The Impact of Cognitive Impairment on Pedal Control and Crash Risk Following Stroke: A Pilot Study

Abstract

Driving after stroke requires complex coordination of cognitive and motor systems, yet the influence of post-stroke cognitive impairment on lower limb motor control during driving remains poorly understood. This pilot study examined the association between cognitive function and lower limb motor control of gas/brake pedal control in stroke survivors. We hypothesized that compromised cognitive function would be associated with worse gas and brake pedal control. Twenty stroke survivors (65.89 ± 9.67 years; 6 females) participated. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) and Useful Field of View (UFOV) test scores for divided and selective attention. Participants performed a car-following task in a driving simulator requiring precise gas and brake control. Pedal control was quantified by gas pedal error, brake force error, and brake response time. Participants were categorized into cognitively normal and cognitively impaired groups (n=10 each). Driving behavior was assessed using the Driving Habits Questionnaire (DHQ), and crash risk was determined via UFOV classification. Increased gas pedal error was associated with poorer MoCA scores and selective attention deficits. Delayed brake response times correlated with lower MoCA scores and poorer divided and selective attention. Although self-reported driving behavior was comparable between groups, 60% of cognitively impaired participants demonstrated moderate to high crash risk compared to cognitively normal participants, who exhibited low crash risk. Cognitive impairment after stroke is significantly linked to impaired lower limb control during driving and elevated crash risk. These findings highlight an urgent need to integrate cognitive assessment along with motor assessments in post-stroke rehabilitation. Future advances in neuroengineering technologies, and personalized motor-cognitive interventions could play a critical role in restoring safe driving capabilities and mobility independence after stroke.

Keywords

gas control brake control reaction time simulator driving cognition

Introduction

In the United States, driving is a primary mode of transportation and a key contributor to independence, access to services, and social engagement (Chihuri et al., 2016; Fonda et al., 2001). Pedal control is central for vehicle operation and driver responsiveness to road and traffic conditions. Additionally, intact cognition is critical for safe driving. For example, sustained attention and the ability to shift focus between tasks is essential for navigating dynamic traffic (Hasegawa et al., 2020). Cognitive impairments following stroke can reduce driving performance and lead to driving cessation and lower autonomy. Despite this, the impact of cognitive deficits on lower limb motor control during driving in stroke survivors remains unexplored.

Previous reports showcase the contribution of motor skills to driving performance. Age-related motor decline delays braking and reduces gas pedal accuracy (Robertsen et al., 2022), and age-related motor inconsistency is associated with poorer reactive driving (Lodha et al., 2016). Our previous work showed that grip force unsteadiness predicts steering deficits in stroke survivors during a simulated driving task (Patel et al., 2021). Despite this strong work demonstrating the impact of motor abilities on driving performance, the influence of cognitive impairment on pedal control in stroke survivors has not been investigated.

Over 60% of stroke survivors experience compromised cognitive functioning in addition to motor impairments following their stroke (El Husseini et al., 2023). These deficits span multiple domains, most notably attention and processing speed (Cumming et al., 2012; Loetscher et al., 2019; Su et al., 2015). Compromised attention and processing speed are especially relevant while driving and may potentially influence optimal control of the pedals. For example, adequate attention and processing speed are needed to recognize relevant traffic events and execute skilled foot movements between the gas and brake pedals (Hird et al., 2014). Additionally, stroke survivors with impaired attention demonstrate reduced on-road driving skills and are less likely to resume driving (Hird et al., 2014). However, evidence supporting the effect of cognitive impairment post-stroke on pedal control is currently absent.

This pilot study aimed to address the following primary question: Is compromised cognitive function in stroke survivors associated with impaired lower limb control during simulated driving? To address this question, we measured gas and brake pedal control during a car-following task in a driving simulator. We hypothesized that compromised cognitive function and delayed processing speed during divided and selective attention tasks would be associated with worse gas and brake pedal control in individuals with stroke. In addition, on-road driving behavior and crash risk are key indicators of overall driving performance and safety (McCarty & Kim, 2024). Therefore, we propose a secondary question: Do post-stroke individuals with lower cognitive status report reduced engagement in on-road driving and higher crash risk than post-stroke individuals with higher cognitive status? To address this question, we classified our stroke cohort into cognitively impaired and cognitively normal groups using the Montreal Cognitive Assessment (MoCA, cut off of 26/30). We compared the two groups on self-reported driving behavior using Driving Habits Questionnaire (DHQ) scores and crash risk derived from the Useful Field of View (UFOV). We hypothesized that DHQ would be lowered and crash risk would be elevated in the cognitive impaired stroke group relative to stroke individuals with normal cognition.

