stops Kept talking (even after several verbal cues); tailgates often; drives too slow often

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114 Table 3-4. Driving re commendations categories Category Recommendations Reduced traveling recommendations (a) avoid rush hour (b) avoid high traffic (c) avoid highways (d) avoid night driving (e) avoid long distance driving (f) avoid driving to new lo cations or unfamiliar places (g) avoid unprotected lefts (h) plan for driving retirement (i) do not drive or retire from driving Scanning for road markings (a) pay more attention to roadway markings (b) avoid drifting right (c) avoid drifting left (d) watch lane maintenance (e) stay in your lane when turning (encroaches/too wide) Speed recommendations (a) do not drive too slow (b) do not drive too fast (c) watch speed related to posted limit (d) reduce aggressive accel eration and/brake earlier Stop recommendations (a) come to complete stops (b) stop behind the white lines/don't stop in intersection Distance recommendations (a) increase following distance (b) increase stopping distance Signal recommendations (a) always use turn signals (b) don't use turn signals too early (c) turn off signal Merging recommendations (a) increase speed when merging or driving on highway (b) use merging lane, do not cross solid lines Attention (a) limit noise and conversation in the vehicle; and/or pay more attention to driving environment

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115 Table 3-4. Continued Hands positioning (a) change positioning of hands on steering wheel (b) move apart or use both hands on driving wheel. Education or training recommendations (a) take AARP driving refresher course (b) re-read FL Driver's Handbook (c) take driving lessons (d) take Behind-the-Wheel Trai ning (BTW with or without adaptive equipment) Referral recommendations (a) referral to physician (b) referral to neurologist (c) referral to eye care specialist (d) referral to physical or occupational therapist (e) referral to driver re-evaluation

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116 Figure 3-5. SOC Driving Recommendations conceptual model. Alternative transportation/ Assistive Devices Driving Recommendations Selection Driving Recommendations Compensation Driving Recommendations Optimization Driving Recommendations Driving avoidance recommendations Plan driving retirement or stop driving Visual Scanning Scanning road markings Speed Stops Distances Signaling Merging Hands position Attention Optimization Specific Behind-thewheel training Optimization Global Driving Education Referrals

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117 Table 3-5. Expected trends based on th e categories of driv ing recommendations Aims Hypotheses Analyses Identify the most common types of driving recommendations that a DRS provides to older drivers Selection recommendations: a. Older adults with global rating scores of 0 will have more recommendations to stop driving or plan for driving retirement than drivers with scores of 1, 2, and 3. b. Older adults with scores of 1 and 2 will have more recommendations to reduce travel than groups with scores of 0 and 3. Optimization specific recommendations: a. Older adults with scores of 1 and 2 will have more driving behavior errors, and will have more specific optimization recommendations than groups with global rating scores of 0 and 3. Optimization global recommendations: a. Older adults with global rating scores of 1 will have more recommendations for education and training than all other groups b. Older adults with global rating scores of 1 will have more referrals than older adults w ith scores of 2 and 3. Frequencies of recommendations Examine what combination of cognitive, motor, and sensory clinical tests help predict the DRS decision to determine whether an older driver is: (1) unsafe, (2) unsafe but remediable, (3) safe with recommendations, or (4) safe driver. Expected a combination of motor, sensory, and cognitive tests to predict specific optimization recommendations and selection recommendations based on the DRS road test assessment. Discriminant function analysis

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118 Table 3-5. Continued Longitudinally evaluate whether or not older drivers remember driving recommendations made by a DRS, with and without cueing. a. On a 1.5 to 3 year post driving assessment follow-up, older adults would recall general safety recommendations, but give less detail on the specific recommendations they obtained. b. A mismatch of recommendations was expected between recommendations given at baseline and recommendations recalled in the follow-up telephone interview. Frequencies of recalled recommendations with and without cues Determine if older adults driving habits, measured as driving exposure and avoidance of problematic driving situations, change over time and are predicted by driving performance determined as (1) unsafe, (2) unsafe but remediable, (3) safe with recommendations, or (4) safe driver. Trends in selection of places driven: a. Older adults with lower driving performance scores will significantly differ in the self-selection of places driven compared to older drivers with higher driving scores. b. All drivers will have reduced the places driven; times they drive per week, and miles driven, and have higher levels of driving avoidance. Univariate ANOVAs of time*driving habit*driving performance group

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119 CHAPTER 4 RESULTS Overview This section is divided into five subsecti ons and by aim s of th e study. The first part describes the sample at baseline and followup. The next section describes frequencies of common recommendations given by the DRS at ba seline, considering th e classification of recommendations by type, as described in the met hods chapter. Third is a discriminant function analysis to determine what clinical measures at baseline predicted classi fication of participants as safe, safe with recommendations, or unsafe drivers. Then, recall of follow-up recommendations is described. Finally, older adults driving habits at time 2 are predicted as a function of global rating scores of driving performance at baseline. Sample At baseline, 118 older adults were adm inistered clinical a ssessments and drove through a road test. Participants age was 63-89 years old (M=74.40, SD=6.3); 50% of the sample was male. At follow-up, mean age of participan ts was 75.25 (6.2); 25.4% were males and 29.7% were females. Participants were mostly white and highly educated. Table 4-1 shows descriptives of baseline and follow-up samples. Response rate at follow-up was 55% (n = 65), which were 15 participants less than expected. Cognitive status scores at baseline, meas ured by the Telephone Interview for Cognitive Status modified (TICS-m), ranged from 14-49 (M = 35.58, SD = 4.5). As stated in the methods chapter, it was expected that older adults with lower levels of driving performance would be older and have lower scores on mental status A MANOVA using global ra ting scale scores as the independent variable and age and total TICS score as dependent variables showed significant differences among groups (F (3, 116) = 4.781, p < .0001, 2 = .114). Follow-up univariate

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120 analysis of variance showed a linear trend of in creased age and lower global rating scores F (1, 112) = 18.118, p < .0001, 2 = .46 (polynomial trend was not significant); Bonferroni post hoc analysis showed that significant differences origin ated between safe drivers (GRS of 3) and each of the other groups (GRS of 0, p = .001; GRS of 1, p < .0001; GRS of 2, p = .003). Safe drivers had the highest scores in TICS-m total score but the results of the univariate test were not significant (p =.08). Figure 4-1 show s the age by global rating groups. From this point on, the groups of drivers are referred to as driving performance groups of Safe (for drivers with GRS 3); Safe-R (for drivers with GRS 2); Unsafe-R (for drivers with GRS of 1); and Unsafe (drivers with GRS of 0). Aim 1: Driving Recommendations To identify the m ost common types of driving recommendations that a DRS provided to older drivers, baseline recommendations were classified using the SOC driving recommendations conceptual model explained in the methods section (Figure 3-5). As a reminder, selection recommendati ons included recommendations to reduce traveling such as avoid driving at night, in long distances, or at rush hour. Sp ecific Optimization recommendations included recommendations to ch ange driving behaviors such as increase following distance, come to complete stops, or scan the environment more often. Global Optimization recommendations included recommendations to take an education refresher c ourse or referrals to health care professionals, and compensation recommendations included behind-the-wheel training with the rehabilitation specialist or taking driving lessons. Frequencies of driving recommendations are divided based on the Selection, Optimization (specific and global), and Compensation (SOC) types explained in the methods chapter. From this point on, the groups of drivers are refe rred to as driving performance groups of Safe (for drivers with GRS 3); Safe-R (for drivers with GRS 2); Unsafe-R (for drivers with GRS of 1); and

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121 Unsafe (drivers with GRS of 0).The next section describes the percentage s of drivers who had recommendations in the total sample and for each driving group, for each SOC type. At the end of this section, tables with frequencies for all subcategories of recommendations and by SOC type are also provided (Tables 4-3 to 4-5). Overall, 78.8% of the sample (n =93) received recommendations; 22% of drivers (n=26) had s election driving recommendations, 78% (n=92) had specific optimization recommendations 22.9% (n=27) had global optimization recommendations and 11.9% (n=14) had compensation recommendations; Figure 4-2 shows the percentage of drivers who had driving recommendations for each driving performance group and dived by SOC types. The majority of drivers were given recommendations to improve their driving beha viors (specific optimization recommendations). Table 4-2 provides means and ranges of recomme ndations by SOC categories. Overall, drivers had a mean of 2 driving behavi or recommendations, an average of 3 total recommendations, and less than 1 selection, driving performa nce, and compensation recommendations. Recommendations Suggesting Selection Frequencies analyses of selection driving r ecommendations showed that none of the safe drivers received selectio n recomm endations, 21% (n = 14) of safe -R drivers, 77% (n = 10) of unsafe-R drivers, and 50% (n = 2) of unsaf e drivers received recommendations suggesting selection. Figure 4-3 shows the percentage of drivers within each driving category who had recommendations suggesting selection. As was expected, unsafe-R and safe-R drivers had more recommendations to reduce travel than unsafe an d safe drivers. Unsafe drivers only received selection recommendations to st op driving and safe drivers did not receive selection driving recommendations. Contrary to the expected trends, unsafe driv ers did not receive recommendations to plan for driving retirement; and four safe-R drivers (6%), who had between 9 to 12 recommendations, were advised to plan for driving retirement.

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122 Selection recommendations for unsafe-r and safe-r drivers: Excepting the recommendation to retire from dr iving, the relative frequency of recommendations was about 4 to 5 times higher for the unsafe-R group than the sa fe-R group of drivers (Fi gure 4-3). In order of prevalence, the unsafe-R drivers had recomme ndations to avoid high traffic (69%), avoid highways (54%), avoid driving in unfamiliar places (39%), avoid night driving (31%), avoid rush hour (23%), avoid long distances or plan for driving retirement (15%). Avoiding unprotected left turns (8%) was only recommended for unsafe-R driv ers. For safe-R drivers, the most common recommendations were to avoid highways (9%), a void rush hour or plan for driving retirement (6%); the rest of recommendations were given to less than five percent of drivers and included avoiding night driving or driving in unfamilia r places (4%), and avoiding high traffic (2%). Recommendations Suggesting Specific Optimization All the g roups received driv ing behavior recommendations. Specifically, 75% (n = 3) of unsafe drivers, 100% (n = 13) of unsafe-R drivers, 90% (n = 61) of safe-R drivers, and 46% (n =15) of safe drivers had recommendations suggesting specific optimization. The most common recommendations were related to stops, spee d, lane maintenance, and signaling. As was expected, older adults in the unsafe-R and safe -R groups had the highest number of driving behavior recommendations. The unsafe group did not have any lane maintenance, stops, distance, merging, or hand positioning recommendations; safe drivers did not have speed, attention or hands positioning r ecommendations. Figure 4-4 shows the percent of drivers within each driving performance group who had specific optimization recommendations. Specific optimization for unsafe and unsafe-r drivers : Most unsafe drivers (75%) had recommendations in the speed category that refe rred to better controlling the vehicle and the speed (e.g., avoid braking, accelerating abruptly or in consistently); half of the unsafe drivers also had recommendations to increase visual scanni ng and pay more attention to the driving

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123 environment. Among unsafe-R drivers 70% had lane maintenance recommendations that included avoid drifting left, watching lane maintenance, or stay ing in the lane when turning (avoiding making turns too wide or encroachi ng). After lane maintenance recommendations, unsafe-R drivers most common recommendations were speed recommendations (for 62% of drivers) of not driving too slowly, watching the speed limit, or controlling the vehicle and the speed. More than half of unsafe-R drivers (54 %) had recommendations to increase following or stopping distances; Thirty-nine percent (39%) of unsafe-R drivers also ha d recommendations to complete stops, stop behind the white lines, always use the turn signal, and pay more attention to driving. Other recommendations for unsafe-R dr ivers were scanning recommendations for 31% of drivers in this group, 15% had recommendations to modify the hand positioning or use both hands while driving, and 7.7% had merging recommendations. Specific optimization recommendation s for safe-r and safe drivers: Specific optimization recommendations for safe-R drivers were mostly stop recommendations (53%), predominantly to come to complete stops (n = 33). Stop recommendations were followed by speed recommendations (32%), specifically to avoid driving slow (n = 16); 31% of safe-R drivers had lane maintenance recommendations; mainly avoid drifting left and watching lane maintenance. Signaling recommendations were given to 28% of safe-R drivers, 15% had merging or distance recommendations, 10% had scanning recommendations, and 7% had attention or hand positioning recommendations The most frequent recommendation for safe drivers was to always use the turn signal (27% of safe drivers). Next were recommendations to come to complete stops (12%), scanning reco mmendations and distance recommendations (9%), lane maintenance (6%), and merging (3%).

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124 Recommendations Suggesting Global Optimization As expected, within driving performance categories, m ore unsafe-R drivers (77%, n =10) than safe-R drivers (22%, n = 15) had recomm endations suggesting global optimization of driving. Only one unsafe and one safe driver had referral recommendati ons; and only one safe driver had recommendations to take the AARP co urse and re-read the Florida (FL) Drivers Handbook. The unsafe driver was referred to the physic ian and neurologist, and the safe driver was advised to maintain the follow-up visits to the physician. Figure 4-5 shows the percent of drivers within each driving performance group who had global optimization recommendations. Global optimization for unsafe-r and safe-r drivers: Among the unsafe-R group, recommendations to take the AARP driving refr esher course were the most common (46% of drivers), followed by a referral to be re-tested for driving performance (6-12 months after the assessment) (31%), and re-reading the FL Driver s Handbook or referrals to a neurologist (23%). Besides referrals to the neurologist, unsafe-R drivers were referred to other health care practitioners including referrals to an eye care specialist (15%), and referrals to a physician, to physical or occupational thera py, and other referrals such as follow-up appointments with a physician or seeing an audiologist (8%). The sa fe-R drivers most common recommendations were referrals to see an eye care specialist (12%) and referrals to take the AARP course or other referrals (7%), and driving-reevaluations (6%) ; and less than 5% had recommendations to reread the FL drivers handbook or see a neurologist (3%), referrals to a physician or rehabilitation specialists (1.5%). Education suggestions such as taking the AAR P course or re-reading the Fl Drivers Handbook were never suggested for unsafe drivers and rarely suggested for safe-R and safe drivers (less than 8% of drivers in these categories).

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125 Compensation Driving Recommendations Unsafe and safe drivers did not receive com pensation recommendations. Seventy-seven percent of unsafe-R drivers and only 6% of sa fe-R drivers had compensation recommendations. Figure 4-6 show the percent of drivers w ithin each driving performance group who had compensation recommendations. As expected, driv ers with lower level of performance (unsafeR) were more frequently recommended to take BTW training (69%) or driving lessons (23%); only one unsafe-R driver was advised to take BTW with assistive devices because the participant had hemiparesis caused by a stroke. Among safe-R drivers, 4% had BTW recommendations and 1.5% had recommendations to take driving lesso ns. Tables 4-3-and 4-4 below provide the detailed frequencies for selection, specific optimization, global optimization, and compensation recommendations. Aim 2: Prediction of Driving Performance The second aim was to examine what combination of cognitive, motor, and sensory clinical tests might undergird th e DRS decision to determine whet her an older driver is: (1) unsafe or unsafe-R (unsafe remediable); Unsafe and unsafe-R drivers were combined in one group, since sample sizes were small (n = 4 in unsafe group and n = 13 in unsafe-R group, (2) safe-R (safe with recommendations), or (3) safe. A discriminant function was used to determine what clinical measures predicted participants classification under the th ree categories of unsafe driving performance (n = 17), safe with recommenda tions (n = 68), and safe (n = 33). Table 4-6 lists the domains, tests, scores, and source of clin ical measures used for the discriminant function analysis. Exploring the Discriminant Function Variables Inspection o f normality distribution of the variables to use in the discriminant function showed significant deviations from normality fo r all groups in total peripheral vision and total

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126 ROM (p<.0001); the unsafe group also deviated from normality in contrast sensitivity, total number of medications, and rapid pace walk. Th e distributions for the safe-R group were group, scores deviated from normality for the trails B, visual acuity, other vision, co-morbidities, and strength. Total TICS, contrast sensitivity, peripheral vision, other vision, ROM, and strength variables were negatively skewed. Table 4-7 lists the variables, va lues of skewness, kurtosis, and Levenes test of homogeneity of variances. Levenes test for homogeneity of variance showed significant diffe rences among groups for trails B (F (2, 96) = 7.071, p = .001), rapid pace walk (F (2, 96) = 3.323, p = .04), and ROM (F (2, 96) =4.830, p = .01). Discriminant function analysis is often thought to be fairly robust to violations of normality and homogeneity of variance (Meyers, Gamst, & Guarino, 2006). Since this analysis was exploratory, the discriminant function was conducted for inferential purposes regarding driving performance groups separatio n. The implications of this violation are considered in the limitations s ection of the disc ussion chapter. Table 4-8 shows the means and standard devi ations of available variables by driving performance group. The cognitive domain variable s show lower levels of performance for the unsafe group in all variables. Spec ifically, the unsafe group took l onger to complete the Trails B (F (2, 96) = 6.9, p = .002, 2 = .12) and UFOV tests (F (2, 96) = 12.532, p = .000, 2 = .20). For the vision variables, all groups differed in cont rast sensitivity scores (F (2, 96) = 20.714, p = .000, 2 = .30). Contrast sensitivity was the only c linical tests that showed differences between the safe and safe-R groups. Differences in phys ical and motor variables among groups were mainly reflected in rapid pace walk scores where the unsafe group took two seconds more than the other groups (F (2, 96) = 12.760, p = .000, 2 = .21).

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127 Discriminant Function The discrim inant function analysis yielde d only one significant discriminant function = .564, X2 (4, 99) = 43.410, p< .0001. A second trivial f unction was extracted, but it was not significant and produced no inte rpretable group separations. Th e significant function had a canonical correlation of .595 that explained 35.4% of the variance in the dependent variables (driving performance groups). Not all covariates entered in the discriminant function analysis met the homogeneity of variance assumption (Box M = 24.342, p = .001) as expected, based on significant Levenes tests explai ned in the section above. The discriminant function, using a stepwise me thod, retained only the variables of contrast sensitivity and rapid pace walk as unique predictors of group membership. Standardized coefficients, that represent th e independent contribution of ea ch variable, were .756 for total contrast sensitivity score and -.485 for rapid pace walk. Thus, the strongest predictor of the function was contrast sensitivity; higher scores on contrast sensitivity and lower scores in rapid pace walk differentiated safe and safe-R drivers from unsafe drivers. Figure 4-7 is a visual representation of groups classifi cation; it shows the centroids or mean variate scores for each group. Co-variates that had strong a ssociations with the second function were only rapid pace walk (.729) and total contrast sensitivity (.468). Although the s econd function was not significant, it showed that the same visual and motor variables as in function 1 are unique predictors of differences in performance among safe drivers. Table 4-9 shows the correlation coefficients of all variables initially offered to the stepwise function as predictors of group membership. A lthough most variables did not emerge as unique predictors, the association between these covariat es and the discriminant function are useful for interpreting the discriminant function. Correlations of .30 are important to determine group

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128 differences (Meyers et al., 2006). In function 1, safe and safe-R drivers were differentiated from unsafe drivers by higher scores in contrast sensi tivity, less time to complete the rapid pace walk, higher scores in other visual functions, less time to complete the trails B, less time to complete the UFOV, lower scores of visual acuity, high er scores on range of motion, higher scores on peripheral vision, taking less medications, havi ng less co-morbidities, and higher cores on cognitive function. The strong as sociation of these co-variates with groups classification indicates that although cognitive tests were not uni que predictors of group classification in this sample, measures of visual at tention and executive function we re strongly associated with driving performance. Visual predictors were highly predictive of groups separation as all measures of visual function, excluding periphera l vision, had association of .30 or higher. Range of motion had an association of .28 with the di scriminant function, which was close to the .30 and suggests a fair contribution to groups sepa ration. The rest of co-variates ranged in associations from .24 to .10, and were in order of contribution to the function, peripheral vision, cognitive status (TICS), total number of co-m orbidities, total stre ngth, and number of medications. The discriminant function correctly classified 51% of unsafe, safe-R, and safe drivers (n = 114). Cross-validation was used to verify the classification analysis and this resulted in 49% of drivers correctly classified by the discrimina nt function Table 4-10 s hows the classification results of the discriminant func tion. To rule out the possibility that demographic variables were strong predictors of group classification beyond the contribution of the performance based tests used in the previous discriminant function, a second discriminant func tion adding demographic variables is described below.

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129 Exploring Discriminant Functions with Clinical and Demographic Variables The discrim inant function analysis yielde d only one significant discriminant function = .510, X2 (2, 108) = 71.402, p< .0001. The second function was not significant. The discriminant function included five predictors: (1) total contrast sensitivity sc ore, (2) rapid pace walk score, (3) age, (4) gender, and (5) e ducation. This function had a ca nonical correlation of .675 that explained 45.5% of the variance. Compared w ith the previous discriminant function, adding demographic variables to the analysis helped explained 10% more of the variance in driving performance. As in the first discriminant func tion, not all covariate s met the assumption of homogeneity of variance and this resulted in a significant Box M test (73.964, p = < .0001). The standardized coefficients, that represent th e independent contribution of each variable, were .641 for total years of education, -.576 for cont rast sensitivity, .569 for rapid pace walk, .279 for age, and .175 for gender. This suggests that non-performance measures helped explained the groups separation in this sample of drivers. The strongest associ ations between the co-variates and the function continued to be rapid pace walk (.636) and contrast se nsitivity (-.619), followed by age (.488), education (.217), and gender (.03). Following the association cut-off score of .30, only age seemed to add predictive value in the groups differences. High er scores on rapid pace walk and age were associated with lower leve ls of driving performance, and higher scores on contrast sensitivity were associated with safe driving. The same finding was reported in the ANOVA results in aim1suggesting age differences in the driv ing performance groups. When adding demographic variables, the model corr ectly classified 58.6% of the sample. Crossvalidation to verify the classifi cation showed that 54.1% of the sa mple was correctly classified. This classification was slightly higher than the first function with 49%. In this sample, clinical

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130 performance and demographic variables were not strong predictors of th e participants driving performance scores. Aim 3: Recall of Driving Recommendations Ai m 3, to longitudinally evaluate whether or not older drivers remembered driving recommendations made by a DRS, was base d on analysis of frequencies of recalled recommendations for the follow-up sample of 65 participants. In the follow-up interview, one participant who had Alzheimers di sease was unable to complete a ll the questions and therefore, recall of recommendations is based on a sample of 64 drivers. Seven of the 65 drivers were not driving at follow-up, including the person with Al zheimers disease (who ha d a driving score of 2 at baseline), 1 safe driver who had a score of 3 at baseline, 4 drivers who had scores of 1 at baseline, and 1 driver who had a score of 0. For this drivers (except the person with dementia), the follow-up telephone interview was administered excluding the questions about driving habits. As stated in the methods chapter, particip ants were first asked uncued whether they recalled any driving recommenda tions provided by the DRS at the driving evaluation at Independence Drive. In a second cued step, the investigator read the list of driving recommendations and let the participant say whet her those recommendations (cued) were given to them. Responses were divided into cued a nd uncued recall, and by recommendations correctly or falsely recalled; six possible outcomes of recommendations recall were recorded: 1. Recommendation not given at baseline and not reported at follow-up (no recommendations) 2. Recommendation given at base line and reported at followup without cues (uncued recall) 3. Recommendation at baseline and repo rted with cues (cued recall) 4. Recommendation not given at baseline yet st ated at follow-up (f alse uncued recall) 5. Recommendation not given at baseline yet st ated following cues (false cued recall) 6. Recommendation given at baseline and not reported after cues (no cued recall)

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131 Frequencies of driving recommendations r ecalled at follow-up are divided based on Selection, Optimization (specific and global), an d Compensation (SOC) as in aim 1; and also divided by correct (cued and uncued) and false re call (cued and uncued), as listed above. At the end of this section, tables with frequencies fo r all subcategories of recommendations and by SOC type are also provided (Tables 4-14 to 4-24). Overview of Recalled Recommendations Total num ber of recommendations given to the follow-up sample was 221; from this total number of recommendations, 56 were correctly recalled and 17 were inco rrectly recalled. Table 4-11 provides total number of recommendations given, number of recommendations correctly and incorrectly recalled for each type of recommendation categor y and table 4-12 provides the means of recalled recommendations for each driv ing performance group. On average, 64 drivers at follow-up correctly recalled .87 (SD = .96) re commendations and incorrectly recalled .26 (SD = .51) recommendations. Differences in tota l recommendations were not significant for incorrectly recalled recommendations (p = .860); and there was a significant difference in correctly recalled recommendations (F (3, 60) = 4.786, p = 0.005). The most common recommendations given to drivers were related to stops, lane maintenance, speed, and signaling. Table 4-13 sh ows the number of recommendations given to drivers at follow-up, and the number of recomme ndations correctly and incorrectly recalled. In general, drivers recalled mo re recommendations related to driving behaviors than recommendations suggesting driving selec tion. None of the global optimization and compensation recommendations were recalled. For purposes of testing the differences in recall of recommendations among drivers, ANOVAS were conducted for cued and uncued reca ll of recommendations suggesting selection, specific optimization, global optimization, and compensation. For this analysis, unsafe and

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132 unsafe-R drivers were grouped in one category since ANOVAs could not be conducted with a sample of just one driver in the unsafe group. To determine if social desi rability was related to false recall of recommendations, correlations betw een total social desirability scores and false recall of recommendations were tested. The mean score of social desirability was 23.53 (SD = 4.8) and the scores were not signi ficantly correlated with false cued and uncued recall (Appendix N provides a correlational table). False Recall of Recommendations Suggesting Selection Falsely recalled recommendation s were th e instances when (a) a recommendation not given at baseline was stated at follow-up, and is here labeled as uncued false recall, or (b) a recommendation not given at baseline was stated at follow-up after the participant heard the cues (the recommendations read in the telephone intervie w) and is here labeled as cued false recall. Uncued false recall: Unsafe and unsafe-R drivers did not falsely recall any selection recommendations without cues. One safe-R and one safe driver reported a recommendation to avoid driving at night. Uncued recall of selection recommen dations was not significant among groups (p = .785). Cued false recall: False cued recall of selection recommendations was more prevalent for unsafe-R drivers, although differences in cued recall among groups were not significant (p = .295). The unsafe driver did not falsely recall any selection recommendations after cues. One unsafe-R driver (out of 2), fals ely recalled selection recommendati ons to avoid rush hour, avoid night driving, and avoid left turns. Among safe-R drivers, 11% (n =4) reported the DRS gave a recommendation to avoid night driving, and 8.3% (n =3) falsely recalled recommendations to avoid rush hour, high traffic, and highways, afte r being cued. One safe driver had a false cued recall of avoiding rush hour. Figure 4-9 shows the percentage of drivers in each driving performance group that falsely recalled selec tion recommendations before and after cues.

