When is it safe to return to driving after distal radius fracture fixation? A prospective study.

INTRODUCTION: After surgical fixation of distal radius fractures, many patients are keen to return to driving. There are however limited guidelines assisting surgeons. The aims of this study were to determine when patients could return to driving safely after distal radius fracture fixation and determine the clinical parameters (range of motion and grip strength) that patients needed to achieve before return to safe driving could be advised. MATERIALS AND METHODS: A prospective grant-funded clinical study was conducted. Patients above the age of 21 years who underwent surgical fixation with a volar plate, possessed a class 3 standard motorcar license, and were regular drivers were recruited in a single institution from 2017 to 2019. A hand surgeon and an occupational therapist who sees routine hand therapy cases, assessed the patients at regular intervals from 2 to 12-weeks post-surgery. Clinical parameters of pain, wrist range of motion and grip strength were measured. Patients underwent off and on-road driving assessments. RESULTS: A total of 26 patients were recruited, with 21 successfully completing the driving assessment. Median time post-surgery to passing the driving test was 6 and 8-weeks for off and on-road assessments respectively. Pain score was observed to decrease over time, with a significant decrease from week 2 to week 4. Range of motion improved over time, with maximal improvement between 2 to 4-weeks post-surgery. When compared with the unaffected wrist, the difference in pronation, supination and radial deviation in the affected hand was consistently no longer statistically significant 4 to 6-weeks post-surgery. CONCLUSION: Patients with isolated surgically treated distal radius fractures can be recommended for a driving assessment as early as 4-6 weeks post-surgery if pain control is adequate, and clinical parameters for pronation and supination are met.

Driving and cognitive function in people with stroke and healthy age-matched controls.

The decision to return to driving is both complex and difficult. It is often made with clinician support, perhaps on the basis of results from standardized paper and pencil tests, and less frequently an assessment of on-road driving. However, the resources required and inherent reliability and validity challenges suggest that greater use of computerization and driving simulation may play a useful role in the assessment process. In this study, thirty-six age-matched healthy and post-stroke drivers completed standard psychometric assessments (including NART, MMSE, BADS, IADL), computer delivered cognitive assessments (including SART, N-Back, Simple Reaction Time), as well as simulated and on-road assessments of driving. While significantly different in terms of psychometric and computer-based assessments, the healthy and post-stroke drivers who completed the on-road test did not differ. In contrast, driving in the portable simulator showed impaired driving, on some but not all driving manoeuvres, for those who failed the on-road test or had voluntarily ceased to drive. These results are discussed in terms of the implications they have for the multi-faceted nature of driving as a skill, and the need to involve simulation when assessing fitness to drive.

Novel use of a virtual driving assessment to classify driver skill at the time of licensure.

Motor vehicle crash rates are highest immediately after licensure, and driver error is one of the leading causes. Yet, few studies have quantified driving skills at the time of licensure, making it difficult to identify at-risk drivers before independent driving. Using data from a virtual driving assessment implemented into the licensing workflow in Ohio, this study presents the first population-level study classifying degree of skill at the time of licensure and validating these against a measure of on-road performance: license exam outcomes. Principal component and cluster analysis of 33,249 virtual driving assessments identified 20 Skill Clusters that were then grouped into 4 major summary "Driving Classes"; i) No Issues (i.e. careful and skilled drivers); ii) Minor Issues (i.e. an average new driver with minor vehicle control skill deficits); iii) Major Issues (i.e. drivers with more control issues and who take more risks); and iv) Major Issues with Aggression (i.e. drivers with even more control issues and more reckless and risk-taking behavior). Category labels were determined based on patterns of VDA skill deficits alone (i.e. agnostic of the license examination outcome). These Skill Clusters and Driving Classes had different distributions by sex and age, reflecting age-related licensing policies (i.e. those under 18 and subject to GDL and driver education and training), and were differentially associated with subsequent performance on the on-road licensing examination (showing criterion validity). The No Issues and Minor Issues classes had lower than average odds of failing, and the other two more problematic Driving Classes had higher odds of failing. Thus, this study showed that license applicants can be classified based on their driving skills at the time of licensure. Future studies will validate these Skill Cluster classes in relation to their prediction of post-licensure crash outcomes.

Simulator Pre-Screening of Underprepared Drivers Prior to Licensing On-Road Examination: Clustering of Virtual Driving Test Time Series Data.

