- Email: [email protected]
Driving simulators in the clinical assessment of fitness to drive in sleepy individuals: A systematic review
Accepted Manuscript Driving simulators in the clinical assessment of fitness to drive in sleepy individuals: a systematic review David R. Schreier, Christina Banks, Johannes Mathis PII:
S1087-0792(17)30094-1
DOI:
10.1016/j.smrv.2017.04.004
Reference:
YSMRV 1032
To appear in:
Sleep Medicine Reviews
Received Date: 13 April 2016 Revised Date:
10 January 2017
Accepted Date: 28 April 2017
Please cite this article as: Schreier DR, Banks C, Mathis J, Driving simulators in the clinical assessment of fitness to drive in sleepy individuals: a systematic review, Sleep Medicine Reviews (2017), doi: 10.1016/j.smrv.2017.04.004. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Title page
Driving simulators in the clinical assessment of fitness to drive in sleepy individuals: a systematic review
David R. Schreier1*, Christina Banks1,2, Johannes Mathis1 1
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(Driving simulators in the clinical assessment)
Sleep-Wake-Epilepsy-Centre, Department of Neurology, Inselspital, Bern University
Hospital, and University of Bern, Switzerland 2
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Department of Primary Care and Public Health, Imperial College London, United
Kingdom
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Title characters = 93, Summary words = 249, Keywords = 9,
Manuscript words = 8166, Figures = 3, Tables = 4, References = 63, Supplementary material = 2 tables and 1 appendix
*corresponding author:
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Dr. med. David Schreier Sleep-Wake-Epilepsy-Centre
Dept. of Neurology, Inselspital, Bern University Hospital CH-3010 Bern, Switzerland
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Phone: +41 31 632 30 54. Fax: +41 31 632 94 48.
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Email: [email protected]
Acknowledgments
We thank Lukas Oesch for illustration support. Disclosure: Dr. Schreier received financial support from UCB-Pharma AG to attend a conference (12/2015). Dr. Schreier and Prof. Mathis are both collaborating with SAFEmine AG (a Hexagon Mining company) in a project funded by the Commission of Technology and Innovation (CTI) from the Swiss Government (grant 17864.1 PFLS-LS).
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ACCEPTED MANUSCRIPT Summary Road traffic injuries are projected to be the leading cause of death for those aged between 15 and 29 years by the year 2030, and sleepiness is estimated to be the underlying cause in up to 15-20% of all motor vehicle accidents. Sleepiness at the
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wheel is most often caused by socially induced sleep deprivation or poor sleep hygiene in otherwise healthy individuals, medical disorders, or the intake of drugs. Validated methods for objectifying sleepiness are urgently sought, particularly in the
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context of driving. Based on the assumption that the identification and treatment of sleepiness, and its causes, may prevent motor vehicle accidents, driving simulators
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are a seemingly promising diagnostic tool. Despite the rising use of these in research, the reliability of their conclusions in healthy sleepy individuals and patients is still unclear. This systematic review aims to evaluate the practical value of driving simulators in a clinical environment when judging fitness to drive in sleepy
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individuals. From the 1674 records screened, 12 studies on sleepy individuals containing both simulated and real driving data were included. In general, simulated driving did not reliably predict real-life motor vehicle accidents, and especially not on
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an individual level, despite the moderate relationship between simulated and realroad test driving performance. The severity of sleepiness is most likely not the critical
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factor leading to motor vehicle accidents, but rather the perception of sleepiness. The self-perception of sleepiness related impairment, and risky behaviour while at the wheel should also be considered as key influencing factors. Keywords sleepiness; excessive daytime sleepiness; vigilance assessment; fitness to drive; driving simulation; real driving; near-miss accident; motor vehicle accident; risk-taking behaviour
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ACCEPTED MANUSCRIPT Abbreviations AusEdTM Australia Edinburgh driving simulator Centre for fitness to drive evaluation and car adaptations
CCAT
Crowe critical appraisal tool
DASS
Divided attention steering simulator
EDS
Excessive daytime sleepiness
EEG
Electroencephalography
ESS
Epworth sleepiness scale
IQR
Interquartile range
KDS
Karolinska drowsiness score
KSS
Karolinska sleepiness scale
M
Mean
Md
Median
MVA
Motor vehicle accident
MSLT
Multiple sleep latency test
MWT
Maintenance of wakefulness test
OSA(S)
Obstructive sleep apnoea (syndrome)
PD
Parkinson’s disease
POMS
Profile of mood states
PVT
Psychomotor vigilance test
SDLP
Standard deviation of lateral position
SSS
Stanford sleepiness scale
TLC
Time to line crossing
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CARA
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ACCEPTED MANUSCRIPT Glossary of terms Epworth sleepiness scale (ESS): The most frequently used subjective sleepiness rating scale referring to a preceding time period. The likeliness of dozing off or falling asleep is rated from zero (would never doze) to three (high chance of dozing) for
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nine daily life situations. The ratings are then accumulated, with scores ranging from 0 to 24. Scores ≤10 are interpreted as normal, while values >10 are considered to indicate pathological daytime sleepiness.
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Maintenance of wakefulness test (MWT): The MWT measures the ability to maintain wakefulness in a monotonous, standardised, environment, and in the
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absence of any external stimuli or influences. Individuals sit in a semi-darkened room and have to stay awake for as long as possible throughout the 40-minute trial which is scheduled four times distributed over the day.
Motor vehicle accident(s) (MVA): The number of motor vehicle accidents either
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according to self-reported or derived from police/hospital based reports and registries, over a defined time period in real life which results in an accident per year rate.
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Multiple sleep latency test (MSLT): The MSLT measures the how prone
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individuals are to falling asleep in the absence of any external stimuli or influences. It is internationally standardised, and consists of five nap opportunities of 20 minutes duration that are scheduled in/at/on/for two hour intervals throughout the day. Psychomotor vigilance test (PVT): The PVT lasts 10 minutes while individuals must react as quickly as possible to visual stimuli by pressing a button. The stimuli appear with a random latency, and the corresponding reaction times are presented immediately to the subject and stored. Reaction times above 0.5 seconds are considered as lapses.
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ACCEPTED MANUSCRIPT Glossary of terms (continued) Real-life driving: Any information regarding real driving in daily life, including nearmisses and/or motor vehicle accidents. Real-road test driving: Continuous real-road driving in a study or test setting but
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not in daily life situations (e.g. measuring the standard deviation of lateral position). Real driving: Any condition related to or any information derived from driving in a real motor vehicle in reality, independent of whether it is a test or real-life condition
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(i.e. real-road test driving and real-life driving), excluding any simulation.
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ACCEPTED MANUSCRIPT 1. Introduction Excessive daytime sleepiness (EDS) is present in 10-20% of the general population 1–4
. EDS may negatively affect reaction time, vigilance, attention, and the judgement
of performance while driving, and consequently result in accidents related to human 5–9
. Motor vehicle accidents (MVA) are responsible for the majority of the 1.24
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error
million road traffic deaths which occur every year worldwide, with sleepiness being estimated to be the underlying cause in 10-20% of MVA
10–12
. By 2030, road traffic
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injuries will be the third most common cause of burden and disease for all age groups, and the leading cause of death for those aged between 15 and 29 years. A
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recent survey conducted in Norway found that 63% of cargo and 52% of passenger train operators, 29% of maritime watch officers, and 26% of bus and truck drivers stated they had nodded off or slept at least once while driving in the three months preceding the survey
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. Sleepiness which ultimately presents as a risk factor for
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MVA typically originates from socially induced sleep deprivation, poor sleep hygiene, or driving at the nadir of the circadian wake phase in otherwise healthy individuals, particularly young males, rather than from disease related causes (i.e. sleep-wake 14–18
. However, sleep-wake disorders significantly increase the risk for
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disorders)
MVA, with obstructive sleep apnoea (OSA) being associated with a 2.5-fold increase
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in comparison to controls
19–22
. Among medical disorders, sleep-wake disorders are
associated with the greatest accident risk (relative risk 3.7), surpassing epilepsy (relative risk 1.8), and exceeding legal limits of alcohol (relative risk 2.0)
23
. With
respect to the significantly increased accident risk combined with the high prevalence of EDS in the general population, it is crucial that physicians are aware of their responsibility to advise their patients on the risk of accidents, effective countermeasures, and the potential legal consequences.
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ACCEPTED MANUSCRIPT Despite the knowledge and awareness in the scientific community, the public and authorities are not sufficiently aware of the dimension and risks of driving while sleepy
24
. Consequently, the prevalence of sleepiness-induced accidents is greatly
underestimated in the official statistics of many countries, such as Switzerland
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,
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which in turn negatively affects the implementation of prevention measures. Based on the assumption that sleepiness is subjectively perceived before the deterioration of driving performance, publicity campaigns such as the “Wake-Up Bus”
risks of driving while sleepy
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ran by the European Sleep Research Society seek to inform the public about the 12
. Awareness is, however, just the first step towards
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preventing MVA, and many factors involved in the disastrous process ultimately resulting in sleepiness-induced accidents (figure 1) must be addressed. From a human centred perspective, three main research domains focusing on sleepiness-induced MVA currently exist: 1) quantification of sleepiness, 2)
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recognition and prevention of socially induced sleepiness, and 3) diagnosis and treatment of the underlying causes of disease related sleepiness. It should be noted that terms similar to sleepiness such as “drowsiness”, “fatigue”, and “tiredness” are
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often used synonymously. While they can occur at the same time, and their effects
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can even add up to a certain extent, they do not necessarily describe identical conditions. Fatigue in its physiological sense was originally considered as “time on task performance decrement”
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, while tiredness indicates a loss of energy and
initiative. Both differ from sleepiness and drowsiness, which indicate increased sleep pressure leading to shortened sleep latencies. All of these conditions, especially if combined, can cause driving accidents which evolve conceptually in the same way (figure 2), and while differentiating between them is challenging and highlights the complexity of the topic, always being able to distinguish them clearly is not critical.
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ACCEPTED MANUSCRIPT In a laboratory setting, the degree of sleepiness is objectively assessed by the multiple sleep latency test (MSLT). Alertness, which is not only the reciprocal condition of sleepiness but also includes compensation capacities affected by fatigue and tiredness, is most often quantified through the maintenance of wakefulness test
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(MWT). In addition, sleepy individuals with sleep latencies < 20 minutes in the MWT in general show a higher number of inappropriate line crossings than individuals with sleep latencies of > 20 minutes
27
. Furthermore, it was shown that simulated driving
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performance correlates stronger with the sleep latency in the MWT than with the sleep latency in the MSLT, underlining that ability to maintain wakefulness is more 28
. In addition
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important regarding driving performance than the degree of sleepiness
to alertness, vigilance as a critical basis of safe driving also requires focused attention, coherent thinking, and adequate performance and reactions, which can be assessed in a psychomotor vigilance test (PVT), in driving simulators and other
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performance tests.
The number of devices for commercial and research purposes, and methods and models for objectifying sleepiness in a driving condition (simulated and/or real-road
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test driving), is growing. Ahlstrom et al., for instance, created a sleep/wake predictor model based on eye movement measurements in a real-road test driving condition
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which correctly classified 82.4% of the drivers with high levels of subjective sleepiness (Karolinska sleepiness scale (KSS) ≥ 8) 29. Regarding the underlying causes of disease related sleepiness, physicians not only have the duty to diagnose and treat these accordingly, but also to judge whether their patients can perceive sleepiness at the wheel early enough and react appropriately. Research on risk-taking behaviour while sleepy at the wheel up until now has been scarce, and it is debatable whether built-in driving assistant systems will be able to
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ACCEPTED MANUSCRIPT fully compensate for that in near future. Most research still focuses on the causes and treatment of sleepiness and EDS, but only a minority sets objective in-laboratory tests in relation to the number of real-life MVA 30,31, which could be considered as the gold standard in this context. Similarly, the relationship between simulated- or real-
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road test driving performance and real-life MVA is currently unclear. Frequently, reallife driving performance is simply projected from the simulated driving performance. When searching PubMed for “driving simulation”, the low number of 32 articles
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published in 1999 stands in sharp contrast to the 327 articles published in 2015, indicating an increase in the use of driving simulators in medical research. However,
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they most often include healthy individuals and rarely address clinical situations. Theoretically, driving simulation in increasingly realistic scenarios, without the real-life risks, would be advantageous in terms of a high face validity. Nevertheless, only a limited number of studies investigating fitness to drive are based on such an
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approach and they vary in driving simulation type, on topic (e.g. neurological, ophthalmological, orthopaedic impairments, etc.), and they all lack validity. Nonetheless, the technique might be used to theoretically assess fitness to drive in
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sleepy patients.
1.1. Aims and Objectives The aim of this systematic review is to evaluate the evidence available regarding the momentary practical value of driving simulators in a clinical environment to accurately judge fitness to drive in sleepy patients. The objectives derived from this aim were to 1) evaluate whether simulated driving is comparable with real-road test driving performance, 2) evaluate whether simulated driving allows a long-term means of prediction for MVA rates, and 3) identify which 9
ACCEPTED MANUSCRIPT driving simulator measures or variables are best suited for predicting real-road test
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driving performance and MVA rates.
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ACCEPTED MANUSCRIPT 2. Methods This systematic review has been conducted in accordance with the PRISMA Guidelines
32
. A protocol has been written but not registered. Conducting a meta-
analysis was considered but would have been inappropriate due to the heterogeneity
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of variables and parameters used to assess simulated and real driving performance and sleepiness. In addition, we aimed for a broader overview through including more studies rather than reducing the potential number of full-text articles for screening by
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using more restrictive inclusion criteria which would have been necessary for a meta-
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analysis.
2.1. Information sources and literature search
The following databases were searched most recently on 17 February 2016: PubMed (without time restrictions), Ovid Embase (1947 – present), Web of Science
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(databases and range of years available in the supplementary material, appendix 1), and SafetyLit (without time restrictions). The exact search terms and search
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specifications can be found in appendix 1 (supplementary material).
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2.2. Eligibility criteria and study selection This review focusses on studies containing both simulated driving and real driving data in relation to sleepy individuals. Eligibility criteria were not restricted by study type, date of publication, age, gender, disease or disorder. An overview of inclusion and exclusion criteria can be found in table 1, which shall subsequently be explained in more detail.
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ACCEPTED MANUSCRIPT We did not clearly define sleepy individuals, since a clear definition is not available and too detailed a definition in the present study could be disputed. We tried to assess whether sleepiness or similar conditions were mentioned or could be indirectly assumed. In studies reporting Epworth Sleepiness Scale (ESS) 34
values ≥ 7, Stanford Sleepiness Scale (SSS)
35
values ≥
values ≥ 6, exposure to
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10, KSS
33
sleep deprivation and assessments during the night, individuals were regarded as being sleepy. Studies looking at the effect of potentially sleep inducing
impairment in healthy individuals were not included.
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pharmacological substances, such as diazepines and alcohol, on performance
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“Motor vehicles” were defined according to the standard of the International Organization for Standardization (ISO 3833:1977(en)) as “types of road vehicles … designated for road circulation, except for agricultural tractors”. Therefore, the likes of
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trains and trams were excluded, and we additionally excluded two wheeled vehicles.
The reporting of simulated and real driving in sleepy individuals (point 4 of table 1) was deemed to be conducted independently if assessments were not conducted in
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the same individuals, a lack of data meant that simulated and real driving were not comparable, or data was reported only on a group level as opposed to individual. In
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addition, if results were reported for combined groups of patients and healthy controls, for example, for either the real or simulated driving condition, the study was excluded. Review articles were only eligible if original data was present (i.e. metaanalysis). References from reviews and grey literature (books, reports, theses) were screened by title and if appropriate the corresponding abstract and/or full-text was considered for eligibility. The screening was conducted by two of the authors (DS, CB). Disagreement was resolved by discussion. If doubts existed regarding whether
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ACCEPTED MANUSCRIPT to include or exclude an article at the stage of abstract screening, it was included for full-text screening.
2.3. Data collection process and data items
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Duplicate records were removed using Citavi 5 reference manager software. The relevant data from each study was extracted into the corresponding critical appraisal form (described in the subsequent chapter 2.4) and into the tables 2 and 3. More
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descriptive information regarding study setting was extracted into table 2, and outcomes, i.e. driving performance and sleepiness outcomes, were extracted into
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table 3.
2.4. Quality assessment and risk of bias
The quality of studies (including risk of bias) was assessed using the Crowe critical
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36
. All studies included have been appraised and rated by
appraisal tool (CCAT) v1.4
two of the authors (DS, CB). The CCAT has the advantage that it can be used across 37
and consists of the CCAT rating form and the CCAT
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a variety of research designs
User Guide. The rating form contains 8 categories, each of which must be scored
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between 0 to 5 points resulting in a maximum total score of 40 points for each study. The total score is then calculated as a percentage ranging from 0 to 100% according to the CCAT User Guide. Even though the CCAT contains a risk of bias appraisal to a certain extent, a more detailed risk of bias appraisal was conducted in addition (CB). For this the Cochrane Risk of Bias Tool was used (chapter 8.6 of the Cochrane handbook for systematic reviews of interventions)
38
. Risk of bias was stated as “high”, “low”, or “unclear” for
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ACCEPTED MANUSCRIPT every bias category: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and other sources of bias.
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2.5. Additional analyses
To gauge how well simulated- and real driving performance variables were investigated, the mean CCAT values of all studies containing the corresponding
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variable in simulated and real driving were gathered, and the median and range of
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CCAT values reported for each variable were calculated (table 4).
3. Results 3.1. Study selection
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The database search generated 3057 records of which 1383 were duplicates (figure 3). Consequently, the titles and abstract of 1674 records were screened. A high number of abstracts (n = 670) were included for full-text screening. Most were
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original work published in peer-reviewed journals but some were part of grey
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literature. The majority of the discarded studies (1347 titles from abstract or full-text screening) were excluded due them missing at least one item from the triad of driving simulation, real driving, sleepy individuals, or additionally, data from these not being reported in relation to each other. From this triad, the presence of real driving performance data was the most frequently missing item, followed by individuals being sleepy, while driving simulation was present in most of the studies. Two titles were excluded as they were editorials, 24 titles as they were reviews (narrative and systematic, without meta-analysis), and 4 systematic reviews with meta-analysis as
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ACCEPTED MANUSCRIPT they did not meet eligibility criteria. No studies from grey literature or further reference screening could be included. Another 285 titles were excluded due to the remaining exclusion criteria. Twelve full-texts qualified for inclusion.
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3.2. Study characteristics
The twelve studies eligible for the review consisted of cross-over, cohort, prospective experiment, and quasi experiment studies (see table 2), and all were published in
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English, between 1995 and 2015. The number of participants ranged from 10 – 282 per study, and mostly consisted of sleep apnoea patients and healthy, but sleep
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deprived, controls. The male gender was dominant, and most individuals were aged between 40 and 50 years old. Sleepiness was assessed in the included studies through self-reporting (ESS, KSS, SSS, effort to stay awake, fatigue, basic Nordic sleep questionnaire), performance tests (Oxford sleep resistance test, Psychomotor
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vigilance test (PVT), continuous performance test), and biophysiological measures (electroencephalography
(EEG),
electrooculography
(EOG)
and
eye
video
recording). The driving simulators used were: Australia Edinburgh driving simulator
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(AusEdTM), STISIM, VTI driving simulator, Steer clear, Divided attention steering
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simulator (DASS), and custom based. Real driving data was derived from selfreported MVA, official reports and registry based MVA extraction, real-road test driving in specially equipped cars (on a real road and a test drive location) and from a fitness-to-drive assessment by legal authorities. How exactly sleepiness and driving performance were assessed in the simulated and real driving condition is detailed in table 3. Driving scenarios, and performance measures in simulated and real driving were heterogeneous (see tables 2 and 3).