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133 False Recall of Recommendations Suggesting Specific Op timization Uncued false recall : No significant differences am ong groups were found for false uncued recall of recommendations suggesting sp ecific optimization (p = .789). One unsafe-R driver falsely recalled a recomm endation to stop behind the white lines; and two safe drivers falsely recalled a recommendation to make comple te stops. One safe driver also incorrectly recalled recommendations to watch lane mainte nance, not drive too fast, and increase the stopping distance with the car in front. Am ong safe-R drivers, 2 falsely reported recommendations related to lane maintenance, and one driver falsely recalled recommendations of scanning, using the turn signal, increasing sto pping distance, and not driving too slow. Cued false recall: The unsafe driver falsely recalle d driving recommendations suggesting specific optimization for all driving behaviors except attention and hands positioning. Unsafe-R drivers incorrectly reported recommendations to merge (n = 3), scan the environment (n = 2), coming to complete stops, incr ease stopping distance, and watchi ng the speed (n = 1). Safe-R drivers incorrectly recalled recommendations for all driving behavi ors except signaling. Specifically, safe-R drivers incorrectly recalled scanning recommendations (n = 7), followed by merging and distance recommendations (n = 6); five safe-R dr ivers recalled stops recommendations of not leaving too much space with the car in front or stopping behind the white lines, 3 drivers recalled hands positioning recommendations and two recalled attention and speed recommendations. Safe drivers also fals ely recalled recommendations for all behaviors after cued, except attention recommendations. Most of the safe drivers false recall was for recommendations to complete stops, signaling, lane maintenance, followed by increase speed when merging, increase stopping di stance and scanning (n = 2); a nd hands positioning and speed (n = 1). No significant differences in false cued recall were found (p = .332). Figure 4-10 shows

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134 the percentage of drivers in each driving pe rformance group that fals ely recalled specific optimization recommendations before and after cues. False Recall of Recommendations Suggestin g Global Optimiz ation and Compensation Uncued false recall : None of the drivers in any of the performance groups had false recalls of global optimization or compensation recommendations. Cued false recall : Only one safe-R driver reported a recommendation to take the AARP course after being cued. All ot her drivers did not falsely recal l any recommendations suggesting global optimization such as referr als to health care specialists or driving education; and there were no compensation recommendations falsely recalled. No Cued Recall of Recommendations Suggesting Selection No cued rec all recommendations constitute d the cases when drivers were given recommendations at baseline but did not reca ll them at follow-up even after the list of recommendations was read and the driv ers were asked, one by one, whether the recommendations were provided to them. This s ection reviews the recommendations that were not recalled and the recalled recommendations are addressed in the next section. Figure 4-11 shows the percentage of drivers in each driv ing performance group that did not recall given selection recommendations after getting cues. Ta ble 4-15 shows the number of drivers in each group that had recommendations suggesting selection. Results showed significant differences among groups (F (1, 63) = 6.557, p = 0.01). Upon Bonferroni post-hoc test differe nces were between unsafe and sa fe-R (p<.0001), and unsafe and safe groups (p<.0001). Two unsafe-R drivers had recommendations to avoid rush hour, night, and long distances and did not recall them. Two of five unsafe-R drivers forgot the recommendation to avoid highways, 2 of 3 forgot the recommendation to avoid high traffic; and one unsafe-R driver forgot a recommendation to avoid driving long distances and plan for

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135 driving retirement. Two of three safe-R driv ers did not recall recommendations to avoid highways and avoid high traffic; 50% (1 of 2) did not recall avoiding rush hour. Safe drivers did not have selection recommendations. No Cued Recall of Recommendations Suggesting Specific Optimiz ation Significant differences were also found for drivers no cued recall of specific optimization recommendations (F (1, 63) = 6.557, p = .01). Bonferroni post-hoc tests showed significant differences between safe and unsafe (p<.0001), and safe-R and unsafe groups (p<.0001). Figure 4-12 shows the percentage of driv ers within each category that did not recall specific optimization recommendations after cues Table 4-16 shows the number of drivers who had specific optimization recommendations. Unsafe drivers: The unsafe driver did not recall a recommendation of vehicle control, although this driver followed the main r ecommendation and stopped driving after the assessment. Seventy five percent (75 %) of unsafe-R drivers did not recall signaling recommendations and speed related recommenda tions; 66% (2 of 3) did not recall scanning recommendations; sixty percent (60%) forgot lane maintenance recommendations such as avoiding drifting left, watching thei r lane maintenance, or staying in their lane when turning; 50% forgot recommendations to increase the following distance and recommendations to pay more attention. None of thr ee unsafe-R drivers recalled reco mmendations to make complete stops and one did not recall th e recommendation to change the hand positioning on the steering wheel. Safe drivers: The safe-R drivers forgot recommendations to use the turn signal (54%), watch the speed (46%), change the positioning of the hands (40%), make complete stops and watch the lane maintenance (36%), pay more a ttention or scan the environment more often (33%), and increase speed when merging on a highways (25%). Among safe drivers, 83% did

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136 not recall the recommendation to signal, 50% (1 of 2) drivers fo rgot a recommendation to scan more the environment, and one driver did not recall the lane maintenance and stops recommendations. No Cued Recall of Recommendations Suggesting Global Optimization No cued recall differed between saf e and unsafe (p =.004) and safe-R and unsafe groups (p =.0001), (F (1, 63) = 4.693, p = .034). Figure 4-13 shows the percentage of drivers within each category that did not recall globa l optimization recommendations after cues. Table 4-17 has the number of drivers that were given global optimi zation recommendations. In this category, all the unsafe-R drivers that were given recommendations to see the neurol ogist (n = 3), be re-evaluated (n = 2), see an eye care speci alist (n = 1), see a physician (n = 1), and see a physical or occupational therapist (n = 1) di d not recall these recommendations; and 3 of 4 drivers forgot the recommendation to take the AARP course. All safe-R drivers (n = 5) who had referrals to see an eye care specialist did not recall this recommendation; one safe-R driver did not recall referral to re-evaluation and physical/occupational therapy. And 1 of 2 drivers did not recall othe r referrals. One safe driver did not recall any of the global recommendations. No Cued Recall of Recommendations Suggesting Compensation None of the safe-R drivers (n = 3) and 75% of unsafe-R drivers (3 of 4) did not recall the recomm endation to take BTW training. One unsafeR driver did not recall the recommendations to take driving lessons and BTW with assistive devices. Unsafe and safe drivers did not have compensation recommendations. Bonferroni pos t-hoc showed differences in recall were significant between unsafe and safe-R, and unsafe and safe drivers (p<.0001), (F (1, 63) = 6.760, p =.01, quadratic trend). Figure 4-14 shows the per centage of drivers within each category that

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137 did not recall compensation recommendations after cues. Table 4-17 shows the number of drivers who had compensation recommendations. Recall of Recommendation s Suggesting Selection Recalled recomm endations are divided into tw o categories: (1) recommendations given at baseline and reported at follow-up without cues, which are here la beled as uncued recall, and (2) recommendations given at baseline and recalled after cues, referred to as cued recall. Figure 415 shows the uncued and cued re call selection recommendations. Uncued recall: Significant differences in unc ued recall among groups were observed between the unsafe and safe groups (F (2,63) = 3.250, p = .046, Bonferroni post hoc p = .04). The unsafe driver recalled the only selection recommendation to stop driving. One of three unsafe-R drivers recalled the commendation to a void high traffic without being cued. One of 3 safe-R driver recalled recommendations to avoid high traffic and highways. Safe drivers did not have any selection reco mmendations at baseline. Cued recall: After cued, 3 of 5 unsafe-R driver s recalled the recommendation to avoid highways, 1 of 3 recalled the recommendation to avoid high traffic, 1 of 2 recalled the recommendation to avoid night driving, and one driver recalled the recommendation to avoid driving in unfamiliar places. Among safe-R dr ivers, 1 of 2 safe-R drivers recalled a recommendation to avoid high tra ffic (after cues); 1 of 2 r ecalled a recommendation to avoid rush hour; 2 of 2 recalled a recommendation to pl an for driving retirement, and one recalled the recommendation to avoid night driving. Differences in cued recall were significant for the safe and unsafe group (p = .002) and the unsafe and safe-R group (p =.01), F (2, 63) = 6.375, p = .003.

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138 Recall of Recommendations Sugge sti ng Specific Optimization Uncued recall: Significant differences were observed be tween the safe and safe-R groups (p = .002), F (2, 63) = 6.345, p = .003. Figure 4-16 shows the uncued and cued recall specific optimization recommendations. Without cues, 2 of 5 unsafe-R drivers reca lled lane maintenance recommendations, and 50% recalled speed recomm endations. Half of the unsafe-R drivers with distance recommendations (n = 1) and signaling recommendations (n = 2) recalled these without cues. More than 50% of safe-R drivers recalled r ecommendations of distance (75%), attention (66%), stops (60%), and lane maintenance (5 4%) without cues. This was followed by uncued recall of hand positioning (40%), signaling (36%), merging (25%), and speed (23%) recommendations. One safe driver recalled the r ecommendations to scan more, watch the lane maintenance, and signal without cues; and 2 of 3 safe drivers recalled distance recommendations. Cued recall: Significant differences between safe a nd safe-R groups, F (2. 63) = 3.730, p = .003. After cues, the unsafe driver recalled the scanning and attention recommendations. Two of five unsafe-R drivers recalled lane maintenance r ecommendations after cues ; and 2 of 3 recalled attention recommendations after cues. One unsaf e-R driver needed cues to recall scanning recommendations. Among safe-R drivers, cues helped 66% recall distance recommendations, 62% recalled merging recommendations, 45% recal led lane maintenance recommendations, and 32% of drivers recall stop reco mmendations; 20% recalled recomm endations to change the hand positioning and 10% recalled signali ng recommendations with cues. With cues, one safe driver recalled recommendations related to distance and lane maintenance. Recall of Recommendations Sugge sting Global Optimiz ation Uncued recall: None of the participants recalled global optimization recommendations without cues.

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139 Cued recall: After cues, one of three safe-R driver recalled the recommendations to take the AARP course. One safe-R driver recalled the recommendation to read the FL drivers handbook, and a referral recommendation. One of four unsafe-R drivers recalled the AARP course recommendation after cues, and one driver recalled the r ecommendation to read the FL drivers handbook. Recall of Recommendations Suggesting Comp ensation Uncued recall: None of the participants recall fell in this category. Cued recall: Cued recall showed significant differ ences between unsafe and safe-R drivers (p = .002), and safe and unsafe drivers (p = .004), F (2, 63) = 3.813, p = .028. With cues, only one of four unsafe-R drivers who had recommen dations to take BTW training recalled this recommendation. AIM 4: Driving Habits Changes The last aim was to dete rmine if older adults driving habits, measured as driving exposure and avoidance of problematic driving situations change over time and are predicted by driving performance determined as (1) unsafe, (2) unsaf e but remediable (unsafe-R), (3) safe with recommendations (safe-R), or (4) safe driver. Driving habits data were available for 116 participants at baseline and for 58 participants at follow-up. At follow-up, 7 older adults were not driving, so data on driving habits were not coll ected; of these 7 participants, 1 scored 0 at baseline and stopped driving after the DRS reco mmendation to stop; 4 scored 1 at baseline, 1 scored 2, and 1 scored 3 at baseline. Six repeated measures ANOVAS were conducted to evaluate whether global rating scores (GRS) at baseline predicted cha nges in driving habits over time Table 4-25 provides the means and standard deviations of the driving exposur e and driving avoidance variables for the total sample at baseline, the follow-up sample at baseline and follow-up, and p-values of ANOVA

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140 main effects of time and time by GRS. Significa nt main effects of time were found for days driven per week (F(1, 55) = 8.425, p = .005, 2 = .133) and number of places driven per week (F(1, 54) = 4.746, p = .034, 2 = .081). The time by group interactions were not significant for any of the driving exposure and avoidance variables. Driving Exposure At follow-up, all drivers reduced the num ber of days they drove by approximately one day. Unsafe-R drivers were driving an average of 6 days per week a nd reduced driving to 5 days per week; safe-R and safe drivers reduced driving from 5 to 4 days. Although this sample was driving fewer days at follow-up, th ey were making more trips, goi ng to more places, and driving more miles on a weekly basis at follow-up. On average, unsafe-R drivers were making 2 more trips at follow-up (M = 6 at baseline vs. M = 8 at follow-up), safe-R drivers made 1 more trip at follow-up (M = 6 and M = 7 at follow-up); and safe drivers maintained an average of 8 trips at follow-up. Table 4-26 provides descri ptive statistics of driving exposure and avoidance variables by driving performance group. Figures 4-17 to 4-22 are visual representa tions of changes in driving exposure by time and gr oup. All drivers were driving more miles at follow-up. Even though these differences were not significant, unsafe-R and safe drivers were driving approximately 37 and 32 miles more per week at follow-up, respectively; and safe-R drivers drove approximately 11 miles more per week. Driv ers in each group were going to 1 more place at follow-up; the unsafe-R group drove to 3 places, the safe-R group drove o 4 places, and the safe group drove to 5 places. Overall, the places that older adults went more frequently included going to the store and running errands, work or volunteer, out to eat, and church. Table 4-27 shows frequencies and percentages of places driven at baseline and follow-up.

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141 Driving Avoidance On average, drivers were avoi ding the sam e number of conditions at baseline and followup. Specifically, unsafe-R drivers avoided an av erage of 2.33 conditions at follow-up, safe-R drivers avoided a mean of 1.81, and safe driver s avoided 1.26 conditions. However, frequencies of drivers who avoided conditions showed that among the follow-up sample, higher percentages of drivers were avoiding rush hour, high traffic, left turns across oncoming traffic, and driving in the rain. Frequency of drivers w ho avoid driving alone did not ch ange from baseline to followup, and fewer drivers were avoiding merging on a highway. Table 4-28 shows frequencies of avoidance of driving conditions for the fo llow-up sample at baseline and follow up.

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142 Table 4-1. Sample descriptives Variable Baseline Follow-upc p-value Agea M (SD) 74.40 (6.3) 75.25 (6.2)c .10 Gender N (%) Male Female 59 (50) 59 (50) 30 (46) 35 (54) .23 Raceb N (%) White Non-White 108 (91.5) 9 61 (51.7) 4 (.0) .38 Educationa M (SD) 16.0 (2.9) 16.15 (2.6) .77 a n = 116. bn = 117. cn = 65. Figure 4-1. Global rating sc ore and age at baseline Road test global rating scalesafe safe R unsafe R unsafe Age 82 80 78 76 74 72 70 70.61 75 78.75 82.5

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143 1.7 8.5 0 2.5 11 51.7 12.7 78 0.8 8.5 0.8 0 8.5 3.4 0 22 11.9 22.9 12.7 11.90 10 20 30 40 50 60 70 80 Unsafe (n = 4)Unsafe-R (n = 13)Safe-R (n = 68)Safe (n = 33)Total % Drivers Total Selection Total Specific Optim Total Global Optim Total Compensation Figure 4-2. Total percent of drivers with recommendations Table 4-2. Mean number of recommendations for older drivers Recommendations Mean (Range) Selection .55 (0-5) Optimization Specific Optimization Global 2.20 (0-8) .47 (0-5) Compensation .14 (0-2) TOTAL 3.37 (0-13)

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144 0 10 20 30 40 50 60 70 80A v Rush Ho ur Av. H igh T r affi c Av. Highways Av. Night Drivin g Av. L on g Di st a nce A v U n f a m i li a r P l aces A v U n prot e ct e d L eft s Plan Driving R eti r e m ent Do n ot Dr ive% Drivers Unsafe Unsafe-R Safe-R Safe Figure 4-3. Percent of drivers in driving perf ormance groups with selection recommendations 0 10 20 30 40 50 60 70 80S can n i n g Lane Maintenanc e Speed S t o ps Di stan ce S igna l in g Merg i n g At t e n t i o n Hands Position% Driver s Unsafe Unsafe-R Safe-R Safe Figure 4-4. Percent of drivers in driving pe rformance groups with specific optimization recommendations

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145 0 10 20 30 40 50Take AA R P course Re-Read FL Ha n dboo k Re f Ph y sici a n Re f Neuro l ogist R ef Ey e C ar e R ef. PT/O T Re f Ot h er Re f D r ive rRe ev al u at i on% Drivers Unsafe Unsafe-R Safe-R Safe Figure 4-5. Percent of drivers in driving performance groups with global optimization recommendations 0 10 20 30 40 50 60 70 80 Take Driving LessonsBTW Training BTW with Devices% Driver s Unsafe Unsafe-R Safe-R Safe Figure 4-6. Percent of drivers in driv ing performance groups with compensation recommendations

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146Table 4-3. Selection Driving Recommendations aPercentages and frequencies are based on the total sample (n = 118). b Percentages are for number of drivers within each category. Selection Driving Recommendations Total Samplea N % Unsafe b (n = 4) N % Unsafe-R b (n = 13) N % Safe-R b (n = 68) N % Safe b (n = 33) N % Avoid rush hour Avoid high traffic Avoid highways Avoid night driving Avoid long distance driving Avoid unfamiliar places Avoid unprotected lefts Plan for driving retirement Do not drive 7 17 13 7 4 8 1 6 2 5.9 14.4 11.0 5.9 3.4 6.8 .8 5.1 1.7 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 50 3 9 7 4 2 5 1 2 0 23.1 69.2 53.8 30.8 15.4 38.5 7.7 15.4 0 4 8 6 3 2 3 0 4 0 5.9 1.8 8.8 4.4 2.9 4.4 0 5.9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Total (% of total sample) 26 22.0 2 50 1.7 10 76.9 8.5 14 20.6 11.9 0 0 0

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147Table 4-4. Specific Opti mization Recommendations Optimization Specific Recommendationsa,b Total Sample N % Unsafe (n = 4) N % Unsafe-R (n =13) N % Safe-R (n = 68) N % Safe (n = 33) N % Scanninga N (%) Turn head more often Use mirrors more often Increase visual scanning 16 1 1 15 13.6 .8 .8 12.7 2 0 0 2 1.7 0 0 50 4 0 0 4 3.4 0 0 30.8 7 0 1 7 5.9 0 1.5 10.3 3 1 0 2 2.5 3.0 0 6.1 Lane maintenancea N (%) Attention to roadway markings Avoid drifting right Avoid drifting left Watch lane maintenance Turns too wide/encroaches 32 6 1 13 14 9 27.1 5.1 .8 11.0 11.9 7.6 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 5 4 4 7.6 0 0 38.5 30.8 30.8 21 6 1 7 8 4 17.8 8.8 1.5 10.3 11.8 5.9 20 0 1 2 1 1.7 0 0 3.0 6.1 3.0 Speeda N (%) Drives too slow Drives too fast Watch speed (posted limit) Speed control acceleration/break 33 15 6 7 13 28.00 12.7 5.1 5.9 11.0 3 0 0 1 3 2.5 0 0 25 75 8 4 1 2 5 6.8 30.8 7.7 15.4 38.5 22 11 5 4 5 18.6 16.2 7.4 5.9 7.4 0 0 0 0 0 0 0 0 0 0 Stopsa N (%) Come to complete stops Stop behind white lines Dont stop too far behind 45 40 21 4 38.1 33.9 17.7 3.4 0 0 0 0 0 0 0 0 5 4 2 0 4.2 30.8 15.4 0 36 33 19 3 30.5 48.5 27.9 4.4 4 3 0 1 3.4 9.1 0 3.0 Distancea N (%) Increase following distance Increase stopping distance 20 16 11 16.9 13.6 9.3 0 0 0 0 0 0 7 5 2 5.9 38.5 15.4 10 10 7 8.5 14.7 10.3 3 1 2 2.5 3.0 6.1

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148Table 4-4. Continued Signalinga N (%) Always use turn signals Dont use turn signals too early Turn off signal 34 29 4 2 28.8 24.6 3.4 1.7 1 1 0 1 .8 25 0 25 5 4 1 0 4.2 30.8 7.7 0 19 17 1 1 16.1 25 1.5 1.5 9 7 2 0 7.6 21.2 6.1 0 Merginga N (%) Increase speed merging on hgw Use merging lane/dont cross solid lines 12 11 2 10.2 9.3 1.7 0 0 0 0 0 0 1 1 0 .8 7.7 0 10 9 2 8.5 13.2 2.9 1 1 0 .8 3.0 0 Attentiona N (%) (% within GRS) 12 10.2 2 1.7 50 5 4.2 38.5 5 4.2 7.4 0 0 0 Hands positioninga N (%) (% within GRS) 7 5.9 0 0 2 1.7 15.4 5 4.2 7.4 0 0 0 Total (% within GRS) 92 78.0 3 2.5 75 13 11.0 100 61 51.7 89.7 15 12.7 45.5 aNumber of persons and percentage of total samp le are given for each driving behavior category. bSubcategories of behaviors show the number of persons and percentage within each driving performance group who received recommendations.

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149Table 4-5. Optimization Global an d Compensation Recommendations Recommendations Total Sample N % Unsafe (n = 4) N % Unsafe-R (n = 13) N % Safe-R (n = 68) N % Safe (n = 33) N % Optimization Globala N (%) (% within GRS) AARP refresher courseb Read FL drivers handbookb 27 12 6 22.9 10.2 5.1 1 0 0 .8 25 0 0 10 6 3 8.5 76.9 46.2 23.1 15 5 2 12.7 22.1 7.4 2.9 1 1 1 .8 3.0 3.0 3.0 Referralsa N (%) (% within GRS) Physicianb Neurologistb Eye care specialistb PT/OTb Otherb Driver re-evaluationb 21 3 5 11 2 6 8 17.8 2.5 4.2 9.3 1.7 5.1 6.8 1 1 0 1 0 0 0 .8 25 25 0 25 0 0 0 7 1 3 2 1 1 4 5.9 53.8 7.7 23.1 15.4 7.7 7.7 30.8 12 1 2 8 1 5 4 10.2 17.6 1.5 2.9 11.8 1.5 7.4 5.9 1 3.0 0 0 0 0 1 0 .8 3.0 0 0 0 0 3.0 0 Compensationa N (%) (% within GRS) Driving lessonsb BTW training (no devices)b With assistive devicesb 14 4 12 1 11.9 3.4 10.2 .8 0 0 0 0 0 0 0 0 0 10 3 9 1 8.5 76.9 23.1 69.2 7.7 4 1 3 0 3.4 5.9 1.5 4.4 0 0 0 0 0 0 0 0 0 0 0 Total (% of total) 29 24.6 1 25 .8 12 92.3 10.2 15 22.1 12.7 1 3.0 .8 aNumber of persons and percentage of total sample. bSubcategories of recommenda tions show the number of persons and percentage within each driving performance group who received recommendations.

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150 Table 4-6. Tests used in discriminant function Domain Tests Scores (Range) Source Cognitive 1. UFOV 2. Trails B 3. TICS-m Composite score (48-1500ms) Time in minutes (varies) Total score (0-50) (Edwards et al., 2006) (Reitan & Wolfson, 2004) (Welsh et al., 1993) Sensory Vision 1. Contrast Sensitivity 2. Peripheral Visiona 3. Visual Acuityb 4. Other visionc Total score (0-45) Total range (0-170)a (20/20 to 20/200 b) Total score (0-4)c (see notes below)c (Ginsburg, 1984) (www.stereooptical.com ) (www.stereooptical.com ) (www.stereooptical.com ) Motor/ Phy sical 1. Total ROMd 2. Total Strengthe 3. RPW 4. Co-morbidities 5. Medications WFL or not WFL (0-10)d WFL or not WFL (0-8)e Time in seconds (varies) Total number Total number Clinician judgmentf Clinician judgmentf (Wang et al., 2003) NA NA Notes. UFOV = Useful Field of View. TICS -m = Telephone Interview for Cognitive Status modified. ROM = Range of moti on. RPW = Rapid Pace Walk. WFL = Within Functional Limits. aTotal range of peripheral vision wa s scored as the sum of the highe st degrees for right and left; degrees measured were 35, 55, 70, and 85. bVisual acuity scores were entered in the analys is using only the denominator values of visual acuity (20, 30, 40, 50, 70, 100, 200). cOther vision included the total score for colo r discrimination, depth perception, lateral and vertical phorias; scores for these tests were impair ed (0) or intact (1), higher scores indicate better vision. dTotal ROM included sum of not WFL (0) or WFL (1) scores for right and left neck rotation, shoulder and elbow flexion, ankle planta r flexion, and ankle dorsiflexion. eTotal Strength included sum of not WFL (0) or WFL (1) of right and left shoulder flexion, hip flexion, ankle plantar flexi on, and ankle dorsiflexion. fGuidelines are provided in Wang et al. (2003).

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151Table 4-7. Tests of normality and homogeneity of variance of discriminant function variables Variable by domain Skewness Skewness SE Kurtosis Kurtosis SE Levenes Test p-value Kolmogorov-Smirnov Test p-value Unsafe Safe-R Safe Cognitive TICS Totala UFOV Composite Scorea Trails Ba -.723 .999 1.842 .224 .224 .224 3.811 .506 4.282 .444 .444 .444 .903 .131 .001* .200 .200 .200 .031* .010* .000* .200 .200 .000* Vision Total Contrast Sensitivitya Total Peripheral Visionc Acuity Both Eyesd Other Visiona -.829 -2.692 5.413 -.629 .224 .225 .235 .224 1.122 7.723 38.597 -.415 .444 .446 .465 .444 .265 .272 .187 .227 .044* .000* .080 .200 .001* .000* .000* .000* .126 .000* .000* .000* Motor/Physical Total Medicationsb Total Co-morbiditiesa RPWe Total ROMb Total Strengtha .983 .646 3.197 -4.55 -6.080 .223 .224 .226 .223 .224 1.634 -.397 14.997 25.063 35.580 .442 .444 .447 .442 .444 .270 .187 .040* .010* .932 .025* .200 .007* .000* NT .200 .000* .175 .000* .000* .200 .020* .140 .000* .000* an = 117. bn = 118. cn = 116. dn=106. en=115. NT = Not tested, constant variable (omitted from normality test). *p<.005

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152Table 4-8. Means and standard de viations by driving performance Variable (Range) Unsafe N = 16 Safe-R N = 66 Safe N = 32 Total N = 114 p-value Post-Hoc Groups, (p-value) Cognitive TICS Total (0-50) 33.7 (3.8) 36.0 (4.1) 36.6 (3.9) 35.8 (4.0) .123 NA UFOV (48-1500ms) Composite Score Processing Speeda Divided Attentiona Selective Attentiona 668.5 (254.2) 48.3 (42.2) 237.8 (149.5) 382.2 (118.7) 396.3 (208.3) 28.0 (24.3) 108.2 (100.1) 260.1 (111.7) 319.1 (168.6) 21.2 (11.5) 69.8 (72.8) 228.1 (108.9) 407.5 (226.7) 28.5 (25.5) 113.0 (111.2) 265.8 (119.7) .000 .007 .000 .001 U-SR, U-S (.000) U-SR (.03); U-S (.005) U-SR, U-S (.000) U-SR (.003); U-S (.000) Trails B (varies, minutes) 2.1 (1.3) 1.4 (.75) 1.1 (.5) 1.4 (.8) .002 U-SR (.001); U-S (.001) Vision Total Contrast Sensitivityb Contrast A (0-9) Contrast B (0-9) Contrast C (0-9) Contrast D (0-9) Contrast E (0-9) 20.4 (7.9) 6.1 (2.1) 6.0 (2.2) 4.9 (2.5) 1.7 (1.6) 1.5 (1.7) 28.8 (5.2) 6.8 (1.0) 7.1 (.97) 7.2 (1.1) 4.1 (2.0) 3.4 (1.8) 32.9 (5.3) 8.0 (.8) 7.8 (.7) 7.8 (1.0) 5.2 (2.0) 3.9 (2.2) 28.9 (6.6) 7.1 (1.3) 7.2 (1.2) 7.1 (1.5) 4.1 (2.2) 3.3 (2.0) .000 .000 .000 .000 .000 .002 U-SR, U-S (.000), SR-S (.006) U-S, SR-S (.000) U-SR (.01), U-S (.000), SR-S (.02) U-S, U-SR (.000) U-S (.000), U-SR (.001) U-S (.001), U-SR (.008) Total Peripheral visionc 162.5 (17.5) 165.1 (11.2) 166.2 (9.6) 165.1 (11.7) .654 NA Acuity Both Eyesd 36.6 (8.8) 37.2 (26.6) 28.2 (8.6) 34.6 (21.5) .177 NA Other Vision (0-4) 2.5 (1.1) 2.7 (.9) 3.2 (.8) 2.8 (.9) .044 NS

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153Table 4-8. Continued Motor/Physical Total Medications 6.2 (4.0) 7.7 (4.7) 7.2 (4.0) 7.4 (4.4) .566 NA Total Co-morbidities 4.5 ( 3.3) 3.8 (2.6) 3.3 (2.4) 3.8 (2.6) .435 NA RPW (seconds) 7.6 (2.8) 5.4 (1.3) 5.0 (1.1) 5.6 (1.6) .000 U-S, U-SR (.000) ROM (0-10) 9.5 (.7) 9.4 (1.4) 9.8 (.3) 9.5 (1.2) .264 NA Strength (0-8) 8 (.0) 7.9 (.1) 7.9 (.1) 7.9 (.1) .812 NA Note. U = Unsafe. SR = Safe with Recomm endations. S = Safe. NA = Not applicable. aRange16 to 500ms for each subtest. bRange 0 to 45. cRange 0 to 170. dScores indicate mean of de nominators of visual acuity.

