Yuya Nakajima, MSc1,2, Hidenori Onishi, PhD2, Yasutaka Mizukami, PhD2, Tomoko Okamoto, MS3, Taisei Inoue, MS4, Akemi Koujimoto, Associate Degree5, Naohiro Konoshita, PhD6, Tokuharu Tanaka, PhD7, Akiko Matsunaga, PhD6, Masafumi Kubota, PhD8, Masamichi Ikawa, PhD6, Hideaki Hori, PhD1, Yasutaka Kobayashi, PhD9, Hiroyuki Hayashi, MD7,10, Osamu Yamamura, PhD2
1Department of Rehabilitation Medicine, Faculty of Health Science, Fukui Health Science University, Fukui;
2Department of Community Medicine, Faculty of Medical Sciences, University of Fukui, Fukui;
3Division of Nursing, Faculty of Medical Sciences, University of Fukui, Fukui;
4Division of Physical Therapy and Rehabilitation Medicine, University of Fukui Hospital, Fukui;
5Department of Radiotechnology, Fukui Red Cross Hospital, Fukui;
6Department of Community Health Science, Faculty of Medical Sciences, University of Fukui, Fukui;
7Department of Family Medicine, University of Fukui Hospital, Fukui;
8Department of Rehabilitation Science, School of Health Sciences, College of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Ishikawa;
9Graduate School of Health Science, Fukui Health Science University, Fukui;
10Department of Emergency, University of Fukui Hospital, Fukui, JapanDOI: https://doi.org/10.5770/cgj.29.917
ABSTRACT
Background
Deciding whether to continue driving or transition to alternative means of transportation is a challenging issue for older adults in preventive care settings. This study aimed to identify potential associations between the Japanese version of the Montreal Cognitive Assessment (MoCA-J) and driving as the primary mode of transport among older adults.
Methods
The participants of this cross-sectional study were community-dwelling older adults participating in a long-term preventive care program. Participants were divided into two groups (DRIVING or OTHER) based on their questionnaire response regarding the main mode of transport used when going out, where the OTHER group included all participants who selected any other mode than a car driven by themselves. Cognitive function was measured using 13 MoCA-J tasks. Binary logistic regression analysis was used to identify associations between MoCA-J results and inclusion in the DRIVING group.
Results
Among the 199 participants, 156 were categorized into DRIVING group and 43 into OTHER group. The DRIVING group showed significantly higher task achievement rates than the OTHER group in trail-making, digit span, and phonemic verbal fluency tests. Of these, only the trail-making test results were associated with inclusion in the DRIVING group (odds ratio, 2.82; 95% confidence interval, 1.22–6.51; p =.016).
Conclusions
The trail-making task of MoCA-J may assist health-care professionals in providing driving guidance to older adults.
Key words: health promotion, mobility, preventive care, screening tool, cognition, frontal lobe, transportation mode
Transport is essential for allowing community-dwelling older adults to participate in social activities.(1) Driving is the most common mode of transportation among older adults residing in both urban and rural areas; however, as people age, they gradually transition to alternative modes of transportation.(2,3) This is attributed to the increased risk of older adults developing dementia and mild cognitive impairment (MCI), conditions that impair the cognitive functions required for driving, such as attention, executive function, visuospatial cognition, and memory.(4–9) Cognitive decline is an important contributing factor in accidents involving older drivers.(4,5,9,10)
The Mini-Mental State Examination(11) and the Japanese version of the Montreal Cognitive Assessment (MoCA-J)(12) are part of cognitive tests commonly used to assess the driving fitness of community-dwelling older adults. The MoCA-J predicts driving fitness and risks among older drivers more effectively than the Mini-Mental State Examination.(9,10,13,14) Many previous analyses are based solely on the total MoCA-J score.(13) However, when diagnosing dementia and MCI, the scores obtained for each cognitive domain of the MoCA-J may be more useful than the MoCA-J total score.(15) We hypothesize that the results of specific MoCA-J domains and tasks may also yield new insights into the driving fitness of community-dwelling older adults.
