Cognitive Alterations Related to Driving Performance in Parkinson’s Disease: A Cross-Sectional Proof-of-Concept Study Using a Driving Simulator

Cognitive Alterations Related to Driving Performance in Parkinson’s Disease: A Cross-Sectional Proof-of-Concept Study Using a Driving Simulator | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Cognitive Alterations Related to Driving Performance in Parkinson’s Disease: A Cross-Sectional Proof-of-Concept Study Using a Driving Simulator Almudena Cerezo-Zarzuelo, Francisco José Sánchez-Cuesta, Carlota Trigo, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7752190/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Driving ability in individuals with Parkinson’s disease (PD) can be compromised early in the course of the illness, even before clear cognitive deficits emerge on standard neuropsychological tests. This study investigated subtle driving impairments in a group of non-demented people with PD using a high-fidelity driving simulator. Seven PD participants and seven healthy controls, matched for age and sex, completed cognitive assessments, reaction time tasks, and five simulated driving scenarios that measured lane keeping, steering control, and reaction to events. While most cognitive scores were comparable between groups, PD participants exhibited slower response times in basic tasks and showed reduced lane control, particularly during left turns. These difficulties were associated with disease severity and medication dosage. The simulator proved more sensitive than conventional tests in detecting early impairments related to attention and visuospatial processing. These findings suggest that driving simulators may play a key role in improving the assessment of driving competence in PD, providing insight into real-world challenges faced by this population. Health sciences/Diseases Health sciences/Neurology Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Parkinson Disease Driving Simulation Neuropsychological Tests Reaction Time Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Driving is a complex task that requires the integration of multiple perceptual, motor, and cognitive processes, with cognitive function being essential for effective real-time decision-making and safe responses ( 1 ). In most countries, fitness to drive is evaluated through standardized psychotechnical assessments that include a variety of tasks aimed at measuring visual perception, reaction time, coordination, and attention. Many of these variables are directly influenced by cognitive functioning. In individuals with Parkinson’s disease (PD), a neurodegenerative condition characterized by degeneration of dopaminergic circuits, motor symptoms such as rigidity rest tremor and bradykinesia are usually the basis of the diagnosis but cognitive disfunctions, including attention, executive function, and working memory may also be present since early stages ( 2 ). Cognitive impairments in Parkinson’s disease (PD) can significantly affect driving ability by compromising critical functions such as divided attention, sustained vigilance, and executive functioning. These deficits hinder the capacity to respond effectively to constantly changing traffic environments—such as reacting to signals, avoiding obstacles, or managing interactions with other vehicles ( 3 ). Individuals with PD often exhibit delayed reaction times and reduced accuracy in tasks requiring rapid decision-making and multitasking ( 1 , 4 ). Since reaction time is crucial for accident avoidance, its prolongation in PD increases the risk of collisions, especially in complex driving scenarios ( 5 ). Furthermore, declines in executive function complicate planning and the ability to respond flexibly to unexpected events, thereby reducing overall fitness to drive ( 6 ). Standard driving assessment exams generally include tests for reaction time, visual processing, attention, and motor coordination, domains frequently impaired in PD patients ( 7 ). In Spain, standard psychotechnical driving assessments consist of basic medical evaluations—such as visual and auditory acuity, and the control of chronic conditions, including neurological disorders, which typically do not lead to test adjustments or additional cognitive evaluations. These medical checks are complemented by automated testing in a psychotechnical cabin, usually involving a screen and manual controls. The most common tasks assess visuomotor coordination (e.g., maintaining a ball centered within a moving lane using two joysticks) and simple reaction time to visual or auditory stimuli. While these tests aim to evaluate essential driving-related skills such as coordination, reaction time, and anticipatory capacity, they are performed in abstract, decontextualized settings with no direct resemblance to real-world driving situations. A recent systematic review by Stamatelos et al.( 8 ) emphasized that while international guidelines recognize the impact of cognitive decline in PD on driving performance, there is considerable variability in how fitness to drive is assessed across countries. The review also highlighted a lack of consensus regarding the most reliable cognitive tools for driving evaluation and called for more evidence-based, individualized approaches that account for the heterogeneous progression of PD and its cognitive symptoms. Assessing fitness to drive in individuals with PD requires specific tools that evaluate the cognitive and psychomotor functions crucial to driving safety. In an era of increasing automation in driving and the development of new technologies that enable more immersive experiences and control over a wider range of variables, it is possible to identify safety indicators behind the wheel that may be overlooked in standard neuropsychological assessments or superficial evaluations of reaction times. As a response to these challenges, driving simulators have emerged as a promising alternative, allowing for detailed and controlled measurement of key driving parameters, such as trajectory accuracy and lane-centered positioning ( 9 , 10 ). This study aims to identify objective differences in key driving safety variables between individuals with Parkinson’s disease (PD) and healthy controls using a driving simulator. We hypothesize that PD patients will show altered reaction times and reduced vehicle control precision compared to controls. By providing empirical evidence, this study seeks to support future efforts toward the development of more objective tools for assessing driving fitness, taking into account the specific neuropsychological profiles associated with different neurodegenerative diseases. Methods 2.1. Participants Seven drivers with PD were recruited and matched in age and gender with seven healthy regular drivers. There were no significant differences between groups regarding years of driving experience (Patients: 43,57 years ± 8,84; Controls: 44,86 years ± 8,70), daily driving distance average (Patients: 20,42 km ± 15,84; Controls: 42,86 km ± 21,57) nor frequency of driving (Patients: 3,43 days ± 2,23, Controls: 5 days ± 1,77) per week. Selection criteria for participants with PD were: ( 1 ) PD diagnosis, ( 2 ) younger than 75 years old, ( 3 ) they must hold a driver’s license and have driving experience, ( 4 ) no changes in medication for the past 30 days and ( 5 ) attend the evaluations on ON state (1 hour after last oral dose of levodopa), ( 6 ) a Hoehn&Yahr stage lower or equal to 3, ( 7 ) a punctuation higher or equal to 24 in the Montreal Cognitive Assessment (MoCA), ( 8 ) not having visuoperceptual impairments. The Unified Parkinson’s Disease Rating Scale (UPDRS) was assessed for each patient, to assure the ON state when conducting the evaluation. The selection criteria for the control participants were: ( 1 ) holding a driver’s license and driving experience, ( 2 ) younger than 75 years and ( 3 ) not having visuoperceptual impairments. The recruitment, as well as the evaluation, were conducted in the Beata Maria Ana Hospital, from March 1st to March 12th and June 3rd to June 14th, 2024. All participants were informed of the details of the evaluation and signed their consent to participate in this study, in accordance with the declaration of Helsinki. The Ethics Committee of the 12 de Octubre Hospital approved the study on 6th February 2024 with code 23/603. 2.2. Experimental design and procedures This is a proof-of-concept study with a cross-sectional measurement. Evaluations of all the participants were carried out in a single session with two different parts: a cognitive assessment and the driving simulation tests, with a total duration of 120 minutes. Demographic and clinical data from participants are shown in Tables 1 and 2 . Table 1 Participants’ demographic characteristics Participant Gender Age (years) Laterality Control 1 Masculine 60 Right-handed Control 2 Masculine 74 Right-handed Control 3 Masculine 60 Right-handed Control 4 Masculine 63 Right-handed Control 5 Masculine 66 Right-handed Control 6 Masculine 59 Right-handed Control 7 Masculine 74 Right-handed Experimental 1 Masculine 71 Right-handed Experimental 2 Masculine 69 Right-handed Experimental 3 Masculine 66 Right-handed Experimental 4 Masculine 56 Right-handed Experimental 5 Masculine 70 Right-handed Experimental 6 Masculine 54 Right-handed Experimental 7 Masculine 63 Right-handed Table 2 Clinical characteristics of the participants with PD Participant Years since diagnosis Hoehn&Yahr stage Levodopa equivalents Daily Dose (mg) Experimental 1 1 2 400 Experimental 2 5 2 1125 Experimental 3 7 2 1050 Experimental 4 3 1,5 300 Experimental 5 11 2,5 1050 Experimental 6 3 2 791,08 Experimental 7 9 2 2074,8 2.2.1. Cognitive assessment All participants completed a battery of the following neuropsychological tests prior the driving simulation tests for 60 minutes: MoCA: it is a brief screening tool for general cognition functioning ( 11 ). Stroop test: this test measures cognitive flexibility, selective attention, cognitive inhibition and information processing speed. It includes four different parts: words, colour, words-colour and interference. ( 12 ) Wechler adult intelligence scale (WAIS-IV): This battery is the gold standard for the evaluation of cognitive abilities in adults. We selected two tests from this battery: symbol search and digit-symbol substitution to assess information processing speed, visual attention, work memory and incidental learning. ( 13 ) Computerized reaction time tasks: reaction time tasks have been previously used with patients with PD when assessing deficits in the information processing ( 14 ). We performed two tasks, using a 27-inch monitor, controlled by Presentation® software (Neurobehavioral Systems Inc, Albany, California, United States) ( 15 ), based on the Arroyo et al. study ( 14 ). The average reaction time in each of the tasks and the percentage of correct answers were measured. The two tasks were displayed in the following order: Finger Tapping (FT): The FT task is a common test of sensory-motor performance ( 16 ). It has been applied following the Strauss application norms: participants were asked to press the keyboard spacebar as repeatedly with the index finger as fast as they can, with 5 attempts with each hand in 10-seconds trials. The collected data is the average time between taps. Simple Reaction Time (SRT): It is a test designed to measure simple perception and sustained alertness. Participants were asked to press the left mouse button as fast as they can when a “+” appeared in the centre of the screen. It involved 50 trials with a total duration of 2–3 minutes. Simple Reaction Time-Sustained Attention to Response Task (SRT-SART): This task was applied to measure response strategy-inhibition. It consists of 168 Go trials and 21 No/Go trials, with a duration of 4 minutes. The stimulus size varied between 12 and 9 mm. Choice Reaction Time (CRT): This task was employed to measure visual perceptual decision time. It adds the processing of uncertainty to the processes involved in the SRT. Participants were asked to press the left button of the mouse when they see a square at the centre of the screen, and the right button when a circle appeared. It included 80 tests with a duration of 3 minutes. Choice Reaction Time-Search (CRT-Search): This task was applied to measure visual search. Participants were asked to press the left button of the mouse when they see a “Z” in a 6-letter sequence, or to press the right one if there was no “Z”. Stimuli were classified in two groups: whether there was a “Z” or not and the visual characteristics of the other letters of the sequence. Therefore, four different combinations were obtained: target-low interference, target-high interference, non-target-low interference and no-target-high interference. The task involved 128 trials, executed between 5 to 8 minutes. 