Mild cognitive impairment,Alzheimer's disease dementia,and predictors of driving cessation: A 7-year longitudinal prospective study

Abstract

Background

Patients with dementia face driving difficulties and, at some point, cease driving.

Objective

We sought to identify predictors of driving cessation among patients with mild cognitive impairment (MCI) or mild Alzheimer's disease dementia (AD).

Methods

We enrolled in this longitudinal study patients with MCI, AD (Clinical Dementia Rating < 2) and cognitively normal (NC) individuals. At baseline evaluation, participants underwent a neurological, neuropsychological and driving simulator assessment. Re-evaluations after 48 and 84 months included a structured interview with the patients and their caregiver. Primary endpoints were driving cessation, death and progression to dementia.

Results

109 individuals were included (32 NC, mean age 65.8 years/47 MCI, mean age 69.1 years/30 AD, mean age 72.8 years). Dangerous driving events during follow-up were referred for 45% and 59% of MCI and AD patients, respectively. 18 MCI (38%, mean time to cease 35 months) and 25 AD (83%, mean time to cease 15 months) patients ceased driving during follow-up. 36% of MCI patients progressed to dementia during follow-up. Cox Regression multivariate analysis revealed age (Hazard Ratio-HR 1.080), semantic verbal fluency-SVF (HR 0.822) and Tandem Walking Test modified with simultaneous reverse number counting-mTWT (HR 1.099) as significant predictors of driving cessation. Simulator accident probability reached statistical significance only in the univariate model (HR 1.040).

Conclusions

Age, SVF and mTWT are significant predictors of driving cessation among MCI and AD patients. Driving simulator may be a promising component of driving evaluation. Large-scale studies are prerequisite for the implementation of a multi-disciplinary driving fitness evaluation protocol.

Keywords

Alzheimer's disease automobile driving dementia driving cessation mild cognitive impairment traffic crashes

Introduction

Driving is indispensable for everyday functionality in modern developed societies.¹ Despite being a common practice, it remains a very demanding task, requiring the integrity and the cooperation of cognitive and motor functions,² both of which are vulnerable to neurodegenerative diseases. Even mild cognitive impairment (MCI), which by definition does not interfere with instrumental activities of daily living,³ has a negative impact on driving ability.^4,5 This impact is more pronounced in multiple-domain MCI,^6–8 while MCI patients have an increased risk to cease driving earlier than healthy controls.⁹

Alzheimer's disease (AD) is the most common neurodegenerative disease, affecting more than 50 million people worldwide with increasing prevalence.¹⁰ Cognitive impairment caused by AD has an insidious onset and a gradually progressive course, leading to loss of autonomy.^11,12 AD patients face driving difficulties even in the early stages of the disease;¹³ these difficulties increase as the disease progresses and, at some point of the disease course, lead to driving cessation.^14–16

Driving cessation of the elderly contributes to psychosocial isolation, depression and loss of self-dependence.^17–19 The magnitude of these profoundly negative consequences is greater among cognitively impaired patients,^20–23 affecting the carers as well (given the common co-existence of depressive symptoms and the lack of insight of individuals with dementia). These subjective experiences of patients and caregivers are sufficiently circumscribed in the literature.^20–26 However, clear-cut directives and guidelines regarding the procedures of driving fitness evaluation²⁷ and driving cessation^28–30 are lacking.

With this longitudinal prospective study, we aim to identify any possible demographic, neurological, neuropsychological or driving simulator predictors of driving cessation among MCI and AD patients.

Methods

Our longitudinal prospective study was approved by the ethics committee of “Attikon” University General Hospital. Participants were recruited and examined in the Cognitive Disorders/Dementia Outpatient Unit at the 2nd University Department of Neurology in “Attikon” University General Hospital of Athens, a tertiary center in Western Attika, Greece. Signed informed consent for research participation in this prospective study was obtained from all participants and their caregivers. We prepared and conducted the present study, adhering to the STROBE guidelines.³¹

The initial evaluation was performed between January 2013 and December 2015 and it included: (a) A general medical and ophthalmological assessment, (b) A driving profile questionnaire, answered by the patient and the caregiver, (c) A neuropsychological, neuropsychiatric and everyday functionality assessment, (d) Assessment of neurological status, emphasizing on motor function and (e) A driving simulator experiment, which took place at the Department of Transportation Planning and Engineering of the National Technical University of Athens. This driving simulator is a motion-base quarter-cab (FOERST Company, Gummersbach, Germany), consisting of 3 LCD 40-in. wide, full HD screens, a driving position and support a motion base. The driving experiment began with an initial driving practice (15 min), to help participants familiarize with the driving simulator, followed by 2 driving sessions (15 min each) in a rural and in an urban environment. During each session, 2 unexpected incidents occurred (a donkey crossing the road in the rural environment and a boy chasing a ball or the sudden appearance of a car in the urban environment). Driving behavior in the simulator has been validated against on-road behavior.³² The above-mentioned data were collected over three days, with an overall duration of 4 to 5 h.