In Japan, individuals renewing their driver’s license after age 75 are required to undergo cognitive function testing, with the results determining whether they may continue driving.(16) Furthermore, physicians may voluntarily report examination findings to the Public Safety Commission if a licensed driver diagnosed with dementia or other specified conditions refuses to heed advice to refrain from driving.(17) However, assessments of driving fitness based on cognitive function and the selection of appropriate screening tools remain challenging.(14,18,19) Health-care professionals can also provide driving guidance in relation to cognitive function,(20) and are often involved in the process of helping individuals in dementia care settings to stop driving.(21) Therefore, a better understanding of the cognitive tasks that can inform driving decisions among older adults is crucial for healthcare professionals.
The study aims to identify MoCA-J-based screening tools for informing driving decisions among older adults. To achieve this purpose, we analyze the associations between individual MoCA-J task results and driving as the primary mode of transportation among community-dwelling older adults. The results of this study provide a more scientific basis for driving guidance in primary care and health promotion settings.
This cross-sectional study is based on survey data from a wider study analyzing the effect of remote exercise classes on preventing frailty and frailty-related issues among community-dwelling older adults.(22) The wider study was conducted in collaboration with local governments in Sakai City and Katsuyama City, Fukui Prefecture, Japan. Participants in the exercise classes underwent regular physical and cognitive function assessments, as well as medical interviews, to understand their living conditions. The survey data were obtained from an initial assessment conducted prior to participation in the exercise classes.
Participants were recruited for the exercise classes through public information magazines and websites in each municipality. Residents who wished to participate applied directly to their respective municipality. From April 2022 to March 2024, 209 older adults participated in the exercise classes. Ten participants were excluded due to incomplete medical interviews or difficulties conducting the cognitive assessments, resulting in a final sample of 199 participants.
All participants were provided with written explanations of the risks of participation and the emergency procedures and medical follow-up required in the event of an accident, with the approval of Sakai City and Katsuyama City municipalities. Written informed consent was obtained from all participants. This study was approved by the Medical Ethics Review Board of Fukui University (20220048) and conducted in compliance with the Declaration of Helsinki (revised in Fortaleza in 2013) and the Ethical Guidelines for Life Science and Medical Research Involving Human Subjects (Notification No. 1 of the Ministry of Education, Culture, Sports, Science and Technology; Ministry of Health, Labour and Welfare; and Ministry of Economy, Trade and Industry on March 23, 2021).
The modes of transportation used by the participants were determined during an interview by asking, “What is your main mode of transport when going out?” The answer choices included walking, cycling, motorbike, car (driving yourself), car (driven by someone else), train, bus (local), bus (hospital/facility), wheelchair, electric wheelchair, walker, senior mobility scooters, and taxi. Multiple responses were allowed because older adults gradually transition from driving to alternative modes of transport as they age.(3,23) In this study, participants were categorized into two groups according to the selected mode of transportation. Individuals who included car (driving yourself) among their responses were classified into the DRIVING group, whereas those who selected only other modes of transportation were classified into the OTHER group. This categorization was adopted because driving a car represents the most common mode of transportation among older adults.(2,3)
We used the MoCA-J as the cognitive test in this study.(12) The MoCA-J, which is designed to detect MCI, is scored out of 30, with the total score representing a composite of six cognitive function domains: visuospatial, executive, attention, memory, language, and orientation, measured through 13 individual tasks.(12) One point was added if the participant had 12 years or less of formal education.(12) The MoCA-J identifies MCI in participants with a score of 26 points or more, whereas 25 points or less indicates a normal cognitive range (sensitivity, 93%; specificity, 87%).(12) The MoCA-J test was administered by medical and nursing students, physicians, clinical laboratory technicians, nurses, and occupational therapists, all under the supervision of a neurologist and nurse with at least seven years of experience in neuropsychological testing. Physicians, nurses, clinical laboratory technicians, and occupational therapists assisted the medical and nursing students. Nurses with more than seven years of experience verified the scoring accuracy. In this study, each MoCA-J task was classified as either achieved or not achieved. For tasks with a single point value, receiving a point was considered “achieved.” For tasks with multiple point values, obtaining the maximum score was considered “achieved,” whereas any score below the maximum was classified as “not achieved.”
