Are interventions effective at improving driving in older ...

RESEARCH ARTICLE Open Access

Are interventions effective at improvingdriving in older drivers?: A systematicreviewH. I. Castellucci1, G. Bravo2, P. M. Arezes3 and M. Lavallière4,5,6,7*

Abstract

Background: With the aging of the population, the number of older drivers is on the rise. This poses significantchallenges for public health initiatives, as older drivers have a relatively higher risk for collisions. While many studiesfocus on developing screening tools to identify medically at-risk drivers, little research has been done to developtraining programs or interventions to promote, maintain or enhance driving-related abilities among healthyindividuals. The purpose of this systematic review is to synopsize the current literature on interventions that aretailored to improve driving in older healthy individuals by working on components of safe driving such as: self-awareness, knowledge, behaviour, skills and/or reducing crash/collision rates in healthy older drivers.

Methods: Relevant databases such as Scopus and PubMed databases were selected and searched for primaryarticles published in between January 2007 and December 2017. Articles were identified using MeSH search terms:(“safety” OR “education” OR “training” OR “driving” OR “simulator” OR “program” OR “countermeasures”) AND (“olderdrivers” OR “senior drivers” OR “aged drivers” OR “elderly drivers”). All retrieved abstracts were reviewed, and fulltexts printed if deemed relevant.

Results: Twenty-five (25) articles were classified according to: 1) Classroom settings; 2) Computer-based training forcognitive or visual processing; 3) Physical training; 4) In-simulator training; 5) On-road training; and 6) Mixedinterventions. Results show that different types of approaches have been successful in improving specific drivingskills and/or behaviours. However, there are clear discrepancies on how driving performance/behaviours areevaluated between studies, both in terms of methods or dependent variables, it is therefore difficult to make directcomparisons between these studies.

Conclusions: This review identified strong study projects, effective at improving older drivers’ performance andthus allowed to highlight potential interventions that can be used to maintain or improve older drivers’ safetybehind the wheel. There is a need to further test these interventions by combining them and determining theireffectiveness at improving driving performance.

Keywords: Elderly drivers, Road safety, Prevention, Collisions

© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you giveappropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtainpermission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to thedata made available in this article, unless otherwise stated in a credit line to the data.

* Correspondence: [email protected] de Kinésiologie, Département des Sciences de la Santé, Universitédu Québec à Chicoutimi (UQAC), Saguenay, QC, Canada5Laboratoire de recherche biomécanique & neurophysiologique enréadaptation neuro-musculo-squelettique - Lab BioNR, UQAC, Saguenay, QC,CanadaFull list of author information is available at the end of the article

Castellucci et al. BMC Geriatrics (2020) 20:125 https://doi.org/10.1186/s12877-020-01512-z

http://crossmark.crossref.org/dialog/?doi=10.1186/s12877-020-01512-z&domain=pdf

http://creativecommons.org/licenses/by/4.0/

http://creativecommons.org/publicdomain/zero/1.0/

mailto:[email protected]

BackgroundThe number of older drivers is rapidly rising due to theaging population [1–4]. It is projected that, by 2030, 20%of the population will be 65 years or older [5]. InCanada, it is expected that, by 2026, 1 driver out of 5will be 65 years or older [6]. With increasingly active life-styles, seniors are expected to rely even more on theirvehicles, taking more trips, driving further distances, andkeeping their licenses longer than prior generations [7].In fact, it is anticipated that a large proportion of bothmen and women will continue driving well into their80’s [8]. For example, a majority of Canadian seniorshold a valid driver’s license (4.7 million in 2017. repre-senting 75% of all seniors) [9]. These trends pose signifi-cant public health concerns, as older drivers aredisproportionately involved in collisions [9] causing ser-ious injury and death, when exposure (kilometres driven)is taken into account [10].The higher crash rates in older adults may be due to

age-related medical conditions. For example, seniorsmay develop vision impairment [11, 12], mild cognitiveimpairment, early dementia, Parkinson’s disease andother neurodegenerative disorders, or may have suffereda stroke. All these conditions produce symptoms thatimpair the skills that are required to drive safely. Manystudies show that these conditions lead to worse drivingperformances both on-road [13, 14] and in-simulatorevaluations [15] compared to the general older adultpopulation. Despite these numerous health conditionsassociated with aging that might negatively impact driv-ing performance, the proportion of older drivers who areconsidered healthy still represents the largest segment ofthese drivers. Therefore, there is a need to assess inter-ventions that are tailored for them.Multiple assessment tools are available for use within

clinical settings to screen for at-risk drivers. Althoughmany assessments/tools are quick and easy to adminis-ter, a screening battery has not yet been developed [16,17]. The potential to detect unsafe drivers versus suc-cessfully identifying safe drivers is an important consid-eration, particularly as removing one’s license can havenegative consequences [18, 19]. Prior studies have foundthat driving cessation is associated with increased de-pression, social isolation, institutionalization and evenearly mortality [20, 21]. A recent survey conducted byVrkljan et al. [22] showed inconsistency in practiceamong evaluations in a sample of driver assessment cen-tres for medical fitness to drive (n = 47). Their resultshighlight the necessity of evidence-based guidelines forthe training and assessment of at-risk drivers.While licensing authorities must consider public safety

when delivering driver’s licenses, it is important to helpseniors drive for as long as possible to facilitate their au-tonomy and independence. This is particularly the case

since there are few programs to help seniors adjust tonon-driving.Alternatively, interventions aimed at improving or

maintaining driving skills offer new opportunities to helpseniors drive safer, longer. Several studies have examinedthe impact of workbooks, seminars, and cognitive, simu-lator or on-road training on driving performance inolder adults in general, and in those with various med-ical conditions. The purpose of this systematic review isto synopsize the current literature on interventions thatare tailored to improve driving: Self-awareness, know-ledge, behaviour, skills and/or reducing of the number ofcollisions in healthy older drivers.

MethodsA systematic literature review (SLR) methodology [23]was used to synopsize the current literature on interven-tions that are tailored to improve older individuals’ driv-ing. This methodology is scientifically transparent,replicable, and useful to generate an in-depth analysis ofthe scientific literature [24]. An initial exploratory reviewwas produced prior to conduct the full SLR [25]. Thismethod allows to elucidate common knowledge of thetopic, to identify if the proposed SLR fits the existingknowledge in the area, to determine the key conceptsand to refine the research question. Also, this SLRfollowed a five-step approach proposed by Denyer andTranfield [25]: 1) Question formulation; 2) Locatingstudies; 3) Study selection and evaluation; 4) Analysisand synthesis; and 5) Reporting and using the results.Based on witch, a review protocol was used regardingthe formulation of the research question, on the selec-tion of scientific databases and search terms, and on theinclusion and exclusion criteria for searching and analys-ing retrieved publications.

Step 1: question formulationA PICO framework (Population, Intervention, Control,Outcomes) was used to generate the research questionof this study (Step 1). This approach allows for a moresystematic approach regarding the identification of rele-vant information and its understanding by using thesefour categories [24, 26]. Therefore, the research questionformulated for this SLR was: In healthy older drivers (P),which type of intervention program (I), education,computer-based, physical training, on-road, simulator-based or mixed program (C) improved driving: Self-awareness, knowledge, behaviour, skills and/or crashrates (O)?

Step 2: locating studiesBased on the research question defined in Step 1, searchstrings to be used and appropriate bibliographic data-bases were defined in Step 2. Scopus and PubMed

Castellucci et al. BMC Geriatrics (2020) 20:125 Page 2 of 25

databases were used as they encompass a wide array ofscientific areas as well as the most relevant peer-reviewed publications [27]. Articles were identified usingMeSH search terms and strings (in English only):(“safety” OR “education” OR “training” OR “driving” OR“simulator” OR “program” OR “countermeasures”) AND(“older drivers” OR “senior drivers” OR “aged drivers”OR “elderly drivers”). The EndNote version X9.2 man-agement software package was used to manage all theinformation.

Step 3: study selection and evaluationTo select the most relevant scientific articles to includein the review, the inclusion and exclusion criteria weredefined in Step 3. The following key inclusion criteriawere defined prior to the search:

� Original articles written in English and published inpeer-reviewed journals;

� Published or in press between January 2007 andDecember 2017.

� Articles were excluded if the sample presenteddrivers with specific health conditions (e.g.traumatic brain injury, vision impairment associatedwith specific pathologies, stroke or Parkinson’sDisease).

Titles and abstracts of papers were scanned independ-ently by three of the authors to identify relevant articlesto retrieve for full text analysis. In cases where the pa-pers seemed potentially eligible, but no abstract wasavailable, the full text of the paper was retrieved. Dis-agreements between authors led to a deeper joint ana-lysis of the paper; and a decision was then maderegarding its inclusion. Full texts were independentlyreviewed for inclusion by the same three authors.The literature search purposely only included studies

between 2007 and 2017, since previous systematic re-views on the topic had already been conducted [28, 29].These reviews cover researches completed prior to 2008,and despite being well-conducted systematic reviews,more recent studies on different interventions to im-prove older drivers’ performance have been conductedbut not yet been synthesized. To our knowledge, there isno more recent review in the literature, despite the needfor guiding evidence-based practices.

Step 4: analysis and synthesisStep 4 consisted in analysing, extracting and managingpapers’ information to identify and highlight key compo-nents of the research conducted and its results. Primarystudies meeting the inclusion criteria and reported inthe included reviews were identified, and the corre-sponding data was extracted using a standardized data

extraction form. The Quality Assessment Tool setknown as “QualSyst tools” was selected as it allows ap-praisal of quality while assessing potential bias over awide variety of research designs, from experimental toobservational [30]. Furthermore, this set of tools has oneversion for quantitative studies and another one forqualitative studies, and in this review, the first one wasused. The quantitative version consists in a checklist of14 questions, with possible answers of: yes, no, partial ornot applicable. The score for a “yes” answer is 2 points,for a “partial” answer 1 point, and for “no” 0 points. Thesum of all answers is then calculated from the corre-sponding points and divided by the total of applicableresponses.The QualSyst was used by three of the authors to

evaluate internal and external validity of the consideredstudies. The QualSyst tool was originally created as athreshold allowing a study to be included in a reviewthrough a cut-off point (0.55 to 0.75) [30]. In this review,the QualSyst cut-off score of 0.55 was chosen to capture75% of the articles initially deemed as relevant for the re-view, as well as to ensure the inclusion of several de-scriptive articles containing valuable data [31]. Morespecifically, papers with a score higher than 0.8 wereclassified as having a strong methodology (> 0.8), be-tween 0.79 and 0.71 as being good, and adequate if thescore was between 0.7–0.55, or limited and therefore ex-cluded if the score was lower than 0.55 [32, 33].By using an approach adapted from Sackett et al. [24],

identified papers were also categorized using a standard-ized value system to grade biomedical practices accord-ing to the following system:

� Level I: Systematic reviews, meta-analyses, random-ized controlled trials

� Level II: Two groups, nonrandomized studies (e.g.,cohort, case control)

� Level III: One group, nonrandomized (e.g., beforeand after, pre-test and post-test)

� Level IV: Descriptive studies including analysis ofoutcomes (e.g., single-subject design, case series)

� Level V: Case reports and expert opinions includingnarrative literature reviews and consensusstatements.

Using such an approach while conducting a reviewalso provides a scheme of references for the clini-cians interested in using such methods/approachesin their practicum. Evidence-based practices are builton the assumption that scientific evidence of the ef-fectiveness of an intervention can be deemed moreor less strong and valid according to a hierarchy ofresearch designs, the assessment of the quality of theresearch, or both.


Step 5: reporting and using the resultsFor Step 5, the results were grouped (Tables 1, 2, 3, 4, 5,6, 7 and 8) according to the specific type of programs(independent variables) used by Golisz [59], who consid-ered 5 different options such as: (1) education-basedtraining programs, (2) computer-based training, (3)physical training, (4) simulator-based training, and (5)route-based or actual driving training. Moreover, an-other independent variable was considered in thecurrent study (6 - Mixed programs) since there weremany investigations that used two types of interventions,therefore making it difficult to differentiate which one ofthe variables is responsible for the obtained results. It isnoteworthy that the route-based or actual driving train-ing (5) alone was not used in any of the studies evalu-ated and is therefore not presented in the tables.The dependent variables were categorized according

to the methods used to collect them (Table 1): Tests/questionnaires, on-road evaluations, simulator and thecombination of all. Also, the dependant variables weregrouped to infer on the impact of the given program:Self-awareness and/or knowledge, behaviour, skills andcrash rates (Tables 2, 3, 4, 5, 6, 7 and 8).Self-awareness/Knowledge: Self-awareness of one’s

ability to drive and of their capacities and/or limitationsto do so safely is mainly evaluated by conducting inter-views or via questionnaire. As for knowledge, it is oftenassociated with traffic regulations and laws, as well asthe effect of health and other factors on driving.Behaviour: The drivers’ behaviour is documented as

moment of the day, roads used, driven speed, or whathas been described by Michon [60] in his model of driv-ing as strategic and tactical levels.Skills: Described as the operational level of Michon’s

model [60], skills are linked with direct control of thevehicle as well as with visual searches surrounding amanoeuvre.Crash rates (or collisions/accidents): Either collected

as self-reported value by participants or by cross-referencing available databases, crash rates (ex. Colli-sions/accidents per distance driven or per year) are usedas a predictor of an intervention’s effectiveness.