The follow-up evaluation was performed via telephone after a mean period of 48 months (January 2017–December 2019) from the initial evaluation. It included: (a) A semi-structured interview with the patient and the caregiver regarding driving history and status of the participants and (b) A basic assessment of everyday functionality and global cognitive status (using IADL and CDR scales, respectively). The primary endpoints were driving cessation (including the interval from the initial evaluation until driving cessation, measured in months) and death (including the interval from the initial evaluation until death, measured in months). For those participants who did not reach the primary endpoints during the follow-up period, we performed a final evaluation after a mean period of 84 months (January 2020–December 2022) from the initial evaluation. It included the same aspects, as the second evaluation.

The flow diagram of our study is depicted in Figure 1.

Figure 1.

The flow diagram of our study. NC: normal controls; MCI: mild cognitive impairment; AD: Alzheimer's disease dementia.

Participants

Our study sample was composed of a control group of cognitively healthy individuals (NC, N = 32), a group of patients with MCI (N = 47) and a group of patients with mild AD dementia (N = 30, Clinical Dementia Rating- CDR < 2). All participants were older than 55 years old, active drivers of private vehicles at the time of the study (>3 years of driving experience, >2500 km of driving during the last year, driving at least once/week during the last year and driving at least 10 km/week). MCI and AD dementia (CDR < 2) diagnosis was made by S.G.P. based on Petersen^3,33 and McKhann³⁴ diagnostic criteria, respectively. Cognitively healthy individuals with subjective cognitive decline (SCD) were excluded from the control group, given the increased risk of cognitive impairment in the future.³⁵ Patients with clinicoradiological evidence of non-AD dementia, vascular dementia, or other neuropsychiatric disorder, as well as individuals with severe motor or vision impairment or alcohol and drugs abuse were excluded. We summarize inclusion and exclusion criteria in Table 1.

Table 1.

Inclusion and exclusion criteria.

Inclusion criteria	Exclusion criteria
Active driver of private vehicle >3 years of driving experience >2500 km of driving during the last year driving at least once/week during the last year driving at least 10 km/week	Subjective Cognitive Decline (control group)
	Non-AD Dementia
	Clinicoradiological evidence of Vascular Dementia
	Psychiatric or other Neurological disorder
Valid driving license	Alcohol or other drug abuse
Age > 55 years old	Any severe motor impairment
MCI or mild ADD (CDR ≤ 1)	Any severe vision impairment

MCI: mild cognitive impairment; AD: Alzheimer's disease; ADD: Alzheimer's disease dementia; CDR: Clinical Dementia Rating.

Variables

The independent (predictor) variables are divided in the following categories:

Demographic variables, including age, sex, education years, and driving experience.

Neuropsychological variables, including tests of global cognition—Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA)—and tests of specific neurocognitive domains—Trail Making Test (TMT)-A & B, Frontal Assessment Battery (FAB), Hopkins Verbal Learning Test (HVLT), Verbal Fluency Test (VFT), Judgement of Line Orientation (JLO), Spatial Span Test (SST).

Everyday functionality variables, including Instrumental Activities of Daily Living (IADL) and CDR scales.

Motor function variables, including Rapid Pace Walk (RPW), Tandem Walking Test (TWT) and TWT modified with simultaneous reverse number counting (mTWT). During RPW, participants were asked to walk a 3-meter distance, turn around and walk back to the starting point. For TWT, patients walked in a 3-meter straight line with one foot immediately in front of the other, while during mTWT participants were asked to count backwards (from twenty to zero) while performing the same procedure. For TWT and mTWT, we measured both time to completion (in seconds) and errors during the procedure (errors were defined as: falls, step(s) towards a different direction or need of external assistance, e.g., steadying against the wall).

Simulator variables. For the purposes of the present study, we used the following driving variables extracted by the simulator: (1) Reaction time (RT, the time between the obstacle's first move toward the road and the braking time, measured in ms) and (2) Accident probability (AP, the ratio Accidents/Unexpected events, measured as a discrete variable ranging from 0 to 100).

The dependent variables (primary outcomes) of our study were the following:

Driving cessation (including the interval from the initial evaluation until driving cessation, measured in months) and death (including the interval from the initial evaluation until death, measured in months). We included death among the primary outcomes despite being multi-factorial because it is a robust, objective variable, possibly correlating with disease progression.

Progression to dementia (measured with CDR and IADL scales in the follow-up evaluations). This variable was measured only among NC and MCI patients.

We also examined dangerous driving events during follow-up (crash, near crash or dangerous driving error), based on the answers of the participants and their caregivers. However, we did not include them in the primary endpoints, given the internal subjectivity and potential heterogeneity among the answers.

Statistical analysis

Our sample was composed of three groups (NC, MCI, and AD). As a first step, we performed between-groups description and comparison of the independent and dependent variables. As a second step, we explored the correlations between dependent and independent variables both across the MCI and AD groups and treating MCI and AD as one group (NC were excluded from these analyses, as no individuals reached primary endpoints). Initially, we graphically depicted (using box plots) the possible correlations between numerous independent variables and driving cessation or death, excluding from further analysis those variables with no evident differences among the categories of dependent variables. We performed point biserial correlation (for the remaining variables) to quantify these correlations and identify the level of statistical significance. In our final step we performed univariate and multivariate Cox Regression Analysis for dependent variables as a function of follow-up time. We included only those independent variables that reached statistical significance in the previous step, excluding from multivariate analysis the independent variables with strong intercorrelation to avoid multicollinearity. The above mentioned along with the detailed methodology used are summarized in Supplementary Table 1.