Methods

Participants

Twenty individuals with stroke volunteered to participate in the current study. Table 1 shows participant characteristics. Participant inclusion criteria were: (1) diagnosed with a unilateral cerebrovascular accident at least 9 months prior to testing, (2) current or past drivers, (3) ability to press and release gas and brake pedal to drive in the simulator. Exclusion criteria were (1) presence of any other neurological or musculoskeletal disorder, (2) pain or injury affecting limb movements, (3) spatial neglect, or uncorrected vision and hearing impairments, (4) untreated psychiatric illness (such as clinical depression or anxiety) or untreated sleep disorder, and (5) history of simulator sickness. This research complied with the tenets of the Declaration of Helsinki and was approved by the Institutional Review Board. Informed consent was obtained from each participant.

Table 1.

Participant Characteristics.

	Stroke Survivors (N=20)
Age (years)	65.89 ± 9.67
Sex (M/F)	14 / 6
Time Since Stroke (years)	5.17 ± 4.03
Driving Leg (Affected/Not Affected)	10 / 10
Stroke Side (Left/Right/Unknown)	10 / 5 / 5
Motor Function
FMA-LE (_/66)	25.45 ± 7.36
Dorsiflexion Strength (N)	163.44 ± 75.98
Plantarflexion Strength (N)	72.74 ± 41.53
Cognition
MoCA (_/30)	23.65 ± 4.79
Divided attention (ms)	35.1 ± 25.58
Selective attention (ms)	133.7 ± 68.8

FMA-LE – Fugl-Meyer assessment for lower extremity; N – Newtons; MoCA – Montreal cognitive assessment; DHQ – Driving Habits Questionnaire; ms – milliseconds. All scores are mean ± standard deviation.

Experimental Protocol

The experimental protocol consisted of a single two-hour session during which we characterized motor function and assessed cognition, pedal control, driving habits, and crash risk.

Motor Function

We characterized motor function using the Fugl-Meyer Assessment–Lower Extremity (FMA-LE) subtest and ankle strength. The FMA-LE is a clinically relevant and comprehensive test of motor impairment in stroke survivors that assesses reflexes, voluntary movements, coordination and sensation (Duncan Millar et al., 2019; Gladstone et al., 2002; Kwakkel et al., 2017). Lower scores on the FMA-LE indicate reduced motor function. In addition, strength is an important outcome for motor recovery following stroke (Nadeau et al., 1999). We measured ankle dorsiflexion and plantarflexion strength, which are critical for pedal control during driving. Participants sat with the hip and knee joint at ∼90° flexion and the ankle in a neutral position. We instructed participants to exert maximal force for 3 s without engaging or moving the hip, knee, or trunk. Each completed 3–5 trials for dorsiflexion and plantarflexion, with a 60-s rest between trials. Strength was defined as the highest force achieved.

Cognition

We assessed cognition using the Montreal Cognitive Assessment (MoCA) (Nasreddine et al., 2005) and the Useful Field of View (UFOV) test (Figure 1A).

Figure 1.

Simulated Driving and UFOV Tasks. A) We Assessed Attentional Capacity Using the Useful Field of View (UFOV) Task. We Focused on the Divided and Selective Attention Tests. The Divided Attention Test Displayed an Object Inside a White Box and an Object Around the Periphery. Participants were then Asked to Select Which Object was Inside the White Box and the Location of the Outside Object. The Selective Attention Subtest was Identical to the Divided Attention Subtest but with Added Upside-Down Triangles Placed Throughout the Screen Used as Distractors. B) Participants Completed a Car Following Simulated Driving Task. The Task Required Participants to Track a Sinusoidal Target with the Gas Pedal and Respond as Quickly as Possible TO Illumination of the Lead Car Lights (i.e., Stimulus) by Pressing on the Brake Pedal with a Specific Amount of Force (40 N). We Quantified Gas Pedal Error as the Root Mean Squared Error (RMSE) of the Gas Pedal Relative to the Sinusoidal Target. Brake Response Time was Calculated as the Latency Between Stimulus Onset and Onset of Brake Force and Brake Force Error was Calculated as the Difference Between the Maximal Brake Force Produced and the Target Brake Force.