We compared medical interviews and MoCA-J results for participants in the DRIVING and OTHER groups. The results for sex, chronic disease, and main mode of transport are presented as the number of participants (percentage), whereas those for age and education are presented as the mean (standard deviation [SD]). MoCA-J results are presented as the mean (SD) of the total score and the number (percentage) of participants who achieved each task.
The chi-square test was used to compare the DRIVING and OTHER groups for nominal variables, including sex, chronic disease, and the number of people who achieved each MoCA-J task. The Mann–Whitney U test was used for continuous variables, including age, education, and the total MoCA-J score. We performed binary logistic regression analysis to identify cognitive functions associated with the main transportation mode of older adults. The dependent variable was the primary mode of transportation (DRIVING or OTHER), and the independent variables were the results of each MoCA-J task that differed significantly between DRIVING and OTHER groups in the univariate analysis. The moderating variables included age, sex, and education, which were selected based on previous studies.(10,19,24) A receiver operating characteristic curve was constructed to determine the discrimination ability of the regression model, with model validity determined using the area under the curve.
All statistical data were analyzed using EZR version 1.68 (Saitama Medical Center, Jichi Medical University, Japan).(25) The level of statistical significance was set to p = .05.
Among the 199 participants, 162 (81.4%) were female and 37 (18.6%) were male, and the mean age was 74.8 ± 6.2 years. Regarding the main mode of transportation, 156 (78.4%) were assigned to the DRIVING group and 43 (21.6%) to the OTHER group.
Table 1 compares the baseline characteristics and MoCA-J scores between the DRIVING and OTHER groups. No significant difference was noted in sex between the groups. However, participants in the DRIVING group were significantly younger (p=.001) and more highly educated (p<.001). Chronic diseases such as dementia and mental disorders were significantly more common in the OTHER group (p=.032 and p=.043, respectively). The total MoCA-J score was significantly higher in the DRIVING group (p<.001).
TABLE 1 Participant characteristics, main mode of transportation, and MoCA-J scores
Table 2 shows the results of each MoCA-J task (achieved or not achieved) for the two groups. The DRIVING group had a significantly higher proportion of participants who achieved the trail-making, digit span (forward and backward), and phonemic verbal fluency tasks (p<.001, p=.036, p=.017, and p=.025, respectively). Among these tasks, the results of the trail-making task were significantly associated with inclusion in the DRIVING group (odds ratio, 2.82; 95% CI, 1.22–6.51; p=.016; Table 3). The area under the receiver operating characteristic curve was 0.733 (95% CI, 0.644–0.822).
TABLE 2 Comparison of MoCA-J task results between groupsa
TABLE 3 Relationship between inclusion in the DRIVING group and achievement rates for each MoCA-J task
In this study, we analyzed potential cognitive functions associated with driving as the primary mode of transport among community-dwelling older adults by examining the results of individual MoCA-J tasks. Older adults who drove themselves had significantly higher achievement rates for trail-making, digit span, and phonemic verbal fluency tasks than those who used other modes of transportation. Multiple regression analysis identified trail-making as a key item associated with older adults who drove as their main mode of transportation.
The trail-making task in MoCA-J involves alternately connecting numbers and hiragana characters, and corresponds to the trail-making test part B (Trails B) in the MoCA. Trails B requires frontal lobe functions such as attention switching, planning, and executive function.(26,27) Although visual information processing and memory are required for driving, executive functioning is particularly important.(5,7,19) Therefore, the observed identified association between the MoCA-J trail-making test and older adults driving themselves indicates that this test sensitively captures the frontal lobe functions necessary for driving.