Results

Our final study sample consisted of 109 participants: 32 NC (Mean Age 65.8 years, 57% Women), 47 MCI patients (Mean Age 69.1 years, 40% Women) and 30 AD patients (Mean Age 72.8 years, 7% Women). Basic demographic data (at baseline evaluation) are presented in Table 2. As expected, we noticed statistically significant differences among the three groups in the neuropsychological tests used (Table 3(a)), as well as in motor tests (RPW and mTWT- Table 3(b)) and in simulator measurements (reaction time in the rural environment and accident probability- Table 3(c)).

Table 2.

Basic demographic data (at baseline evaluation) and outcomes of the participants.

	NC (N = 32)	MCI (N = 47)	AD (N = 30)	p
Age (Mean, ±SD)	65.8 (±6) p = 0.07	69.1 (±10) p = 0.06	72.8 (±6)	<0.001**
Sex (F/M)	18/14	19/28	2/28	<0.001**
Education years (Mean, ±SD)	14 (±3)	11 (±4)	10 (±4)	<0.001**
Driving experience (Mean, ±SD)	40 (±5)	41 (±10)	43 (±7)	0.559
Months to last follow-up (Mean, min-max)	91 (75–122)	71 (36–89)	68 (48–86)	N/A
Pts with dangerous events during follow-up (N, %)	4 (12%) p = 0.003	21 (45%) p = 0.20	16 (59%)	0.001**
Cease driving (N, %)	0 p < 0.001	18 (38%) p < 0.001	25 (83%)	<0.001**
Months to cease (Mean, min-max)	N/A	35 (1–72)	15 (1–54)	0.001**
Unwilling to cease (N, %)	N/A	9 (50%)	14 (58%)	0.591
Decision to cease (N, %)	N/A	Patient: 1 (6%) / Family: 13 (72%) / Doctor: 1 (6%)	Patient: 4 (16%) / Family: 17 (68%) / Doctor: 2 (8%)	N/A
Death (N, %)	1 (3.1%) p = 0.30	4 (8.5%) p = 0.002	11 (36.7%)	<0.001**
Months to death (Mean, min-max)	N/A	70 (36–88)	48 (12–82)	0.126

p-values in the MCI column refer to differences between MCI-AD. p-values in the NC column refer to differences between NC-MCI.

*p < 0.05, **p < 0.01.

MMSE: Mini-Mental State Examination; NC: normal controls; MCI: mild cognitive impairment; AD: Alzheimer's disease dementia; SD: standard deviation.

Table 3.

(a) Basic neuropsychological test results of the participants at baseline evaluation.

Neuropsychological tests	NC (N = 32)	MCI (N = 47)	AD (N = 30)	ANCOVA
Neuropsychological tests	Mean (±SD)			p
MMSE	29 (±1) p = 0.001**	27 (±2) p < 0.001**	22 (±4)	<0.001**
MoCA	26 (±3) p < 0.001**	22 (±3) p < 0.001**	16 (±5)	<0.001**
HVLT total recall	24 (±5) p < 0.001**	18 (±5) p = 0.001**	12 (±5)	<0.001**
FAB	16 (±2) p = 0.003**	13 (±3) p < 0.001**	10 (±3)	<0.001**
TMT-A sec	44 (±17) p = 0.037*	69 (±47) p = 0.005**	115 (±69)	<0.001**
TMT-B sec	83 (±48) p < 0.001**	175 (±87) p < 0.001**	267 (±59)	<0.001**
TMT-(B-A) sec (Mean, 95% CI)	39 (19–59) p < 0.001**	105 (80–130) p = 0.028*	152 (116–187)	<0.001**
SVF	22 (±4) p < 0.001**	16 (±5) p < 0.001**	9 (±4)	<0.001**
PVF	14 (±4) p = 0.015*	10 (±4) p < 0.001**	6 (±3)	<0.001**
JLO	18 (±2) p = 0.001**	14 (±4) p = 0.053	11 (±5)	<0.001**
SST Forward	7 (±2) p = 0.685	7 (±2) p = 0.017*	6 (±2)	0.024*
SST Backward	8 (±2) p = 0.001**	5 (±2) p = 0.030*	4 (±2)	<0.001**

p-values in the MCI column refer to differences between MCI-AD. p-values in the NC column refer to differences between NC-MCI.

NC: normal controls; MCI: mild cognitive impairment; AD: Alzheimer's disease dementia; SD: standard deviation; MMSE: Mini-Mental Status Examination; MoCA: Montreal Cognitive Assessment; FAB: Frontal Assessment Battery; HVLT: Hopkins Verbal Learning Test; TMT: Trail Making Test; SST: Spatial Span Test; NC: normal controls; MCI: mild cognitive impairment; ADD: Alzheimer's disease dementia; SD: standard deviation.

(b) Basic mobility test results of the participants at baseline evaluation.

