Driving reaction time (DRT) is one of the most important predictors of motor vehicle crashes in older adults. Although individuals with preclinical Alzheimer’s disease (AD) show subtle cognitive changes that may affect driving, their DRT to emergency events has not been investigated. We compared DRT to an emergency event between 19 drivers with preclinical AD and 21 controls in a driving simulator. All drivers engaged in a car-following task with and without distracters. After the car-following event, a crash prompted participants to brake and maneuver around the accident scene. Drivers with preclinical AD took longer to respond to the emergency event compared to controls when they were not distracted by an additional task (7.56 ± 1.46 s v 6.42 ± 1.17 s; p = .02). There were no group differences when a distraction was added to the car-following task. These pilot results have important implications on driving safety for older adults with preclinical AD when confirmed in larger on-road studies.
AhmadnezhadP.BurnsJ. M.AkinwuntanA. E.RanchetM.KondyliA.MahnkenJ. D.DevosH. (2023). Driving automation for older adults with preclinical Alzheimer’s disease. Gerontology, 69(11), 1307–1314. https://doi.org/10.1159/000531263
2.
AkinwuntanA. E.WachtelJ.RosenP. N. (2012). Driving simulation for evaluation and rehabilitation of driving after stroke. Journal of Stroke and Cerebrovascular Diseases, 21(6), 478–486. https://doi.org/10.1016/j.jstrokecerebrovasdis.2010.12.001
3.
AndriutaD.DioufM.RousselM.GodefroyO. (2019). Is reaction time slowing an early sign of Alzheimer’s disease? A meta-analysis. Dementia and Geriatric Cognitive Disorders, 47(4-6), 281–288. https://doi.org/10.1159/000500348
4.
AschenbrennerA. J.CrawfordJ. L.PeelleJ. E.FaganA. M.BenzingerT. L.MorrisJ. C.HassenstabJ.BraverT. S. (2023). Increased cognitive effort costs in healthy aging and preclinical Alzheimer’s disease. Psychology and Aging, 38(5), 428–442. https://doi.org/10.1037/pag0000742
5.
BédardM.ParkkariM.WeaverB.RiendeauJ.DahlquistM. (2010). Assessment of driving performance using a simulator protocol: Validity and reproducibility. American Journal of Occupational Therapy, 64(2), 336–340. https://doi.org/10.5014/ajot.64.2.336
6.
CarrD. B.BarcoP. P.BabulalG. M.StoutS. H.JohnsonA. M.XiongC.MorrisJ. C.RoeC. M. (2016). Association of functional impairments and Co-morbid conditions with driving performance among cognitively normal older adults. PLoS One, 11(12), Article e0167751. https://doi.org/10.1371/journal.pone.0167751
7.
CarrD. B.BeyeneK.DohertyJ.MurphyS. A.JohnsonA. M.DomashH.RileyN.WalkerA.SabapathyA.MorrisJ. C.BabulalG. M. (2023). Medication and road test performance among cognitively healthy older adults. JAMA Network Open, 6(9), e2335651. https://doi.org/10.1001/jamanetworkopen.2023.35651
8.
ClarkC. M.PontecorvoM. J.BeachT. G.BedellB. J.ColemanR. E.DoraiswamyP. M.FleisherA. S.ReimanE. M.SabbaghM. N.SadowskyC. H.SchneiderJ. A.AroraA.CarpenterA. P.FlitterM. L.JoshiA. D.KrautkramerM. J.LuM.MintunM. A.SkovronskyD. M.AV-45-A16 Study Group (2012). Cerebral PET with florbetapir compared with neuropathology at autopsy for detection of neuritic amyloid-β plaques: A prospective cohort study. The Lancet Neurology, 11(8), 669–678. https://doi.org/10.1016/S1474-4422(12)70142-4
9.
ClassenS.JeghersM.Morgan-DanielJ.WinterS.KingL.StruckmeyerL. (2019). Smart in-vehicle technologies and older drivers: A scoping review. OTJR: Occupation, Participation and Health, 39(2), 97–107. https://doi.org/10.1177/1539449219830376
Delphin-CombeF.CosteM.-H.BacheletR.LlorensM.GentilC.GirouxM.Paire-FicoutL.RanchetM.Krolak-SalmonP. (2022). An innovative therapeutic educational program to support older drivers with cognitive disorders: Description of a randomized controlled trial study protocol. Frontiers in Neurology, 13, Article 901100. [Study Protocol]. https://doi.org/10.3389/fneur.2022.901100
13.