Older adults who drove themselves also showed significantly higher achievement rates on the digit span and verbal fluency tasks than those who used other transportation methods; however, the results of these tasks were not associated with inclusion in the DRIVING group. Although the digit span and verbal fluency tasks reflect working memory, cognitive flexibility, and executive function in the frontal lobe,(28,29) these tasks were verbally presented, whereas trail-making is a visual task that activates a broad range of brain regions, including the bilateral frontal (executive function), temporal (recollection of numbers and words), and occipital–parietal (visuospatial information processing) lobes, all of which are essential for driving.(8,9,27) Thus, the digit span and verbal fluency tasks only partially capture the brain functions required for driving, whereas the trail-making task comprehensively assesses these functions, which may explain the lack of association. Additionally, driving requires complex visual information processing,(8,9,30) which may not have been effectively captured in the linguistic delivery of the digit span or verbal fluency tasks.
Trail B is considered a useful tool for health-care professionals assessing driving fitness from the perspective of functional impairment in clinical settings.(31) In Japan, Trail B is a required neuropsychological test related to automobile driving when age-related cognitive decline is suspected.(32) However, Trail B only assesses functional impairment, whereas driving fitness should be determined using comprehensive clinical findings.(31) Although our results demonstrate that the MoCA-J trail-making test is associated with driving as the primary mode of transport among older adults, decisions regarding whether an older adult should continue driving or transition to alternative transportation methods should be based on comprehensive findings, incorporating the insights gained from this study alongside conventional driving aptitude assessments,(31,32) such as driving simulator and road evaluations. Furthermore, the MoCA-J trail-making test is a shortened version of Trail B that evaluates only whether participants have achieved/not achieved the task, unlike the full version that evaluates task completion time and error count. Therefore, caution is required when interpreting the results of this study. However, conducting detailed driving aptitude tests is often impractical in community health promotion settings.(33) Under these circumstances, the MoCA-J trail-making test represents a simple and useful screening tool for measuring cognitive function and assessing driving fitness in community-dwelling older adults.
This study has some methodological limitations. First, we did not obtain detailed information on the participants’ driving status. Previous studies have highlighted the limitations of capturing driving as a binary variable (driving/not driving), and recommended collecting more detailed information on driving behaviour, such as driving frequency and distance.(3) Driver’s license possession is also associated with cognitive function.(34) Therefore, future research should collect more detailed driving data. Second, the MoCA-J trail-making task is a shortened version of Trail B. Consequently, detailed analyses of task completion time and error counts, which are typically used to assess driving fitness, could not be performed.(31) Furthermore, analyzing error types in Trail B can help identify areas of brain function decline.(35) Thus, using the full version of Trail B in future research could provide a more detailed analysis of the relationship between driving decisions and cognitive function among older adults. Third, the generalizability of our findings is limited by differences in the transportation environments between rural and urban areas.(24,36) Future research should include urban-dwelling older adults and data compared with the findings of this study. Nevertheless, driving remains the primary mode of transportation among older adults in both rural and urban areas.(2,3) Therefore, we suggest that this study makes a valuable contribution to assessments of driving fitness among older adults, regardless of whether they reside in rural or urban areas.
The results of the MoCA-J trail-making task are associated with driving as the primary mode of transportation in community-dwelling older adults. This finding suggests that the MoCA-J trail-making task represents a useful indicator for older adults considering their daily transportation options, including whether to refrain from driving. This study provides valuable driving guidance support for health-care professionals, as well as older adults and their families in primary care and health promotion settings.
We express our gratitude to the medical staff of the University of Fukui, Fukui Health Science University, Kanazawa University, University of Fukui Hospital, Fukui Kosei Hospital, office workers, and the students who participated in this study. We thank Nice METS Inc., Sakai City Office, Macnica Inc., and the Katsuyama City Office. We express our gratitude to Yuki Niida, Ph.D., of Jin-ai University, for their valuable technical assistance throughout this study. We also thank Kazue Fujita, Kumiko Ito, Yuka Nakamura, Mie Yamashita, Noriko Sadakane, and Satoko Hirose for their assistance with the office work, preparation, and technical support. We thank Editage (https://www.editage.jp) for the English language editing.