2.2.2. Driving simulation The driving simulator was displayed on a computer with three 27-inch screens, which offers a field of view of approximately 130º, enabling safe management of scenarios such as intersections and roundabouts. Interaction with the simulation software is achieved using the Logitech G29 kit (Logitech, Laussane, Switzerland), which includes force-feedback steering wheel and a pedal set with realistic resistance. The simulation software used was the SCANeR Studio by AV Simulation (AVSimulation, Boulogne-Billancourt, France)( 17 ). This automotive simulation software, commercially available, allows for the assessment of driving performance by humans and autonomous driving systems. The tool provides all the necessary modules to construct a realistic virtual environment, including road settings, vehicle dynamics, traffic and sensor configurations, weather conditions, and scenario customization (Fig. 1 ). The simulation protocol includes 5 experiments assessing different aspects related to fitness to drive that are executed in 45–60 minutes: Experiment 1 : Participants are asked to follow the car in front of them at a constant speed and to stop the movement when the other car stops. The task consists of initiating movement and maintaining a constant speed following a straight line. At the end of the experiment, the lead car stops at an aleatory time to test the participant's reaction. Metrics derived: Reaction time, defined as the time difference between when the simulated car begins to brake and when the participant initiates braking (ms). Experiment 2 : Participants are asked to follow the car in front of them and adapt their speed to match the pace of the lead vehicle. At the end of the experiment, the car in front stops at an aleatory time to test the participant’s reaction. Metrics derived: ( 1 ) Reaction time, defined as the time difference between when the simulated car begins to accelerate and when the participant begins to accelerate (ms) measured only at the beginning of the experiment. ( 2 ) Mean distance between the simulated car and the participant's car (m). ( 3 ) Mean difference in velocity between the simulated car and the participant's car (km/h). Experiment 3 : Participants are asked to make a right turn from the right lane. They must initiate the movement, approach the intersection, execute a tight right turn without trespassing the left lane, and stop at a specific spot. Metrics derived: ( 1 ) Percentage of time the car remained within the lane (%). ( 2 ) Maximum lateral distance from the center of the lane (m). ( 3 ) Area between the edge of the road and the vehicle's trajectory outside the lane (m²). Experiment 4 : Participants are asked to make a left turn from the left lane. They must initiate the movement, approach the intersection, execute a wider left turn while maintaining the car within the left lane, and stop at a specific spot. Metrics derived: ( 1 ) Percentage of time the car remained within the lane (%). ( 2 ) Maximum lateral distance from the center of the lane (m). ( 3 ) Area between the edge of the road and the vehicle's trajectory outside the lane (m²). Experiment 5 : Participants are asked to make a reverse turn covering approximately 10 meters and stop in a specific spot. Metrics derived: ( 1 ) Percentage of time the car remained within the lane (%). ( 2 ) Maximum lateral distance from the center of the lane (m). ( 3 ) Area between the edge of the road and the vehicle's trajectory outside the lane (m²). Experiments 1 and 2 aim to assess the driver’s ability to adapt dynamically to a controlled traffic environment, paying attention to the stimulus from the vehicle before them. Experiment 1 specifically assesses selective and sustained attention, processing speed, inhibitory control, and executive functions. Experiment 2 examines divided attention, working memory, motor planning, and cognitive flexibility related to motor initiation. Experiments 3 and 4 assess the driver's ability to perform turns at intersections—to the right and left, respectively—while maintaining a centred position within the lane during the turn. Experiment 5 assesses the ability to make a turn while driving in reverse. These tasks require precise integration of visuospatial planning, visuomotor coordination, attentional control, and executive functions. All experiments are designed to detect cognitive impairments commonly observed in Parkinson’s disease, which can compromise motor adaptation and driving safety. 2.3. Statistical analysis Differences between control participants and PD patients were determined through Student’s t and Wilcoxon tests regarding demographic variables, driving experience, driving distance average and frequency of driving per week, neuropsychological tests, reaction time tasks and variables involved in the driving simulator. For multiple comparisons, the level of significance was adopted p < .05. An ANCOVA was used to identify the cognitive components contributing to the SRT Task: the slowness in the processing of the information associated with the perceptual and sustained alert components was analysed with the SRT task as the dependent variable and the response time in the FT task as the covariate. Use of FT as a covariate allows controlling the shared “motor” component with the STR task. The significance level was adopted p < .05. To compare the experiment execution in experimental and control group, linear mixed models were applied. This approach was selected to account for the repeated measures structure of the data, as multiple trials were collected per participant. In the models, group (control, experimental) was included as a fixed effect while subject was modelled as a random effect to account for within-subject variability. Spearman correlations were performed with the PD patients’ data between variables considered in the driving simulation and the different reaction time tasks, and PD-related variables: the HY stage, years of evolution of the disease and levodopa equivalents daily dose. The significance level was adopted p < .05. Analyses were performed using R (4.4.2. version, Posit, Boston, MA, United States) and MATLAB (2023b version, MathWorks, Natick, MA, United States). ( 18 ) Results 3.1. Cognitive assessment 3.1.1. Neuropsychological tests Wilcoxon tests showed no significant differences between groups in the MoCA (W = 15 [-3,99 − 1,99]; p = 0,14) nor Stroop test (words: W = 21,5 [-38,00–22,00]; p = 0,49; colour: W = 29,5 [-8,99–10,99], p = 0,90; words-colour: W = 19,5 [-21,99–8,99]; p = 0,35; interference: W = 17 [-16,30–1,45]; p = 0,22) between control and experimental groups. Student´s t tests were used with scalar punctuations of symbol search (t = 2,5955 [0,37–4,21]; p = 0,02354) and digit-symbol (t = 1,1793 [-1,38, – 4,52]; p = 0,2645), which showed a significant difference between groups in symbol search punctuations. Table 3 Neuropsychological tests results Neuropsychological test Control group Experimental group Statistic P value MoCA 27,72 26,25 W = 15(-3,99 − 1,99) 0,14 Stroop test Words 115,43 108,13 W = 21,5(-38,00 22,00) 0,49 Colour 73,71 74,13 W = 29,5(-8,99 − 10,99) 0,90 Words-Colours 52,57 45,25 W = 19,5(-21,99 − 8,99) 0,35 Interference 6,96 0,55 W = 17 (-16,30–1,45) 0,22 Symbol search* 29,71/13,43 24,5/11,14 t = 2,6 (0,37–4,21) 0,02* Digit-symbol substitution 57,29/12,43 50,75/10,86 t = 1,2 (-1,38, – 4,52) 0,26 3.1.2. Reaction time tasks Wilcoxon tests revealed no significant differences between groups in FTT (non-dominant hand: W = 32 [-27,89–45,80]; p = 0,69; dominant hand: W = 26 [-23,63–23,58]; p = 0,87), SRT-SART (average time: W = 36 [-26,85–79,41]; p = 0,40; correct answers: W = 22,5 [-3,99–1,99]; p = 0,55), CRT (average time: W = 36 [-26,85–79,41]; p = 0,40; correct answers: W = 22,5 [-3,99–1,99]; p = 0,55) and CRT-Search (average time: W = 28 [-93,18–119,61]; p = 1; correct answers: W = 12,5 [-29,99–1,99]; p = 0,08). In the SRT task, a significant difference was found regarding the time average reaction time (W = 49 [9,60–83,96]; p = 0,013), whereas it was not significant when considering the percentage of correct answers (W = 17 [-0.99–4,00]; p = 0,22). The ANCOVA conducted to address the perceptual and sustained alert components of the SRT task showed a significant difference between the PD patients and the healthy controls (F ( 1 , 15 ) = 15,613; p = 0,002; η 2 part = 0,565). 