ResultsFigure 1 shows the results of the search strategy usingPRISMA. An initial number of 1510 papers was identi-fied through search of databases (SCOPUS: 934 andPubmed: 576), from which 484 duplicates were removed.After screening the remaining 1026 title, abstract andkeywords of each article, 36 papers were identified as be-ing potentially relevant. Following a complete review ofthe corresponding full-texts, 29 papers were then se-lected based on the previously mentioned inclusion cri-teria. Seven (7) papers were not considered due todifferent situations [61–67], for example: the objective ofthe paper by Joanisse et al. [61] was to report the find-ings from an evaluability assessment of the 55 alive ma-ture driver-refresher course offered by the Canada SafetyCouncil. Another example is the study by Musselwhite[63] where different issues were addressed through anexpert group opinion identifying age related physio-logical and cognitive changes that may be involved incollisions. Finally, after applying the QualSyst [30], 4 pa-pers were removed due to the methodology quality.Thus, 25 papers were included in the final review.Table 1 shows the summary of studies reviewed. It

should be noted that none of the reviewed studies con-sidered route-based or actual driving training as a pureindependent variable. Also, it can be seen that 8 of the25 studies evaluated a combination of more than onetype of intervention (Mixed approach), followed bycomputer-based and education interventions with 7 and6 studies, respectively. On the other hand, interventionswith less studies in this review are those based on phys-ical training and simulator-based training, with 4 and 2studies each. For more specific information on studiesidentified in this literature review, detailed descriptionsof protocols can be found in Tables 4, 5, 6, 7 and 8.Tests/questionnaires were used more frequently to

evaluate programs, secondly, on-road evaluations andthe combination of these two approaches and thirdly, in-simulator alone or combined with on-road evaluation(Table 1).Table 2 presents the principal dependant variables

used to infer on the impact of the given program: Self-

Table 1 Summary of the reviewed studies

Type of Programs Method used to collect the dependent variables

On-road (OR) Simulator (S) Test/Questionnaire (TQ) OR/TQ S/TQ OR/S Total

Education 1 0 4 1 0 0 6

Computer based 1 1 4 0 1 0 7

Physical training 2 0 0 0 0 0 2

Simulator-based training 0 2 0 0 0 0 2

Mixed 1 0 0 4 0 3 8

Total 5 3 8 5 1 3 25


Awareness/Knowledge (SAK), Behaviour (B), Skills (S)and Crash Rates (CR). Although 25 studies werereviewed, the number of dependent variables was 28 asthree of the studies presented more than 1 dependentvariable [35, 52, 53]. The most studied dependent vari-able was Skills, tested 12 times, followed by studies thatconsidered Behaviour, tested 10 times.Before presenting the summary of the results (Table 3)

and the synthesis of reviewed studies (Tables 4, 5, 6, 7and 8), it is important to highlight that the effect of theindependent variable was classified as (+) when the ef-fect resulted in a significant improvement in thedependent variable, (−) when the effect was significantlynegative or no change was observed in the dependentvariable and (+/−) when the obtained results were notclear (i.e. non-significant effect or a combination of sig-nificantly positive and negative effects on driving).Overall results show that 60% of reviewed studies pre-

sented positive (+) results, 24% presented negative (−),and the remaining 16% of the studies showed unclear re-sults (+/−). For example, from Table 4, the study byCoxon et al. [34] presented unclear overall results (+/−)since self-regulatory driving practices generally showedpositive results, but a negative result in the distancedriven per week, the restriction of driving space, the useof alternate transportation and community participation12-months + post-intervention.Regarding independent variables, the highest overall

positive results are for physical training (Table 6) and

mixed programs (Table 8) with values of 100 and 88%,respectively. Finally, the lowest overall positive resultscan be observed when the reviewed studies considerededucation (43%) and computer-based programs (33%).In all reviewed studies, primary research approaches

were randomized controlled trial (RCT), observed in 17studies, followed by a non-randomized controlled study(NRCT) and a cohort study used 4 and 3 times respect-ively. Also, only 1 study presented a pre-test and post-test approach. Finally, regarding sample size, studiesevaluated ranged from 11 to 4880 drivers.

Education-based training programsEducation-based training programs were quite variablein terms of their duration (Table 4), some of them last-ing from 1 day up to a full month, the number of classesranging between 1 and 4. Programs were developedmainly in classroom format. The follow-up evaluationfrom the intervention was also very variable, going fromimmediately to 2, 3, 6, and up to 12 months post-intervention. Four (4) of the studies were conductedusing a RCT (Level 1) while the remaining 2 used aretro-cohort design (Level 3).Regarding the dependent variables, 5 of the reviewed

studies used questionnaires/tests to evaluate the pro-grams with self-reported driving knowledge, driving be-haviours, and driving habits, among others. Driversreported changing their driving habits following the pro-gram and add increased knowledge of road safety facts,

Table 2 Summary of the dependent variables considered in the reviewed studies

Dependent variables

Type of Programs Self-Awareness and/or Knowledge (SA/K) Behavior (B) Skills (S) Crash Rates (CR) Total

Education 2 4 0 1 7

Computer based 0 2 4 1 7

Physical training 0 0 2 0 2

Simulator-based training 0 0 2 0 2

Mixed 2 4 4 0 10

Total 4 10 12 2 28

Each study may have had more than one type of dependent variable, explaining why the totals add up to more than 25

Table 3 Summary of the results

Dependent variables

Type of Programs Self-Awareness and/or Knowledge (SA/K) Behavior (B) Skills (S) Crash Rates (CR) Total

(+) (−) (+/−) (+) (−) (+/−) (+) (−) (+/−) (+) (−) (+/−) (+) (−) (+/−)

Education 2 0 0 1 2 1 0 0 0 0 1 0 3 3 1

Computer based 0 0 0 1 1 0 3 1 0 0 0 1 4 2 1

Physical training 0 0 0 0 0 0 2 0 0 0 0 0 2 0 0

Simulator-based training 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0

Mixed 2 0 0 4 0 0 3 1 0 0 0 0 9 1 0

Total 4 0 0 6 3 1 9 3 0 0 1 1 19 7 2

Each study may have had more than one type of dependent variable, explaining why the totals add up to more than 25


Table

4Synthe

sisof

interven

tionstud

iesinvolvinged

ucation-basedtraining

prog

rams

Autho

rsNum

ber(n),

Age

(yr)Cou

ntry

(c)

R.D.

Objective

Stud

yde

scrip

tion

Dep

ende

ntVariable

Relevant

results

QS

Coxon

etal.,

[34]

n=366

yr=be

tween75

and94

yearsold.

c=Australia

RCT

Toascertainwhe

ther

asafe-

transportatio

nprog

ram

canchange

drivingexpo

sure

whilemaintaining

commun

ityparticipationof

olde

rdrivers

Participantswererand

omized

in2

grou

ps:

-Interven

tiongrou

p(n:190):They

hadto

participatein

twosessions

held

1mon

thapart.Thesessionwas

delivered

byan

occupatio

nal

therapistface

toface

andlasted

120

and45

min,the

firstandsecond

session,respectively

-Con

trol

grou

p(n:190):Did

not

receiveanyed

ucation.

Before

therand

omizationallthe

participantspe

rform

edthebaseline

assessmen

tandself-repo

rted

ques-

tionn

aires.Also,thedrivingexpo

sure

was

measure

over

the12

mon

thsof

stud

ythroug

han

invehiclemon

itor-

ingde

vice.

(B)Drivingexpo

sure

measure

over

the12-m

onth

stud

ype

riodwith

anin-veh

iclemon

itorin

gde

vice

(B):Drivingspaceanduseof

alternatetransportatio

nwere

measuredusingaqu

estio

nnaire.

(B):Dep

ressivesymptom

swere

measuredusingtheGeriatric

Dep

ressionScale.

B(−):Aned

ucationprog

ram

does

nottranslateto

sign

ificant

differences

inthedistance

driven

perweek,therestrictio

nof

driving

space,theuseof

alternate

transportatio

nandthecommun

ityparticipationafter12-m

onth

post

interven

tion.Also,therewas

nodif-

ferencebe

tweenthecontroland

interven

tiongrou

pin

prop

ortio

nof

participantswith

twoor

morede

-pressive

symptom

sat

12mon

ths.

B(+):Participantsin

theinterven

tion

grou

pweremorelikelyto

becloser

toadop

tingself-regu

latory

driving

practices

at12

mon

thsthan

control

grou

p.Theod

dsof

theparticipants

intheinterven

tiongrou

pbe

ingin

ahigh

erbe

havioralprofile

were1.6

times

greaterthan

thosein

thecon-

trol

grou

p.

0,89

Nasvadi

and

Vavrik,

[35]

n=139(was

considered

phase

2) yr=be

tween55

and94

yearsold.

c=Canada

Retro-

Coh

ort

Determineifthecrashrate

ofaging

driverscanbe

mitigatedby

post-

licen

sedriver

education

Theparticipantsweredivide

din

2grou

p:-Driverswho

attend

edthe55

alive/

maturedrivingcoursesbe

tween

Janu

ary1,2000

andJuly31,2003(n:

ns).

-Driverswho

didno

tattend

the

educationalp

rogram

(n:ns).

Then

werecomparedthecrashe

sratesafterthedate

ofattend

ance

atthecourse,untilDecem

ber31,2003

(CR):C

rashes

andviolations

were

obtained

from

Insurance

Corpo

ratio

nof

British

Colum

bia.

CR(−):Older

men

andwom

enwho

attend

edthe55

Alive/Mature

Drivingcourse

hada1.5tim

esgreaterod

dsof

beinginvolved

ina

crashthan

theirmatched

controls.

Theseresults

weremarginally

sign

ificant

(p=.078).Forwom

enseparately,

therewas

nodifferencebe

tween

subjectsandcontrolsforthe

numbe

rof

post-cou

rsecrashe

s,re-

gardless

ofagecatego

ry.H

owever,

formen

,driversaged

75yearsand

olde

rwho

attend

ed55

Alive/Mature

Drivingwere3.8tim

esmorelikelyto

beinvolved

inacrash(p=.050).

0,81

Nasvadi,

[36]

n=367

yr=be

tween55

and94

yearsold.

c=Canada

Retro-

Coh

ort

Exam

inelong

-term

learning

out-

comes

ofasampleof

olde

rdrivers

who

attend

edamaturedriver

edu-

catio

nprog

ram.

Thecoho

rtconsistedof

driversaged

55yearsandolde

rwho

attend

edthe55

alive/maturedrivingcourse.

Allparticipantsweresurveyed

byteleph

one.

(SA/K)and(B):Thesurvey

containe

dop

en-end

edandclosed

questio

nsandaskedrespon

dentsto

recall

whatthey

hadlearne

din

thecourse,

andho

wtheirdrivingbe

havior

had

change

dbe

causeof

attend

ing.

SA/K

(+)B(+):Threequ

artersof

participantssaid

they

change

dtheir

drivinghabitsas

aresultof

attend

ingthecourse

(55alive/

mature)

includ

ing:

increased

awaren

essandvisualskills;change

sin

attitud

e;im

proved

speedand

spacemargins;avoidance

ofhazards;

usingmorecaution;ob

eyingroad

rules;andim

proved

vehicle

maneuvers.M

enweremorelikelyto

0,79


Table

4Synthe

sisof

interven

tionstud

iesinvolvinged


prog

rams(Con

tinued)

Autho

rsNum

ber(n),

Age

(yr)Cou

ntry

(c)

R.D.

Objective

Stud

yde

scrip

tion

Dep

ende

ntVariable

Relevant

results

QS

repo

rttheirdrivingskillshad

improved

sincetaking

thecourse,

andolde

rmen

repo

rted

sign

ificantly

high

ermeancomfortscores

with

theirdriving.

Jone

set

al.,

[37]

n=58

yr=averageage

70.9yearsold(age

sN/S).c=USA

RCT

Describedrivingexpe

riences

and

habitsof

acommun

itysampleof

olde

rdrivers(60+

years)andto

determ

inewhe

ther

theprog

ram

redu

cestheseolde

radults’d

riving

riskexpo

sures.


omized

in2

grou

ps:

-Interven

tiongrou

p(n:33):4

weeks

oftraining

inaclassroo

msetting

with

2hof

training

perweek.The4

sessions

includ

e:roadwisereview

,road

smart,saferdrivingandbe

ing

med

wiseto

stay

roadwise

med

ication.

-Con

trol

grou

p(n:25):D

idno

treceivetraining

Thebo

thgrou

pcompleted

the

baselinequ

estio

nnaire

andafterthe

4weeks

completed

the

questio

nnaire

again.

(B):Drivinghabitriskexpo

sure

were

divide

intw

otype

s-Highe

rdrivingriskexpo

sure:

defined

asfre

quen

cyof

driving

furthe

rthan

10milesfro

mho

me,

afterdark,b

etween5pm

and7pm

,andon

interstates.

-Low

erdrivingriskexpo

sure:d

efined

asfre

quen

cyof

drivingless

than

2milesfro

mho

meandbe

fore

9am

.(B):They

also

askedfordriving

expe

rience

B(−):Therewereno

statistical

differences

inlower

andhigh

erdrivingriskexpo

sure

whe

ncomparin

gtheinterven

tionand

controlg

roup

.

0,79

Gaine

set

al.,

[38]

n=195

yr=be

tween79

and84

yearsold

c=NM

RCT

Assesstheprocessandshort-term

effectsof

theCarFitprog

ram.


omized

in2

grou

ps:

-CarFitgrou

p(n:83):the

interven

tionwas

carriedou

tin

one

daythroug

han

individu

alappo

intm

entwith

thecommun

ity’s

CarFiteven

tcoordinator.Each

CarFit

assessmen

trequ

iredapproxim

ately

15min

forcompletion.Therefore,

thetotaltim

ecouldbe

150min.

-Com

parison

grou

p(n:112):Did

not

receivetraining

Alltheparticipantsansw

ered

the

drivingqu

estio

nnaire

inthebaseline

andsixmon

thsaftertheCarFit

interven

tion.

(B):Adrivingqu

estio

nnaire

basedin

3partswas

applied:

-DrivingActivity:a

high

erscore

indicatesgreaterdrivingactivity.

-Com

fortableDriving:

alower

score

indicatesgreatercomfortdu

ringthe

drivingactivities.

-DrivingBehaviors:alower

score

indicatessaferdrivingbe

haviors.

B(−):Therewas

nostatistically

sign

ificant

differencebe

tween

having

aCarFitinterven

tionor

not

receivingtraining

afterthesix-

mon

thpo

stCarFitinterven

tionin

drivingbe

haviors.

0,75

Jone

set

al.,

[39]

n=44

yr=averageage

79yearsold(age

sN/S).c=USA

RCT

Com

pare

theim

pact

ofamulti-

sessioninteractive,expe

rt-ledver-

sion

ofthetraining

prog

ram

(Se-

niorson

theMOVE

–Version-A)to

aself-gu

ided

andless

resource

inten-

sive

versionof

theprog

ram

(Sen

iors

ontheMove–Version-B)

onolde

rdrivers’know

ledg

eandbe

havior

pertaining

todriving.


omized

in2

grou

ps:

-SOM-A

(n:20):C

onsisted

inthefour

sessions

utilizedin

theJone

set

al.,

2011

andtheCarFitassessmen

t.-SOM-B

(n:24):C

onsisted

inaself-

guided

andless

intensiveversionof

theSO

M-A.The

participantshadto

assistto

aon

esessionandtw

oop

-tio

nal(on

ewas

inclassandthe

othe

rtheCarFitassessmen

t)Atthebe

ginn

ingof

each

sessionthe

(SA/K):Self-repo

rted

drivingknow

-ledg

e15

itemswerede

velope

dby

theauthorsto

assess

specificde

tails

taug

htdu

ringthesessions.