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154 Function 15.0 2.5 0.0 -2.5 -5.0 Function 25.0 2.5 0.0 -2.5 -5.0 Safe Safe-R Unsafe Figure 4-7. Discriminant function Table 4-9. Correlation coefficients by discriminant functions Function 1 2 Total Contrast Sensitivity .884 .468 Other vision .445 .187 Trails B Scorea -.426 .045 UFOV Composite Score -.403 -.003 Visual Acuity Both Eyes -.303 -.067 ROM Total .282 .039 Peripheral vision .243 -.081 Total medication -.153 -.060 Total Co-morbidities -.115 .039 TICS total .101 .049 Rapid pace walk -.684 .729 Total strength .119 -.188

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155 Table 4-10. Classification results Discriminant Predicted Group Membership Total Unsafe Safe-R Safe Unsafe Original Count Unsafe 10 6 0 16 Safe-R 13 26 27 66 Safe 3 7 22 32 % Unsafe 62.5 37.5 .0 100.0 Safe-R 19.7 39.4 40.9 100.0 Safe 9.4 21.9 68.8 100.0 Cross-validated Count Unsafe 10 6 0 16 Safe-R 14 25 27 66 Safe 3 8 21 32 % Unsafe 62.5 37.5 .0 100.0 Safe-R 21.2 37.9 40.9 100.0 Safe 9.4 25.0 65.6 100.0 Note. Cross validation is done only for those cases in the analysis. In cross validation, each case is classified by the functions derived from all cases other than that case. 50.9% of original grouped cas es correctly classified. 49.1% of cross-validated grouped cases correctly classified. Table 4-11. Number of drivers with recommendations at follow-up Number of recommendations (n=64) GRS 0 (n=1) GRS 1 (n=7) GRS 2 (n=20) GRS 3 (n=36) Total (n=64) Selection (number given) Correctly recalled Incorrectly recalled 1 1 0 19 1 0 11 1 1 0 0 1 31 3 2 Optimization specific Correctly recalled Incorrectly recalled 3 0 0 27 7 1 102 41 9 15 5 5 147 53 15 Optimization global Correctly recalled Incorrectly recalled 0 0 0 14 0 0 17 0 0 3 0 0 34 0 0 Compensation Correctly recalled Incorrectly recalled 0 0 0 6 0 0 3 0 0 0 0 0 9 0 0 Total Correctly recalled Incorrectly recalled 4 1 0 66 8 1 133 42 10 18 5 6 221 56 17

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156 Table 4-12. Number of drivers w ith recommendations at follow-up Driving group N Mean Std. Deviation Total incorrect recall 0 1 .0000 1 7 .1429 .37796 2 36 .2778 .56625 3 20 .3000 .47016 Total 64 .2656 .51152 Total correct recall 0 1 1.0000 1 7 1.1429 1.34519 2 36 1.1667 .97101 3 20 .2500 .44426 Total 64 .8750 .96773

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157 Table 4-13. Most common recomm endations given and recalled Number of recommendations (n=64) GRS 0 GRS 1 GRS 2 GRS 3 Total Stops Correctly recalled (Number) Incorrectly recalled (Number) 0 0 0 2 0 1 34 16 0 1 0 2 37 16 3 Lane maintenance Correctly recalled Incorrectly recalled 0 0 0 7 3 0 14 6 2 3 0 1 24 9 3 Speed Correctly recalled Incorrectly recalled 1 0 0 6 2 0 14 3 1 0 1 1 21 6 2 Signaling Correctly recalled Incorrectly recalled 0 0 0 3 1 0 11 4 1 6 1 0 20 6 1 Distance Correctly recalled Incorrectly recalled 0 0 0 2 1 0 7 5 1 3 2 1 12 8 2 Scanning (given) Correctly recalled Incorrectly recalled 0 0 0 3 0 0 4 0 2 2 1 0 10 1 2 Merging Correctly recalled Incorrectly recalled 0 0 0 0 0 0 8 2 0 0 0 0 8 2 0 Attention Correctly recalled Incorrectly recalled 1 0 0 3 0 0 4 3 2 0 0 0 8 3 2 Hands position Correctly recalled Incorrectly recalled 0 0 0 1 0 0 6 2 0 0 0 0 7 2 0 AARP course Correctly recalled Incorrectly recalled 0 0 0 4 0 0 3 0 0 1 0 0 8 0 0 Referral eye care specialist Correctly recalled Incorrectly recalled 0 0 0 1 0 0 5 0 0 0 0 0 6 0 0 Re-read handbook Correctly recalled Incorrectly recalled 0 0 0 2 0 0 2 0 0 1 0 0 5 0 0 Other referral Correctly recalled Incorrectly recalled 0 0 0 0 0 0 4 0 0 1 0 0 5 0 0 Referral neurologist Correctly recalled Incorrectly recalled 0 0 0 3 0 0 0 0 0 0 0 0 3 0 0

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158 Table 4-13. Continued Referral rehabilitation Correctly recalled Incorrectly recalled 0 0 0 1 0 0 2 0 0 0 0 0 3 0 0 Drive re-evaluation Correctly recalled Incorrectly recalled 0 0 0 2 0 0 1 0 0 0 0 0 3 0 0 Referral physician Correctly recalled Incorrectly recalled 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 Avoid high traffic Correctly recalled Incorrectly recalled 0 0 0 5 1 0 3 0 0 0 0 0 8 1 0 Avoid highways Correctly recalled Incorrectly recalled 0 0 0 5 0 0 3 1 0 0 0 0 8 1 0 Avoid rush hour Correctly recalled Incorrectly recalled 0 0 0 2 0 0 2 0 0 0 0 0 4 0 0 Avoid night driving Correctly recalled Incorrectly recalled 0 0 0 2 0 0 1 0 1 0 0 1 3 0 2 Plan driving retirement Correctly recalled Incorrectly recalled 0 0 0 1 0 0 2 0 0 0 0 0 3 0 0 Avoid long distance driving Correctly recalled Incorrectly recalled 0 0 0 2 0 0 0 0 0 0 0 0 2 0 0 Avoid driving to new locations Correctly recalled Incorrectly recalled 0 0 0 2 0 0 0 0 0 0 0 0 2 0 0 Do not drive Correctly recalled Incorrectly recalled 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 BTW training Correctly recalled Incorrectly recalled 0 0 0 4 0 0 2 0 0 0 0 0 6 0 0 Driving lessons Correctly recalled Incorrectly recalled 0 0 0 1 0 0 1 0 0 0 0 0 2 0 0 BTW training with devices Correctly recalled Incorrectly recalled 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0

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159 0 10 20 30 40 50 60 70 80 90 100 unsafe (n = 1)unsafe-R (n = 7)Safe-R (n = 36)Safe (n = 20)% drivers Total selection fup Total specific opt. fup Total global opt. fup Total comp. fup Figure 4-8. Total recommendations of follow-up sample Table 4-14. Number of drivers with recommendations at follow-up Selection Specific opt. Global opt. Comp. Unsafe (n = 1) 1 1 0 0 Unsafe-R (n = 7) 5 7 6 5 Safe-R (n = 36) 7 34 7 3 Safe (n = 20) 0 10 1 0 Notes. Opt. = optimization. Comp. = Compen sation. Sample sizes for each group are in parenthesis.

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160 0 2 4 6 8 10 12 14 16 unsafe (1)unsafe-R (7)Safe-R (36)Safe (20)% Drivers Rush hour uncued FR Rush hour cued FR High trafic uncued FR High traffic cued FR Highways uncued FR Highwways cued FR Night uncued FR Night cued FR Left turns uncued FR Left turns cued FR Figure 4-9. Uncued and cued false recall of selection recommendations (Cued recommendations in solid colors and uncued recommen dations in colors with patterns) 0 10 20 30 40 50 60 70 80 90 100 110 unsafe (1) unsafe-R (7)Safe-R (36) Safe (20)% Driver s Scanning uncued FR Scanning cued FR Lane maint. uncued FR Lane maint. cued FR Speed uncued FR Speed cued FR Stops uncued FR Stops cued FR Distance uncued FR Distance cued FR Signaling uncued FR Signaling cued FR Merging uncued FR Merging cued FR Hands uncued FR Hands cued FR Attention uncued FR Attention cued FR Figure 4-10. Uncued and cued false recall of specific optimization recommendations (Cued recommendations in solid colors and uncued recommendations in colors with patterns)

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161 0 5 10 15 20 25 30 35 unsafe (1)unsafe-R (7)Safe-R (36)Safe (20)% drivers Rush hour no cued recall High traffic no cued recall Highways no cued recall Night no cued recall Long dist no cued recall Retire no cued recall Figure 4-11. No cued recall of selection recommendations Table 4-15. Number of drivers w ho had selection recommendations Notes. Recommendations referred to avoidance driv ing conditions stated in the first row. Hghw = Highways. Long dist. = long distance. Unfamilia r = unfamiliar places or new locations. Lefts = unprotected lefts. Retire = Plan for driv ing retirement. No drive = stop driving. Rush Traffic Hghw Night Long dist Unfamiliar Lefts Retire No drive Unsafe 0 0 0 0 0 0 0 0 1 Unsafe-R 2 3 5 2 2 1 0 1 0 Safe-R 2 3 3 1 0 0 0 2 0 Safe 0 0 0 0 0 0 0 0 0

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162 0 20 40 60 80 100 unsafe (1)unsafe-R (7)Safe-R (36)Safe (20)% Driver s Scanning NCR Lane maint. NCR Speed NCR Stops NCR Distance NCR Signaling NCR Merging NCR Hands NCR Attention NCR Figure 4-12. No cued recall of sp ecific optimization recommendations Notes. NCR = Not cued recall. Percentage shown is for drivers within each group. Table 4-16. Number of drivers who ha d specific optimization recommendations Scan Lane Maint. Speed Stops Distance Signal Merge Hands Att. Unsafe 1 0 1 0 0 0 0 0 1 Unsafe-R 3 5 4 3 2 4 0 1 4 Safe-R 3 11 13 22 4 11 8 5 3 Safe 2 1 1 1 3 6 0 0 0 Notes: Lane maint. = lane maintenance. Att = Attention.

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163 0 10 20 30 40 50 unsafe (1)unsafe-R (7)Safe-R (36)Safe (20)% Driver s aarp NCR read handbook NCR physician NCR neurologist NCR eye care NCR pt/ot NCR other referrals NCR re-evaluation NCR Figure 4-13. No cued recall of gl obal optimization recommendations 0 10 20 30 40 50 unsafe (1)unsafe-R (7)Safe-R (36)Safe (20)% Driver s drive lessons NCR BTW NCR BTW with AT NCR Figure 4-14. No cued recall of compensation recommendations Table 4-17. Number of drivers who had global optimization recommendations AARP Handbook Physician Neurologist Eye Care Pt/ Ot Other referral Reeval Unsafe 0 0 0 0 0 0 0 0 Unsafe-R 4 1 1 3 1 1 0 2 Safe-R 3 1 0 0 5 1 2 1 Safe 1 1 1 0 0 0 0 0

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164 Table 4-18. Number of drivers w ho had compensation recommendations Lessons BTW BTW with AT Unsafe (1) 0 0 0 Unsafe-R (7) 1 4 1 Safe-R (36) 0 3 0 Safe (20) 0 0 0 0 10 20 30 40 50 60 70 80 90 100 unsafe (1)unsafe-R (7)Safe-R (36)Safe (20)% Driver s Rush hour CR High traffic UR High traffic CR Highways UR Highwways CR Night CR Unfamiliar CR Retire CR No drive UR Figure 4-15. Uncued and cued recall of sel ection recommendations.(Cued recommendations in solid colors and uncued recommenda tions in colors with patterns)

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165 0 5 10 15 20 25 30 35 40 45 50 unsafe (1)unsafe-R (7)Safe-R (36)Safe (20)% Driver s Scanning uncued UR Scanning CR Lane maint. UR Lane maint. CR Speed UR Speed CR Stops UR Stops CR Distance UR Distance CR Signaling UR Signaling CR Merging UR Merging CR Hands UR Hands CR Attention UR Attention CR Figure 4-16. Uncued and cued recall specific optimization recommendations. (Cued recommendations in solid colors and uncue d recommendations in colors with patterns)

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166Table 4-19. Selection reco mmendations follow-up sample Selection driving recommendations Total sample (n = 64) N % Unsafe (n = 1) N % Unsafe-R (n = 7) N % Safe-R (n = 36) N % Safe (n = 20) N % Avoid rush hour Avoid high traffic Avoid highways Avoid night driving Avoid long distance driving Avoid unfamiliar places Avoid unprotected lefts Plan for driving retirement Do not drive 4 8 8 3 2 2 0 3 1 6.3 12.5 12.5 4.7 3.1 3.1 0 4.7 1.6 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 100 2 5 5 2 2 2 0 1 0 28.6 71.4 71.4 28.6 28.6 28.6 0 14.3 0 2 3 3 1 0 0 0 2 0 5.6 8.3 8.3 2.8 0 0 0 5.6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Total (% of total sample) 13 20.3 1 100 1.6 5 71.4 7.8 7 19.4 10.9 0 0 0 Table 4-20. Optimization specific recommendations follow-up sample Optimization specific recommendations Total sample (n = 64) N % Unsafe (n = 1) N % Unsafe-R (n = 7) N % Safe-R (n = 36) N % Safe (n = 20) N % Scanning N (%) Turn head more often Use mirrors more often Increase visual scanning 9 1 1 8 14.1 1.6 1.6 12.5 1 0 0 1 1.6 0 0 100 3 0 0 3 42.9 0 0 42.9 3 0 1 3 8.3 0 2.8 8.3 2 1 0 1 10 .3 0 1.6 Lane maintenance N (%) Attention to roadway markings Avoid drifting right Avoid drifting left Watch lane maintenance Turns too wide/encroaches 16 4 1 7 5 7 25.0 6.3 1.6 10.9 7.8 10.9 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 2 2 3 6.3 0 0 28.6 28.6 42.9 11 4 1 4 2 3 17.2 6.3 1.6 11.1 5.6 8.3 1 0 0 1 1 1 1.6 0 0 5.0 5.0 5.0

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167Table 4-20. Continued Speed N (%) Dont drive too slow Dont drive too fast Watch speed (posted limit) Reduce aggressive acceleration/break earlier 17 7 4 5 5 26.6 10.9 6.3 7.8 7.8 1 0 0 0 1 100 0 0 0 100 4 2 0 2 2 57.1 28.6 0 28.6 28.6 12 5 4 3 2 33.3 13.9 11.1 8.3 5.6 0 0 0 0 0 0 0 0 0 0 Stops N (%) Come to complete stops Stop behind white lines (not in intersection) Dont stop too far behind 25 22 12 3 39.1 34.4 18.8 4.7 0 0 0 0 0 0 0 0 2 2 0 0 28.6 28.6 0 0 22 20 12 2 61.1 55.6 33.3 5.6 1 0 0 1 5.0 0 0 5.0 Distance N (%) Increase following distance Increase stopping distance 9 7 5 14.1 10.9 7.8 0 0 0 0 0 0 2 2 0 28.6 28.6 0 4 4 3 1.1 11.1 8.3 3 1 10.0 15.00 5.0 6.1 Signaling N (%) Always use turn signals Dont use turn signals too early Turn off signal 20 19 1 0 31.3 29.7 1.6 0 0 0 0 0 0 0 0 0 3 3 0 0 42.9 42.9 0 0 11 11 0 0 30.6 30.6 0 0 6 5 1 0 30 25.0 5.0 0 Merging N (%) Increase speed merging on hgw Use merging lane/dont cross solid lines 7 6 2 10.9 9.4 3.1 0 0 0 0 0 0 0 0 0 0 0 0 7 6 2 19.4 16.7 5.6 0 0 0 0 0 0 Attention N (%) (% within GRS) 7 10.9 1 1.6 100 3 4.7 42.9 3 4.7 8.3 0 0 0 Hands positioning N (%) (% within GRS) 6 9.4 0 0 1 1.6 14.3 5 7.8 13.9 0 0 0 Total Persons who got Optim ization Recommendations (% within GRS) 52 81.3 1 1.6 100 7 10.9 100 34 53.1 94.4 10 15.6 50.0

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168Table 4-21. Optimization global follow-up sample Recommendations Total samplea (n = 64) N % Unsafeb (n = 1) N % Unsafe-Rb (n = 7) N % Safe-Rb (n = 36) N % Safeb (n = 20) N % Optimization Global N (%) (% within GRS) Take AARP refresher course Re-read FL drivers handbook 14 8 5 21.9 12.5 7.8 0 0 0 0 0 0 0 6 4 2 9.4 85.7 57.1 28.6 7 3 2 10.9 19.4 8.3 5.6 1 1 1 1.6 5.0 5.0 5.0 Referrals N (%) (% within GRS) Physician Neurologist Eye care specialist PT/OT Other Driver re-evaluation 11 1 3 6 2 4 3 17.2 1.6 4.7 9.4 3.1 6.3 4.7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 1 3 1 1 0 2 6.3 57.1 14.3 42.9 14.3 14.3 0 28.6 6 0 0 5 1 3 1 9.4 16.7 0 0 13.9 2.8 8.3 2.8 1 0 0 0 0 1 0 1.6 5.0 0 0 0 0 5.0 0 Compensation N (%) (% within GRS) Take driving lessons BTW training without devices With assistive devices 8 2 6 1 12.5 3.1 9.4 1.6 0 0 0 0 0 0 0 0 0 5 1 4 1 7.8 71.4 14.2 57.1 14.3 3 1 2 0 4.7 8.3 2.8 5.6 0 0 0 0 0 0 0 0 0 0 0 Total (% of total) 14 21.9 0 0 0 6 9.4 85.7 7 10.9 19.4 1 5.0 1.6 aNumber of persons and percentage of total sample. bSubcategories of recommenda tions show the number of persons and percentage within each driving performance group who received recommendations.

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169Table 4-22. Follow-up recall of selection recommendations Recommendations GRS Recall N (%) No Uncued Cued False Recall N (%) Uncued Cued No Cued Totals (N) Recall False recall Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 4 (57.1) 0 0 0 1 (14.3) 2 (28.6) 0 3 Safe-R 31 (86.1) 0 1 (2.8) 0 3 (8.3) 1 (2.8) 1 4 Safe 19 (95.0) 0 0 0 1 (5.0) 0 0 1 Avoid rush hour Subtotal 55 (85.9) 0 1 (1.6) 0 5 (7.8) 3 (4.7) 1 8 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 3 (42.9) 1 (14.3) 1 (14.3) 0 0 2 (28.6) 2 2 Safe-R 30 (83.3) 0 1 (2.8) 0 3 (8.3) 2 (5.6) 1 5 Safe 20 (100) 0 0 0 0 0 0 0 Avoid high traffic Subtotal 54 (84.4) 1 (1.6) 2 (3.1) 0 3 (4.7) 4 (6.3) 3 7 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 2 (28.6) 0 3 (42.9) 0 0 2 (28.6) 3 2 Safe-R 30 (83.3) 1 (2.8) 0 0 3 (8.3) 2 (5.6) 1 5 Safe 20 (100) 0 0 0 0 0 0 0 Avoid highways Subtotal 53 (82.8) 1 (1.6) 3 (4.7) 0 3 (4.7) 4 (6.3) 4 7 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 4 (57.1) 0 1 (14.3) 0 1 (14.3) 1 (14.3) 1 2 Safe-R 30 (83.3) 0 1 (2.8) 1 (2.8) 4 (11.1) 0 1 5 Safe 19 (95.0) 0 0 1 (5.3) 0 0 0 1 Avoid night driving Subtotal 54 (84.4) 0 2 (3.2) 2 (3.2) 5 (7.9) 1 (1.6) 2 8 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 5 (71.4) 0 0 0 0 2 (28.6) 0 2 Safe-R 36 (100) 0 0 0 0 0 0 0 Safe 20 (100) 0 0 0 0 0 0 0 Avoid long distance driving Subtotal 62 (96.9) 0 0 0 0 2 (3.1) 0 2

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170Table 4-22. Continued Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 6 (85.7) 0 1 (14.3) 0 0 0 1 0 Safe-R 36 (100) 0 0 0 0 0 0 0 Safe 20 (100) 0 0 0 0 0 0 0 Avoid unfamiliar places Subtotal 63 (98.4) 0 1 (1.6) 0 0 0 1 0 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 6 (85.7) 0 0 0 1 (14.3) 0 0 1 Safe-R 36 (100) 0 0 0 0 0 0 0 Safe 20 (100) 0 0 0 0 0 0 0 Avoid unprotected lefts Subtotal 63 (98.4) 0 0 0 1 (1.6) 0 0 1 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 6 (85.7) 0 0 0 0 1 (14.3) 0 1 Safe-R 34 (94.4) 0 2 (5.6) 0 0 0 2 0 Safe 20 (100) 0 0 0 0 0 0 0 Plan for driving retirement Subtotal 61 (95.3) 0 2 (3.1) 0 0 1 (1.6) 2 1 Unsafe 0 1 (100) 0 0 0 0 1 0 Unsafe-R 7 (100) 0 0 0 0 0 0 0 Safe-R 36 (100) 0 0 0 0 0 0 0 Safe 20 (100) 0 0 0 0 0 0 0 Do not drive Subtotal 63 (98.4) 1 (1.6) 0 0 0 0 1 0

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171Table 4-23. Follow-up recall of optimi zation specific recommendations Recommendations GRS Matches N (%) N-N Y-Y Y-YC Mismatches N (%) N-Y N-YC Y-NC Totals (N) Matches Mismatches Unsafe 0 0 0 0 1 (100) 0 0 1 Unsafe-R 6 (85.7) 0 0 0 1 (14.3) 0 0 1 Safe-R 30 (83.3) 0 1 (2.8) 1 (2.8) 4 (11.1) 0 1 5 Safe 18 (90.0) 0 0 0 1 (5.0) 1 (5.0) 0 2 Turn head more often N (% of GRS) N (% of Total) Subtotal 54 (84.4) 0 1 (1.6) 1 ( 1.6) 7 (10.9) 1 (1.6) 1 9 Unsafe 0 0 0 0 1 (100) 0 0 1 Unsafe-R 6 (85.7) 0 0 0 1 (14.3) 0 0 1 Safe-R 29 (80.6) 0 0 1 (2.8) 5 (13.9) 1 (2.8) 0 7 Safe 19 (95.0) 0 0 0 1 (5.0) 0 0 1 Use mirrors more often Subtotal 54 (84.4) 0 0 1 (1.6) 8 (12.5) 1 (1.6) 0 10 Unsafe 0 0 1 (100) 0 0 0 1 0 Unsafe-R 4 (57.1) 0 1 (14.3) 0 0 2 (28.6) 1 3 Safe-R 32 (88.9) 0 2 (5.6) 0 2 (5.6) 0 2 4 Safe 19 (95.0) 1 (5.0) 0 0 0 0 1 1 Scan environment more Subtotal 55 (85.9) 1 (1.6) 4 (6.3) 0 2 (3.1) 2 (3.1) 5 4 Unsafe 0 0 0 0 1 (100) 0 0 1 Unsafe-R 7 (100) 0 0 0 0 0 0 0 Safe-R 28 (77.8) 2 (5.6) 2 (5.6) 1 (2.8) 2 (5.6) 0 4 3 Safe 18 (90.0) 0 0 0 2 (10) 1 (2.8) 0 2 Attention to road marks Subtotal 53 (82.8) 2 (3.1) 2 (3.1) 1 (1.6) 5 (7.8) 1 (1.6) 4 7 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 7 (100) 0 0 0 0 0 0 0 Safe-R 34 (94.4) 1 (2.8) 0 0 1 (2.8) 0 1 1 Safe 20 (100) 0 0 0 0 0 0 0 Avoid drifting right Subtotal 62 (96.9) 1 (1.6) 0 0 1 (1.6) 0 1 1 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 5 (71.4) 1 (14.3) 0 0 0 1 (14.3) 1 1 Safe-R 31 (86.1) 2 (5.6) 1 (2.8) 0 1 (2.8) 1 (2.8) 3 2 Safe 19 (95.0) 0 1 (5.0) 0 0 0 1 0 Avoid drifting left Subtotal 56 (87.5) 3 (4.7) 2 (3.1) 0 1 (1.6) 2 (3.1) 5 3

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172Table 4-23. Continued Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 4 (57.1) 2 (28.6) 0 0 0 1 (14.3) 2 1 Safe-R 32 (88.9) 0 0 1 (2.8) 1 (2.8) 2 (5.6) 0 4 Safe 18 (90.0) 0 1 (5.0) 1 (5.0) 0 0 1 1 Watch lane maintenance Subtotal 55 (85.9) 2 (3.1) 1 (1.6) 2 (3.1) 1 (1.6) 3 (4.7) 3 6 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 4 (57.1) 0 2 (28.6) 0 0 1 (14.3) 2 1 Safe-R 30 (83.3) 1 (2.8) 2 (5.6) 0 3 (8.3) 0 3 3 Safe 18 (90.0) 0 0 0 1 (5.0) 1 (5.0) 0 2 Stay in lane (in turns) Subtotal 53 (82.8) 1 (1.6) 4 (6.3) 0 4 (6.3) 2 (3.1) 5 6 Unsafe 0 0 0 0 1 (100) 0 0 1 Unsafe-R 5 (71.4) 1 (14.3) 0 0 0 1 (14.3) 6 1 Safe-R 28 (77.8) 0 3 (8.3) 1 (2.8) 2 (5.6) 2 (5.6) 3 5 Safe 18 (90.0) 1 (5.0) 0 0 1 (5.0) 0 19 1 Do not drive too slow Subtotal 51 (79.7) 2 (3.1) 3 (4.7) 1 (1.6) 4 (6.3) 3 (4.7) 5 8 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 7 (100) 0 0 0 0 0 0 0 Safe-R 31 (86.1) 3 (8.3) 0 0 1 (2.8) 1 (2.8) 3 2 Safe 19 (95.0) 0 0 1 (5.0) 0 0 0 1 Do not drive too fast Subtotal 58 (90.6) 3 (4.7) 0 1 (1.6) 1 (1.6) 1 (1.6) 3 3 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 4 (57.1) 0 0 0 1 (14.3) 2 (28.6) 0 3 Safe-R 31 (86.1) 0 1 (2.8) 0 1 (2.8) 3 (8.3) 1 4 Safe 20 (100) 0 0 0 0 0 0 0 Adhere to speed limit Subtotal 56 (87.5) 0 1 (1.6) 0 2 (3.1) 5 (7.8) 1 7 Unsafe 0 0 0 0 0 1 (100) 0 1 Unsafe-R 5 (71.4) 1 (14.3) 0 0 0 1 (14.3) 1 1 Safe-R 33 (91.7) 0 1 (2.8) 0 1 (2.8) 1 (2.8) 1 2 Safe 20 (100) 0 0 0 0 0 0 0 Braking/acceleration Subtotal 58 (90.6) 1 (1.6) 1 0 1 (1.6) 3 (4.7) 2 4

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173Table 4-23. Continued Unsafe 0 0 0 0 1 (100) 0 0 1 Unsafe-R 3 (42.9) 0 0 0 1 (14.3) 3 (42.9) 0 4 Safe-R 15 (41.7) 9 (25.0) 6 (16.7) 0 1 (2.8) 5 (13.9) 15 6 Safe 14 (70.0) 0 0 2 (10) 4 (20) 0 0 6 Make complete stops Subtotal 32 (50.0) 9 (14.1) 6 (9.4) 2 (3.1) 7 (10.9) 8 (12.5) 15 17 Unsafe 0 0 0 0 1 (100) 0 0 1 Unsafe-R 6 (85.7) 0 0 1 (14.3) 0 0 0 1 Safe-R 21 (58.3) 5 (13.9) 4 (11.1) 0 3 (8.3) 3 (8.3) 9 6 Safe 19 (95.0) 0 0 0 1 (5.0) 0 0 1 Stop behind white lines Subtotal 46 (71.9) 5 (7.8) 4 (6.3) 1 (1.6) 5 (7.8) 3 (4.7) 9 9 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 7 (100) 0 0 0 0 0 0 0 Safe-R 32 (88.9) 2 (5.6) 0 0 2 (5.6) 0 2 2 Safe 19 (95.0) 0 0 0 0 1 (5.0) 0 1 Other stop (too far back) Subtotal 59 (92.2) 2 (3.1) 0 0 2 (3.1) 1 (1.6) 2 3 Unsafe 0 0 0 0 1 (100) 0 0 1 Unsafe-R 5 (71.4) 1 (14.3) 0 0 0 1 (14.3) 1 1 Safe-R 27 (75.0) 2 (5.6) 2 (5.6) 0 5 (13.9) 0 4 5 Safe 19 (95.0) 0 1 (5.0) 0 0 0 1 0 Increase following dist. Subtotal 51 (79.7) 3 (4.7) 3 (4.7) 0 6 (9.4) 1 (1.6) 6 7 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 6 (85.7) 0 0 0 1 (14.3) 0 0 1 Safe-R 29 (80.6) 3 (8.3) 0 1 (2.8) 3 (8.3) 0 3 4 Safe 15 (75.0) 2 (10) 0 1 (5.0) 2 (10) 0 2 3 Increase stopping dist. Subtotal 51 (79.7) 5 (7.8) 0 2 (3.1) 6 (9.4)0 5 8 Unsafe 0 0 0 0 1 (100) 0 0 1 Unsafe-R 3 (42.9) 1 (14.3) 0 0 0 3 (42.9) 1 3 Safe-R 24 (66.7) 4 (11.1) 1 (2.8) 1 (2.8) 0 6 (16.7) 5 7 Safe 11 (55.0) 1 (5.0) 0 0 4 (20.0) 4 (20.0) 1 8 Always use signal Subtotal 38 (59.4) 6 (9.4) 1 (1.6) 1 (1.6) 5 (7.8) 13 (20.3)7 19

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174Table 4-23. Continued Unsafe 1 (100) 0 0 0 0 0 1 0 Unsafe-R 7 (100) 0 0 0 0 0 7 0 Safe-R 36 (100) 0 0 0 0 0 36 0 Safe 19 (95.0) 0 0 0 0 1 (5.0) 19 1 Dont signal too early Subtotal 63 (98.4) 0 0 0 0 1 (1.6) 63 1 Unsafe 0 0 0 0 1 (100) 0 0 1 Unsafe-R 4 (57.1) 0 0 0 3 (42.9) 0 4 3 Safe-R 24 (66.7) 1 (2.8) 4 (11.1) 0 5 (13.9) 2 (5.6) 29 7 Safe 18 (90.0) 0 0 0 2 (10.0) 0 18 2 Increase speed merging Subtotal 46 (71.9) 1 (1.6) 4 (6.3) 0 11 (17.2)2 (3.1) 51 13 Unsafe 1 (100) 0 0 0 0 0 1 0 Unsafe-R 6 (85.7) 0 0 0 1 (14.3) 0 6 1 Safe-R 33 (91.7) 1 (2.8) 1 (2.8) 0 1 (2.8) 0 35 1 Safe 20 (100) 0 0 0 0 0 20 0 Use merging lane Subtotal 60 (93.8) 1 (1.6) 1 (1.6) 0 2 (3.1) 0 62 2 Unsafe 1 (100) 0 0 0 0 0 1 0 Unsafe-R 6 (85.7) 0 0 0 0 1 (14.3) 6 1 Safe-R 28 (77.8) 2 (5.6) 1 (2.8) 0 3 (8.3) 2 (5.6) 31 5 Safe 19 (95.0) 0 0 0 1 (5.0) 0 19 1 Change hands position Subtotal 54 (84.4) 2 (3.1) 1 (1.6) 0 4 (6.3) 3 (4.7) 57 7 Unsafe 0 0 1 (100) 0 0 0 1 0 Unsafe-R 3 (42.9) 0 2 (28.6) 0 0 2 (28.6) 5 2 Safe-R 29 (80.6) 2 (5.6) 0 2 (5.6) 2 (5.6) 1 (2.8) 31 5 Safe 20 (100) 0 0 0 0 0 20 0 Pay more attention Subtotal 52 (81.3) 2 (3.1) 3 (4.7) 2 (3.1) 2 (3.1) 3 (4.7) 57 7