3.2. Driving simulation Results from statistical analyses regarding driving simulator execution are shown in Table 4 . Although statistical significance was not reached—likely due to the limited sample size and the proof-of-concept nature of this study—clear differences were observed between patients and controls in the reaction times of Experiment 1 and the driving speed in Experiment 2 (Fig. 2 ). We found a relationship between the execution of the symbol search test and the performance in experiment 1 by a linear mixed model (estimate = -0,2, p = 0,004*). Although differences in the ability to maintain lane position were observed in Experiment 3, they did not reach statistical significance. However, significant differences were found in Experiment 4 regarding both the maximum distance from the centre of the lane and area outside the lane (Figs. 3 and 4 ). No significant statistical differences were found concerning experiment 5, although a difference between group means is noticeable. Table 4 Differences between groups in driving simulation experiments Experiment Metric Control group Patient group P value Statistic Confidence Interval 1 Reaction time (s) 2,02 ± 1,38 3,9 ± 2,36 0,113 1,621 [-0,338, 3,074] 2 Reaction time (s) 4,26 ± 1,61 4,55 ± 1,53 0,657 0,450 [-1,032, 1,611] Diff. Velocity (km/h) 6,83 ± 2,63 9,81 ± 4,06 0,080 1,822 [-0,382, 6,348] Distance (m) 56,34 ± 13,69 57,74 ± 30,94 0,906 0,119 [-22,703, 25,507] 3 % Time in lane (%) 91,49 ± 15,17 83,47 ± 18,07 0,182 –1,358 [-19,945, 3,914] Area (m2) 1,76 ± 3,21 6,23 ± 11,30 0,082 1,785 [-0,590, 9,521] Max. Distance (m) 2,24 ± 0,69 9,81 ± 4,06 0,386 0,877 [-0,374, 0,947] 4 % Time in lane (%) 88,36 ± 10,19 70,84 ± 25,04 0,052 -2,007 [-35,163, 0,120] Area (m2) 2,46 ± 1,73 17,75 ± 19,65 0,013* 2,615 [3,471, 27,112] Max. Distance (m) 2,64 ± 0,86 3,58 ± 1,27 0,039* 2,132 [0,050, 1,849] 5 % Time in lane (%) 95,01 ± 10,88 82,47 ± 30,21 0,242 -1,188 [-33,910, 8,809] Area (m2) 9,64 ± 30,47 79,90 ± 201,25 0,330 0,986 [-73,808, 214,330] Max. Distance (m) 2,62 ± 2,12 5,23 ± 8,86 0,411 0,830 [-3,746, 8,971] 3.3. Correlations Significant correlations regarding PD evolution variables and PD treatment with simulation execution were found between reaction time in experiment 2 and levodopa dose (S = 102,42, p = 0,0211, ρ=-0,8288) and distance from the centre of the lane in experiment 4 with the levodopa dose (S = 12,611, p = 0,04077, ρ = 0,77) and years of evolution (S = 6,56, p = 0,00845, ρ = 0,88). Percentage of maintenance inside the lane in experiments 3 (S = 100,9, p = 0,0301, ρ=-0,80) and 4 (S = 100,9, p = 0,0301, ρ=-0,80) and area outside the lane in experiment 4 (S = 11,1, p = 0,0301, ρ = 0,80) correlates with Hoehn & Yahr stage. This relationship indicates that disease progression is directly associated with a worsening of the ability to maintain vehicle trajectory and a centred lane position. The cognitive components of the SRT task correlates with all the components considered in experiment 5: distance from the centre of the lane (S = 102, p = 0,0340, ρ=-0,82), area outside the lane (S = 103,43, p = 0,0162, ρ=-0,85) and percentage inside the lane (S = 8,57, p = 0,0162, ρ = 0,85). Discussion Parkinson’s disease affects multiple functional domains—including motor control, sensory integration, and cognitive processing, all of which are essential for maintaining safe driving behaviour. PD patients are more prone to cease driving earlier than their contemporaries, as the disease progresses and associates a gradual decrease of their ability to drive ( 3 ). According to a recent review ( 8 ) the evaluation for driving fitness in PD patients should include 6 different aspects: general patient characteristics like age and Hoehn&Yahr stage, driving history, motor impairment, usually measured with the MDS-UPDRS-III; cognitive evaluation with neuropsychological tests and other PD-related symptoms like sleep disorders, motor fluctuations and adverse effects. All these data were included in our study in the initial evaluation of the participants. When comparing the clinical interview and neuropsychological test results with the driving simulator data, we observed that standard cognitive assessments failed to detect significant differences between PD patients and controls, while the simulator revealed clear impairments in reaction time and visuospatial accuracy, both critical for safe driving. The inclusion of computerized tasks further supported these findings: participants with PD showed deficits in perceptual processing and sustained alertness during the SRT task, in line with previous studies ( 14 , 19 – 21 ). These results suggest that traditional assessments may not capture subtle but functionally relevant cognitive alterations. Notably, performance in Experiment 1, which was designed to assess sustained attention and processing speed, is predicted by performance on the Symbol Search test, as both tasks evaluate similar cognitive functions. Likewise, the association between SRT performance and reverse driving suggests that simple computerized reaction time tasks—relying on visual stimulus detection—may help predict functional abilities in real-world scenarios such as backing into a parking space, which also requires visual detection skills. These visuospatial impairments may also contribute to the increased difficulty observed when executing left turns, particularly among right-handed individuals, considering that the typical driving position in the vehicle is not centered but shifted to the left Our findings reinforce the growing evidence that standard psychomotor tests may be insufficient to detect the early-stage cognitive and visuospatial impairments that affect driving ability in PD ( 22 ). Driving simulators, as used in this study, provide a multidimensional assessment environment that better reflects the real demands of driving. The simulator used in this study was intentionally programmed to address domains known to be affected in early PD, such as sustained attention, visuospatial control, and reaction time. The scenarios were designed to simulate real-world driving tasks like left turns, lane positioning, and reverse driving, allowing for objective measurement of trajectory control and response time. These features were developed to reflect both clinical needs and the literature on common driving challenges in PD, and to go beyond the limited scope of standard cognitive assessments. While driving simulators inevitably differ from real world driving due to the lack of full sensory feedback and risk perception, they offer a reproducible, safe, and ethically sound alternative for evaluating performance under controlled conditions. Moreover, their flexibility makes them valuable for future implementation of personalized driving assessments in clinical settings. This study has several limitations. First, the sample size was relatively small, and participants were not randomly selected, which limits both the generalizability of the findings and the reliability of the statistical comparisons. Second, the sample was not fully balanced in terms of socioeconomic and educational background, which may influence neuropsychological performance. Third, although gender was matched across groups, the overall gender distribution may not reflect the general driving population. Finally, although simulators provide a valuable controlled environment, they cannot fully replicate the sensory, emotional, and contextual variables inherent in actual driving situations. By bridging the gap between clinical evaluation and real-world functional performance, simulator-based assessments offer a crucial opportunity to redefine how we understand and measure driving fitness in neurodegenerative conditions like Parkinson’s disease. Conclusion This proof-of-concept study highlights that routine cognitive assessments in patients with Parkinson’s disease may not always be efficient enough to detect impairments that could potentially compromise driving safety, such as visuospatial skills and sustained alert impairments. Previous studies have confirmed that this type of impairment can emerge in the early stages of the disease, even in the absence of other indicators of cognitive decline. While instrumental tests such as computerized reaction time assessments can detect these deficits, they are not currently used in real-life tasks like driving ability. The present study demonstrates that immersive driving environments targeting specific cognitive functions guided by the known characterized deficits of the disease may help identify deficits that go unnoticed with conventional assessments. Although the actual impact of these deficits on driving safety remains to be fully evaluated, the refinement of driving simulators—such as the one used in this experiment—represents a promising step toward improving the detection of variables that may affect driving performance in people with PD. Declarations Funding: This study received no specific funding from public, commercial, or not-for-profit organizations. Ethical approval: The study was approved by the Ethics Committee of the Hospital 12 de Octubre (approval code 23/603, issued on February 6, 2024), and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants. Conflict of interest: The authors declare no conflicts of interest. Reporting guidelines: This manuscript was prepared in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist for cross-sectional observational studies. Author Contributions : Conceptualization: JPR/ER/JV; Methodology: ACZ/FJSC/JPR; Software: ER/JV/JFML/VT; Validation: ER/JV/JFML/VT; Formal analysis: ACZ/FJSC/CT; Resources: JPR/ER/JV; Data curation: ACZ/FJSC/CT; Writing – original draft: ACZ/FJSC/JPRM; Writing review & editing: ACZ/FJSC/CT/ER/JV/JFML/VT/JPR; Supervision: ER/JV/JPR; Project administration: ER/JV/JPM Data Availability Statement: The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. References Anderson, DE., Ghate, DA. and Rizzo, M. Vision, attention, and driving. Handb. Clin. Neurol. 178 , 337–360 (2021). Álvarez F.J. Parkinson’s disease, antiparkinson medicines, and driving. Expert Rev. Neurother. 16 , 1023–1032 (2016). Ranchet, M., Devos, H. and Uc, EY. Driving in Parkinson disease. Clin. Geriatr. Med. 36 , 141–148 (2020). Classen, S. and Holmes, J. Executive functions and driving in people with Parkinson’s disease. Mov. 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A state-space model for finger tapping with applications to cognitive inference. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2012 , 21–24 (2012). SCANeR - AVSimulation. [Internet]. 2024 [cited 2024 Nov 15]. Available from: https://www.avsimulation.com/en/scaner/ Posit. [Internet]. [cited 2024 Nov 18]. Available from: https://www.posit.co/ Herman, T., Weiss, A., Brozgol, M., Giladi, N. and Hausdorff JM. Identifying axial and cognitive correlates in patients with Parkinson’s disease motor subtype using the instrumented Timed Up and Go. Exp. Brain Res. 232 , 713–721 (2014). Dunet, V. et al. Episodic memory decline in Parkinson’s disease: relation with white matter hyperintense lesions and influence of quantification method. Brain Imaging Behav. 13 , 810–818 (2019). Dujardin, K et al. The pattern of attentional deficits in Parkinson’s disease. Parkinsonism Relat. Disord. 19 , 300–305. Alvarez, FJ., Fierro, I., Vicondoa, A., Ozcoidi, M. and Gómez-Talegón, T. Assessment of fitness to drive and cardiovascular diseases at the Spanish medical traffic centres. Circ. J. 71 , 1800–1804 (2007). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 31 Oct, 2025 Reviews received at journal 27 Oct, 2025 Reviews received at journal 25 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviews received at journal 21 Oct, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers invited by journal 14 Oct, 2025 Editor assigned by journal 14 Oct, 2025 Editor invited by journal 13 Oct, 2025 Submission checks completed at journal 10 Oct, 2025 First submitted to journal 10 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7752190","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":535891338,"identity":"d2186aca-65ff-410d-8bbd-313856b79b3f","order_by":0,"name":"Almudena Cerezo-Zarzuelo","email":"","orcid":"","institution":"National University of Distance Education","correspondingAuthor":false,"prefix":"","firstName":"Almudena","middleName":"","lastName":"Cerezo-Zarzuelo","suffix":""},{"id":535891340,"identity":"e9c14e96-5bcf-4882-9903-dc65eae36602","order_by":1,"name":"Francisco José 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Rico","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Felipe","lastName":"Medina-Lee","suffix":""},{"id":535891345,"identity":"066194d5-e0eb-4d9a-8fd8-a1e117a37145","order_by":6,"name":"Vinicius Trentin","email":"","orcid":"","institution":"Centre for Automation and Robotics","correspondingAuthor":false,"prefix":"","firstName":"Vinicius","middleName":"","lastName":"Trentin","suffix":""},{"id":535891346,"identity":"30d0d686-127c-4b2f-a189-9cc9215d267c","order_by":7,"name":"Juan Pablo Romero","email":"","orcid":"","institution":"Francisco de Vitoria University","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Pablo","lastName":"Romero","suffix":""}],"badges":[],"createdAt":"2025-09-30 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04:19:17","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98934,"visible":true,"origin":"","legend":"","description":"","filename":"419b93c55c584c5e9b04f443e2b3966f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7752190/v1/1cb0a71e7774b706c64db829.xml"},{"id":94623439,"identity":"e24925f8-6a53-4531-b80a-fd6a285d84cd","added_by":"auto","created_at":"2025-10-29 04:19:09","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110997,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7752190/v1/fe45869f367f0938a22286ca.html"},{"id":94623279,"identity":"a74cd8cd-f0af-4316-812d-6f159b0263c1","added_by":"auto","created_at":"2025-10-29 04:19:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":98770,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDriving simulator setup used in the experiments, consisting of a steering wheel with pedals, three monitors displaying the virtual driving environment, and a participant’s chair.