Motor tasks	NC (N = 32)	MCI (N = 47)	AD (N = 30)	ANCOVA
Motor tasks	Mean (±SD)			p
RPW (sec)	5.3 (±1) p = 0.007**	6.3 (±1.2) p = 0.397	6 (±1.1)	0.01**
TWT (sec)	6.4 (±1)	7.2 (±1.9)	7.3 (±2)	0.3
TWT (errors)	0	0.3 (±0.7)	0.2 (±0.5)	0.19
mTWT (sec)	7.6 (±1.8) p = 0.016*	9.4 (±3.4) p = 0.011*	12.5 (±4.8)	0.001**
mTWT (errors)	0	0.5 (±1.3)	0.9 (±1.3)	0.004**

NC: normal controls; MCI: mild cognitive impairment; AD: Alzheimer's disease dementia; SD: standard deviation; RPW: Rapid Pace Walk Test; TWT: Tandem Walking Test; mTWT: Tandem Walking Test modified with reverse number counting.

(c) Basic driving simulator results of the participants at baseline evaluation. Reaction time refers to the rural driving scene.

Simulator variables	NC (N = 18)	MCI (N = 36)	AD (N = 23)	ANCOVA
Simulator variables	Mean (±SD)			p
Reaction Time (msec)	1806 (±363) p = 0.003**	2204 (±530) p = 0.11	2443 (±532)	<0.001**
Accident probability	0.06 (±0.07) p = 0.001**	0.14 (±0.12) p = 0.04*	0.24 (±0.19)	0.002**

NC: normal controls; MCI: mild cognitive impairment; AD: Alzheimer's disease dementia; SD: standard deviation.

Regarding the main outcomes of our study (Table 2), no NC ceased driving during follow-up, in strong contrast to 21/47 MCI patients (45%, Mean time to cease: 35 months, unwilling to cease: 50%) and 25/30 AD patients (83%, Mean time to cease: 15 months, unwilling to cease: 58%). Only one participant of the NC group died, while 4/47 MCI (8.5%, Mean time to death: 70 months) and 11/30 AD (36%, Mean time to death: 48 months) passed away during the follow-up period. Figure 2(a) and (b) depict the Kaplan-Meier plots for driving cessation and survival, respectively. Due to some degree of heterogeneity observed in basic demographic parameters of the three groups, we initially performed a binomial logistic regression, which confirmed the statistically significant differences in outcome variables described above (driving cessation and death) after controlling for age, sex, and education years.

Figure 2.

Kaplan-Meier plots for driving cessation (A) and survival (B) among the three groups and for driving cessation of MCI patients as a function of progression to dementia (C). MCI: mild cognitive impairment; AD: Alzheimer's disease.

Of note, 45% and 59% of the MCI and AD patients respectively, had at least one dangerous driving event (crash, near crash or dangerous driving error) during follow-up, while only 12% of NC participants had a similar event. Analyzing further this finding, only one participant from NC and AD group reported a car crash (3% and 4%, respectively). In contrast to that, a significantly higher percentage of MCI participants (12/47, 25%) admitted involvement in an accident. Participants and their caregivers denied any injuries or fatalities, as a consequence of the above-mentioned crashes. Concerning the dangerous driving errors, the higher percentage was observed in the AD group (16/30, 53%), followed by the MCI (17/47, 36%) and the NC group (3/32, 9%).

Furthermore, 17/47 (36%) MCI patients progressed to dementia during the follow-up period and our findings show that 16/17 MCI patients who progressed to dementia, ceased driving too, while on the other hand, 28/30 MCI patients who did not progress, continued to drive regularly. A statistically significant linear correlation was evident between driving cessation and progression to dementia (using Chi-Square test, p < 0.001). Both age and AP in the simulator had a strong correlation with progression to dementia (p < 0.001, r = +0.61). Figure 2(c) depicts the Kaplan-Meier plot for driving cessation among the MCI patients, as a function of progression to dementia during follow-up.

Moving forward to the second step of our statistical analysis, we identified eight independent variables as possible predictors of driving cessation using box plots: one demographic (age), four neuropsychological (MMSE, TMT-B, semantic VFT and HVLT total recall), one motor (mTWT) and two derived from the driving simulator experiment (RT in the rural environment and AP). Point biserial correlation confirmed the statistical significance of the correlations at the level of p = 0.01 for six out of these eight variables (apart from mTWT and RT) among the MCI group (Table 4). No significant correlations were found among the AD patients, possibly due to the very high percentage of driving cessation among these patients. All the above-mentioned eight variables were found to be significantly correlated to driving cessation when we analyzed MCI and AD patients as a single group. Statistical significance was maintained at the level of p = 0.01, after Bonferroni correction.

Table 4.

Correlation (using point biserial correlation) between independent variables and primary endpoints (driving cessation and survival).