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175Table 4-24. Follow-up recall of optimization global and compensation recommendations Recommendations GRS Matches N (%) N-N Y-Y Y-YC Mismatches N (%) N-Y N-YC Y-NC Totals (N) Matches Mismatches Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 3 (42.9) 0 2 (28.6) 0 0 2 (28.6) 2 2 Safe-R 32 (88.9) 0 2 (5.6) 0 1 (2.8) 1 (2.8) 2 2 Safe 19 (95.0) 0 0 0 0 1 (5.0) 0 1 Take AARP course Subtotal 55 (85.9) 0 4 (6.3) 0 1 (1.6) 4 (6.3) 4 5 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 6 (85.7) 0 1 (14.3) 0 0 0 1 0 Safe-R 35 (97.2) 0 1 (2.8) 0 0 0 1 0 Safe 19 (95.0) 0 0 0 0 1 (5.0) 0 1 Read drivers handbook Subtotal 61 (95.3) 0 2 (3.1) 0 0 1 (1.6) 2 1 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 6 (85.7) 0 0 0 0 1 (14.3) 0 1 Safe-R 36 (100) 0 0 0 0 0 0 0 Safe 19 (95.0) 0 0 0 0 1 (5.0) 0 1 Referral-Physician Subtotal 62 (96.9) 0 0 0 0 2 (3.1) 0 2 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 4 (57.1) 0 0 0 0 3 (42.9) 0 3 Safe-R 36 (100) 0 0 0 0 0 0 0 Safe 20 (100) 0 0 0 0 0 0 0 Referral-Neurologist Subtotal 61 (95.3) 0 0 0 0 3 (4.7) 0 3 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 6 (85.7) 0 0 0 0 1 (14.3) 0 1 Safe-R 31 (86.1) 0 0 0 0 5 (13.9) 0 5 Safe 20 (100) 0 0 0 0 0 0 0 Referral-Eye care Subtotal 58 (90.6) 0 0 0 0 6 (9.4) 0 6 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 6 (85.7) 0 0 0 0 1 (14.3) 0 1 Safe-R 35 (97.2) 0 0 0 0 1 (2.8) 0 1 Safe 20 (100) 0 0 0 0 0 0 0 Referral-PT/OT Subtotal 62 (96.9) 0 0 0 0 2 (3.1) 0 2

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176Table 4-24. Continued Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 7 (100) 0 0 0 0 0 0 0 Safe-R 34 (94.4) 0 1 (2.8) 0 0 1 (2.8) 1 1 Safe 20 (100) 0 0 0 0 0 0 0 Other Referral Subtotal 62 (96.9) 0 1 (1.6) 0 0 1 (1.6) 2 1 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 5 (71.4) 0 0 0 0 2 (28.6) 0 2 Safe-R 35 (97.2) 0 0 0 0 1 (2.8) 0 1 Safe 20 (100) 0 0 0 0 0 0 0 Driver re-evaluation Subtotal 61 (95.3) 0 0 0 0 3 (4.7) 0 3 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 6 (85.7) 0 0 0 0 1 (14.3) 0 1 Safe-R 36 (100) 0 0 0 0 0 0 0 Safe 20 (100) 0 0 0 0 0 0 0 Take driving lessons Subtotal 63 (98.4) 0 0 0 0 1 (1.6) 0 1 Unsafe 1 (100) 0 0 0 0 0 1 0 Unsafe-R 3 (42.9) 0 1 (14.3) 0 0 3 (42.9) 1 3 Safe-R 33 (51.6) 0 0 0 0 3 (8.3) 0 3 Safe 20 (31.3) 0 0 0 0 0 0 0 BTW training (no dev.) Subtotal 57 (89.1) 0 1 (1.6) 0 0 6 (9.4) 1 6 Unsafe 1 (100) 0 0 0 0 0 0 0 Unsafe-R 6 (85.7) 0 0 0 0 1 (14.3) 0 1 Safe-R 36 (100) 0 0 0 0 0 0 0 Safe 20 (100) 0 0 0 0 0 0 0 BTW with devices Subtotal 63 (98.4) 0 0 0 0 1 (1.6) 0 1

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177Table 4-25. Driving exposure a nd avoidance of follow-up sample Variable (Range) Total Sample Baseline n = 116 Follow-up Sample Baseline n = 58 Follow-up Sampled n = 58 Follow-up Sample ANOVAS (pvalues) Time Time*GRS Days per week (0-7) M (SD) 5.37 (.1)a 5.29 (1.)d 4.74 (1.6) .005 .499 Trips per week (Varies) M (SD) 7.51 (.4)b 7.53 (4.5)c 7.78 (4.4) .393 .725 Number of places per week (Varies) M (SD) 3.71 (.1)b 3.79 (1.4)c 4.86 (1.8) .034 .825 Miles per week (Varies) M (SD) 121.12 (12.6)b 109.77 (118.8)c 127.25 (135.56) .295 .797 Distance driven last month (0-6) M (SD) 4.95 (.1)a 4.98 (1.3)d 4.60 (1.4) .650 .673 Avoidance of driving (0-6) M (SD) 1.91 (.1)a 1.74 (1.0)d 1.66 (1.3) .801 .956 an = 116. bn = 113. cn = 57. dn = 58. Table 4-26. Driving exposure and avoidance Variable (Range) Unsafe-R Baseline Follow-up Safe-R Baseline Follow-up Safe Baseline Follow-up Days per week (0-7) M (SD) 6.33 (1.1) 5.00 (1.0) 5.17 (1.8) 4.61 (1.7) 5.37 (1.8) 4.95 (1.7) Trips per week (Varies) M (SD) 6.66 (2.0) 8.17 (3.1) 6.8 (3.0) 7.23 (3.5) 8.9 (6.5) 8.9 (5.9) Number of places per week (Varies) M (SD) 2.67 (.5) 3.33 (1.5) 3.57 (1.3) 4.80 (1.8) 4.37 (1.3) 5.32 (1.7) Miles per week (Varies) M (SD) 61.00 (21.5) 98.33 (66.3) 85.62 (93.1) 96.79 (86.1) 159.52 (160.4) 192.38 (192.5) Distance driven last month (0-6) M (SD) 3.00 (1.7) 3.33 (1.5) 4.92 (1.2) 4.44 (1.6) 5.42 (1.2) 5.11 (.9) Avoidance of driving (0-6) M (SD) 2.33 (1.1) 2.33 (2.0) 1.86 (1.0) 1.81 (1.3) 1.42 (.9) 1.26 (1.1)

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178 Time2 1 Means7 6 5 4 3 2 1 0 safe under any condition safe with restrictions and recommendations unsafe remediableRoad test global rating scaleFollow-up SampleNumber of Driving Days per Week Figure 4-17. Days per week

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179 Time2 1 Means10.00 8.00 6.00 4.00 2.00 0.00 safe under any condition safe with restrictions and recommendations unsafe remediableRoad test global rating scaleFollow-u p Sam p leNumber of Tri p s p er Week Figure 4-18. Number of trips per week

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180 Time2 1 Means7 6.5 6 5.5 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 safe under any condition safe with restrictions and recommendations unsafe remediableRoad test global rating scaleFollow-u p Sam p leNumber of Places p er Week Figure 4-19. Number of places per week

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181 Time2 1 Means200.00 175.00 150.00 125.00 100.00 75.00 50.00 25.00 0.00 safe under any condition safe with restrictions and recommendations unsafe remediableRoad test global rating scaleFollow-u p Sam p leMean of Weekl y Miles Figure 4-20. Average miles per week

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182 Time2 1 Means7 6 5 4 3 2 1 0 safe under any condition safe with restrictions and recommendations unsafe remediableRoad test global rating scaleFollow-u p Sam p leDistance Driven ( Past Month ) Figure 4-21. Distance driven past month

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183 Time2 1 Means6 5 4 3 2 1 0 safe under any condition safe with restrictions and recommendations unsafe remediableRoad test global rating scale Follow-up SampleDriving Avoidance (Past Month) Figure 4-22. Driving avoidance past month

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184 Table 4-27. Places driven Places driven N (% that went places) Follow-up at baseline N = 58 Follow-up N = 58 Store and errands 56 (96.6) 57 (98.3) Church 21 (35.6) 26 (44.8) Work/Volunteer 23 (39) 21 (36.2) Medical Appointments 14 (24.1) 37 (63.8) Other shopping 10 (16.9) 18 (31) Relatives House 14 (23.7) 16 (27.6) Friends House 17 (29.3) 26 (44.8) Out to Eat 23 (39.7) 18 (31) Gym/Exercise 10 (16.9) 14 (24.1) Leisure (Movies, theater, golf, crafts) 8 (13.6) 13 (22.4) Library 4 (6.8) 4 (6.9) Other places (Meetings, pleasure driving, others) 17 (29.3) 24 (41.4) Table 4-28. Driving avoidan ce at baseline and follow-up Driving avoidance N (% that avoided) Follow-up sample -baseline N = 58 Follow-up sample N = 58 Driving in the rain 8 (13.8) 23 (19.5) Driving alone 1 (1.7) 1 (1.7) Driving in high traffic 6 (10.3) 20 (34.5) Driving in rush hour 16 (27.6) 34 (58.6) Merging into highway/expressway 6 (10.3) 4 (6.9) Making left turns 7 (12.1) 14 (24.1) Driving at night 7 (38.9) (data for 18 drivers only) 22 (37.9) Note: Driving at night was not included in the an alysis since data of 40 older adults were missing at baseline.

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185 CHAPTER 5 DISCUSSION This chapter discusses the results of each study aim, followed by the lim itations of this study, and suggestions for future research in this area. Aim 1: Driving Recommendations The f irst aim of this study was to iden tify the most common types of driving recommendations that a DRS pr ovides to older drivers. Sample sizes differed among groups, which was a important limitation in st udying the frequency of recommendations. Recommendations studied at baseline were based on 4 unsafe drivers, 13 unsafe but remediable drivers (unsafe-R), 68 safe driv ers with recommendations (safe-R) and 33 safe drivers. It was therefore not surprising that the majority of recommendations were related to driving behaviors as the type of recommendations were somewhat dr iven by the larger sample in the safe-R drivers group. Despite this limitation, an interesting patt erns of DRS recommendations emerged across driving performance groups as described below. Recommendations Suggesting Selection As expected, unsafe drivers only received selection recomm en dations to stop driving, and safe drivers did not receive selection drivi ng recommendations. Safe-R drivers were mostly advised to avoid high traffic a nd highways. Driving in highways and in high traffic situations demand prolonged attention, control of the speed of the vehicle, leavi ng safe distances in following cars, and safe reaction time, among othe r driving behaviors. Since approximately 12% of safe-R and 69% of unsafe-R drivers had driving recommendations to avoid high traffic, the findings suggest that these are complex driving situations wher e older adults start to show difficulties driving. Avoiding high traffic and avoi ding highways were also the most frequent recommendations in the unsafe-R group, but more than a third of these drivers were also advised

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186 to avoid unfamiliar places and night driving. This suggests that per DRS judgments, the unsafe-R drivers were having more cognitive difficulties that put them at risk of getting lost, or they became fatigued when driving in unfamiliar places. They also had more attention and visual difficulties that potentially made night driving unsafe. Overall, the recommendations suggesting sele ction showed an increasing need to limit places to drive as a persons driving performance declines. The data on self-report of avoidance of situations (at baseline) showed that more than 80% of drivers did not a void driving in the rain, driving alone, making lefts, merg ing, or driving in high traffic. Drivers tended to avoid night driving (70%) and rush hour (67%), but more than half of the baseline sample reported that they did not avoid these situations. Recent reports of older adults and driving that suggest avoidance of driving situations is a se lf-selection process not always related to levels of driving performance (Baldock et al., 2006). Recommendations Suggesting Specific Optimization As drivers move along the spectrum of bei ng safe and safe-R to unsafe-R and unsafe, driving behavior errors are m ore likely to be a ssociated with awareness of the vehicle and the environment. Unsafe drivers recommendations were mainly to pay more attention to the driving environment, to scan the environment more of ten, and to reduce speed-related errors. When drivers made these type of erro rs, the DRS was more likely to i ntervene by using the auxiliary brake or giving cues such as watch the people. Th is finding is in line with previous research (Dobbs, 1997; Dobbs et al., 1998; Hunt et al., 1997) suggesting that errors requiring evaluators intervention are predictive of road test failure or cognitive impairment. In contrast to the studies by Hunt et al. and Dobbs et al. mentioned above, this study did not include cognitively impaired participants. The cognitive measure scores for the unsafe drivers in this study (based on the

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187 TICS) ranged from 26 to 36 with a mean of 31, wh ich is above the cut-off score of 30 suggested by Welsh et al. (Welsh et al., 1993). The speed related errors in cluded poor vehicle control by not maintaining a constant pace. In the DRS comments, some speed recommenda tions were to start breaking earlier, avoid braking abruptly or not accelerating too fast. In their recent study explaining confusion of the brake with the accelerator, Freund et al. (2008) suggested these functi ons were related to executive function impairments. Although this stu dy did not specifically analyze errors based on clinical measures, it seems that the vehicle control and speed regulation are main components of safe driving ability. Hunt et al. (1997) and Dobbs et al. (1998) also suggested that lane maintenance and speed errors were common for dr ivers with cognitive impairments. In this sample, 30% of the unsafe-R drivers had recommendations related to drifting, lane maintenance, or not staying in th e line when turning. Among safe-R and safe drivers there was a hi gh number of stop and signaling errors. This is similar to previous studies suggesting these are errors common for older and younger drivers, or errors commonly seen in a population of healthy older adults and thus, not reflective of declines in competency to drive (Carr, Jackson, Madden, & Cohen, 1992; Dobbs et al., 1998; Hunt et al., 1997). Other signali ng errors reflected cautious be havior. Four drivers had a recommendation to not turn the signal too earl y before the maneuver, and two persons had a recommendation to turn off the signal. Having th e signal turned on longer than needed may be problematic because other drivers can be confused about the drivers intentions and may engage in aggressive behaviors such as accelerating abruptly and passing th e driver or honking. Not turning off the signal can be related to attention difficulties or hearing impairment. In this sample

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188 both individuals who forgot to turn off the signa l had scores that were among the lowest TICS scores, but reported fair hearing. The safe-R group accounted for most of the to tal percentage of recommendations related to stops. This finding supports Dobb s et al. results that not maki ng complete stops (e.g., rolling stops) are common errors among yo ung and older adults (Dobbs et al ., 1998). Stop errors in this study also related to stopping in the middle of an inte rsection when waiting to turn. Three safe-R and one safe driver had stop errors relating to leaving too much space between them and the car in front, which reflects some drivers are overcautious. Dobbs et al. (1998) found that stop positioning errors included stopping too close or t oo far back from the car in front and these errors were not significantly diffe rent across young, older adults, and participants with dementia. Older adults in the unsafe-R and safe -R groups had the highest number of recommendations suggesting specific optimizat ion. These recommendations relate to Michons tactical and operational levels of driving. Besides stop and sign aling recommendations described above, most of safe-R drivers recommendations re lated to driving too slowly, lane maintenance, and need to increase speed while merging on a highway. In parallel to Dobbs et al. (1998) findings, driving slowly was la beled as over-cautiousness. These errors did not suggest declined competency to drive, and did not differ among old and young dr ivers. In this study, speed-related errors that differentiated unsafe a nd safe drivers were rela ted to vehicle control such as abruptly braking or accelerating. Most merging recommendations were given to the safeR drivers which relates to safe-r drivers ge tting recommendations to avoid highways. Recommendations Suggesting Global Optimization In this category, m ost recommendations were for unsafe-R drivers. Again, a different pattern of recommendations emerged for each gr oup. The unsafe drivers were not given any recommendations to remediate the driving perf ormance as they were instead advised to stop

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189 driving. The unsafe-R drivers however, were gi ven varied recommendations to improve their driving ability. These recommendations includ ed taking the AARP cour se and reading the Florida (FL) drivers handbook. The studies that have looked at the effectiveness of the AARP course have used samples of healthy, community volunteers (Bedard et al., 2004; Bedard et al., 2008; Nasvadi, 2007; Nasvadi & Vavrik, 2007). This study suggests that if unsafe-R drivers are the drivers to whom driving educ ation is mostly advised, traffic research has not answered the question of driving educ ation effectiveness. It is also questionable wh ether a recommendation to re -read the FL driving handbook would improve unsafe-R drivers performance. This handbook mostly targ ets the rules of the road and how to handle emergency situations, which in cognitive terms relates to declarative type of knowledge. In other word s, learning about rules of the road helps a driver understand what to do in unsafe situations. Although the handbook might be a good reminder of safety on the road, whether a driver applie s these optimization strategies w ould be more objectively tested with on-road performance (i.e., pr ocedural type of knowledge). Along with recommendations of driving edu cation, unsafe drivers were the only drivers referred to a neurologist. This recommendation highlights the cognitive and executive function difficulties that the DRS might have observed in these drivers, such as decreased attention, decreased reaction time, decreased memory recall, visual-perception. In this regard, recent visual attention and speed of processing interventions s how promising benefits to older drivers safety (Roenker, Cissell, Ball, Wadley, & Edward, 2003). The most prevalent recommendation to drivers in the safe-R group was a referral to s ee an eye care specialist. This is not surprising considering the age related declines in vision and the impact of visual abilities in the driving task, such as keeping safe distances with cars, perceiving road marks, and scanning the

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190 environment to change lanes, make turns, or me rge onto a highway. It was also evident from the driving recommendations that the DRS considered it important to re-evaluate older drivers periodically. The DRS referred 31% of unsafeR drivers for driving re-evaluations. When referring to driving-reev aluations the DRS noted: Driving skills absolutely differ from person to person. I may evaluate a 75 year old man with no medical problems that drives adequately and then see a 65 year old man with cognitive impairments that hinder his ability to drive safely I really look at the person as a whole and not just at age. The DRS perception of drivers ability and perf ormance supported evidence that age alone is not adequate to predict driv ers ability or crash risk (Hakam ies-Blomqvist et al., 1995; Janke & Eberhard, 1998; Langford, Fitzharris, Koppel, & Newstead, 2004; Langfor d, Methorst et al., 2006) Recommendations suggesting compensation The DRS referred 9 of 13 unsafe-R drivers for BTW training, suggesting that older adults may improve driving performance with traini ng. Current evidence on th e effectiveness of behind-the-wheel interventions for healthy older adults is very limited. Marottoli et al. (2007) have recently shown improvements in driving pe rformance for drivers after a physical exercise intervention. The evidence on compensation stra tegies resulting from this study should be viewed with caution since the sample sizes of unsafe and unsafe-R driver were small. Why did some drivers receive driving lesson recommendations instead of BT W training? Financial issued could be the case why 4 of the 16 driv ers who had training recommendations were advise to take training behind the wheel (DRS, pers onal communication). Driv ing training from a DRS compared to practice lessons with a Certified Driv ing Instructor (CDI) are more expensive. This raises some concern because DRS are more trai ned and specialized on ag ing declines, assistive

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191 technology, and medical impairments. Yet many olde r adults with limited fi nancial resources and specific physical, motor, visual, or cognitive declines may choose to receive non-specialized training. Aim 2: Prediction of Driving Performance The second aim was to examine what combination of cognitive, motor, and sensory clinical tests helped predict the DRS decision to determine whether an older driver was: (1) unsafe, (2) unsafe but remediable, (3) safe with recommendations, or (4) safe driver. A group of potential cognitive, sensory, and motor predictors were used in a discriminant function to determine what performance measures differentia ted unsafe from safe-R, and safe drivers. The main limitation of the results in aim 2 was the violation of the assumption of homogeneity of variance. This violation implies that the va riances among groups were not the same and therefore, the significant differe nces observed in driving performa nce may have been subject to an inflated type I error. This limitation was c ounteracted by a relatively large sample size (n = 118) and the fair robustness of discriminant func tions to violations of homogeneity of variance (Meyers et al., 2006). In this samp le, levels of driving performan ce were predicted by scores of contrast sensitivity and rapid pace walk scores Possible explanations for these findings are described below. Since visual information comprises more than 90% of the information required for driving (Hills, 1980) one would expect a visual m easure to explain a large percentage of driving performance. In this sample, total scores of contrast sensitivity were the main predictor of driving ability. Other driving rese arch has reported that contrast sensitivity predicted driving abilities at night and overall driving performance on a road test (Wood & Owens, 2005; Wood & Troutbeck, 1995). Most studies of driving perfor mance have not used the test of contrast

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192 sensitivity reported in this study, the Functional Acuity Contrast Test (F.A.C.T.). However, Decina et al. (1991) report ed that adding the F.A.C.T. to measur es of horizontal fi elds and visual acuity was a strong predictor of cr ash frequencies in a sample of 12,400 drivers. Results of this study replicate findings by Stav el al. that reported on a sample of 123 older adults, most of whom were participants in this study (Stav et al., 2008). In Stav et al. contrast sensitivity B was the main predictor of driving performance accounting for .26 of the variance in a multiple regression model. This study used a total score of contrast sens itivity since the main interest was on overall performance on clinical measures. However, repo rted means and standard deviations for each contrast sensitivity subtest (in table 4-8 of the me thods chapter) show that the scores for contrast B were significantly different among the three gr oups of driving performance; all subtests of contrast sensitivity were significantly different between unsafe and safe-R or unsafe and safe drivers. An important observation is that for the higher frequencies of contrast sensitivity (C, D, and E), the mean score differences are higher between unsafe and safe drivers than the differences in the lower frequencies; and contrast sensitivity scores for all groups were lower in the higher frequencies. This is in line with the literature that suggests older adults have more difficulty identifying visual targets in the hi gher frequencies of cont rast (Greene & Madden, 1987; Owsley & Sloane, 1987; Wood & Owens, 2005). Each frequency of the F.A.C.T. represents different regions of the visual system (F.A.C.T. manual, Stereo Optical, Inc.): (1) Contrast Aneurologi c region, (2) Contrast B and Coptic nerve and retina region, (3) Contrast D-.optic nerve, retina, and macula, and (4) Contrast Emacula. Each contrast also has equivalent scores of visual acuity and thus, this is a comprehensive measure of visual function. Vi sual acuity was not predictive of driving

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193 performance, which has also been argued befo re as a concern for re -licensing assessment, suggesting that a combination of visual and cognitiv e measures is better to predict driving ability (Ball et al., 1993; Janke & Eberhard, 1998; Owsley et al., 1991; Wood & Owens, 2005). Surprisingly, none of the cognitive measures emerged as a significant predictor in the discriminant function. Some explan ations of this finding are that the sample of unsafe, safe-R, and safe drivers in this study consisted of mostly healthy older adults and the results of predictors of driving performance among groups may not be reflective of unsafe and safe drivers in the population. When compared to UFOV normative scor es by age and education (Edwards et al., 2006), mean composite scores of this sample were lower than the mean score of the norms for the safe-R and safe groups. The mean compos ite score in the norms is 500.56 (SD = 218.54) for ages 75-79 (and more than 12 years of edu cation); and 417.99 (SD = 207.24) for ages 70-74 (more than 12 years of education). In this sa mple, mean scores were 688.5 (SD = 254.2) for the unsafe group (mean age 79.6); 396.3 (SD = 208.3) for the safe-R group (mean age 75), and 319.1 (168.6) (mean age 70). Although the mean scores were lower than the norms, the means fell under the range of standard devia tions in the norm scores. Thus, in this sample of young older adults (M = 75), declines in visual atten tion were not predominantly affecting drivers performance on the road. The correlation of UFOV with the discriminant function was -.40, a lower association than those recently repor ted in a meta-analysi s of UFOV and driving performance (.42-.50) (Clay et al., 2005). An interesting finding was the predictive valu e of the rapid pace walk (RPW). RPW was recently described as a good measure of frailty over time (Staplin, Lococo et al., 2003). For drivers who took more than 9 seconds to complete the RPW, Staplin et al. (2003) reported an increase in the odds (OR 2.64 to 3.32) of at fault cr ashes over two years. Pr evious findings from

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194 the National Older Driver Research Training Cent er (NODRTC) also reported RPW scores were correlated to driving performance on the ro ad (McCarthy & Mann, 2006; Stav et al., 2008). Since the AMA Guidelines for physicians assessment of older drivers includes the RPW as an easy to administer test to evaluate fitness to drive (Wang et al., 2003), these findings support the use of RPW for screening older dr ivers in clinical settings. However, since the discriminant function only correctly classified approximately half of the drivers, interp retations of clinical predictors should be taken with caution. To rule out the possibility that demographi c factors explained some of the unexplained variance in the discriminant function, a second f unction that included measures of age, gender, and education was conducted. The addition of age helped explain 10% more of the variance, but the percent of correctly classifi ed drivers remained low (54%). Age alone is not a good predictor of driving performance (Waller, 1991). However, the salience of age as a predictor may be a result of a conglomeration of age-related declines or frailty; or it could re flect a bias in the DRS assessment since the DRS was not blinded to clin ical assessments. Since older drivers tended to have higher scores on measures of cognitive status, these results may have affected the DRS decisions to score drivers. The clinical tests differentiated unsafe from sa fe-R and safe drivers. However, only the low frequencies of contrast sensitiv ity (i.e., contrast A and B) differentiated the safe-R and safe drivers. This finding suggests that mild differences on contrast sensitivity differentiated the safe drivers. The differences in visual function on co ntrast A and B among safe drivers differed by 1 or 2 points only; and the scores were within 6 to 8 on a range from 0 to 9. This finding may have reflected the un-blinded assessment by the DRS or it could be related to the highly functional sample. The DRS could have intuit ively considered contrast sensitivity scores when giving the

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195 global rating scores for safe dr ivers. Another possibility is that since this sample did not significantly differ in measures of visual attention and speed of pr ocessing, the drivers abilities to react to stimuli, integrate information from the periphery, and divide attention was not an important component of safe drivi ng in this population. This is c ontrast with large effect sizes reported between poor driving performance and performance on the useful field of view (Clay et al., 2005). However, studies that reported associations of dr iving performance and visual attention have used samples with visual impairme nts, crash data as outcome measure, or have included patient populations (Ba ll et al., 1993; Clay et al., 20 05; Owsley et al., 1998), which could be a reason why the driving scores of th e functional and independent sample were not predicted by visual attention. Sim ilarly, safe-R and safe drivers sp eed of processing in the Trails B was approximately one minute, which is faster than findings suggesting that drivers who took 147 seconds or longer to complete the tests were mo re likely to crash 4 to 5 years after a clinical assessment (Ball et al., 2006). Since approximately only half of the sample was correctly classified, the results on predictors of driving performance are a ma jor limitation in this study. Differentiating among levels of safe drivers remains a challenge for future research. The differences between safe-R and safe drivers are relevant for clinical practice since adoption of recommendations or optimizing driving through recommendations can gui de clinical and research interventions. The choice of predictors or the violation of homogeneity of variances ma y have affected the ability of the discriminant function to separate the highe r function groups. For example, some limitations were using the total number of medications and total number of co-morbidities since polypharmacy and specific medical conditions impair driving in different ways (Lococo & Staplin, 2006).