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDriving simulator setup used in the study. The system includes a steering wheel with pedals, three monitors providing a wide field of view of the virtual driving environment, and a participant’s chair positioned in front of the workstation. This setup was used to conduct all trials and to record participants’ driving performance under controlled conditions.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7752190/v1/f68040e7a59a104178d126ef.png"},{"id":94623611,"identity":"4cdb8dff-0703-4606-a8cb-dcaff731db37","added_by":"auto","created_at":"2025-10-29 04:19:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":434759,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVelocity profiles as a function of distance in Experiment 2, comparing the leader (black), control participants (red), and patients (blue).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVelocity profiles along the path in Experiment 2 for the leader (black), control group (red), and patient group (blue). The leader profile shows the target trajectory and speed modulation, while the control group closely followed these patterns with relatively consistent velocities. In contrast, the patient group exhibited greater variability, with several deviations at different sections of the path. Higher dispersion was observed especially in the segments between 400–800 m and 1200–1600 m, indicating less stable speed control compared to the control group.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7752190/v1/9ecad6da0d8e38b49ab9213e.png"},{"id":94623528,"identity":"bc18a5d0-d228-4d25-ad1a-d2647960b4f0","added_by":"auto","created_at":"2025-10-29 04:19:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":143166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVehicle paths in Experiment 4 for the control group (red) and patient group (blue) when negotiating the intersection.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVehicle paths in Experiment 4 for the control group (red) and patient group (blue) when negotiating a right-turn intersection. Both groups generally followed the intended turning trajectory, but patient trajectories displayed a wider spread, with some paths cutting closer to the inner curve or drifting toward the outer lane. This increased variability suggests reduced precision in lane-keeping during turning maneuvers among patients compared with control participants.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7752190/v1/8fddbaec22ea412bb37ade22.png"},{"id":94623582,"identity":"963ea408-d14f-48e9-a7fd-cb3983574327","added_by":"auto","created_at":"2025-10-29 04:19:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55390,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;\u003cstrong\u003eBox plots comparing group performance in experiment 4.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDistributions of three performance measures are shown for the two experimental groups: control (cm, blue) and Parkinson’s disease (pa, orange). Left panel: Final performance percentage (porc_final) was higher and more consistent in the control group, while the patient group showed greater variability and lower median scores. Middle panel: The patient group exhibited a wider range and higher values in total deviation area (area), suggesting less precise control. Right panel: Maximum deviation distance from the lane (maxDist) was greater in the patient group, indicating impaired steering accuracy. Each box plot shows the interquartile range, median (horizontal line), and whiskers representing the full range excluding outliers.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7752190/v1/55b68206b9d51595e76296d5.png"},{"id":98244778,"identity":"553293cb-b02d-436f-a25f-04862696a90a","added_by":"auto","created_at":"2025-12-15 16:15:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1770395,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7752190/v1/d2979859-3ad7-4693-a42e-e79dedb90e5a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cognitive Alterations Related to Driving Performance in Parkinson’s Disease: A Cross-Sectional Proof-of-Concept Study Using a Driving Simulator","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDriving is a complex task that requires the integration of multiple perceptual, motor, and cognitive processes, with cognitive function being essential for effective real-time decision-making and safe responses (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In most countries, fitness to drive is evaluated through standardized psychotechnical assessments that include a variety of tasks aimed at measuring visual perception, reaction time, coordination, and attention. Many of these variables are directly influenced by cognitive functioning.\u003c/p\u003e\u003cp\u003eIn individuals with Parkinson\u0026rsquo;s disease (PD), a neurodegenerative condition characterized by degeneration of dopaminergic circuits, motor symptoms such as rigidity rest tremor and bradykinesia are usually the basis of the diagnosis but cognitive disfunctions, including attention, executive function, and working memory may also be present since early stages (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCognitive impairments in Parkinson\u0026rsquo;s disease (PD) can significantly affect driving ability by compromising critical functions such as divided attention, sustained vigilance, and executive functioning. These deficits hinder the capacity to respond effectively to constantly changing traffic environments\u0026mdash;such as reacting to signals, avoiding obstacles, or managing interactions with other vehicles (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Individuals with PD often exhibit delayed reaction times and reduced accuracy in tasks requiring rapid decision-making and multitasking (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Since reaction time is crucial for accident avoidance, its prolongation in PD increases the risk of collisions, especially in complex driving scenarios (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Furthermore, declines in executive function complicate planning and the ability to respond flexibly to unexpected events, thereby reducing overall fitness to drive (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStandard driving assessment exams generally include tests for reaction time, visual processing, attention, and motor coordination, domains frequently impaired in PD patients (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Spain, standard psychotechnical driving assessments consist of basic medical evaluations\u0026mdash;such as visual and auditory acuity, and the control of chronic conditions, including neurological disorders, which typically do not lead to test adjustments or additional cognitive evaluations. These medical checks are complemented by automated testing in a psychotechnical cabin, usually involving a screen and manual controls. The most common tasks assess visuomotor coordination (e.g., maintaining a ball centered within a moving lane using two joysticks) and simple reaction time to visual or auditory stimuli. While these tests aim to evaluate essential driving-related skills such as coordination, reaction time, and anticipatory capacity, they are performed in abstract, decontextualized settings with no direct resemblance to real-world driving situations.\u003c/p\u003e\u003cp\u003eA recent systematic review by Stamatelos et al.(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) emphasized that while international guidelines recognize the impact of cognitive decline in PD on driving performance, there is considerable variability in how fitness to drive is assessed across countries. The review also highlighted a lack of consensus regarding the most reliable cognitive tools for driving evaluation and called for more evidence-based, individualized approaches that account for the heterogeneous progression of PD and its cognitive symptoms.\u003c/p\u003e\u003cp\u003eAssessing fitness to drive in individuals with PD requires specific tools that evaluate the cognitive and psychomotor functions crucial to driving safety. In an era of increasing automation in driving and the development of new technologies that enable more immersive experiences and control over a wider range of variables, it is possible to identify safety indicators behind the wheel that may be overlooked in standard neuropsychological assessments or superficial evaluations of reaction times. As a response to these challenges, driving simulators have emerged as a promising alternative, allowing for detailed and controlled measurement of key driving parameters, such as trajectory accuracy and lane-centered positioning (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study aims to identify objective differences in key driving safety variables between individuals with Parkinson\u0026rsquo;s disease (PD) and healthy controls using a driving simulator. We hypothesize that PD patients will show altered reaction times and reduced vehicle control precision compared to controls. By providing empirical evidence, this study seeks to support future efforts toward the development of more objective tools for assessing driving fitness, taking into account the specific neuropsychological profiles associated with different neurodegenerative diseases.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Participants\u003c/h2\u003e\u003cp\u003eSeven drivers with PD were recruited and matched in age and gender with seven healthy regular drivers. There were no significant differences between groups regarding years of driving experience (Patients: 43,57 years\u0026thinsp;\u0026plusmn;\u0026thinsp;8,84; Controls: 44,86 years\u0026thinsp;\u0026plusmn;\u0026thinsp;8,70), daily driving distance average (Patients: 20,42 km\u0026thinsp;\u0026plusmn;\u0026thinsp;15,84; Controls: 42,86 km\u0026thinsp;\u0026plusmn;\u0026thinsp;21,57) nor frequency of driving (Patients: 3,43 days\u0026thinsp;\u0026plusmn;\u0026thinsp;2,23, Controls: 5 days\u0026thinsp;\u0026plusmn;\u0026thinsp;1,77) per week. Selection criteria for participants with PD were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) PD diagnosis, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) younger than 75 years old, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) they must hold a driver\u0026rsquo;s license and have driving experience, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) no changes in medication for the past 30 days and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) attend the evaluations on ON state (1 hour after last oral dose of levodopa), (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) a Hoehn\u0026amp;Yahr stage lower or equal to 3, (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) a punctuation higher or equal to 24 in the Montreal Cognitive Assessment (MoCA), (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) not having visuoperceptual impairments. The Unified Parkinson\u0026rsquo;s Disease Rating Scale (UPDRS) was assessed for each patient, to assure the ON state when conducting the evaluation.