Driving cessation	MCI (N = 47)				MCI + AD (N = 76)
Driving cessation	Continue (Ν=29)	Cease (Ν=18)	r	p	Continue (Ν=33)	Cease (Ν=43)	r	p
Age	65 (±8)	76 (±8)	+0.58	<0.001**	65 (±8)	74 (±8)	+0.52	<0.001**
MMSE	28 (±1)	26 (±2)	−0.45	0.009**	28 (+/2)	24 (±4)	−0.49	<0.001**
TMT-B	136 (±76)	236 (±69)	+0.56	0.001**	150 (±81)	260 (±62)	+0.61	<0.001**
SVF	19 (±4)	12 (±3)	−0.67	<0.001**	18 (±5)	10 (±4)	−0.67	<0.001**
HVLT-TR	20 (±4)	14 (±4)	−0.54	0.001**	19 (±5)	13 (±5)	−0.52	<0.001**
mTWT	7 (±3)	11 (±4)	+0.33	0.06	9 (±3)	12 (±5)	0.42	0.002**
RT msec	2099 (±535)	2368 (±497)	+0.25	0.14	2093 (±509)	2469 (±510)	0.35	0.007**
AP	0.08 (±0.1)	0.21 (±0.1)	+0.52	0.001**	0.10 (±0.1)	0.23 (±0.1)	0.40	0.002**

Survival	AD (N = 30)				MCI + AD (N = 77)
Survival	Survival (Ν=19)	Death (Ν=11)	r	p	Survival (Ν=62)	Death (Ν=15)	r	p
Age	72 (±7)	74 (±5)	+0.18	0.34	69 (±9)	76 (±6)	+0.30	0.007**
MMSE	24 (±4)	20 (±3)	−0.46	0.04*	26 (+/3)	21 (±4)	−0.52	<0.001**
TMT-B	243 (±68)	301 (±69)	+0.48	0.03*	189 (±87)	285 (±52)	+0.43	0.001**
SVF	19 (±4)	12 (±3)	−0.67	<0.001**	15 (±5)	7 (±3)	−0.53	<0.001**
HVLT-TR	14 (±5)	10 (±5)	−0.31	0.18	17 (±6)	11 (±4)	−0.38	0.005**
mTWT	9 (±2)	16 (±4)	+0.72	<0.001**	9 (±3)	16 (±5)	0.61	<0.001**
RT msec	2124 (±297)	2940 (±425)	+0.76	<0.001**	2172 (±481)	2789 (±484)	0.46	<0.001**
AP	0.14 (±0.1)	0.39 (±0.1)	+0.64	0.001**	0.13 (±0.1)	0.35 (±0.1)	0.55	<0.001**

*p < 0.05, **p < 0.01.

MMSE: Mini-Mental State Examination; MCI: mild cognitive impairment; AD: Alzheimer's disease dementia; SD: standard deviation; TMT: Trail Making Test; SVF: Semantic Verbal Fluency; HVLT-TR: Hopkins Verbal Learning Test-Total Recall; mTWT: modified Tandem Walking Test; RT: reaction time; AP: accident probability.

Concerning death probability, we identified the same eight independent variables as possible predictors using box plots (age, MMSE, TMT-B, semantic VFT, HVLT total recall, mTWT, RT in the rural environment and AP). Point biserial correlation confirmed the statistical significance at the level of p = 0.01 for four out of these eight variables (semantic VFT, mTWT, RT, AP) among the AD group (Table 4). No significant correlations were found among the MCI patients, possibly due to the very low mortality in this group. All the above-mentioned eight variables were found to be significantly correlated to death when we analyzed MCI and AD patients as a single group. Statistical significance was maintained at the level of p = 0.01, after Bonferroni correction.

The final step was the Cox Regression analysis (CRA) for the primary endpoints of our study (driving cessation and death). We included age in our models, as it is a strong, established predictor of driving cessation, cognitive impairment and death, being, thus, a potential confounder. Initially, we performed a univariate CRA separately for each independent variable (both across the MCI group alone and taking together MCI plus AD patients), finding that seven out of eight variables mentioned above were statistically significant predictors of driving cessation, i.e., Age, MMSE, TMT-B, Semantic verbal fluency, HVLT-TR, mTWT, and RT (Table 5). Due to strong intercorrelation among variables of the same category (neuropsychological variables- MMSE, TMT-B, VFT, HVLT- and simulator variables- RT and AP), we included in multivariate CRA only the variables with the highest correlation with the outcome variable (per category). Thus, four variables were included: age, semantic VFT, mTWT and AP. Age (HR = 1.080, p = 0.007), semantic VFT (HR = 0.822, p = 0.001) and mTWT (HR = 1.099, p = 0.043) were statistically significant independent predictors of driving cessation among the cognitively impaired participants (MCI plus AD patients, as one group- Table 5). AP in the simulator failed to reach statistical significance in the multivariate model, possibly because of the relatively small sample and its moderate correlation with age.

Table 5.

Cox regression univariate and multivariate analysis for driving cessation, including the main independent variables.

Univariate analysis	MCI + AD (N = 76)			MCI (N = 47)			AD(N = 29)
Univariate analysis	Hazard ratio	95% CI	p	Hazard ratio	95% CI	p	Hazard ratio	95% CI	p
Age	1.106	1.059–1.154	<0.001**	1.175	1.092–1.264	<0.001**	1.043	0.968–1.124	0.27
MMSE	0.850	0.785–0.919	<0.001**	0.671	0.499–0.902	0.008**	0.953	0.845–1.075	0.431
TMT-B	1.011	1.006–1.017	<0.001**	1.011	1.004–1.018	0.003**	1.004	0.991–1.017	0.552
SVF	0.811	0.746–0.842	<0.001**	0.787	0.691–0.897	<0.001**	0.879	0.757–1.020	0.089
HVLT-TR	0.898	0.845–0.954	0.001**	0.884	0.804–0.971	0.01**	0.985	0.890–1.090	0.766
mTWT	1.151	1.067–1.242	<0.001**	1.195	1.020–1.401	0.028*	1.058	0.962–1.164	0.242
RT msec	1.040	1.017–1.063	0.001**	1.063	1.022–1.107	0.003**	1.009	0.982–1.038	0.506

Multivariate analysis	MCI + AD (N = 76)
Age	1.080	1.021–1.142	0.007**
SVF	0.822	0.736–0.919	0.001**
mTWT	1.099	1.003–1.203	0.043*
AP	0.992	0.959–1.026	0.634

*p < 0.05, **p < 0.01.