We have read and understood the Canadian Geriatrics Journal’s policy on conflicts of interest disclosure and declare the following interests: HO has signed a nondisclosure agreement with Nice METS Inc. and Macnica Inc. The other authors declare no conflicts of interest.
This study was supported by the University of Fukui Fund for the promotion of Collaborative Research “Reff,” 2022–2023. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
1. Unsworth C, Dickerson A, Gélinas I, Harries P, Margot-Cattin I, Mazer B, et al. Linking people and activities through community mobility: an international comparison of the mobility patterns of older drivers and non-drivers. Ageing Soc. 2022 Aug;42(8):1938–63. doi:10.1017/S0144686X20001968.
Crossref
2. Cabinet Office (online). Tokyo. 2019 White Paper on Aging Society (full version). Section 3. Situation regarding going out and driving a car (cited 2025 February 24). Available from: https://www8.cao.go.jp/kourei/whitepaper/w-2019/html/zenbun/s1_3_1_3.html
3. Pristavec T. Social participation in later years: the role of driving mobility. J Gerontol B. 2018 Oct 10;73(8):1457–69. doi:10.1093/geronb/gbw057.
Crossref
4. Anstey KJ, Wood J, Lord S, Walker JG. Cognitive, sensory and physical factors enabling driving safety in older adults. Clin Psychol Rev. 2005 Jan 1;25(1):45–65. doi:10.1016/j.cpr.2004.07.008.
Crossref
5. Depestele S, Ross V, Verstraelen S, Brijs K, Brijs T, van Dun K, et al. The impact of cognitive functioning on driving performance of older persons in comparison to younger age groups: a systematic review. Transp Res F Traffic Psychol Behav. 2020 Aug 1;73:433–52.
Crossref
6. Hird MA, Egeto P, Fischer CE, Naglie G, Schweizer TA. A systematic review and meta-analysis of on-road simulator and cognitive driving assessment in Alzheimer’s disease and mild cognitive impairment. J Alzheimers Dis. 2016 Jul 13;53(2):713–29. doi:10.3233/JAD-160276. Epub 2016 May 11.
Crossref PubMed
7. Racheva R, Totkova Z. Reliability and validity of a method for assessment of executive functions in drivers. Behav Sci. 2020 Jan 19;10(1):37. doi:10.3390/bs10010037.
Crossref PubMed PMC
8. Shimada H, Bae S, Harada K, Makino K, Chiba I, Katayama O, et al. Association between driving a car and retention of brain volume in Japanese older adults. Exp Gerontol. 2023 Jan 1; 171:112010. doi:10.1016/j.exger.2022.112010.
Crossref
9. Wagner JT, Müri RM, Nef T, Mosimann UP. Cognition and driving in older persons. Swiss Med Wkly. 2011 Jan 14;140:w13136. doi:10.4414/smw.2011.13136.
Crossref PubMed
10. Kim JS, Bae JB, Han K, Hong JW, Han JH, Kim TH, et al. Driving-related adverse events in the elderly men: a population-based prospective cohort study. Psychiatry Investig. 2020 Jul 21; 17(8):744–50. doi:10.30773/pi.2019.0219.
Crossref PubMed PMC
11. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975 Nov;12(3):189–98.
Crossref PubMed
12. Fujiwara Y, Suzuki H, Yasunaga M, Sugiyama M, Ijuin M, Sakuma N, et al . Brief screening tool for mild cognitive impairment in older Japanese: validation of the Japanese version of the Montreal Cognitive Assessment. Geriatr Gerontol Int. 2010 Jul;10(3):225–32.