SA/K

(+):They

foun

dsign

ificant

differences

inSO

M-A

grou

pbe

-tw

eenthebaselineandthefirstfol-

low

upin

theknow

ledg

eof

the

prop

erplacem

entof

thehe

adre-

straint,thetim

echecking

tirepres-

sure,m

usclerelaxersdo

notaffect

drivingandthede

finition

ofmod

er-

atedrinking

forolde

radults.But

this

sign

ificant

differencewith

thefollow

uptw

owas

onlywith

theitem

ofde

finition

ofdrinking

forolde

r

0,68


Table

4Synthe

sisof

interven

tionstud

iesinvolvinged


prog

rams(Con

tinued)

Autho

rsNum

ber(n),

Age

(yr)Cou

ntry

(c)

R.D.

Objective

Stud

yde

scrip

tion

Dep

ende

ntVariable

Relevant

results

QS

grou

pscompleted

thebaseline

questio

nnaire,the

nim

med

iately

afterthecompletionof

the

prog

ramsandfinallyafter6mon

ths

afterthebaseline.

adults.For

theothe

rhand

,the

SOM-

Bde

mon

stratedasign

ificant

differ-

ence

betw

eenfollow

uptw

oon

know

ledg

eabou

tmusclerelaxers.

SA/K

(+/−):Com

parin

gthemean

totalkno

wledg

escores

baselineand

immed

iatelyafterthecompletionof

thetraining

betw

eenthegrou

psthey

foun

dthat

theprog

ram

with

4ob

ligatorysessions

was

sign

ificant

greaterthan

theself-gu

ided

pro-

gram

with

onlyon

erequ

iredsession.

How

ever,thisdifferencewas

notsig-

nificantafter6mon

ths.


Table

5Synthe

sisof

interven

tionstud

iesinvolvingcompu

ter-basedtraining

Autho

rsNum

ber(n),

Age

(yr)Cou

ntry

(c)

R.D.

Objective

Stud

yde

scrip

tion

Dep

ende

ntVariable

Relevant

results

QS

Edwards

etal.,[40]

n=500

yr=averageage74

and75

yearsold

(age

sN/S).c=USA

andUK

Coh

ort

Thecurren

tanalyses

were

cond

uctedto

exam

ine

whe

ther

completingthis

speedof

processing

training

regimen

delays

driving

cessation.

Thecoho

rtwas

form

edby

participantsfro

m2different

stud

y.Theinterven

tionwas

basedin

10speedof

processing

training

sessions

ledby

atraine

rin

which

thesubjects

practiced

compu

terized

exercisesof

visualattentionaimed

aten

hancing

thespeedandaccuracy

ofvisual

perfo

rmance.The

sessions

lasted

1h,

twiceaweekfor5weeks.

Theassessmen

tcarriedou

tat

baseline,im

med

iatelypo

sttraining

,andwas

repe

ated

3yearsafter

training

.

(B):Drivingstatus

andthenu

mbe

rof

days

perweekdriven

was

evaluated

with

theMob

ility

DrivingHabits

Questionn

aire.

-Far

visualacuity

was

evaluatedwith

astandard

letter

chart.

-Men

talstatuswas

assessed

with

the

Mini-M

entalState

Exam

ination.

B(+):Speedof

processing

training

participationwas

protectiveagainst

drivingcessation,mainlyin

those

driverswho

drovemoreoftenand

thosewith

better

vision

.Thu

s,the

participantswho

completed

the

training

were40%

less

likelyto

cease

drivingacross

thesubseq

uent

3years

ascomparedwith

controls

0,86

Ballet

al.,

[41]

n=908

yr=be

tween65

and91

yearsold.

c=USA

RCT

Totesttheeffectsof

cogn

itive

training

onsubseq

uent

motor

vehicle

collision

(MVC

)involvem

ent

ofolde

rdrivers.


omized

in4

grou

ps:

-Con

trol

grou

p(n:298):Notraining

-Mem

orytraining

(n:103):Basedin

mne

mon

icstrategies.

-Reasoning

training

(n:133):Basedin

strategies

forfinding

thepatternin

aletter

orwordseriesandiden

tifying

thene

xtitem

intheseries.

-Spe

edof

processing

training

(n:129):

basedin

practiceof

visualattention

skillsandtheability

toiden

tifyand

locate

visualinform

ationqu

icklyin

increasing

lyde

manding

visual

displays.

Thesessions

wereledby

traine

rs,

cond

uctedin

grou

psof

2–4

participantsdu

ringapprox.70min

sessions

over

ape

riodof

5to

6weeks.Ineach

interven

tion

cond

ition

,10training

sessions

were

carriedou

ttw

iceaweekover

a5-

weekpe

riod

Participantscompleted

assessmen

tsat

baseline,im

med

iatelyfollowing,

andannu

allyat

1,2,3,and5years.

(CR):The

prim

aryou

tcom

ewas

state-

recorded

motor

vehiclecollision

(MVC

)ob

tained

from

theDep

art-

men

tsof

Motor

Vehicles

inthestates

ofAlabama,Indiana,Maryland,

and

Penn

sylvania.

Thevariablewere:Totalcollisions,at-

faultcollision

s,pe

rson

-tim

e(in

years),

person

-miles,at-faultcrashe

s/year,at-

faultcrashe

s/mile

rate

ratio

s.Mileage—

Thenu

mbe

rof

milesdriven

perweekwas

repo

rted

bypartici-

pantson

theMob

ility

DrivingHabits

Questionn

aire

andwas

used

tocalcu-

late

thede

pend

entvariableof

inter-

est,rate

ofMVC

spe

rpe

rson

mile

driven

CR(−):Theparticipantswho

carried

outthemem

orytraining

dono

tshow

asign

ificant

associationin

the

redu

ctionof

rate

ofat

faultMVC

per

year

CR(+):Theparticipantsun

derspeed

processing

training

andreason

ing

training

expe

rienced

asign

ificantly

lower

rate

ofat

faultMVC

peryear

ofdrivingexpo

sure

orpe

rpe

rson

mile

driven

.

0,86

Horsw

illet

al.,[42]

n=75

yr=be

tween6

5and89

yearsold.

c=Australia

RCT

Exam

inethelong

er-term

ef-

fectsof

hazard

percep

tion.


omized

in3

grou

ps:

-Trainingwith

outbo

oster(n:26):

perfo

rmed

thehazard

percep

tion

training

that

consistedin

aninstructionalvideo

followed

byvide

o-basedexercises.

-Trainingwith

booster(n:25):A

fter

(S):SimplespatialR

Ttest:the

participantsmusttouchas

quicklyas

possible15

high

contrastrectangles

that

appe

ared

oneafteranothe

ron

thecompu

terscreen

atrand

omintervals.(S):H

azardpe

rcep

tiontests:

4hazard

percep

tiontestspe

rparticipantwerege

neratedfro

ma

S(+):Theparticipantsthat

carriedou

tthetraining

respon

ded0.81

sfaster

than

baselinecomparedwith

those

intheplaceb

ocond

ition

.This

differenceitwas

maintaine

dafteron

eandthreemon

thsof

followingwith

0,67

sand0,45

sfaster,respe

ctively.

S(+):Theparticipantswho

were

0,79


Table

5Synthe

sisof

interven

tionstud

iesinvolvingcompu

ter-basedtraining

(Con

tinued)

Autho

rsNum

ber(n),

Age

(yr)Cou

ntry

(c)

R.D.

Objective

Stud

yde

scrip

tion

Dep

ende

ntVariable

Relevant

results

QS

onemon

thof

receivethesame

training

ofthegrou

pwith

outbo

oster

they

received

22min

ofadditio

nal

training

vide

o.-Placebo

(n:24):the

yhadaplaceb

ointerven

tionwatchinganothe

rvide

owith

clipsof

adrivinginstructor

discussing

aspe

ctsof

safe

driving.

Allthegrou

pspe

rform

edthehazard

percep

tiontestin

thefirstsession

priorto

thetraining

andthen

after

oneandthree-mon

thpo

stinterven

tion.

pool

of153vide

osfilmed

from

the

driver’spe

rspe

ctive.

unde

rinterven

tionhadasign

ificant

immed

iate

effect

oftraining

onhazard

percep

tion

S(+/−):Thehazard

percep

tion

training

with

boosterdidno

tshow

asign

ificant

differencerelativeto

baselinethan

training

with

out

boosterbasedon

thehazard

percep

tiontestscores.

Edwards

etal.,[43]

n=500

yr=averageage

72.08,74.13and

74.52yearsold

(age

sN/S).

c=UK

RCT

Toexam

ineho

wcogn

itive

speedof

processing

training

affectsdrivingmob

ility

across

a3-year

perio

dam

ongolde

rdrivers.

Basedup

ontheirUFO

Vtest

perfo

rmance

theparticipantswere

rand

omized

in2grou

ps:

-Cog

nitivespeedof

processing

training

(n:66):the

tasksin

the

compu

terinvolved

iden

tifiedand

localizevisualandauditory

targets.

-Com

putercontactinternet

training

(n:68):p

articipantsreceived

instructions

oncompu

terhardware,

how

touseamou

se,how

touseand

e-mailand

how

toaccess

anduse

web

page

s.Theinterven

tionhad10

sessions,60

min

indu

ratio

n,gu

ided

byatraine

randinvolving1–3participantspe

rclass.

Oncefinalized

thetraining

follow

upinterviewsoccurred

with

in3years

+/−

3mon

thsof

theparticipantslast

assessmen

t.

(B):Drivingbe

haviorswas

assessed

with

theMob

ility

Questionn

aire:

-Drivingexpo

sure:Totalnu

mbe

rof

challeng

ingcond

ition

sen

coun

tered

whiledriving-Drivingspace:Extent

into

environm

entdriven

-Drivingdiffi-

culty

3(Alone

,Laneandchange

s):

Left-hand:

Ratin

gof

difficulty

while

drivingin

each

situation;1=no

diffi-

culty

to4=extrem

edifficulty.-D

riving

difficulty

5(Rushho

ur,H

ightraffic,

Night,RainandMerging

into

traffic):

Ratin

gof

difficulty

whiledrivingin

each

situation;1=no

difficulty

to4=

extrem

edifficulty.

B(+):Theparticipantsthat

didno

treceivethespeedof

processing

training

expe

rienced

steepe

rde

cline

indrivingmob

ility

across

the3-year

perio

drelativeto

thereferencegrou

pas

indicatedby

increaseddrivingdiffi-

culty

andde

creaseddrivingexpo

sure

andspace.

B(−):Theparticipantsthat

completed

thespeedof

processing

training

expe

rienced

increaseddriving

difficulty

across

timewhe

ndriving

alon

e,makinglane

change

s,and

makingleft-handturnsacross

oncom-

ingtraffic

than

didthereference

grou

p(drivingdifficulty

three-item

compo

site).

B(−):Theparticipantsthat

were

traine

ddidno

tdifferacross

time

from

thereferencegrou

pin

driving

expo

sure,d

rivingspace,or

the

degree

ofdrivingdifficulty

asindicatedby

thefive-item

compo

site

0,79

Cuene

net

al.,[44]

n=56

yr=averageage

70.84,69.84and

73.06yearsold

(age

sN/S).

c=Belgium

and

Holland

RCT

Thepu

rposeof

thepresen

tstud

ywas

toinvestigatethe

effect

ofacompu

terized

WM

training

onaspe

ctsof

olde

rdrivers’cogn

itive

ability

and

drivingability.


omized

in2

grou

psandthecontrolg

roup

was

collected

:-AdaptativeTraining

Group

(n:19):

thedifficulty

levelo

fthetraining

was

automaticallyadjusted

onatrial-b

y-trialb

asis.

-Non

AdaptativeTraining

Group

(n:

19):thedifficulty

levelo

fthetraining

was

notadjusted

-Con

trol

Group

(n:18):N

oTraining

Thetw

o-training

interven

tiongrou

p

(S):ThreeCog

nitivemeasureswere

evaluated:

-Working

mem

ory

-Atten

tion-In

hibitio

n(S):Sixspecific

drivingmeasureswereevaluated:

-Drivingspeed(km/h)

-SDLP

(m)

-Gap

acceptance

(s)

-Com

pletestop

sat

stop

sign

s-Givingrig

htof

way

-Crashes

(num

ber)

S(+):Theparticipantsun

der

compu

tertraining

achieved

asign

ificant

differenceforworking

mem

oryandthedrivingmeasure

ofgiving

right

ofway.Inparticular,

participantswho

notwereun

der

training

hadlower

working

mem

ory

capacity

andgave

less

right

ofway

than

theothe

rtw

otraining

grou

ps.

How

ever,the

rewas

anim

provem

ent

intheadaptivetraining

grou

pin

cogn

itive

ability,smallerin

theno

n-

0,71


Table

5Synthe

sisof

interven

tionstud

iesinvolvingcompu

ter-basedtraining

(Con

tinued)

Autho

rsNum

ber(n),

Age

(yr)Cou

ntry

(c)

R.D.

Objective

Stud

yde

scrip

tion

Dep

ende

ntVariable

Relevant

results

QS

consistedin

working

mem

orytrain-

ingbasedin

3subtasks:visuo

-spatial

task,a

backwarddigitspan

task

anda

letter

span

task.The

training

was

con-

ducted

atho

me,on

aPC

,viathe

internet

with

atotaln

umbe

rof

ses-

sion

sbe

tween20

and25.

Afte

rthetraining

theparticipants

develope

dthepo

st-testthat

was

the

samepre-testandconsistedin

cogn

i-tivetasksanddrivingin

asimulator

scen

ario.

adaptivetraining

grou

pandon

lyminim

alin

theno

-trainingcontrol

grou

psupp

ortedforworking

mem

ory.