\u003c/p\u003e\u003cp\u003eThe selection criteria for the control participants were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) holding a driver\u0026rsquo;s license and driving experience, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) younger than 75 years and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) not having visuoperceptual impairments.\u003c/p\u003e\u003cp\u003eThe recruitment, as well as the evaluation, were conducted in the Beata Maria Ana Hospital, from March 1st to March 12th and June 3rd to June 14th, 2024. All participants were informed of the details of the evaluation and signed their consent to participate in this study, in accordance with the declaration of Helsinki. The Ethics Committee of the 12 de Octubre Hospital approved the study on 6th February 2024 with code 23/603.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Experimental design and procedures\u003c/h2\u003e\u003cp\u003eThis is a proof-of-concept study with a cross-sectional measurement. Evaluations of all the participants were carried out in a single session with two different parts: a cognitive assessment and the driving simulation tests, with a total duration of 120 minutes. Demographic and clinical data from participants are shown in Tables \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParticipants\u0026rsquo; demographic characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParticipant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLaterality\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMasculine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRight-handed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMasculine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRight-handed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMasculine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRight-handed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMasculine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRight-handed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMasculine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRight-handed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMasculine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRight-handed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMasculine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRight-handed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMasculine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRight-handed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMasculine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRight-handed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMasculine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRight-handed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMasculine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRight-handed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMasculine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRight-handed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMasculine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRight-handed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMasculine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRight-handed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical characteristics of the participants with PD\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParticipant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYears since diagnosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHoehn\u0026amp;Yahr stage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLevodopa equivalents Daily Dose (mg)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e791,08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2074,8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1. Cognitive assessment\u003c/h2\u003e\u003cp\u003eAll participants completed a battery of the following neuropsychological tests prior the driving simulation tests for 60 minutes:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eMoCA: it is a brief screening tool for general cognition functioning (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStroop test: this test measures cognitive flexibility, selective attention, cognitive inhibition and information processing speed. It includes four different parts: words, colour, words-colour and interference. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eWechler adult intelligence scale (WAIS-IV): This battery is the gold standard for the evaluation of cognitive abilities in adults. We selected two tests from this battery: symbol search and digit-symbol substitution to assess information processing speed, visual attention, work memory and incidental learning. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eComputerized reaction time tasks: reaction time tasks have been previously used with patients with PD when assessing deficits in the information processing (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). We performed two tasks, using a 27-inch monitor, controlled by Presentation\u0026reg; software (Neurobehavioral Systems Inc, Albany, California, United States) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), based on the Arroyo et al. study (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The average reaction time in each of the tasks and the percentage of correct answers were measured. The two tasks were displayed in the following order:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eFinger Tapping (FT): The FT task is a common test of sensory-motor performance (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). It has been applied following the Strauss application norms: participants were asked to press the keyboard spacebar as repeatedly with the index finger as fast as they can, with 5 attempts with each hand in 10-seconds trials. The collected data is the average time between taps.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSimple Reaction Time (SRT): It is a test designed to measure simple perception and sustained alertness. Participants were asked to press the left mouse button as fast as they can when a \u0026ldquo;+\u0026rdquo; appeared in the centre of the screen. It involved 50 trials with a total duration of 2\u0026ndash;3 minutes.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSimple Reaction Time-Sustained Attention to Response Task (SRT-SART): This task was applied to measure response strategy-inhibition. It consists of 168 Go trials and 21 No/Go trials, with a duration of 4 minutes. The stimulus size varied between 12 and 9 mm.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eChoice Reaction Time (CRT): This task was employed to measure visual perceptual decision time. It adds the processing of uncertainty to the processes involved in the SRT. Participants were asked to press the left button of the mouse when they see a square at the centre of the screen, and the right button when a circle appeared. It included 80 tests with a duration of 3 minutes.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eChoice Reaction Time-Search (CRT-Search): This task was applied to measure visual search. Participants were asked to press the left button of the mouse when they see a \u0026ldquo;Z\u0026rdquo; in a 6-letter sequence, or to press the right one if there was no \u0026ldquo;Z\u0026rdquo;.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eStimuli were classified in two groups: whether there was a \u0026ldquo;Z\u0026rdquo; or not and the visual characteristics of the other letters of the sequence. Therefore, four different combinations were obtained: target-low interference, target-high interference, non-target-low interference and no-target-high interference. The task involved 128 trials, executed between 5 to 8 minutes.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2. Driving simulation\u003c/h2\u003e\u003cp\u003eThe driving simulator was displayed on a computer with three 27-inch screens, which offers a field of view of approximately 130\u0026ordm;, enabling safe management of scenarios such as intersections and roundabouts. Interaction with the simulation software is achieved using the Logitech G29 kit (Logitech, Laussane, Switzerland), which includes force-feedback steering wheel and a pedal set with realistic resistance.\u003c/p\u003e\u003cp\u003eThe simulation software used was the SCANeR Studio by AV Simulation (AVSimulation, Boulogne-Billancourt, France)(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). This automotive simulation software, commercially available, allows for the assessment of driving performance by humans and autonomous driving systems. The tool provides all the necessary modules to construct a realistic virtual environment, including road settings, vehicle dynamics, traffic and sensor configurations, weather conditions, and scenario customization (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe simulation protocol includes 5 experiments assessing different aspects related to fitness to drive that are executed in 45\u0026ndash;60 minutes:\u003c/p\u003e\u003cp\u003e\u003cb\u003eExperiment 1\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eParticipants are asked to follow the car in front of them at a constant speed and to stop the movement when the other car stops. The task consists of initiating movement and maintaining a constant speed following a straight line. At the end of the experiment, the lead car stops at an aleatory time to test the participant's reaction.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eMetrics derived: Reaction time, defined as the time difference between when the simulated car begins to brake and when the participant initiates braking (ms).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eExperiment 2\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eParticipants are asked to follow the car in front of them and adapt their speed to match the pace of the lead vehicle. At the end of the experiment, the car in front stops at an aleatory time to test the participant\u0026rsquo;s reaction.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eMetrics derived: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Reaction time, defined as the time difference between when the simulated car begins to accelerate and when the participant begins to accelerate (ms) measured only at the beginning of the experiment. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Mean distance between the simulated car and the participant's car (m). (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Mean difference in velocity between the simulated car and the participant's car (km/h).