DevosH.AhmadnezhadP.LiaoK.GustafsonK. M.FakoredeS.MartinL. E.NguyenT.-T.AkinwuntanA. E.BurnsJ. M. (2023). Cortical dynamics of automated driving across the spectrum of cognitive aging: A proof-of-concept study. Transportation Research Part F: Traffic Psychology and Behaviour, 96(July), 58–65. https://doi.org/10.1016/j.trf.2023.06.004
14.
DoraiswamyP. M.SperlingR. A.ColemanR. E.JohnsonK. A.ReimanE. M.DavisM. D.GrundmanM.SabbaghM. N.SadowskyC. H.FleisherA. S.CarpenterA.ClarkC. M.JoshiA. D.MintunM. A.SkovronskyD. M.PontecorvoM. J.AV45-A11 Study Group (2012). Amyloid-β assessed by florbetapir F 18 PET and 18-month cognitive decline: A multicenter study. Neurology, 79(16), 1636–1644. https://doi.org/10.1212/WNL.0b013e3182661f74
15.
EconomouA.PavlouD.BeratisI.AndronasN.PapadimitriouE.PapageorgiouS. G.YannisG. (2020). Predictors of accidents in people with mild cognitive impairment, mild dementia due to Alzheimer’s disease and healthy controls in simulated driving. International Journal of Geriatric Psychiatry, 35(8), 859–869. https://doi.org/10.1002/gps.5306
16.
FausM.AlonsoF.EstebanC.UsecheS. (2023). Are adult driver education programs effective? A systematic review of evaluations of accident prevention training courses. International Journal of Educational Psychology, 12(1), 62–91. https://doi.org/10.17583/ijep.8805
17.
FoxG. K.BowdenS. C.BashfordG. M.SmithD. S. (1997). Alzheimer's disease and driving: Prediction and assessment of driving performance. Journal of the American Geriatrics Society, 45(8), 949–953. https://doi.org/10.1111/j.1532-5415.1997.tb02965.x
18.
Fraade-BlanarL. A.HansenR. N.ChanK. C. G.SearsJ. M.ThompsonH. J.CraneP. K.EbelB. E. (2018). Diagnosed dementia and the risk of motor vehicle crash among older drivers. Accident Analysis and Prevention, 113(April), 47–53. https://doi.org/10.1016/j.aap.2017.12.021
19.
GorrieC. A.RodriguezM.SachdevP.DuflouJ.WaiteP. M. (2007). Mild neuritic changes are increased in the brains of fatally injured older motor vehicle drivers. Accident Analysis and Prevention, 39(6), 1114–1120. https://doi.org/10.1016/j.aap.2007.02.008
20.
GreenM. (2000). “How long does it take to stop?” Methodological analysis of driver perception-brake times. Transportation Human Factors, 2(3), 195–216. https://doi.org/10.1207/sthf0203_1
21.
HaatveitB. C.SundetK.HugdahlK.UelandT.MelleI.AndreassenO. A. (2010). The validity of d prime as a working memory index: Results from the “bergen n-back task”. Journal of Clinical and Experimental Neuropsychology, 32(8), 871–880. https://doi.org/10.1080/13803391003596421
22.
HarnN. R.HuntS. L.HillJ.VidoniE.PerryM.BurnsJ. M. (2017). Augmenting amyloid PET interpretations with quantitative information improves consistency of early amyloid detection. Clinical Nuclear Medicine, 42(8), 577–581. https://doi.org/10.1097/RLU.0000000000001693
23.
HartS. G.StavelandL. E. (1988). Development of NASA-TLX (task Load index): Results of empirical and theoretical research. In Advances in psychology (52, pp. 139–183). Elsevier. https://doi.org/10.1016/s0166-4115(08)62386-9
24.
IversonD. J.GronsethG. S.RegerM. A.ClassenS.DubinskyR. M.RizzoM.Quality Standards Subcomittee of the American Academy of Neurology. (2010). Practice parameter update: Evaluation and management of driving risk in dementia: Report of the quality standards subcommittee of the American academy of neurology. Neurology, 74(16), 1316–1324. https://doi.org/10.1212/WNL.0b013e3181da3b0f
25.