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196 Despite the correlations of clinical tests with driving performance sc ores, the discriminant function analyses indicated that 51% of drivers were ac curately classified. According to this results, there is not enough eviden ce to suggest that clinical measur es that correlated with driving performance should help in DRS decision making about driving. A combination of clinical assessments may be valuable to clinical practice only when patients are correctly classified as safe and unsafe drivers, which was not shown in the discriminant function of this study. Future research is needed to find clinical measures th at predict driving performa nce ensuring safety of drivers with varied leve ls of performance. At least in this population drivin g performance was determined by the clinical and subjective perspec tive of the DRS road test assessment. This is shown in the DRS interview (Appendix M) when answering the questi on: How do you think your evaluations and clinical appr oach differ from standard practi ce in Driving Rehab? Part of the DRS answer was: () Some programs do not do the road test if they fail the clinical. I do not agree with that. I think this information is the most va luable part of the dr iving evaluation. I give everyone a chance to prove themselves in the car no matter how poorly they do in the clinical portion. There are cer tain situations that I cannot perform the road test. For example, if the person does not meet Florida requirements for visual acuity. Aside from the exceptions, I always give my client the oppo rtunity to show me his/her driving ability in the real world, in a real car. Aim 3: Recall of Driving Recommendations The third aim was to longitudin ally evaluate whether or not older dr ivers recalled driving recommendations made by a DRS, with and with out cueing. This is the first driving study of recall of DRS driving recommendations. The interp retations described below explore different hypothetical scenarios. Further study of driving recommendations is needed to fully understand their relationship to recall. The comparisons of driving recommendations recall among driving performance groups, like the recommendations de scribed before, violated assumptions of

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197 homogeneity of variance. The results should be interpreted under the assumption that the variance differences might have affected the analysis. Falsely Recalled Recommendations Four interpretations of why recomm endations were falsely recalled follow. One possibility is that drivers falsely recalled recommendations because they have difficulties with specific driving conditions, such as night driving or driving in high tra ffic. Thus, drivers could have adopted driving restrictions on their own, such as avoiding these driving conditions. For drivers who recalled recommendations without cues, it is also possible that they had made changes in their driving but not necessarily as a result of the DRS recomme ndations. A third explanation is that the recommendations were indeed given by the DRS at baseline but the information was only verbally transmitted and there was no written record on either the recommendation forms or road tests and thus, this recommendations might have inaccurately fallen under the category of uncued false recall. Last, false uncued recall could have been related to social desirability. In other words, the drivers may have felt that the appropriate answers to the questionnaire were to recall driving recommendations. A social desirability scale was administered to control for this possibility, however zero-orde r correlations between fa lse recall and not recalled recommendations and social desirabili ty were not significant (Appendix N). Post-hoc Observation Post-hoc exam ination of the data showed that out of seven unsafe-R drivers at follow-up, 4 had stopped driving. These 4 driver s gave the following reasons for having stopped: (1) Could not see at night and too old to keep driving, (2) lived in a retirement living community that provided all the transportation, (3) the car was too old, sold the car, and stopped driving for health, (4) kept only one car at home, and no ne ed to go places. The recall of recommendations questionnaire was administered to all unsafeR drivers at follow-up. For drivers who stopped

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198 driving, recall of recommendations seems irrele vant. However, some observations will be addressed. For clarity, the three unsafe-R drivers who continued driving at follow-up will be referred to as unsafe-R1, unsafe-R2, and unsaf e-R3; and the unsafe-R drivers who stopped driving will be referred to as unsafe-R stoppe d. From the unsafe-R stopped, one driver had a recommendation to retire from driving this was forgotten, suggesting the DRS recommendation may have not influenced the deci sion. Obviously, the sample sizes in this study were very small and observations are very specific to the participants involved in the study. Also, this study only tested recall of information so any assumpti ons about adopting DRS recommendations are only speculative. For each type of recommendation, specific observed responses for unsafe-R 1, 2, and 3 will be reported in addition to the general discussion. Selection At baseline unsafe-R1 driver reported avoiding left tu rns but was not avoiding any driving conditions at followup, although this person had reco mmendations to avoid rush hour, high traffic, highways, night driv ing, and driv ing in unfamiliar places. After cued, the driver recalled recommendations to a void highways, night driving, and unfamiliar places. However, from the follow-up self-report of avoidance, it appeared that DRS recommendations were not adopted. The unsafe-R2 driver re ported avoiding left turns and merging at baseline and was avoiding left turns, high traffi c, and rush hour at follow-up. For this person, self-report of avoiding left turns at follow-up was a false re call, perhaps because this driver had already adopted this behavior when driv ing. However, for avoidance of hi gh traffic, the unsafe-R2 driver reported avoiding high traffic at follow-up but forgot this was a DRS recommendation. In the third case, the unsafe-R3 driver was a voiding left turns and avoiding merging onto highways, at baseline. At follow-up the driver reported avoiding merging, night driving, rain, high traffic, and rush hour. The initial DRS recommendations to avoid highways and high traffic

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199 were recalled after cues, but this driver fals ely recalled recommendations to drive at night and rush hour. DRS recommendations suggesting selec tion did not seem to affect these drivers selection choices. These three ex amples of unsafe-R drivers who continued driving at follow-up, illustrate how individualized the avoidance behavi ors were at baseline and follow-up. There is variance among older adults, and si nce the driving groups and sample sizes in this study were small, we can not generalize to the older adult population. What was falsely recalled? Avoiding driving at night and avoiding rush hour were falsely recalled with cues by drivers in all gr oups, except the unsafe driv er; and falsely recalled without cues in the safe-R and safe groups. Did drivers avoid rush hour and night driving because they had difficulties in these situations ? Self-report of driving di fficulties among drivers in the follow-up sample showed that 86% did not have difficulty driving in rush hour and 64% did not have difficulty driving at night. These se lf-reports might not reflect real difficulties that drivers have, which is a challenging question to test in research studies because road performance is typically measured in pre-determined driving cour ses and consistent times of day. Drivers may have selected to avoid rush hour for personal preference as suggested in other studies (Baldock et al., 2006; Charlton et al., 2006) or self-re gulated their driving due to vision impairments (Brabyn et al., 2005; West et al., 2003). Specific optimization After cues, the unsafe driver fals ely recal led almost all the specific optimization recommendations. It is possible that besides the discussion this driver had with the DRS to stop driving, these were reasons that the DRS explai ned as main motivators for her decision to recommend driving cessation. Again, the veracity of a false recall is hard to determine. For those who were still driving from the unsafeR group, the unsafe-R1 driver falsely recalled a recommendation to stop behind the white line s; the unsafe-R2 driv er falsely recalled

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200 recommendations to increase the stopping distan ce and increase speed when merging; and the unsafe-R3 driver did not have false recalls. One po ssibility is that these were considered difficult situations. But this assumption c ould only be tested with a follo w-up road test assessment. For other drivers, cued false recall of scanning was prevalent among safe-R drivers, and false recall of stops and signaling recommendations were preval ent for safe-R drivers. This finding may be related to aspects that the drivers considered important. But as was mentioned before, these recommendations may have been mentioned by th e DRS as preventive or safe behaviors. Global optimization and compensation Unsafe-R and safe-R drivers were the onl y groups that had global and compensation recomm endations at baseline. None of these dr ivers falsely recalled recommendations and most forgot the recommendations. One safe driver mentioned the AARP course as a false recommendation after cues. This driver had not taken the course, but might have been a recommendation that sounded important for the driver. Correctly Recalled Recommendations This study used uncued and cued recall that a llowed exam ination of the mismatches or discrepancies between recalled information w ith and without cues. In this study, 73.5% of drivers recalled recommendations, 17.2% did no t recall recommendations, and 9.3%were drivers who did not have recommendations (e.g., were sa fe drivers or no written recommendations appeared on the records). When participants re called information without cues, most of the recommendations recalled were specific and very similar to the DRS recommendations. Some examples of recommendations reca lled were: (a) Too slow, go to posted limit (safe driver), (b) Keep distance with vehicle in fr ont by being able to see tires; and not crossing the median line (safe-R driver); (b) Watch the sp eed limit, to drive faster, turn on signal sooner, to stay on right of road and out of bicycl e lane (unsafe-R driver).

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201 These findings contrast with the results of Nasvadi (2007) who interviewed drivers after taking the AARP course. The author reported that in telephone interv iews, drivers recalled mostly rules of the road and some strategies to change lanes, merge, or passing vehicles, and approximately 20% gave only general comments about the education course (Nasvadi, 2007). One reason to support this finding is the context in which dr ivers learned about recommendations. The driving evalua tion was a real driving situation that could have facilitated the recall of information since recommendations were specific and individualized to each drivers needs. In a meta-analysis to examine the best instructional me thods for older adults training in the work setting (Callahan, Kiker, & Cross, 2003) the authors reported self-pacing, modeling, and active participation e xplained more variance of older adults learning than lectures, using supplementary material, and feedback. Th e recent evidence by Marottoli et al. (2007) on physical exercise interventions and driving and a recent meta-analysis of interventions in the driving literature, (Kua et al ., 2007) suggest that studying the e ffectiveness of older driver interventions will be an important area of future driving research. If self-paced and active participa tion is the best approach to retrain older drivers, then the format of educational courses may have to be re-evaluated. A supplementary question in the follow-up interviews asked whether the drivers had taken the AARP course. Out 64 drivers, 37 (57.8%) responded they had. Inform ation on why they took the course was collected for 26 of these 37 drivers. Out of 26 drivers, 53.8% (n = 14) responded they took the course to get the insurance discount; 15.4% (n = 4) said they took the course for the insurance discount and because it was a good thing, one driver said it wa s a good thing, and interestingly, 26.9% (n = 7) participants in this study were AARP instructors. Previous research found that driving classroom

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202 education programs may be taken for insurance di scounts rather than the safety benefits (Hunt, 1993; Stutts & Wilkins, 2003). Selection Recommendations recalled by the un safe-R drivers were addressed in the false recall selection section. The fact that unsafe-R drivers accurately r ecalled recommendations after being cued is a promising finding since it suggests that reminding of recommendations on an individual basis was a successful approach to help improve driver s safety. DRS gave recommendations to safe-R drivers to start planning for driving retirement and drivers recalled th is recommendation after cues. Giving safe-R drivers a recommendation to plan for retirement might have been more of a preventive measure by the DRS. This is related to the high percentage of drivers-93% in this follow-up sample who reported not having talked to someone about retiring from driving. Specific optimization In the unsaf e-R group, unsafe-R1 driver recal led being told to be too far out in intersection, too careful, a nd wait too long for car to clear out; after cues, this driver recalled the recommendation to pay more attention. Forgotte n recommendations for the unsafe-R1 driver were driving too slowly and hands position on the steering wheel. The unsafe-R2 driver recalled Drive too close to front car; don' t drive in the middle of the lan e, and with cues recalled the recommendation to scan the environment more and stay in the lane when turning. This driver forgot recommendations to signal and pay more a ttention. For the unsafe-R3 driver, there were no uncued recommendations recalled except a broad statement of stopping. This driver forgot all driving behavior related recommendations including scanni ng, speed, stopping distances, and stops. It seemed that overall, the unsafe-R driver s benefited from the cued recall like with the selection recommendations; but interesting pattern of forge tting emerged for the unsafe-R3 driver. One could speculate this is the beginning of unsafe driving behaviors that may or may not

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203 lead to crash risk. It al so shows that unsafe driving behavior s are hard to pinpoint in a general sample of drivers unless one speci fically studies dr iving errors. Among safe-R drivers it was noticeable that drivers recalled recomm endations to improve their driving without cues. This suggests that this group of driv ers had good insight and benefited from DRS recommendations. For safe drivers, re commendations were not recalled in 83% of the cases. This is similar to the findings reported in the recommendations section that suggest signaling is a habitual error among older adults, not one that indica tes declining ability to drive. This study also shows that safe older drivers do not recall this recommendation. Global optimization and compensation In general, drivers of all groups did not recall global and com pensation recommendations. This was striking because referral to a health care specialist can help drivers remediate or maintain their safe driving abilities. Yet, 3 unsaf e-R drivers forgot referrals to a neurologist, 1 unsafe-R forgot a referral to th e physician, 5 safe-R and 1 unsafe-R drivers forgot referrals to an eye care specialist. From the unsafe-R drivers w ho were still driving at follow-up, unsafe-R1 did not have any global recommendati ons at baseline, unsafe-R2 recalled global recommendations after cues (AARP course and BTW training), and the unsafe-R3 driver forgot all global recommendations to see a neurologist, take BTW training, and take the AARP course. Taking the AARP course was the most common global r ecommendation followed by referrals to an eye care specialist. The possibility that follow-up remi nders help drivers recal l these referrals should be incorporated in future studi es of driving recommendations. Not Recalled Recommendations Not recalled recommendations are those reco mmendations that drivers were given at baseline but forgot over time. Why do drivers forget recommendations? Some possibilities are declines in memory, irrelevance of the materi al given or lack of motivation. The main study

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204 limitation here was the long time that elapsed between initial assessment and the follow-up interviews. One could argue that memory d eclines influenced the ability to recall recommendations. However, TICS score for th e sample at follow-up was M = 36.09, which is 6 points higher than the suggested cut-off scor e for impairment (Welsh et al., 1993). The longitudinal component in this study was instructive because in contrast to studies that evaluated what drivers recalled from educa tion courses after taking the course (Bedard et al., 2004; Bedard et al., 2008), we were able to see what driv ers recalled over a longer period of time. Another explanation for forgotten recommendati ons is that recall of recommendations is a subjective experience and drivers may have forgotten what th ey did not consider relevant or helpful. Drivers choose their goals and actions base d on their motivations. This is in line with the SOC where drivers select their goals based on ch oices. For older adults, choices may be time and space constrained, or based on loss of resources (Freund & Baltes, 1998). Older drivers may therefore try to spend their energy and cognitive resources trying to achieve emotional satisfaction. Their motivations may continuously be drawn to positive events that reinforce their emotions and goals, which Carstensen et al. (C arstensen & Mikels, 2005; Carstensen, Mikels, & Mather, 2006) have labeled the positivity e ffect. If a driver does not consider DRS recommendations useful or applicable to th eir own situation, then a main component of motivation to modify the behavior is lacking. As reviewed in the recalled recommendations section, training and educational efforts have to be carefully designed considering how older drivers learn best and what motivates them. Aim 4: Driving Habits Changes The last aim was to study older drivers e xposure and avoidance as a function of driving performance group. The findings showed increa sed levels of exposure and no changes in

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205 avoidance of situations. There were no significant differences in longitudinal changes in driving habits among driving performance groups. Explana tions for these findings are suggested below. The first explanation is that increases in dr iving exposure could be related to interviewer biases. Specifically, at baseline and follow-up, di fferent interviewers administered the telephone interview. The question about pla ces driven in a week, number of weekly trips, and miles is calculated based on open-ended responses. The way interviewers asked the question such as giving participants response op tions, allowing more or less time for answers, or asking for clarifications can influence th e outcome. Although all indices of driving e xposure increased over time, older adults were driving fewer days pe r week. This question was more structured and perhaps showed more objective re sults of the changes in drivi ng. Other research has suggested that drivers age 72 and older are more likely to drive fewer days (Bauer et al., 2003). Over time, the distant places that drivers went to remained relatively stable, which supports the Collia et al. (2003) findings that long distance trips of 50 m iles by young and older drivers were mostly taken in the same state (Collia et al., 2003). On average, the furthest pl aces that unsafe-R drivers drove to were to neighboring towns, safe-R drivers tende d to go to more distant towns or cities, and on average, safe drivers went to places outside the county. In this study, drivers exposure was not significantly different over time, c ontrary to studies reporting that older adults decrease trips for vacationing, recreational shopping, going to the beauty or barber shop, and volunteering or working (Bauer et al., 2003). The results of this study were more in line with Collia et al. (2003) who suggested that older drivers travel is characterized by trip s for pleasure such as vacations and sightseeing excursions, trips for relaxation and rest, trips to visit family, and outdoor recreation; or trips for shopping, medical reasons, or running errands (C ollia et al., 2003). Considering the SOC and

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206 Michons Hierachical Model, the places that older adults drov e to at baseline and follow-up reflected the selection of places based on time and resources as suggested by the SOC model. With more leisure time as they aged, drivers we re going to more socially related places at follow-up. These included more visits to friends and relatives, more leis ure activities such as playing cards, playing golf, or going to the m ovies; more outings to the mall or other shopping, more long distance drives for pleasure, and exer cising more. Drivers were also selecting more religious activities on a weekly basis. Other outings such as grocery shopping, work and volunteering, and going to the library had simila r frequencies over time. As expected, drivers were going to more medical appointments over time. Contrary to other authors sugge sting that drivers with lower levels of driving performance avoided more driving situations (De Raedt & Ponjaert-Kristoffersen, 2000a), in this study driving performance did not have any effect on driv ers selection of places to avoid. In fact, after longitudinal evaluation of driving habits, drivers with different le vels of driving performance did not differ in most driving expos ure and avoidance patterns. Drivers mostly avoided driving in rush hour and night driving. Although data for ni ght driving was missing at baseline, this avoidance pattern was still highly reported at foll ow-up. As in other studies (Charlton et al., 2006; Raitanen et al., 2003), older a dults often made comments of not liking to drive in rush hour and choosing to avoid it. Other studies reported that rush hour avoidance was related to retirement from work and more flexible schedu les (Baldock et al., 2006; Ball et al., 1998; Lyman et al., 2001; Raitanen et al., 2003). Th is reflects the application of Michons strategical level and indicates that older adults selec tively choose to avoid driving in some situations, such as rush hour, high traffic, and night drivi ng. It also implies that a driv ing restriction on ones driver

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207 license to avoid rush hour need s to be better prescribed since drivers may already be compensating for declines in that way In this study we did not as k drivers why they avoided specific driving conditions. However, results from driving recommendations and recall of recommendations supported recent findings in traffic safety sugges ting that driving avoidance of s ituations among older adults is a selective process (Baldock et al., 2006; Charlton et al., 2006). A second explanation for drivers not changing or even increasing their driving exposure over time is reflective of the need to drive. Perhaps in this highly functional a nd relatively young sample, driving exposure and avoidance did not change over time because all drivers want to maintain their independence, freedom, and quality of life. Authors have studied the impact of losing the freedom and independence to drive (Bonnel, 1999; Johnson, 1998; Peel et al., 2002; Ragland et al., 2005; Ralston et al., 2001). If driving is a major rite of passage in American society (Ralston et al., 2001) and embedded as a societal value, then an y intervention to maintain or even optimize drivers performance on the road should be consid ered an intervention to sustain the identity and way of life (Bonnel, 1999; Peel et al., 2002). Driving recommendations, provided we can transmit them in a relevant and meaningful mann er to older drivers, can serve part of that purpose. Study Limitations Lim itations of this study incl ude the use of secondary data analysis, which is subject to variability in data collection a nd missing values. Some participan ts had to be excluded because data were missing such as the road test or tele phone number. The collecti on of some self-report measures can be problematic when differe nt evaluators interview participants. This study used only the perspe ctive and clinical judgment of one driving rehabilitation specialist. To help explain the DRS clinical e xpertise, a written interv iew is included (Appendix

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208 M). From the DRS point of view, her methods of clinical evaluation a nd driving ability are similar to others who evaluate older driver performance. A nother limitation was relying on written driving recommendations from different forms because not all dr ivers received a copy of their recommendations those who had copies coul d have better recalled the recommendations. Since it was a secondary data analysis, the wa y that the DRS provided the recommendations was not pre-established, and drivers could have accurately recalled recommendations that were not kept in written records. Although using data of two baseline studies helped increase the sample size, all clinical tests did not overlap between studies and so me potential predictors of driving performance were not in cluded in the analysis. Driving recommendations were classified per agreement among three raters but this agreement was not statistically tested. Future st udies can better classify driving recommendations among DRSs quantifying the raters agreem ent. The relationships among driving recommendations under the SOC framework can be more accurately classified by including BTW training as a practice to optimize driving a nd evaluate compensation strategies such as the use of assistive devices or alternate means of transportation. Follow-up interviews relied on self-report, whic h is subject to recall bias and inaccuracy of information. Follow-up interviews were given 1.5 to 3 years after the road test and many older adults participated in more than one driving study, which could have influenced their recall of driving recommendations. However, for those w ho recalled recommendations, the descriptions they used were very specific and similar to the information that the DRS commonly uses when counseling drivers. Follow-up interviews had at trition rates and although the follow-up sample did not significantly differ from baseline particip ants, attrition and small sample sizes were a limitation of this study.

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209 Future Directions Future studies of driving recomm endations for older drivers would benefit from more standard and systematic ways to measure re commendations. A standard questionnaire of recommendations can be validated to test wh ether older drivers adopt recommendations over time. These recommendations should be reviewed by a group of driving re habilitation specialists and tested in more than one clinical setting a nd based on a larger sample of DRS clinical and road test assessments. This would make the dr iving recommendations more valid and easier to generalize to the population of older drivers. A larger scale study of older driver abilities over time is needed to examine whether driving recommendations are implemented as dr iving behaviors behind -the-wheel; or if recommendations to avoid drivi ng situations or optimize drivi ng abilities through education or compensation strategies are adopted. In addition to studying recall of driving recommendations, a future study should examine whether older dr ivers accept the driving recommendations and what specific changes in driv ing they report over time. Future studies should include larger and more balanced samples of drivers with different driving abilities to make more objective co mparison among driving performance groups. The range of interventions that a DRS provides to olde r drivers such as behind -the-wheel or training in the use of assistive devices can also be tested in scientifically to compare and contrast the different interventions that can benefit older drivers safety. Future studies in driving can examine the way older adults put in practice the recommendations they select to adopt, what stra tegies help them optimi ze their driving behaviors behind-the-wheel, and how do they compensate fo r loss of resources or driving abilities over time. Using the SOC model of successful aging in conjunction with a driving behavior model is strength for future research because it allows researchers to conceptua lize and help explain the

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210 ways in which drivers use their abilities and maxi mize their potential. This is the ultimate goal of driving safety: maintaining driving inde pendence, mobility, and quality of life.

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211 APPENDIX A TELEPHONE INTERVIEW Demographic Information Participant #CDC_______________ Participant #NHTSA_____________ Date of interview___/__/___ 1. Driver License # ___________________________ Date of Birth: ___/___/____ mm/ dd/ yyyy 2. Gender: 1. M 2. F 3. Race and Ethnicity: Do you consider yourself to be: 1. White: A person having origins in any of th e original peoples of Europe, the Middle East, or North Africa. 2. Black or African American: A person having origins in any of the black racial groups of Africa. 3. Hispanic or Latino : A person of Cuban, Mexican, Pu erto Rican, South or Central American, or other Spanish culture or origin 4. American Indian or Alaska Native: A person having origins in any of the original peoples of North, Central, or South America, and who maintains tribal affiliation or community attachment. 5. Asian: A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam. 6. Native Hawaiian or Other Pacific Isla nder: A person having origins in any of the original peoples of Hawaii, Gu am, Samoa, or other Pacific Islands.

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212 4. Level of Completed Education: DID NOT GO TO SCHOOL 00 GRADE 1 01 GRADE 2 02 GRADE 3 03 GRADE 4 04 GRADE 5 05 GRADE 6 06 GRADE 7 07 GRADE 8 08 GRADE 9 09 GRADE 10 10 GRADE 11 11 GRADE 12/GED 12 VOCATIONAL/TRAINING/ SOME COLLEGE AFTER HS GRAD 13 ASSOCIATE DEGREE 14 COLLEGE GRAD/BA-BS 16 SOME PROFESSIONAL SCHOOL AFTER COLLEGE GRAD 17 MASTER'S DEGREE 18 DOCTORAL DEGREE (PhD, MD, DVM, DDS, JD, etc.) 20 Physical Health 5. Do you wear (or own) a hearing aid? 1. Yes 2. No Skip 5a 5a. How is your heari ng (WITH HEARING AID)? 1. Excellent 2. Good 3. Fair 4. Poor 5. Totally Deaf 5b. How is your hearing (WITHOUT HEARING AID)? 1. Excellent 2. Good 3. Fair 4. Poor 5. Totally Deaf 6. Do you ever feel periods of drowsiness? YES...................................................................... 1 NO ....................................................................... 2

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213 7. Have you had any falls in the last 6 months YES......................................................................1 NO....................................................................... 2 If yes, how many_________ 8. Do you use eyeglasses? 1. Yes 2. No 9. How much difficulty do you have reading or dinary print in a newspaper with your glasses/contacts on (if applicable)? (or difficulty reading ordinary print if they dont read the paper). a. No difficulty b. A little or some difficulty c. Extreme difficulty or you stopped reading because of your eyesight d. Unable to see/ legally blind Co-morbidity List Do you have any of the following conditions? Yes No Comments Heart Disease Heart trouble (Including CHF, a ngina, cardiac arrest) High blood pressure/low blood pressure including syncope Circulation trouble in arms or legs, peripheral vascular disease including aneurysm Anemia Respiratory Disease Asthma Emphysema or Chronic Bronchitis

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214 Yes No Comments Tuberculosis Other respiratory disorder (Pneumonia, COPD) Musculoskeletal Disorders Arthritis Hip fracture/replacement Knee Replacement Effects of Polio Cerebral Palsy Muscular Dystrophy Other musculoskeletal disord er (Degenerative vertebral disc problems, Osteoporosis, sc oliosis, fracture, shoulder replacement/dislocati on, torn ligament) Kidney Disease Other urinary tract disorders (I ncluding prostrate trouble) Glaucoma Diabetic Retinopathy

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215 Yes No Comments Cataracts Macular Degeneration Other vision impairment (hemorrhage, trauma, enucleation, retinitis, pigmentosa) Diabetes Thyroid or other glandular disorders Ulcers (of the digestive system) Liver disease Other stomach or intestinal disorders or gallbladder problems Neurologic Disease Brain Disorder (Brain tumor, cerebral atrophy, TIA) Peripheral Neuropathy Peripheral Nerve Disorder (Bells Senile Tremor, pinched nerve, sciatica, trigeminal neuralgia, Tic Douloureux) Dementia Multiple Sclerosis

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216 Yes No Comments Epilepsy Parkinsons Disease Effects of Stroke or CVA, subarachnoid hemorrhage Other Dementia (Binswangers dementia, multi-infarction) Seizure Disorder Other Cancer or Leukemia Type? Affective/anxiety disorder (anorexia-psychiatric Dx, Clinical depression) Skin disorders such as sores, pressure, leg ulcers, or severe burns Sleep disorders including narcolepsy and sleep apnea Hearing problem or impairments Speech impediment or impairment Foot problems (Bunions, corns, calluses, fungal infections) Other (gout)

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217 INSTRUMENTAL ACTIVITIES OF DAILY LIVING (IADL) Now Id like to question you about some of your daily living activities, things that we all need to do as part of our daily lives. I would like to know if you can do these activities without any help at all, or if y ou need some help to do them, or if you cant do them at all. (BE SURE TO READ ALL ANSWER CHOICES IF APPLICABLE TO RESPONDENT.) 1. Can you use the telephone...? 2 without help, including l ooking up numbers and dialing; 1 with some help (can answer phone or dial operator in an emergency, but need a special phone or help in getting the number or dialing); or 0 are you completely unable to use the telephone? 2. Can you get to places out of walking distance...? 2 without help (drive your own car, or travel al one on buses or taxis); 1 with some help (need someone to help you or go with you when traveling); or 0 are you unable to travel unless emerge ncy arrangements are made for a specialized vehicl e like an ambulance? 3. Can you go shopping for groceries or clothes (ASSUMING SUBJECT HAS TRANSPORTATION)...? 2 without help (taking care of all shopping needs yourself, assuming you had transportation); 1 with some help (need someone to go with you on all shopping trips); or 0 are you completely unable to do any shopping? 4. Can you prepare your own meals...? 2 without help (plan and cook full meals yourself); 1 with some help (can prepare some things but unable to cook full meals yourself); or 0 are you completely unable to prepare any meals? 5. Can you do your housework...? 2 without help (can clean floors, etc.); 1 with some help (can do light housework but need help with heavy work); or 0 are you completely unable to do any housework? 6. Can you take your own medicine...? 2 without help (in the righ t dose at the right time); 1 with some help (able to take medicine if someone prepares it for you and/or reminds you to take it); 0 or are you completely unab le to take your medicines? 7. Can you handle your own money...? 2 without help (write ch ecks, pay bills, etc.); 1 with some help (manage day-to-day buying but need help with managing your checkbook and paying your bills); or 0 are you completely unable to handle money?