BACKGROUND: A large Midwestern state commissioned a virtual driving test (VDT) to assess driving skills preparedness before the on-road examination (ORE). Since July 2017, a pilot deployment of the VDT in state licensing centers (VDT pilot) has collected both VDT and ORE data from new license applicants with the aim of creating a scoring algorithm that could predict those who were underprepared. OBJECTIVE: Leveraging data collected from the VDT pilot, this study aimed to develop and conduct an initial evaluation of a novel machine learning (ML)-based classifier using limited domain knowledge and minimal feature engineering to reliably predict applicant pass/fail on the ORE. Such methods, if proven useful, could be applicable to the classification of other time series data collected within medical and other settings. METHODS: We analyzed an initial dataset that comprised 4308 drivers who completed both the VDT and the ORE, in which 1096 (25.4%) drivers went on to fail the ORE. We studied 2 different approaches to constructing feature sets to use as input to ML algorithms: the standard method of reducing the time series data to a set of manually defined variables that summarize driving behavior and a novel approach using time series clustering. We then fed these representations into different ML algorithms to compare their ability to predict a driver's ORE outcome (pass/fail). RESULTS: The new method using time series clustering performed similarly compared with the standard method in terms of overall accuracy for predicting pass or fail outcome (76.1% vs 76.2%) and area under the curve (0.656 vs 0.682). However, the time series clustering slightly outperformed the standard method in differentially predicting failure on the ORE. The novel clustering method yielded a risk ratio for failure of 3.07 (95% CI 2.75-3.43), whereas the standard variables method yielded a risk ratio for failure of 2.68 (95% CI 2.41-2.99). In addition, the time series clustering method with logistic regression produced the lowest ratio of false alarms (those who were predicted to fail but went on to pass the ORE; 27.2%). CONCLUSIONS: Our results provide initial evidence that the clustering method is useful for feature construction in classification tasks involving time series data when resources are limited to create multiple, domain-relevant variables.

Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior.

The aim of this paper was to provide a methodological framework for estimating the amount of driving data that should be collected for each driver in order to acquire a clear picture regarding their driving behavior. We examined whether there is a specific discrete time point for each driver, in the form of total driving duration and/or the number of trips, beyond which the characteristics of driving behavior are stabilized over time. Various mathematical and statistical methods were employed to process the data collected and determine the time point at which behavior converges. Detailed data collected from smartphone sensors are used to test the proposed methodology. The driving metrics used in the analysis are the number of harsh acceleration and braking events, the duration of mobile usage while driving and the percentage of time driving over the speed limits. Convergence was tested in terms of both the magnitude and volatility of each metric for different trips and analysis is performed for several trip durations. Results indicated that there is no specific time point or number of trips after which driving behavior stabilizes for all drivers and/or all metrics examined. The driving behavior stabilization is mostly affected by the duration of the trips examined and the aggressiveness of the driver.

Aiding medical professionals in fitness-to-drive screenings for elderly drivers: development of an office-based screening tool.

ABSTRACTBackground:Elderly drivers are an increasing group in society. Previous research has found that functional and cognitive abilities are more important for driving abilities than biological age. In an attempt to conserve independent mobility for elderly drivers, many researchers have focused on elderly drivers diagnosed with cognitive decline (mild cognitive impairment or mild Dementia). This study is the first to focus on elderly drivers with cognitive complaints or suspected of diminished fitness to drive by an (in)formal caregiver as an at-risk group. METHODS: The main objective of this study was to develop a fitness to drive screening tool for elderly drivers to be used in a doctor's office. Furthermore, this study investigated the additional value of driving simulator tests in the assessment of fitness to drive. Both screenings (functional abilities and driving simulator test) were benchmarked against the official Belgian fitness to drive licensing procedure. RESULTS: One-hundred thirty-six elderly drivers participated in a functional abilities screening, a driving simulator assessment and an on-road driving test. Sixty-five percent of the sample was considered fit to drive. Visual acuity, physical flexibility, and knowledge of road signs were found to be the best predictive set of tests for the on-road fitness to drive outcome. A performance based driving simulator assessment increased predictive accuracy significantly. CONCLUSION: The proposed screening procedure saves part of the at-risk elderly driver population from stressful and costly on-road driving evaluations. This procedure provides more information of an individual driver's specific driving parameters. This opens doors for personalized older driver training to maintain independent mobility in later life.

On the road again: assessing driving ability in patients with neurological conditions.

Clinicians may not be aware of the specialised methods and adaptations that are used to help people with disabilities to drive a car. We describe a driving assessment process as carried out by one of the UK's flagship assessment centres, including an overview of the available assessments, adaptations and relevant legislation to guide practitioners about how best to signpost and counsel their patients appropriately about driving.