S(−):Theeffectsof

thetraining

did

notachieveastatisticallysign

ificant

differenceforthecogn

itive

abilitiesof

attentionandinhibitio

n.S(+):Thedrivingabilitiessuch

asdrivingspeedandcompletestop

sat

stop

sign

sachieved

onlymarginally

asign

ificant

effect.H

owever,the

othe

rdrivingmeasuressuch

asSD

LP,g

apacceptance,g

ivingrig

htof

way,and

crashe

sdidno

tfindstatistically

sign

ificant

difference.

Cassavaug

handKram

er,

[45]

n=21

yr=averageage

71.7yearsold(age

sN/S).

c=USA

Pre-

Post

test

Thepresen

tstud

y’smain

objectivewas

toinvestigate

whe

ther

training

inlabo

ratory

taskswou

ldtransfer

todrivingpe

rform

ance

inolde

radults.

Alltheparticipantswereun

derthe

samecompu

ter-basedtraining

.Theinterven

tionconsistedin

8-training

sessionlasted

90min

and

carriedou

tin

different

days.The

pro-

gram

haddifferent

tasks(atten

tion,

visuo-spatialw

orking

mem

ory,man-

ualcon

trol

anddu

altasks).

Theassessmen

tconsistedin

two

initialdrivingin

simulator

andtw

ofinalpo

st-in

terven

tiondrivingsimula-

torsession,iden

ticalto

thefirst.

(S):Respon

seaccuracy

andrespon

setim

eweremeasuredin

theselective

attentionandN-backtasks.Ro

otmeansquare

tracking

errorandtim

e-on

-targe

twereanalyzed

forthetrack-

ingtask.

-Trackingtask

-Visualselectiveattentiontask

-Visual–spatialN

-backtask

-Dualtasks

(S):DrivingSimulator

-Lanepo

sitio

nandfollowingdistance

wereassessed

interm

sof

root

mean

square

error.-Respon

setim

eto

lead-

vehiclebrakeeven

tswas

measuredin

millisecon

ds.

S(+):Theparticipantswho

were

unde

racompu

ter-basedtraining

achieved

improvem

entsin

sing

leand

dualcogn

itive

tasks.Theseim

prove-

men

tsweretranslateto

anim

prove-

men

tin

drivingsimulator

perfo

rmance

across

thecourse

ofthe

stud

y

0,69

John

ston

etal.,[46]

n=53

yr=averageage

68.83yearsold

(age

sN/S).

c=Canada

NRC

TThecurren

tstud

yassessed

theeffectiven

essof

DriveSharp

intraining

olde

rdriversin

anaturalistic

class

setting


din

2grou

ps:

-Con

trol

grou

p(n:18)

-Experim

entalg

roup

(n:24)

Theinterven

tionwas

theDrivesharp

course

that

lasted

5weeks

with

2sessions

forweekandeach

session

was

ledby

afacilitator

with

adu

ratio

nof

60min.Thiscourse

was

develope

din

aclassroo

men

vironm

enton

individu

alde

sktop

compu

terwith

3games

that

incorporates

divide

dattentionand

multip

leob

ject,inten

dedto

enlarge

theUFO

Vandtrains

speedof

processing

.

(S):Abriefversionof

theHazard

Percep

tionTestwas

utilized

(S):TrailsAandB:wereutilizedto

measure

theprocessing

speed,

working

mem

ory,andexecutive

control.

S(−):Afte

rthefiveweeks

oftraining

theanalysisof

perfo

rmance

data

did

notrevealed

anysign

ificant

bene

fits

totheDrivesharp

course.

0,68


Table

5Synthe

sisof

interven

tionstud

iesinvolvingcompu

ter-basedtraining

(Con

tinued)

Autho

rsNum

ber(n),

Age

(yr)Cou

ntry

(c)

R.D.

Objective

Stud

yde

scrip

tion

Dep

ende

ntVariable

Relevant

results

QS

Allparticipantscompleted

trailsthat

assess

visualsearch,m

emory,and

attentionandashortversionof

the

HazardPercep

tionTestin

thepre-

testing.

After

the5fiveweeks

oftraining

theparticipantsattend

edthe

post-testin

gsessionthat

was

the

samepre-testingwith

onlyon

ediffer-

ence

that

theexpe

rimen

talg

roup

completed

ausability

questio

nnaire.


Table

6Synthe

sisof

interven

tionstud

iesinvolvingph

ysicaltraining

Autho

rsNum

ber(n),

Age

(yr)Cou

ntry

(c)

R.D.Objective

Stud

yde

scrip

tion

Dep

ende

ntVariable

Relevant

results

QS

Marottoli

etal.,[47]

n=178

yr=averageage

77.4and77.2years

old(age

sN/S).

c=USA

RCT

Tode

term

inewhe

ther

amulticom

pone

ntph

ysical

cond

ition

ingprog

ram

targeted

toaxialand

extrem

ityflexibility,

coordinatio

n,andspeedof

movem

entcouldim

provedriving

perfo

rmance

amon

golde

rdrivers.

Theparticipantswererand

omized

in2grou

ps-Con

trol

grou

p(n:90):the

yreceived

mon

thlyin-hom

eed

uca-

tionmod

ules

review

ingge

neral

safety

issues

abou

tho

mesafety,fall

preven

tion,andvehiclecare.

-Interven

tiongrou

p(n:88):12weeks

ofdaily

training

of15

min

atho

me

participants.The

participants

received

amanualw

ithim

ages

and

instructions.A

lso,they

hadaweekly

visitby

aph

ysicaltherapistto

review

theexercises.

Alltheparticipantspe

rform

edthe

baselineassessmen

tandthen

the

change

inon

-roaddrivingpe

rform

-ance

(prim

aryou

tcom

e)at

3mon

thswas

measured.

(S):Chang

ein

on-roaddrivingpe

r-form

ance

at3mon

thsrelativeto

baseline.

(S):Second

aryou

tcom

eswerethe

drivingevaluatorsoverallratingand

numbe

rof

criticalerrorsat

3mon

ths

S(+):Theparticipantsafterthe12

weeks

ofdaily

training

atho

mein

theinterven

tiongrou

pmaintaine

dthedrivingpe

rform

ance

meanw

hile

inthecontrolg

roup

they

declined

.Interven

tiongrou

pmade27.1%

fewer

criticalerrorsthan

control

grou

pdu

ringon

-roadassessmen

t

0,89

Marmeleira

etal.,[48]

n=26

yr=be

tween55

and78

yearsold.

c=Po

rtug

al

RCT

Themainaim

ofthisresearch

was

tostud

ytheeffectsof

asimilarexercise

prog

ram

onthespeedof

behavior

ofolde

radultsdu

ringon

theroad

driving.


omized

in2grou

ps:

-Con

trol

grou

p(n:13):D

idno

treceiveinterven

tion

-Exercisegrou

p(n:13):W

asbased

inan

exercisesprog

ram

of8weeks

with

3days

perweekwith

asession

of60

min.

Alltheparticipantspe

rform

edthe

on-roadbaselineassessmen

tand

after8weeks

carriedou

tthepo

st-

interven

tionassessmen

t.

(S):BrakeRespon

seTimeTask:The

participantshadto

brakeas

quickly

aspo

ssiblewhe

nevertheleading

car’s

rear

brakelightswereactivated

.(S):Perip

heralR

espo

nseTimeTask:

Theparticipantshadto

reactby

depressing

with

theirleftthum

ba

microsw

itchattached

totheleft

side

ofthesteerin

gwhe

el.

(S):Cho

iceRespon

seTimeTask:The

participantswereinstructed

tofollow

theleadingcarandreactas

quicklyas

possibleto

either

using

thebrakeor

depressing

the

microsw

itchon

thesteerin

gwhe

el.

Theleadingcar’s

rear

brakelights

wereactivated

.(S):D

ual-TaskCon

di-

tion:Theparticipantshadto

brake

asfastas

possiblewhe

ntheleading

car’s

rear

brakelightswereactivated

andmustrealizeamen

tal-

calculationtask.

S(+):Theparticipantsun

derthe

exercise

grou

pshow

edasign

ificant

improvem

entforthesimple,tw

ochoice

andpe

riphe

ralreactiontim

etasksandin

thedu

altask

cond

ition

.Moreo

ver,acompo

site

score

reflectingallreactiontim

emeasuremen

tsshow

edasign

ificant

improvem

ent.

0,79


Table

7Synthe

sisof

interven

tionstud

iesinvolvingsimulator-based

training

Autho

rsNum

ber(n),

Age

(yr)Cou

ntry

(c)

R.D.

Objective

Stud

yde

scrip

tion

Dep

ende

ntVariable

Relevant

results

QS

Marchal-

Crespo

etal.,

[49]

n=32.

yr=be

tween65

and92

yearsold.

c=USA

RCT

One

goalof

thepresen

tstud

ywas,

therefore,to

determ

ineifthegu

idance-

relatedlearning

enhancem

entpe

rsistsat

along

-term

(1weeklater)retentiontest.

Evaluated4grou

pstw

omainlygrou

pdivide

dby

ageandthen

everygrou

pwas

rand

omlyassign

edin

guidance

andno

guidance:

-Guidancegrou

p-youn

g(n:15):D

rove

15tim

eswith

hapticgu

idance

and5

with

out.

-Guidancegrou

p-old(n:17):D

rove

15tim

eswith

hapticgu

idance

and5

with

out.

-Nogu

idance

grou

p-youn

g(n:15):

Drove

thecircuit20

times

with

outro-

botic

guidance.

-Nogu

idance

grou

p-old(n:14):D

rove

thecircuit20

times

with

outrobo

ticgu

idance

Theinterven

tionconsistedin

3expe

rimen

talsession

ondifferent

days.Inthefirstandthird

session

werecarriedou

ttests.Thesecond

sessionparticipantspe

rform

edthe

training

.

(S):Thetracking

error:de

fined

asthe

meanof

theabsolute

valuebe

tweenthe

center

ofthesimulated

whe

elchairand

theblackline,was

measured

(S):Trajectoriesfollowed

.(S):Lon

g-term

redu

ctionin

steerin

gpe

rform

ance.

(S):Perfo

rmance

(error

redu

ction).

S(−):Training

with

guidance

sign

ificantly

improved

long

-term

retentionof

thetask

onlyforyoun

gerdrivers.Furthe

rmore,

improved

long

-term

retentionmorefor

initiallyless

skilled

driversandfinallyim

-proved

learning

ofthesteerin

gtask

incurves,w

hereas

itdidno

taffect

learning

durin

gstraight

lines.

S(−):Older

driversdidno

tfind

sign

ificant

differencein

training

with

guidance

orwith

out.

S(−):Therewas

aneffect

ofageon

drivingpe

rform

ance

andretention.

The

olde

rdrivershave

aworse

perfo

rmance

andalso

learne

dmoreslow

lyandforgot

thelearne

dtask.

0,75

Rogé

etal.,

[50]

n=31

yr=be

tween63

and78

yearsold.

c=France

RCT

Our

aim

inthisstud

ywas

totestthetw

ofollowinghypo

theses:thatspecific

training

givendu

ringsimulated

driving

wou

ldim

proveelde

rlydrivers’useful

visualfield;and

that

thetraining

given

wou

ldallow

them

tode

tect

moreeasily

vulnerableroad

usersthan

untraine

delde

rlydriversdu

ringsimulated

driving.

The31

participantsweredivide

in2

grou

ps:

-Experim

ental(n:

15):training

insimulator

toincrease

theuseful

visual

field.

-Con

trol

(n:16):d

rivingin

the

simulator

maintaining

aconstant

distance

betw

eenthevehiclein

front.

Theinterven

tions

werebasedin

two

visitsto

thelabseparatedwith

12days

onaverage.Theen

tiretw

osessions

lasted

5hand4hand15

min

totheexpe

rimen

taland

control

grou

prespectively.

(S):Usefulvisualfield

size:W

asestim

ated

durin

gdriving.

Participantshadto

detect

achange

incolor(cen

tralsign

al)of

adisc

which

appe

ared

briefly

and

interm

itten

tlyon

therear

windo

wof

the

vehiclethey

werefollowing.

22central

sign

alsappe

ared

durin

gthetest.The

yalso

hadto

detect

48pe

riphe

ralsignals

which

appe

ared

briefly

at3eccentricities

ontheroad

over

8different

meridians.(S):Visibilitydistance

ofvulnerableroad

users

S(+):Therewas

asign

ificant

effect

inthe

useful

visualfield

size,w

ereun

traine

dparticipantsde

tected

alower

numbe

rof

sign

alsin

thecentraltaskcomparedto

thetraine

dgrou

p.Also,in

thepe

riphe

ral

task

theexpe

rimen

talg

roup

detected

agreaternu

mbe

rof

sign

alsthan

untraine

dparticipants(49,48%

vs27.93%

)whe

nthistestwas

administeredat

theen

dof

theexpe

rimen

t.S(+):Thetraining

wou

ldallow

elde

rlydriversto

improvetheirability

tode

tect

vulnerableroad

userswhiledriving.

Visibilitydistance

forvulnerableroad

userswas

greaterin

theexpe

rimen

tal

grou

pthan

inthecontrolg

roup

andthe

visibilitydistance

was

greaterin

session2

than

insession1.Also,thetype

ofvulnerableroad

user

also

hada

sign

ificant

effect

onvisibilitydistance

which

was

greaterforpe

destrians

than

fortw

o-whe

eled

motorized

vehicles,

werethetraine

dgrou

pwas

better

tode

-tect

pede

strians

intheroad

environm

ent

0,68


Table

8Synthe

sisof

interven

tionstud

iesinvolvingmixed

training

Autho

rsNum

ber(n),

Age

(yr)Cou

ntry

(c)

R.D.

Objective

Stud

yde

scrip

tion

Dep

ende

ntVariable

Relevant

results

QS

Porter,

[51]

n=54

yr=averageage

77.6,77.1and73.6

yearsold.

c=Canada

RCT

Thepu

rposeof

thisstud

ywas

toexam

inean

alternateform

ofdriver

training

byutilizing

vide

oandglob

alpo

sitio

ning

system

(GPS)

techno

logy,incombinatio

nwith

aclassroo

m-based

educationprog

ram.


omized

in3grou

ps:

-Classroom

education(n:18):55

AliveMatureDrivingprog

ram,2

sessions

of4h.