Montreal Cognitive Assessment (MoCA): The MoCA is a valid and sensitive screening tool to assess cognitive impairments. MoCA evaluates cognitive function in 7 domains: 1) visuospatial/executive function; 2) naming; 3) memory; 4) attention; 5) language; 6) abstraction; and 7) orientation. A score less than 26 (out of 30) indicates cognitive impairment(Nasreddine et al., 2005).

Useful Field of View (UFOV) test: The UFOV test is a highly robust tool used to capture processing speed in divided and selective attention tasks. Extensive research demonstrates that performance on the UFOV test is strongly linked to crash risk in older adults (Owsley et al., 1998; Rubin et al., 2007; Wood et al., 2012) and stroke survivors (Marshall et al., 2007). To perform the UFOV test, participants sat upright approximately 60 cm from a 32-inch monitor (Sync Master™ 275t+, Samsung Electronics America, NJ, USA) positioned at eye level. The monitor displayed the divided and selective attention tasks.

Divided attention. The divided attention task required participants to identify a central stimulus (car or truck) while simultaneously locating a peripheral stimulus (car). Divided attention measures the ability to attend to central and peripheral stimuli simultaneously where longer times indicates poorer divided attention.

Selective attention. The selective attention task was similar to the divided attention task, except the peripheral stimulus was embedded among distractors (triangles). Selective attention measures the ability to focus on a specific stimulus while voluntarily suppressing attention to distractors. Longer times on this task indicates poorer selective attention.

Pedal Control

We assessed lower limb control during driving using a simulated driving task, modeled after prior studies (Lodha et al., 2016) (Figure 1B). Participants sat in a professional driving cab with custom driving simulator environment. The driving scene was displayed on a 32-in. computer monitor (Samsung Sync Master 320MP-2; 1920×1080 resolution, 60 Hz) located 1.25 m away at eye level. Participants followed a lead car along a straight road with their foot on a custom gas pedal adjacent to a brake pedal. Steering was automated via custom software, allowing participants to control speed using the gas pedal to match a visible target. At random intervals, the lead car's brake lights illuminated, prompting participants to release the gas as quickly as possible and apply sufficient brake force (40 N) to slow the car. Participants returned their foot to the gas pedal to resume following the lead car. Each trial lasted 20 s, with a 60-s rest between trials to minimize fatigue. Participants completed three familiarization trials and affirmed they fully understood how to perform the task before performing 10 test trials. All data were analyzed offline using a custom-written program in Matlab (Math Works Inc, Natick, MA, USA). We quantified pedal control with gas pedal error, brake response time, and brake force error.

Gas pedal error. The gas pedal position was measured using CSR Elite Pedals 134 (Fanatec, Endor AG, Germany). The gas pedal position data were sampled at 100 Hz. Gas pedal error was calculated as the root mean square error (RMSE) between the participant's gas pedal position and the target position.

Brake response time. The force applied to the brake pedal was measured using a force transducer (Model LAU200, 100 lb. capacity, FUTEK Advanced Sensor Technology, Irvine, CA) embedded in the brake pedal. The brake force data were sampled at 1000 Hz and band-pass filtered from 0.03 to 20 with NI-DAQ board (Model USB6218, National Instruments, Austin, TX, USA). Brake response time was calculated as the latency between the onset of the lead car's brake lights and the onset of the brake pedal force (i.e., time when the exerted force was equal to 2 N).

Brake force error. Brake force error was calculated as the absolute difference between the maximal brake force applied and the target brake force of 40 N.

Driving Behavior and Crash Risk

To assess self-reported driving behavior, we used the driving habits questionnaire (DHQ). DHQ measures driving exposure, space, avoidance, and citations. Higher DHQ scores indicated greater participation in on-road driving (Owsley et al., 1999). In addition, we obtained crash risk from the UFOV test. UFOV employs a complex and robust algorithm that integrates performance across three attentional tasks to categorize individuals into low-, moderate-, or high-risk groups based on their ability to process and respond to visual information. Notably, UFOV-derived crash risk captures aspects of risk category not evident from divided or selective attention tasks alone. Importantly, UFOV-derived crash risk is strongly associated with real-world crash risk per mile driven and is considered a reliable and valid indicator of crash risk in controlled settings (Ball et al., 1993).