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ACCEPTED MANUSCRIPT 3.3. Quality and risk of bias The CCAT total score of the included studies (see supplementary material, table S1) ranged from 55% to 95%, with a mean value (M) of 80.25%, a median (Md) of 81.5%, and a standard deviation of 13%.
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The risk of bias was mostly only usable for the categories of incomplete outcome data, selective reporting, and for other sources of bias. It was “unclear” for the other categories (supplementary material, table S2). If a risk of bias was present, it was
3.4. Results of individual studies
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risk of bias for other sources, mainly for selection.
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mostly “low”. However, in six out of the twelve studies included, there was a “high”
An overview of methodologies and results from each study included for this review
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can be found in tables 2 and 3. In line with table 2, studies are discussed alphabetically in this section. Demirdöğen et al.
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assessed EDS using the Turkish version of the ESS and real
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driving performance data by self-reporting of any MVA in the past, exact time frame unspecified, in 282 commercial male vehicle drivers. Among other tests, they
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underwent an approximately 60-minute driving simulation. Only a few drivers reported EDS (ESS: Md = 2, range = 0-20). A positive history of MVA was present in 26% (n = 72) of the drivers who were reported to have higher ESS values (Md = 2, range = 0-19) compared to drivers without MVA (Md = 2, range 0-20; p = 0.022). As mean values and standard deviations were not reported, it was difficult to conceive a difference between the two groups. In addition, with a median ESS of 2, EDS was not present in most of the cases and the ESS was the only variable that significantly
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ACCEPTED MANUSCRIPT differed between those with positive and negative MVA history in the multiple logistic regression analysis. In addition, the speed-distance estimation was the only simulated driving test that significantly differed, with the positive MVA history group failing less often (22.2%) than the group without MVA (41.8%). 40
assessed 13 patients with mild obstructive sleep apnoea (OSA), and
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Desai et al.
16 healthy controls in a well-rested, and in a sleep deprived condition in a driving simulation among other tests. OSA was defined by a total respiratory disturbance
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index > 5 and reported to be M = 12 ± 1.4 in the patient group. The ESS was 9 ± 6 for the patient, and 7 ± 4.2 for the control group. Concerning real driving, the MVA
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rate over the last 3 years was evaluated based on self-reported questionnaires and was similar in patients (M = 0.3 ± 0.5) and controls (M = 0.6 ± 1.3). Driving simulation performance was negatively affected by sleep deprivation and time of day (increase in mean reaction time, its standard deviation, and average speed deviation) but again
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no significant differences were found between the patient and control groups. The primary aim of Devos et al.
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was to develop a short clinical battery to assist
physicians in identifying safe and unsafe drivers with Parkinson’s disease (PD), a
42,43
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condition which is associated with higher rates of excessive daytime sleepiness (PD)
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. Their secondary aim was to determine the value of driving simulation to predict
fitness to drive. Forty patients with PD and forty age- and sex-matched controls were included (further details can be found in table 2). In Belgium, where the study was conducted, the Centre for fitness to drive evaluation and car adaptations (CARA) of the Belgian road safety institute is responsible for determining fitness to drive in all people with functional disabilities. The CARA team consisted of a physician, a neuropsychologist, and an expert in practical fitness to drive. All patients underwent the official CARA driving evaluation, also involving a real-road drive. The authors
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ACCEPTED MANUSCRIPT dichotomised the original CARA decisions into fail (fit to drive with restriction or unfit) and pass, and compared these two groups (fail n = 11, pass n = 29). EDS based on an ESS score ≥ 10 was rarely present but occurred more frequently (p = 0.28) in the fail group (ESS: M = 7.55 ± 4.87) than in the pass group (5.86 ± 4.16). The number of
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accidents and traffic penalties were assessed by self-reported questionnaires. They did not differ between the fail group and pass group. In the driving simulation, the fail group performed (mostly significantly) worse than the pass group. The patient group
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overall performed (mostly significantly) worse than the control group. To summarise, although there was a tendency of higher ESS scores in the fail group as opposed to
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the pass group, EDS was predominantly absent. In addition, the differences in the driving simulation results may have been caused primarily by impairments related to PD since PD patients performed worse than controls in general. Lastly, no driving accidents occurred and the stratification of the fail and pass group was based on the
Filtness et al.
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CARA decision.
investigated sleep-related eye symptoms and their association with
driver sleepiness in both simulated and real-road test driving conditions. Sixteen
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healthy individuals were tested twice in each driving condition (the times of the trials were as follows: 15:30-17:15, 17:45-19:30 / 00:15-02:00, 02:45-04:30). Throughout
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each drive, participants had to rate their sleepiness on the KSS every 5 minutes. The “sleepiness indicators” used were grouped and reported overall for both real-road tests and simulated driving. No statistical comparison of the two conditions was reported. However, the following results with mean and standard deviation were reported: mean proportion of blinks >0.15 sec (s) (real-road test drive: 0.113 ± 0.129; simulation: 0.277 ± 0.206), mean blink duration (s) (0.115 ± 0.025; 0.147 ± 0.043), median blink duration (s) (0.108 ± 0.015; 0.128 ± 0.025), maximum KSS (6.781 ±
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ACCEPTED MANUSCRIPT 1.699; 7.726 ± 1.343), mean KSS (5.7 ± 1.417; 7.439 ± 1.385), effort to stay awake (1 = no effort, 7 = very much effort) (3.656 ± 2.149; 5.438 ± 2.031), left line crossings per km (right hand sided traffic) (0.038 ± 0.068; 0.073 ± 0.128), right line crossings per km (0.008 ± 0.125; 0.004 ± 0.007). 31
investigated, among other hypotheses, if impaired vigilance is
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Findley et al.
associated with a higher rate of automobile accidents. In total, the study included 114 participants: 62 patients with untreated OSA (51 ± 4 apnoea plus hypopnoea per
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hour) and 10 patients with untreated narcolepsy (3.7 ± 0.6 min mean sleep latency in the MSLT, 3.1 ± 0.5 REM naps per patient), with age- and sex-matched patients (12
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for OSA and 10 for narcolepsy), and an additional 10 age- and sex-matched volunteers each for OSA and narcolepsy. The driving simulation was conducted in a 30-minute Steer clear test between 7 and 9 pm. The number of automobile accidents (property damage > $500) was obtained from the legal authorities for each individual
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patient. In the driving simulation, OSA patients hit significantly more obstacles (4.3 ± 0.6%) than controls (1.4 ± 0.3%) and volunteers (1.2 ± 0.3%) (p<0.05). In addition, driving simulator performance significantly correlated with the severity of sleep
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apnoea syndrome (mild, moderate, severe). Similarly, narcolepsy patients hit more obstacles (7.7 ± 3.2%) compared to controls (1.2 ± 0.3%) and volunteers (0.9 ±0.3%)
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(p<0.05). Driving simulator performance in narcolepsy patients did not significantly correlate with the MSLT indicators of narcolepsy severity. Normal (<1.8%), poor (1.84.5%), and very poor (>4.5%) driving simulator performance was significantly associated with the number of automobile accident rates in the preceding 5 years (normal: 0.05, poor: 0.20, very poor: 0.38 MVA / driver / 5 y) in OSA and narcolepsy patients.
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Hallvig et al.
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ACCEPTED MANUSCRIPT compared real-road test driving with simulated driving under day and
night conditions after an extended time awake in 10 healthy individuals (ESS: 7.4 ± 3.1). The time to line crossing (TLC) was shorter in the driving simulation but did not significantly differ compared to the real-road test driving condition. In contrast, the
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KSS, the mean Karolinska drowsiness score (KDS), the maximum KDS, blink duration, lateral position, standard deviation of lateral position (SDLP), and speed, significantly differed between the simulated and the real-road test driving condition.
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Values were higher in the driving simulation with the exception of the lateral position, which showed a shift to the midline in the real-road test driving condition. Absolute
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values were graphically illustrated but not reported in detail. Although there was a significant difference resulting in a low absolute comparability, result patterns for day and night tests were more similar in simulated driving than real-road test driving. Mazza et al.
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compared 20 OSAS patients before treatment with 20 matched
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controls (ESS: 11.4 ± 3.4 vs. 8.4 ± 2.7; p < 0.02). After 3 months of treatment, ten patients were retested, as well as their matched controls (to control for a learning effect). The real-road test driving consisted of two separate 150 metre long one-way
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tracks which were equipped with cameras and sensors. Reaction time, distance to stop, and the number of collisions with an obstacle were assessed in simple,
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distraction, and anticipation conditions. Before treatment, reaction time and (in simple and anticipation conditions) distance to stop was significantly worse in OSAS patients compared to controls. After treatment, no significant differences between existed between the groups. The driving simulator was used for 20 minutes with a divided attention (DASS) assessing: duration of trial (termination if > 15 s off-road), reaction time, and the number of off-road events. All three were significantly worse in OSAS patients compared to controls before treatment. After treatment, only reaction time
20
ACCEPTED MANUSCRIPT remained impaired compared to controls, but less significantly. Between real-road and simulated driving, an association was found for patients before treatment (r = 0.43, p = 0.05) but this did not allow a prediction from simulated to real-road test driving. 47
assessed 12 healthy men in two driving conditions: real-road tests and
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Philip et al.
simulated driving, each throughout a whole day (6 sessions) after normal sleep and after two hours of sleep in a sleep deprived condition. A significant effect of sleep
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deprivation on either real-road test driving or simulated driving performance was found (F1,10 = 60.013, p < 0.001), as well as a significant effect of the driving
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condition on driving performance with more inappropriate line crossings for simulated driving seen (F1,10 = 156.184, p < 0.001). In the rested condition, no overall correlation was found for the “driving condition”. In the sleep deprived condition, an overall correlation was found (r = 0.712, p = 0.008) but this was a result of there
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being an increased number of inappropriate line crossings during the 11am to 1pm driving session compared to the other sessions in simulated driving, and compared to real-road test driving. Individuals had to rate their sleepiness on the KSS in the
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middle of each driving session, for which an effect of “driving condition” was found (F1,11 = 8.036, p = 0.016), with higher KSS values in the driving simulation. Fatigue
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ratings on a visual analogue scale did not reveal significant effects for the “driving condition”.
Pichel et al.
48
assessed 93 patients in whom OSA was suspected (confirmed in 77)
in the Steer Clear and the DASS, where patients were grouped according to normal and poor performance for vigilance in the Steer Clear, tracking error in the DASS, and reaction time in the DASS. The ESS did not differ between these 6 groups (Md range 10-11, IQR range 2-7). In females, and participants who regularly consumed
21
ACCEPTED MANUSCRIPT alcohol, tracking errors in the DASS were significantly associated with the presence of an MVA in the preceding year. Poor reaction time in the DASS was independently associated with dozing while driving during the previous year (self-reporting: from “only one time” to “always driving”). The tendency to fall asleep was also predicted by
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tracking error. The regression analysis revealed no significant association between MVA and the Steer clear or DASS, and a more significant association with tracking error at p = 0.094. 49
assessed 43 male OSA patients using the ESS, the MWT, simulated
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Pizza et al.
driving, and a driving questionnaire asking for the occurrences of MVA in the
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previous 5 years. All patients were grouped into crash (47% with MVA) and no crash groups (53% without MVA). Seventy percent of the MVA was reported to have been sleepiness related. In the crash group, the ESS score was higher (12 ± 4.2 vs. 9.2 ± 4, p = 0.038), and the time to the first crash in simulated driving shorter (22.3 ± 8.6
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min vs. 27.1 ± 5.1 min, p = 0.05) when compared to the no crash group. No significant differences were found for the other simulated driving performance measures (the number of crashes, and lane position variability), for real-life near-
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miss accidents, as well as for the mean sleep latency in the MWT. In the subgroup of patients showing risky behaviour in real-life driving (65% of patients self-reported
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continuing to drive while sleepy), those with MVA performed significantly worse in the driving simulator (more crashes, shorter time to first crash, and higher lane position variability) compared to the group without MVA. In contrast, in the subgroup showing safe behaviour in real-life driving (35% reported stopping when sleepy), no such difference in driving simulator performance was observed. In the risky-behaving group, 71.4% of the MVA were sleepiness related, compared to 66.7% in the safebehaving group. In the risky-behaving group, among the ones with MVA, 42.9%
22
ACCEPTED MANUSCRIPT additionally had real-life near-miss accidents, compared to 28.6% among the ones without MVA. In the safe-behaving group, among the ones with MVA, 16.7% additionally had real-life near-miss accidents, compared to 0% among the ones without MVA. 50
assessed 150 patients (ESS: Md = 13, IQR = 8-16) in whom
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Turkington et al.
snoring or sleep apnoea was suspected. Poor tracking errors in the DASS were significantly associated with regular alcohol intake, older age, female sex, and real-
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life near-miss accidents, but not previous MVA. A higher number of off-road events/h in the DASS was also significantly associated with older age, female sex, and real-
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life near-miss accidents but again not with MVA. A longer reaction time in the DASS was only significantly associated with older age. The ESS had a highly significant association with reporting of nodding off at the wheel, and real-life near-miss accidents (both in the previous 3 years). The hierarchical logistic regression analysis
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that contained (among others) simulated and real-life driving performance, and the ESS, revealed a significant association between the number of off-road events/h in the driving simulation and real-life MVA in the previous year. However, using that
51
assessed 9 healthy individuals (8 males, 1 female) in a day (~9am –
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Valck et al.
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model, only 10% of those who had an accident could be correctly identified.
7pm) and after a period of prolonged wakefulness in a night (~9pm – 6am) driving condition. The assessment consisted of an 800 km real-road test drive (from Belgium to France) with three breaks in-between, which was compared to a 25 minute driving simulation before and after the drive, and during the 3rd break. Subjective sleepiness was rated on a 7-likert scale with a range of 0-28 (Fatigue subscale of the Profile of Mood States (POMS) assessment) before and after the real-road drive, and in every break. Several data and whole datasets were excluded due to technical problems
23
ACCEPTED MANUSCRIPT and reported lack of compliance (e.g. in the driving simulation). The main focus of the investigation was on the effects of prolonged wakefulness. In real-road test driving, the standard deviation in steering position increased throughout the drive but decreased in the last segment (after 3rd break) which the authors explained as the
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“coming home effect”. Individuals generally drove faster during the night than during the day, however, driving speed was also influenced by other traffic. In the driving simulation, no overall effects were found for lane drifting between the day and night
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condition, and over time. No effects were found for mean speed. A significant increase in speed deviation and a trend for accidents (the only two crashes in the
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study) were found upon arrival in the night condition, compared to the preceding segments and the day condition. The POMS increased until arrival.
3.5. Synthesis of results
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Sleepy individuals consisted mainly of OSA and narcolepsy patients, as well as sleep deprived healthy individuals (table 2). Simulated driving was compared with real-road
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test driving (mostly road position measures), other real-life driving measures (e.g. self-reported tendency to fall asleep while driving), and the number of MVA (mostly
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within the three preceding years) (table 3). Both real-road test driving performance and MVA were simultaneously assessed only by Devos et al.
41
. The duration of
simulated driving was mostly short (i.e. 20-30 minutes) while the duration of real-road test driving lasted at least 30 minutes, but in both conditions the duration varied and was not always specified. The rather simple DASS was the most frequently used driving simulator (one third of the studies). MVA were assessed in half of the studies, however, only in the study of Pizza et al. 49 was MVA related to sleepiness presented separately. In addition, MVA were not equally defined, if at all.
24
ACCEPTED MANUSCRIPT Comparing simulated and real-road test driving, the number of inappropriate line crossings was comparable but lower absolute values were seen in real-road test driving. With increasing levels of sleepiness, the average lateral position shifted much less towards the midline in simulated than in real-road test driving. The
sleepiness in the study of Desai et al.
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standard deviation of the lateral road position in simulated driving was not affected by 40
, in contrast to the study of Hallvig et al.
45
where the standard deviation of the lateral position increased in parallel with
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sleepiness levels in both the simulated and real-road test driving condition. Speed measures in simulated and real-road test driving studies revealed contradictory
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results. Reaction times were longer in sleepy individuals compared to non-sleepy individuals in both the simulated and real-road test driving condition, but this did not allow a means of prediction from simulated to real-road driving. Comparing simulated with real-life driving, tracking errors in simulated driving
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moderately predicted the self-reported tendency to fall asleep during real-life driving, and were also associated with real-life near-misses and MVA
48
. Tracking errors
during simulated driving were additionally associated with female gender, regular
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alcohol intake, and older age. Similarly, in the presence of higher numbers of off-road events/h in the driving simulation, female gender and older age were also associated
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with real-life near-miss accidents. A higher number of off-road events/h were also associated with real-life MVA in the previous year, however the predictability was weak. Among those who had had an MVA in the last 5 years compared to those who did not, a significantly higher lane variability during simulated driving was only found for the subgroup of risky-behaving drivers (those continuing to drive when sleepy) but not for the subgroup of safe-behaving drivers (those stopping when sleepy).
25
ACCEPTED MANUSCRIPT Poor reaction time in simulated driving was associated with self-reported dozing off during real-life driving, and older age. No association was found between longer reaction times in simulated driving and real-life near-miss accidents. In one study, the percentage of obstacle hits in simulated driving was associated with the number of
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MVA and also with the severity (based upon oxyhaemoglobin desaturation) of the sleep apnoea syndrome 31.
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When assessing sleepiness on a regular basis and with short intervals in-between (e.g. the KSS), values obtained in the simulated driving condition were higher than in
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the real-road test driving condition, but were mostly comparable on a relative basis. Impaired simulated or real-road test driving performance, nodding off at the wheel, real-life near-miss accidents, and MVA were associated with higher ESS values. Driving simulation performance was not found to be able to accurately predict MVA
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on an individual level. While significant relationships between simulated driving performance and the number of MVA could be found on a group level, the
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associations were modest at best, or only the fact that values were statistically significant was reported without detailing the strength of the relation itself.
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Associations between simulated and real-road test driving performance seemed to be stronger than between simulated driving performance and MVA.
3.6. Additional analyses Calculated CCAT scores (see section 2.5.) for driving performance items are reported in table 4. Road position variables (simulated and real driving), road position (simulated driving) and near-misses and/or MVA (real driving), reaction time
26
ACCEPTED MANUSCRIPT (simulated driving) and near-misses and/or MVA (real driving), and near-misses and/or MVA (simulated and real driving) were the four most commonly reported combination of items. Out of these four, studies reporting the combination of road position in simulated and real driving showed the highest CCAT values (Md = 90.5%,
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range 78-93%). In contrast, studies reporting the combination of road position in simulated driving with near-misses and/or MVA in real driving showed the lowest
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CCAT values out of these four item combinations (Md = 69.5%, range 55-95%).
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4. Discussion
The systematic review of the literature revealed a rather low number of studies and currently low evidence for accuracy, validity, and predictability of simulated driving for either real-road test driving or even more MVA risk in real-life driving. The data in the twelve articles eligible for this review showed that simulated driving performance, in
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sleepy patients and healthy sleep deprived individuals, is only moderately associated with real-road test driving performance. This was on a relative basis due to higher
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driving performance measure values (e.g. SDLP) in simulated compared to real-road test driving, and mainly limited by the non-reporting of effect sizes or the strength of
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associations. Moreover, MVA in sleepy individuals could not reliably be predicted, especially not on an individual level. The quality of the studies might have contributed to these results. Several studies were mainly limited by small sample sizes, and the questionnaire based method of self-reporting of MVA which possibly suffers from recall bias, especially due to the rather rare occurrence of an MVA. Furthermore, one might assume that there is a high chance of publication bias with a high number of studies not published due to inconclusive results, since many statistical comparisons in studies published did not reach levels of significance. Study results that reached
27
ACCEPTED MANUSCRIPT the level of significance and therefore were published should hence be judged with caution. When comparing simulated with real driving there are several important points to address:
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Firstly, a generally accepted definition how to assess simulated driving and which parameters to use in sleepy individuals is urgently needed. Among the included studies, the number of near-misses or accidents in simulated driving was the item
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most frequently compared with real driving and the parameter with the strongest association to real-life near-miss accidents or MVA. This association, however, was
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found and reported on a group level and no sensitivity or specificity values for predicting the risk for an MVA have been published yet. The number of inappropriate line crossings and the SDLP are other parameters frequently compared between simulated and real driving, both increasing with higher levels of sleepiness.