Characteristics of on-road driving by persons with central vision loss: Learning to drive with a bioptic telescope.

There is limited research on the driving performance and safety of bioptic drivers and even less regarding the driving skills that are most challenging for those learning to drive with bioptic telescopes. This research consisted of case studies of five trainee bioptic drivers whose driving skills were compared with those of a group of licensed bioptic drivers (n = 23) while they drove along city, suburban, and controlled-access highways in an instrumented dual-brake vehicle. A certified driver rehabilitation specialist was positioned in the front passenger seat to monitor safety and two backseat evaluators independently rated driving using a standardized scoring system. Other aspects of performance were assessed through vehicle instrumentation and video recordings. Results demonstrate that while sign recognition, lane keeping, steering steadiness, gap judgments, and speed choices were significantly worse in trainees, some driving behaviors and skills, including pedestrian detection and traffic light recognition were not significantly different from those of the licensed drivers. These data provide useful insights into the skill challenges encountered by a small sample of trainee bioptic drivers which, while not generalizable because of the small sample size, provide valuable insights beyond that of previous studies and can be used as a basis to guide training strategies.

Driving Trail Making Test part B: a variant of the TMT-B.

[Purpose] The Trail Making Test part B (TMT-B) is used in evaluating driving abilities and includes testing for the executive function. A driving simulator version of this test (DTMT-B) was developed to measure drivers' executive abilities in three-dimensional space. The purpose of the present study was to assess the validity of the DTMT-B for driving assessment. [Subjects] Thirty stroke patients and 65 healthy subjects were recruited. [Methods] Participants performed the TMT-B and DTMT-B. The DTMT-B was run on a driving simulator in which the individual performed a task on virtual roads connecting the lettered and numbered TMT-B points by simulated driving instead of connecting them with lines as in the paper or computerized TMT-B. Intra-class correlation coefficients (ICCs) were used to assess validities. Significant correlations were found between the TMT-B and DTMT-B. [Results] Participants performed the TMT-B and DTMT-B. Intra-class correlation coefficients (ICCs) were used to assess validities. Significant correlations were found between the TMT-B and DTMT-B. [Conclusion] The results suggest that the DTMT-B may be useful as part of driver screening assessment using a driving simulator for stroke patients and that it may also be used to assess the executive functions for healthy people.

A systematic review of major evaluation metrics for simulator-based automatic assessment of driving after stroke.

Background: Simulator-based driving assessments (SA) have recently been used and studied for various purposes, particularly for post-stroke patients. Automating such assessment has potential benefits especially on reducing financial cost and time. Nevertheless, there currently exists no clear guideline on assessment techniques and metrics available for SA for post-stroke patients. Therefore, this systematic review is conducted to explore such techniques and establish guidelines for evaluation metrics. Objective: This review aims to find: (a) major evaluation metrics for automatic SA in post-stroke patients and (b) assessment inputs and techniques for such evaluation metrics. Methods: The study follows the PRISMA guideline. Systematic searches were performed on PubMed, Web of Science, ScienceDirect, ACM Digital Library, and IEEE Xplore Digital Library for articles published from January 1, 2010, to December 31, 2023. This review targeted journal articles written in English about automatic performance assessment of simulator-based driving by post-stroke patients. A narrative synthesis was provided for the included studies. Results: The review included six articles with a total of 239 participants. Across all of the included studies, we discovered 49 distinct assessment inputs. Threshold-based, machine-learning-based, and driving simulator calculation approaches are three primary types of assessment techniques and evaluation metrics identified in the review. Discussion: Most studies incorporated more than one type of input, indicating the importance of a comprehensive evaluation of driving abilities. Threshold-based techniques and metrics were the most commonly used in all studies, likely due to their simplicity. An existing relevant review also highlighted the limited number of studies in this area, underscoring the need for further research to establish the validity and effectiveness of simulator-based automatic assessment of driving (SAAD). Conclusions: More studies should be conducted on various aspects of SAAD to explore and validate this type of assessment.

Driving assistant using generative AI pre-generated messages in simulator-based driving assessment: A step towards low-cost simulator-based driving assessment.