Statistical Analysis

A sample size of 12 is a recommended minimum for pilot studies (Julious, 2005). Therefore, we oversampled and recruited 20 stroke participants. To examine the association between cognition and lower limb control during driving in stroke survivors while accounting for the potential confounding effect of motor function, we conducted a partial correlation analysis between cognitive measures (MoCA, divided and selective attention) and pedal control measures controlling for FMA-LE and ankle strength. To supplement the correlational analysis, we compared lower limb control during driving between the cognitively impaired and cognitive normal stroke groups using a Mann-Whitney U test. For this, we classified participants into a cognitively normal stroke group or cognitively impaired stroke group based on their MoCA scores with a cutoff of < 26 (Nasreddine et al., 2005). To address our secondary question, we compared cognitively normal and cognitive impaired stroke participants on DHQ using a Mann-Whitney U test and assessed group differences in crash risk using a Chi-square test. The alpha level was set at p < 0.05 and r was used to report effect size for correlations, Hedge's g was used to report effect sizes for Mann-Whitney U comparisons, and Cramér's V was used to report effect size for chi-square comparisons, regardless of statistical significance. All analyses were performed using IBM SPSS 26.0 (IBM, Armonk, NY).

Results

Group means and SDs on participant characteristics, motor function, and cognition are presented in Table 1.

Gas pedal error. The relationship between cognitive performance and gas pedal error is presented in Figure 2. We observed a negative trend between MoCA scores and gas pedal error which did not reach statistical significance (r = −0.42; p = 0.108). Divided attention was not associated with gas pedal error (r = 0.31; p = 0.237). Importantly, selective attention time was positively correlated with gas pedal error (r = 0.53; p = 0.037). Ten stroke participants were classified into a cognitively normal group (67.8 ± 9.3 yrs; 2 females; MoCA = 27.6 ± 1.1) and 10 were classified into a cognitively impaired group (63.9 ± 10.1 yrs; 4 females; MoCA = 19.7 ± 3.4). Although on average the cognitively impaired stroke group (15.5 ± 5.8^o) showed greater gas pedal error than the cognitively normal stroke group (11.7 ± 2.0^o), this difference did not reach statistical significance (Mann-Whitney U = 33, p = 0.218, g = 0.85).

Figure 2.

Associations between Cognition and Gas Pedal Error. A) Although Marginally Significant (0.05 < p < 0.15; Dotted Line), Lower MoCA Score Showed a Negative Trend with Higher Gas Pedal Error. B) Divided Attention Did Not Significantly Associate with Gas Pedal Error (p > 0.15; no line). C) However, Longer Selective Attention Significantly Associated with Higher Gas Pedal Error (p < 0.05; Solid Line).

Brake response time. The relationship between cognitive performance and brake response time is presented in Figure 3. MoCA scores showed a negative trend with brake response time (r = −0.45; p = 0.081). Although not reaching statistical significance, divided attention time (r = 0.39; p = 0.081) and selective attention time (r = 0.41; p = 0.111) showed a positive trend with brake response time. Importantly, the cognitively impaired stroke group (1.0 ± 0.2 s) demonstrated longer brake response time compared with the cognitively normal stroke group (0.9 ± 0.1 s; Mann-Whitney U = 22, p = 0.035, g = 1.03).

Figure 3.

Associations between Cognition and Brake Response Time. A) Lower MoCA Score Showed a Negative Trend with Longer Brake Response Time (0.05 < p < 0.15; dotted line). Moreover, Longer Divided Attention (B) and Selective Attention (C) Showed a Positive Trend with Longer Brake Response Time. However, these Associations were Marginally Significant.

Brake force error. The relationship between cognitive performance and brake force error is presented in Figure 4. Neither MoCA scores (r = −0.31; p = 0.246) nor selective attention time (r = 0.37; p = 0.157) were correlated with brake force error. We found a positive trend between brake force error and divided attention time, but this did not reach statistical significance (r = 0.42; p = 0.104). Finally, brake force error was not significantly different between the two stroke groups (impaired: 20.5 ± 10.2 N, normal: 20.8 ± 9.6 N; Mann-Whitney U = 52, p = 0.912, g = 0.03).

Figure 4.

Associations between Cognition and Brake Force Error. Neither MoCA (A) Nor Selective Attention (C) Significantly Associated with Brake Force Error (0.05 < p < 0.15; Dotted Line). B) Although Marginally Significant (0.05 < p < 0.15; Dotted Line), Longer Divided Attention Showed a Positive Trend with Higher Brake Force Error.