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Secondly, when driving performance is evaluated through quantitative performance measures (e.g. SDLP), simulated driving is generally seen to be worse, with higher
EP
absolute values than real-road test driving, and therefore only relatively comparable. These discrepancies may be due to the risk present during real-road test driving, and
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differences which are seen on an individual level may be explained by varying motivation levels.
Thirdly, the large heterogeneity among driving simulator types and settings decreases internal comparability between simulated driving performance itself. Norm values and standardised in-laboratory conditions are preconditions for a clinical assessment. The definition and standardisation of these is urgently needed and should consider the relevant simulated driving performance measures, the type of
28
ACCEPTED MANUSCRIPT driving simulator used, and the condition of the assessment. Ideally, these would have to be compared with rates of real-life near-miss accidents and/or MVA. It remains unclear if the predictive value of more sophisticated simulators in sleepy drivers surpasses that of more simple simulations. Therefore, studies using any type
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of driving simulation were eligible for this review. Studies only using the MSLT or MWT as a predictor of real-life driving performance or MVA rate were outside the scope of this review. However, Drake et al.
30
found an association between sleep 28
al found associations
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latency in the MSLT and 10-year MVA risk, and Pizza et al.
between sleep latency in the MSLT and simulated driving performance. Similarly, a
performance impairments
27,52,53
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sleep latency of less than 20 minutes in the MWT is associated with simulated driving . Evidence from such tests needs to be
systematically reviewed and the predictive value for real driving performance analysed.
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The main advantage of using driving simulation instead of real-road test driving is the safety aspect. The rapid advances in technology, and increased affordability, have contributed to the availability of much more realistic, sophisticated but also more
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complex scenarios, which increases the likelihood of patients accepting negative
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fitness-to-drive decisions in comparison to the MWT 54. A major disadvantage in the use of driving simulation is the dropout rate of individuals due to simulator sickness
55
. The prevalence of simulator sickness varies
among studies is estimated to range from 10 to 20%
56,57
. This rate is affected by the
setting, including scenario and size of screen among other factors 58,59. Despite the advantage and disadvantages of simulated driving, it remains unknown whether real-life MVA can be predicted by in-laboratory tests at all. From a clinical perspective, even if multiple parameters derived from car position, velocity, steering 29
ACCEPTED MANUSCRIPT wheel behaviour or secondary task performance are assessed, the perception of sleepiness and the safe or risky behaviours which follow should be evaluated. In this context, it is important to understand the factors which may ultimately lead to a sleepiness-induced MVA. This disastrous process can be conceptually divided into at
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least 3 consecutive steps: 1) Any cause leading to sleepiness at the wheel, 2) perception of sleepiness and its severity, and 3) how prudently one reacts to the perceived sleepiness (including the use of countermeasures), defining risky or safe
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behaviour respectively (figure 1).
As mentioned in the introduction, sleepiness and its similar terms are complex and
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not always easy to differentiate. Sleepiness at the wheel can be a consequence of socially related sleep deprivation, or due to medical diseases and/or drugs, resulting in an increased sleep propensity. Vigilance as an important precondition for driving performance in every driver is not simply the reciprocal of sleepiness, but is also
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influenced by the capacity to maintain alertness, which in turn depends upon factors such as motivation and attention. Both the severity of sleepiness and capacity to maintain alertness can differ within healthy sleep deprived individuals and also
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between patients of different diagnoses and within the same diagnosis. Generally,
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the cause(s) can be identified and treated, thereby reducing sleepiness as the first influencing factor in the evolution of an accident, but not necessarily resolving this risk completely.
The second factor influencing the evolution of an accident is the subjective and spontaneous perception of sleepiness. Spontaneously perceived sleepiness has been seen to be accurate in sleep-deprived healthy individuals (aged 23 ± 1.3 years old) in a simulated driving condition 60, but not in the MWT where one third fell asleep without prior perception of their sleepiness. Nevertheless, this is limited to only the
30
ACCEPTED MANUSCRIPT qualitative perception and does not give an accurate judgement about the quantitative level of sleepiness or impact on driving. However, Kaplan et al. have shown that sleep onset cannot be recognised precisely, and the judgment of sleep onset is impaired with increased sleepiness
61
. This leads to the third and probably
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most neglected factor relevant to sleepiness-induced MVA: safe or risky behaviour at the wheel. Some drivers tend to declare themselves as behaving safely when in fact this is not always the case when sleepy. Others might perceive their sleepiness but 61
.
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misjudge its severity and their tendency to fall asleep as Kaplan et al. have shown
Therefore, self-reported safe behaviour might not be accurate and objectively safe,
In fact Pizza et al.
49
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even if the driver believes it is.
showed that in 27% (n = 4) of patients with self-reported safe
behaviour (i.e. who state they stop driving when sleepy), the occurrence of sleepiness related real-life MVA was not much lower compared to patients with self-
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reported risky behaviour (36%, n = 10) (i.e. those who continued to drive). This raises the question of how accurate the judgement of sleepiness-induced driving impairment is. Nevertheless, the combination of accurately perceiving the first signs
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of sleepiness with the corresponding safe behaviour in real driving might have a more significant impact on MVA prevention compared to focusing on self-perception
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or safe behaviour only. Based on this concept, a sleepiness-induced MVA would most likely happen if sleepiness is present and has been perceived, but ignored or underestimated, resulting in inadequate countermeasures and risky behaviour. Therefore, the level of driver sleepiness may be initially of less relevance, and impaired driving performance the last warning sign preceding an MVA. Further studies are needed to define the contribution of each of these components hypothesised to be relevant in the development of a sleepiness-induced MVA. This
31
ACCEPTED MANUSCRIPT may also have an impact on the development of the frequently discussed role of advanced driving assistant systems (ADAS) for preventing MVA
62
. Moreover, it
might also change the approach of future “driving rehabilitation”. Before appropriate studies will be available, clinicians must evaluate sleepiness and
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fitness to drive in on an individual basis as accurately as possible by using a combination of available tests, i.e. a vigilance battery. Driving simulation might be one of them, but should not be used exclusively. Furthermore, the physician is still
SC
responsible for interpreting results in the clinical context, considering any possible influences from co-morbidities. However, physicians not only should diagnose and
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treat the patient’s underlying cause of sleepiness, but also offer education and support, and take behaviour into account. The priority shouldn’t be to get patients off the road based solely on laboratory results such as impaired simulated driving performance. Finally, it is still the driver who is ultimately responsible for acting safely
not change.
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when driving, and unless there is a major shift towards autonomous driving this will
From a clinical perspective, future research on safe driving in sleepy individuals
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should not only take the number of real-life near-miss accidents or MVA into account,
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but use them as the gold standard. Furthermore, vigilance tests and simulated driving should be tested for their power in predicting MVA under real life conditions. A possible bottleneck limiting these investigation is the quality and availability of nearmiss or MVA data. Police officers and authorities do not always accurately consider sleepiness as a cause of MVA, possibly due to a lack of knowledge. However, sleepiness-induced MVA often occur in a typical context, such as a single vehicle running off the road in good weather conditions with an absence of braking signs, alcohol consumption, and mechanical vehicle defects, and often the driver would
32
ACCEPTED MANUSCRIPT have been able to see the run off point for several seconds prior to the crash
11
. This
knowledge should enable police officers to detect sleepiness-induced MVA more accurately, and allow them to refer the responsible driver for medical investigations on disease related EDS to prevent further accidents.
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From a population perspective, the same question as for other medical disorders concerning driving (e.g. epilepsy) must be answered: what is the acceptable range of risk society accepts to still allow an individual to drive in public traffic? Introducing the
in cardiac patients,
63
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risk of harm formula for drivers with sleepiness, similar to judging the fitness to drive may be a reasonable approach. However, evidence is still
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scarce and further research is needed.
4.1. Limitations
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Arguably, the main limitation of this review is how the inclusion and exclusion criteria were defined. One might have included studies involving sleepy individuals containing only simulated driving and real-road test driving, or simulated driving and
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MVA, or real-road test driving and MVA, and compared them with each other. Furthermore, an exclusion criteria for patients in which sleepiness might not be the
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only factor limiting driving ability could have been defined. However, patients are judged individually and in multiple tests in clinical routine, and usually suffer from more than one disorder. Group effects of patients with only one disorder or only assessed in one test might therefore only be applicable to a certain extent. One might also argue about the definition of a “driving simulator”. The Steer clear test for example, which represents a go no-go paradigm, is a borderline variety of driving simulator. It is, however, open to discussion whether more realistic driving simulators
33
ACCEPTED MANUSCRIPT show more reliable prognostic power compared to less realistic driving simulators. Although, several skills used during the Steer clear test anticipate real driving, and as Findley et al.
31
demonstrated, the outcome in the Steer clear is associated with
MVA. Another limitation of this study is the use of the CCAT rating for assessing the
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quality of the evidence for driving performance items comparing real and simulated driving. The quality of a study might not be an applicable criterion for assessing how well a driving performance item was investigated. However, we aimed to create an
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overview with a greater degree of sensitivity. Third, the risk of bias was assessed by the Cochrane Risk of Bias Tool, which was only applicable to a certain extent due to
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the absence of randomised controlled trials, and was rated by one author (CB).
5. Conclusion
The driving performance of sleepy individuals in a driving simulator is partially related
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to their real-road test driving performance, illustrating the potential practical value of driving simulators. However, based on the current evidence, simulated driving
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performance is not able to reliably predict real-life rates of near-misses or MVA especially not on an individual level. When comparing simulated and real driving, the
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strongest association was found for the most frequently investigated combination: driving simulation near-misses or accidents with real-life rates of near-misses or MVA; followed by the number of inappropriate line crossings and the SDLP. However, and in general, studies lacked accuracy, validity, adequate sample size, and were affected by several forms of bias. No consensus among the research community defining the test setting of a simulated driving assessment exists. Finally, studies do not take into account other important factors such as the perception and accurate judgement of sleepiness, and the risk-taking behaviour.
34
ACCEPTED MANUSCRIPT Practice Points
•
•
•
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•
Evidence regarding simulated driving in a clinical environment is scarce and lacking in methodologically high quality studies with sufficient power. Real-life motor vehicle accidents cannot be reliably predicted by simulated driving performance. In order to assess and judge fitness to drive in sleepy patients, driving simulation can be used as part of a combination of tests (i.e. vigilance battery) which as a whole aims to assess the individual risk of sleepiness-induced motor vehicle accidents. An individual’s sleepiness perception and risky behaviour while driving sleepy are important but still neglected factors relevant to sleepiness-induced motor vehicle accidents. This should be addressed more consistently in a clinical setting. Driving rehabilitation instead of restrictions.
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•
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Research Agenda
Evidence should be improved by further research
•
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•
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• • • •
assessing a standardised set of different driving parameters in larger groups of sleepy patients and sleep deprived healthy individuals. using comparable types of driving simulators. using comparable conditions including environmental factors and time of day. including the perception of sleepiness and risky behaviour in a driving condition. comparing simulated driving with real-life near-misses and motor vehicle accidents using objective information (e.g. databases from legal authorities, insurances, or hospital records) in addition to self-reporting. ideally including and analysing different patient groups with excessive daytime sleepiness (and matched controls) to define specific constraints in certain patient groups. assessing fitness to drive by using multiple neurophysiological (maintenance of wakefulness test, multiple sleep latency test) and performance tests (simulated driving, psychomotor vigilance test, etc.) in parallel.
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•
Several of the aforementioned points can only be investigated by using a multicentre approach. A consensus among the research community is mandatory in defining the conditions of driving simulation similar to the maintenance of wakefulness test or multiple sleep latency test: number of sessions per day, optimal duration, time of day, driving simulation scenario and settings, and relevant outcome measures and how they will be judged (defined norm values). Furthermore, it would be helpful to restrict the different types of driving simulators to only a handful of comparable types, including at least one easy-to-use model for a clinical environment
35
ACCEPTED MANUSCRIPT References 1.
Ford ES, Cunningham TJ, Giles WH, Croft JB. Trends in insomnia and
excessive daytime sleepiness among U.S. adults from 2002 to 2012. Sleep medicine 2015;16(3):372–8. Hayley AC, Williams LJ, Kennedy GA, Holloway KL, Berk M, Brennan-Olsen
RI PT
2.
SL, et al. Excessive daytime sleepiness and falls among older men and women:
cross-sectional examination of a population-based sample. BMC Geriatr 2015;15:74. Hara C, Lopes Rocha F, Lima-Costa MFF. Prevalence of excessive daytime
SC
3.
Sleep medicine 2004;5(1):31–6. 4.
M AN U
sleepiness and associated factors in a Brazilian community: The Bambuı́ study.
Young TB. Epidemiology of daytime sleepiness: definitions, symptomatology,
and prevalence. J Clin Psychiatry 2004;65 Suppl 16:12–6.
Thomann J, Baumann CR, Landolt H, Werth E. Psychomotor vigilance task
TE D
5.
demonstrates impaired vigilance in disorders with excessive daytime sleepiness. J Clin Sleep Med 2014;10(9):1019–24.
Ward KL, Hillman DR, James A, Bremner AP, Simpson L, Cooper MN, et al.
EP
6.
AC C
Excessive daytime sleepiness increases the risk of motor vehicle crash in obstructive sleep apnea. J Clin Sleep Med 2013;9(10):1013–21. 7.
Braeckman L, Verpraet R, van Risseghem M, Pevernagie D, Bacquer D de.
Prevalence and correlates of poor sleep quality and daytime sleepiness in Belgian truck drivers. Chronobiol. Int. 2011;28(2):126–34. 8.
Landrigan CP, Rothschild JM, Cronin JW, Kaushal R, Burdick E, Katz JT, et
al. Effect of reducing interns' work hours on serious medical errors in intensive care units. N. Engl. J. Med. 2004;351(18):1838–48. 36
ACCEPTED MANUSCRIPT 9.
Barger LK, Cade BE, Ayas NT, Cronin JW, Rosner B, Speizer FE, et al.
Extended work shifts and the risk of motor vehicle crashes among interns. N. Engl. J. Med. 2005;352(2):125–34. 10.
Organization WH. WHO Global Status Report on Road Safety 2013:
11.
RI PT
Supporting a Decade of Action. Geneva: World Health Organization; 2013. Horne JA, Reyner LA. Sleep related vehicle accidents. BMJ
1995;310(6979):565–7.
Gonçalves M, Amici R, Lucas R, Åkerstedt T, Cirignotta F, Horne J, et al.
SC
12.
M AN U
Sleepiness at the wheel across Europe: a survey of 19 countries. Journal of sleep research 2015. 13.
Phillips RO, Sagberg F, Torkel B. Fatigue in operators of land- and sea-based
transport forms in Norway: Risk Profiles. Fatigue in Transport Report IV; 2015. Connor J, Norton R, Ameratunga S, Robinson E, Civil I, Dunn R, et al. Driver
TE D
14.
sleepiness and risk of serious injury to car occupants: population based case control study. BMJ 2002;324(7346):1125.
McConnell CF, Bretz KM, Dwyer WO. Falling asleep at the wheel: aclose look
EP
15.
AC C
at 1,269 fatal and serious injury-producing crashes. Behav Sleep Med 2003;1(3):171–83. 16.
Pack AI, Pack AM, Rodgman E, Cucchiara A, Dinges DF, Schwab CW.
Characteristics of crashes attributed to the driver having fallen asleep. Accid Anal Prev 1995;27(6):769–75. 17.
Stevenson MR, Elkington J, Sharwood L, Meuleners L, Ivers R, Boufous S, et
al. The role of sleepiness, sleep disorders, and the work environment on heavyvehicle crashes in 2 Australian states. Am J Epidemiol 2014;179(5):594–601. 37
ACCEPTED MANUSCRIPT 18.
Martiniuk ALC, Senserrick T, Lo S, Williamson A, Du W, Grunstein RR, et al.
Sleep-deprived young drivers and the risk for crash: the DRIVE prospective cohort study. JAMA Pediatr 2013;167(7):647–55. 19.
Ellen RLB, Marshall SC, Palayew M, Molnar FJ, Wilson KG, Man-Son-Hing M.
RI PT
Systematic review of motor vehicle crash risk in persons with sleep apnea. J Clin Sleep Med 2006;2(2):193–200. 20.
Mulgrew AT, Nasvadi G, Butt A, Cheema R, Fox N, Fleetham JA, et al. Risk
21.
M AN U
apnoea/hypopnoea. Thorax 2008;63(6):536–41.
SC
and severity of motor vehicle crashes in patients with obstructive sleep
Karimi M, Hedner J, Habel H, Nerman O, Grote L. Sleep apnea-related risk of
motor vehicle accidents is reduced by continuous positive airway pressure: Swedish Traffic Accident Registry data. Sleep 2015;38(3):341–9.
Philip P, Chaufton C, Orriols L, Lagarde E, Amoros E, Laumon B, et al.
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22.
Complaints of Poor Sleep and Risk of Traffic Accidents: A Population-Based CaseControl Study. PLoS ONE 2014;9(12):e114102. W. Klemenjak, E. Braun, J. Alvarez, I.M. Bernhoft, L. Fjerdingen. Final
EP
23.
*24.
AC C
Programme Report Public, IMMORTAL. Project Consortium; 2005. Powell NB, Chau JK. Sleepy driving. The Medical clinics of North America
2010;94(3):531–40. 25.
Beratungsstelle für Unfallverhütung. SINUS-Report: Sicherheitsniveau und
Unfallgeschehen im Strassenverkehr 2013. Bern: bfu; 2014. *26.
Brown ID. Driver fatigue. Hum Factors 1994;36(2):298–314.
38
ACCEPTED MANUSCRIPT 27.
Philip P, Chaufton C, Taillard J, Sagaspe P, Leger D, Raimondi M, et al.
Maintenance of Wakefulness Test scores and driving performance in sleep disorder patients and controls. Int J Psychophysiol 2013;89(2):195–202. 28.
Pizza F, Contardi S, Mondini S, Trentin L, Cirignotta F. Daytime sleepiness
RI PT
and driving performance in patients with obstructive sleep apnea: comparison of the MSLT, the MWT, and a simulated driving task. Sleep 2009;32(3):382–91. 29.
Ahlstrom C, Nyström M, Holmqvist K, Fors C, Sandberg D, Anund A, et al. Fit-
SC
for-duty test for estimation of drivers’ sleepiness level: Eye movements improve the
2013;26:20–32. *30.
M AN U
sleep/wake predictor. Transportation Research Part C: Emerging Technologies
Drake C, Roehrs T, Breslau N, Johnson E, Jefferson C, Scofield H, et al. The
10-year risk of verified motor vehicle crashes in relation to physiologic sleepiness.
*31.
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Sleep 2010;33(6):745–52.
Findley L, Unverzagt M, Guchu R, Fabrizio M, Buckner J, Suratt P. Vigilance
and automobile accidents in patients with sleep apnea or narcolepsy. Chest
32.
EP
1995;108(3):619–24.
Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred Reporting Items for
AC C
Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 2009;6(7):e1000097. 33.
Johns MW. A new method for measuring daytime sleepiness: the Epworth
sleepiness scale. Sleep 1991;14(6):540–5. 34.
Akerstedt T, Gillberg M. Subjective and objective sleepiness in the active
individual. Int. J. Neurosci 1990;52(1-2):29–37.
39
ACCEPTED MANUSCRIPT 35.
Hoddes E, Zarcone V, Smythe H, Phillips R, Dement WC. Quantification of
Sleepiness: A New Approach. Psychophysiology 1973;10(4):431–6. 36.