This paper presents a novel approach for a low-cost simulator-based driving assessment system incorporating a speech-based assistant, using pre-generated messages from Generative AI to achieve real-time interaction during the assessment. Simulator-based assessment is a crucial apparatus in the research toolkit for various fields. Traditional assessment approaches, like on-road evaluation, though reliable, can be risky, costly, and inaccessible. Simulator-based assessment using stationary driving simulators offers a safer evaluation and can be tailored to specific needs. However, these simulators are often only available to research-focused institutions due to their cost. To address this issue, our study proposes a system with the aforementioned properties aiming to enhance drivers' situational awareness, and foster positive emotional states, i.e., high valence and medium arousal, while assessing participants to prevent subpar performers from proceeding to the next stages of assessment and/or rehabilitation. In addition, this study introduces the speech-based assistant which provides timely guidance adaptable to the ever-changing context of the driving environment and vehicle state. The study's preliminary outcomes reveal encouraging progress, highlighting improved driving performance and positive emotional states when participants are engaged with the assistant during the assessment.

Esterman Visual Field Testing Using a Virtual Reality Headset in Glaucoma.

Purpose: To test the use of a virtual reality visual field headset (VRVF) for implementation of the Esterman visual field (EVF) test as compared with standard automated perimetry (SAP) among people with glaucoma. Design: Experimental design. Subjects: Patients with mild to severe glaucoma ranging from 10 to 90 years who presented for follow-up at a glaucoma clinic in Miami, Florida were eligible. Methods: Participants performed the EVF test on both SAP and VRVF. Five glaucoma-trained ophthalmologists were then asked to rate all anonymized SAP and RVF tests as a "pass" or "failure" based on Florida state law. Main Outcome Measures: Point-by-point concordance between original VRVF EVF test results and SAP EVF test results was calculated using the Kappa statistic. Concordance between SAP and VRVF was secondarily assessed with a conditional logistic regression based on the pass-failure determinations by the glaucoma-trained ophthalmologists. Interrater agreement on test pass-failure determinations was also calculated. Finally, test results on SAP versus VRVF were compared based on Esterman efficiency score (EES), the number of correct points divided by the number of total points, and duration of testing. Results: Twenty-two subjects were included in the study with ages ranging from 14 to 78 years old. Concordance between VRVF and SAP test using point-by-point analysis was poor (κ = 0.332, [95% confidence intervals {CI}: 0.157, 0.506]) and somewhat increased using pass-failure determinations from ophthalmologists (κ = 0.657, [95% CI: 0.549, 0.751]). Ophthalmologists were more likely to agree amongst themselves on pass-failure determinations for VRVF tests (κ = 0.890, [95% CI: 0.726, 0.964]) than for SAP (κ = 0.590, [95% CI: 0.372, 0.818]); however, VRVF demonstrated significantly lower EES than SAP (median EES difference: 4.5 points, P = 0.021). Conclusions: This pilot study is the first to assess the implementation of the EVF test using a virtual reality headset. Based on the weak overall agreement between VRVF and SAP, the current VRVF EVF test is not an acceptable determinant of driver's licensing. However, ophthalmologists were more likely to agree amongst themselves on VRVF test reports than on SAP reports. With further testing and improvement, virtual reality may eventually become a portable and convenient method for administering the EVF test. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Digital clock drawing test reflects visuospatial ability of older drivers.

Objectives: To keep older drivers safe, it is necessary to assess their fitness to drive. We developed a touch screen-based digital Clock Drawing Test (dCDT) and examined the relationship between the dCDT scores and on-road driving performance of older drivers in a community-setting. Methods: One hundred and forty-one community-dwelling older drivers (range; 64-88 years old) who participated in this study were included in the analysis. Participants completed the dCDT, the Mini-Mental State Examination-Japanese (MMSE-J), and an on-road driving assessment. We examined the relationship between dCDT scores using the method by Rouleau et al. (maximum 10 points) and the on-road driving performance based on a driving assessment system originally developed by Nagoya University. Results: Multiple regression analyses showed that errors in the driving test were associated with dCDT score for the items "confirmation," "turning left" and "maintains driving lane position". Discussion: This study confirmed the relationship between the dCDT score and driving errors, such as confirmation, turning left and maintaining driving lane position. The increase in these errors indicates a decline in visuospatial ability while driving. The dCDT score may reflect older drivers' visuospatial abilities while driving.

Driving scenarios and environmental settings in simulator-based driving assessment systems for stroke: a systematic review.