JackC. R.Jr.AndrewsJ. S.BeachT. G.BuracchioT.DunnB.GrafA.HanssonO.HoC.JagustW.McDadeE.MolinuevoJ. L.OkonkwoO. C.PaniL.RafiiM. S.ScheltensP.SiemersE.SnyderH. M.SperlingR.TeunissenC. E.CarrilloM. C. (2024). Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup. Alzheimers Dement, 20(8), 5143–5169. https://doi.org/10.1002/alz.13859
26.
JackC. R.Jr.BennettD. A.BlennowK.CarrilloM. C.DunnB.HaeberleinS. B.HoltzmanD. M.JagustW.JessenF.KarlawishJ.LiuE.MolinuevoJ. L.MontineT.PhelpsC.RankinK. P.RoweC. C.ScheltensP.SiemersE.SnyderH. M.Contributors (2018). NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimers Dement, 14(4), 535–562. https://doi.org/10.1016/j.jalz.2018.02.018
27.
JohanssonK.BogdanovicN.KalimoH.WinbladB.ViitanenM. (1997). Alzheimer's disease and apolipoprotein E epsilon 4 allele in older drivers who died in automobile accidents. Lancet, 349(9059), 1143–1144. https://doi.org/10.1016/s0140-6736(97)24016-x
28.
JoshiA. D.PontecorvoM. J.ClarkC. M.CarpenterA. P.JenningsD. L.SadowskyC. H.AdlerL. P.KovnatK. D.SeibylJ. P.AroraA.SahaK.BurnsJ. D.LowreyM. J.MintunM. A.SkovronskyD. M.Florbetapir F 18 Study Investigators (2012). Performance characteristics of amyloid PET with florbetapir F 18 in patients with Alzheimer's disease and cognitively normal subjects. Journal of Nuclear Medicine, 53(3), 378–384. https://doi.org/10.2967/jnumed.111.090340
29.
KahyaM.LiaoK.GustafsonK. M.AkinwuntanA. E.ManorB.DevosH. (2022). Cortical correlates of increased postural task difficulty in young adults: A combined pupillometry and EEG study. Sensors, 22(15), 5594. https://doi.org/10.3390/s22155594
30.
KahyaM.LyonsK. E.PahwaR.AkinwuntanA. E.HeJ.DevosH. (2021a). Pupillary response to postural demand in Parkinson’s disease. Frontiers in Bioengineering and Biotechnology, 9, Article 617028. https://doi.org/10.3389/fbioe.2021.617028
31.
KahyaM.LyonsK. E.PahwaR.AkinwuntanA. E.HeJ.DevosH. (2021b). Reliability and validity of pupillary response during dual-task balance in Parkinson Disease. Archives of Physical Medicine and Rehabilitation, 102(3), 448–455. https://doi.org/10.1016/j.apmr.2020.08.008
32.
KahyaM.WoodT. A.SosnoffJ. J.DevosH. (2018). Increased postural demand is associated with greater cognitive workload in healthy young adults: A pupillometry study. Frontiers in Human Neuroscience, 12, 288. https://doi.org/10.3389/fnhum.2018.00288
33.
KennedyR. S.LaneN. E.BerbaumK. S.LilienthalM. G. (1993). Simulator sickness questionnaire: An enhanced method for quantifying simulator sickness. The International Journal of Aviation Psychology, 3(3), 203–220. https://doi.org/10.1207/s15327108ijap0303_3
LempkeL. B.LynallR. C.HoffmanN. L.DevosH.SchmidtJ. D. (2021). Slowed driving-reaction time following concussion-symptom resolution. Journal of Sport and Health Science, 10(2), 145–153. https://doi.org/10.1016/j.jshs.2020.09.005
36.
MarshallS. P. (2007). Identifying cognitive state from eye metrics. Aviation Space & Environmental Medicine, 78(5 Suppl), B165–B175.
37.
MaselloL.CastignaniG.SheehanB.MurphyF.McdonnellK. (2022). On the road safety benefits of advanced driver assistance systems in different driving contexts. Transportation Research Interdisciplinary Perspectives, 15(September), Article 100670. https://doi.org/10.1016/j.trip.2022.100670
38.