Crowe M. Crowe Critical Appraisal Tool (v1.4). Available at:
https://conchra.com.au/2015/12/08/crowe-critical-appraisal-tool-v1-4/ [accessed
37.
RI PT
31.03.2016].
Crowe M, Sheppard L, Campbell A. Reliability analysis for a proposed critical
appraisal tool demonstrated value for diverse research designs. J Clin Epidemiol
Higgins J, Green S. Cochrane Handbook for Systematic Reviews of
M AN U
38.
SC
2012;65(4):375–83.
Interventions. Available at: http://handbook.cochrane.org/; 2011 [accessed 31.03.2016]. 39.
Demirdöğen Çetinoğlu E, Görek Dilektaşlı A, Demir NA, Özkaya G, Acet NA,
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Durmuş E, et al. The relationship between driving simulation performance and obstructive sleep apnoea risk, daytime sleepiness, obesity and road traffic accident history of commercial drivers in Turkey. Sleep Breath 2015. Desai AV, Marks GB, Jankelson D, Grunstein RR. Do sleep deprivation and
EP
40.
time of day interact with mild obstructive sleep apnea to worsen performance and
41.
AC C
neurobehavioral function? J Clin Sleep Med 2006;2(1):63–70. Devos H, Vandenberghe W, Nieuwboer A, Tant M, Baten G, Weerdt W de.
Predictors of fitness to drive in people with Parkinson disease. Neurology 2007;69(14):1434–41. 42.
Tholfsen LK, Larsen JP, Schulz J, Tysnes O, Gjerstad MD. Development of
excessive daytime sleepiness in early Parkinson disease. Neurology 2015;85(2):162–8.
40
ACCEPTED MANUSCRIPT 43.
Cochen De Cock V, Bayard S, Jaussent I, Charif M, Grini M, Langenier MC, et
al. Daytime Sleepiness in Parkinson’s Disease: A Reappraisal. PLoS ONE 2014;9(9):e107278. 44.
Filtness AJ, Anund A, Fors C, Ahlström C, Akerstedt T, Kecklund G. Sleep-
RI PT
related eye symptoms and their potential for identifying driver sleepiness. J Sleep Res 2014;23(5):568–75. *45.
Hallvig D, Anund A, Fors C, Kecklund G, Karlsson JG, Wahde M, et al. Sleepy
SC
driving on the real road and in the simulator--A comparison. Accid Anal Prev
46.
M AN U
2013;50:44–50.
Mazza S, Pépin J, Naëgelé B, Rauch E, Deschaux C, Ficheux P, et al. Driving
ability in sleep apnoea patients before and after CPAP treatment: evaluation on a road safety platform. Eur. Respir. J. 2006;28(5):1020–8.
Philip P, Sagaspe P, Taillard J, Valtat C, Moore N, Akerstedt T, et al. Fatigue,
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47.
sleepiness, and performance in simulated versus real driving conditions. Sleep 2005;28(12):1511–6.
Pichel F, Zamarrón C, Magán F, Rodríguez JR. Sustained attention
EP
48.
measurements in obstructive sleep apnea and risk of traffic accidents. Respir Med
49.
AC C
2006;100(6):1020–7.
Pizza F, Contardi S, Mondini S, Cirignotta F. Simulated driving performance
coupled with driver behaviour can predict the risk of sleepiness-related car accidents. Thorax 2011;66(8):725–6. 50.
Turkington PM, Sircar M, Allgar V, Elliott MW. Relationship between
obstructive sleep apnoea, driving simulator performance, and risk of road traffic accidents. Thorax 2001;56(10):800–5.
41
ACCEPTED MANUSCRIPT 51.
Valck E de, Quanten S, Cluydts R, Berckmans D. Day versus night driving in
real traffic and on a driving simulator during an 800 km all-highway drive. INTERNATIONAL JOURNAL OF VEHICLE DESIGN 2006;42(1-2):119–33. 52.
Sagaspe P, Taillard J, Chaumet G, Guilleminault C, Coste O, Moore N, et al.
RI PT
Maintenance of wakefulness test as a predictor of driving performance in patients with untreated obstructive sleep apnea. Sleep 2007;30(3):327–30. 53.
Bioulac S, Chaufton C, Taillard J, Claret A, Sagaspe P, Fabrigoule C, et al.
SC
Excessive daytime sleepiness in adult patients with ADHD as measured by the
Maintenance of Wakefulness Test, an electrophysiologic measure. J Clin Psychiatry
*54.
M AN U
2015;76(7):943–8.
Fisher DL. Handbook of driving simulation for engineering, medicine, and
psychology. Boca Raton: Taylor & Francis Group; 2011.
Matas NA, Nettelbeck T, Burns NR. Dropout during a driving simulator study:
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*55.
A survival analysis. Journal of safety research 2015;55:159–69. 56.
Lee HC, Cameron D, Lee AH. Assessing the driving performance of older
EP
adult drivers: on-road versus simulated driving. Accident; analysis and prevention
57.
AC C
2003;35(5):797–803.
Kawano N, Iwamoto K, Ebe K, Aleksic B, Noda A, Umegaki H, et al. Slower
adaptation to driving simulator and simulator sickness in older adults. Aging Clin Exp Res 2012;24(3):285–9. 58.
Bridgeman B, Blaesi S, Campusano R. Optical correction reduces simulator
sickness in a driving environment. Hum Factors 2014;56(8):1472–81.
42
ACCEPTED MANUSCRIPT 59.
Jäger M, Gruber N, Müri R, Mosimann UP, Nef T. Manipulations to reduce
simulator-related transient adverse health effects during simulated driving. Med Biol Eng Comput 2014;52(7):601–10. *60.
Schreier DR, Roth C, Mathis J. Subjective perception of sleepiness in a driving
RI PT
simulator is different from that in the maintenance of wakefulness test. Sleep Med 2015;16(8):994–8. *61.
Kaplan KA, Itoi A, Dement WC. Awareness of sleepiness and ability to predict
SC
sleep onset: can drivers avoid falling asleep at the wheel? Sleep Med 2007;9(1):71–
62.
Evans L. The dominant role of driver behavior in traffic safety. Am J Public
Health 1996;86(6):784–6. *63.
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Canadian Cardiovascular Society. Assessment of the cardiac patient for
Figures Legend
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fitness to drive. Can J Cardiol 1992;8(4):406–19.
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accident.
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Figure 1. Factors relevant for and leading to a sleepiness-induced motor vehicle
Figure 2. Evolution of a sleepiness-induced motor vehicle accident. Figure 3. Driving simulators in the clinical assessment
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1. The article must contain all of the items addressed below: a. sleepy individuals (incl. sleepiness, fatigue, drowsiness, tiredness) b. simulated motor vehicle driving with reported performance
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c. real motor vehicle driving (any of the items below) real-road test driving performance measures
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number of real-life near-miss or motor vehicle accidents in a certain time period
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Table 2 Study aim
Study setting and design
Study population (n)
Gender
Age
Sleepiness
Demirdöğen et al. 2015 39
Determine the relationship between obesity, risk of obstructive sleep apnoea, daytime sleepiness, history of road traffic accidents, and driving simulator performance.
Quasi experiment
282 commercial vehicle drivers (mandatory assessment every 5 years)
All male
29-76, median = 45
Epworth sleepiness scale
Desai et al. 2006 40
Examine the interactive effect of sleep deprivation, time of day, and mild OSA on performance and neurobehavioral function, especially driving simulator performance.
Cross over study
With OSA: 13
All male
With OSA: 45(±14.8)
1. Development of clinical battery to identify safe and unsafe drivers with PD.
Prospective experiment
40 PD patients.
Real driving (assessment / setting)
CCAT (%)
Psycho-technical assessment system
Self-reported road traffic accident history
56
Epworth sleepiness scale, Stanford sleepiness scale, Oxford sleep resistance test, Psychomotor vigilance test
personal computer-based driving simulator (AusEdTM ) (Sydney, Australia; Edinburgh, Scotland)
Self-reported motor vehicle accidents
95
Epworth sleepiness scale,
STISIM Drive system, model 300, Version 1.03.05 manufactured by Systems Technology Inc., Hawthorne, CA
Self-reported driving habits, traffic penalties and accidents over the last 5 years. Drivers with PD asked to self-appraise their fitness to drive. CARA assessment – on road driving evaluation for PD patients scored on the Test ride for investigating practical fitness to drive checklist.
89
41.3± 8.7 (SD)
Karolinska sleepiness scale, selfreported effort required to stay awake. Electrooculography measured at 512 Hz for measuring eye blinks. Smart Eye Pro 5.7 recorded at 60 Hz for eye gaze
VTI driving simulator III is a moving base simulator with a Saab 9-3 cabin (automatic gearbox) and a 120 degrees forward field of view.
Automatic Volvo XC70. Vehicle data were recorded at 10 Hz.
90
With OSA: 51± 1 (SEM)
EEG, EOG, submental electromyography, oxyhaemoglobin saturation
Steer clear computer programme
Driving records from the Department of motor vehicles – automobile accident rate, defined as property damage greater than $500.
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64 males, 16 females
40 age and sex matched controls
2. Determine value of a driving simulator evaluation in the prediction of fitness to drive.
Investigation of sleep-related eye symptoms and their association with driver sleepiness.
Cross over study
16 healthy individuals
Findley et al. 1995 31
Investigation of impaired vigilance in patients with sleep apnoea and narcolepsy during prolonged, monotonous tasks, whether degree of impairment is related to severity of illness, and whether impaired vigilance is associated with a higher than expected rate of automobile accidents.
Prospective experiment
62 patients with OSA, 12 controls and 10 volunteers (both age and sex matched)
Not explicitly given
8 males, 8 females
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OSA patients and controls: 63 males, 11 females
10 patients with untreated narcolepsy, 10 controls and 10 volunteers (both age and sex matched)
Narcolepsy patients and controls: 14 females, 6 males
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Without OSA: 38(±14.7)
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Healthy controls: 16
Driving simulation (type / setting)
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Author, year
Without OSA: 50±3 With narcolepsy: 37±5 Without narcolepsy: 36±4
ii
Comparison of real driving with simulated driving under conditions of extended time awake.
Prospective experiment
10 healthy individuals
5 males, 5 females
Mazza et al. 2006 46
Evaluating the performance of a group of apnoeic patients and controls on a road safety platform.
Cross over study
20 patients with OSA, 20 matched controls
advanced moving base driving simulator, car body consisted of the front part of a Volvo 850 with a manual 5-shift gearbox. three channels of forward view of 120◦ × 30◦
Volvo S80, model year
Epworth sleepiness scale, Oxford sleep resistance test, Continuous performance test
DASS
Miniature driving performance evaluation
78
Philip et al. 2005 47
To determine whether real life driving would produce results different from those obtained in a driving simulator.
Cross over study
12 healthy individuals
All males
21.1±1.6 (SD)
Karolinska sleepiness scale, selfreported fatigue
DASS
Car equipped with dual controls
93
Pichel et al. 2006 48
To identify associations between performance on driving simulators, subject sleep complaints and risk of traffic accidents in a population undergoing OSAS investigation.
Cohort study
93 OSA patients
78 males, 15 females
50.8±10.7 (SD)
Epworth sleepiness scale, Basic nordic sleep questionnaire
Steer clear computer programme and DASS 2D
Self-reported questionnaire
80
Pizza et al 2011 49
To determine the predictors of sleepiness and crash risk in patients with OSA.
Cohort study
43 OSA patients
All males
53±9 (SD)
Epworth sleepiness scale
STISIM 300 Driving simulator systems technology
Self-reported questionnaire
55
Turkington et al. 2001 50
To determine the relationship between driving simulator performance, patient symptoms, sleep study results and driving history and establish if the driving simulator data is additionally of help in advising fitness to drive.
Cohort study
150 OSA patients
124 males, 26 females
49.8±10.7 (SD)
Epworth sleepiness scale
DASS
Self-reported questionnaire
83
Valck et al. 2006 51
To examine driving behaviour – as measured by the standard deviation in steering position and speed - during prolonged driving in real traffic during daytime as opposed to night time.
Cross over study
9 healthy individuals
40±11(SD)
Fatigue of the profile of mood states
York driving simulator with Drivesim 3000 software
Day and night drives from Antwerp to Lyon
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35 males and 5 females
40±11(SD)
Patients: 54.1±5.9 (SD)
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Controls: 52.2±8.3
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8 males, 1 female
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Epworth sleepiness scale, sleep diaries, wrist actigraphy, EEG, EOG (simulator study), driver sleepiness detection and warning system (real driving), Karolinska sleepiness scale
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2000, equipped with dual command.
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Table 2. Characteristics of included studies. Abbreviations: AusEdTM: Australia Edinburgh driving simulator, CARA: Centre for fitness to drive evaluation and car adaptations, DASS: Divided attention steering simulator, EEG: Electroencephalography, EOG:
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Electrooculography, OSA: Obstructive sleep apnoea, PD: Parkinson’s disease, SD: Standard deviation, SEM: Standard error of the
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Table 3 Author, year
Driving simulation
Duration or length of drive
others
Inappropriate line crossings: RNP
6 x 105 minutes
Reaction time: RNP
KSS Day: range 4.3-6.1 Night: 7.6-8.9 KDS mean (%): Day range: 31-44 Night: range 47-53
Lateral position (m): Day range: -0.19 - 0.04 Night range: -0.2 - -0.38 Standard deviation of lateral position (m): Day range: 0.18-0.19 Night range: 0.19-0.23
90 minutes
Time to line crossing (sec): Day range: 9-10.1 Night range: 8.510.3
KSS mean (SD): 5.7 (1.417) KSS max (SD): 6.781 (1.699) Effort to stay awake mean (SD) (1-7): 3.656 (2.149)
Left line crossings/km, mean (SD): 0.038 (0.068) Right line crossings/km, mean (SD): 0.008 (0.125)
90 minutes
Real driving
Sleepiness
Road position
Near-misses and accidents Crashes: 0 for all subjects
Length of drive
Other
Sleepiness
30 minutes
Velocity deviation: RNP Mean reaction time: RNP
ESS mean With OSA: 9±6 Without OSA: 7±4.2 KSS: RNP
Desai et al. 2006 40
Oxford sleep resistance test: sleep latency: RNP
SDLP: Results not published
93
Philip et al. 2005 47
KSS: Results not published
Inappropriate line crossings: RNP
6 x 105 minutes
Reaction time: RNP
91
Hallvig et al. 2013 45
KSS: Day range: 4.3-6.1 Night range: 7.5-8.9 KDS mean (%): Day range: 32-42 Night range: 48-50
Lateral position (m): Day range: 0-0.02 Night range: -0.05 - -0.18 Standard deviation of lateral position (m): Day range: 0.18-0.25 Night range: 0.28-0.35
60 minutes
Time to line crossing (sec): Day range: 6.2-6.4 Night range: 5.8-6.2
90
Filtness et al. 2014 44
KSS mean (SD): 7.439 (1.385) KSS max (SD): 7.726 (1.343) Effort to stay awake mean (SD) (1-7): 5.438 (2.031)
Left line crossings/km, mean (SD): 0.073 (0.128) Right line crossings/km, mean (SD): 0.004 (0.007)
75-80 minutes
89
Devos et al. 2007 41
ESS: PD patients passed to drive: 5.86±4.16 PD patients not passed for driving: 7.55±4.87
83
Turkington et al. 2001 50
ESS: Md (IQR): 12 (7-17)
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Pichel et al. 2006 48
ESS: Tracking error: Normal performance: 11±6 Poor performance: 10±4 Reaction time: Normal performance: 10±2 Poor performance: 10±7
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Off road events/hour: Md (IQR) 24 (3-104)
Road position
Near-misses and motor vehicle accidents Self-reported MVAs in last 3 years: With OSA: 0.3±0.5 Without OSA: 0.6±1.3
TRIP score: PD patients passed: 116 PD patients failed: 100 Reaction time (Md): PD patients passed:2.52 PD patients failed: 2.92
Accidents & traffic penalties (Md, IQR) for CARA: pass: 0 (0-0), 0 (0-1) fail: 0 (0-0), 0 (0-0)
20 minutes
Reaction time: Md (IQR): 2.85 (1.99-4.23)
Accidents in last 3 years (%): 25
20 minutes
Tracking error: Normal performance: 53.7% Poor performance: 46.3% Reaction time: Normal performance: 18.2% Poor performance: 81.8%
MVA in last year: 22.6%
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Traffic accidents (Md): PD patients passed: 0 PD patients failed: 1 SDLP: Md (IQR) 0.321 (0.218-1.172)
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CCAT score
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15 km
CARA decision 72.5% fit to drive 25% fit to drive with restrictions 2.5% not fit to drive
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Mazza et al. 2006 (results before treatment were used)
ESS (mean ± SD for all): OSA patients: 11.4±3.4 Controls: 8.4±2.7
Off-road events (n): OSA patients: 89.5±116.7 Controls: 10.0±12.5
20 minutes
Accident liability (0 if no crashes, 1 if at least 2): RNP
25 minutes
% of obstacles hit: Sleep apnea patients: 4.3±0.6 Controls: 1.4±0.3 Narcolepsy patients: 7.7±3.2 Controls: 1.2±0.3
30 minutes
Number of off road events: Results not published
Not stated
Mean reaction time (sec): OSA patients: 3.98±2.47 Controls: 1.95±0.87
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Findley et al. 1995 31
Subjective sleepiness not measured
56
Demirdöğen et al. 2015 39
Turkish version of ESS With history of RTA: Med = 2 (0-19) Without history of RTA: Md = 2 (0-20)
Time spent off road: Results not published Speed-distance estimation test: Drivers without RTA performed poorer than those with past RTA
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Pizza et al. 2011 49
ESS (mean ± SD) With crash history: 12±4.2 Without crash history: 9.2±4
Lane position variability (m, mean ± SD) With crash history: 0.6±0.3 Without crash history: 0.4±0.1
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Fatigue of the profile of mood states: Fatigue score day drive mean (SE): 6.5(0.5) at start 11.17(1.56) at end. Night drive: 7.0(0.71) at start, 12.14(1.78) at end
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Standard deviation of the road position (cm): RNP
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Not recorded
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Crashes: With crash history: 2.7±5.9 Without crash history: 0.5±1.0
30 minutes
Number of collisions: OSA patients: 0.9±0.7 Controls: 0.4±0.5
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Standard deviation steering position: RNP
Reaction time (sec): OSA patients: 1.51±0.35 Controls: 0.91±0.21 Distance to stop (m): OSA patients: 36.6±10.7 Controls: 27.9±6.5
Day drive: 807 km, night drive: 794 km
MVA rate (accident/driver/5y): Normal vigilance performance: 0.05 Poor vigilance performance: 0.2 Very poor vigilance performance: 0.38
Reaction time: 46.7% of those with high OSA risk had poor reaction times vs 28.1% with low OSA risk Peripheral vision test: NA
Self-reported RTA history: 26% of 274 drivers had been involved in traffic accidents
Time to first crash (min, mean ± SD) With crash history: 22.3±8.6 Without crash history: 27.1±5.1
Near miss accidents in last 5 y (%): With crash history: 35 Without crash history: 17.4
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30 minutes
Driving > 20’000 km/y (%) with crash history: 50 without crash history: 60.9
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Table 3. Measurements and results from included studies. Abbreviations: CARA: Centre for fitness to drive evaluation and car
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adaptations, ESS: Epworth sleepiness scale, IQR: Interquartile range, KSD: Karolinska drowsiness scale, KSS: Karolinska sleepiness scale, Md: Median, MVA: motor vehicle accident, OSA: Obstructive sleep apnea, PD: Parkinson’s disease, RNP: Results not published (in usable format), RTA: Road traffic accident, SD: Standard deviation, SDLP: Standard deviation of the
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lateral position (m), SE: Standard error, SEC: Seconds TRIP: Test ride for investigating practical fitness to drive.