-Video

(n:17):receivedvide

oand

GPS

feed

back

+classroo

med

ucation.Theparticipantwatched

thevide

oof

theirow

npre-testdrive

with

thedrivinginstructor

andwere

givenvery

specificinstructions

onho

wto

improvetheirow

ndriving

-Con

trol

(n:19):N

otspecified

Theon

-roadtestwerepe

rform

edin

thepreandpo

st.

(S):DrivingTest(errors):Participants

drovea26

kmin

astandardized

all

testwerepe

rform

edwith

adigital

vide

ocameraandthen

were

watchingto

scorethedrivingerrors.

S(−):Participantsthat

carriedou

tthe55

AliveMatureDrivingprog

ram

didno

tsign

ificantlychange

from

pre-

topo

st-testin

g.S(+/−):Participantsin

thevide

ogrou

pandGPS

feed

back

sign

ificantlyredu

cedtheirdriving

errorsaftertheprog

ram.Inthis

grou

p9of

17subjectsim

proved

,whe

reas

only4of

18im

proved

intheEducationgrou

p,andjust1of

19im

proved

intheCon

trol

grou

p.

0,96

Marottoli

etal.,[52]

n=126

yr=averageage

80.4and79.7years

old(age

sN/S).

c=USA

RCT

Thisstud

ywas

design

edto

determ

inewhe

ther

aned

ucation

prog

ram

consistin

gof

classroo

mandon

-roadtraining

coulden

hance

drivingpe

rform

ance.


omized

in2grou

ps:

-Classroom

+OnRo

addriving

training

(n:69):Thisgrou

preceived

8hof

classroo

mand2hof

onroad

training

.-Con

trol

(n:57):Thisgrou

preceive

mod

ules

directed

atvehicle,ho

me

anden

vironm

entalsafety.

Both

grou

pspe

rform

edtheir

training

at8weeks

andfinallyhad

drivingandknow

ledg

etest.

(S):DrivingPerfo

rmance:The

road

testwas

basedon

theCon

necticut

Dep

artm

entof

Motor

Vehicles

test

andassessed

awiderang

eof

drivingabilities.

(SA/K):Kn

owledg

eTest:20road

know

ledg

equ

estio

nsfro

mtheAAA

Driver

Improvem

ent.Prog

ram

and

eigh

troad

sign

questio

nsused

inou

rearlier

stud

ies.

(SA/K):Interven

tionParticipant

Percep

tions:the

participantswere

askedifthey

liked

theprog

ram

adhe

rence.

S(+):Theprog

ram

basedin

aclassroo

med

ucationplus

onroad

drivingshow

someim

provem

entsin

thedrivingpe

rform

ance

comparin

gto

acontrolg

roup

.After

8weeks

oftraining

thetraining

grou

pwas

2.87

pointshigh

erthan

thecontrol

grou

pin

theroad

testscore.

SA/K

(+):Moreo

ver,therewere

differencein

know

ledg

etestscore

after8weeks,3.45po

intshigh

erin

theinterven

tionthan

inthecontrol

grou

p.Overall,theparticipantssaid

that

they

likethistype

ofprog

ram

andfoun

ditbe

neficial.

0,89

Bédard

etal.,[53]

n=75

yr=be

tween65

and87

yearsold.

c=Canada

RCT

ifthecombinatio

nof

anin-class

educationprog

ram

with

on-road

educationwou

ldlead

toim

prove-

men

tsin

olde

rdrivers’know

ledg

eof

safe

drivingpractices

andon

-road

drivingevaluatio

ns.


omized

in2

grou

ps-In

terven

tiongrou

p(n:38):received

the55-Alive/Maturedrivingpro-

gram

,aswellas2sessions

of40-m

inon

road

practice.

-Con

trol

grou

p(n:37):

Theproced

ureof

thisstud

ywas

the

initialon

road

drivingevaluatio

n.4–

8weeks

aftercompletingthe

training

werepe

rform

edthesecond

onroad

drivingevaluatio

n.

(B):Ro

adevaluatio

n:Theon

-road

evaluatio

nlasted

approxim

ately35

min

consistedof

varyingtype

sof

roadwaysandspeeds

andleftand

right

turnsat

controlledandun

con-

trolledintersectio

ns.

-Starting/stop

ping

/backing

-Signalviolatio

n/rig

htof

way/

inattention

-Movingin

roadway.

-Passing

/spe

edandturning

(SA/K):Theknow

ledg

ewas

evaluatedwith

aqu

estio

nnaire

that

consistsof

15multip

le-cho

ice

questio

ns

B(+):Ontheothe

rhand

,the

on-

road

evaluatio

nresults

sugg

estim

-provem

entson

someaspe

ctsof

safe

drivingsuch

asmovingin

the

roadway.

SA/K

(+):Thisstud

yrevealed

asign

ificant

improvem

entafterthe

educationandon

road

practice,

with

anincrease

intheknow

ledg

etestfro

m61%

ofqu

estio

nscorrectly

answ

ered

atbaselineto

81%

atfollow-up.

0,89

Hay

etal.,

[54]

n=67

yr=averageage

75yearsold(age

s

NRC

TCom

pare

theeffectiven

essof

two

training

prog

rams:pu

recogn

itive

training

andthesamecogn

itive


din

2grou

ps-Cog

nitivetraining

(CT,n:40):hada

(The

cogn

itive

perfo

rmance

was

evaluatedwith

the:

(S):TrailM

akingTest(A

andB):

S(+):Bo

thgrou

pof

training

show

sim

provem

ent;adiminutionin

the

numbe

rof

perseverations

inthe

0,82


Table

8Synthe

sisof

interven

tionstud

iesinvolvingmixed

training

(Con

tinued)

Autho

rsNum

ber(n),

Age

(yr)Cou

ntry

(c)

R.D.

Objective

Stud

yde

scrip

tion

Dep

ende

ntVariable

Relevant

results

QS

N/S).

c=France

training

coup

ledwith

threedriving

simulator

training

sessions,b

oth

prog

ramsbe

ingaddressedto

olde

rdriverspresen

tingacogn

itive

self-

assessmen

tbias.

duratio

nof

35hfor12

weeks

and

was

compo

sedof

20cogn

itive

exerciseswith

15difficulty

levels

each,focused

on:atten

tion,

mem

ory,visuospatialabilities,

executivefunctio

ns-Cog

nitivetraining

+driving

simulator

(CT+DS,n:27):Was

the

sametraining

plus

1hof

simulated

driving,

3sessions

of20

min.

Theevaluatio

nscarriedou

twere

before

andaftertraining

trou

ghthe

cogn

itive

evaluatio

nandon

road

evaluatio

n.

assessed

processing

speed,

executivefunctio

n,andvisual

scanning

ability

andinvolved

two

parts.

(S):DigitSubstitutionSymbo

lTest:

assessed

psycho

motor

processing

speed.

(S):Thespeedof

processing

and

visualattentionwas

evaluatedwith

theUsefulo

fFieldof

View

.(S):The

on-roaddrivingevaluatio

nwas

basedon

twodifferent,b

uteq

uiva-

lent

road

tripscombine

durban,sub-

urbandruralcircuitsandasection

ofrin

groad/highw

ay.A

lso,there

weretw

ogrid,the

first,assessed

eleven

dimen

sion

sof

driving.

The

second

grid

was

completed

inreal

timedu

ringthetrip

bytheexpe

ri-men

terseated

behind

thedriver.

TMT,an

increase

inthenu

mbe

rof

correctsymbo

lsfortheDSST,shorter

intervalpresen

tatio

nof

thetarget

towhich

they

reacted(visualatten

tion),

participantsanticipated

thetraffic

andtheen

vironm

entalchang

esbe

tter

anddrivingpe

rform

ance.

S(+):Participantsfro

mtheCTgrou

ptend

edto

makemoreplanning

errorsthan

participantsfro

mthe

CT+DSgrou

p,regardless

ofthe

timeof

evaluatio

nS(+/−):Thedrivingsimulator

expe

riencedidno

tinfluen

cethe

drivers’be

havior

ontheroad.The

participantsfro

mtheCT+DSgrou

pdidno

tmakesign

ificantlyfewer

drivingerrorsthan

thosefro

mthe

CTgrou

p.Therefore,theadditio

nof

drivingin

asimulator

tothe

cogn

itive

prog

ram

ledto

ade

terio

ratio

nin

speedadaptatio

nandcarcontrolh

andling

perfo

rmances,whe

reas

thepu

reCT

ledto

anim

provem

entof

these

drivingpe

rform

ances.

Casutt

etal.,[55]

n=91

yr=be

tween62

and87

yearsold.

c=Sw

itzerland

RCT

NM


omized

in3grou

ps:

-Sim

ulator

Training

Group

(n:39):A

training

sessiontook

40min.The

goalof

thistraining

approach

was

toincrease

themen

talw

orkload

ofcorrectdrivingin

arealistic

multitasking

drivingsetting.

-Cog

nitiveTraining

Group

(n:26):

Thego

alof

thistraining

approach

was

toincrease

specificdriving

relevant

cogn

itive

functio

ns.Eachof

the10

training

sessions

was

compo

sedof

10min

intrinsic

alertnesstraining

,followed

by10

min

ofph

asicalertnesstraining

and

20min

ofvigilancetraining

.-Con

trol

Group

(n:26):N

otraining

.Thestud

yde

sign

was

apre-po

stde

-sign

.Duringthepre-po

sttestwere

cond

uctedthecogn

itive

andon

road

tests.Theexpe

rimen

talg

roup

sbe

tweenthepre-po

sttests

(S):Duringtheon

-roadtestdriving

assessmen

ttheinstructorsmade

notesin

theevaluatio

nsheetbu

ton

lyevaluatin

gcogn

itive

aspe

ctsof

drivingbe

havior.D

rivingpe

rform

-ance

was

measuredusingdrivinger-

rors,top

speed,

meanspeed,

lane

accuracy,lanevariability,andreac-

tiontim

eto

hazardou

seven

ts.

(S):Cog

nitivetestbatteryevaluated:

Reactio

ntest,C

ognitron

etest,

determ

inationtest,p

eriphe

ral

percep

tiontest,adaptive

tachistoscop

ictraffic

percep

tiontest

andadaptivematrices

test.

S(+):Thedrivingsimulator-training

grou

pshow

edan

improvem

entin

on-roaddrivingpe

rform

ance

com-

paredto

theattention-training

grou

pandbo

thtraining

grou

psin-

creasedcogn

itive

perfo

rmance

com-

paredto

thecontrolg

roup

.

0,82


Table

8Synthe

sisof

interven

tionstud

iesinvolvingmixed

training

(Con

tinued)

Autho

rsNum

ber(n),

Age

(yr)Cou

ntry

(c)

R.D.

Objective

Stud

yde

scrip

tion

Dep

ende

ntVariable

Relevant

results

QS

perfo

rmed

thetraining

.

Romoser,

[56]

n=21

yr=“activelearning

grou

p”age

rang

e=73–82,avg.

=77.4,SD=3.47

“con

trol

grou

p”agerang

e=72–81;

avg.=76.5,SD=

3.20

c=USA

NRC

TDeterminethelong

-term

effectsof

activetraining

onolde

rdrivers’scan-

ning

inintersectio

ns.

Participantswho

participated

inthe

stud

yof

Romoser

andFisher

(2009)

wererecruited.

-Activelearning

grou

p(n:11):

Received

custom

ized

feed

back

from

areplay

ofhisow

nsimulator

and

field

drives

evaluatio

ns.

-Con

trol

grou

p(n:10):Receivedno

training

Allparticipantspe

rform

ed6

individu

alsessions

andthetraining

sessionwas

thenu

mbe

r4,theothe

rsessions

werede

sign

ated

tothepre

andpo

stevaluatio

ns(sim

ulator

and

onroad).

(B):Vide

osshow

ingtheindividu

alintersectio

nmaneuversof

the

participantswereanalyzed

tode

term

ineifthedriver

madea

correctsecond

arylook

atthe

intersectio

n.Themainou

tcom

eis

percen

tage

ofsecond

arylooks,

defined

asthenu

mbe

rof

intersectio

nswhe

rethedriver

took

aprop

ersecond

arylook

divide

dby

thetotaln

umbe

rof

intersectio

nsthe

driver

navigated,

was

calculated

for

B(+):The2009

stud

y,olde

rdrivers

intheactivelearning

grou

ptook

second

arylooksin

46.3%

ofintersectio

nspriorto

activetraining

inasimulator

andin

79.6%

ofintersectio

ns.Twoyearslater,the

sameactivelearning

grou

pdrivers

continuedto

executesecond

ary

looksin

intersectio

ns72.7%

ofthe

timearesultthat

was

still

sign

ificantlyhigh

erthan

their2009

pretrainingpe

rform

ance.The

6.9%

decrease

was

notstatistically

sign

ificant

B(+):Older

driversin

thecontrol

grou

pwho

received

notraining

in2009

took

second

arylooksin

40.7%

ofintersectio

nsdu

ringthefirstfield

driveandin

38.5%

ofintersectio

ns6

to8weeks

later.Tw

oyearslater,

thesesamecontrolg

roup

drivers

took

second

arylooksin

42.9%

ofintersectio

nsagain,

nostatistically

sign

ificant

change

inpe

rform

ance.

0,82

Romoser

and

Fisher,

[57]

n=54

yr=be

tween70

and89

yearsold

(rang

e=70

to88;

samplemean=

77.54;sample

STD=4.55)

c=USA

NRC

TDeterminewhe

ther

olde

rdrivers

looked

less

oftenforpo

tential

threatswhileturningthan

youn

ger

driversandto

compare

the

effectiven

essof

activeandpassive

training

onolde

rdrivers’

perfo

rmance

andevaluatio

nof

their

drivingskillsin

intersectio

ns.

Participantsweredivide

dinto

three

agegrou

ps(70–74;75–79;80–89),

then

each

grou

pwereassign

edto

oneof

thene

xt3grou

ps:

-Activelearning

grou

p(n:18):

Received

custom

ized

feed

back

from

areplay

ofhisow

nsimulator

and

field

drives

evaluatio

ns.

-Passive

learning

grou

p(n:18):

Received

atradition

allecture-style

training

sessionconsistin

gof

power

pointsslides,texts,figures

and

anim

ations.