MMSE: Mini-Mental State Examination; MCI: mild cognitive impairment; AD: Alzheimer's disease dementia; SD: standard deviation; TMT: Trail Making Test; mTWT: modified Tandem Walking Test; SVF: Semantic Verbal Fluency; HVLT-TR: Hopkins Verbal Learning Test-Total Recall; RT: reaction time; AP: accident probability.

Univariate CRA for death probability revealed mTWT, RT and AP as possible predictors across the AD group (Table 6). However, in multivariate analysis only RT in the rural scene (HR = 1.003, p = 0.035) was found to be statistically significant predictor of death (Table 6). MCI patients were not included in this analysis, due to the very low mortality of this group.

Table 6.

Cox regression univariate and multivariate analysis for survival, including the main independent variables.

Univariate analysis	AD (N = 30)			MCI + AD (N = 77)
Univariate analysis	Hazard ratio	95% CI	p	Hazard ratio	95% CI	p
Age	1.048	0.935–1.175	0.417	1.099	1.026–1.178	0.008**
MMSE	0.869	0.752–1.006	0.062	0.821	0.733–0.921	0.001**
TMT-B	1.033	0.980–1.090	0.227	1.015	1.001–1.030	0.035*
SVF	0.814	0.658–1.007	0.058	0.749	0.631–0.890	0.001**
HVLT-TR	0.917	0.800–1.053	0.220	0.871	0.779–0.974	0.015*
mTWT	1.132	1.015–1.264	0.026*	1.199	1.079–1.332	0.001**
RT msec	1.003	1.001–1.004	0.001**	1.002	1.001–1.003	0.001**
AP	1.047	1.010–1.086	0.012**	1.068	1.032–1.105	<0.001**

Multivariate analysis	AD (N = 30)
mTWT	1.022	0.871–1.198	0.790
Reaction Time msec	1.003	1.000–1.005	0.035*
Accident probability	0.990	0.936–1.048	0.733

*p < 0.05, **p < 0.01.

Discussion

With this longitudinal study, we aim to describe the driving status and its evolution over time among individuals with cognitive impairment. We stratified cognitively normal individuals and patients with MCI or mild AD and performed a baseline and two follow-up evaluations (after a mean period of 48 and 84 months, respectively). Our main research target with clear clinical implications was the identification of specific prognostic markers of driving cessation. The present study was based on the rationale that these markers should be derived from a multi-disciplinary clinical evaluation, including demographic evidence, motor and neuropsychological tests and, in selected cases, driving assessment (either on-road or in a driving simulator). The implementation of a driving simulator experiment in the evaluation protocol, along with the long-term follow-up (up to seven years), may add a different perspective in the current knowledge and practice of driving assessment among individuals with cognitive impairment.

We performed a relative systematic review of the literature, searching for English-written, prospective studies, regarding driving cessation of patients with cognitive impairment (MCI, AD, or dementia). We identified only nine articles^9,14,36–42 addressing the same issue, which used heterogenous methodology and arrived at mixed findings (Table 7). Only one study included simulator driving assessment in the evaluation protocol of AD patients, with a relatively short follow-up period (seven months).³⁶ Despite the lack of strong literature evidence, the issue of driving cessation among cognitively impaired individuals is of utmost importance in everyday clinical setting and in terms of public health policy. Both the appropriate timing of driving cessation and the early identification of at-risk patients are key questions for treating neurologists and converge to the same target: the description of specific prognostic markers.

Table 7.

Existing longitudinal studies regarding driving cessation among patients with cognitive impairment.