MattayV. S.FeraF.TessitoreA.HaririA. R.BermanK. F.DasS.Meyer-LindenbergA.GoldbergT. E.CallicottJ. H.WeinbergerD. R. (2006). Neurophysiological correlates of age-related changes in working memory capacity. Neuroscience Letters, 392(1-2), 32–37. https://doi.org/10.1016/j.neulet.2005.09.025
39.
MoonS.KahyaM.LyonsK. E.PahwaR.AkinwuntanA. E.DevosH. (2020). Cognitive workload during verbal abstract reasoning in Parkinson’s disease: A pilot study. International Journal of Neuroscience, 131(5), 504–510. https://doi.org/10.1080/00207454.2020.1746309
40.
MoonS.RanchetM.AkinwuntanA. E.TantM.CarrD. B.RajiM. A.DevosH. (2018). The impact of advanced age on driving safety in adults with medical conditions. Gerontology, 64(3), 291–299. https://doi.org/10.1159/000486511
41.
MorrisM. E.AdairB.MillerK.OzanneE.HansenR.PearceA. J.SantamariaN.ViegaL.LongM.SaidC. M. (2013). Smart-home technologies to assist older people to live well at home. Journal of aging science, 1(1), 1–9.
42.
MullenN.CharltonJ.DevlinA.BedardM. (2011). Simulator validity: Behaviours observed on the simulator and on the road. In Handbook of driving simulation for engineering, medicine and psychology (pp. 1–18). CRC Press.
43.
NasreddineZ. S.PhillipsN. A.BédirianV.CharbonneauS.WhiteheadV.CollinI.CummingsJ. L.ChertkowH. (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
44.
NucciM.MapelliD.MondiniS. (2012). Cognitive reserve index questionnaire (CRIq): A new instrument for measuring cognitive reserve. Aging Clinical and Experimental Research, 24(3), 218–226. https://doi.org/10.3275/7800
45.
O'ConnorM. L.EdwardsJ. D.WadleyV. G.CroweM. (2010). Changes in mobility among older adults with psychometrically defined mild cognitive impairment. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 65(3), 306–316.
46.
OttB. R.DaielloL. A. (2010). How does dementia affect driving in older patients?Aging Health, 6(1), 77–85. https://doi.org/10.2217/ahe.09.83
47.
OttB. R.JonesR. N.NotoR. B.YooD. C.SnyderP. J.BernierJ. N.CarrD. B.RoeC. M. (2017). Brain amyloid in preclinical Alzheimer’s disease is associated with increased driving risk. Alzheimer’s and Dementia: Diagnosis, Assessment and Disease Monitoring, 6, 136–142. https://doi.org/10.1016/j.dadm.2016.10.008
48.
PapantoniouP.PapadimitriouE.YannisG. (2017). Review of driving performance parameters critical for distracted driving research. Transportation Research Procedia, 25, 1796–1805. https://doi.org/10.1016/j.trpro.2017.05.148
49.
ParasuramanR.SheridanT. B.WickensC. D. (2008). Situation awareness, mental workload, and trust in automation: Viable, empirically supported cognitive engineering constructs. Journal of Cognitive Engineering and Decision Making, 2(2), 140–160. https://doi.org/10.1518/155534308x284417
50.
ParkG. D.AllenR. W.FiorentinoD.RosenthalT. J.CookM. L. (2006). Simulator sickness scores according to symptom susceptibility, age, and gender for an older driver assessment study. In Proceedings of the Human Factors and Ergonomics Society - Annual Meeting. Sage.
51.
PerkinsonM. A.Berg-WegerM. L.CarrD. B.MeuserT. M.PalmerJ. L.BucklesV. D.PowlishtaK. K.FoleyD. J.MorrisJ. C. (2005). Driving and dementia of the Alzheimer type: Beliefs and cessation strategies among stakeholders. The Gerontologist, 45(5), 676–685. https://doi.org/10.1093/geront/45.5.676
52.
RanchetM.MorganJ. C.AkinwuntanA. E.DevosH. (2017). Cognitive workload across the spectrum of cognitive impairments: A systematic review of physiological measures. Neuroscience and Biobehavioral Reviews, 80, 516–537. https://doi.org/10.1016/j.neubiorev.2017.07.001
53.