-Con

trol

grou

p(n:18):Receivedno

training

Allparticipantspe

rform

ed6

individu

alsessions

andthetraining

sessionwas

thenu

mbe

r4,theothe

rsessions

werede

sign

ated

tothepre

andpo

stevaluatio

ns(sim

ulator

and

onroad).

(B):Vide

osshow

ingtheindividu

alintersectio

nmaneuversof

the

participantswereanalyzed

tode

term

ineifthedriver

madea

correctsecond

arylook

atthe

intersectio

n.Themainou

tcom

eis

percen

tage

ofsecond

arylooks,

defined

asthenu

mbe

rof

intersectio

nswhe

rethedriver

took

aprop

ersecond

arylook

divide

dby

thetotaln

umbe

rof

intersectio

nsthe

driver

navigated,

was

calculated

for

B(+):Theparticipantsin

theactive

learning

grou

pincrease

their

second

arylooksmorethan

the

doub

lethat

they

took

before

training

andtend

edto

rate

this

training

tobe

moreeffective.

Betw

eentheactiveandpassive

grou

pstherewas

asign

ificant

difference,as

was

that

betw

eenthe

activeandcontrolg

roup

sin

their

second

arylooks.Finally,the

rewere

sign

ificant

differences

betw

een

activeandpassivegrou

psand

betw

eenactiveandcontrolg

roup

sbu

tno

tbe

tweenthepassiveand

controlg

roup

s.

0,81

Lavallière

etal.,[58]

n=22

yr=be

tween65

NRC

TIfsimulator

training

,cou

pled

with

vide

o-basedfeed

back

canmod

ifyTheparticipantswererand

omized

in2grou

ps(B):Alldriversdrove12

kmin

the

samevehicleon

thesameop

enB(+):Theparticipantsin

the

feed

back

grou

pafterthetraining

0,75


Table

8Synthe

sisof

interven

tionstud

iesinvolvingmixed

training

(Con

tinued)

Autho

rsNum

ber(n),

Age

(yr)Cou

ntry

(c)

R.D.

Objective

Stud

yde

scrip

tion

Dep

ende

ntVariable

Relevant

results

QS

and85

yearsold.

c=Canada

visualsearch

behaviorsof

olde

rdriverswhilechanging

lane

s(Feed-

back

drivers).

-Feedb

ackgrou

p(n:10)

-Con

trol

grou

p(n:12)

Then

participated

in5sessions,

whe

rethefirstandlastsession

includ

edtheon

road

andin

simulator

assessmen

ts.The

3sessions

betw

eenthepre-po

sttests

werethesameforbo

thgrou

pswith

thege

nerald

river

refre

sher

course

(based

onthe55-alivedriver

safety)

anddrivingsimulator

training

.The

onlyon

edifferencewas

that

the

feed

back

grou

preceived

driving

specificfeed

back.

road

circuitforbo

ththepreand

post-trainingsessions.For

each

on-

road

lane

change

,20sof

data

were

extractedfro

mtherecords;15

sprior

totheinitialdisplacemen

tof

theve-

hicletowards

thetarget

lane

and5s

afterthisinitialdisplacemen

t.The

principalo

utcomes

were:

-Frequ

ency

ofvisualinspectio

nsdu

ringlane

change

s-Tem

poralinspe

ctionof

theblind

spot.

increasedthefre

quen

cyof

verificationof

theblindspot

increasing

from

32,3%

before

the

interven

tionto

a64,9%

post

interven

tion.Add

ition

ally,the

rewas

anincrease

ofvisualinspectio

nsoccurringpriorto

theon

setof

the

lane

change

s(after

thetraining

,96%

oftheverifications

occurred

priorto

theon

setof

thelane

change

s).


but the impact of the intervention faded over time [39].Only one study used on-road data, measured throughcollisions and violations of traffic regulations. Theremaining study used a mix of questionnaires and moni-toring of driving. For the study that evaluated implica-tions in collision [36], drivers who participated in theprogram add 1.5-time greater odds of being involved ina crash than their matched controls.

Computer-based trainingTable 5 presents reviewed papers regarding computer-based training. Four (4) of the studies were conductedusing a RCT (Level 1), 1 used a non-RCT 2-groupapproach (Level 2), 1 a pre/post-test intervention(Level 3) and 1 a cohort design (Level 3). The moststudied dependent variable was assessed throughquestionnaires/tests (3 of 7 studies). It is also import-ant to acknowledge that 3 of the studies additionallyused the on-road assessment or simulator driving forevaluation purposes. Despite differences in the formof their interventions, 4 out of the 7 reviewed studiespresented computer-based training based on 10 clas-ses. Follow-up of these studies varied between imme-diate evaluations, up to 1 to 5 years post-intervention.This later evaluation of the program’s impact wasbased on crash rates [41]. Two (2) studies assessed

behaviours with mixed results on reported outcome,some factors improving such as less driving cessationover 3 years [40] while other specific manoeuvres de-teriorated [43]. Speed of processing training showed apositive impact on reducing driving cessation andlowering at fault motor vehicle collisions, as well asimproving reaction time [42, 45].

Physical trainingOnly 2 studies used physical training to improve drivingskills. Both used a RCT (Level 1), 1 using an evalu-ation scheme similar to an instructor looking at adriver’s overall performance [47] while the secondstudy used different types of evaluations more associ-ated with processing and movement time, such asbrake reaction time and peripheral response timetasks [48]. Both programs showed relatively similaractive time for the older drivers, Marottoli et al.’sprogram lasting 21 h [47] and Marmeleira et al.’s pro-gram 24 h [48]. The 2 interventions showed a positiveimpact on driving performance by either maintainingdriving capacity after 12 weeks or even improvingscores of reaction time measurements. However, it isnot possible to identify if there was any issue withadherence to training in these 2 studies.

Fig. 1 Flow diagram of paper selection process based on PRISMA


Simulator-based trainingOf the studies that investigated the effects ofsimulator-based training on driving performance, only2 that met the inclusion criteria were reviewed(Table 7). These studies used a randomized controlledtrial approach (Level 1), and the dependent variableswere gathered using the same simulator. Furthermore,characteristics of the intervention are not very clear,so it is not known how long each class lasted andonly one study indicates that the intervention lasted2 days. Marchal-Crespo et al.’s [49] study showed that,despite being useful for improving driving perform-ance for both younger and older drivers, a guidancesystem using haptic feedback was not sufficient totransfer learned skills for older drivers and this train-ing was not useful at improving their skills, long-term. Once the guidance system was removed, olderdrivers returned to their initial driving performance.Rogé et al.’s [50] study showed a positive effect of their

training using a simulator to improve drivers’ useful vis-ual field size. Trained participants showed an improvedcentral and peripheral capacity to detect signals whilethe control group who drove the same simulator with nospecific training did not improve their detection rates.

Mixed programsThe most studied independent variable in the revisedpapers was the mixed programs. The most commoncombination was done by using a classroom setting pluseither an on-road intervention with a driving specialist[51–53] or a series of in-simulator interventions withdriving-specific feedback combined with active practicein the simulator [57, 58]. Research designs were equallydistributed, 4 for each, between Level 1 (randomizedcontrolled trials) and Level 2 (2 or more groups ran-domly assigned to conditions but not as a RCT). Whencomparing the types of interventions that received mixedtraining, a consistent finding is that groups that only re-ceived classroom information with no specific feedbackand practice of their driving (control groups) did not im-prove their driving when compared to other combina-tions of interventions [51, 57, 58]. These results aresimilar to those observed in the education-based trainingprograms section above.With the use of a specific data collection system, it is

interesting to note that the approaches used by Porter[51], Romoser and Fisher [57] and Lavallière et al. [58]allowed the trainees to receive specific feedback fromtheir own driving performance by using video collectedduring the initial on-road evaluation.

DiscussionThe purpose of this study was to assess, by a critical re-view, whether interventions designed for healthy older

drivers improve driving on the following components ofsafe driving: Self-awareness and/or knowledge (SA/K),behaviour (B), skills (S) and/or reduced crash rates (CR).Reviewing the 25 papers selected according to pre-defined criteria, 60% (15 papers) report a positive impacton different levels of driving indicating that interventionsare feasible and useful at improving older drivers’ situ-ational awareness/knowledge, behaviours, skills and/orcrash rates. However, it is important to mention thatsome of these results are different from the findings pre-sented by Golisz [59] in their review assessing specificinterventions within the scope of occupational therapypractice and including a population with specific healthconcerns such as stroke survivors [68].In this section, the primary review findings are dis-

cussed separately according to each independent variable(i.e., the effects of education-based training programs,the effects of computer-based training, etc.). The authorsrealized that the diverse nature of the studies and thevariables used in the reviewed studies were quite differ-ent, even when testing similar variables, and that differ-ent approaches have their own specific strengths andweaknesses.

Education-based training programsEducation programs present a variety of types of inter-ventions, from the number of classes, number of partici-pants per class, program and class duration. In thecurrent review, the typical class used was based on the55 alive driving course. This was also observed in mixedtraining that used part of an education-based approach.On the other hand, when considering the type of class,most interventions were guided by an expert, but therewere also programs developed through educational vid-eos, more flexible programs guided by the participants,or simply reading a document. Therefore, it is difficultto propose a “standard” type of intervention or to gener-ate a recommendation on how an intervention based onan education program should be.Despite the positive findings related to self-regulatory driv-

ing practices found in some of the programs [34, 36, 39], theresults must be considered with caution since 2 out of 3studies with positive results related to education programswere informed by self-reports and/or questionnaires [36,39]. This type of dependent variables could create someproblems, for example, in the study developed by Selanderet al. [69], all the participants self-reported as capable ofdriving, however, when evaluated by an objective measure-ment such as a test on route, 20% of them failed. In anotherstudy developed by Freund et al. [70], 38% of the partici-pants were categorized after a simulator test as unsafedrivers, however, all of them self-reported driving perform-ance that was equal or better than other drivers of their agegroup. Ross et al. [71] found that 85% of older drivers self-


reported as being good or excellent drivers regardless oftheir previous citations or crash rates.It is also important to highlight that 4 out of 6 of the

reviewed studies, including the ones with strong meth-odology [34, 39], did not produce positive results sup-porting this type of intervention [37, 38]. Furthermore,the results obtained by Nasvadi and Vavrik [35] indicatethat men and women who attended this educationcourse had a 1.5-time greater chance of being involvedin a crash than their matched controls. The previous re-sults coincide with Janke’s study [72], which concludedthat completing a course of education is not associatedwith a decrease in crashes after the analysis of 2 cohorts,conversely, those killed and injured in motor vehicle col-lisions increased. Furthermore, there is a systematic re-view that indicates that there is no scientific evidence tosupport the effectiveness of post-licensing educationprograms in the reduction or prevention of accidents[73]. This could translate into an increased risk of driv-ing since the effects of the education program are notpositive and participants feel more confident after par-ticipating in a program or maybe that drivers takingthese types of classes have concerns about their drivingand might already be at risk drivers due to decliningskills and/or health conditions.Overall, despite their widespread use among older

drivers and organizations who provide these classes,their proven efficacy to increase driver’s knowledge andself-awareness are not enough to improve one’s abilityto drive safely or reduce crashes. Therefore, they shouldnot be used as a single method for an older driver whowants to continue driving. The results obtained from thecurrent review confirm the one from Owsley et al. [74]and McKnight et al. [75]. They showed that educationalinterventions did not show positive results in improvingdriving performance and safety, even though drivers in-creased their knowledge on road safety. Moreover, theseresults have been confirmed by the mixed interventionsidentified in the current review showing that with aclassroom intervention only, older drivers did not im-prove their driving performance [51, 52, 57, 58].

Computer-based trainingDespite the huge variability in the methodologies used,only 2 of the 7 studies presented positive overall resultson behaviour [40] and skills [45].Considering the study by Edwards et al. [45], it can be

concluded that this type of intervention could positivelyaffect driving mobility, since the participants who com-pleted the processing speed training were 40% less likelyto cease driving during the subsequent 3 years, as com-pared with controls. In addition, another paper showedthat the older adults with risk for mobility decline whocompleted the processing speed training experienced a

trajectory of driving mobility similar to the subjects whowere not at risk [43]. On the other hand, those who wereat risk for mobility decline and did not undergo trainingexperienced greater decrease and difficulty in mobilityfor the 3 subsequent years. However, those resultsshould be analysed with caution since the dependentvariable is based on self-report through the mobilityquestionnaire. Contrary to the positive findings men-tioned above, another reviewed study showed that 5weeks of training with 2 weekly 60-min sessions guidedby a facilitator did not reveal any significant benefit asso-ciated with the intervention [46]. This could be due inpart to the type of variable used, i.e. a short version ofthe “Hazard Perception Test”, which corresponds to amore objective tool than the application of a self-reported questionnaire, which has shown a sensitivity of75% and a specificity of 61% in the ability to predict safeolder drivers or dangerous older drivers [76].Three (3) out of the 7 studies presented unclear results

(+/−) [41, 43, 44]. Despite it being important to mentionthat in the study by Ball et al. [41] 2 out of 3 interven-tion groups (speed of processing training and reasoningtraining) reduced at-fault collision involvement over thesubsequent 6-year period relative to controls, whichwould indicate that this type of computer-based pro-gram could improves driving performance. This is theonly reviewed study on a computer-based program dem-onstrating an improvement for older drivers.One of the advantages of computer-based programs

are that they can be a great training alternative whenconsidering costs, since today’s access to the Internethas been facilitated, and the widespread use of com-puters and mobile devices is not a barrier to the imple-mentation of these interventions but rather anopportunity for people to improve their driving skillsfrom their homes [44, 77].Overall, computer-based interventions are an interest-

ing opportunity for older drivers, since some of themhave been shown to reduce the risk of crash involvementover time. However, more research is required to betterunderstand how these interventions improve one’s abil-ity to drive safely, beyond the speed of processing andreduction of reaction time. With computers and smartdevices now widely available, training could be done al-most anywhere for interested drivers.