Crossref PubMed
13. Hollis AM, Duncanson H, Kapust LR, Xi PM, O’Connor MG. Validity of the Mini-Mental State Examination and the Montreal Cognitive Assessment in the prediction of driving test outcome. J Am Geriatr Soc. 2015 May;63(5):988–92. doi:10.1111/jgs.13384.
Crossref PubMed
14. Piersma D, Fuermaier ABM, de Waard D, De Deyn PP, Davidse RJ, De Groot J, et al. The MMSE should not be the sole indicator of fitness to drive in mild Alzheimer’s dementia. Acta Neurol Belg. 2018 Dec;118(4):637–42. doi:10.1007/s13760-018-1036-3.
Crossref PubMed PMC
15. Goldstein FC, Milloy A, Loring DW, the Alzheimer’s Disease Neuroimaging Initiative. Incremental validity of Montreal Cognitive Assessment index scores in mild cognitive impairment and Alzheimer Disease. Dement Geriatr Cogn Disord. 2018 Apr 11;45(1–2):49–55. doi:10.1159/000487131.
Crossref PubMed PMC
16. Metropolitan Police Department (online). Tokyo. Cognitive function test and senior driver training (for license renewal by individuals aged 75 and older) (cited 2025 December 7). Available from: https://www.keishicho.metro.tokyo.lg.jp/menkyo/koshu/koshu/over75.html
17. Metropolitan Police Department (online). Tokyo. Exams and renewals for individuals with certain medical conditions, and safe driving consultations for the individual or their family. (cited 2025 February 24). Available from: https://www.keishicho.metro.tokyo.lg.jp/menkyo/menkyo/sodan/tekisei00.html
18. Crizzle AM, Mullen N, Mychael D, Meger N, Toxopeus R, Gibbons C, et al. The SIMARD-MD is not an effective driver screening tool for determining fitness-to-drive. Can Geriatr J. 2021 Mar 2;24(1):14–21. doi:10.5770/cgj.24.444.
Crossref PubMed PMC
19. Wood I, Bhojak T, Jia Y, Jacobsen E, Snitz BE, Chang CC, et al. Predictors of driving cessation in older adults: a 12-year population-based study. Alzheimer Dis Assoc Disord. 2023 Jan 1; 37(1):13–19. doi:10.1097/WAD.0000000000000541.
Crossref PubMed PMC
20. Dobbs BM, Shergill SS. How effective is the Trail Making Test (Parts A and B) in identifying cognitively impaired drivers? Age Ageing. 2013 Sep 1;42(5):577–81. doi:10.1093/ageing/aft073.
Crossref PubMed
21. Sommer D, Stasiulis E, Rapoport MJ, Kelm P, Naglie G. Care partner perspectives on driving cessation in dementia in the province of Saskatchewan, Canada. Can Geriatr J. 2025 Sep 3; 28(3):271–80. doi:10.5770/cgj.28.819.
Crossref PubMed PMC
22. Nakajima Y, Onishi H, Mizukami Y, Niida Y, Okamoto T, Konoshita N, et al. Effect of remote exercise interventions on the risk of incident functional disability certification among community-dwelling older adults: before-and-after study. Asian J Occup Ther. 2025;21(1):27–36. doi:10.11596/asiajot.21.27.
Crossref
23. Dellinger AM, Sehgal M, Sleet DA, Barrett-Connor E. Driving cessation: what older former drivers tell us. J Am Geriatr Soc. 2001 Apr;49(4):431–35. doi:10.1046/j.1532-5415.2001.49087.x.
Crossref PubMed
24. Bhojak T, Jia Y, Jacobsen E, Snitz BE, Chang CH, Ganguli M. Driving habits of older adults: a population-based study. Alzheimer Dis Assoc Disord. 2021 Jul 1;35(3):250–57. doi:10.1097/WAD.0000000000000443.
Crossref PubMed PMC
25. Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant. 2013 Mar;48(3):452–58. doi:10.1038/bmt.2012.244.