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Table 4 Time to line crossing
Reaction time
90.5 % (78-93)
91 %
93 %
44,45,47,51
45
47
Distance to stop
Reaction time
91 %
91 %
45
45
Near-misses and/or accidents 69.5 % (55-95) 39,40,49,50
80 % 48
93 %
85.5 % (78-93)
78 %
89 %
81.5 % (56-95)
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46,47
46
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39–40,46,48,50
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Velocity deviation
78 % 51
95 % 40
89 %
89 %
41
41
55 % 49
78 %
78 %
89 %
78 % (55-95)
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46
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31, 39–41,46,49,50
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Time to first crash
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Near-misses and/or accidents
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Tracking error Time to line crossing
CARA decision
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Road position
Road position
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Real driving → Driving simulation ↓
Table 4. Median of CCAT scores of studies reporting on the corresponding item (and range, if more than one study was available). Abbreviations: CARA: Centre for fitness to drive evaluation and car adaptations, TRIP: Test ride for investigating practical fitness to drive.
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S1087-0792(17)30094-1
DOI:
10.1016/j.smrv.2017.04.004
Reference:
YSMRV 1032
To appear in:
Sleep Medicine Reviews
Received Date: 13 April 2016 Revised Date:
10 January 2017
Accepted Date: 28 April 2017
Please cite this article as: Schreier DR, Banks C, Mathis J, Driving simulators in the clinical assessment of fitness to drive in sleepy individuals: a systematic review, Sleep Medicine Reviews (2017), doi: 10.1016/j.smrv.2017.04.004. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Title page
Driving simulators in the clinical assessment of fitness to drive in sleepy individuals: a systematic review
David R. Schreier1*, Christina Banks1,2, Johannes Mathis1 1
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(Driving simulators in the clinical assessment)
Sleep-Wake-Epilepsy-Centre, Department of Neurology, Inselspital, Bern University
Hospital, and University of Bern, Switzerland 2
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Department of Primary Care and Public Health, Imperial College London, United
Kingdom
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Title characters = 93, Summary words = 249, Keywords = 9,
Manuscript words = 8166, Figures = 3, Tables = 4, References = 63, Supplementary material = 2 tables and 1 appendix
*corresponding author:
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Dr. med. David Schreier Sleep-Wake-Epilepsy-Centre
Dept. of Neurology, Inselspital, Bern University Hospital CH-3010 Bern, Switzerland
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Phone: +41 31 632 30 54. Fax: +41 31 632 94 48.
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Email: [email protected]
Acknowledgments
We thank Lukas Oesch for illustration support. Disclosure: Dr. Schreier received financial support from UCB-Pharma AG to attend a conference (12/2015). Dr. Schreier and Prof. Mathis are both collaborating with SAFEmine AG (a Hexagon Mining company) in a project funded by the Commission of Technology and Innovation (CTI) from the Swiss Government (grant 17864.1 PFLS-LS).
1
ACCEPTED MANUSCRIPT Summary Road traffic injuries are projected to be the leading cause of death for those aged between 15 and 29 years by the year 2030, and sleepiness is estimated to be the underlying cause in up to 15-20% of all motor vehicle accidents. Sleepiness at the
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wheel is most often caused by socially induced sleep deprivation or poor sleep hygiene in otherwise healthy individuals, medical disorders, or the intake of drugs. Validated methods for objectifying sleepiness are urgently sought, particularly in the
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context of driving. Based on the assumption that the identification and treatment of sleepiness, and its causes, may prevent motor vehicle accidents, driving simulators
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are a seemingly promising diagnostic tool. Despite the rising use of these in research, the reliability of their conclusions in healthy sleepy individuals and patients is still unclear. This systematic review aims to evaluate the practical value of driving simulators in a clinical environment when judging fitness to drive in sleepy
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individuals. From the 1674 records screened, 12 studies on sleepy individuals containing both simulated and real driving data were included. In general, simulated driving did not reliably predict real-life motor vehicle accidents, and especially not on
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an individual level, despite the moderate relationship between simulated and realroad test driving performance. The severity of sleepiness is most likely not the critical
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factor leading to motor vehicle accidents, but rather the perception of sleepiness. The self-perception of sleepiness related impairment, and risky behaviour while at the wheel should also be considered as key influencing factors. Keywords sleepiness; excessive daytime sleepiness; vigilance assessment; fitness to drive; driving simulation; real driving; near-miss accident; motor vehicle accident; risk-taking behaviour
2
ACCEPTED MANUSCRIPT Abbreviations AusEdTM Australia Edinburgh driving simulator Centre for fitness to drive evaluation and car adaptations
CCAT
Crowe critical appraisal tool
DASS
Divided attention steering simulator
EDS
Excessive daytime sleepiness
EEG
Electroencephalography
ESS
Epworth sleepiness scale
IQR
Interquartile range
KDS
Karolinska drowsiness score
KSS
Karolinska sleepiness scale
M
Mean
Md
Median
MVA
Motor vehicle accident
MSLT
Multiple sleep latency test
MWT
Maintenance of wakefulness test
OSA(S)
Obstructive sleep apnoea (syndrome)
PD
Parkinson’s disease
POMS
Profile of mood states
PVT
Psychomotor vigilance test
SDLP
Standard deviation of lateral position
SSS
Stanford sleepiness scale
TLC
Time to line crossing
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CARA
3
ACCEPTED MANUSCRIPT Glossary of terms Epworth sleepiness scale (ESS): The most frequently used subjective sleepiness rating scale referring to a preceding time period. The likeliness of dozing off or falling asleep is rated from zero (would never doze) to three (high chance of dozing) for
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nine daily life situations. The ratings are then accumulated, with scores ranging from 0 to 24. Scores ≤10 are interpreted as normal, while values >10 are considered to indicate pathological daytime sleepiness.
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Maintenance of wakefulness test (MWT): The MWT measures the ability to maintain wakefulness in a monotonous, standardised, environment, and in the
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absence of any external stimuli or influences. Individuals sit in a semi-darkened room and have to stay awake for as long as possible throughout the 40-minute trial which is scheduled four times distributed over the day.
Motor vehicle accident(s) (MVA): The number of motor vehicle accidents either
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according to self-reported or derived from police/hospital based reports and registries, over a defined time period in real life which results in an accident per year rate.
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Multiple sleep latency test (MSLT): The MSLT measures the how prone
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individuals are to falling asleep in the absence of any external stimuli or influences. It is internationally standardised, and consists of five nap opportunities of 20 minutes duration that are scheduled in/at/on/for two hour intervals throughout the day. Psychomotor vigilance test (PVT): The PVT lasts 10 minutes while individuals must react as quickly as possible to visual stimuli by pressing a button. The stimuli appear with a random latency, and the corresponding reaction times are presented immediately to the subject and stored. Reaction times above 0.5 seconds are considered as lapses.
4
ACCEPTED MANUSCRIPT Glossary of terms (continued) Real-life driving: Any information regarding real driving in daily life, including nearmisses and/or motor vehicle accidents. Real-road test driving: Continuous real-road driving in a study or test setting but
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not in daily life situations (e.g. measuring the standard deviation of lateral position). Real driving: Any condition related to or any information derived from driving in a real motor vehicle in reality, independent of whether it is a test or real-life condition
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(i.e. real-road test driving and real-life driving), excluding any simulation.
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ACCEPTED MANUSCRIPT 1. Introduction Excessive daytime sleepiness (EDS) is present in 10-20% of the general population 1–4
. EDS may negatively affect reaction time, vigilance, attention, and the judgement
of performance while driving, and consequently result in accidents related to human 5–9
. Motor vehicle accidents (MVA) are responsible for the majority of the 1.24
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error
million road traffic deaths which occur every year worldwide, with sleepiness being estimated to be the underlying cause in 10-20% of MVA
10–12
. By 2030, road traffic
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injuries will be the third most common cause of burden and disease for all age groups, and the leading cause of death for those aged between 15 and 29 years. A
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recent survey conducted in Norway found that 63% of cargo and 52% of passenger train operators, 29% of maritime watch officers, and 26% of bus and truck drivers stated they had nodded off or slept at least once while driving in the three months preceding the survey
13
. Sleepiness which ultimately presents as a risk factor for
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MVA typically originates from socially induced sleep deprivation, poor sleep hygiene, or driving at the nadir of the circadian wake phase in otherwise healthy individuals, particularly young males, rather than from disease related causes (i.e. sleep-wake 14–18
. However, sleep-wake disorders significantly increase the risk for
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disorders)
MVA, with obstructive sleep apnoea (OSA) being associated with a 2.5-fold increase
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in comparison to controls
19–22
. Among medical disorders, sleep-wake disorders are
associated with the greatest accident risk (relative risk 3.7), surpassing epilepsy (relative risk 1.8), and exceeding legal limits of alcohol (relative risk 2.0)
23
. With
respect to the significantly increased accident risk combined with the high prevalence of EDS in the general population, it is crucial that physicians are aware of their responsibility to advise their patients on the risk of accidents, effective countermeasures, and the potential legal consequences.
6
ACCEPTED MANUSCRIPT Despite the knowledge and awareness in the scientific community, the public and authorities are not sufficiently aware of the dimension and risks of driving while sleepy
24
. Consequently, the prevalence of sleepiness-induced accidents is greatly
underestimated in the official statistics of many countries, such as Switzerland
25
,
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which in turn negatively affects the implementation of prevention measures. Based on the assumption that sleepiness is subjectively perceived before the deterioration of driving performance, publicity campaigns such as the “Wake-Up Bus”
risks of driving while sleepy
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ran by the European Sleep Research Society seek to inform the public about the 12
. Awareness is, however, just the first step towards
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preventing MVA, and many factors involved in the disastrous process ultimately resulting in sleepiness-induced accidents (figure 1) must be addressed. From a human centred perspective, three main research domains focusing on sleepiness-induced MVA currently exist: 1) quantification of sleepiness, 2)
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recognition and prevention of socially induced sleepiness, and 3) diagnosis and treatment of the underlying causes of disease related sleepiness. It should be noted that terms similar to sleepiness such as “drowsiness”, “fatigue”, and “tiredness” are
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often used synonymously. While they can occur at the same time, and their effects
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can even add up to a certain extent, they do not necessarily describe identical conditions. Fatigue in its physiological sense was originally considered as “time on task performance decrement”
26
, while tiredness indicates a loss of energy and
initiative. Both differ from sleepiness and drowsiness, which indicate increased sleep pressure leading to shortened sleep latencies. All of these conditions, especially if combined, can cause driving accidents which evolve conceptually in the same way (figure 2), and while differentiating between them is challenging and highlights the complexity of the topic, always being able to distinguish them clearly is not critical.
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ACCEPTED MANUSCRIPT In a laboratory setting, the degree of sleepiness is objectively assessed by the multiple sleep latency test (MSLT). Alertness, which is not only the reciprocal condition of sleepiness but also includes compensation capacities affected by fatigue and tiredness, is most often quantified through the maintenance of wakefulness test
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(MWT). In addition, sleepy individuals with sleep latencies < 20 minutes in the MWT in general show a higher number of inappropriate line crossings than individuals with sleep latencies of > 20 minutes
27
. Furthermore, it was shown that simulated driving
SC
performance correlates stronger with the sleep latency in the MWT than with the sleep latency in the MSLT, underlining that ability to maintain wakefulness is more 28
. In addition
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important regarding driving performance than the degree of sleepiness
to alertness, vigilance as a critical basis of safe driving also requires focused attention, coherent thinking, and adequate performance and reactions, which can be assessed in a psychomotor vigilance test (PVT), in driving simulators and other
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performance tests.
The number of devices for commercial and research purposes, and methods and models for objectifying sleepiness in a driving condition (simulated and/or real-road
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test driving), is growing. Ahlstrom et al., for instance, created a sleep/wake predictor model based on eye movement measurements in a real-road test driving condition
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which correctly classified 82.4% of the drivers with high levels of subjective sleepiness (Karolinska sleepiness scale (KSS) ≥ 8) 29. Regarding the underlying causes of disease related sleepiness, physicians not only have the duty to diagnose and treat these accordingly, but also to judge whether their patients can perceive sleepiness at the wheel early enough and react appropriately. Research on risk-taking behaviour while sleepy at the wheel up until now has been scarce, and it is debatable whether built-in driving assistant systems will be able to
8
ACCEPTED MANUSCRIPT fully compensate for that in near future. Most research still focuses on the causes and treatment of sleepiness and EDS, but only a minority sets objective in-laboratory tests in relation to the number of real-life MVA 30,31, which could be considered as the gold standard in this context. Similarly, the relationship between simulated- or real-
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road test driving performance and real-life MVA is currently unclear. Frequently, reallife driving performance is simply projected from the simulated driving performance. When searching PubMed for “driving simulation”, the low number of 32 articles
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published in 1999 stands in sharp contrast to the 327 articles published in 2015, indicating an increase in the use of driving simulators in medical research. However,
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they most often include healthy individuals and rarely address clinical situations. Theoretically, driving simulation in increasingly realistic scenarios, without the real-life risks, would be advantageous in terms of a high face validity. Nevertheless, only a limited number of studies investigating fitness to drive are based on such an
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approach and they vary in driving simulation type, on topic (e.g. neurological, ophthalmological, orthopaedic impairments, etc.), and they all lack validity. Nonetheless, the technique might be used to theoretically assess fitness to drive in
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sleepy patients.
1.1. Aims and Objectives The aim of this systematic review is to evaluate the evidence available regarding the momentary practical value of driving simulators in a clinical environment to accurately judge fitness to drive in sleepy patients. The objectives derived from this aim were to 1) evaluate whether simulated driving is comparable with real-road test driving performance, 2) evaluate whether simulated driving allows a long-term means of prediction for MVA rates, and 3) identify which 9
ACCEPTED MANUSCRIPT driving simulator measures or variables are best suited for predicting real-road test
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driving performance and MVA rates.
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ACCEPTED MANUSCRIPT 2. Methods This systematic review has been conducted in accordance with the PRISMA Guidelines
32
. A protocol has been written but not registered. Conducting a meta-
analysis was considered but would have been inappropriate due to the heterogeneity
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of variables and parameters used to assess simulated and real driving performance and sleepiness. In addition, we aimed for a broader overview through including more studies rather than reducing the potential number of full-text articles for screening by
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using more restrictive inclusion criteria which would have been necessary for a meta-
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analysis.
2.1. Information sources and literature search
The following databases were searched most recently on 17 February 2016: PubMed (without time restrictions), Ovid Embase (1947 – present), Web of Science
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(databases and range of years available in the supplementary material, appendix 1), and SafetyLit (without time restrictions). The exact search terms and search
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specifications can be found in appendix 1 (supplementary material).
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2.2. Eligibility criteria and study selection This review focusses on studies containing both simulated driving and real driving data in relation to sleepy individuals. Eligibility criteria were not restricted by study type, date of publication, age, gender, disease or disorder. An overview of inclusion and exclusion criteria can be found in table 1, which shall subsequently be explained in more detail.
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ACCEPTED MANUSCRIPT We did not clearly define sleepy individuals, since a clear definition is not available and too detailed a definition in the present study could be disputed. We tried to assess whether sleepiness or similar conditions were mentioned or could be indirectly assumed. In studies reporting Epworth Sleepiness Scale (ESS) 34
values ≥ 7, Stanford Sleepiness Scale (SSS)
35
values ≥
values ≥ 6, exposure to
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10, KSS
33
sleep deprivation and assessments during the night, individuals were regarded as being sleepy. Studies looking at the effect of potentially sleep inducing
impairment in healthy individuals were not included.
SC
pharmacological substances, such as diazepines and alcohol, on performance
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“Motor vehicles” were defined according to the standard of the International Organization for Standardization (ISO 3833:1977(en)) as “types of road vehicles … designated for road circulation, except for agricultural tractors”. Therefore, the likes of
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trains and trams were excluded, and we additionally excluded two wheeled vehicles.
The reporting of simulated and real driving in sleepy individuals (point 4 of table 1) was deemed to be conducted independently if assessments were not conducted in
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the same individuals, a lack of data meant that simulated and real driving were not comparable, or data was reported only on a group level as opposed to individual. In
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addition, if results were reported for combined groups of patients and healthy controls, for example, for either the real or simulated driving condition, the study was excluded. Review articles were only eligible if original data was present (i.e. metaanalysis). References from reviews and grey literature (books, reports, theses) were screened by title and if appropriate the corresponding abstract and/or full-text was considered for eligibility. The screening was conducted by two of the authors (DS, CB). Disagreement was resolved by discussion. If doubts existed regarding whether
12
ACCEPTED MANUSCRIPT to include or exclude an article at the stage of abstract screening, it was included for full-text screening.
2.3. Data collection process and data items
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Duplicate records were removed using Citavi 5 reference manager software. The relevant data from each study was extracted into the corresponding critical appraisal form (described in the subsequent chapter 2.4) and into the tables 2 and 3. More
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descriptive information regarding study setting was extracted into table 2, and outcomes, i.e. driving performance and sleepiness outcomes, were extracted into
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table 3.
2.4. Quality assessment and risk of bias
The quality of studies (including risk of bias) was assessed using the Crowe critical
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36
. All studies included have been appraised and rated by
appraisal tool (CCAT) v1.4
two of the authors (DS, CB). The CCAT has the advantage that it can be used across 37
and consists of the CCAT rating form and the CCAT
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a variety of research designs
User Guide. The rating form contains 8 categories, each of which must be scored
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between 0 to 5 points resulting in a maximum total score of 40 points for each study. The total score is then calculated as a percentage ranging from 0 to 100% according to the CCAT User Guide. Even though the CCAT contains a risk of bias appraisal to a certain extent, a more detailed risk of bias appraisal was conducted in addition (CB). For this the Cochrane Risk of Bias Tool was used (chapter 8.6 of the Cochrane handbook for systematic reviews of interventions)
38
. Risk of bias was stated as “high”, “low”, or “unclear” for
13
ACCEPTED MANUSCRIPT every bias category: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and other sources of bias.
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2.5. Additional analyses
To gauge how well simulated- and real driving performance variables were investigated, the mean CCAT values of all studies containing the corresponding
SC
variable in simulated and real driving were gathered, and the median and range of
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CCAT values reported for each variable were calculated (table 4).
3. Results 3.1. Study selection
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The database search generated 3057 records of which 1383 were duplicates (figure 3). Consequently, the titles and abstract of 1674 records were screened. A high number of abstracts (n = 670) were included for full-text screening. Most were
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original work published in peer-reviewed journals but some were part of grey
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literature. The majority of the discarded studies (1347 titles from abstract or full-text screening) were excluded due them missing at least one item from the triad of driving simulation, real driving, sleepy individuals, or additionally, data from these not being reported in relation to each other. From this triad, the presence of real driving performance data was the most frequently missing item, followed by individuals being sleepy, while driving simulation was present in most of the studies. Two titles were excluded as they were editorials, 24 titles as they were reviews (narrative and systematic, without meta-analysis), and 4 systematic reviews with meta-analysis as
14
ACCEPTED MANUSCRIPT they did not meet eligibility criteria. No studies from grey literature or further reference screening could be included. Another 285 titles were excluded due to the remaining exclusion criteria. Twelve full-texts qualified for inclusion.
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3.2. Study characteristics
The twelve studies eligible for the review consisted of cross-over, cohort, prospective experiment, and quasi experiment studies (see table 2), and all were published in
SC
English, between 1995 and 2015. The number of participants ranged from 10 – 282 per study, and mostly consisted of sleep apnoea patients and healthy, but sleep
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deprived, controls. The male gender was dominant, and most individuals were aged between 40 and 50 years old. Sleepiness was assessed in the included studies through self-reporting (ESS, KSS, SSS, effort to stay awake, fatigue, basic Nordic sleep questionnaire), performance tests (Oxford sleep resistance test, Psychomotor
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vigilance test (PVT), continuous performance test), and biophysiological measures (electroencephalography
(EEG),
electrooculography
(EOG)
and
eye
video
recording). The driving simulators used were: Australia Edinburgh driving simulator
EP
(AusEdTM), STISIM, VTI driving simulator, Steer clear, Divided attention steering
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simulator (DASS), and custom based. Real driving data was derived from selfreported MVA, official reports and registry based MVA extraction, real-road test driving in specially equipped cars (on a real road and a test drive location) and from a fitness-to-drive assessment by legal authorities. How exactly sleepiness and driving performance were assessed in the simulated and real driving condition is detailed in table 3. Driving scenarios, and performance measures in simulated and real driving were heterogeneous (see tables 2 and 3).