RazaM. S.MurtazaM.ChengC. T.MuslamM. M. A.AlbahlalB. M. (2024). Systematic review of cognitive impairment in drivtrf:wdrers through mental workload using physiological measures of heart rate variability. Frontiers in Computational Neuroscience, 18, Article 1475530. https://doi.org/10.3389/fncom.2024.1475530
54.
ReimerB.D'AmbrosioL. A.CoughlinJ. E.KafrissenM. E.BiedermanJ. (2006). Using self-reported data to assess the validity of driving simulation data. Behavior Research Methods, 38(2), 314–324. https://doi.org/10.3758/bf03192783
55.
RodsethJ.WashabaughE. P.Al HaddadA.KartjeP.TateD. G.KrishnanC. (2017). A novel low-cost solution for driving assessment in individuals with and without disabilities. Applied Ergonomics, 65(November), 335–344. https://doi.org/10.1016/j.apergo.2017.07.002
56.
RoeC. M.BabulalG. M.HeadD. M.StoutS. H.VernonE. K.GhoshalN.GarlandB.BarcoP. P.WilliamsM. M.JohnsonA.FierbergR.FagueM. S.XiongC.MorminoE.GrantE. A.HoltzmanD. M.BenzingerT. L. S.FaganA. M.OttB. R.…MorrisJ. C. (2017). Preclinical Alzheimer's disease and longitudinal driving decline. Alzheimer’s and Dementia: Translational Research & Clinical Interventions, 3(1), 74–82. https://doi.org/10.1016/j.trci.2016.11.006
57.
RoskerJ.LeskovecT.TomazinK.Majcen RoskerZ. (2024). Pupillometry as a biomarker of postural control: Deep-learning models reveal side-specific pupillary responses to increased intensity of balance tasks. Psychophysiology, 61(12), Article e14667. https://doi.org/10.1111/psyp.14667
58.
SenapatiP. R. R.DasS.VoraP. B. (2017). Intelligent braking and maneuvering system for an automobile application.
59.
SevincV.BerkmanM. I. (2020). Psychometric evaluation of Simulator Sickness Questionnaire and its variants as a measure of cybersickness in consumer virtual environments. Applied Ergonomics, 82(January), Article 102958. https://doi.org/10.1016/j.apergo.2019.102958
60.
SmailagicN.LafortuneL.KellyS.HydeC.BrayneC. (2018). 18F-FDG PET for prediction of conversion to Alzheimer’s disease dementia in people with mild cognitive impairment: An updated systematic review of test accuracy. Journal of Alzheimer's Disease, 64(4), 1175–1194. https://doi.org/10.3233/JAD-171125
61.
SummalaH. (2000). Brake reaction times and driver behavior analysis. Transportation Human Factors, 2(3), 217–226. https://doi.org/10.1207/sthf0203_2
62.
ViitanenM. H.JohanssonK.BogdanovicN.BerkowiczA.DruidH.ErikssonA.KrantzP.LaaksonenH.SandlerH.SaukkoP. J.ThiblinI.WinbladB.KalimoH. O.KalimoH. (1998). Alzheimer changes are common in aged drivers killed in single car crashes and at intersections. Forensic Science International, 96(2-3), 115–127. https://doi.org/10.1016/s0379-0738(98)00114-5
63.
WeintraubS.BesserL.DodgeH. H.TeylanM.FerrisS.GoldsteinF. C.GiordaniB.KramerJ.LoewensteinD.MarsonD.MungasD.SalmonD.Welsh-BohmerK.ZhouX. H.ShirkS. D.AtriA.KukullW. A.PhelpsC.MorrisJ. C. (2018). Version 3 of the alzheimer disease centers’ neuropsychological test battery in the Uniform data set (UDS). Alzheimer Disease and Associated Disorders, 32(1), 10–17. https://doi.org/10.1097/WAD.0000000000000223
64.
WenH.SzeN.ZengQ.HuS. (2019). Effect of music listening on physiological condition, mental workload, and driving performance with consideration of driver temperament. International Journal of Environmental Research and Public Health, 16(15), 2766. https://doi.org/10.3390/ijerph16152766
65.
YiJ.LeeH. C.-Y.ParsonsR.FalkmerT. (2014). The effect of the global positioning system on the driving performance of people with mild Alzheimer's disease. Gerontology, 61(1), 79–88. https://doi.org/10.1159/000365922
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