Self-reported driving behavior and crash risk. The two stroke groups did not differ significantly on the DHQ scores (Mann-Whitney U = 65, p = 0.280, g = 0.47; Figure 5A). Interestingly, there was a significant difference in crash risk category between the two stroke groups (χ² (2, N = 20) = 8.57, p = 0.014, Cramér's V = 0.655; Figure 5B). Specifically, 100% of individuals in the cognitively normal stroke group were classified into the low-risk category. In contrast, among the cognitively impaired stroke group, 40% were classified as having low-risk while 30% were classified in the moderate-risk category and remaining 30% were placed in the high-risk category.

Figure 5.

Driving Behavior and Crash Risk between Cognitively Impaired and Cognitively Normal Stroke Survivors. A) Self-Reported Driving Behavior Assessed with the Driving Habits Questionnaire (DHQ). DHQ Scores were Not Significantly Different between the Two Stroke Groups. Effect Size is Indicated by Hedge's g. B) Crash Risk was Determined Using the Useful Field of View test for Cognitively Normal (left) and Cognitively Impaired (right) Stroke Groups. Sixty Percent of the Participants in Cognitively Impaired Stroke Group Exhibit Moderate (30%) to High (30%) Driving Crash Risk as Compared to None of the Participants in the Cognitively Normal Group.

Furthermore, neither FMA-LE (Cognitively Normal: 25.7 ± 7.9; Cognitively Impaired: 25.2 ± 7.2; p = 0.796, g = 0.063), dorsiflexion strength (Cognitively Normal: 192.8 ± 88.0 N; Cognitively Impaired: 137.0 ± 54.9 N; p = 0.211, g = 0.737), nor plantarflexion strength (Cognitively Normal: 81.7 ± 44.6 N; Cognitively Impaired: 64.7 ± 39.1 N; p = 0.356, g = 0.388) were significantly different between the two groups.

Discussion

The purpose of this pilot study was to examine the association between cognitive function and lower limb motor control during driving following stroke. Preliminary findings from this study provide the first line of exploratory evidence that lower overall cognition and reduced attentional processing speed contribute to greater gas pedal error and longer brake response times. Importantly, these findings do not imply causation but rather an association. Moreover, cognitively impaired individuals with stroke showed greater crash risk relative to the cognitively normal stroke group. Our findings shed light on the potential impact of post-stroke cognitive impairment on lower limb motor control during driving and emphasizes the critical role of attention in maintaining safe driving skills.

Compromised Cognitive Function is Linked to Impaired Pedal Control in Stroke Survivors

Effective pedal control is critical for safe driving. Gas pedal control ensures a safe distance from lead vehicles while driving (Aarts & Van Schagen, 2006). In our stroke cohort, slower attentional processing speed (UFOV) was significantly associated with more gas pedal errors and showed a positive trend with longer brake response time. Further, lower overall cognitive function (MoCA) showed negative trend with more gas pedal errors and longer brake response time. Interestingly, the effect sizes of these associations were in the medium-large range. Previous work has linked unsafe driving behaviors, such as speeding and hard braking, to early stages of cognitive impairment due to Alzheimer's disease (Bayat et al., 2021). In addition, fast and accurate braking response is essential for responding promptly to avoid collisions (Schweitzer et al., 1995). Our findings extend this work by suggesting that impaired gas pedal accuracy and delayed brake response time are likely linked to cognitive impairment following stroke. Indeed, Montgomery et al. (2014) reported a relationship between delayed braking responses and cognitive impairment severity (Montgomery et al., 2014). Together, these findings introduce the potential importance of assessing and addressing cognitive impairments in stroke survivors to promote safer driving behaviors.

Cognitive Impairment Increases Driving Crash Risk in Stroke Survivors

A notable finding in our study is that 60% of the cognitively impaired stroke group had a moderate to high driving crash risk, compared to 0% in the cognitively normal stroke group and demonstrated a large effect size. The UFOV test is a reliable predictor of car crashes in older adults (Owsley et al., 1998), with previous work highlighting increased risk among those with cognitive impairments (Ball et al., 1993; Owsley et al., 1991). Our findings reinforce this work by providing evidence that cognitive impairment following stroke significantly elevates crash risk.