15
ACCEPTED MANUSCRIPT 3.3. Quality and risk of bias The CCAT total score of the included studies (see supplementary material, table S1) ranged from 55% to 95%, with a mean value (M) of 80.25%, a median (Md) of 81.5%, and a standard deviation of 13%.
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The risk of bias was mostly only usable for the categories of incomplete outcome data, selective reporting, and for other sources of bias. It was “unclear” for the other categories (supplementary material, table S2). If a risk of bias was present, it was
3.4. Results of individual studies
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risk of bias for other sources, mainly for selection.
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mostly “low”. However, in six out of the twelve studies included, there was a “high”
An overview of methodologies and results from each study included for this review
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can be found in tables 2 and 3. In line with table 2, studies are discussed alphabetically in this section. Demirdöğen et al.
39
assessed EDS using the Turkish version of the ESS and real
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driving performance data by self-reporting of any MVA in the past, exact time frame unspecified, in 282 commercial male vehicle drivers. Among other tests, they
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underwent an approximately 60-minute driving simulation. Only a few drivers reported EDS (ESS: Md = 2, range = 0-20). A positive history of MVA was present in 26% (n = 72) of the drivers who were reported to have higher ESS values (Md = 2, range = 0-19) compared to drivers without MVA (Md = 2, range 0-20; p = 0.022). As mean values and standard deviations were not reported, it was difficult to conceive a difference between the two groups. In addition, with a median ESS of 2, EDS was not present in most of the cases and the ESS was the only variable that significantly
16
ACCEPTED MANUSCRIPT differed between those with positive and negative MVA history in the multiple logistic regression analysis. In addition, the speed-distance estimation was the only simulated driving test that significantly differed, with the positive MVA history group failing less often (22.2%) than the group without MVA (41.8%). 40
assessed 13 patients with mild obstructive sleep apnoea (OSA), and
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Desai et al.
16 healthy controls in a well-rested, and in a sleep deprived condition in a driving simulation among other tests. OSA was defined by a total respiratory disturbance
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index > 5 and reported to be M = 12 ± 1.4 in the patient group. The ESS was 9 ± 6 for the patient, and 7 ± 4.2 for the control group. Concerning real driving, the MVA
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rate over the last 3 years was evaluated based on self-reported questionnaires and was similar in patients (M = 0.3 ± 0.5) and controls (M = 0.6 ± 1.3). Driving simulation performance was negatively affected by sleep deprivation and time of day (increase in mean reaction time, its standard deviation, and average speed deviation) but again
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no significant differences were found between the patient and control groups. The primary aim of Devos et al.
41
was to develop a short clinical battery to assist
physicians in identifying safe and unsafe drivers with Parkinson’s disease (PD), a
42,43
EP
condition which is associated with higher rates of excessive daytime sleepiness (PD)
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. Their secondary aim was to determine the value of driving simulation to predict
fitness to drive. Forty patients with PD and forty age- and sex-matched controls were included (further details can be found in table 2). In Belgium, where the study was conducted, the Centre for fitness to drive evaluation and car adaptations (CARA) of the Belgian road safety institute is responsible for determining fitness to drive in all people with functional disabilities. The CARA team consisted of a physician, a neuropsychologist, and an expert in practical fitness to drive. All patients underwent the official CARA driving evaluation, also involving a real-road drive. The authors
17
ACCEPTED MANUSCRIPT dichotomised the original CARA decisions into fail (fit to drive with restriction or unfit) and pass, and compared these two groups (fail n = 11, pass n = 29). EDS based on an ESS score ≥ 10 was rarely present but occurred more frequently (p = 0.28) in the fail group (ESS: M = 7.55 ± 4.87) than in the pass group (5.86 ± 4.16). The number of
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accidents and traffic penalties were assessed by self-reported questionnaires. They did not differ between the fail group and pass group. In the driving simulation, the fail group performed (mostly significantly) worse than the pass group. The patient group
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overall performed (mostly significantly) worse than the control group. To summarise, although there was a tendency of higher ESS scores in the fail group as opposed to
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the pass group, EDS was predominantly absent. In addition, the differences in the driving simulation results may have been caused primarily by impairments related to PD since PD patients performed worse than controls in general. Lastly, no driving accidents occurred and the stratification of the fail and pass group was based on the
Filtness et al.
44
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CARA decision.
investigated sleep-related eye symptoms and their association with
driver sleepiness in both simulated and real-road test driving conditions. Sixteen
EP
healthy individuals were tested twice in each driving condition (the times of the trials were as follows: 15:30-17:15, 17:45-19:30 / 00:15-02:00, 02:45-04:30). Throughout
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each drive, participants had to rate their sleepiness on the KSS every 5 minutes. The “sleepiness indicators” used were grouped and reported overall for both real-road tests and simulated driving. No statistical comparison of the two conditions was reported. However, the following results with mean and standard deviation were reported: mean proportion of blinks >0.15 sec (s) (real-road test drive: 0.113 ± 0.129; simulation: 0.277 ± 0.206), mean blink duration (s) (0.115 ± 0.025; 0.147 ± 0.043), median blink duration (s) (0.108 ± 0.015; 0.128 ± 0.025), maximum KSS (6.781 ±
18
ACCEPTED MANUSCRIPT 1.699; 7.726 ± 1.343), mean KSS (5.7 ± 1.417; 7.439 ± 1.385), effort to stay awake (1 = no effort, 7 = very much effort) (3.656 ± 2.149; 5.438 ± 2.031), left line crossings per km (right hand sided traffic) (0.038 ± 0.068; 0.073 ± 0.128), right line crossings per km (0.008 ± 0.125; 0.004 ± 0.007). 31
investigated, among other hypotheses, if impaired vigilance is
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Findley et al.
associated with a higher rate of automobile accidents. In total, the study included 114 participants: 62 patients with untreated OSA (51 ± 4 apnoea plus hypopnoea per
SC
hour) and 10 patients with untreated narcolepsy (3.7 ± 0.6 min mean sleep latency in the MSLT, 3.1 ± 0.5 REM naps per patient), with age- and sex-matched patients (12
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for OSA and 10 for narcolepsy), and an additional 10 age- and sex-matched volunteers each for OSA and narcolepsy. The driving simulation was conducted in a 30-minute Steer clear test between 7 and 9 pm. The number of automobile accidents (property damage > $500) was obtained from the legal authorities for each individual
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patient. In the driving simulation, OSA patients hit significantly more obstacles (4.3 ± 0.6%) than controls (1.4 ± 0.3%) and volunteers (1.2 ± 0.3%) (p<0.05). In addition, driving simulator performance significantly correlated with the severity of sleep
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apnoea syndrome (mild, moderate, severe). Similarly, narcolepsy patients hit more obstacles (7.7 ± 3.2%) compared to controls (1.2 ± 0.3%) and volunteers (0.9 ±0.3%)
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(p<0.05). Driving simulator performance in narcolepsy patients did not significantly correlate with the MSLT indicators of narcolepsy severity. Normal (<1.8%), poor (1.84.5%), and very poor (>4.5%) driving simulator performance was significantly associated with the number of automobile accident rates in the preceding 5 years (normal: 0.05, poor: 0.20, very poor: 0.38 MVA / driver / 5 y) in OSA and narcolepsy patients.
19
Hallvig et al.
45
ACCEPTED MANUSCRIPT compared real-road test driving with simulated driving under day and
night conditions after an extended time awake in 10 healthy individuals (ESS: 7.4 ± 3.1). The time to line crossing (TLC) was shorter in the driving simulation but did not significantly differ compared to the real-road test driving condition. In contrast, the
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KSS, the mean Karolinska drowsiness score (KDS), the maximum KDS, blink duration, lateral position, standard deviation of lateral position (SDLP), and speed, significantly differed between the simulated and the real-road test driving condition.
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Values were higher in the driving simulation with the exception of the lateral position, which showed a shift to the midline in the real-road test driving condition. Absolute
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values were graphically illustrated but not reported in detail. Although there was a significant difference resulting in a low absolute comparability, result patterns for day and night tests were more similar in simulated driving than real-road test driving. Mazza et al.
46
compared 20 OSAS patients before treatment with 20 matched
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controls (ESS: 11.4 ± 3.4 vs. 8.4 ± 2.7; p < 0.02). After 3 months of treatment, ten patients were retested, as well as their matched controls (to control for a learning effect). The real-road test driving consisted of two separate 150 metre long one-way
EP
tracks which were equipped with cameras and sensors. Reaction time, distance to stop, and the number of collisions with an obstacle were assessed in simple,
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distraction, and anticipation conditions. Before treatment, reaction time and (in simple and anticipation conditions) distance to stop was significantly worse in OSAS patients compared to controls. After treatment, no significant differences between existed between the groups. The driving simulator was used for 20 minutes with a divided attention (DASS) assessing: duration of trial (termination if > 15 s off-road), reaction time, and the number of off-road events. All three were significantly worse in OSAS patients compared to controls before treatment. After treatment, only reaction time
20
ACCEPTED MANUSCRIPT remained impaired compared to controls, but less significantly. Between real-road and simulated driving, an association was found for patients before treatment (r = 0.43, p = 0.05) but this did not allow a prediction from simulated to real-road test driving. 47
assessed 12 healthy men in two driving conditions: real-road tests and
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Philip et al.
simulated driving, each throughout a whole day (6 sessions) after normal sleep and after two hours of sleep in a sleep deprived condition. A significant effect of sleep
SC
deprivation on either real-road test driving or simulated driving performance was found (F1,10 = 60.013, p < 0.001), as well as a significant effect of the driving
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condition on driving performance with more inappropriate line crossings for simulated driving seen (F1,10 = 156.184, p < 0.001). In the rested condition, no overall correlation was found for the “driving condition”. In the sleep deprived condition, an overall correlation was found (r = 0.712, p = 0.008) but this was a result of there
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being an increased number of inappropriate line crossings during the 11am to 1pm driving session compared to the other sessions in simulated driving, and compared to real-road test driving. Individuals had to rate their sleepiness on the KSS in the
EP
middle of each driving session, for which an effect of “driving condition” was found (F1,11 = 8.036, p = 0.016), with higher KSS values in the driving simulation. Fatigue
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ratings on a visual analogue scale did not reveal significant effects for the “driving condition”.
Pichel et al.
48
assessed 93 patients in whom OSA was suspected (confirmed in 77)
in the Steer Clear and the DASS, where patients were grouped according to normal and poor performance for vigilance in the Steer Clear, tracking error in the DASS, and reaction time in the DASS. The ESS did not differ between these 6 groups (Md range 10-11, IQR range 2-7). In females, and participants who regularly consumed
21
ACCEPTED MANUSCRIPT alcohol, tracking errors in the DASS were significantly associated with the presence of an MVA in the preceding year. Poor reaction time in the DASS was independently associated with dozing while driving during the previous year (self-reporting: from “only one time” to “always driving”). The tendency to fall asleep was also predicted by
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tracking error. The regression analysis revealed no significant association between MVA and the Steer clear or DASS, and a more significant association with tracking error at p = 0.094. 49
assessed 43 male OSA patients using the ESS, the MWT, simulated
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Pizza et al.
driving, and a driving questionnaire asking for the occurrences of MVA in the
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previous 5 years. All patients were grouped into crash (47% with MVA) and no crash groups (53% without MVA). Seventy percent of the MVA was reported to have been sleepiness related. In the crash group, the ESS score was higher (12 ± 4.2 vs. 9.2 ± 4, p = 0.038), and the time to the first crash in simulated driving shorter (22.3 ± 8.6
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min vs. 27.1 ± 5.1 min, p = 0.05) when compared to the no crash group. No significant differences were found for the other simulated driving performance measures (the number of crashes, and lane position variability), for real-life near-
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miss accidents, as well as for the mean sleep latency in the MWT. In the subgroup of patients showing risky behaviour in real-life driving (65% of patients self-reported
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continuing to drive while sleepy), those with MVA performed significantly worse in the driving simulator (more crashes, shorter time to first crash, and higher lane position variability) compared to the group without MVA. In contrast, in the subgroup showing safe behaviour in real-life driving (35% reported stopping when sleepy), no such difference in driving simulator performance was observed. In the risky-behaving group, 71.4% of the MVA were sleepiness related, compared to 66.7% in the safebehaving group. In the risky-behaving group, among the ones with MVA, 42.9%
22
ACCEPTED MANUSCRIPT additionally had real-life near-miss accidents, compared to 28.6% among the ones without MVA. In the safe-behaving group, among the ones with MVA, 16.7% additionally had real-life near-miss accidents, compared to 0% among the ones without MVA. 50
assessed 150 patients (ESS: Md = 13, IQR = 8-16) in whom
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Turkington et al.
snoring or sleep apnoea was suspected. Poor tracking errors in the DASS were significantly associated with regular alcohol intake, older age, female sex, and real-
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life near-miss accidents, but not previous MVA. A higher number of off-road events/h in the DASS was also significantly associated with older age, female sex, and real-
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life near-miss accidents but again not with MVA. A longer reaction time in the DASS was only significantly associated with older age. The ESS had a highly significant association with reporting of nodding off at the wheel, and real-life near-miss accidents (both in the previous 3 years). The hierarchical logistic regression analysis
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that contained (among others) simulated and real-life driving performance, and the ESS, revealed a significant association between the number of off-road events/h in the driving simulation and real-life MVA in the previous year. However, using that
51
assessed 9 healthy individuals (8 males, 1 female) in a day (~9am –
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Valck et al.
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model, only 10% of those who had an accident could be correctly identified.
7pm) and after a period of prolonged wakefulness in a night (~9pm – 6am) driving condition. The assessment consisted of an 800 km real-road test drive (from Belgium to France) with three breaks in-between, which was compared to a 25 minute driving simulation before and after the drive, and during the 3rd break. Subjective sleepiness was rated on a 7-likert scale with a range of 0-28 (Fatigue subscale of the Profile of Mood States (POMS) assessment) before and after the real-road drive, and in every break. Several data and whole datasets were excluded due to technical problems
23
ACCEPTED MANUSCRIPT and reported lack of compliance (e.g. in the driving simulation). The main focus of the investigation was on the effects of prolonged wakefulness. In real-road test driving, the standard deviation in steering position increased throughout the drive but decreased in the last segment (after 3rd break) which the authors explained as the
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“coming home effect”. Individuals generally drove faster during the night than during the day, however, driving speed was also influenced by other traffic. In the driving simulation, no overall effects were found for lane drifting between the day and night
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condition, and over time. No effects were found for mean speed. A significant increase in speed deviation and a trend for accidents (the only two crashes in the
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study) were found upon arrival in the night condition, compared to the preceding segments and the day condition. The POMS increased until arrival.
3.5. Synthesis of results
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Sleepy individuals consisted mainly of OSA and narcolepsy patients, as well as sleep deprived healthy individuals (table 2). Simulated driving was compared with real-road
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test driving (mostly road position measures), other real-life driving measures (e.g. self-reported tendency to fall asleep while driving), and the number of MVA (mostly
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within the three preceding years) (table 3). Both real-road test driving performance and MVA were simultaneously assessed only by Devos et al.
41
. The duration of
simulated driving was mostly short (i.e. 20-30 minutes) while the duration of real-road test driving lasted at least 30 minutes, but in both conditions the duration varied and was not always specified. The rather simple DASS was the most frequently used driving simulator (one third of the studies). MVA were assessed in half of the studies, however, only in the study of Pizza et al. 49 was MVA related to sleepiness presented separately. In addition, MVA were not equally defined, if at all.
24
ACCEPTED MANUSCRIPT Comparing simulated and real-road test driving, the number of inappropriate line crossings was comparable but lower absolute values were seen in real-road test driving. With increasing levels of sleepiness, the average lateral position shifted much less towards the midline in simulated than in real-road test driving. The
sleepiness in the study of Desai et al.
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standard deviation of the lateral road position in simulated driving was not affected by 40
, in contrast to the study of Hallvig et al.
45
where the standard deviation of the lateral position increased in parallel with
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sleepiness levels in both the simulated and real-road test driving condition. Speed measures in simulated and real-road test driving studies revealed contradictory
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results. Reaction times were longer in sleepy individuals compared to non-sleepy individuals in both the simulated and real-road test driving condition, but this did not allow a means of prediction from simulated to real-road driving. Comparing simulated with real-life driving, tracking errors in simulated driving
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moderately predicted the self-reported tendency to fall asleep during real-life driving, and were also associated with real-life near-misses and MVA
48
. Tracking errors
during simulated driving were additionally associated with female gender, regular
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alcohol intake, and older age. Similarly, in the presence of higher numbers of off-road events/h in the driving simulation, female gender and older age were also associated
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with real-life near-miss accidents. A higher number of off-road events/h were also associated with real-life MVA in the previous year, however the predictability was weak. Among those who had had an MVA in the last 5 years compared to those who did not, a significantly higher lane variability during simulated driving was only found for the subgroup of risky-behaving drivers (those continuing to drive when sleepy) but not for the subgroup of safe-behaving drivers (those stopping when sleepy).
25
ACCEPTED MANUSCRIPT Poor reaction time in simulated driving was associated with self-reported dozing off during real-life driving, and older age. No association was found between longer reaction times in simulated driving and real-life near-miss accidents. In one study, the percentage of obstacle hits in simulated driving was associated with the number of
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MVA and also with the severity (based upon oxyhaemoglobin desaturation) of the sleep apnoea syndrome 31.
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When assessing sleepiness on a regular basis and with short intervals in-between (e.g. the KSS), values obtained in the simulated driving condition were higher than in
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the real-road test driving condition, but were mostly comparable on a relative basis. Impaired simulated or real-road test driving performance, nodding off at the wheel, real-life near-miss accidents, and MVA were associated with higher ESS values. Driving simulation performance was not found to be able to accurately predict MVA
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on an individual level. While significant relationships between simulated driving performance and the number of MVA could be found on a group level, the
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associations were modest at best, or only the fact that values were statistically significant was reported without detailing the strength of the relation itself.
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Associations between simulated and real-road test driving performance seemed to be stronger than between simulated driving performance and MVA.
3.6. Additional analyses Calculated CCAT scores (see section 2.5.) for driving performance items are reported in table 4. Road position variables (simulated and real driving), road position (simulated driving) and near-misses and/or MVA (real driving), reaction time
26
ACCEPTED MANUSCRIPT (simulated driving) and near-misses and/or MVA (real driving), and near-misses and/or MVA (simulated and real driving) were the four most commonly reported combination of items. Out of these four, studies reporting the combination of road position in simulated and real driving showed the highest CCAT values (Md = 90.5%,
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range 78-93%). In contrast, studies reporting the combination of road position in simulated driving with near-misses and/or MVA in real driving showed the lowest
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CCAT values out of these four item combinations (Md = 69.5%, range 55-95%).