BACKGROUND: Driving simulators are effective tools to evaluate the driving abilities of patients with stroke. They can introduce various driving scenarios which will greatly benefit both the assessors and drivers. However, there is still no guidelines by which driving scenarios should be introduced in the driving assessment. OBJECTIVES: We conducted a systematic review to examine the utilization of driving scenarios and environments in the simulator-based driving assessment for patients with stroke. METHODS: A systematic review was conducted following PRISMA. We searched PubMed, Web of Science, ScienceDirect, ACM Digital Library, and IEEE Xplore Digital Library databases in January and June 2022 to identify eligible articles published since 2010. RESULTS: Our searches identified 1,614 articles. We included 12 studies that applied driving simulators to assess the driving performance of patients with stroke. The driving scenarios were categorized into three categories - vehicle controls scenarios, hazard perception scenarios, and trajectory planning scenarios - based on a certain set of driving abilities. The most common driving scenarios are simple navigation (n = 8) and emergency stop (n = 8). The most frequently used driving area is urban (n = 9), and a variety of roads and traffic conditions were found in the included studies. Only 2 studies applied weather conditions, such as the clear and sunny condition or the windy condition. CONCLUSION: It is recommended for future research to consider covering scenarios from the aforementioned three categories and further investigate the benefits of introducing complex weather conditions and localized traffic conditions in the driving assessment.

Driving safety: Investigating the cognitive foundations of accident prevention.

Driving is a crucial aspect of personal independence, and accurate assessment of driving skills is vital for ensuring road safety. This study aimed to identify reliable cognitive predictors of safe driving through a driving simulator experiment. We assessed the driving performance of 66 university students in two distinct simulated driving conditions and evaluated their cognitive skills in decision-making, attention, memory, reasoning, perception, and coordination. Multiple regression analyses were conducted to determine the most reliable cognitive predictor of driving outcome. Results revealed that under favorable driving conditions characterized by good weather and limited interactions with other road users, none of the variables tested in the study were able to predict driving performance. However, in a more challenging scenario with adverse weather conditions and heavier traffic, cognitive assessment scores demonstrated significant predictive power for the rate of traffic infractions committed. Specifically, cognitive skills related to memory and coordination were found to be most predictive. This study underscores the significance of cognitive ability, particularly memory, in ensuring safe driving performance. Incorporating cognitive evaluations in driver licensing and education/training programs can enhance the evaluation of drivers' competence and promote safer driving practices.

Using a virtual reality power mobility device simulator to assess the driving skills of people with brain diseases.

Introduction: No previous study has explored the effectiveness of current prescription standards for evaluating power mobility device (PMD) maneuverability. To verify the current prescription standards for PMDs using a virtual reality (VR)-based PMD simulator and to present the possibility of using a VR-based PMD simulator as an alternative to current evaluation standards. Methods: A total of 52 patients with brain diseases were enrolled. All participants were over 18 years old and had gait disturbance or limited outdoor walking ability. Participants performed a driving ability test using a VR PMD simulator. Results: The driving ability test using the VR PMD simulator indicated that cognitive impairment, measured by the K-MMSE (p = 0.017), and unilateral neglect, measured by line bisection (p = 0.031), led to reduced driving ability and safety. In addition, patients with cognitive impairment or neglect presented driving stability problems, which were observed in the driving trajectory. There was also no correlation between driving scores and MBI subitems. Conclusion: In patients with brain lesions, a driving ability test using a VR PMD simulator can be a safe, objective method for comprehensively evaluating a driver's capacity, offering an alternative to the current prescription standards for PMDs.

Driving practice effects for older drivers with mild cognitive impairment: A preliminary study.

BACKGROUND: Older drivers with mild cognitive impairment (MCI) often show declining driving performance. Evidence is lacking regarding whether their driving skills can be improved after practice. AIMS/OBJECTIVES: To compare the practice effects of older drivers with MCI and drivers with normal cognition in an unfamiliar, standardized driving course with three practices. MATERIALS AND METHODS: Single-blind two-group observational design. Twelve drivers with confirmed MCI as the experimental group and ten with normal cognition (NC) as the control, all ≥ 55 years old. The primary outcome was to assess the practice effects, measured with an in-car global-positioning-system mobile application to compare the speed and directional control of a complex manoeuvre after practices. Secondary outcomes were to assess the pass/fail rate and mistakes observed for the 3rd/final on-road driving practice. No instructions were given during practice. Descriptive statistics and the Mann-Whitney U test were used for data analysis. RESULTS: No significant inter-group difference in the pass/fail rate and number of mistakes. Some MCI drivers performed better in the speed and directional control of the S-Bend manoeuvre after practices. CONCLUSIONS: The driving performance of drivers with MCI may improve with practice. SIGNIFICANCE: Older drivers with MCI may potentially benefit from driver retraining. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov (Identifier: NCT04648735).