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eExperiment 3\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eParticipants are asked to make a right turn from the right lane. They must initiate the movement, approach the intersection, execute a tight right turn without trespassing the left lane, and stop at a specific spot.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eMetrics derived: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Percentage of time the car remained within the lane (%). (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Maximum lateral distance from the center of the lane (m). (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Area between the edge of the road and the vehicle's trajectory outside the lane (m\u0026sup2;).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eExperiment 4\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eParticipants are asked to make a left turn from the left lane. They must initiate the movement, approach the intersection, execute a wider left turn while maintaining the car within the left lane, and stop at a specific spot.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eMetrics derived: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Percentage of time the car remained within the lane (%). (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Maximum lateral distance from the center of the lane (m). (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Area between the edge of the road and the vehicle's trajectory outside the lane (m\u0026sup2;).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eExperiment 5\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eParticipants are asked to make a reverse turn covering approximately 10 meters and stop in a specific spot.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eMetrics derived: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Percentage of time the car remained within the lane (%). (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Maximum lateral distance from the center of the lane (m). (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Area between the edge of the road and the vehicle's trajectory outside the lane (m\u0026sup2;).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eExperiments 1 and 2 aim to assess the driver\u0026rsquo;s ability to adapt dynamically to a controlled traffic environment, paying attention to the stimulus from the vehicle before them. Experiment 1 specifically assesses selective and sustained attention, processing speed, inhibitory control, and executive functions. Experiment 2 examines divided attention, working memory, motor planning, and cognitive flexibility related to motor initiation.\u003c/p\u003e\u003cp\u003eExperiments 3 and 4 assess the driver's ability to perform turns at intersections\u0026mdash;to the right and left, respectively\u0026mdash;while maintaining a centred position within the lane during the turn.\u003c/p\u003e\u003cp\u003eExperiment 5 assesses the ability to make a turn while driving in reverse. These tasks require precise integration of visuospatial planning, visuomotor coordination, attentional control, and executive functions.\u003c/p\u003e\u003cp\u003eAll experiments are designed to detect cognitive impairments commonly observed in Parkinson\u0026rsquo;s disease, which can compromise motor adaptation and driving safety.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Statistical analysis\u003c/h2\u003e\u003cp\u003eDifferences between control participants and PD patients were determined through Student\u0026rsquo;s t and Wilcoxon tests regarding demographic variables, driving experience, driving distance average and frequency of driving per week, neuropsychological tests, reaction time tasks and variables involved in the driving simulator. For multiple comparisons, the level of significance was adopted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e\u003cp\u003eAn ANCOVA was used to identify the cognitive components contributing to the SRT Task: the slowness in the processing of the information associated with the perceptual and sustained alert components was analysed with the SRT task as the dependent variable and the response time in the FT task as the covariate. Use of FT as a covariate allows controlling the shared \u0026ldquo;motor\u0026rdquo; component with the STR task. The significance level was adopted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e\u003cp\u003eTo compare the experiment execution in experimental and control group, linear mixed models were applied. This approach was selected to account for the repeated measures structure of the data, as multiple trials were collected per participant. In the models, group (control, experimental) was included as a fixed effect while subject was modelled as a random effect to account for within-subject variability.\u003c/p\u003e\u003cp\u003eSpearman correlations were performed with the PD patients\u0026rsquo; data between variables considered in the driving simulation and the different reaction time tasks, and PD-related variables: the HY stage, years of evolution of the disease and levodopa equivalents daily dose. The significance level was adopted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05. Analyses were performed using R (4.4.2. version, Posit, Boston, MA, United States) and MATLAB (2023b version, MathWorks, Natick, MA, United States). (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Cognitive assessment\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1. Neuropsychological tests\u003c/h2\u003e\u003cp\u003eWilcoxon tests showed no significant differences between groups in the MoCA (W\u0026thinsp;=\u0026thinsp;15 [-3,99\u0026thinsp;\u0026minus;\u0026thinsp;1,99]; p\u0026thinsp;=\u0026thinsp;0,14) nor Stroop test (words: W\u0026thinsp;=\u0026thinsp;21,5 [-38,00\u0026ndash;22,00]; p\u0026thinsp;=\u0026thinsp;0,49; colour: W\u0026thinsp;=\u0026thinsp;29,5 [-8,99\u0026ndash;10,99], p\u0026thinsp;=\u0026thinsp;0,90; words-colour: W\u0026thinsp;=\u0026thinsp;19,5 [-21,99\u0026ndash;8,99]; p\u0026thinsp;=\u0026thinsp;0,35; interference: W\u0026thinsp;=\u0026thinsp;17 [-16,30\u0026ndash;1,45]; p\u0026thinsp;=\u0026thinsp;0,22) between control and experimental groups. Student\u0026acute;s t tests were used with scalar punctuations of symbol search (t\u0026thinsp;=\u0026thinsp;2,5955 [0,37\u0026ndash;4,21]; p\u0026thinsp;=\u0026thinsp;0,02354) and digit-symbol (t\u0026thinsp;=\u0026thinsp;1,1793 [-1,38, \u0026ndash; 4,52]; p\u0026thinsp;=\u0026thinsp;0,2645), which showed a significant difference between groups in symbol search punctuations.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNeuropsychological tests results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuropsychological test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExperimental group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27,72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26,25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;15(-3,99\u0026thinsp;\u0026minus;\u0026thinsp;1,99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eStroop test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWords\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115,43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108,13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;21,5(-38,00 22,00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73,71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74,13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;29,5(-8,99\u0026thinsp;\u0026minus;\u0026thinsp;10,99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWords-Colours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52,57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45,25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;19,5(-21,99\u0026thinsp;\u0026minus;\u0026thinsp;8,99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;17 (-16,30\u0026ndash;1,45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSymbol search*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29,71/13,43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24,5/11,14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;2,6 (0,37\u0026ndash;4,21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,02*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigit-symbol substitution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57,29/12,43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50,75/10,86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1,2 (-1,38, \u0026ndash; 4,52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2. Reaction time tasks\u003c/h2\u003e\u003cp\u003eWilcoxon tests revealed no significant differences between groups in FTT (non-dominant hand: W\u0026thinsp;=\u0026thinsp;32 [-27,89\u0026ndash;45,80]; p\u0026thinsp;=\u0026thinsp;0,69; dominant hand: W\u0026thinsp;=\u0026thinsp;26 [-23,63\u0026ndash;23,58]; p\u0026thinsp;=\u0026thinsp;0,87), SRT-SART (average time: W\u0026thinsp;=\u0026thinsp;36 [-26,85\u0026ndash;79,41]; p\u0026thinsp;=\u0026thinsp;0,40; correct answers: W\u0026thinsp;=\u0026thinsp;22,5 [-3,99\u0026ndash;1,99]; p\u0026thinsp;=\u0026thinsp;0,55), CRT (average time: W\u0026thinsp;=\u0026thinsp;36 [-26,85\u0026ndash;79,41]; p\u0026thinsp;=\u0026thinsp;0,40; correct answers: W\u0026thinsp;=\u0026thinsp;22,5 [-3,99\u0026ndash;1,99]; p\u0026thinsp;=\u0026thinsp;0,55) and CRT-Search (average time: W\u0026thinsp;=\u0026thinsp;28 [-93,18\u0026ndash;119,61]; p\u0026thinsp;=\u0026thinsp;1; correct answers: W\u0026thinsp;=\u0026thinsp;12,5 [-29,99\u0026ndash;1,99]; p\u0026thinsp;=\u0026thinsp;0,08). In the SRT task, a significant difference was found regarding the time average reaction time (W\u0026thinsp;=\u0026thinsp;49 [9,60\u0026ndash;83,96]; p\u0026thinsp;=\u0026thinsp;0,013), whereas it was not significant when considering the percentage of correct answers (W\u0026thinsp;=\u0026thinsp;17 [-0.99\u0026ndash;4,00]; p\u0026thinsp;=\u0026thinsp;0,22).\u003c/p\u003e\u003cp\u003eThe ANCOVA conducted to address the perceptual and sustained alert components of the SRT task showed a significant difference between the PD patients and the healthy controls (F (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;15,613; p\u0026thinsp;=\u0026thinsp;0,002; \u003cem\u003eη\u003c/em\u003e \u003csup\u003e2\u003c/sup\u003e part\u0026thinsp;=\u0026thinsp;0,565).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Driving simulation\u003c/h2\u003e\u003cp\u003eResults from statistical analyses regarding driving simulator execution are shown in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eAlthough statistical significance was not reached\u0026mdash;likely due to the limited sample size and the proof-of-concept nature of this study\u0026mdash;clear differences were observed between patients and controls in the reaction times of Experiment 1 and the driving speed in Experiment 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe found a relationship between the execution of the symbol search test and the performance in experiment 1 by a linear mixed model (estimate = -0,2, p\u0026thinsp;=\u0026thinsp;0,004*).\u003c/p\u003e\u003cp\u003eAlthough differences in the ability to maintain lane position were observed in Experiment 3, they did not reach statistical significance. However, significant differences were found in Experiment 4 regarding both the maximum distance from the centre of the lane and area outside the lane (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNo significant statistical differences were found concerning experiment 5, although a difference between group means is noticeable.