Physical trainingFor both studies under review in this current analysis,results are positive in terms of impact on driving per-formance following a physical training interventionaimed at older drivers. The first study showed that phys-ical training allowed older drivers to maintain drivingperformance over the course of the interventions, whilethe second program showed improved response time to


different secondary tasks while driving. Over 2 differentperiods of time, 12 vs 8 weeks, they both showed that aregimen equivalent to about 21 to 24 h of exercisescould be beneficial to older drivers. Further evaluationshould be performed to analyse the best and most effi-cient modality of interventions with this clientele (ex.Short daily period of exercise or longer period spreadacross the week).Moreover, since most interventions aimed at either in-

creasing range of motion and speed of movement, littleis known on the impact of cardiovascular training andits transferability to driving capacity. Positive transferhas been shown on cognitive tasks [78] and it would beof interest to evaluate this in a driving context.

Simulator-based trainingOnly 2 studies used simulator-based training and pre-sented contradictory results. The negative results re-ported by Marchal-Crespo et al. [49] showed that olderdrivers did not benefit from training with haptic guid-ance, and long-term improvements (1 week) were onlyobserved among younger drivers. On the other hand,Rogé et al. [50] showed that simulator training can im-prove visual field, allowing older adults to better identifyvulnerable users on the road. Dependent variables fromthe 2 reviewed studies were obtained through the use ofsimulators, and normally indicate that performance in adriving simulator is strongly related to real driving per-formance and less to cognitive performance [55]. There-fore, driving tests in simulators could be used toevaluate older adults, as previously suggested by Leeet al. [79–81]. Despite the small number of reviewedstudies using simulator-based training as the only inter-vention, it is interesting to report that in the mixed pro-gram, 5 out of 9 studies used simulators with anotherindependent variable [54–58].Something interesting having repercussions in future

research from the study by Rogé et al. [50], is the reportof simulator sickness by participants that rendered themunable to continue with the study. Studies using simula-tors in the mixed interventions below also reported theloss of participants due to simulator sickness. This im-portant issue related to the use of simulator or virtualreality is at the forefront of their widespread use forclinics and programs aimed at older individuals, sincethey present a higher prevalence of symptoms than theiryounger counterparts [82]. Fortunately, interventionscan be tailored to reduce the importance of such symp-toms to allow the driver to accommodate to this newdriving environment [83].

Mixed programsAll the Programs using a mixed approach included spe-cific driving practice either on-road or in-simulator.

Bédard et al. [53] and Marottoli et al. [52] showed thatinterventions using on-road sessions with an instructorproviding specific feedback improved driving scores afterthe intervention. Unfortunately, the use of general scoresto describe a driver’s improvement does not make it pos-sible to extract the specific effect of the intervention orthe remaining generalization (ex. moving in the roadway).Romoser and Fisher [57] compared the effectiveness of

active, passive and no training on older drivers’ perform-ance in intersections. Active training in-simulator increaseda driver’s probability of looking for a hazard during a turnby nearly 100% in both post-training simulator and on-roaddriving sessions. Lavallière et al. [58] showed similar resultsin-simulator with an analysis of visual search strategies dur-ing on-road lane changes. Their results revealed that thedriving-specific Feedback group increased their blind spotverifications (from 32.3 to 64.9% of the lane changes - anincrease of 100%), whereas the control group did not. Por-ter et al. [51] also used a video intervention but the feed-back was not specific to a particular set of driving skills.Porter et al. [51] used a similar paradigm utilizing videoand global positioning system (GPS) data, in combinationwith a classroom-based education program. Their resultsshowed a mitigated impact on driving, since only 9 out of17 improved their driving by reducing their errors after theprogram. This difference between Porter et al.’s results andthe 2 previous studies might be due to the number of ses-sions to provide feedback and practice for drivers, sincePorter et al. do not report any specific practice of drivingweaknesses after receiving feedback.Only the study conducted by Romoser [56] evaluated

the retention of the initial intervention and showed apositive impact 2 years after the program was completed.Only the group who received specific feedback on theirdriving and appropriate practice in-simulator initiallycontinued to execute secondary looks in intersectionsprior to turning [57].For all the studies using customized feedback [51, 57,

58], interventions were successful at modifying drivers’perception of their driving abilities and positively modify-ing their subsequent driving skills when returning on-road. The difference between them is that, when particularfeedback is aimed specifically at one driving manoeuvre(ex. visual search while turning left [57] or changing lanes[58]), one can expect that these specific manoeuvresshould improve after the interventions.Overall, the mixed approach interventions presented

the highest overall score on Qualtsyst and a high fre-quency of randomized controlled trials (Level 1) (n = 4)or multiple groups comparisons (Level 2) (n = 4).

Limitations of this reviewA probable limitation of this review includes the searchprocess itself, which may not have allowed the


identification of all studies showing the effects of the dif-ferent types of programs on driving skills. The use ofadditional databases such as CINAHL, PsychInfo andERIC might have led to slightly different results [27].For the study using a simulator, either as a single

intervention or with a mixed approach, simulator sick-ness still remains an obstacle for the large-scale use ofsuch interventions, in particular if we cannot betterunderstand the underlying mechanisms. The fact re-mains that some techniques, at best, reduce the inci-dence and impact of such events on subjectparticipation, and should be used to prevent the preva-lence of such impediments on individuals who followthese driving curriculums. For some of the studies, hav-ing self-reported collisions as an indicator of driving per-formance should be used with caution, since self-reportsof collision involvement may lack validity [84, 85]. Des-pite this limitation, this type of reporting remains ofinterest, as official records might also face an issue ofunderreporting when addressing older drivers’ involve-ment for fear of losing their driver’s license [86] whensuch events are reported to official agencies [87].Finally, limitation of the systematic review itself is due

to lack of consistency when reporting results in trainingprograms aimed at older individuals. The wide variety ofresearch approaches adopted by the reviewed studiesalso made it difficult to summarize and obtain directrelevant findings, since not all driving parameters wereassessed similarly, and the specific driving componentsthat were evaluated were not mentioned. Some of theon-road evaluations used general score checklists similarto the one used by driving specialists [47, 52, 53] whileothers have assessed specific driving manoeuvres, suchas visual inspection during lane change or while turningat intersections [56, 58]. In some studies, it is hard to ex-tract proper information on the used design and methodand there is limited reporting on the training program,per se. Moreover, few studies have presented a follow-upevaluation of their program to evaluate the mid or long-term retention of their interventions [56]. Despite identi-fying 25 interventions aimed at improving older drivers’performance in hopes of reducing their crash risk, al-most all the studies failed to show or did not addresscollision rate outcomes.

ConclusionOverall, the most valuable approaches in terms of specificimprovement of driving skills and performance are theones that have put in place specific training curriculumfor every single driver to tackle their specific weakness be-hind the wheel. This is not surprising, considering theknown key-concept defined as transfer-appropriate prac-tice [88]. One must develop his/her own capacity of error-

detection in their driving if they want to be able to modifycurrent behaviours and implement appropriate responses.With the recent development of technology aimed at

collecting driving information over longer periods oftime (for example, see the SHRP 2 project: www.fhwa.dot.gov/goshrp2), evaluation of one driving’s abilitycould encompass more and longer driving periods, dis-tance driven and more manoeuvres, allowing for aclearer depiction of what needs to be addressed by adriving instructor or an occupational therapist while de-veloping specific interventions.Moreover, the availability of computing power and

artificial intelligence has brought a new set of tools thatallows for automatic detection of driving errors and thepossibility to provide automated feedback to the trainees[89]. However, since few of the studies have evaluatedthe long-term retentions of such interventions [56], fu-ture efforts should be made to include this importantmilestone in their projects. It is of real interest to knowwhen a “refresher” session should be provided to preventa decrease in performance following an improvement intheir abilities [90].Despite its complex implementation, attempts to com-

bine the most efficient interventions presented in thecurrent review are promising for the development of anefficient way to allow older drivers to maintain and evenimprove their skills, driving behaviours, and decreasetheir involvement in motor vehicle collisions.

AbbreviationsB: Behaviour; CR: Crash Rates; NRCT: Non-randomized controlled study;PICO: Population, Intervention, Control, Outcomes; RCT: Randomizedcontrolled trial; SAK: Self-Awareness/Knowledge; S: Skills; SLR: Systematicliterature review

AcknowledgementsAn preliminary version of this work covering January 2007 to December2014 was presented as an abstract at the CARSP Conference in 2015 (http://www.carsp.ca/research/research-papers/research-papers-search/download-info/are-interventions-effective-at-improving-skills-in-older-drivers/) [91].

Authors’ contributionsC.H.I., B.G., A.P.M. and L.M. contributed equally to this manuscript. All authorshave read and approved the final version of the manuscript.

FundingThis research project has received no specific funding.

Availability of data and materialsThe datasets used and/or analysed during the current study are availablefrom the corresponding author upon reasonable request.

Ethics approval and consent to participateNot applicable.

Consent for publicationNot applicable.

Competing interestsThe authors declare that they have no competing interests.


http://www.fhwa.dot.gov/goshrp2

http://www.fhwa.dot.gov/goshrp2

http://www.carsp.ca/research/research-papers/research-papers-search/download-info/are-interventions-effective-at-improving-skills-in-older-drivers/



Author details1Centro de Estudio del Trabajo y Factores Humanos, Escuela de Kinesiología,Facultad de Medicina, Universidad de Valparaíso, Valparaiso, Chile. 2Facultadde Ciencias de la Salud, Universidad de Las Américas, Providencia, Chile.3ALGORITMI Centre, School of Engineering of the University of Minho,Guimarães, Portugal. 4Module de Kinésiologie, Département des Sciences dela Santé, Université du Québec à Chicoutimi (UQAC), Saguenay, QC, Canada.5Laboratoire de recherche biomécanique & neurophysiologique enréadaptation neuro-musculo-squelettique - Lab BioNR, UQAC, Saguenay, QC,Canada. 6Centre intersectoriel en santé durable – UQAC, Saguenay, QC,Canada. 7Centre de recherche-Charles-Le Moyne-Saguenay–Lac-Saint-Jeansur les innovations en santé (CRCSIS), Longueuil, Canada.

Received: 3 May 2019 Accepted: 10 March 2020

References1. Dickerson AE, Molnar L, Bedard M, Eby DW, Classen S, Polgar J.

Transportation and aging: an updated research agenda for advancing safemobility. J Appl Gerontol. 2017;733464817739154..

2. Dickerson AE, Molnar LJ, Eby DW, Adler G, Bedard M, Berg-Weger M,Classen S, Foley D, Horowitz A, Kerschner H, et al. Transportation and aging:a research agenda for advancing safe mobility. Gerontologist. 2007;47(5):578–90.

3. Dobbs BM. Aging baby boomers—a blessing or challenge for driverlicensing authorities. Traffic Inj Prev. 2008;9(4):379–86.

4. Coughlin JF, D'Ambrosio LA. Aging America and transportation: personalchoices and public policy. New-York: Springer publishing company, LLC;2012. p. 288.

5. Bureau of Labor Statistics. Census of Fatal Occupational Injuries. Washington,DC: U.S. Department of Labor; 2014.

6. Canada Safety Council. Seniors behind the wheel. 2005. http://www.safetycouncil.org/news/sc/2000/Eng-1-00.pdf.

7. Burkhardt J, Berger AM, Creedon MA, McGavock AT. Mobility andIndependence Changes and Challenges for Older Drivers. Washington, D.C:US Department of Health and Human Services and the National HighwayTraffic Safety Administration; 1998. p. 226.

8. Turcotte M. Profile of seniors’ transportation habits. In: Component ofStatistics Canada. vol. Catalogue no. 11–008-x. Ottawa: Statistics Canada;2012. p. 3–16.

9. Canadian Motor Vehicle Traffic Collision Statistics : 2017 [https://www.tc.gc.ca/eng/motorvehiclesafety/canadian-motor-vehicle-traffic-collision-statistics-2017.html].

10. Staplin L, Lococo KH. Model driver screening and evaluation program:Volume 3: Guidelines for Motor Vehicle Administrators. Washington: Officeof Research and Traffic Records, National Highway Traffic SafetyAdministration; 2003. p. 77.

11. Owsley C, McGwin G Jr. Vision and driving. Vis Res. 2010;50(23):2348–61.12. Owsley C. Aging and vision. Vis Res. 2011;51(13):1610–22.13. Devos H, Vandenberghe W, Nieuwboer A, Tant M, De Weerdt W, Dawson

JD, Uc EY. Validation of a screening battery to predict driving fitness inpeople with Parkinson's disease. Mov Disord. 2013;28(5):671–4.

14. Uc EY, Rizzo M, Anderson SW, Sparks J, Rodnitzky RL, Dawson JD. Impairedvisual search in drivers with Parkinson's disease. Ann Neurol. 2006;60(4):407–13.

15. Rizzo M, McGehee DV, Dawson JD, Anderson SN. Simulated car crashes atintersections in drivers with Alzheimer disease. Alzheimer Dis Assoc Disord.2001;15(1):10–20.

16. Bédard M, Weaver B, Darzins P, Porter MM. Predicting driving performancein older adults: we are not there yet! Traffic Inj Prev. 2008;9(4):336–41.

17. Dickerson AE, Bedard M. Decision tool for clients with medical issues: aframework for identifying driving risk and potential to return to driving.Occup Ther Health Care. 2014;28(2):194–202.

18. Marottoli RA, de Leon CFM, Glass TA, Williams CS, Cooney LM Jr, BerkmanLF. Consequences of driving cessation: decreased out-of-home activitylevels. J Gerontol B Psychol Sci Soc Sci. 2000;55(6):S334–40.

19. Fonda SJ, Wallace RB, Herzog AR. Changes in driving patterns andworsening depressive symptoms among older adults. J Gerontol B PsycholSci Soc Sci. 2001;56(6):S343–51.

20. Ragland DR, Satariano WA, MacLeod KE. Driving cessation and increaseddepressive symptoms. J Gerontol A Biol Sci Med Sci. 2005;60(3):399–403.

21. Windsor TD, Anstey KJ, Butterworth P, Luszcz MA, Andrews GR. The role ofperceived control in explaining depressive symptoms associated withdriving cessation in a longitudinal study. Gerontologist. 2007;47(2):215–23.

22. Vrkljan BH, Myers AM, Crizzle A, Blanchard RA, Marshall SC. Evaluatingmedically at-risk drivers: a survey of assessment practices in Canada. Can JOccup Ther. 2013;80(5):295–303.

23. Tranfield D, Denyer D, Smart P. Towards a methodology for developingevidence-informed management knowledge by means of systematicreview. Br J Manag. 2003;14(3):207–22.