Validation of a computerized driving simulator test of cognitive abilities for fitness-to-drive assessments.

Background: Driving requires a series of cognitive abilities, many of which are affected by age and medical conditions. The psychosocial importance of continued driving ushers the need for valid measurements in fitness-to-drive assessments. A driving simulator test could prove useful in these assessments, having greater face validity than other off-road tests and being more cost-effective and safer than ordinary on-road testing. The aim of this study was to validate a driving simulator test for assessment of cognitive ability in fitness-to-drive assessments. Methods: The study included 67 healthy participants. Internal consistency of the simulator subtests was estimated. A correlation analysis between results on the simulator and the cognitive tests Trail Making Test (TMT) A and B and the Useful field of View test (UFOV) and multiple regression analysis were conducted. Finally, a comparison of results between age groups (>65 years) and (<65 years) was done. Results: Results showed good internal consistency. Significant and moderate correlations were found for all reaction time in the simulator's subtests and UFOV 3, and all but two with TMT A. Lane positioning in the simulator showed significant and low to moderate correlations with UFOV 3 in all subtests. Reaction time and Double reaction time on subtest 3 were significantly correlated with UFOV 2 and UFOV 3 and TMT A, respectively. Test on Centerline (position) in subtest 3 as dependent variable was significantly correlated with UFOV 3. Significant means differences and large effect sizes between the age groups were found for all reaction time and lane positioning tests. Conclusion: The findings of concurrent validity, especially with TMT A and UFOV 3 and its sensitivity for age-related differences, indicate potential for the simulator to be used as a complement in fitness-to-drive assessments. However, a clinical study is necessary to further examine its usefulness for patients with cognitive deficits.

Back on the Road: Comparing Cognitive Assessments to Driving Simulators in Moderate to Severe Traumatic Brain Injuries.

Objective: To compare established clinical outcome assessments for predicting behind the wheel driving readiness and driving simulator results across age groups and in traumatic brain injury. Methods: Participants included adults who had a traumatic brain injury ranging in age from 31 to 57 years and a non-impaired adult population ranging in age from 18 to 80 years. Physical and cognitive outcomes measures were collected included range of motion and coordination, a "Rules of the Road Test" a "Sign Identification Test," Trails A and B, and the clock drawing test. Visual measures included the Dynavision D2 system and motor-free visual perceptual test-3 (MVPT-3). Finally, the driving simulators (STIÒ version M300) metro drive assessment was used, which consisted of negotiating several obstacles in a metropolitan area including vehicles abruptly changing lanes, pedestrians crossing streets, and negotiating construction zones. Results: Our findings suggest that the standard paper-pencil cognitive assessments and sign identification test significantly differentiate TBI from a non-impaired population (Trails A, B and Clock drawing test p < 0.001). While the driving simulator did not show as many robust differences with age, the TBI population did have a significantly greater number of road collisions (F3, 78 = 3.5, p = 0.02). We also observed a significant correlation between the cognitive assessments and the simulator variables. Conclusions: Paper-pencil cognitive assessments and the sign identification test highlight greater differences than the STI Driving Simulator between non-impaired and TBI populations. However, the driving simulator may be useful in assessing cognitive ability and training for on the road driving.

Offering alphabet support in the Trail Making Test: Increasing validity for participants with insufficient automatization of the alphabet.

Insufficient automatization of the alphabet may falsely impair performance on the Trail Making Test among persons with dyslexia or persons not accustomed to the Latin alphabet. We analyze whether writing the alphabet on top of the test sheet changes performance in these risk groups, and whether alphabet support reduces the complexity of the set-shifting task.One-hundred and seventy patients referred to neuropsychological assessment participated and were given both a TMT-version offering alphabet support and the D-KEFS TMT. The discrepancy between the D-KEFS subtask where lines are drawn successively between numbers only, and the task where lines are drawn between letters only, was operationalized as measuring insufficient alphabet automatization.Both the possible dyslexia group, and persons taught to read with another alphabet, had a larger discrepancy score than the remaining sample. Regression analyses showed that the discrepancy scores explained 3.4% of the variance beyond age and speed when giving alphabet support. The corresponding percentage for the D-KEFS Switching task was 17.5%. The findings indicate that alphabet support alleviated effects of non-automatization. The TMT-B-NR: TMT-A ratio score was equivalent to what is found when not applying alphabet support, showing that alphabet support did not contaminate the test as a EF-measure.