Author (Year)	N (%F)	Diagnosis	Mean Age	Duration (Follow-up)	Baseline evaluation	Endpoint	Risk factors
Cotrell (1998) ⁴⁰	35 (40%)	AD	74 years	12–18 months	ADL, CDR, Questionnaire	Cease: 88% (51% of them ceased before study)	MMSE, IADL
Adler (2003) ⁴¹	43 (0)	Dementia	75 years	25–39 months	Questionnaire, MMSE	Cease: 53%	Age, MMSE, disease duration
Herrmann (2006) ³⁸	719 (46%)	Dementia	76 years	3 years (6-months)	3MS, GDS, NPI, SMAF	Cease: 85% (84% of them ceased before study)	GDS, MMSE, ≥ 3 Behavioral, Education
Asse (2016) ³⁷	512 (51%)	CN (Incident Dementia during study)	82 years	10 years (2 years)	NeuroPsych, Questionnaire	Cease: 69% (87% of them ceased before diagnosis)Probability 1–3–5 y after diagnosis:0.26–0.41–0.63(M)/0.27–0.46–0.64(F)	AD (Protective)
Connors (2017) ⁹	144 (40%)	MCI	74 years	3 years (3–6–12–24–36 months)	NeuroPsych (MMSE-SMAF-NPI), Questionnaire	Cease: 27% (58% progressed to Dementia during the study)Mean time to cease: 14 months	Age, MMSE, MMSE decline, SMAF, SMAF decline
Connors (2018) ³⁹	779 (48%)	Dementia	78 years	3 years (3–6–12–24–36 follow-up)	NeuroPsych (MMSE-SMAF-NPI), Questionnaire	Cease: 74% (75% of them ceased before diagnosis)Mean time to cease: 12 months	Age, Sex(F), MMSE, MMSE decline, SMAF, SMAF decline
Stout (2018) ¹⁴	1724 (52%)	CN (53%), MCI (38%, Dementia (9%)	73 years	24 years (annual)	Questionnaire. CDR, CSF	Cease: 99%Median years to cease: 15, 6, 2 for CDR 0, 0.5, and 1, respectively	Age, Sex(F), ApoE4, Driving problems, CDR, CSF biomarkers
Piersma (2018) ³⁶	172 (26%)	Dementia	71 years	7 months	NeuroPsych, Simulator, On-road	Cease: 40%	Age, AD, Sex(F), CDR, MMSE, Health general, Contrast Sensitivity, Less alternative modes of transport
Babulal (2024) ⁴²	283 (51%)	CN (Baseline)	72 years	5.6 years (annual)	NeurologicalNeuroPsychCSF, AmyloidPET	Cease: 8%Death: 5%	Age, Female, CDR, PACC

CN: cognitively normal; AD: Alzheimer's disease; CDR: Clinical Dementia Rating; MMSE: Mini-Mental Status Examination; MCI: mild cognitive impairment; SMAF: Functional Autonomy Measurement System; NPI: Neuropsychiatric Inventory; 3MS: Modified Mini-Mental Status Examination; GDS: Geriatric Depression Scale; ADL: Activities of Daily Living; IADL: Instrumental Activities of Daily Living; CSF: cerebrospinal fluid; NeuroPsych: Neuropsychological Assessment not further defined; PACC: Preclinical Alzheimer Cognitive Composite.

Examining at first our descriptive findings, we highlight the very high percentage of AD patients who ceased driving (83%) and the remarkable percentage of 59% who experienced dangerous driving events (including accidents or near-accidents and disorientation) during the study. The mean period until driving cessation was 15 months, with 60% of our mild AD patients ceasing driving during the first two years of the study. 58% of those who ceased were unwilling to do so. Prospective studies from the literature had similar findings, as driving cessation percentage ranges from 48–71% after 1–3 years of follow-up.^38–40 In the largest prospective study so far, mean period to driving cessation was two years after inclusion in the study.¹⁴ These findings underlie the need to discuss (early on the disease course) with our patients the possibility of driving cessation in the near future and implement alternative transportation methods, to corroborate the therapeutic relationship and minimize the negative consequences on patients and family.^21,43–45

Concerning the MCI group, the percentages are significantly lower (as expected), but still quite informative: almost 30% ceased driving during the first four years of follow-up, while the total percentage of driving cessation during the study was 38%. Two previous longitudinal studies among MCI patients had comparable results, as in the first 27% of the participants ceased driving over a 3-year period,⁹ while in the second almost 50% of the participants ceased driving after 5 years.¹⁴ Among the MCI patients, 45% had at least one dangerous driving event during follow-up. It is noteworthy that MCI patients exhibited the highest percentage of crashes among the three groups. Progression to dementia among MCI patients is well studied in the literature. Age is recognized as the most important risk-factor, while annual conversion rates vary across different studies from 5% to 20%.⁴⁶ Our study supports these data, as we found a 36% cumulative probability of progression to dementia over a 6-year period and a strong positive correlation with age and AP in the simulator. A possible predictive role of driving simulator is discussed below.

The main research and clinical question is the identification of prognostic markers for driving cessation, to individualize patients’ risk. We found age, semantic VFT and mTWT as possible predictors of driving cessation. Accident probability (AP) in the simulator experiment was also significantly higher among the participants who ceased driving. However, it did not reach statistical significance when incorporated in the multivariate CRA, possibly because of the small sample size and its intercorrelation with the other variables in the model. Only one longitudinal study examined simulator measurements as possible predictors of driving cessation and failed to demonstrate any significant correlations.³⁶ Age is recognized in four other longitudinal studies as a main risk factor for driving cessation,^9,36,39,42 while VFT is recognized in a cross-sectional study⁴⁷ and mTWT has not been correlated so far with driving cessation. The heterogeneity of these findings is not surprising, given the highly heterogenous methodology across the studies of driving fitness, along with the plethora of neuropsychological tests proposed^8,48 and the simultaneous absence of motor function tests.²⁷

Verbal fluency tasks are widely used neuropsychological tools, testing verbal ability and, most importantly, executive functions (working memory, clustering, inhibition) and complex attention (processing speed, sustained attention). Tandem walking is a part of neurological examination, testing balance and motor coordination. mTWT (TWT modified with reverse number counting) entails a delicate cognitive component requiring the participation of executive functions (working memory, inhibition) and complex attention (sustained, divided attention, processing speed). The cognitive domains involved in VFT and mTWT are essential for safe driving and this fact may explain the above-mentioned findings and the value of these tests as predictors of driving fitness.⁸ Recent robust data point out the detrimental effect of dual decline in cognition and motor performance on dementia risk.⁴⁹ Thus, we want to underscore the potential role of mTWT in driving fitness evaluation, as it encompasses three notable characteristics: easy administration (quick, office based, part of clinical neurological examination), quantitative measurement (useful for research purposes and for patients’ serial re-evaluations) and assessment of both motor and cognitive (executive) function.