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferences between groups in driving simulation experiments\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperiment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetric\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePatient group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eConfidence Interval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eReaction time (s)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2,02\u0026thinsp;\u0026plusmn;\u0026thinsp;1,38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e3,9\u0026thinsp;\u0026plusmn;\u0026thinsp;2,36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[-0,338, 3,074]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eReaction time (s)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4,26\u0026thinsp;\u0026plusmn;\u0026thinsp;1,61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e4,55\u0026thinsp;\u0026plusmn;\u0026thinsp;1,53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[-1,032, 1,611]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDiff. Velocity (km/h)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e6,83\u0026thinsp;\u0026plusmn;\u0026thinsp;2,63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e9,81\u0026thinsp;\u0026plusmn;\u0026thinsp;4,06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[-0,382, 6,348]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDistance (m)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e56,34\u0026thinsp;\u0026plusmn;\u0026thinsp;13,69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e57,74\u0026thinsp;\u0026plusmn;\u0026thinsp;30,94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[-22,703, 25,507]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e% Time in lane (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e91,49\u0026thinsp;\u0026plusmn;\u0026thinsp;15,17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e83,47\u0026thinsp;\u0026plusmn;\u0026thinsp;18,07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;1,358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[-19,945, 3,914]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eArea (m2)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1,76\u0026thinsp;\u0026plusmn;\u0026thinsp;3,21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e6,23\u0026thinsp;\u0026plusmn;\u0026thinsp;11,30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[-0,590, 9,521]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMax. Distance (m)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2,24\u0026thinsp;\u0026plusmn;\u0026thinsp;0,69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e9,81\u0026thinsp;\u0026plusmn;\u0026thinsp;4,06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[-0,374, 0,947]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e% Time in lane (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e88,36\u0026thinsp;\u0026plusmn;\u0026thinsp;10,19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e70,84\u0026thinsp;\u0026plusmn;\u0026thinsp;25,04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2,007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[-35,163, 0,120]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eArea (m2)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2,46\u0026thinsp;\u0026plusmn;\u0026thinsp;1,73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e17,75\u0026thinsp;\u0026plusmn;\u0026thinsp;19,65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,013*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[3,471, 27,112]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMax. Distance (m)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2,64\u0026thinsp;\u0026plusmn;\u0026thinsp;0,86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e3,58\u0026thinsp;\u0026plusmn;\u0026thinsp;1,27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,039*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[0,050, 1,849]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e% Time in lane (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e95,01\u0026thinsp;\u0026plusmn;\u0026thinsp;10,88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e82,47\u0026thinsp;\u0026plusmn;\u0026thinsp;30,21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1,188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[-33,910, 8,809]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eArea (m2)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9,64\u0026thinsp;\u0026plusmn;\u0026thinsp;30,47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e79,90\u0026thinsp;\u0026plusmn;\u0026thinsp;201,25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[-73,808, 214,330]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMax. Distance (m)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2,62\u0026thinsp;\u0026plusmn;\u0026thinsp;2,12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e5,23\u0026thinsp;\u0026plusmn;\u0026thinsp;8,86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[-3,746, 8,971]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Correlations\u003c/h2\u003e\u003cp\u003eSignificant correlations regarding PD evolution variables and PD treatment with simulation execution were found between reaction time in experiment 2 and levodopa dose (S\u0026thinsp;=\u0026thinsp;102,42, p\u0026thinsp;=\u0026thinsp;0,0211, ρ=-0,8288) and distance from the centre of the lane in experiment 4 with the levodopa dose (S\u0026thinsp;=\u0026thinsp;12,611, p\u0026thinsp;=\u0026thinsp;0,04077, ρ\u0026thinsp;=\u0026thinsp;0,77) and years of evolution (S\u0026thinsp;=\u0026thinsp;6,56, p\u0026thinsp;=\u0026thinsp;0,00845, ρ\u0026thinsp;=\u0026thinsp;0,88). Percentage of maintenance inside the lane in experiments 3 (S\u0026thinsp;=\u0026thinsp;100,9, p\u0026thinsp;=\u0026thinsp;0,0301, ρ=-0,80) and 4 (S\u0026thinsp;=\u0026thinsp;100,9, p\u0026thinsp;=\u0026thinsp;0,0301, ρ=-0,80) and area outside the lane in experiment 4 (S\u0026thinsp;=\u0026thinsp;11,1, p\u0026thinsp;=\u0026thinsp;0,0301, ρ\u0026thinsp;=\u0026thinsp;0,80) correlates with Hoehn \u0026amp; Yahr stage. This relationship indicates that disease progression is directly associated with a worsening of the ability to maintain vehicle trajectory and a centred lane position.\u003c/p\u003e\u003cp\u003eThe cognitive components of the SRT task correlates with all the components considered in experiment 5: distance from the centre of the lane (S\u0026thinsp;=\u0026thinsp;102, p\u0026thinsp;=\u0026thinsp;0,0340, ρ=-0,82), area outside the lane (S\u0026thinsp;=\u0026thinsp;103,43, p\u0026thinsp;=\u0026thinsp;0,0162, ρ=-0,85) and percentage inside the lane (S\u0026thinsp;=\u0026thinsp;8,57, p\u0026thinsp;=\u0026thinsp;0,0162, ρ\u0026thinsp;=\u0026thinsp;0,85).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eParkinson\u0026rsquo;s disease affects multiple functional domains\u0026mdash;including motor control, sensory integration, and cognitive processing, all of which are essential for maintaining safe driving behaviour.\u003c/p\u003e\u003cp\u003ePD patients are more prone to cease driving earlier than their contemporaries, as the disease progresses and associates a gradual decrease of their ability to drive (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). According to a recent review (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) the evaluation for driving fitness in PD patients should include 6 different aspects: general patient characteristics like age and Hoehn\u0026amp;Yahr stage, driving history, motor impairment, usually measured with the MDS-UPDRS-III; cognitive evaluation with neuropsychological tests and other PD-related symptoms like sleep disorders, motor fluctuations and adverse effects. All these data were included in our study in the initial evaluation of the participants.\u003c/p\u003e\u003cp\u003eWhen comparing the clinical interview and neuropsychological test results with the driving simulator data, we observed that standard cognitive assessments failed to detect significant differences between PD patients and controls, while the simulator revealed clear impairments in reaction time and visuospatial accuracy, both critical for safe driving. The inclusion of computerized tasks further supported these findings: participants with PD showed deficits in perceptual processing and sustained alertness during the SRT task, in line with previous studies (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). These results suggest that traditional assessments may not capture subtle but functionally relevant cognitive alterations.\u003c/p\u003e\u003cp\u003eNotably, performance in Experiment 1, which was designed to assess sustained attention and processing speed, is predicted by performance on the Symbol Search test, as both tasks evaluate similar cognitive functions. Likewise, the association between SRT performance and reverse driving suggests that simple computerized reaction time tasks\u0026mdash;relying on visual stimulus detection\u0026mdash;may help predict functional abilities in real-world scenarios such as backing into a parking space, which also requires visual detection skills. These visuospatial impairments may also contribute to the increased difficulty observed when executing left turns, particularly among right-handed individuals, considering that the typical driving position in the vehicle is not centered but shifted to the left\u003c/p\u003e\u003cp\u003eOur findings reinforce the growing evidence that standard psychomotor tests may be insufficient to detect the early-stage cognitive and visuospatial impairments that affect driving ability in PD (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Driving simulators, as used in this study, provide a multidimensional assessment environment that better reflects the real demands of driving.\u003c/p\u003e\u003cp\u003eThe simulator used in this study was intentionally programmed to address domains known to be affected in early PD, such as sustained attention, visuospatial control, and reaction time. The scenarios were designed to simulate real-world driving tasks like left turns, lane positioning, and reverse driving, allowing for objective measurement of trajectory control and response time. These features were developed to reflect both clinical needs and the literature on common driving challenges in PD, and to go beyond the limited scope of standard cognitive assessments.\u003c/p\u003e\u003cp\u003eWhile driving simulators inevitably differ from real world driving due to the lack of full sensory feedback and risk perception, they offer a reproducible, safe, and ethically sound alternative for evaluating performance under controlled conditions. Moreover, their flexibility makes them valuable for future implementation of personalized driving assessments in clinical settings.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the sample size was relatively small, and participants were not randomly selected, which limits both the generalizability of the findings and the reliability of the statistical comparisons. Second, the sample was not fully balanced in terms of socioeconomic and educational background, which may influence neuropsychological performance. Third, although gender was matched across groups, the overall gender distribution may not reflect the general driving population. Finally, although simulators provide a valuable controlled environment, they cannot fully replicate the sensory, emotional, and contextual variables inherent in actual driving situations.