Crossref PMC
26. Shibuya-Tayoshi S, Sumitani S, Kikuchi K, Tanaka T, Tayoshi SY, Ueno SI, et al. Activation of the prefrontal cortex during the Trail-Making Test detected with multichannel near-infrared spectroscopy. Psychiatry Clin Neurosci. 2007 Dec;61(6):616–21.doi: 10.1111/j.1440-1819.2007.01727.x.
Crossref PubMed
27. Talwar N, Churchill NW, Hird MA, Tam F, Graham SJ, Schweizer TA. Functional magnetic resonance imaging of the trail-making test in older adults. PLoS One. 2020 May 12; 15(5):e0232469. doi:10.1371/journal.pone.0232469.
Crossref PubMed PMC
28. Kaneko H, Yoshikawa T, Nomura K, Ito H, Yamauchi H, Ogura M, et al. Hemodynamic changes in the prefrontal cortex during digit span task: a near-infrared spectroscopy study. Neuropsychobiology. 2011 Dec 20;63(2):59–65. doi:10.1159/000323446.
Crossref
29. Song M, Suda M, Aoyama Y, Takei Y, Sato T, Fukuda M, et al. Similar activation patterns in the prefrontal cortex for Chinese and Japanese verbal fluency tests with syllable cues as revealed by near-infrared spectroscopy. J Clin Exp Neuropsychol. 2020 Oct 20;42(9):924–31. doi:10.1080/13803395.2020.1825637.
Crossref PubMed
30. Kawabata K, Nakajima Y, Fujita K, Sato M, Hayashi K, Kobayashi Y. Pilot study on gaze characteristics of older drivers while watching driving movies. Geriatrics. 2024 Oct 10;9(5):132. doi:10.3390/geriatrics9050132.
Crossref PubMed PMC
31. Roy M, Molnar F. Systematic review of the evidence for Trails B cut-off scores in assessing fitness-to-drive. Can Geriatr J. 2013 Sep 4;16(3):120–42. doi:10.5770/cgj.16.76.
Crossref PubMed PMC
32. Japan Society for Higher Brain Function (online). Tokyo [Indications and judgment for neuropsychological testing methods regarding automobile driving when cognitive impairment due to aging or other factors is suspected] (Japanese.) (cited 2025 September 10). Available from: https://www.higherbrain.or.jp/wp/wp-content/uploads/2023/10/9fcc42a5fe8bd48dd1a9a0af2e000b98-1.pdf
33. Lee L, Molnar F. Driving and dementia: efficient approach to driving safety concerns in family practice. Can Fam Phys. 2017 Jan 1;63(1):27–31.
34. Dawson JD, Anderson SW, Uc EY, Dastrup E, Rizzo M. Predictors of driving safety in early Alzheimer disease. Neurology. 2009 Feb 10;72(6):521–27. doi:10.1212/01.wnl.0000341931.35870.49.
Crossref PubMed PMC
35. Kopp B, Rösser N, Tabeling S, Stürenburg HJ, de Haan B, Karnath HO, et al. Errors on the Trail Making Test are associated with right hemispheric frontal lobe damage in stroke patients. Behav Neurol. 2015;2015(1):309235. doi:10.1155/2015/309235.
Crossref PubMed PMC
36. Hajek A, König HH. Frequency and correlates of driving status among the oldest old: results from a large, representative sample. Aging Clin Exp Res. 2022 Dec;34(12):3083–88. doi:10.1007/s40520-022-02252-3.
Crossref PubMed PMC
Correspondence to: Hidenori Onishi, PhD, Department of Community Medicine, Faculty of Medical Sciences, University of Fukui, 23-3 Matsuokashimoaizuki, Yoshida-gun Eiheiji-cho, Fukui 910-1193 Japan, E-mail: o-hide68@u-fukui.ac.jp
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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial No-Derivative license (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits unrestricted non-commercial use and distribution, provided the original work is properly cited.
Canadian Geriatrics Journal, Vol. 29, No. 2, JUNE 2026