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4. Discussion
The systematic review of the literature revealed a rather low number of studies and currently low evidence for accuracy, validity, and predictability of simulated driving for either real-road test driving or even more MVA risk in real-life driving. The data in the twelve articles eligible for this review showed that simulated driving performance, in
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sleepy patients and healthy sleep deprived individuals, is only moderately associated with real-road test driving performance. This was on a relative basis due to higher
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driving performance measure values (e.g. SDLP) in simulated compared to real-road test driving, and mainly limited by the non-reporting of effect sizes or the strength of
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associations. Moreover, MVA in sleepy individuals could not reliably be predicted, especially not on an individual level. The quality of the studies might have contributed to these results. Several studies were mainly limited by small sample sizes, and the questionnaire based method of self-reporting of MVA which possibly suffers from recall bias, especially due to the rather rare occurrence of an MVA. Furthermore, one might assume that there is a high chance of publication bias with a high number of studies not published due to inconclusive results, since many statistical comparisons in studies published did not reach levels of significance. Study results that reached
27
ACCEPTED MANUSCRIPT the level of significance and therefore were published should hence be judged with caution. When comparing simulated with real driving there are several important points to address:
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Firstly, a generally accepted definition how to assess simulated driving and which parameters to use in sleepy individuals is urgently needed. Among the included studies, the number of near-misses or accidents in simulated driving was the item
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most frequently compared with real driving and the parameter with the strongest association to real-life near-miss accidents or MVA. This association, however, was
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found and reported on a group level and no sensitivity or specificity values for predicting the risk for an MVA have been published yet. The number of inappropriate line crossings and the SDLP are other parameters frequently compared between simulated and real driving, both increasing with higher levels of sleepiness.
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Secondly, when driving performance is evaluated through quantitative performance measures (e.g. SDLP), simulated driving is generally seen to be worse, with higher
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absolute values than real-road test driving, and therefore only relatively comparable. These discrepancies may be due to the risk present during real-road test driving, and
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differences which are seen on an individual level may be explained by varying motivation levels.
Thirdly, the large heterogeneity among driving simulator types and settings decreases internal comparability between simulated driving performance itself. Norm values and standardised in-laboratory conditions are preconditions for a clinical assessment. The definition and standardisation of these is urgently needed and should consider the relevant simulated driving performance measures, the type of
28
ACCEPTED MANUSCRIPT driving simulator used, and the condition of the assessment. Ideally, these would have to be compared with rates of real-life near-miss accidents and/or MVA. It remains unclear if the predictive value of more sophisticated simulators in sleepy drivers surpasses that of more simple simulations. Therefore, studies using any type
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of driving simulation were eligible for this review. Studies only using the MSLT or MWT as a predictor of real-life driving performance or MVA rate were outside the scope of this review. However, Drake et al.
30
found an association between sleep 28
al found associations
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latency in the MSLT and 10-year MVA risk, and Pizza et al.
between sleep latency in the MSLT and simulated driving performance. Similarly, a
performance impairments
27,52,53
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sleep latency of less than 20 minutes in the MWT is associated with simulated driving . Evidence from such tests needs to be
systematically reviewed and the predictive value for real driving performance analysed.
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The main advantage of using driving simulation instead of real-road test driving is the safety aspect. The rapid advances in technology, and increased affordability, have contributed to the availability of much more realistic, sophisticated but also more
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complex scenarios, which increases the likelihood of patients accepting negative
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fitness-to-drive decisions in comparison to the MWT 54. A major disadvantage in the use of driving simulation is the dropout rate of individuals due to simulator sickness
55
. The prevalence of simulator sickness varies
among studies is estimated to range from 10 to 20%
56,57
. This rate is affected by the
setting, including scenario and size of screen among other factors 58,59. Despite the advantage and disadvantages of simulated driving, it remains unknown whether real-life MVA can be predicted by in-laboratory tests at all. From a clinical perspective, even if multiple parameters derived from car position, velocity, steering 29
ACCEPTED MANUSCRIPT wheel behaviour or secondary task performance are assessed, the perception of sleepiness and the safe or risky behaviours which follow should be evaluated. In this context, it is important to understand the factors which may ultimately lead to a sleepiness-induced MVA. This disastrous process can be conceptually divided into at
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least 3 consecutive steps: 1) Any cause leading to sleepiness at the wheel, 2) perception of sleepiness and its severity, and 3) how prudently one reacts to the perceived sleepiness (including the use of countermeasures), defining risky or safe
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behaviour respectively (figure 1).
As mentioned in the introduction, sleepiness and its similar terms are complex and
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not always easy to differentiate. Sleepiness at the wheel can be a consequence of socially related sleep deprivation, or due to medical diseases and/or drugs, resulting in an increased sleep propensity. Vigilance as an important precondition for driving performance in every driver is not simply the reciprocal of sleepiness, but is also
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influenced by the capacity to maintain alertness, which in turn depends upon factors such as motivation and attention. Both the severity of sleepiness and capacity to maintain alertness can differ within healthy sleep deprived individuals and also
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between patients of different diagnoses and within the same diagnosis. Generally,
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the cause(s) can be identified and treated, thereby reducing sleepiness as the first influencing factor in the evolution of an accident, but not necessarily resolving this risk completely.
The second factor influencing the evolution of an accident is the subjective and spontaneous perception of sleepiness. Spontaneously perceived sleepiness has been seen to be accurate in sleep-deprived healthy individuals (aged 23 ± 1.3 years old) in a simulated driving condition 60, but not in the MWT where one third fell asleep without prior perception of their sleepiness. Nevertheless, this is limited to only the
30
ACCEPTED MANUSCRIPT qualitative perception and does not give an accurate judgement about the quantitative level of sleepiness or impact on driving. However, Kaplan et al. have shown that sleep onset cannot be recognised precisely, and the judgment of sleep onset is impaired with increased sleepiness
61
. This leads to the third and probably
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most neglected factor relevant to sleepiness-induced MVA: safe or risky behaviour at the wheel. Some drivers tend to declare themselves as behaving safely when in fact this is not always the case when sleepy. Others might perceive their sleepiness but 61
.
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misjudge its severity and their tendency to fall asleep as Kaplan et al. have shown
Therefore, self-reported safe behaviour might not be accurate and objectively safe,
In fact Pizza et al.
49
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even if the driver believes it is.
showed that in 27% (n = 4) of patients with self-reported safe
behaviour (i.e. who state they stop driving when sleepy), the occurrence of sleepiness related real-life MVA was not much lower compared to patients with self-
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reported risky behaviour (36%, n = 10) (i.e. those who continued to drive). This raises the question of how accurate the judgement of sleepiness-induced driving impairment is. Nevertheless, the combination of accurately perceiving the first signs
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of sleepiness with the corresponding safe behaviour in real driving might have a more significant impact on MVA prevention compared to focusing on self-perception
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or safe behaviour only. Based on this concept, a sleepiness-induced MVA would most likely happen if sleepiness is present and has been perceived, but ignored or underestimated, resulting in inadequate countermeasures and risky behaviour. Therefore, the level of driver sleepiness may be initially of less relevance, and impaired driving performance the last warning sign preceding an MVA. Further studies are needed to define the contribution of each of these components hypothesised to be relevant in the development of a sleepiness-induced MVA. This
31
ACCEPTED MANUSCRIPT may also have an impact on the development of the frequently discussed role of advanced driving assistant systems (ADAS) for preventing MVA
62
. Moreover, it
might also change the approach of future “driving rehabilitation”. Before appropriate studies will be available, clinicians must evaluate sleepiness and
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fitness to drive in on an individual basis as accurately as possible by using a combination of available tests, i.e. a vigilance battery. Driving simulation might be one of them, but should not be used exclusively. Furthermore, the physician is still
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responsible for interpreting results in the clinical context, considering any possible influences from co-morbidities. However, physicians not only should diagnose and
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treat the patient’s underlying cause of sleepiness, but also offer education and support, and take behaviour into account. The priority shouldn’t be to get patients off the road based solely on laboratory results such as impaired simulated driving performance. Finally, it is still the driver who is ultimately responsible for acting safely
not change.
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when driving, and unless there is a major shift towards autonomous driving this will
From a clinical perspective, future research on safe driving in sleepy individuals
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should not only take the number of real-life near-miss accidents or MVA into account,
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but use them as the gold standard. Furthermore, vigilance tests and simulated driving should be tested for their power in predicting MVA under real life conditions. A possible bottleneck limiting these investigation is the quality and availability of nearmiss or MVA data. Police officers and authorities do not always accurately consider sleepiness as a cause of MVA, possibly due to a lack of knowledge. However, sleepiness-induced MVA often occur in a typical context, such as a single vehicle running off the road in good weather conditions with an absence of braking signs, alcohol consumption, and mechanical vehicle defects, and often the driver would
32
ACCEPTED MANUSCRIPT have been able to see the run off point for several seconds prior to the crash
11
. This
knowledge should enable police officers to detect sleepiness-induced MVA more accurately, and allow them to refer the responsible driver for medical investigations on disease related EDS to prevent further accidents.
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From a population perspective, the same question as for other medical disorders concerning driving (e.g. epilepsy) must be answered: what is the acceptable range of risk society accepts to still allow an individual to drive in public traffic? Introducing the
in cardiac patients,
63
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risk of harm formula for drivers with sleepiness, similar to judging the fitness to drive may be a reasonable approach. However, evidence is still
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scarce and further research is needed.
4.1. Limitations
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Arguably, the main limitation of this review is how the inclusion and exclusion criteria were defined. One might have included studies involving sleepy individuals containing only simulated driving and real-road test driving, or simulated driving and
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MVA, or real-road test driving and MVA, and compared them with each other. Furthermore, an exclusion criteria for patients in which sleepiness might not be the
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only factor limiting driving ability could have been defined. However, patients are judged individually and in multiple tests in clinical routine, and usually suffer from more than one disorder. Group effects of patients with only one disorder or only assessed in one test might therefore only be applicable to a certain extent. One might also argue about the definition of a “driving simulator”. The Steer clear test for example, which represents a go no-go paradigm, is a borderline variety of driving simulator. It is, however, open to discussion whether more realistic driving simulators
33
ACCEPTED MANUSCRIPT show more reliable prognostic power compared to less realistic driving simulators. Although, several skills used during the Steer clear test anticipate real driving, and as Findley et al.
31
demonstrated, the outcome in the Steer clear is associated with
MVA. Another limitation of this study is the use of the CCAT rating for assessing the
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quality of the evidence for driving performance items comparing real and simulated driving. The quality of a study might not be an applicable criterion for assessing how well a driving performance item was investigated. However, we aimed to create an
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overview with a greater degree of sensitivity. Third, the risk of bias was assessed by the Cochrane Risk of Bias Tool, which was only applicable to a certain extent due to
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the absence of randomised controlled trials, and was rated by one author (CB).
5. Conclusion
The driving performance of sleepy individuals in a driving simulator is partially related
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to their real-road test driving performance, illustrating the potential practical value of driving simulators. However, based on the current evidence, simulated driving
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performance is not able to reliably predict real-life rates of near-misses or MVA especially not on an individual level. When comparing simulated and real driving, the
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strongest association was found for the most frequently investigated combination: driving simulation near-misses or accidents with real-life rates of near-misses or MVA; followed by the number of inappropriate line crossings and the SDLP. However, and in general, studies lacked accuracy, validity, adequate sample size, and were affected by several forms of bias. No consensus among the research community defining the test setting of a simulated driving assessment exists. Finally, studies do not take into account other important factors such as the perception and accurate judgement of sleepiness, and the risk-taking behaviour.
34
ACCEPTED MANUSCRIPT Practice Points
•
•
•
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•
Evidence regarding simulated driving in a clinical environment is scarce and lacking in methodologically high quality studies with sufficient power. Real-life motor vehicle accidents cannot be reliably predicted by simulated driving performance. In order to assess and judge fitness to drive in sleepy patients, driving simulation can be used as part of a combination of tests (i.e. vigilance battery) which as a whole aims to assess the individual risk of sleepiness-induced motor vehicle accidents. An individual’s sleepiness perception and risky behaviour while driving sleepy are important but still neglected factors relevant to sleepiness-induced motor vehicle accidents. This should be addressed more consistently in a clinical setting. Driving rehabilitation instead of restrictions.
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•
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Research Agenda
Evidence should be improved by further research
•
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•
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• • • •
assessing a standardised set of different driving parameters in larger groups of sleepy patients and sleep deprived healthy individuals. using comparable types of driving simulators. using comparable conditions including environmental factors and time of day. including the perception of sleepiness and risky behaviour in a driving condition. comparing simulated driving with real-life near-misses and motor vehicle accidents using objective information (e.g. databases from legal authorities, insurances, or hospital records) in addition to self-reporting. ideally including and analysing different patient groups with excessive daytime sleepiness (and matched controls) to define specific constraints in certain patient groups. assessing fitness to drive by using multiple neurophysiological (maintenance of wakefulness test, multiple sleep latency test) and performance tests (simulated driving, psychomotor vigilance test, etc.) in parallel.
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•
Several of the aforementioned points can only be investigated by using a multicentre approach. A consensus among the research community is mandatory in defining the conditions of driving simulation similar to the maintenance of wakefulness test or multiple sleep latency test: number of sessions per day, optimal duration, time of day, driving simulation scenario and settings, and relevant outcome measures and how they will be judged (defined norm values). Furthermore, it would be helpful to restrict the different types of driving simulators to only a handful of comparable types, including at least one easy-to-use model for a clinical environment
35
ACCEPTED MANUSCRIPT References 1.
Ford ES, Cunningham TJ, Giles WH, Croft JB. Trends in insomnia and
excessive daytime sleepiness among U.S. adults from 2002 to 2012. Sleep medicine 2015;16(3):372–8. Hayley AC, Williams LJ, Kennedy GA, Holloway KL, Berk M, Brennan-Olsen
RI PT
2.
SL, et al. Excessive daytime sleepiness and falls among older men and women:
cross-sectional examination of a population-based sample. BMC Geriatr 2015;15:74. Hara C, Lopes Rocha F, Lima-Costa MFF. Prevalence of excessive daytime
SC
3.
Sleep medicine 2004;5(1):31–6. 4.
M AN U
sleepiness and associated factors in a Brazilian community: The Bambuı́ study.
Young TB. Epidemiology of daytime sleepiness: definitions, symptomatology,
and prevalence. J Clin Psychiatry 2004;65 Suppl 16:12–6.
Thomann J, Baumann CR, Landolt H, Werth E. Psychomotor vigilance task
TE D
5.
demonstrates impaired vigilance in disorders with excessive daytime sleepiness. J Clin Sleep Med 2014;10(9):1019–24.
Ward KL, Hillman DR, James A, Bremner AP, Simpson L, Cooper MN, et al.
EP
6.
AC C
Excessive daytime sleepiness increases the risk of motor vehicle crash in obstructive sleep apnea. J Clin Sleep Med 2013;9(10):1013–21. 7.
Braeckman L, Verpraet R, van Risseghem M, Pevernagie D, Bacquer D de.
Prevalence and correlates of poor sleep quality and daytime sleepiness in Belgian truck drivers. Chronobiol. Int. 2011;28(2):126–34. 8.
Landrigan CP, Rothschild JM, Cronin JW, Kaushal R, Burdick E, Katz JT, et
al. Effect of reducing interns' work hours on serious medical errors in intensive care units. N. Engl. J. Med. 2004;351(18):1838–48. 36
ACCEPTED MANUSCRIPT 9.
Barger LK, Cade BE, Ayas NT, Cronin JW, Rosner B, Speizer FE, et al.
Extended work shifts and the risk of motor vehicle crashes among interns. N. Engl. J. Med. 2005;352(2):125–34. 10.
Organization WH. WHO Global Status Report on Road Safety 2013:
11.
RI PT
Supporting a Decade of Action. Geneva: World Health Organization; 2013. Horne JA, Reyner LA. Sleep related vehicle accidents. BMJ
1995;310(6979):565–7.
Gonçalves M, Amici R, Lucas R, Åkerstedt T, Cirignotta F, Horne J, et al.
SC
12.
M AN U
Sleepiness at the wheel across Europe: a survey of 19 countries. Journal of sleep research 2015. 13.
Phillips RO, Sagberg F, Torkel B. Fatigue in operators of land- and sea-based
transport forms in Norway: Risk Profiles. Fatigue in Transport Report IV; 2015. Connor J, Norton R, Ameratunga S, Robinson E, Civil I, Dunn R, et al. Driver
TE D
14.
sleepiness and risk of serious injury to car occupants: population based case control study. BMJ 2002;324(7346):1125.
McConnell CF, Bretz KM, Dwyer WO. Falling asleep at the wheel: aclose look
EP
15.
AC C
at 1,269 fatal and serious injury-producing crashes. Behav Sleep Med 2003;1(3):171–83. 16.
Pack AI, Pack AM, Rodgman E, Cucchiara A, Dinges DF, Schwab CW.
Characteristics of crashes attributed to the driver having fallen asleep. Accid Anal Prev 1995;27(6):769–75. 17.
Stevenson MR, Elkington J, Sharwood L, Meuleners L, Ivers R, Boufous S, et
al. The role of sleepiness, sleep disorders, and the work environment on heavyvehicle crashes in 2 Australian states. Am J Epidemiol 2014;179(5):594–601. 37
ACCEPTED MANUSCRIPT 18.
Martiniuk ALC, Senserrick T, Lo S, Williamson A, Du W, Grunstein RR, et al.
Sleep-deprived young drivers and the risk for crash: the DRIVE prospective cohort study. JAMA Pediatr 2013;167(7):647–55. 19.
Ellen RLB, Marshall SC, Palayew M, Molnar FJ, Wilson KG, Man-Son-Hing M.
RI PT
Systematic review of motor vehicle crash risk in persons with sleep apnea. J Clin Sleep Med 2006;2(2):193–200. 20.
Mulgrew AT, Nasvadi G, Butt A, Cheema R, Fox N, Fleetham JA, et al. Risk
21.
M AN U
apnoea/hypopnoea. Thorax 2008;63(6):536–41.
SC
and severity of motor vehicle crashes in patients with obstructive sleep
Karimi M, Hedner J, Habel H, Nerman O, Grote L. Sleep apnea-related risk of
motor vehicle accidents is reduced by continuous positive airway pressure: Swedish Traffic Accident Registry data. Sleep 2015;38(3):341–9.
Philip P, Chaufton C, Orriols L, Lagarde E, Amoros E, Laumon B, et al.
TE D
22.
Complaints of Poor Sleep and Risk of Traffic Accidents: A Population-Based CaseControl Study. PLoS ONE 2014;9(12):e114102. W. Klemenjak, E. Braun, J. Alvarez, I.M. Bernhoft, L. Fjerdingen. Final
EP
23.
*24.
AC C
Programme Report Public, IMMORTAL. Project Consortium; 2005. Powell NB, Chau JK. Sleepy driving. The Medical clinics of North America
2010;94(3):531–40. 25.
Beratungsstelle für Unfallverhütung. SINUS-Report: Sicherheitsniveau und
Unfallgeschehen im Strassenverkehr 2013. Bern: bfu; 2014. *26.
Brown ID. Driver fatigue. Hum Factors 1994;36(2):298–314.
38
ACCEPTED MANUSCRIPT 27.
Philip P, Chaufton C, Taillard J, Sagaspe P, Leger D, Raimondi M, et al.
Maintenance of Wakefulness Test scores and driving performance in sleep disorder patients and controls. Int J Psychophysiol 2013;89(2):195–202. 28.
Pizza F, Contardi S, Mondini S, Trentin L, Cirignotta F. Daytime sleepiness
RI PT
and driving performance in patients with obstructive sleep apnea: comparison of the MSLT, the MWT, and a simulated driving task. Sleep 2009;32(3):382–91. 29.
Ahlstrom C, Nyström M, Holmqvist K, Fors C, Sandberg D, Anund A, et al. Fit-
SC
for-duty test for estimation of drivers’ sleepiness level: Eye movements improve the
2013;26:20–32. *30.
M AN U
sleep/wake predictor. Transportation Research Part C: Emerging Technologies
Drake C, Roehrs T, Breslau N, Johnson E, Jefferson C, Scofield H, et al. The
10-year risk of verified motor vehicle crashes in relation to physiologic sleepiness.
*31.
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Sleep 2010;33(6):745–52.