\u003c/p\u003e\u003cp\u003eBy bridging the gap between clinical evaluation and real-world functional performance, simulator-based assessments offer a crucial opportunity to redefine how we understand and measure driving fitness in neurodegenerative conditions like Parkinson\u0026rsquo;s disease.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis proof-of-concept study highlights that routine cognitive assessments in patients with Parkinson\u0026rsquo;s disease may not always be efficient enough to detect impairments that could potentially compromise driving safety, such as visuospatial skills and sustained alert impairments. Previous studies have confirmed that this type of impairment can emerge in the early stages of the disease, even in the absence of other indicators of cognitive decline. While instrumental tests such as computerized reaction time assessments can detect these deficits, they are not currently used in real-life tasks like driving ability. The present study demonstrates that immersive driving environments targeting specific cognitive functions guided by the known characterized deficits of the disease may help identify deficits that go unnoticed with conventional assessments. Although the actual impact of these deficits on driving safety remains to be fully evaluated, the refinement of driving simulators\u0026mdash;such as the one used in this experiment\u0026mdash;represents a promising step toward improving the detection of variables that may affect driving performance in people with PD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eFunding:\u0026nbsp;\u003c/em\u003eThis study received no specific funding from public, commercial, or not-for-profit organizations.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical approval:\u0026nbsp;\u003c/em\u003eThe study was approved by the Ethics Committee of the Hospital 12 de Octubre (approval code 23/603, issued on February 6, 2024), and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConflict of interest:\u0026nbsp;\u003c/em\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eReporting guidelines:\u0026nbsp;\u003c/em\u003eThis manuscript was prepared in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist for cross-sectional observational studies.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eConceptualization: JPR/ER/JV; Methodology: ACZ/FJSC/JPR; Software: ER/JV/JFML/VT; Validation: ER/JV/JFML/VT; Formal analysis: ACZ/FJSC/CT; Resources: JPR/ER/JV; Data curation: ACZ/FJSC/CT; Writing \u0026ndash; original draft: ACZ/FJSC/JPRM; Writing review \u0026amp; editing: ACZ/FJSC/CT/ER/JV/JFML/VT/JPR; Supervision: ER/JV/JPR; Project administration: ER/JV/JPM\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData Availability Statement:\u0026nbsp;\u003c/em\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnderson, DE., Ghate, DA. and Rizzo, M. Vision, attention, and driving. \u003cem\u003eHandb. Clin. Neurol.\u003c/em\u003e\u003cstrong\u003e178\u003c/strong\u003e, 337\u0026ndash;360 (2021).\u003c/li\u003e\n\u003cli\u003e\u0026Aacute;lvarez F.J. Parkinson\u0026rsquo;s disease, antiparkinson medicines, and driving. \u003cem\u003eExpert Rev. Neurother.\u003c/em\u003e\u003cstrong\u003e16\u003c/strong\u003e, 1023\u0026ndash;1032 (2016).\u003c/li\u003e\n\u003cli\u003eRanchet, M., Devos, H. and Uc, EY. Driving in Parkinson disease. \u003cem\u003eClin. Geriatr. Med.\u003c/em\u003e\u003cstrong\u003e36\u003c/strong\u003e, 141\u0026ndash;148 (2020).\u003c/li\u003e\n\u003cli\u003eClassen, S. and Holmes, J. Executive functions and driving in people with Parkinson\u0026rsquo;s disease. \u003cem\u003eMov. Disord.\u003c/em\u003e\u003cstrong\u003e28\u003c/strong\u003e, 1909\u0026ndash;1911 (2013).\u003c/li\u003e\n\u003cli\u003eThompson, T et al. 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Pract.\u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e, 198\u0026ndash;208 (2024).\u003c/li\u003e\n\u003cli\u003ePhokaewvarangkul, O., Krootjohn, S., Yanthitirat, P., Anan C. and Bhidayasiri, R. Objective monitoring of driving behavior in Parkinson\u0026rsquo;s disease: the utility of the Chula Parkinson Car\u0026reg;. \u003cem\u003eEur. Neurol.\u003c/em\u003e\u003cstrong\u003e81\u003c/strong\u003e, 128\u0026ndash;138 (2019).\u003c/li\u003e\n\u003cli\u003eLloyd, K et al. Driving in Parkinson\u0026rsquo;s disease: a retrospective study of driving and mobility assessments. \u003cem\u003eAge Ageing\u003c/em\u003e\u003cstrong\u003e49\u003c/strong\u003e, 1097\u0026ndash;1101 (2020).\u003c/li\u003e\n\u003cli\u003eOjeda, N., Del Pino, R., Ibarretxe-Bilbao, N., Schretlen, DJ. and Pe\u0026ntilde;a, J. [Montreal cognitive assessment test: normalization and standardization for Spanish population]. \u003cem\u003eRev. Neurol.\u003c/em\u003e\u003cstrong\u003e63\u003c/strong\u003e, 488\u0026ndash;496 (2016).\u003c/li\u003e\n\u003cli\u003eRognoni, T et al. Spanish normative studies in young adults (NEURONORMA young adults project): norms for Stroop color-word interference and Tower of London-Drexel University tests. \u003cem\u003eNeurologia\u003c/em\u003e\u003cstrong\u003e28\u003c/strong\u003e, 73\u0026ndash;80 (2013).\u003c/li\u003e\n\u003cli\u003ePearson Clinical \u0026amp; Talent Assessment. \u003cem\u003eWAIS-IV, Escala de inteligencia de Wechsler para adultos-IV\u003c/em\u003e [Internet]. [cited 2024 Nov 14]. Available from: https://www.pearsonclinical.es/wais-iv-escala-de-inteligencia-de-wechsler-para-adultos-iv\u003c/li\u003e\n\u003cli\u003eArroyo, A et al. Components determining the slowness of information processing in Parkinson\u0026rsquo;s disease. \u003cem\u003eBrain Behav.\u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e, e02031 (2021).\u003c/li\u003e\n\u003cli\u003eNeurobehavioral Systems. [Internet]. [cited 2024 Nov 14]. Available from: https://www.neurobs.com/\u003c/li\u003e\n\u003cli\u003eAustin, D., Petersen, J., Jimison, H. and Pavel, M. A state-space model for finger tapping with applications to cognitive inference. \u003cem\u003eAnnu. Int. Conf. IEEE Eng. Med. Biol. Soc.\u003c/em\u003e\u003cstrong\u003e2012\u003c/strong\u003e, 21\u0026ndash;24 (2012).\u003c/li\u003e\n\u003cli\u003eSCANeR - AVSimulation. [Internet]. 2024 [cited 2024 Nov 15]. Available from: https://www.avsimulation.com/en/scaner/\u003c/li\u003e\n\u003cli\u003ePosit. [Internet]. [cited 2024 Nov 18]. Available from: https://www.posit.co/\u003c/li\u003e\n\u003cli\u003eHerman, T., Weiss, A., Brozgol, M., Giladi, N. and Hausdorff JM. Identifying axial and cognitive correlates in patients with Parkinson\u0026rsquo;s disease motor subtype using the instrumented Timed Up and Go. \u003cem\u003eExp. Brain Res.\u003c/em\u003e\u003cstrong\u003e232\u003c/strong\u003e, 713\u0026ndash;721 (2014).\u003c/li\u003e\n\u003cli\u003eDunet, V. et al. Episodic memory decline in Parkinson\u0026rsquo;s disease: relation with white matter hyperintense lesions and influence of quantification method. \u003cem\u003eBrain Imaging Behav.\u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e, 810\u0026ndash;818 (2019).\u003c/li\u003e\n\u003cli\u003eDujardin, K et al. The pattern of attentional deficits in Parkinson\u0026rsquo;s disease. \u003cem\u003eParkinsonism Relat. Disord.\u003c/em\u003e\u003cstrong\u003e19\u003c/strong\u003e, 300\u0026ndash;305.\u003c/li\u003e\n\u003cli\u003eAlvarez, FJ., Fierro, I., Vicondoa, A., Ozcoidi, M. and G\u0026oacute;mez-Taleg\u0026oacute;n, T. Assessment of fitness to drive and cardiovascular diseases at the Spanish medical traffic centres. \u003cem\u003eCirc. J.\u003c/em\u003e\u003cstrong\u003e71\u003c/strong\u003e, 1800\u0026ndash;1804 (2007).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Parkinson Disease, Driving Simulation, Neuropsychological Tests, Reaction Time","lastPublishedDoi":"10.21203/rs.3.rs-7752190/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7752190/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDriving ability in individuals with Parkinson\u0026rsquo;s disease (PD) can be compromised early in the course of the illness, even before clear cognitive deficits emerge on standard neuropsychological tests. This study investigated subtle driving impairments in a group of non-demented people with PD using a high-fidelity driving simulator. Seven PD participants and seven healthy controls, matched for age and sex, completed cognitive assessments, reaction time tasks, and five simulated driving scenarios that measured lane keeping, steering control, and reaction to events. While most cognitive scores were comparable between groups, PD participants exhibited slower response times in basic tasks and showed reduced lane control, particularly during left turns. These difficulties were associated with disease severity and medication dosage. The simulator proved more sensitive than conventional tests in detecting early impairments related to attention and visuospatial processing. These findings suggest that driving simulators may play a key role in improving the assessment of driving competence in PD, providing insight into real-world challenges faced by this population.\u003c/p\u003e","manuscriptTitle":"Cognitive Alterations Related to Driving Performance in Parkinson’s Disease: A Cross-Sectional Proof-of-Concept Study Using a Driving Simulator","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 04:11:57","doi":"10.21203/rs.3.rs-7752190/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-31T10:57:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-27T19:23:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-25T14:16:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137948495066658905647339743359926041288","date":"2025-10-22T16:07:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-21T04:52:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27781914130585753175331932084642643416","date":"2025-10-21T03:44:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27166858976254953665256392165780062141","date":"2025-10-16T12:36:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-14T12:42:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-14T12:11:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-13T11:31:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-10T10:05:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-10T10:02:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0355d652-82fd-487a-9538-3082b434926a","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56977927,"name":"Health sciences/Diseases"},{"id":56977928,"name":"Health sciences/Neurology"},{"id":56977929,"name":"Biological sciences/Neuroscience"},{"id":56977930,"name":"Biological sciences/Psychology"},{"id":56977931,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2025-12-15T16:09:10+00:00","versionOfRecord":{"articleIdentity":"rs-7752190","link":"https://doi.org/10.1038/s41598-025-31585-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-09 15:59:08","publishedOnDateReadable":"December 9th, 2025"},"versionCreatedAt":"2025-10-29 04:11:57","video":"","vorDoi":"10.1038/s41598-025-31585-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-31585-y","workflowStages":[]},"version":"v1","identity":"rs-7752190","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7752190","identity":"rs-7752190","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}