24. Sackett D, Richardson WJ, Rosenberg W, Haynes R. How to practice and teachevidence-based medicine. New York: Churchill Livingstone; 1997. p. 118–28.

25. Denyer D, Tranfield D. Producing a systematic review. In: The SageHandbook of Organizational Research Methods; 2009. p. 671–89.

26. Thabane L, Thomas T, Ye C, Paul JH. Posing the research question: not sosimple. Can J Anesth Can d’anesthésie. 2009;56(1):71.

27. Falagas M, Pitsouni E, Malietzis GA, Pappas G. Comparison of PubMed,Scopus, Web of Science, and Google Scholar: strengths and weaknesses.FASEB J. 2008;22(2):338–42.

28. Korner-Bitensky N, Kua A, von Zweck C, Van Benthem K. Older driverretraining: an updated systematic review of evidence of effectiveness. J SafRes. 2009;40(2):105–11.

29. Kua A, Korner-Bitensky N, Desrosiers J, Man-Son-Hing M, Marshall S. Olderdriver retraining: a systematic review of evidence of effectiveness. J Saf Res.2007;38(1):81–90.

30. Kmet LM, Lee RC, Cook LS. Standard quality assessment criteria forevaluating primary research from a variety of fields. Technol Assess. 2004.

31. Kandula T, Park SB, Cohn RJ, Krishnan AV, Farrar MA. Pediatric chemotherapyinduced peripheral neuropathy: a systematic review of current knowledge.Cancer Treat Rev. 2016;50:118–28.

32. Lee L, Packer TL, Tang SH, Girdler S. Self-management education programsfor age-related macular degeneration: a systematic review. Aust J Ageing.2008;27(4):170–6.

33. Maharaj S, Harding R. The needs, models of care, interventions andoutcomes of palliative care in the Caribbean: A systematic review of theevidence. BMC Palliat Care. 2016;15(1).

34. Coxon K, Chevalier A, Brown J, Clarke EF, Billot L, Boufou S, Ivers R, Keay L.Effects of a safe transportation educational program for older drivers ondriving exposure and community participation: a randomized controlledtrial. J Am Geriatr Soc. 2017;65(3):540–9.

35. Nasvadi GE, Vavrik J. Crash risk of older drivers after attending a maturedriver education program. Accid Anal Prev. 2007;39(6):1073–9.

36. Nasvadi GE. Changes in self-reported driving behaviour following attendanceat a mature driver education program. Transp Res F. 2007;10:358–69.

37. Jones VC, Cho JM, Abendschoen-Milani J, Gielen A. Driving habits and riskexposure in older drivers: lessons learned from the implementation of aself-regulation curriculum. Traffic Inj Prev. 2011;12(5):468–74.

38. Gaines JM, Burke KL, Marx KA, Wagner M, Parrish JM. Enhancing older driversafety: a driving survey and evaluation of the CarFit program. J Saf Res.2011;42:351–8.

39. Jones V, Gielen A, Bailey M, Rebok G, Agness C, Soderstrom C,Abendschoen-Milani J, Liebno A, Gaines J, Parrish J. The effect of a low andhigh resource intervention on older drivers' knowledge, behaviors and riskydriving. Accid Anal Prev. 2012;49:486–92.

40. Edwards JD, Delahunt PB, Mahncke HW. Cognitive speed of processingtraining delays driving cessation. J Gerontol A Biol Sci Med Sci. 2009;64(12):1262–7.

41. Ball K, Edwards JD, Ross LA, McGwin G Jr. Cognitive training decreasesmotor vehicle collision involvement of older drivers. J Am Geriatr Soc. 2010;58(11):2107–13.

42. Horswill MS, Falconer EK, Pachana NA, Wetton M, Hill A. The longer-termeffects of a brief Hazard perception training intervention in older drivers.Psychol Aging. 2015;30(1):62–7.

43. Edwards JD, Myers CE, Ross LA, Roenker DL, Cissel GG, McLaughlin AM, BallKK. The longitudinal impact of cognitive speed of processing training ondriving mobility. Gerontologist. 2009;49(4):485–94.

44. Cuenen A, Jongen EMM, Brijs T, Brijs K, Houben K, Wets G. Effect of aworking memory training on aspects of cognitive ability and driving abilityof older drivers: merits of an adaptive training over a non-adaptive training.Transp Res Part F Traffic Psychol Behav. 2016;42:15–27.

45. Cassavaugh ND, Kramer AF. Transfer of computer-based training tosimulated driving in older adults. Appl Ergon. 2009;40(5):943–52.


https://www.safetycouncil.org/news/sc/2000/Eng-1-00.pdf

https://www.safetycouncil.org/news/sc/2000/Eng-1-00.pdf

https://www.tc.gc.ca/eng/motorvehiclesafety/canadian-motor-vehicle-traffic-collision-statistics-2017.html



46. Johnston KA, Borkenhagen D, Scialfa CT. Driving skills training for olderadults: an assessment of DriveSharp. Can J Aging / La Rev Can du Vieil.2015;34(4):532–44.

47. Marottoli RA, Allore H, Araujo KL, Iannone LP, Acampora D, Gottschalk M,Charpentier P, Kasl S, Peduzzi P. A randomized trial of a physicalconditioning program to enhance the driving performance of olderpersons. J Gen Intern Med. 2007;22(5):590–7.

48. Marmeleira JF, Soares De Melo FM, Tlemcani M, Adriano M, Godinho B.Exercise can improve speed of behavior in older drivers. J Aging Phys Act.2011;19:48–61.

49. Marchal-Crespo L, McHughen S, Cramer SC, Reinkensmeyer DJ. The effect ofhaptic guidance, aging, and initial skill level on motor learning of a steeringtask. Exp Brain Res. 2010;201(2):209–20.

50. Rogé J, Ndiaye D, Vienne F. Useful visual field training: a way to improveelderly car drivers’ ability to detect vulnerable road users. Transp Res F.2014;26:246–57.

51. Porter MM. Older driver training using video and global positioning systemtechnology--a randomized controlled trial. J Gerontol A Biol Sci Med Sci.2013;68(5):574–80.

52. Marottoli RA, Ness PH, Araujo KL, Iannone LP, Acampora D, Charpentier P,Peduzzi P. A randomized trial of an education program to enhance olderdriver performance. J Gerontol. 2007;62(10):A1113–9.

53. Bédard M, Porter MM, Marshall S, Isherwood I, Riendeau J, Weaver B, Tuokko H,Molnar F, Miller-Polgar J. The combination of two training approaches toimprove older adults' driving safety. Traffic Inj Prev. 2008;9(1):70–6.

54. Hay M, Adam N, Bocca ML, CGabaude C. Effectiveness of two cognitivetraining programs on the performance of older drivers with a cognitive self-assessment bias. Eur Transp Res Rev. 2016;8(3).

55. Casutt G, Theill N, Martin M, Keller M, Jancke L. The drive-wise project:driving simulator training increases real driving performance in healthyolder drivers. Front Aging Neurosci. 2014;6:85.

56. Romoser MR. The long-term effects of active training strategies onimproving older drivers' scanning in intersections: a two-year follow-up toRomoser and Fisher (2009). Hum Factors. 2013;55(2):278–84.

57. Romoser MRE, Fisher DL. The effect of active versus passive trainingstrategies on improving older drivers’ scanning in intersections. HumFactors. 2009;51(5):652–68.

58. Lavallière M, Simoneau M, Laurendeau D, Teasdale N. Active training anddriving-specific feedback improve older drivers’ visual search prior to lanechanges. BMC Geriatr. 2012;12(5).

59. Golisz K. Occupational therapy interventions to improve driving performancein older adults: a systematic review. Am J Occup Ther. 2014;68(6):662–9.

60. Michon JA. A critical view of driver behavior models: What do we know,what should we do? In: Evans L, Schwing R, editors. Human Behavior andTraffic Safety. New York: Plenum Press; 1985. p. 485–520.

61. Joanisse M, Stinchcombe A, Yamin S. Evaluability assessment of a nationaldriver retraining program: are we evaluating in the right lane? Can J ProgrEval. 2010;25(1):27–50.

62. Levasseur M, Audet T, Gelinas I, Bedard M, Langlais ME, Therrien FH, RenaudJ, Coallier JC, D'Amours M. Awareness tool for safe and responsible driving(OSCAR): a potential educational intervention for increasing interest,openness and knowledge about the abilities required and compensatorystrategies among older drivers. Traffic Inj Prev. 2015;16(6):578–86.

63. Musselwhite CBA. Assessment of computer-based training packages toimprove the safety of older people’s driver behaviour. Transp Plan Technol.2017;40(1):64–79.

64. Tada M, Noma H, Utsumi A, Segawa M, Okada M, Renge K. Elderly driverretraining using automatic evaluation system of safe driving skill. IET IntellTransp Syst. 2014;8(3):266–72.

65. Tuokko HA, McGee P, Gabriel G, Rhodes RE. Perception, attitudes andbeliefs, and openness to change: implications for older driver education.Accid Anal Prev. 2007;39(4):812–7.

66. Tuokko H, Rhodes R, Love J, Cloutier-Fisher D, Jouk A, Schoklitsch A. Changein beliefs about older drivers through applied theater. Educ Gerontol. 2013;39(1):45–56.

67. Tuokko H, Rhodes R, Love J, Cloutier D, Jouk A, Schoklitsch A. Just the facts:changes in older driver attitudes after exposure to educationalinterventions. Traffic Inj Prev. 2015;16(6):558–64.

68. Akinwuntan AE, De Weerdt W, Feys H, Pauwels J, Baten G, Arno P, KiekensC. Effect of simulator training on driving after stroke: a randomizedcontrolled trial. Neurology. 2005;65(6):843–50.

69. Selander H, Lee HC, Johansson K, Falkmer T. Older drivers: on-road and off-road test results. Accid Anal Prev. 2011;43(4):1348–54.

70. Freund B, Colgrove L, Burke B, McLeod R. Self-rated driving performanceamong elderly drivers referred for driving evaluation. Accid Anal Prev. 2005;37(4):613–8.

71. Ross LA, Dodson JE, Edwards JD, Ackerman ML, Ball K. Self-rated driving anddriving safety in older adults. Accid Anal Prev. 2012;48:523–7.

72. Janke MK. Mature driver improvement program in California. Transp ResRec. 1994;1438:77–83.

73. Ker K, Roberts I, Collier T, Beyer F, Bunn F, Frost C. Post-license drivereducation for the prevention of road traffic crashes: a systematic reviewrandomised controlled trials. Accid Anal Prev. 2005;37(2):305–13.

74. Owsley C, McGwin G Jr, Phillips JM, McNeal SF, Stalvey BT. Impact of aneducational program on the safety of high-risk, visually impaired, olderdrivers. Am J Prev Med. 2004;26(3):222–9.

75. McKnight AJ, Simone GA, Weidman JR. Elderly Driver Retraining. Washington,D.C.: National Highway Traffic Safety Administration; 1982. p. 181.

76. Wood JM, Horswill MS, Lacherez PF, Anstey KJ. Evaluation of screening testsfor predicting older driver performance and safety assessed by an on-roadtest. Accid Anal Prev. 2013;50:1161–8.

77. Hiraoka T, Wang TW, Kawakami H. Cognitive Function Training SystemUsing Game-Based Design for Elderly Drivers. IFAC-PapersOnLine. 2016;49(19):579–84.

78. Bherer L, Erickson KI, Liu-Ambrose T. A review of the effects of physicalactivity and exercise on cognitive and brain functions in older adults. JAging Res. 2013;2013:657508.

79. Lee HC, Cameron D, Lee AH. Assessing the driving performance of olderadult drivers: on-road versus simulated driving. Accid Anal Prev. 2003;35(5):797–803.

80. Lee HC, Lee AH, Cameron D. Validation of a driving simulator by measuringthe visual attention skill of older adult drivers. Am J Occup Ther. 2003;57(3):324–8.

81. Lee HC, Lee AH, Cameron D, Li-Tsang C. Using a driving simulator toidentify older drivers at inflated risk of motor vehicle crashes. J Saf Res.2003;34(4):453–9.

82. Stoner HA, Fisher DL, Mollenhauer MJ. Simulator and scenario factorsinfluencing simulator sickness. In: Fisher DL, Rizzo M, Caird JK, Lee JD,editors. Handbook of Driving Simulation for Engineering, Medicine, andPsychology. Boca Raton: CRC Press; 2011. p. 1–24. Chapter 14.

83. Mackrous I, Lavallière M, Teasdale N. Adaptation to simulator sickness inolder drivers following multiple sessions in a driving simulator.Gerontechnology. 2014;12(2):101–11.

84. McGwin G Jr, Owsley C, Ball K. Identifying crash involvement among olderdrivers: agreement between self-report and state records. Accid Anal Prev.1998;30(6):781–91.

85. Af Wåhlberg AE, Dorn L. How reliable are self-report measures of mileage,violations and crashes? Saf Sci. 2015;76:67–73.

86. Kamaluddin NA, Andersen CS, Larsen MK, Meltofte KR, Várhelyi A. Self-reporting traffic crashes – a systematic literature review. Eur Transp Res Rev.2018;10(26):1–18.

87. Marottoli RA, Cooney LM Jr, Tinetti ME. Self-report versus state records foridentifying crashes among older drivers. J Gerontol A Biol Sci Med Sci. 1997;52(3):M184–7.

88. Lee TD, Magill RA. The locus of contextual interference in motor skillacquisition. J Exp Psychol. 1983;9:730–46.

89. Teasdale N, Simoneau M, Hudon L, Moszkowicz T, Laurendeau D, GermainRobitaille M, Bherer L, Duchesne S, Hudon C. Drivers with amnestic mildcognitive impairment can benefit from a multiple-session driving simulatorautomated training program. J Am Geriatr Soc. 2016;64(9):e16–8.

90. Teasdale N, Simoneau M, Hudon L, Germain Robitaille M, Moszkowicz T,Laurendeau D, Bherer L, Duchesne S, Hudon C. Older adults with mildcognitive impairments show less driving errors after a multiple sessionssimulator training program but do not exhibit long term retention. FrontHum Neurosci. 2016;10:653.

91. Lavallière M, Crizzle A. Are interventions effective at improving skills in olderdrivers? Ottawa: CARSP; 2015.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.


Are interventions effective at improving driving in older ...

Documents