Concerning the identification of RT as a predictor of death among the AD group, we evaluate this finding with caution, given the small sample and the low mortality rate. However, in line with the findings in the MCI group, it may signify a predictive role of the driving simulator not only for disease severity, but also for driving cessation. Despite its infrequently use, mainly due to high cost and restricted availability, a driving simulator has several useful characteristics: safe driving environment, quantification of measurements, reproducibility of driving scenarios and correlation with cognitive status.^1,7,12 Future large-scale studies will possibly allow its integration into a driving fitness evaluation protocol.

The main limitation of every prospective driving study among cognitively impaired individuals is the relatively small sample size along with the under-representation of women, especially among the AD group. In our study, the latter is attributed to socio-cultural reasons reflecting the rarity of female elderly drivers in Greek society. Sample size was undoubtedly influenced by the reluctancy of some patients to participate in our study (possibly due to the time-consuming protocol) and the moderate percentage of drop-outs (due to exclusion criteria or as a result of simulator sickness⁵⁰). Finally, we must mention the inclusion of patients only with typical AD dementia in our study. Nevertheless, evidence in the literature regarding driving fitness of non-AD dementias is scarce.⁵⁰ All these factors may mask various significant prognostic markers (especially among simulator measurements), thus limiting the generalizability of our results (e.g., for other dementia subtypes).

Future perspectives arise from the above-analyzed limitations: A larger sample with equal representation of both sexes, no major demographic heterogeneity and inclusion of non-AD dementias will likely lead to robust conclusions. Furthermore, driving rehabilitation is a promising field of research⁵¹ and could be incorporated in similar studies, to avoid early driving cessation and its profoundly negative consequences.

To sum up with, a focused motor assessment along with a neuropsychological evaluation (including tests of executive functions and complex attention) should be the core parts of a multi-disciplinary evaluation of driving fitness and prediction of driving cessation probability. Driving simulator assessment may have a complementary role for Neurologists in their effort to individualize driving risk among cognitively impaired patients.

Supplemental Material

sj-docx-1-alz-10.1177_13872877251333705 - Supplemental material for Mild cognitive impairment, Alzheimer's disease dementia, and predictors of driving cessation: A 7-year longitudinal prospective study

Supplemental material, sj-docx-1-alz-10.1177_13872877251333705 for Mild cognitive impairment, Alzheimer's disease dementia, and predictors of driving cessation: A 7-year longitudinal prospective study by Petros Stamatelos, Ion N Beratis, Panagiota Hatzaki, Alexandra Economou, Nikolaos Andronas, Dimosthenis Pavlou, Styliani P Fragkiadaki, Dionysia Kontaxopoulou, Anastasios Bonakis, Leonidas Stefanis, George Yannis and Sokratis G Papageorgiou in Journal of Alzheimer's Disease

Footnotes

Acknowledgments

The authors have no acknowledgments to report.

ORCID iD

Petros Stamatelos

Alexandra Economou

Dionysia Kontaxopoulou

George Yannis

Ethical considerations

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of “Attikon” University General Hospital of Athens.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Author contributions

Petros Stamatelos (Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Writing - original draft); Ion N Beratis (Conceptualization, Formal analysis, Investigation, Writing - review & editing); Panagiota Hatzaki (Formal analysis, Investigation, Writing - original draft); Alexandra Economou (Formal analysis, Resources, Writing - review & editing); Nikolaos Andronas (Formal analysis, Investigation, Writing - review & editing); Dimosthenis Pavlou (Formal analysis, Investigation, Writing - review & editing); Styliani P Fragkiadaki (Formal analysis, Investigation, Writing - review & editing); Dionysia Kontaxopoulou (Formal analysis, Investigation, Writing - review & editing); Anastasios Bonakis (Investigation, Resources, Writing - review & editing); Leonidas Stefanis (Formal analysis, Investigation, Resources, Writing - review & editing); George Yannis (Formal analysis, Investigation, Resources, Writing - review & editing); Sokratis G Papageorgiou (Conceptualization, Formal analysis, Investigation, Resources, Supervision, Writing - review & editing).

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This article is part of Petros Stamatelos's PhD project titled “Evaluation of Driving Behavior of Patients with MCI, Dementia or Parkinson's Disease: Diagnostic and Prognostic Markers,” funded and supported by Alexander S. Onassis Public Benefit Foundation (grant number: G ZN 060-1/2017-2018).

Declaration of conflicting interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: P. Stamatelos received a scholarship for his PhD project from by Alexander S. Onassis Public Benefit Foundation (grant number: G ZN 060-1/2017-2018). The other authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

The data supporting the findings of this study are available within the article and/or its supplemental material. Data which are not publicly available due to privacy or ethical restrictions, on request from the corresponding author.

Supplemental material

Supplemental material for this article is available online.

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