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218 FUNCTIONAL INDEPENDENCE MEASURE FIM Scoring Codes: No helper 7 Complete Independence (Timely, Safely) 6 Modified Independence (Device) Helper Modified Dependence 5 Supervision 4 Minimal Assist (Subject = 75%+) 3 Moderate Assist (Subject = 50%+) Complete Dependence 2 Maximal Assist (Subject = 25%+) 1 Total Assist (Subject = 0%+) Do you need help with Eating 1. No Do you use any assistive devices to complete task? No score 7 Yes score 6 2. Yes How much of the task do you complete yourself? (see helper scores above s ubject does 50% of task=3) Self Care A. Eating B. Grooming C. Bathing D. Dressing Upper Body E. Dressing Lower Body F. Toileting Sphincter Control G. Bladder Management H. Bowel Management

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219 Transfer I. Bed, Chair, Wheelchair J. Toilet K. Tub, Shower Locomotion L. (W)alk/wheel, (C)hair W ___ C ___ M. Stairs Level of assistance determined by increased time (6) or amount of prompting or cueing (1-5) Communication N. Comprehension (Does subject understand and follow directions ?) O. Expression (Does subject express ideas and needs ?) Social Cognition ________P. Social Interaction (Does subj ect interact appropriately with family/friends?) Q. Problem Solving (Making safe, timely decisions e.g. finances/IADLs) R. Memory (Recognizing people, remembering things) Total FIM Score

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220 TELEPHONE INTERVIEW FOR COGNITIVE STATUS (TICS) For the next few questions, it is very important th at you turn off your televi sion or radio, so that you can concentrate and hear me clearly. Please look around you, and move all papers, newspapers, pens and pencils away from where you are. I will be asking you some questions that require you to use your memory and its important that you dont write anything down for this part. [ NOTE TO SCREENERS : SINGLE REPETITIONS ARE PERMITTED OF ALL ITEMS EXCEPT FOR T5 AND T8.] T1. Please tell me your full name. ( Do not write name down to preserve confidentiality) ______________________________ 1 point for first name, 1 point for last name ____ / 2 T2. What is todays date? ______________________________ Prompt for missing parts (month, date, year, day of week, season) 1 point for month 1 point for date 1 point for year 1 point for day of week 1 point for season ____ / 5 T3. Please tell me your age and phone number. ( Do not write down information ) ____________________________ 1 point for age 1 point for phone number ____ / 2 T4. Count backwards from 20 to 1. ______________________________ 2 points if completely correct on first trial; 1 point if completely correct on second trial; 0 points for anything else ____ / 2 T5. Im going to read you a list of ten words. Please listen carefully and try to remember them. When I am done, tell me as many words as you can, in any order. Ready? The words are: cabin, pipe, elephant, chest, silk, theatre, watch, whip, pillow, giant. Now tell me all the words you can remember. 1 point for each correct response. No penalty for repetitions or intrusions. ( cabin, pipe, elephant, chest, silk, theatre, watch, whip, pillow, giant ) _________________________________ _________________________________ _________________________________ _________________________________ ____ / 10 T6. One hundred minus 7 equals what?_ And 7 from that? ____________ Keep going ____________ Keep going ____________ Keep going ____________ Stop at 5 serial subtractions. 1 point for each corr ect subtraction. ( 9386-79-72-65 ) Do not inform the participant of incorrect responses, but allow subtractions to be made from his/her last response (e.g., -85-78-7165 would get 3 points). ___ / 5 T7. What do people usually use to cut paper? __________________________ 1 point for scissor or shears ____ / 4

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221 How many things are in a dozen ____ What do you call the prickly green plant that lives in the desert? ____________ What animal does wool come from? ______________________________ 1 point for 12 1 point for cactus 1 point sheep or lamb T8. Say this: No ifs, ands, or buts. Say this: Methodist Episcopal. 1 point for complete repetition on the first trial. Repeat only if poorly presented. ____ / 2 T9. What is the full name of the President of the United States right now? ______________________________ What is the full name of the Vice President? ______________________________ 1 point for correct first and last name. ( George W. Bush in 2002/2003 ) 1 point for correct first and last name. ( Richard Cheney in 2002/2003 ) ____ / 4 T10. With your fingernail, tap 5 times on the part of the phone you speak into. 2 points if 5 taps are heard 1 point if participant taps more or less than 5 times ____ / 2 T11. Im going to give you a word and I want you to give me its opposite. For example, the opposite of hot is cold. What is the opposite of west; ______________________________ What is the opposite of generous? _______________________________ 1 point for east 1 point for selfish, greedy, stingy, tight, cheap, mean, meager, skimpy, or other good antonym. ____ / 2 T12. Please tell me all the words you remember from the list I gave you before. _________________________________ _________________________________ _________________________________ _________________________________ _________________________________ 1 point for each correct response. No penalty for repetitions or intrusions. ( cabin, pipe, elephant, chest, silk, theatre, watch, whip, pillow, giant ) ____ / 10 TOTAL TICS SCORE ____/ 50

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222 MOBILITY/DRIVING HABITS FOR CURRENT DRIVERS 1. How long have you been driving (years)? Currently Driving? YES NO 2. Which way do you prefer to get around? Drive yourself 1 Have someone else drive you 2 Drive with someone else in the car with you 3 3. During the past month, have you driven to plac es outside the southeas t region of the country? 1. Yes go to #10 2. No 4. During the past month, have you driv en to places outside the state? 1. Yes go to #10 2. No 5. During the past month, have you driv en to places outside the county? (e.g. Gville/Ocala/Orlando/Ja x areas, respectively) 1. Yes go to #10 2. No 6. During the past month, have you driven to more distant towns or cities? 1. Yes go to #10 2. No 7. During the past month, have you driven to neighboring towns? 1. Yes go to #10 2. No 8. During the past month, have you driven to places beyond your neighborhood? 1. Yes go to #10 2. No 9. During the past month, have you driv en in your immediate neighborhood? 1. Yes go to #10 2. No 10. How fast do you usually drive compared to the general flow of traffic? Would you say Much faster .......................................................... 5 Somewhat faster................................................... 4 About the same .................................................... 3 Somewhat slower................................................. 2 Much slower ........................................................ 1

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223 11. How would you rate the quality of your own driving? Would you say it is Excellent...............................................................5 Good.....................................................................4 Average ................................................................3 Fair .......................................................................2 Poor ......................................................................1 12. In an average week, how ma ny days do you normally drive? 1 2 3 4 5 6 7 13. Consider all of the places that you drive in a typical week. Check the appropriate options (note one-way distance from home) Place How many times a week or month Estimate Miles from home ( one-way ) times/week* miles*2 = Total Miles _____Store _____X W/M ____________ = ____________ _____Church _____X W/M ____________ = ____________ _____Work/Volunteer _____X W/M ____________ = ____________ _____Relatives House _____X W/M ____________ = ____________ _____Friends House _____X W/M ____________ = ____________ _____Out to eat _____X W/M ____________ = ____________ Appointments _____(e.g., doctor, hair) _____X W/M ____________ = ____________ _____Gym/ Exercise _____X W/M ____________ = ____________ _____Other (golf, theater, social) What _________ _____X W/M ____________ = ____________ _____ Other What________ _____X W/M ____________ = ____________ _____ Other What________ _____X W/M ____________ = ____________ TOTAL MILES DRIVEN

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224 14. During the last two months, have you driven when it is raining? YES.......................................................................1 NO........................................................................2 15. Would you say that you drive in the rain with No difficulty at all .............................................1 A little difficulty.................................................2 Moderate difficulty.............................................3 Extreme difficulty ..............................................4 Dont drive in the rain.....5 16. Do you avoid driving in the rain? YES....................................................................1 NO......................................................................2 17. During the last two months, have you driven alone? YES...................................................................... 1 NO....................................................................... 2 18. Would you say that you drive alone with No difficulty at all..1 A little difficulty.2 Moderate difficulty.3 Extreme difficulty...............................................4 Dont drive alone. 19. Do you avoid driving alone? YES.................................................................... 1 NO ..................................................................... 2 20. Do you have difficulty reading road signs? YES.................................................................... 1 NO ..................................................................... 2 21. During the last two months, have you made left -hand turns across oncoming traffic? (This is where you are waiting for traffic to clear before making a left-hand turn.) YES...................................................................... 1 NO....................................................................... 2 22. Would you say that you make left-ha nded turns across oncoming traffic with No difficulty at all...............................................1 A little difficulty..2 Moderate difficulty..3 Extreme difficulty............................................ Dont make left-handed turn s across oncoming traffic...5

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225 23. Do you avoid making left-hand turns across oncoming traffic? YES.................................................................... 1 NO ..................................................................... 2 24. During the last two months, have you merged into traffic while entering a highway or expressway? YES......................................................................1 NO........................................................................2 25. Would you say that you merge into traffic wh ile entering a highway or expressway with No difficulty at all....1 A little difficulty...2 Moderate difficulty...3 Extreme difficulty ............................................4 Dont merge into traffic while entering a highway...5 26. Do you avoid merging into traffic while entering a highway or expressway? YES....................................................................1 NO......................................................................2 27. During the last two months, have you driven on high-traffic roads? YES......................................................................1 NO........................................................................2 28. Would you say that you drive on high-traffic roads with No difficulty at all...1 A little difficulty..2 Moderate difficulty..3 Extreme difficulty ...............................................4 Dont drive on high-traffic roads.5 29. Do you avoid driving on high traffic roads? YES....................................................................1 NO......................................................................2 30. During the last two months, have you driven in rush-hour traffic? YES.......................................................................1 NO........................................................................2 31. Would you say that you drive in rush-hour traffic with No difficulty at all..1 A little difficulty.2 Moderate difficulty.3 Extreme Difficulty ..............................................4 Dont drive in rush-hour traffic

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226 32. Do you avoid driving in rush-hour traffic? YES.................................................................... 1 NO...................................................................... 2 33. During the last two months, have you driven at night? YES...................................................................... 1 NO....................................................................... 2 34. Would you say that you drive at night with No difficulty at all..1 A little difficulty.2 Moderate difficulty.3 Extreme difficulty...............................................4 Dont drive at night. 35. Do you avoid driving at night? YES.......................................................................1 NO........................................................................2 36. Would you say that you make la ne changes while driving with No difficulty at all..1 A little difficulty.2 Moderate difficulty.3 Extreme difficulty ..............................................4 The remaining questions ask about things that mi ght have happened to you in the last twelve months. 37. Do you have any concerns about your own driving? YES NO ______________________________________________________________________________ 38. Has anyone suggested to you in the last tw elve months that you limit your driving or suggested that you stop driving? (PROMPT: Has anyone like your spouse, children, doctor, or a friend suggested that you not dr ive anymore or drive less?) YES......................................................................1 NO........................................................................2 39. Who made the suggestion to limit or stop your driving? List all that apply YES=1 NO=2 1) SPOUSE.......................................................... 1----2 2) SON OR DAUGHTER................................... 1----2 3) FRIEND.......................................................... 1----2 4) YOUR DOCTOR, OR OT HER MEDICAL 1----2 5) EYE DOCTOR .. 1----2 6) OTHER ......................................................... 1----2

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227 40. How many accidents or crashes have you been i nvolved in over the last twelve months when you were the driver? Please indicate the number of accidents, wh ether or not you were at fault. __ __ __ 1=hitting the curb 2= hitting the garage door 3= hitting objects such as the garbage can 4= fender bender 5=accident less than $500.00 in damage 6= accidents leading to tow away 7=accidents where you were injured 8=accidents where others were injured 9=accidents were someone died 41. Have you considered planning for the use of other mobility options? (e.g. use of public transportation) 1. Yes 2. No What options (Para trans it, senior, bus, etc): ___________________________________________________________________________ _______________________________________________________________ 42. Have you considered talking to someone about retirement from driving? 1. Yes 2. No Name of Interviewer________________________________ Signature_________________________________

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228 APPENDIX B MEDICATIONS FORM

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229Participant #_______________Date:______________ MEDICATIONS INSTRUCTIONS: Copy the name of ALL medications the strength (dose) and unit (mg, drop, insulin units), route of administration, frequency, and whether drug is to be taken as need ed (prn) or regularly. Include pills, dermal patches, eye & ear drops, injections, creams and salves. PLEASE PRINT CLEARLY Record whether drug was p rescribed or over the counter. Medication (Print name clearly) Generic Brand Dose Units P rescription or OTC Route ( O ral, P atch, I njection, Inhalation, E ye Drops, S uppository) Freq. As Needed (PRN) Comments 1. P OTC O P I Inh E S D W M Y N 2. P OTC O P I Inh E S D W M Y N 3. P OTC O P I Inh E S D W M Y N 4. P OTC O P I Inh E S D W M Y N 5. P OTC O P I Inh E S D W M Y N 6. P OTC O P I Inh E S D W M Y N 7. P OTC O P I Inh E S D W M Y N 8. P OTC O P I Inh E S D W M Y N 9. P OTC O P I Inh E S D W M Y N 10. P OTC O P I Inh E S D W M Y N

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230 APPENDIX C CDC STUDY CLINICAL ASSESSMENT FORM University of Florida Participant ID #: ______________________________Date____________ Vision: OPTEC 2500 Peripheral F ield: R= 85 70 55 Nasal 35 L= 85 70 55 Nasal 35 Static Acuity: Corrective Lenses: Y / N Both Right Left 1 ZN RO HK RO HK ZN RO 20/200 2 RKS HNC ZOD HNC ZOD RKS HNC 20/100 3 HCDV SKZO RNDS SKZO RNDS HCDV SKZO 20/70 4 ZROD NSCH VZKN NSCH VZKN ZROD NSCH 20/50 5 KHSC OZNR DNVC OZNR DNVC KHSC OZNR 20/40 6 ONRZV DKHCS KDSON DKHCS KDSON ONRZV DKHCS 20/30 7 SDCHN VRZKO HSNRD VRZK O HSNRD SDCHN VRZKO 20/20 Both __________ Right __________ Left ___________ VISUAL PERCEPTION Intact Impaired Comment Color Discrimination 12 5 26 6 16 __ Depth Perception 1-B 2-L 3-B 4-T 5-T 6-L 7-R 8-L 9-R Contrast Sensitivity (see below)

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231 VISUAL PERCEPTION Intact Impaired Comment Lateral Phoria Note # _____ (4-13) Vertical Phoria Note # _____ (3-5) Cognition: MMSE: Total Score____/_30___ Subtests: Orientation (time)___ _/ _5___ Orientation (place)____ /_5__ Orientation total __________/10 Registration____ /_3 Attention and calculation____/_5_ __ Recall____ /_3___ Name __ /_2 Repeat ______/_1___ Command ______/_3___ Obey ______/_1___ Language total _____/7____ Write ____/_1___ Copy ___/_1___ Visual-Perceptual-Motor: MVPT: VC (#22-34 & #56-60): __ /18 SO (#46-50): __ /5

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232 Physical: Cognition: Digit Span Digits Forward Total Score __________ Digit Backward Total Score______ Total Score: (forward + backward)_________(max =30) Cognition: UFOV Rating ___________ Test 1 ______ Test 2 ______ Test 3 ______ Physical Proprioception: Cognition: Trails B Time (mm:ss:ms) ______________ Symbol Digit: #correct________ / #attempted________ Letter Cancellation time: Trial 1 ________ # omissions______ #commissions_______ (mm:ss:ms) Trial 2 _________ # omissions_____ #commissions_______ Physical: Pain: Y / N Where________________ Level 1-10______________ Coordination R Time Errors L Time Errors Comments Finger to Nose ( mm:ss:ms) Toe Tap (mm:ss:ms) Ambulation: Independent Assistive Device Unsteady Dependent Transfer mobility: Independent Assistance Dependent Rapid Pace Walk (mm:ss: ms): _______________ Upper Extremities: Intact Im paired Absent Comments: ________________________ Lower Extremities: Intact Im paired Absent Comments: ________________________

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233 BODY PART ROM Comments WFL Imp. Trunk/Neck Rotation L Trunk/Neck Rotation R ROM Strength Comments R WFL Imp. L WFL Imp R WFL Imp L WFL Imp Upper Extremity Sh flexion Sh Int Rot Sh Ext Rot Elbow flexion Elbow ext Hand (alternate L / R) R / / Avg: L / / Avg: Lower Extremity Hip flexion Hip abd Hip add Knee flexion Knee ext Ankle plantar Ankle dorsiflex

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234 Cognition: Number Recall 5 4 1 6 Trial Score ____ Trial Score ____ Item Score 0 1 2 9 1 4 7 Trial Score ____ Trial Score ____ Item Score 0 1 2 3 7 2 8 1 3 Trial Score ____ Trial Score ____ Item Score 0 1 2 3 8 2 6 1 5 Trial Score ____ Trial Score ____ Item Score 0 1 2 6 4 9 7 4 1 5 8 Trial Score ____ Trial Score ____ Item Score 0 1 2 4 9 7 3 8 5 3 1 Trial Score ____ Trial Score ____ Item Score 0 1 2 2 8 6 3 5 9 4 7 8 3 Trial Score ____ Trial Score ____ Item Score 0 1 2 9 2 6 8 4 1 8 2 6 9 Trial Score ____ Trial Score ____ Item Score 0 1 2 1 2 9 6 5 3 9 4 2 5 7 1 Trial Score ____ Trial Score ____ Item Score 0 1 2 8 6 9 5 7 3 7 5 9 8 6 2 Trial Score ____ Trial Score ____ Item Score 0 1 2 2 8 5 3 1 7 9 2 5 1 3 6 8 4 Trial Score ____ Trial Score ____ Item Score 0 1 2 4 8 1 6 9 5 3 7 4 9 3 5 2 6 Item Score 0 1 2 7 3 6 2 8 5 9 4 3 2 6 7 9 1 4 8 Trial Score ____ Trial Score ____ Item Score 0 1 2 5 2 9 4 8 6 3 1 9 7 5 8 4 1 6 3 Item Score 0 1 2 2 5 3 9 1 4 6 8 7 2 5 1 3 6 8 4 7 9 Trial Score ____ Trial Score ____ Item Score 0 1 2 Item Score 0 1 2 Forward Total Score __________ Backward Total Score _________ Total Score (forward + backward)__________ Reaction Time: Print and Attach SRT & PRT Knowledge: Rules of The Road Test: _____/20 Signs Test: _____/20 Road Test: Global Rating Scale__________ Reported (Yes or No) ________________________________________________________ Comments_________________________________________________________________ Therapist signature ________________________________ Date: _______________

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235APPENDIX D NHTSA STUDY ADRES AND CLINICAL ASSESSMENT FORM University of Florida Date: _____________ mm / dd/ yy Participant ID: NHTSA ____________________ 1. Visual fields Right Left 2. Visual Acuity (Snellen Chart) Was the patient wearing corrective eyeg lasses? Yes No Both eyes (OU) _______ Right eye _________ Left eye _________ 3. Rapid pace Walk: ___ ___ : ___ ____ SEC Was this performed with a walker or cane? If yes, please specify: ___________________________________________________________ 4. Range of Motion (Not WNL or WNL) Right Left Comments (If Not WNL, Why?) Neck Rotation Finger Curl Shoulder and elbow flexion Ankle plantar flexion Ankle dorsiflexion

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236 5. Motor Strength (0-5) Right Left Comments Shoulder adduction Shoulder abduction Shoulder flexion Wrist flexion Wrist extension Hand grip Hip flexion Hip extension Ankle dorsiflexion Ankle plantar flexion 6. Trails B: ___ ___ : ___ ___ : ___ ___ MIN SEC 1/100 S 7. Clock drawing test Yes No All 12 hours are placed in correct numeri c order, starting with 12 at the top Only the numbers 1-12 are included (no duplicates, omissions, or foreign marks) The numbers are drawn inside the clock circle The numbers are spaced equally or nearly equally from each other The numbers are spaced equally or nearly equally from the edge of the circle One clock hand correctly points to two oclock The other hand correctly poi nts to eleven oclock There are only two clock hands

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237 8. OPTEC 2500 Peripheral Field: R= 85 70 55 Nasal 35 L= 85 70 55 Nasal 35 Static Acuity: Corrective Lenses: Y / N Both Right Left 1 ZN RO HK RO HK ZN RO 20/200 2 RKS HNC ZOD HNC ZOD RKS HNC 20/100 3 HCDV SKZO RNDS SKZO RNDS HCDV SKZO 20/70 4 ZROD NSCH VZKN NSCH VZKN ZROD NSCH 20/50 5 KHSC OZNR DNVC OZNR DNVC KHSC OZNR 20/40 6 ONRZV DKHCS KDSON DKHCS KDSON ONRZV DKHCS 20/30 7 SDCHN VRZKO HSNRD VRZK O HSNRD SDCHN VRZKO 20/20 Both __________ Right __________ Left ___________ VISUAL PERCEPTION Intact Impaired Comment Color Discrimination 12 5 26 6 16 __ Depth Perception 1-B 2-L 3-B 4-T 5-T 6-L 7-R 8-L 9-R Contrast Sensitivity (see below)

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238 Intact Impaired Lateral Phoria Note # _____ (4-13) Vertical Phoria Note # _____ (3-5) 9. UFOV UFOV Rating ___________ Test 1 ______ Test 2 ______ Test 3 ______ 10. MMSE Total _____________________ 11. Reported Yes ________ No _________ 12. Global rating _______________________________ 13. Behavioral score (%) _________________________ 14. Driving Recommendations Yes ________ No _________ __________________________________________________________________________ __________________________________________________________________________ __________________________________________________________________________ EVALUATORS SI GNATURE ________________________________ PRINT NAME ________________________________

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239 APPENDIX E LIST OF COMBINED CLINICAL MEASURES Table E-1. L ist of combined clinical measures Notes. UFOV = Useful Field of View. MMSE = Mini-Mental Status Examination. TICS = Telephone Interview for Cognitive Status. *Only range of motion and streng th tests that overlapped between both studies are considered. ** NHTSA study scores for strength were numer ical and CDC scores were categorical. A strength score of 4-5 will be considered with in functional limits to be comparable to the categorical scoring. Cognitive and visuoperceptual Visual Motor/Physical UFOV1 Trails Test Part B (sec) MMSE2 TICS3 OPTEC (vision screener): Visual acuity Peripheral fields (total degrees for both eyes) Color discrimination Depth perception Contrast sensitivity Vertical and lateral phorias Rapid Pace Walk (sec) Range of Motion* Neck rotation Shoulder and elbow flexion Ankle Plantar Flexion Ankle dorsiflexion Strength** Shoulder flexion Hip flexion Ankle plantar flexion Ankle dorsiflexion

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240APPENDIX F RECOMMENDATION FORM FOR GRS OF 1

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241

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242 APPENDIX G RECOMMENDATION FORM-1 FOR GRS OF 2

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244 APPENDIX H RECOMMENDATION FORM-2 FOR GRS OF 2

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245 APPENDIX I MEDICAL REPORTING FORM

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246 APPENDIX J ROAD TEST GRS 0

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261 APPENDIX K ROAD TEST GRS 3

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278 APPENDIX L FOLLOW-UP INTERVIEW NHTSA/ CDC ID ___________ Follow-up Date ___________ Clinical and road test date_______________ Total years and months after clinical__________ 1. Are you currently driving? Yes / No If no, when did you stop driving? _____________________________________ Why did you stop driving? ___________________________________________ 2. Has your medical condition cha nged since you visited the Independence Drive driving program from the University of Florida? (circle yes/no) Yes / No If yes, what? (disease, hospital stay, fall, surgery, etc) __________________________ ______________________________________________________________________________ __________________________________________________________________ 3. Do you recall any driving recommendations provided to you from the driver evaluation at the Independence Drive driving program? Yes / No If yes, what driving recommendations do you recall? (Fill table below)

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279 Recommendation Recommendation given? (Yes/No) Recommendation Recalled (Yes/No) Comments Scanning (to reverse, change lanes, and at intersections) 1. Turn head more often 2. Increase visual scanning 3. Scan the environment more often (look around) 4. Use mirrors more often Scanning for road markings 1. Pay more attention to roadway markings a. Drifts right, drifts left 2. Stay within road way lines/watch lane maintenance Speeding 1. Adhere or Watch speed limit (too slow, too fast/reduce speed limit, watch speed related to posted limit) 2. Reduce aggressive acceleration/do not accelerate fast 3. Start breaking earlier Following and stopping distance 1. Increase following distance 2. Stopping distance (Look for bottom of tires when stopped/leave cushion/not too much space with car in front) Signaling 1. Always use turn signals (throughout turns and lane changes) 2. Dont use turn signals too early 3. Make sure turn signal is off

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280Turns 1.Stay in your lane when turning (Too wide, encroaches) Merging 1.Increase speed when merging on the highway 2.Use merging lane, do not cross solid lines Hand Positioning 1.Change positioning of hands on steering wheel a. Use two hands, move hands apart Attention 1.Limit noise and conversation in the vehicle 2. Pay more attention to driving environment/avoid ditractions Reduced Traveling 1. Avoid rush hour 2. Avoid high traffic 3. Avoid driving to new locations 4. Avoid highways 5. No long distance driving 6. Limit to local/ neighborhood driving 7. Avoid or reduce driving at night 8. Avoid unprotected lefts 9. Plan for driving retirement 10. Do not drive or retire from driving

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281Education/Training 1. Take AARP Refresher course 2. Re-read Florida Drivers Handbook 3. Driving lessons 4. Take BTW training Assessments/Referrals 1. Driving Re-evaluation 2. See neurologist 3. See physician 4. See eye care specialist 5. See Physical or Occupational Therapist 6. Other Overall good driver/ safe

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282 Marlowe-Crowne Social Desirability Scale D. P. Crowne and D. Marlowe Listed below are a number of statements concer ning personal attitudes and traits. Read each item and decide whether the statement is true or false as it pertains to you. T F 1. Before voting I thoroughly investig ate the qualifications of all the conditions. T F 2. I never hesitate to go out of my way to help someone in trouble. T F 3. It is sometimes hard for me to go on with my work if I am not encouraged. T F 4. I have never intensely disliked anyone. T F 5. On occasion I have had doubts about my ability to succeed in life. T F 6. I sometimes feel resentful when I dont get my way. T F 7. I am always careful about my manner of dress. T F 8. My table manners at home are as good as when I eat ou t in a restaurant. 9. If I could get into a movie without paying and be sure I was not seen, I would T F probably do it. 10. On a few occasions, I have given up doing something because I thought too T F little of my ability. T F 11. I like to gossip at times. 12. There have been times when I felt like rebelling against people in authority T F even though I knew they were right. T F 13. No matter whom Im talking to, Im always a good listener. T F 14. I can remember playing sick to get out of something. T F 15. There have been occasions when I took advantage of someone. T F 16. Im always willing to admit when I make a mistake. T F 17. I always try to practice when I preach.

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283 18. I dont find it particularly difficult to get along with loud-mouthed, obnoxious T F people. T F 19. I sometimes try to get even, rather than forgive and forget. T F 20. When I dont know something I dont at all mind admitting it. T F 21. I am always courteous, even to people who are disagreeable. T F 22. At times I have really insi sted on having th ings my own way. T F 23. There have been occasions when I felt like smashing things. T F 24. I would never think of letting someone else be punished for my wrongdoings. T F 25. I never resent being asked to return a favor. 26. I have never been irked when people expressed ideas very different from my T F own. T F 27. I never make a long trip without checking the safety of my car. T F 28. There have been times when I was qu ite jealous of the good fortune of others. T F 29. I have almost never felt th e urge to tell someone off. T F 30. I am sometimes irritated by people who ask favors of me. T F 31. I have never felt that I was punished without cause. 32. I sometimes think when people have a misfortune they only got what they T F deserved. T F 33. I have never deliberately said so mething that hurt someones feelings. D. P. Crowne and D. Marlowe

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284 4. During the past month, have you driven to pl aces outside the sout heast region of the country? Yes go to #14 2. No 5. During the past month, have you driven to places outside the state? 6. 1. Yes go to #14 2. No 9. During the past month, have you driven to places outside the county? (e.g. Gville/O cala/Orlando/Jax areas, respectively) 1. Yes go to #14 2. No 10. During the past month, have you driv en to more distant towns or cities? 1. Yes go to #14 2. No 11. During the past month, have you driven to neighboring towns? 1. Yes go to #14 2. No 12. During the past month, have you driven to places beyond your neighborhood? 1. Yes go to #14 2. No 13. During the past month, have you driv en in your immediate neighborhood? 1. Yes go to #14 2. No 14. How fast do you usually drive compared to the general flow of traffic? Would you say Much faster .......................................................... 5 Somewhat faster................................................... 4 About the same .................................................... 3 Somewhat slower................................................. 2 Much slower ........................................................ 1 15. How would you rate the quality of your own driving? Would you say it is Excellent............................................................... 5 Good..................................................................... 4 Average ............................................................... 3 Fair .......................................................................2 Poor ......................................................................1 16. In an average week, how ma ny days do you normally drive? 1 2 3 4 5 6 7

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285 17. Consider all of the places that you drive in a typical week. Check the appropriate options (note one-way distance from home) 0.25 if only once a month, 0.50 twice a month, 0.75 if three times a month Place How many times a week Estimate Miles from home ( one-way ) Total Miles (one way miles 2) _____Store _____X W _______ ____________ _____Church _____X W _______ ____________ _____Work/Volunteer _____X W _______ ____________ _____Relatives House _____X W _______ ____________ _____Friends House _____X W _______ ____________ _____Out to eat _____X W _______ ____________ Appointments _____(e.g., doctor, hair) _____X W _______ ____________ _____ Other _____X W _______ ____________ _____ Other _____X W _______ ____________ _____ Other _____X W _______ ____________ 18. During the last two months, have you driven when it is raining? YES......................................................................1 NO........................................................................2 19. Would you say that you driv e in the rain with No difficulty at all ............................................. 1 A little difficulty................................................. 2 Moderate difficulty.............................................3 Extreme difficulty ..............................................4 Dont drive in the rain.... 5 20. Do you avoid driving in the rain? YES....................................................................1 NO......................................................................2 21. During the last two months, have you driven alone? YES...................................................................... 1 NO....................................................................... 2

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286 22. Would you say that you drive alone with No difficulty at all..1 A little difficulty. 2 Moderate difficulty.3 Extreme difficulty..........................................4 Dont drive alone...5 23. Do you avoid driving alone? YES.................................................................... 1 NO ..................................................................... 2 24. Do you have difficulty reading road signs? YES.................................................................... 1 NO ..................................................................... 2 25. During the last two months, have you made le ft-hand turns across oncoming traffic? (This is where you are waiting for traffic to clear before making a left-hand turn.) YES...................................................................... 1 NO....................................................................... 2 26. Would you say that you make left-ha nded turns across oncoming traffic with No difficulty at all...............................................1 A little difficulty. ..2 Moderate difficulty. 3 Extreme difficulty............................................ Dont make left-handed turn s across oncoming traffic.. 27. Do you avoid making left-hand turns across oncoming traffic? YES.................................................................... 1 NO ..................................................................... 2 28. During the last two months, have you merged into traffic while entering a highway or expressway? YES......................................................................1 NO........................................................................2 29. Would you say that you merge into traffic wh ile entering a highway or expressway with... No difficulty at all.....1 A little difficulty.. 2 Moderate difficulty.. 3 Extreme difficulty ........................................4 Dont merge into traffic while entering a highway 30. Do you avoid merging into traffic while entering a highway or expressway? YES....................................................................1 NO......................................................................2