Interestingly, self-reported driving behavior did not differ between the groups, suggesting similar perceptions of real-world driving engagement. We propose that cognitive deficits in individuals with stroke may impair self-awareness of their driving abilities, resulting in comparable scores on the DHQ. This reduced perception, coupled with compromised pedal control, may contribute to the observed crash risk findings. Interestingly, both groups demonstrated comparable degree of motor impairment as indicated by FMA scores and ankle strength. Despite these similarities, individuals with cognitive impairments exhibited poorer pedal control. These findings underscore the critical role of cognitive function in lower limb motor control during driving post-stroke and highlight the need to incorporate cognitive assessments into driving evaluations and rehabilitation strategies for stroke survivors. Taken together, our preliminary findings suggest that cognitive impairment could contribute to compromised lower limb motor control during driving and elevates crash risk, even in the absence of changes in self-reported driving behavior and motor function in stroke survivors.

Clinical Implications

Understanding the impact of cognitive impairment on lower limb motor control during driving can inform rehabilitation strategies aimed at improving driving safety in stroke survivors. Our results highlight that post-stroke cognitive deficits, particularly attentional impairments, may compromise vehicle operation and elevate crash risk. Screening for cognitive deficits could help identify individuals at higher risk for pedal errors. We propose that cognitive assessments should be a routine part of post-stroke driving evaluations and promote independence, especially for those intending to return to driving. Driver training programs may benefit from incorporating cognitive training. For example, Nozawa et al., (2015) trained older adults in a vehicle with an onboard cognitive training program and found that those individuals showed significant improvements in a driving aptitude test and during an on-road driving evaluation relative to baseline (Nozawa et al., 2015). The usefulness of these approaches in stroke survivors needs to be investigated. Overall, the presented work has the potential to kickstart the development of rehabilitation approaches that simultaneously target motor and cognitive domains. Emerging neuroengineering tools, particularly adaptive driving simulators, may offer new pathways for personalized, functional recovery. Integrating these technologies into rehabilitation could enhance real-world mobility outcomes and support the goal of restoring safe, independent driving after stroke.

Study Limitations

The present findings require the following considerations: 1) Despite meeting sample size recommendations appropriate for pilot studies (Julious, 2005), the findings should be interpreted with caution and confirmed in larger, adequately powered samples. 2) Although the UFOV is a well-established proxy for crash risk and has been linked to real-world crash incidence, it does not capture actual crash events and may not fully reflect real-world driving safety. 3) We used MOCA and a standard attentional processing assessment to examine cognition. However, these cognitive assessments are relatively broad and do not address other cognitive domains critical for safe and effective driving such as executive function, memory, and visuospatial processing. Future studies should incorporate a more comprehensive battery of neuropsychological assessments to better characterize cognitive deficits following stroke. 4) Lower limb motor control during driving was evaluated in a controlled, simulated environment and DHQ is self-reported and subject to bias. This presents an important limitation to generalizability, especially in the absence of objective, real-world driving data, and highlights the need for naturalistic driving assessments. 5) Lastly, our study focused specifically on pedal manipulation. However, steering is another crucial component of driving performance that is commonly affected by stroke (Patel et al., 2021) and should be examined in future research.

Conclusion

The current study provides preliminary evidence that cognitive impairment following stroke potentially compromises lower limb motor control during driving. While these findings provide valuable early support for including cognitive evaluations in driving assessment and retraining post-stroke, larger, sufficiently powered studies are needed to confirm these results and establish the role of cognitive function in driving safety after stroke.

Footnotes

Ethical Considerations

This research complied with the tenets of the Declaration of Helsinki and was approved by the Institutional Review Board (IRB2014-U-0824).

Consent to Participate

Informed consent was obtained from each participant prior to study procedures.

Consent for Publication

Not applicable.

Author Contributions

Stefan Delmas: Formal analysis, Writing – review & editing, Visualization

Prakruti Patel: Writing – original draft, Formal analysis

Agostina Casamento-Moran: Writing – review & editing, Investigation, Validation

Evangelos A Christou: Supervision, Conceptualization, Methodology, Project administration

Neha Lodha: Conceptualization, Data curation, Funding acquisition, Investigation, Writing – review & editing

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the American Heart Association (Scientist Development Award 14SDG20450151 to NL).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability

The data reported in this manuscript will be provided by the corresponding author upon reasonable request.

ORCID iDs

Stefan Delmas

Neha Lodha

References

Aarts

Van Schagen

(2006). Driving speed and the risk of road crashes: A review. Accident Analysis and Prevention, 38(2), 215–224. https://doi.org/10.1016/J.AAP.2005.07.004

Ball

Owsley

Sloane

M. E.

Roenker

D. L.

Bruni

J. R.

(1993). Visual attention problems as a predictor of vehicle crashes in older drivers. Investigative Ophthalmology & Visual Science, 34(11), 3110–3123.