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287 31. During the last two months, have you driven on high-traffic roads? YES......................................................................1 NO........................................................................2 32. Would you say that you drive on high-traffic roads with No difficulty at all...1 A little difficulty. 2 Moderate difficulty. 3 Extreme difficulty ...........................................4 Dont drive on high-traffic roads 33. Do you avoid driving on high traffic roads? YES....................................................................1 NO......................................................................2 34. During the last two months, have you driven in rush-hour traffic? YES...................................................................... 1 NO....................................................................... 2 35. Would you say that you drive in rush-hour traffic with. No difficulty at all..1 A little difficulty. 2 Moderate difficulty.3 Extreme Difficulty ........................................ 4 Dont drive in rush-hour traffic. 5 36. Do you avoid driving in rush-hour traffic? YES.................................................................... 1 NO ..................................................................... 2 37. During the last two months have you driven at night? YES...................................................................... 1 NO....................................................................... 2 38. Would you say that you drive at night with No difficulty at all..1 A little difficulty.2 Moderate difficulty 3 Extreme difficulty.........................................4 Dont drive at night... 5 39. Do you avoid driving at night? YES.1 NO..2

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288 40. Would you say that you make lane changes while driving with No difficulty at all.. 1 A little difficulty. 2 Moderate difficulty.3 Extreme difficulty .........................................4 The remaining questions ask about things that might have happened to you since you had the driving evaluation in (say name of driving program). 41. Do you have any concerns about your own driving? YES / NO 42. Has anyone suggested to you that you limit your driving or suggested that you stop driving? (PROMPT: Has anyone like your spouse, children, doctor, or a friend suggested that you not drive anymore or drive less?) YES......................................................................1 NO........................................................................2 43. Who made the suggestion to limit or stop your driving? Li st all that apply YES=1 NO=2 1) SPOUSE.......................................................... 1----2 2) SON OR DAUGHTER................................... 1----2 3) FRIEND.......................................................... 1----2 4) YOUR DOCTOR, OR OT HER MEDICAL 1----2 5) EYE DOCTOR .. 1----2 6) OTHER ......................................................... 1----2 SPECIFY: _____________________________ 44. How many accidents or crashes have you been involved in over the last twelve months when you were the driver? Please indicate the numb er of accidents, whether or not you were at fault. __ __ __ 1=hitting the curb 2= hitting the garage door 3= hitting objects such as the garbage can 4= fender bender 5=accident less than $500.00 in damage 6= accidents leading to tow away 7=accidents where you were injured 8=accidents where others were injured 9=accidents were someone died

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289 45. Have you considered planning for the use of other mobility options? (e.g. use of public transportation) 1. Yes 2. No What options (Para trans it, senior, bus, etc): ______________________________________________________________________________ 46. Have you considered talking to someone about retirement from driving? 1. Yes 2. No Name of Interviewer_________________________ Signature_________________________________ Date __________________________

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290 APPENDIX M DRIVING REHABILITATION SPECIALIST INTERVIEW June 2007 1a. How do you conduct the driving evaluations? (typical process) DRS: I usually begin the process with a telephone interview. I get the initial information including all the medical and dr iving history. We set up an ap pointment for the person to come into the office. During the evalua tion I conduct vision, cognition and physical assessments. That may take about one hour to one and a half hours. Following the clinical assessments, I conduct the behind the wheel road test using our (the programs) car. We begin in a parking lot to get the pe rson familiar with the car. Then we travel through a residential area and progress into bus ier roadways with hi gher speeds. The road test lasts approximately one hour. When the road test is completed we return to the office where I review the persons performance on th e clinical and road a ssessments. I give feedback and recommendations based on the results of all the tests. 1b. What happens when someone fails the road test? DRS: If someone fails I first let that person know that I feel th at he/she is unsafe to drive. I review the reasons why and then talk a bout mobility choices. I offer mobility counseling but I have noticed that most never follow up with me. I then report my findings to the Medical Revi ew Board in Tallahassee. I complete a Reporting Form and fax it to them. I do not have the authority to revoke someones license but I have an obligat ion to report in Florida. 2. What are the difficulties of conducting driving evaluations? DRS : Difficulties occur when clients have difficulties trying to complete the tests. Usually it is because they have cogni tive impairments that hinder them from comprehending and/or recalling instructions and simply have problems because the assessments are too complex for them. Some times the assessments are too difficult for them to complete at all. Other difficulties occur when clients become angry or over emotional. Sometimes I have to take time out to talk clients to help them overcome their frustrations, fears, and anger. Sometimes it works and sometimes it does not. Many times, clients do not have the insight to recognize their impairments. Thes e people are most difficu lt to deal with/talk to. Other barriers I face are when the family members or caregivers are not supportive and do not help me reinforce my recommendations. It may include getting someone to retire from driving or restrict to neighborhood driving.

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291 3. How do you think your evalua tions and clinical appro ach differ from standard practice in Driving Rehab? DRS: I do not think my approach differs too mu ch from the typical driving rehab practice except for the research component. Most driv ing rehab programs do not offer a research component to their clients. Fortunately, here I can offer my clients the opportunity to participate in research if they meet the incl usion criteria. Its b een a positive experience for both me and my clients. It makes our program unique and different from other driving rehab programs. Also, some other programs did not conduct the behind the wheel road tests at all. Some programs do not do the road test if they fail the clinical. I do not agre e with that. I think this information is the most valuable part of the driving evaluation. I give everyone a chance to prove themselves in the car no matter how poorly they do in the clinical portion. There are certain situat ions that I cannot perform the road test. For example, if the person does not meet Florida requirem ents for visual acuity. Aside from the exceptions, I always give my client the opportuni ty to show me his/her driving ability in the real world, in a real car. 4a. How much variance do you see from person to person? DRS: Sometimes I see a big variance from pe rson to person. I have clients from all ranges of ages and diagnoses. As long as th ey have a permit or driv ers license I can see them for services. I tend to see a lot more older adults because of the older driver research. But I also have young clients that are new drivers. Ive seen individuals with amputations, brain injury, Cerebral Palsy, Atte ntion Deficit Disorder, Multiple Sclerosis, Alzheimers, and Parkinsons Disease to name some. 4b. Do you see a lot of variance among older ad ults? Have you recogni zed a pattern of driving errors among older adults? Or, do you think older adults driving errors are continuously changing and differe nt from person to person? DRS: Yes, I do see quite a lot of variance among older adults. However, there are some general normal aging changes that affect older drivers. For instance, older drivers have problems with contrast sensitivity. They tend to recognize this on their own and selfregulate by not driving during the evening hours. A lot of older drivers have slowed reaction time but compensate by decreasi ng driving speed and increasing following distance. In addition they may not drive on interstates, highways and busy roadways because they experience cognitive overload (they get overwhelmed easily). I do believe that they experience age related driving errors. Most are able to adapt their driving habits according to the changes. It is those that do not recognize the changes that are unsafe drivers. I do think driving errors for older adults co ntinuously change. A lot depends on their medical status. A progressive disease could ch ange the older persons driving skills over time (e.g.-dementia/ Parkinsons disease). That is why I recommend periodic driving

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292 evaluations to check if driving skills have changed over time. Driv ing skills absolutely differ from person to person. I may evaluate a 75 year old man with no medical problems that drives adequately and then see a 65 year old man with cognitive impairments that hinders his ability to drive safely. I really look at the person as a whole and not just at age. 5. Do you consider your clients to be represen tative of clients se en in other driving rehab settings? Why or why not? DRS: I do not tend see as much variance as so me other programs do. I do not see very involved people such as spinal cord injuries and such. I do not ha ve the capacity to see the lower functioning clients because I do not have an adaptive van and cannot train individuals to use one. These vans are used to train peop le that use wheelchairs to become independent drivers. I think it is because this is not a hospital based program. Hospital based driving programs generall y have more medically involved, lower functioning clients. Not a ll programs have vans.

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293 APPENDIX N CORRELATIONS OF SOCIAL DESIRABI LI TY, FALSE RECALL AND NO RECALL

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294Table N-1. Correlations of social desirability, false recall and no recall Correlations Safe, good driver driver FalseUncSpeci fic FalseUncSelect FalseCuedSpecific Pearson Correlation Sig. Pearson Corr Si g. Pearson Corr Sig. Pearson Corr Sig. safe, good driver 1.000 -0.059 0.321 0.085 0.252 -0.122 0.168 FalseUncuedSpecifi -0.059 0.321 1.000 -0.083 0.257 0.040 0.376 FalseUncuedSelection 0.085 0.252 -0.083 0.257 1.000 -0.125 0.162 FalseUncuedGlobal .(a) .(a) .(a) .(a) FalseUncuedComp. .(a) .(a) .(a) .(a) FalseCuedSpecific -0.122 0.168 0.040 0.376 -0.125 0.162 1.000 FalseCuedSelection -0.103 0.210 -0.070 0.290 -0.065 0.305 0.073 0.282 FalseCuedGlobal 0.091 0.236 -0.058 0.324 -0.023 0.430 0.073 0.284 FalseCuedComp. .(a) .(a) .(a) .(a) nocuedrecallSpecific -0.429 0.000 -0.033 0.397 0.099 0.219 -0.147 0.123 nocuedrecallSelection -0.142 0.131 -0.050 0.347 -0.057 0.329 -0.126 0.161 nocuedrecallGlobal -0.183 0.074 -0.191 0.066 -0.074 0.280 0.047 0.356 nocuedrecallComp. -0.179 0.079 -0.154 0.112 -0.060 0.319 0.060 0.319 Social Desirability Scale 0.066 0.304 0.092 0.236 -0.279 0.013 -0.037 0.388

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295Table N-1. Continued FalseCuedSelection FalseCuedGlobal nocuedrecallSpecific nocuedrecallSelection Pearson Corr Sig. Pearson Corr Sig. Pearson Corr Sig. Pearson Corr Sig. safe, good driver -0.103 0.210 0.091 0.236-0.429 0.000-0.142 0.131 FalseUncuedSpecifi -0.070 0.290-0 .058 0.324-0.033 0.397-0.050 0.347 FalseUncuedSelection -0.065 0. 305-0.023 0.4300.099 0.219-0.057 0.329 FalseUncuedGlobal .(a) .(a) .(a) .(a) FalseUncuedComp. .(a) .(a) .(a) .(a) FalseCuedSpecific 0.073 0.2820. 073 0.284-0.147 0.123-0.126 0.161 FalseCuedSelection 1.000 -0.046 0.3600.007 0.479-0.057 0.328 FalseCuedGlobal -0.046 0.3601.000 0.009 0.471-0.040 0.378 FalseCuedComp. .(a) .(a) .(a) .(a) nocuedrecallSpecific 0.007 0. 4790.009 0.4711.000 0.323 0.005 nocuedrecallSelection -0.057 0. 328-0.040 0.3780.323 0.0051.000 nocuedrecallGlobal 0.183 0.074-0.052 0.3420.352 0.0020.223 0.038 nocuedrecallComp. -0.121 0.171 -0.042 0.3710.223 0.0380.287 0.011 Social Desirability Scale -0.067 0.3010.016 0.4520.087 0.2490.048 0.356 a = cannot be computed because at least one of the variables is constant.

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296Table N-1. Continued Correlations nocuedrecallGlobal nocuedrecallComp. Social Desirability Pearson Correlation Sig. Pearson Correlation Sig. Pearson Correlation Sig. safe, good driver -0.183 0.074-0.179 0.0790.066 0.304 FalseUncuedSpecifi -0.191 0.066-0.154 0.1120.092 0.236 FalseUncuedSelection -0.074 0.280-0.060 0.319-0.279 0.013 FalseUncuedGlobal .(a) .(a) .(a) FalseUncuedComp. .(a) .(a) .(a) FalseCuedSpecific 0.047 0.3560.060 0.319-0.037 0.388 FalseCuedSelection 0.183 0.074-0.121 0.171-0.067 0.301 FalseCuedGlobal -0.052 0.342-0.042 0.3710.016 0.452 FalseCuedComp. .(a) .(a) .(a) nocuedrecallSpecific 0. 352 0.0020.223 0.0380.087 0.249 nocuedrecallSelection 0. 223 0.0380.287 0.0110.048 0.356 nocuedrecallGlobal 1.000 0.463 0.0000.260 0.020 nocuedrecallComp. 0.463 0.0001.000 0.094 0.233 Social Desirability Scale 0.260 0.0200.094 0.2331.000

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297 LIST OF REFERENCES Adler, G., Rottunda, S., & Dysken, M. (2005). The older driver with dem entia: An updated literature review. Journal of Safety Research, 36 399-407. Anstey, K. J., & Smith, G. A. (2003). Associations of biomarkers, cognition and self-reports of sensory function with self-reported driving behaviour and confidence. Gerontology, 49 196-202. Anstey, K. J., Wood, J., Lord, S., & Walker, J. G. (2005). Cognitive, sensory, and physical factors enabling driving sa fety in older adults. Clinical Psychology Review, 25 45-65. AOTA. (2002). Report on the AOTA/NHTSA older driver consensus conference Bethesda: The American Occupational Therapy Association. Ashendorf, L., Jefferson, A. L., O'Connor, M. K., Chaisson, C., Green, R. C., & Stern, R. A. (2008). Trail making test errors in normal aging, mild cognitive impairment, and dementia. Archives of Clinical Neuropsychology, 23 129-137. Ashman, R. D., Bishu, R. R., Foster, B. G., & McCoy, P. T. (1994). Countermeasures to improve the driving performance of older drivers. Educational Gerontology, 20 567-577. Baldock, M. R. J., Mathias, J. L., McLean, A. J., & Berndt, A. (2006). Se lf-regulation of driving and its relationship to drivi ng ability among older adults. Accident Analysis and Prevention, 38 1038-1045. Ball, K., Owsley, C., Sloane, M. E., Roenker, D. L., & Bruni, J. R. (1993). Visual attention problems as a predictor of vehicle crashes in older drivers. Invest Ophthalmol Vis Sci, 34(11), 3110-3123. Ball, K., Owsley, C., Stalvey, B., Roenker, D. L., Sloane, M. E., & Graves, M. (1998). Driving avoidance and functional impa irment in older drivers. Accident Analysis and Prevention, 30(3), 313-322. Ball, K., Roenker, D. L., Wadley, V. G., Edward s, J. D., Roth, D. L., McGwin Jr, G., et al. (2006). Can high-risk older drivers be identified through performance-based measures in a department of motor vehicles setting? JAGS, 54 77-84. Baltes, M. M., & Carstensen, L. L. (1996). The process of succesful ageing. Ageing and Society, 16, 397-422. Barger, S. D. (2002). The Marlowe-Crowne affair : Short forms, psychometric structure, and social desirability. Journal of Personality Assessment, 79 286-305. Bauer, M. J., Adler, G., Kuskowski, M., & Ro ttunda, S. (2003). The influence of age and gender on the driving pattern s of older adults. Journal of Women & Aging, 15 (4), 3-16.

PAGE 298

298 Bedard, M., Isherwood, I., Moore, E., Gibbons, C., & Lindstrom, W. (2004). Evaluation of a retraining program for older drivers. Canadian Journal of Public Health, 95 (4), 295-298. Bedard, M., Porter, M. M., Marshall, S., Isherw ood, I., Riendeau, J., Weaver, B., et al. (2008). The combination of two training approaches to improve older adults' driving safety. Traffic Injury Prevention, 9 70-76. Bogner, H. R., Straton, J. B., Gallo, J. J., Re bok, G. W., & Keyl, P. M. (2004). The role of physicians in assessing older drivers: Barriers, opportunitie s, and strategies. J Am Board Fam Pract, 17 (1), 38. Bonnel, W. B. (1999). Giving up the car: Older women's losses and experiences. J Psychosocial Nur Ment Health Serv, 37 (5), 10-15. Bowers, A., Peli, E., Elgin, J., McGwin Jr, G ., & Owsley, C. (2005). On-road driving with moderate visual field loss. Optometry and Vision Science, 82 (8), 657-667. Brabyn, J. A., Schneck, M. E., Lott, L. A., & Haegerstrom-Portnoy, G. (2005). Night driving self-restriction: Vision func tion and gender differences. Optometry and Vision Science, 82(8), 755-764. Brandt, J., Spencer, M., & Folstein, M. (1988). The telephone interview for cognitive status. Neuropsychiatry, Neuropsychology, and Behavioral Neurology, 1 (2), 111-117. Brayne, C., Dufouil, C., Ahmed, A., Dening, T. R., Chi, L.-Y., McGee, M., et al. (2000a). Very old drivers: findings fr om a population cohort of people aged 84 and over. International Journal of Epidemiology, 29, 704-707. Brayne, C., Dufouil, C., Ahmed, A., Dening, T. R., Chi, L.-Y., McGee, M., et al. (2000b). Very oldr drivers: findings from a populati on cohort of people aged 84 and over. International Journal of Epidemiology, 29, 704-707. Brehmer, B. (1990). Variable e rrors set a limit to adaptation. Ergonomics, 33 (10/11), 1231-1239. Brown, I. D. (1990). Drivers' margins of safety considered as a focus for research on error. Ergonomics, 33 (10/11), 1307-1314. Burkhardt, J. E., & McGavock, A. T. (1999). To morrow's older drivers: Who? How many? What impacts? Transportation Research Board, 1693 62-70. Callahan, J. S., Kiker, D. S., & Cross, T. ( 2003). Does method matter? A meta-analysis of the effects of training method on older learner training performance. Journal of Management, 29(5), 663-680. Carr, D., Jackson, T. W., Madden, D. J., & Cohen, H. J. (1992). The effect of age on driving skills. JAGS, 40 567-573. Carr, D. B. (2000). The older adult driver. American Family Physician, 61 (1), 141-146.

PAGE 299

299 Carr, D. B., Flood, K. L., Steger-May, K., Sc hechtman, K. B., & Binder, E. F. (2006). Characteristics of frail older adult drivers. JAGS, 54 1125-1129. Carstensen, L. L., & Mikels, J. A. (2005). At the intersection of emo tion and cognition: Aging and the positivity effect. Current Directions in Psychological Science, 14 (3), 117-121. Carstensen, L. L., Mikels, J. A., & Mather, M. (2006). Aging and the intersection of cognition, motivation, and emotion. In J.E. Birren & K.W. Schaie (Eds.), Handbook of the Psychology of Aging (6th Edition ed.): Academic Press. Charlton, J. L., Oxley, J., Fildes, B., Oxle y, P., Newstead, S., & Koppel, S. (2006). Characteristics of older drivers who a dopt self-regulatory driving behaviours. Transportation Research Part F, 9 (5), 363-373. Classen, S., Shechtman, O., Stephens, B., Davis, E., Bendixen, R., Belchior, P., et al. (2006). The impact of roadway intersection design on driving performance of young and senior adults: Preliminary results. Topics in Geriatric Rehabilitation, 22 (1), 18-26. Clay, O. J., Wadley, V. G., Edwards, J. D., Ro th, D. L., Roenker, D. L., & Ball, K. (2005). Cumulative meta-analysis of the relationship between useful field of view and driving performance in older adults: Cu rrent and future implications. Optometry and Vision Science, 82 (8), 724-731. Coeckelbergh, T. R. M., Brouwer, W. H., Corneli ssen, F. W., Wolffelaar, P., & Kooijman, A. C. (2002). The effect of visual fiel d defects on driving performance. Arch Ophthalmol, 120, 1509-1516. Collia, D. V., Sharp, J., & Giesbrecht, L. ( 2003). The 2001 national household travel survey: A look into the travel patter ns of older Americans. Journal of Safety Research, 34 461-470. Congdon, N., O'Colmain, B., Klaver, C. C., Klein, R ., Munoz, B., Friedman, D. S., et al. (2004). Causes and prevalence of visual impairme nt among adults in the United States. Arch Ophthalmol, 122, 477-485. Crowne, D. P., & Marlowe, D. (1960). A new s cale of social desirabi lity independent of psychopathology. Journal of Consulting Psychology, 24 349-354. Daigneault, G., Joly, P., & Fri gon, J.-Y. (2002). Executive functions in the evaluation of accident risk of older drivers. Journal of Clinical and Expe rimental Neuropsychology, 24 (2), 221238. De Raedt, R., & Ponjaert-Kristoffersen, I. (2000a). Can strategic and tactical compensation reduce crash risk in older drivers? Age and Aging, 29 517-521. De Raedt, R., & Ponjaert-Kristoffers en, I. (2000b). The relationship between cognitive/neuropsychological factors and car driving perf ormance in older adults. JAGS, 48, 1664-1668.

PAGE 300

300 De Raedt, R., & Ponjaert-Kristoffersen, I. (2001a ). Predicting at-fault car accidents of older drivers. Accident Analysis & Prevention, 33 809-819. De Raedt, R., & Ponjaert-Kristoffersen, I. (2001b). Short cognitive/neuropsychological test battery for first-tier fitness-to-drive assessment of older adults. The Clinical Neuropsychologist, 15 (3), 329-336. Decina, L. E., Staplin, L., Spiegel, A., & Knoebel, K. (1991). Contrast sensitivity and driver vision screening: An accident analysis. Paper presented at the 35th Annual Conference: Association for the Advancement of Automotive Medicine, Toronto, Canada. Dellinger, A. M., Sehgal, M., Sleet, D. A., & Barret-Connor, E. (2001). Dr iving cessation: What older former drivers tell us. Journal of the American Geriatric Society, 49 (4), 431-435. Di Stefano, M., & McDonald, W. (2003). Assessme nt of older drivers: Relationships among onroad errors, medical conditions and test outcome. Journal of Safety Research, 34 415429. Dobbs, A. R. (1997). Evaluating the driv ing competence of dementia patients. Alzheimer Disease and Associated Disorders, 11 (Suppl 1), 8-12. Dobbs, A. R., Heller, R. B., & Schopflocher, D. (1998). A comparative approach to identify unsafe older drivers. Accident Analysis and Prevention, 30 (3), 363-370. Dobbs, B. M. (2005). Medical conditions and driving: A review of the scientific literature (19602000) (No. DOT HS 809 690). Washington, DC: U.S. Department of Transportation National Highway Safety Administration. Eby, D. W., Molnar, L. J., Shope, J. T., Vivoda, J. M., & Fordyce, T. A. (2003). Improving older driver knowledge and self-awareness through self-assessment: The driving decisions workbook. Journal of Safety Research, 34 371-381. Eby, D. W., Trombley, D. A., Moln ar, L. J., & Shope, J. T. (1998). The assessment of older drivers' capabilities: a review of the literature (No. UMTRl-98-24). Ann Arbor, MI: The University of Michigan Transportation Research Institute. Edwards, J. D., Ross, L. A., Wadley, V. G., Clay, O. J., Crowe, M., Roenker, D. L., et al. (2006). The useful field of view: Normative data for older adults. Archives of Clinical Neuropsychology, 21 275-286. Edwards, J. D., Vance, D. E., Wadley, V. G., Cissell, G. M., Roenker, D. L., & Ball, K. K. (2005). Reliability and validity of useful fiel d of view test scores as administered by personal computer. Journal of Clinical and Experimental Neuropsychology, 27 529-543. Fillenbaum, G. G. (1978). Multidimensional f unctional assessment: The OARS methodology. Durham, NC: Duke University Center for the Study of Aging and Human Development.

PAGE 301

301 Fillenbaum, G. G., & Smyer, M. A. (1981). The development, validity, and reliability of the OARS multidimensional functional assessment questionnaire. Journal of Gerontology, 36, 428-431. Finn, J. (2004). Driving evaluation & retraining pr ograms: A report of good practices : AOTA and NHTSA. Fleishman, E. A. (1967). Performance assessme nt based on an empirically derived task taxonomy. Human Factors, 9(4), 349-366. Fleishman, E. A. (1975). Toward a taxonomy of human performance. American Psychologist (December), 1127-1149. Foley, D. J., Heimovitz, H. K., Guralnik, J. M., & Brock, D. B. (2002). Driving life expectancy of persons aged 70 years and older in the United States. American Journal of Public Health, 92(8), 1284-1289. Foley, D. J., Wallace, R. B., & Eberhard, J. (1995 ). Risk factors for motor vehicle crashes among older drivers in a rural community. Journal of the Americ an Geriatric Society, 43 776781. Fonda, S. J., Wallace, R. B., & Herzog, R. (2001) Changes in driving patterns and worsening depressiveymptoms among older adults. Journal of Gerontology: Social Sciences, 56B (6), S343-S351. Fox, G. K., Bowden, S. C., & Smith, D. S. ( 1998). On-road assessment of driving competence after brain impairment: Review of curre nt practice and recommendations for a standardized examination. Arch Phys Med Rehabil, 79, 1288-1296. Freeman, E. E., Munoz, B., Turano, K. A., & West S. (2006). Measures of visual function and their association with driving modification in older adults. Investigative Ophtalmology and Visual Science, 47 (2), 514-520. Freund, A. M., & Baltes, P. B. (1998). Selection, optimization, and compensation as strategies of life management: Correlations with subjective indicators of successful aging. Psychology and Aging, 13, 531-543. Freund, B., Colgrove, L. A., Petrakos, D., & Mc Leod, R. (2008). In my car the brake is on the right: Pedal errors among older drivers. Accident Analysis and Prevention, 40 403-409. Freund, B., & Colgrove, L. A. A. (2008). Error specific restrictions for ol der drivers: Promoting continued independence and public safety. Accident Analysis and Prevention, 40 97-103. Freund, B., Gravenstein, S., Ferris, R., Burke, B. L., & Shaheen, E. (2005). Drawing clocks and driving cars: Use of brief test s of cognition to screen drivi ng competency in older adults. J Gen Intern Med, 20 240-244.

PAGE 302

302 Fricke, J., Unsworth, C., & Worrell, D. (1993) Reliability of the functional independence measure with occupational therapists. The Australian Occupational Therapy Journal, 40(1), 7-15. Fuller, R. (1984). A conceptu alization of driving behaviour as threat avoidance. Ergonomics, 27(11), 1139-1155. Galski, T., Ehle, H. T., McDonald, M. A., & M ackevich, J. (2000). Evalua ting fitness to drive after cerebral injury: basic issues a nd recommendations for medical and legal communities. J Head Trauma Rehabil, 15(3), 895-908. Ginsburg, A. P. (1984). A new contrast sensitivity vision test chart. American Journal of Optometry and Physiological Optics, 61 403-407. Greenberg, H. (1959). An an alysis of traffic flow. Operations Research, 7 79-85. Greene, H. A., & Madden, D. J. (1987). Adult age differences in visual acuity, stereopsis, and contrast sensitivity. American Journal of Optometry and Physiological Optics, 64 (10), 749-753. Gresset, J., & Meyer, F. (1994). Risk of au tomobile accidents among eldery drivers with impairments or chronic diseases. Canadian Journal of Public Health, 85 (4), 282-285. Hakamies-Blomqvist, L. (1998). Older driver's accident risk: conceptual and methodological issues. Accident Analysis and Prevention, 30 (3), 293-297. Hakamies-Blomqvist, L., Johansson, K., & Lundberg, C. (1995). Driver licen ses as a measure of older drivers' exposure: A methodological note. Accident Analysis and Prevention, 27, 853-857. Hakamies-Blomqvist, L., Johansson, K., & Lundbe rg, C. (1996). Medica l screening of older drivers as a traffic saftey measure--A co mparative Finnish-Swedish evaluation study. JAGS, 44 650-653. Hakamies-Blomqvist, L., & Siren, A. (2003). Deconstructing a gender difference: Driving cessation and personal drivi ng history of older women. Journal of Safety Research, 34 383-388. Hakamies-Blomqvist, L., & Wahlstrom, B. (1998). Why older drivers give up driving? Accident Analysis and Prevention, 30 (3), 305-312. Hamilton, B. B., Granger, C. V., Sherwin, F. S., Zielezny, M., & Tashman, J. S. (1987). A uniform national data system for medical rehabilitation. In M.J. Fuhrer (Ed.), Rehabilitation outcomes and measurement (pp. 137-147). Baltimore: Brookes. Herman, R., Montroll, E. W., Potts, R. B., & Ro thery, R. W. (1959). Traffic dynamics: Analysis of stability in car following. Operations Research, 7 86-106.