Bayat

Babulal

G. M.

Schindler

S. E.

Fagan

A. M.

Morris

J. C.

Mihailidis

Roe

C. M.

(2021). GPS Driving: A digital biomarker for preclinical Alzheimer disease. Alzheimer’s Research & Therapy, 13(1), 115. https://doi.org/10.1186/S13195-021-00852-1

Chihuri

Mielenz

T. J.

Dimaggio

C. J.

Betz

M. E.

Diguiseppi

Jones

V. C.

(2016). Driving cessation and health outcomes in older adults. Journal of the American Geriatrics Society, 64(2), 332–341. https://doi.org/10.1111/JGS.13931

Cumming

T. B.

Marshall

R. S.

Lazar

R. M.

(2012) Stroke, cognitive deficits, and rehabilitation: Still an incomplete picture. International Journal of Stroke, 8(1), 38–45. https://doi.org/10.1111/J.1747-4949.2012.00972.X

Duncan Millar

van Wijck

Pollock

Ali

(2019). Outcome measures in post-stroke arm rehabilitation trials: Do existing measures capture outcomes that are important to stroke survivors, carers, and clinicians? Clinical Rehabilitation, 33(4), 737–749. https://doi.org/10.1177/0269215518823248

El Husseini

Katzan

I. L.

Rost

N. S.

Blake

M. L.

Byun

Pendlebury

S. T.

Aparicio

H. J.

Marquine

M. J.

Gottesman

R. F.

Smith

E. E.

(2023). Cognitive impairment after ischemic and hemorrhagic stroke: A scientific statement from the American Heart Association/American stroke association. Stroke, 54(6), E272–E291. https://doi.org/10.1161/STR.0000000000000430

Fonda

S. J.

Wallace

R. B.

Herzog

A. R.

(2001). Changes in driving patterns and worsening depressive symptoms among older adults. The Journals of Gerontology: Series B, 56(6), S343–S351. https://doi.org/10.1093/GERONB/56.6.S343

Gladstone

D. J.

Danells

C. J.

Black

S. E.

(2002). The fugl-meyer assessment of motor recovery after stroke: A critical review of its measurement properties. Neurorehabilitation and Neural Repair, 16(3), 232–240. https://doi.org/10.1177/154596802401105171

10.

Hasegawa

Kimura

Takeda

(2020). Pedal misapplication: Interruption effects and age-related differences. Human Factors, 63(8), 1342. https://doi.org/10.1177/0018720820936122

11.

Hird

M. A.

Vetivelu

Saposnik

Schweizer

T. A.

(2014). Cognitive, on-road, and simulator-based driving assessment after stroke. Journal of Stroke and Cerebrovascular Diseases : The Official Journal of National Stroke Association, 23(10), 2654–2670. https://doi.org/10.1016/J.JSTROKECEREBROVASDIS.2014.06.010

12.

Julious

S. A.

(2005). Sample size of 12 per group rule of thumb for a pilot study. Pharmaceutical Statistics, 4(4), 287–291. https://doi.org/10.1002/PST.185

13.

Kwakkel

Lannin

N. A.

Borschmann

English

Ali

Churilov

Saposnik

Winstein

van Wegen

E. E. H.

Wolf

S. L.

Krakauer

J. W.

Bernhardt

(2017). Standardized measurement of sensorimotor recovery in stroke trials: Consensus-based core recommendations from the stroke recovery and rehabilitation roundtable. International Journal of Stroke : Official Journal of the International Stroke Society, 12(5), 451–461. https://doi.org/10.1177/1747493017711813

14.

Lodha

Moon

Kim

Onushko

Christou

E. A.

Kritchevsky

(2016). Motor output variability impairs driving ability in older adults. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 71(12), glw013. https://doi.org/10.1093/gerona/glw013

15.

Loetscher

Potter

K. J.

Wong

das Nair

(2019) Cognitive rehabilitation for attention deficits following stroke. Cochrane Database of Systematic Reviews, 2013(5), CD002842. https://doi.org/10.1002/14651858.CD002842.PUB3/INFORMATION/EN

16.

Marshall

S. C.

Molnar

Man-Son-Hing

Blair

Brosseau

Finestone

H. M.

Lamothe

Korner-Bitensky

Wilson

K. G.

(2007). Predictors of driving ability following stroke: A systematic review. Topics in Stroke Rehabilitation, 14(1), 98–114. https://doi.org/10.1310/TSR1401-98

17.

McCarty

Kim

H. W.

(2024). Risky behaviors and road safety: An exploration of age and gender influences on road accident rates. PLOS ONE, 19(1), e0296663. https://doi.org/10.1371/JOURNAL.PONE.0296663

18.

Montgomery

Kusano

K. D.

Gabler

H. C.

(2014) Age and gender differences in time to collision at braking from the 100-Car naturalistic driving study. Traffic Injury Prevention, 15(Suppl 1), S15–20. https://doi.org/10.1080/15389588.2014.928703

19.

Nadeau

Arsenault

A. B.

Gravel

Bourbonnais

(1999). Analysis of the clinical factors determining natural and maximal gait speeds in adults with a stroke. American Journal of Physical Medicine & Rehabilitation, 78(2), 123–130. https://doi.org/10.1097/00002060-199903000-00007

20.

Nasreddine

Z. S.

Phillips

N. A.

Bedirian

Charbonneau

Whitehead

Collin

Cummings

J. L.

Chertkow

(2005). The Montreal cognitive assessment, MoCA: A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society, 53(4), 695–699. https://doi.org/10.1111/j.1532-5415.2005.53221.x

21.

Nozawa

Taki

Kanno

Akimoto

Ihara

Yokoyama

Kotozaki

Nouchi

Sekiguchi

Takeuchi

Miyauchi

C. M.

Ogawa

Goto

Sunda

Shimizu

Tozuka

Hirose

Nanbu

Kawashima

(2015). Effects of different types of cognitive training on cognitive function, brain structure, and driving safety in senior daily drivers: A pilot study. Behavioural Neurology, 2015(1), 525901. https://doi.org/10.1155/2015/525901

22.

Owsley

Ball

McGwin

Sloane

M. E.

Roenker

D. L.

White

M. F.

Overley

E. T.

(1998). Visual processing impairment and risk of motor vehicle crash among older adults. JAMA, 279(14), 1083–1088. https://doi.org/10.1001/JAMA.279.14.1083

23.

Owsley

Ball

Sloane

M. E.

Roenker

D. L.

Bruni

J. R.

(1991) Visual/cognitive correlates of vehicle accidents in older drivers. Psychology and Aging, 6(3), 403–415. https://doi.org/10.1037/0882-7974.6.3.403

24.

Owsley

Stalvey

Wells

Sloane

M. E.

(1999) Older drivers and cataract: Driving habits and crash risk. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 54(4), M203–M211. https://doi.org/10.1093/GERONA/54.4.M203

25.

Patel

Alam

Tracy

B. L.

Lodha

(2021). Impaired force control contributes to car steering dysfunction in chronic stroke. Disability and Rehabilitation, 43(14), 1948–1954. https://doi.org/10.1080/09638288.2019.1685603

26.

Robertsen

Lorås

H. W.

Polman

Simsekoglu

Sigmundsson

(2022). Aging and driving: A comparison of driving performance between older and younger drivers in an on-road driving test. SAGE Open, 12(2). https://doi.org/10.1177/21582440221096133

27.

Rubin

G. S.

E. S. W.

Bandeen-Roche

Keyl

P. M.

Freeman

E. E.

West

S. K.

Alston

Donoway

Harrison

Hernandez

Istre-Walker

Johnson

Jones

Long

C. P.

Lynch

Macintosh

Mitchell

J. E.

Townsend

Wilson

(2007). A prospective, population-based study of the role of visual impairment in motor vehicle crashes among older drivers: The SEE study. Investigative Ophthalmology and Visual Science, 48(4), 1483–1491. https://doi.org/10.1167/IOVS.06-0474

28.

Schweitzer

Apter

Ben-David

Liebermann

D. G.

Parush

(1995) A field study on braking responses during driving. II. Minimum driver braking times. Ergonomics, 38(9), 1903–1910. https://doi.org/10.1080/00140139508925238

29.

C. Y.

Wuang

Y. P.

Lin

Y. H.

J. H.

(2015). The role of processing speed in post-stroke cognitive dysfunction. Archives of Clinical Neuropsychology, 30(2), 148–160. https://doi.org/10.1093/ARCLIN/ACU057

30.

Wood

J. M.

Chaparro

Lacherez

Hickson

(2012). Useful field of view predicts driving in the presence of distracters. Optometry and Vision Science, 89(4), 373–381. https://doi.org/10.1097/OPX.0B013E31824C17EE