PAGE 303

303 Hills, B. L. (1980). Vision, visibi lity, and perception in driving. Perception, 9 183-216. Hitchcock, E. M., Dick, R. B., & F., K. E. ( 2004). Visual contrast sensitivity testing: A comparison of two F.A.C.T. test types. Neurotoxicology and Teratology, 26, 271-277. Hoffman, L., Atchley, P., McDowd, J. M., & Dubinsky, R. (2005). The role of visual attention in predicting driving impairment in older adults. Psychology and Aging, 20 (4), 610-622. Hogan, D. B. (2005). Which older patients are comp etent to drive? Approaches to office-based assessment. Canadian Family Physician, 51 362-368. Horberry, T. J., Gale, A. G., & Taylor, S. P. (1997). Visual screeners for display screen equipment users: An experimental evaluation. Displays, 17, 111-117. Hu, P., Trumble, D. A., Foley, D. J., Eberhard, J. W., & Wallace, R. B. (1998). Crash risks of older drivers: A panel data analysis. Accident Analysis and Prevention, 30(5), 569-581. Hunt, L. A. (1993). Evaluation and re training programs for older drivers. Clinics in Geriatric Medicine, 9 (2), 439-447. Hunt, L. A., Murphy, C. F., Carr, D., Duchek, J. M., Buckles, V., & Morris, J. C. (1997). Reliability of the Washington University Road Test. Arch Neurol, 54 707-712. Hutcherson, D. G. (1989). Self-monitoring of driv ing for the elderly: Evidence for use of a driving diary. Physical and Occupational Therapy in Geriatrics, 7, 171-201. Isler, R. B., Parsonson, B. S., & Hansson, G. J. (1997). Age related effect s of restricted head movements on the useful field of view of drivers. Accident Analysis and Prevention, 29(6), 793-801. Ivers, R. O., Mitchell, P., & Cumming, R. G. ( 1999). Sensory impairment and driving: The Blue Mountains Eye Study. American Journal of Public Health, 89 85-87. Janke, M. K., & Eberhard, J. W. (1998). Asse ssing medically impaired older drivers in a licensing agency setting. Accident Analysis and Prevention, 30(3), 347-361. Johnson, C. A., & Keltner, J. L. (1983). Incidenc e of visual field loss in 20,000 eyes and its relationship to dr iving perfomance. Arch Ophthalmol, 101, 371-375. Johnson, J. E. (1998). Older rural ad ults and the decision to top driv ing: The influence of family and friends. Journal of Community Health Nursing, 15 (4), 205-216. Justiss, M. D., Mann, W. C., Stav, W., & Veloz o, C. (2006). Development of a behind-the-wheel driving performance assessment for older adults. Topics in Geriatric Rehabilitation, 22(2), 121-128. Kantor, B., Mauger, L., Richardson, V. E., & Unroe, K. T. (2004). An analysis of an older driver evaluation program. JAGS, 52 1326-1330.

PAGE 304

304 Keeffe, J. E., Jin, C. F., Weih, L. M., McCa rthy, C. A., & Taylor, H. R. (2002). Vision impairment and older drivers: who's driving? Br J Ophthalmol, 86 1118-1121. Kiernan, B. D., Cox, D. J., Kovatchev, B. P., Kiernan, B. S., & Giuliano, A. J. (1999). Improving driving performance of senior drivers th rough self-monitoring with a driving diary. Physical and Occupational Therapy in Geriatrics, 16 55-62. Koepsell, T. D., Wolf, M. E., MC loskey, L., Buchner, D. M., Louie, D., Wagner, E. H., et al. (1994). Medical conditions and motor vehicle collision in juries in older adults. JAGS, 42 695-700. Korner-Bitensky, N., Bitensky, J., Sofer, S., Ma n-Son-Hing, M., & Gelinas, I. (2006). Driving evaluation practices of clinicians work ing in the United States and Canada. The American Journal of Occupational Therapy, 60 428-434. Kua, A., Korner-Bitensky, N., Desrosiers, J., Ma n-Son-Hing, M., & Marshall, S. (2007). Older driver retraining: A systematic revi ew of evidence of effectiveness. Journal of Safety Research, 38 81-90. Lajunen, T., Parker, D., & Summala, H. ( 2004). The Manchester Driver Behaviour Questionnaire: A cross-cultural study. Accid Anal Prev, 36 231-238. Langford, J., Fitzharris, M., Koppel, S., & News tead, S. (2004). Effectiveness of mandatory license testing for older driv ers in reducing crash risk among urban older Australian drivers. Traffic Injury Prevention, 5 326-335. Langford, J., & Koppel, S. (2006). Epidemiology of older driver crashes Identifying older driver risk factors and exposure patterns. Transportation Research Part F, 9 309-321. Langford, J., Koppel, S., Andrea, D., & Fildes, B. (2006). Determining older driver crash responsibility from police and insurance data. Traffic Injury Prevention, 7 343-351. Langford, J., Methorst, R., & Hakamies-Blomqvi st, L. (2006). Older drivers do not have high crash risk--A replication of low mileage bias. Accident Analysis and Prevention, 38 574578. Li, G., Braver, E. R., & Chen, L.-H. (2003). Frag ility versus excessive crash involvement as determinants of high death rates per vehi cle-mile of travel among older drivers. Accident Analysis and Prevention, 35 227-235. Lloyd, S., Cormack, C. N., Blais, K., Messeri, G ., McCallum, M. A., Spicer, K., et al. (2001). Driving and dementia: A re view of the literature. Canadian Journal of Occupational Therapy, 68(3), 149-156. Lococo, K. H., & Staplin, L. (2006). Polypharmacy and older drivers: Identifying strategies to study drug usage and driving functioning among older drivers (No. 801 681). Washington, DC: Office of research and traffic records National Highway Safety Administartion.

PAGE 305

305 Lundberg, C., Hakamies-Blomqvist, L., Almkkvist O., & Johansson, K. (1998). Impairments of some cognitive functions are common in crash-involved older drivers. Accident Analysis and Prevention, 30 (3), 371-377. Lyman, J. M., McGwin Jr, G., & Sims, R. (2001). F actors related to drivin g difficulty and habits in older drivers. Accident Analysis and Prevention, 33 413-421. Mallon, K., & Wood, J. M. (2004). Occupational Therapy assessment of open-road driving performance: Validity of directed and self-directed navigational instructional components. American Journal of Occuptional Therapy, 58 279-286. Margolis, K. L., Kerani, R. P., McGovern, P., Songer, T., Cauley, J. A., & Ensrud, K. E. (2002). Risk factors for motor vehi cle crashes in older women. Journal of Gerontology: Medical Sciences, 57A (3), M186-M191. Marottoli, R. A., Allore, H., Araujo, K. L. B ., Iannone, L. P., Acampora, D., Gottschalk, M., et al. (2007). A randomized trial of a physical conditioning program to enhance the driving performance of older persons. Society of General Internal Medicine, 11, 590-597. Marottoli, R. A., Cooney, L. M., Wagner, D. R., Doucette, J., & Tinetti, M. E. (1994). Predictors of automobile crashes and moving violations among elderly drivers. Ann Inter. Med, 121 842-846. Marottoli, R. A., & Drickamer, M. A. (1993). Psychomotor mobility and the elderly driver. Clinics in Geriatric Medicine, 9 (2), 403-411. Marottoli, R. A., Mendes de Leon, C. F., Glass, T. A., Williams, C. S., Cooney Jr., L. M., & Berkman, L. F. (2000). Consequences of driving cessation: Decreased out-of-home activity levels. Journal of Gerontology: Social Sciences, 55B (6), S334-S340. Marottoli, R. A., Mendes de Leon, C. F., Gla ss, T. A., Williams, C. S., Cooney Jr., L. M., Berkman, L. F., et al. (1997). Driving ce ssation and increased depressive symptoms: Prospective evidence from the New Haven EPESE. JAGS, 45 202-206. Marottoli, R. A., Ostfeld, A. M., Merrill, S. S ., Perlman, G. D., Foley, D. J., & Cooney Jr., L. M. (1993). Driving cessation and changes in m ileage driven among elderly individuals. Journal of Gerontology: Social Sciences, 48(5), S255-S260. Marottoli, R. A., & Richardson, E. D. (1998). C onfidence in, and self-rat ing of driving ability among older drivers. Accident Analysis and Prevention, 30 331-336. Marottoli, R. A., Richardson, E. D., Stowe, M. H ., Miller, E. G., Brass, L. M., Cooney, L. M. J., et al. (1998). Development of a test battery to identify older driver s at risk for selfreported adverse driving events. JAGS, 46, 562-568. Marottoli, R. A., Van Ness, P. H., Araujo, K. L. B., Iannone, L. P., Acampora, D., Charpentier, P., et al. (2007). A randomized trial of an education program to enhance older driver performance. Journal of Gerontology: Medical Sciences, 62A (10), 1113-1119.

PAGE 306

306 Marshall, S. C., & Gilbert, N. (1999). Sa skatchewan physicians' attitudes and knowledge regarding assessment of me dical fitness to drive. Canadian Medical Association Journal, 160(12), 1701-1704. Marshall, S. C., Man-Son-Hing, M., Molnar, F. J., Wilson, K. G., & Blair, R. (2007). The acceptability to older drivers of diffe rent types of licensing restriction. Accident Analysis and Prevention, 39 776-793. Marshall, S. C., Spasoff, R., Nair, R., & van Walr aven, C. (2002). Restricted driver licensing for medical impairments: Does it work? Canadian Medical Association Journal, 167 (7), 747-751. Marsiske, M., Lang, F. R., Baltes, P. B., & Balte s, M. M. (1995). Selective optimization with compensation: Life-span perspectives on succes ful human development. In R.A. Dixon & L. Backman (Eds.), Compensation for Psychological Defects and Declines: Managing Losses and Promoting Gains (pp. 35-79). Mahwah, NJ: Erlbaum. McCarthy, D. P. (2005). Approaches to im proving elders' safe driving abilities. Physical and Occupational Therapy in Geriatrics, 23 (2/3), 25-42. McCarthy, D. P., & Mann, W. C. (2006). Sensitivity and specificity of the Assessment of Driving-Related Skills olde r driver screening tool. Topics in Geriatric Rehabilitation, 22(2), 139-152. McGee, P., & Tuokko, H. (2003). The older and wiser driver: A self-assessment program British Columbia: Capital Regional District Traffic Safety Comission, University of Victoria. McGwin Jr, G., Sims, R. V., Pulley, L., & Rose man, J. M. (1999). Diabetes and automobile crashes in the elderly. Diabetes Care, 22 220-227. McGwin Jr, G., Sims, R. V., Pulley, L., & Roseman, J. M. (2000). Relations among chronic medical conditions, medications, and automob ile crashes in the elderly: A populationbased case-control study. American Journal of Epidemiology, 152 (5), 424-431. McGwin Jr, G., Sims, R. V., Pulley, L., & Roseman, J. M. (2002). Relations among chronic medical conditions, medications, and automob ile crashes in the elderly: A populationbased case-control study. American Journal of Epidemiology, 152 (5), 424-431. McKenna, F. P. (1982). The human factor in driv ing accidents: An overview of approaches and problems. Ergonomics, 25 (10), 867-877. McKenna, F. P. (1988). What role should the con cept of risk play in theories of accident involvement? Ergonomics, 31(4), 469-484. McKnight, A. J., & McKnight, A. S. (1999). Multivar iate analysis of age-related driver ability and performance deficits. Accident Analysis and Prevention, 31 445-454.

PAGE 307

307 McRuer, D. T., Allen, R. W., Weir, D. H., & Klei n, R. H. (1977). New results in driver steering control models. Human Factors, 19(4), 381-397. Meyers, L. W., Gamst, G., & Guarino, A. J. (2006). Applied multivariate research: Design and interpretation Thousand Oaks, CA: Sage Publications. Michon, J. A. (1985). A critical view of driver behavior mode ls: What do we know, what should we do? In E.L. Evans & R. Schwing (Eds.), Human Behavior and Traffic Safety (pp. 485520). New York: Plenum. Michon, J. A. (1989). Explanatory pitfal ls and rule-based driver models. Accident Analysis and Prevention, 21 (4), 341-353. Molnar, F. J., Marshall, S. C., Byszewski, A. M., & Man-Son-Hing, M. (2005). In-office evaluation of medical fitness to drive. Canadian Family Physician, 51 372-379. Molnar, F. J., Patel, A., Marshall, S., Man-SonHing, M., & Wilson, K. G. (2006). Clinical utility of office-based cognitive predictors of fitn ess to drive in persons with dementia: A systematic review. JAGS, 54 1809-1824. Molnar, L. J., Eby, D. W., & Miller, L. L. (2003). Promising approaches for enhancing elderly mobility (No. Highway Safety Project DE-03-01, UMTRI Report No. 2003-24). Michigan: University of Michigan, Transportation Research Institute. Myers, R. S., Ball, K., Kalina, T. D., Roth, D. L., & Goode, K. T. (2000). Relation of useful field of view and other screening tests to on-road driving performance. Perceptual and Motor Skills, 91 279-290. Naatanen, R., & Summala, H. (1974). A model for th e role of motivational factors in drivers' decision making. Accident Analysis and Prevention, 6 243-261. Nasvadi, G. E. (2007). Changes in self-reported driving behaviour following attendance at a mature driver education program. Transportation Research Part F, 10, 358-369. Nasvadi, G. E., & Vavrik, J. (2007). Crash risk of older drivers after attending a mature driver education program. Accident Analysis and Prevention, 39 1073-1079. NHTSA. (2007). Traffic safety facts: Crash Stats, DOT HS 810 779 (pp. 1-2). Washington, DC: NHTSA National Center for Statistics and Analysis. Odell, M. (2005). Assessing fitness to drive: Part 1. Australian Family Physician, 34 (5), 359362. Odenheimer, G. L. (2006). Driver safety in older adults: The physician's role in assessing driving skills of older patients. Geriatrics, 61 14-21.

PAGE 308

308 Odenheimer, G. L., Beaudet, M., Jette, A., Albe rt, M. S., Grande, L., & Minaker, K. L. (1994). Performance-based driving evaluation of the elderly driv er: Safety, reliability, and validity. Journal of Gerontology : Medical Sciences, 49 (4), M153-M159. Ostrow, A. C., Shaffron, P., & McPherson, K. (1992). The effects of a joint range-of-motion physical fitness training program on the auto mobile driving skills of older adults. Journal of Safety Research, 23 207-219. Oswanski, M. F., Sharma, O. P., Raj, S. S., Vassa r, L. A., Woods, K. L., Sargent, W. M., et al. (2007). Evaluation of two assessment tools in pr edicting driving ability of senior drivers. Am J Phys Med Rehabil, 86 190-199. Ottenbacher, K. J., Hsu, Y., Granger, C. V., & Fiedler, R. C. (1996). The reliability of the functional independence meas ure: A quanitative review. Archives of Physical Medicine and Rehabilitation, 77 1226-1232. Ottenbacher, K. J., Mann, W. C., Granger, C. V ., Tomita, M., Hurren, D., & Charvat, B. (1994). Inter-rater agreement and stability of fu cntional assessment in the community-based eldery. Archives of Physical Medicine and Rehabilitation, 75 1297-1301. Owsley, C., Ball, K., McGwin, G., Sloane, M. E., Roenker, D. L., White, M. F., et al. (1998). Visual processing impairment and risk of motor vehicle crash among older adults. JAMA, 279(4), 1083-1088. Owsley, C., Ball, K., Sloane, M. E., Roenker, D. L., & Bruni, J. R. (1991). Visual/cognitive correlates of vehicle accidents in older drivers. Psychology and Aging, 6 (3), 403-415. Owsley, C., McGwin, G., Phillips, J. M., McNeal, S. F., & Stalvey, B. T. (2004). Impact of an educational program on the safety of highrisk, visually impaired, older drivers. American Journal of Preventive Medicine, 26 (3), 222-229. Owsley, C., & Sloane, M. E. (1987). Contrast se nsitivity, acuity, and th e perception of 'realworld' targets. British Journal of Ophthalmology, 71 791-796. Owsley, C., Stalvey, B., Wells, J., & Sloane, M. E. (1999). Older drivers and cataract: Driving habits and crash risk. Journal of Gerontology : Medical Sciences, 54A M203-M211. Owsley, C., Stalvey, B. T., & Phillips, J. M. (200 3). The efficacy of an educational intervention in promoting self-regulation am ong high-risk older drivers. Accident Analysis and Prevention, 35 393-400. Owsley, C., Stalvey, B. T., Wells, J., Sloane, M. E., & McGwin, G. (2001). Visual risk factors for crash involvement in older drivers with cataract. Arch Ophthalmol, 119, 881-887. Ozkan, T., Lajunen, T., & Summala, H. (2006). Driving Behaviour Questionnaire: A follow-up study. Accid Anal Prev, 38 386-395.

PAGE 309

309 Parasuraman, R., & Nestor, P. (1993). Attenti on and driving. Clinics in Geriatric Medicine, 9 (2), 377-387. Parker, D., McDonald, L., Rabbitt, P., & Sutcliffe P. (2000). Elderly drivers and their accidents: The Aging Driver Questionnaire. Accid Anal Prev, 32 751-759. Parker, D., McDonald, L., Rabbitt, P., & Sutcliffe P. (2003). Older drivers and road safety: the acceptability of a range of intervention measures. Accident Analysis and Prevention, 35 805-810. Parker, D., McDonald, L., Sutcliffe, P., & Rabbi tt, P. (2001). Confidence and the older driver. Ageing and Society, 21 (2), 169-182. Parker, D., Reason, J. T., Manstead, A. S., & St radling, S. G. (1995). Driving errors, driving violatons and accident involvement. Ergonomics, 38 (5), 1036-1048. Paulhus, D. L. (1991). Measurement and control of response bias. In J.P. Robinson, P.R. Shaver & L.S. Wrightsman (Eds.), Measures of personality and social attitudes (Vol. 1, pp. 1731). San Diego, California: Academic Press Inc. Peel, N., Westmoreland, J., & Stei nberg, M. (2002). Transport safety for older people: A study of their experiences, perceptions and management needs. Injury Control and Safety Promotion, 9(1), 19-24. Plassman, B. L., Newman, T. T., Welsh, K. A., Helms, M., & Breitner, J. C. S. (1994). Properties of the telephone interview for cogn itive status: Application in epidemiological and longitudinal studies. Neuropsychiatry, Neuropsychology, and Behavioral Neurology, 7(3), 235-241. Portney, L. G., & Watkins, M. P. (2000). Foundations of clinical re search: Applications to practice (2nd ed.). New Jersey: Prentice Hall Health. Ragland, D. R., Satariano, W. A., & MacLeod, K. E. (2004). Reasons given by older people for limitation or avoidance of driving. The Gerontologist, 44 (2), 237-244. Ragland, D. R., Satariano, W. A., & MacLeod, K. E. (2005). Driving cessation and increased depressive symptoms. Journal of Gerontology: Medical Sciences, 60A (3), 399-403. Raitanen, T., Tormakangas, T., Mollenkopf, H., & Marcellini, F. (2003) Why do older drivers reduce driving? Findings from three European countries. Transportation Research Part F, 6, 81-95. Ralston, L. S., Bell, S. L., Mote, J. K., Rain ey, T. B., Brayman, S., & Shotwell, M. (2001). Giving up the car keys: Perceptions of well elders and families. Physical and Occupational Therapy in Geriatrics, 19 (4), 59-70. Ranney, T. A. (1994). Models of driving behavior: A review of their evolution. Accident Analysis and Prevention, 26 (6), 733-750.

PAGE 310

310 Ray, W. A., Fought, R. L., & Decker, M. D. (1992) Psychoactive drugs and the risk of injurious motor vehicle crashes in elderly drivers. American Journal of Epidemiology, 136 (7), 873883. Ray, W. A., Thapa, P. B., & Shorr, R. I. (1993). Medications and the older driver. Clinics in Geriatric Medicine, 9 (2), 413-438. Reason, J., Manstead, A., Stradling, S., Baxter, J., & Campbell, K. (1990). Errors and vilations on the roads: a real distinction? Ergonomics, 33, 1315-1332. Reger, M. A., Welsh, R. K., Watson, G. S., Choler ton, B., Baker, L. D., & Craft, S. (2004). The relationship between neuropsychol ogical functioning and driv ing ability in dementia: A meta-analysis. Neuropsychology, 18 1-9. Reitan, R. M., & Wolfson, D. (2004). Intellectua l and neuropsychological assessment. In D. Goldstein & S. Beers (Eds.), Comprehensive handbook of psychological assessment (Vol. 1). Hoboken, NJ: John Wiley and Sons. Retchin, S. M., & Anapolle, J. (1993) An overview of the older driver. Clin Geriatr Med, 9 279-296. Reuben, D. B., Silliman, R. A., & Traines, M. (1988). The aging driver. JAGS, 36 1135-1142. Richardson, E. D., & Marottoli, R. A. (2003). Visual attention and driving behaviors among community-living older persons. Journal of Gerontology : Medical Sciences, 58 (9), 832836. Rimmo, P. A. (2002). Aberrant driving behaviour: Homogeneity of a fou r-factor structure in samples differing in age and gender. Ergonomics, 45 (8), 569-582. Rizzo, M. (2004). Safe and unsafe driving. In M. Rizzo & P.J. Eslinger (Eds.), Principles and practice of behavioral neurology and neuropsychology (pp. 197-220). US: Saunders. Rizzo, M., & Kellison, I. L. ( 2004). Age, brains, and autos. Arch Ophthalmol, 122, 650-652. Rizzo, M., Reinach, S., McGehee, D., & Dawson, J. (1997). Simulated car crashes and crash predictors in drivers with Alzheimer disease. Arch Neurol, 54 545-551. Roberts, W. N., & Roberts, P. C. (1993). Eval uation of the elderly dr iver with arthritis. Clinics in Geriatric Medicine, 9 (2), 311-322. Roenker, D. L., Cissell, G. M., Ball, K. K., Wa dley, V. G., & Edward, J. D. (2003). Speed-of processing and drivign simulator training result in improved driving performance. Human Factors, 45(2), 218-233. Rothengatter, T. (1988). Risk and the absence of pleasure: a motivational approach to modeling road user behavior. Ergonomics, 31(4), 599-607.

PAGE 311

311 Ruechel, S., & Mann, W. C. (2005). Self-re gulation of driving by older persons. Physical and Occupational Therapy in Geriatrics, 23 (2/3), 91-101. Rumar, K. (1990). The basic dr iver error: late detection. Ergonomics, 33, 1281-1290. Schneidert, M., Hurst, R., Millers, J., & Ust un, B. (2003). The role of environment in the International Classification of Functioning, Disability and Health (ICF). Disability and Rehabilitation, 25 588-595. Shope, J. T., & Eby, D. W. (1998). Improvement of older drivers sa fety through self-evaluation: Focus group results (No. UMTRI-98-29). Ann Arbor, MI : The Univesity of Michigan, Transportation Research Institute. Sims, R. V., Ahmed, A., Sawyer, P., & Allman, R. M. (2007). Self-reported health and driving cessation in community-dwelling older drivers. J Gerontol A Biol Sci Med Sci, 62 (7), 789-793. Sims, R. V., McGwin Jr, G., Allman, R. M., Ba ll, K., & Owsley, C. (2000). Exploratory study of incident vehicle crashe s among older drivers. Journal of Gerontology : Medical Sciences, 55A (1), M22-M27. Stalvey, B. T., & Owsley, C. (2000). Self-percepti ons and current practices of high-risk older drivers: Implications for driver safety interventions. Journal of Health Psychology, 5 (4), 441-456. Stalvey, B. T., & Owsley, C. (2003). The deve lopment and efficacy of a theory-based educational curriculum to promote self-regu lation among high risk older drivers. Health Promotion Practice, 4 (2), 109-119. Staplin, L., & Dinh-Zarr, B. (2006). Promoting re habilitation of safe driving abilities through computer-based clinical and personal screening techniques. Topics in Geriatric Rehabilitation, 22 (2), 129-138. Staplin, L., Gish, K. W., & Wagner, E. K. (2003) MaryPODS revisited: Updated crash analysis and implications for screening program implementation. Journal of Safety Research, 34 389-397. Staplin, L., Lococo, K. H., Gish, K. W., & Decina, L. E. (2003). Model driver screening and evaluation program final technical repor t volume 1: Project summary and model program recommendations (No. DOT HS 809 582). Wash ington, DC: National Highway Traffic Safety Administration. Stav, W. B., Justiss, M. D., McCarthy, D. P., Mann, W. C., & Lanford, D. N. (2008). Predictability of clinical assessments for driving performance. Journal of Safety Research, 39 1-7. Stelmach, G. E., & Nahom, A. (1992). Cognitiv e-motor abilities of the elderly driver. Human Factors, 34(1), 53-65.

PAGE 312

312 Stephens, B., McCarthy, D. P., Marsiske, M., Sch echtman, O., Classen, S., Justiss, M., et al. (2005). International older driver consensus conference on assessment, remediation and counseling for transporta tion alternatives: Summary and recommendations. Physical and Occupational Therapy in Geriatrics, 23 (2/3), 103-121. Stressel, D. L. (2000, July). Driv ing issues of the older adult. OT Practice, CE1-CE8. Stutts, J., Stewart, J. R., & Martell, C. (1998). Cognitive test performance and crash risk in an older driver population. Accident Analysis and Prevention, 30(3), 337-346. Stutts, J. C. (1998). Do older drivers with visual and cognitive im pairments drive less? JAGS, 46 854-861. Stutts, J. C., & Wilkins, J. W. (2003). On-road driving evaluations: A potential tool for helping older adults drive safely longer. Journal of Safety Research, 34 431-439. Summala, H. (1988). Risk control is not risk adjustment: the zero-r isk theory of driver behaviour and its implications. Ergonomics, 31(4), 491-506. Szlyk, J. P., Mahler, C., Seiple, W., Edwar d, D. P., & Wilensky, J. T. (2005). Driving performance of glaucoma patients correla tes with peripheral visual field loss. J Glaucoma, 14, 145-150. Szlyk, J. P., Myers, L., Zhang, Y. X., Wet zel, L., & Shapiro, R. (2002). Development and assessment of a neuropsychological battery to aid in predciting driving performance. Journal of Rehabilitation Research and Development, 39 (4), 483-496. Szlyk, J. P., Seiple, W., & Viana, M. (1995). Re lative effects of age and compromised vision on driving performance. Human Factors, 37(2), 430-436. Tay, R. (2006). Ageing drivers: Storm in a teacup? Accident Analysis and Prevention, 38 112121. Taylor, B. D., & Tripodes, S. (2001). The eff ects of driving cessation on the elderly with dementia and their caregivers. Accid Anal Prev, 33 (4), 519-528. Uc, E. Y., Rizzo, M., Anderson, S. W., Shi, Q ., & Dawson, J. D. (2005). Driver landmark and traffic sign identification in early Alzheimer's disease. J Neurol Neurosurg Psychiatry, 76, 764-768. Unsworth, C. (2007). Using social judgment th eory to study occupational therapists' use of information when making driver licensing recommendations for older and functionally impaired adults. The American Journal of Occupational Therapy, 61 (5), 493-502. Ustun, T. B., Chatterji, S., Bickenback, J ., Kostanjsek, N., & Schneider, M. (2003). The International Classification of Functioning, Disability and Health: a new tool for understanding disabi lity and health. Disability and Rehabilitation, 25 565-571.

PAGE 313

313 Vance, D. E., Roenker, D. L., Cissell, G. M., Ed wards, J. D., Wadley, V. G., & Ball, K. (2006). Predictors of driving exposure and avoidance in a field study of ol der drivers from the state of Maryland. Accident Analysis and Prevention, 38 823-831. Waller, P. F. (1991). The older driver. Human Factors, 33(5), 499-505. Walton, D., & Thomas, J. A. (2005). Naturalist ic observations of driver hand positions. Transportation Research Part F, 8 229-238. Wang, C. C., & Carr, D. B. (2004). Older driver safety: A report fr om the older drivers project. JAGS, 52 143-149. Wang, C. C., Kosinski, C. J., Schw artzberg, J. G., & Shanklin, A. V. (2003). Physician's guide to assessing and counseling older drivers. Washington, D.C.: National Highway Traffic Safety Administration. Weir, D. H., & McRuer, D. T. (1968). A theory of driver steering cont rol of motor vehicles. Road user characteristics, Highway Research Record, 247 7-28. Welsh, K. A., Breitner, J. C. S., & Magruder-Habib, K. M. (1993). Detection of dementia in the elderly using telephone screen ing of cognitive status. Neuropsychiatry, Neuropsychology, and Behavioral Neurology, 6 (2), 103-110. West, C. G., Gildengorin, G., Haegerstrom-Port noy, G., Lott, L. A., Schneck, M. E., & Brabyn, J. A. (2003). Vision and driving se lf-restriction in older adults. JAGS, 51 1348-1355. Wheatley, C. J. (2001). Visual pe rceptual aspects of driving. Physical Disabilities Special Interest Section Quarterly, 24 (3), 1-3. WHO. (2001). International classification of functioning, disability and health: ICF Geneva: World Health Organization. Wilde, G. J. S. (1982). The theory of risk hom eostasis: Implications for safety and health. Risk Analysis, 2 (4), 209-225. Wilde, G. J. S. (1988). Risk homeostasis theory and traffic accidents: propositions, deductions and discussion of dissension in recent reactions. Ergonomics, 31 441-468. Wilde, G. J. S., & Murdoch, P. A. (1982). Incen tive systems for accident-free and violation-free driving in the general population. Ergonomics, 25(10), 879-890. Wolf, J. D., & Barrett, M. F. (1978). Driver-vehicle effectiveness model (No. DOT HS-804 337, v.1 and DOT HS-804 338, v.2). Springfield: Dept. of Transportation, National Highway Traffic Safety Administration. Wood, J. M., & Owens, D. A. (2005). Standard measures of visual acuity do not predict drivers' recognition performance under day or night conditions. Optometry and Vision Science, 82, 698-705.

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314 Wood, J. M., & Troutbeck, R. (1995). Elderly drivers and simulated visual impairment. Optometry and Vision Science, 72 (2), 115-124.

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315 BIOGRAPHICAL SKETCH Cristin a Posse earned her bachelors degree in occupational therapy in Colombia. She finished a Master of Health Sciences at the University of Florida (UF) in 2003. During her rehabilitation science Ph.D. studies, Ms. Posse was a research assistant in driving projects of the National Older Driver Research Center (NODRTC). She also coordinated a telehealth project that provided support and educati on online for caregivers of pers ons with dementia. Ms. Posses interests are aging, cognitive impa irments, driving, and education toward improving older adults independence and well-being.