Findley L, Unverzagt M, Guchu R, Fabrizio M, Buckner J, Suratt P. Vigilance
and automobile accidents in patients with sleep apnea or narcolepsy. Chest
32.
EP
1995;108(3):619–24.
Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred Reporting Items for
AC C
Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 2009;6(7):e1000097. 33.
Johns MW. A new method for measuring daytime sleepiness: the Epworth
sleepiness scale. Sleep 1991;14(6):540–5. 34.
Akerstedt T, Gillberg M. Subjective and objective sleepiness in the active
individual. Int. J. Neurosci 1990;52(1-2):29–37.
39
ACCEPTED MANUSCRIPT 35.
Hoddes E, Zarcone V, Smythe H, Phillips R, Dement WC. Quantification of
Sleepiness: A New Approach. Psychophysiology 1973;10(4):431–6. 36.
Crowe M. Crowe Critical Appraisal Tool (v1.4). Available at:
https://conchra.com.au/2015/12/08/crowe-critical-appraisal-tool-v1-4/ [accessed
37.
RI PT
31.03.2016].
Crowe M, Sheppard L, Campbell A. Reliability analysis for a proposed critical
appraisal tool demonstrated value for diverse research designs. J Clin Epidemiol
Higgins J, Green S. Cochrane Handbook for Systematic Reviews of
M AN U
38.
SC
2012;65(4):375–83.
Interventions. Available at: http://handbook.cochrane.org/; 2011 [accessed 31.03.2016]. 39.
Demirdöğen Çetinoğlu E, Görek Dilektaşlı A, Demir NA, Özkaya G, Acet NA,
TE D
Durmuş E, et al. The relationship between driving simulation performance and obstructive sleep apnoea risk, daytime sleepiness, obesity and road traffic accident history of commercial drivers in Turkey. Sleep Breath 2015. Desai AV, Marks GB, Jankelson D, Grunstein RR. Do sleep deprivation and
EP
40.
time of day interact with mild obstructive sleep apnea to worsen performance and
41.
AC C
neurobehavioral function? J Clin Sleep Med 2006;2(1):63–70. Devos H, Vandenberghe W, Nieuwboer A, Tant M, Baten G, Weerdt W de.
Predictors of fitness to drive in people with Parkinson disease. Neurology 2007;69(14):1434–41. 42.
Tholfsen LK, Larsen JP, Schulz J, Tysnes O, Gjerstad MD. Development of
excessive daytime sleepiness in early Parkinson disease. Neurology 2015;85(2):162–8.
40
ACCEPTED MANUSCRIPT 43.
Cochen De Cock V, Bayard S, Jaussent I, Charif M, Grini M, Langenier MC, et
al. Daytime Sleepiness in Parkinson’s Disease: A Reappraisal. PLoS ONE 2014;9(9):e107278. 44.
Filtness AJ, Anund A, Fors C, Ahlström C, Akerstedt T, Kecklund G. Sleep-
RI PT
related eye symptoms and their potential for identifying driver sleepiness. J Sleep Res 2014;23(5):568–75. *45.
Hallvig D, Anund A, Fors C, Kecklund G, Karlsson JG, Wahde M, et al. Sleepy
SC
driving on the real road and in the simulator--A comparison. Accid Anal Prev
46.
M AN U
2013;50:44–50.
Mazza S, Pépin J, Naëgelé B, Rauch E, Deschaux C, Ficheux P, et al. Driving
ability in sleep apnoea patients before and after CPAP treatment: evaluation on a road safety platform. Eur. Respir. J. 2006;28(5):1020–8.
Philip P, Sagaspe P, Taillard J, Valtat C, Moore N, Akerstedt T, et al. Fatigue,
TE D
47.
sleepiness, and performance in simulated versus real driving conditions. Sleep 2005;28(12):1511–6.
Pichel F, Zamarrón C, Magán F, Rodríguez JR. Sustained attention
EP
48.
measurements in obstructive sleep apnea and risk of traffic accidents. Respir Med
49.
AC C
2006;100(6):1020–7.
Pizza F, Contardi S, Mondini S, Cirignotta F. Simulated driving performance
coupled with driver behaviour can predict the risk of sleepiness-related car accidents. Thorax 2011;66(8):725–6. 50.
Turkington PM, Sircar M, Allgar V, Elliott MW. Relationship between
obstructive sleep apnoea, driving simulator performance, and risk of road traffic accidents. Thorax 2001;56(10):800–5.
41
ACCEPTED MANUSCRIPT 51.
Valck E de, Quanten S, Cluydts R, Berckmans D. Day versus night driving in
real traffic and on a driving simulator during an 800 km all-highway drive. INTERNATIONAL JOURNAL OF VEHICLE DESIGN 2006;42(1-2):119–33. 52.
Sagaspe P, Taillard J, Chaumet G, Guilleminault C, Coste O, Moore N, et al.
RI PT
Maintenance of wakefulness test as a predictor of driving performance in patients with untreated obstructive sleep apnea. Sleep 2007;30(3):327–30. 53.
Bioulac S, Chaufton C, Taillard J, Claret A, Sagaspe P, Fabrigoule C, et al.
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Excessive daytime sleepiness in adult patients with ADHD as measured by the
Maintenance of Wakefulness Test, an electrophysiologic measure. J Clin Psychiatry
*54.
M AN U
2015;76(7):943–8.
Fisher DL. Handbook of driving simulation for engineering, medicine, and
psychology. Boca Raton: Taylor & Francis Group; 2011.
Matas NA, Nettelbeck T, Burns NR. Dropout during a driving simulator study:
TE D
*55.
A survival analysis. Journal of safety research 2015;55:159–69. 56.
Lee HC, Cameron D, Lee AH. Assessing the driving performance of older
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adult drivers: on-road versus simulated driving. Accident; analysis and prevention
57.
AC C
2003;35(5):797–803.
Kawano N, Iwamoto K, Ebe K, Aleksic B, Noda A, Umegaki H, et al. Slower
adaptation to driving simulator and simulator sickness in older adults. Aging Clin Exp Res 2012;24(3):285–9. 58.
Bridgeman B, Blaesi S, Campusano R. Optical correction reduces simulator
sickness in a driving environment. Hum Factors 2014;56(8):1472–81.
42
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Jäger M, Gruber N, Müri R, Mosimann UP, Nef T. Manipulations to reduce
simulator-related transient adverse health effects during simulated driving. Med Biol Eng Comput 2014;52(7):601–10. *60.
Schreier DR, Roth C, Mathis J. Subjective perception of sleepiness in a driving
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simulator is different from that in the maintenance of wakefulness test. Sleep Med 2015;16(8):994–8. *61.
Kaplan KA, Itoi A, Dement WC. Awareness of sleepiness and ability to predict
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sleep onset: can drivers avoid falling asleep at the wheel? Sleep Med 2007;9(1):71–
62.
Evans L. The dominant role of driver behavior in traffic safety. Am J Public
Health 1996;86(6):784–6. *63.
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Canadian Cardiovascular Society. Assessment of the cardiac patient for
Figures Legend
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fitness to drive. Can J Cardiol 1992;8(4):406–19.
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Figure 1. Factors relevant for and leading to a sleepiness-induced motor vehicle
Figure 2. Evolution of a sleepiness-induced motor vehicle accident. Figure 3. Driving simulators in the clinical assessment
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1. The article must contain all of the items addressed below: a. sleepy individuals (incl. sleepiness, fatigue, drowsiness, tiredness) b. simulated motor vehicle driving with reported performance
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number of real-life near-miss or motor vehicle accidents in a certain time period
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Table 2 Study aim
Study setting and design
Study population (n)
Gender
Age
Sleepiness
Demirdöğen et al. 2015 39
Determine the relationship between obesity, risk of obstructive sleep apnoea, daytime sleepiness, history of road traffic accidents, and driving simulator performance.
Quasi experiment
282 commercial vehicle drivers (mandatory assessment every 5 years)
All male
29-76, median = 45
Epworth sleepiness scale
Desai et al. 2006 40
Examine the interactive effect of sleep deprivation, time of day, and mild OSA on performance and neurobehavioral function, especially driving simulator performance.
Cross over study
With OSA: 13
All male
With OSA: 45(±14.8)
1. Development of clinical battery to identify safe and unsafe drivers with PD.
Prospective experiment
40 PD patients.
Real driving (assessment / setting)
CCAT (%)
Psycho-technical assessment system
Self-reported road traffic accident history
56
Epworth sleepiness scale, Stanford sleepiness scale, Oxford sleep resistance test, Psychomotor vigilance test
personal computer-based driving simulator (AusEdTM ) (Sydney, Australia; Edinburgh, Scotland)
Self-reported motor vehicle accidents
95
Epworth sleepiness scale,
STISIM Drive system, model 300, Version 1.03.05 manufactured by Systems Technology Inc., Hawthorne, CA
Self-reported driving habits, traffic penalties and accidents over the last 5 years. Drivers with PD asked to self-appraise their fitness to drive. CARA assessment – on road driving evaluation for PD patients scored on the Test ride for investigating practical fitness to drive checklist.
89
41.3± 8.7 (SD)
Karolinska sleepiness scale, selfreported effort required to stay awake. Electrooculography measured at 512 Hz for measuring eye blinks. Smart Eye Pro 5.7 recorded at 60 Hz for eye gaze
VTI driving simulator III is a moving base simulator with a Saab 9-3 cabin (automatic gearbox) and a 120 degrees forward field of view.
Automatic Volvo XC70. Vehicle data were recorded at 10 Hz.
90
With OSA: 51± 1 (SEM)
EEG, EOG, submental electromyography, oxyhaemoglobin saturation
Steer clear computer programme
Driving records from the Department of motor vehicles – automobile accident rate, defined as property damage greater than $500.
75
64 males, 16 females
40 age and sex matched controls
2. Determine value of a driving simulator evaluation in the prediction of fitness to drive.
Investigation of sleep-related eye symptoms and their association with driver sleepiness.
Cross over study
16 healthy individuals
Findley et al. 1995 31
Investigation of impaired vigilance in patients with sleep apnoea and narcolepsy during prolonged, monotonous tasks, whether degree of impairment is related to severity of illness, and whether impaired vigilance is associated with a higher than expected rate of automobile accidents.
Prospective experiment
62 patients with OSA, 12 controls and 10 volunteers (both age and sex matched)
Not explicitly given
8 males, 8 females
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OSA patients and controls: 63 males, 11 females
10 patients with untreated narcolepsy, 10 controls and 10 volunteers (both age and sex matched)
Narcolepsy patients and controls: 14 females, 6 males
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Healthy controls: 16
Driving simulation (type / setting)
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Without OSA: 50±3 With narcolepsy: 37±5 Without narcolepsy: 36±4
ii
Comparison of real driving with simulated driving under conditions of extended time awake.
Prospective experiment
10 healthy individuals
5 males, 5 females
Mazza et al. 2006 46
Evaluating the performance of a group of apnoeic patients and controls on a road safety platform.
Cross over study
20 patients with OSA, 20 matched controls
advanced moving base driving simulator, car body consisted of the front part of a Volvo 850 with a manual 5-shift gearbox. three channels of forward view of 120◦ × 30◦
Volvo S80, model year
Epworth sleepiness scale, Oxford sleep resistance test, Continuous performance test
DASS
Miniature driving performance evaluation
78
Philip et al. 2005 47
To determine whether real life driving would produce results different from those obtained in a driving simulator.
Cross over study
12 healthy individuals
All males
21.1±1.6 (SD)
Karolinska sleepiness scale, selfreported fatigue
DASS
Car equipped with dual controls
93
Pichel et al. 2006 48
To identify associations between performance on driving simulators, subject sleep complaints and risk of traffic accidents in a population undergoing OSAS investigation.
Cohort study
93 OSA patients
78 males, 15 females
50.8±10.7 (SD)
Epworth sleepiness scale, Basic nordic sleep questionnaire
Steer clear computer programme and DASS 2D
Self-reported questionnaire
80
Pizza et al 2011 49
To determine the predictors of sleepiness and crash risk in patients with OSA.
Cohort study
43 OSA patients
All males
53±9 (SD)
Epworth sleepiness scale
STISIM 300 Driving simulator systems technology
Self-reported questionnaire
55
Turkington et al. 2001 50
To determine the relationship between driving simulator performance, patient symptoms, sleep study results and driving history and establish if the driving simulator data is additionally of help in advising fitness to drive.
Cohort study
150 OSA patients
124 males, 26 females
49.8±10.7 (SD)
Epworth sleepiness scale
DASS
Self-reported questionnaire
83
Valck et al. 2006 51
To examine driving behaviour – as measured by the standard deviation in steering position and speed - during prolonged driving in real traffic during daytime as opposed to night time.
Cross over study
9 healthy individuals
40±11(SD)
Fatigue of the profile of mood states
York driving simulator with Drivesim 3000 software
Day and night drives from Antwerp to Lyon
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35 males and 5 females
40±11(SD)
Patients: 54.1±5.9 (SD)
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2000, equipped with dual command.
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Table 2. Characteristics of included studies. Abbreviations: AusEdTM: Australia Edinburgh driving simulator, CARA: Centre for fitness to drive evaluation and car adaptations, DASS: Divided attention steering simulator, EEG: Electroencephalography, EOG:
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Electrooculography, OSA: Obstructive sleep apnoea, PD: Parkinson’s disease, SD: Standard deviation, SEM: Standard error of the
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Table 3 Author, year
Driving simulation
Duration or length of drive
others
Inappropriate line crossings: RNP
6 x 105 minutes
Reaction time: RNP
KSS Day: range 4.3-6.1 Night: 7.6-8.9 KDS mean (%): Day range: 31-44 Night: range 47-53
Lateral position (m): Day range: -0.19 - 0.04 Night range: -0.2 - -0.38 Standard deviation of lateral position (m): Day range: 0.18-0.19 Night range: 0.19-0.23
90 minutes
Time to line crossing (sec): Day range: 9-10.1 Night range: 8.510.3
KSS mean (SD): 5.7 (1.417) KSS max (SD): 6.781 (1.699) Effort to stay awake mean (SD) (1-7): 3.656 (2.149)
Left line crossings/km, mean (SD): 0.038 (0.068) Right line crossings/km, mean (SD): 0.008 (0.125)
90 minutes
Real driving
Sleepiness
Road position
Near-misses and accidents Crashes: 0 for all subjects
Length of drive
Other
Sleepiness
30 minutes
Velocity deviation: RNP Mean reaction time: RNP
ESS mean With OSA: 9±6 Without OSA: 7±4.2 KSS: RNP
Desai et al. 2006 40
Oxford sleep resistance test: sleep latency: RNP
SDLP: Results not published
93
Philip et al. 2005 47
KSS: Results not published
Inappropriate line crossings: RNP
6 x 105 minutes
Reaction time: RNP
91
Hallvig et al. 2013 45
KSS: Day range: 4.3-6.1 Night range: 7.5-8.9 KDS mean (%): Day range: 32-42 Night range: 48-50
Lateral position (m): Day range: 0-0.02 Night range: -0.05 - -0.18 Standard deviation of lateral position (m): Day range: 0.18-0.25 Night range: 0.28-0.35
60 minutes
Time to line crossing (sec): Day range: 6.2-6.4 Night range: 5.8-6.2
90
Filtness et al. 2014 44
KSS mean (SD): 7.439 (1.385) KSS max (SD): 7.726 (1.343) Effort to stay awake mean (SD) (1-7): 5.438 (2.031)
Left line crossings/km, mean (SD): 0.073 (0.128) Right line crossings/km, mean (SD): 0.004 (0.007)
75-80 minutes
89
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ESS: PD patients passed to drive: 5.86±4.16 PD patients not passed for driving: 7.55±4.87
83
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ESS: Md (IQR): 12 (7-17)
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ESS: Tracking error: Normal performance: 11±6 Poor performance: 10±4 Reaction time: Normal performance: 10±2 Poor performance: 10±7
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Off road events/hour: Md (IQR) 24 (3-104)
Road position
Near-misses and motor vehicle accidents Self-reported MVAs in last 3 years: With OSA: 0.3±0.5 Without OSA: 0.6±1.3
TRIP score: PD patients passed: 116 PD patients failed: 100 Reaction time (Md): PD patients passed:2.52 PD patients failed: 2.92
Accidents & traffic penalties (Md, IQR) for CARA: pass: 0 (0-0), 0 (0-1) fail: 0 (0-0), 0 (0-0)
20 minutes
Reaction time: Md (IQR): 2.85 (1.99-4.23)
Accidents in last 3 years (%): 25
20 minutes
Tracking error: Normal performance: 53.7% Poor performance: 46.3% Reaction time: Normal performance: 18.2% Poor performance: 81.8%
MVA in last year: 22.6%
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Traffic accidents (Md): PD patients passed: 0 PD patients failed: 1 SDLP: Md (IQR) 0.321 (0.218-1.172)
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CCAT score
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15 km
CARA decision 72.5% fit to drive 25% fit to drive with restrictions 2.5% not fit to drive
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Mazza et al. 2006 (results before treatment were used)
ESS (mean ± SD for all): OSA patients: 11.4±3.4 Controls: 8.4±2.7
Off-road events (n): OSA patients: 89.5±116.7 Controls: 10.0±12.5
20 minutes
Accident liability (0 if no crashes, 1 if at least 2): RNP
25 minutes
% of obstacles hit: Sleep apnea patients: 4.3±0.6 Controls: 1.4±0.3 Narcolepsy patients: 7.7±3.2 Controls: 1.2±0.3
30 minutes
Number of off road events: Results not published
Not stated
Mean reaction time (sec): OSA patients: 3.98±2.47 Controls: 1.95±0.87
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Findley et al. 1995 31
Subjective sleepiness not measured
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Demirdöğen et al. 2015 39
Turkish version of ESS With history of RTA: Med = 2 (0-19) Without history of RTA: Md = 2 (0-20)
Time spent off road: Results not published Speed-distance estimation test: Drivers without RTA performed poorer than those with past RTA
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Pizza et al. 2011 49
ESS (mean ± SD) With crash history: 12±4.2 Without crash history: 9.2±4
Lane position variability (m, mean ± SD) With crash history: 0.6±0.3 Without crash history: 0.4±0.1
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Fatigue of the profile of mood states: Fatigue score day drive mean (SE): 6.5(0.5) at start 11.17(1.56) at end. Night drive: 7.0(0.71) at start, 12.14(1.78) at end
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Crashes: With crash history: 2.7±5.9 Without crash history: 0.5±1.0
30 minutes
Number of collisions: OSA patients: 0.9±0.7 Controls: 0.4±0.5
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Standard deviation steering position: RNP
Reaction time (sec): OSA patients: 1.51±0.35 Controls: 0.91±0.21 Distance to stop (m): OSA patients: 36.6±10.7 Controls: 27.9±6.5
Day drive: 807 km, night drive: 794 km
MVA rate (accident/driver/5y): Normal vigilance performance: 0.05 Poor vigilance performance: 0.2 Very poor vigilance performance: 0.38
Reaction time: 46.7% of those with high OSA risk had poor reaction times vs 28.1% with low OSA risk Peripheral vision test: NA
Self-reported RTA history: 26% of 274 drivers had been involved in traffic accidents
Time to first crash (min, mean ± SD) With crash history: 22.3±8.6 Without crash history: 27.1±5.1
Near miss accidents in last 5 y (%): With crash history: 35 Without crash history: 17.4
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30 minutes
Driving > 20’000 km/y (%) with crash history: 50 without crash history: 60.9
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Table 3. Measurements and results from included studies. Abbreviations: CARA: Centre for fitness to drive evaluation and car
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Table 4 Time to line crossing
Reaction time
90.5 % (78-93)
91 %
93 %
44,45,47,51
45
47
Distance to stop
Reaction time
91 %
91 %
45
45
Near-misses and/or accidents 69.5 % (55-95) 39,40,49,50
80 % 48
93 %
85.5 % (78-93)
78 %
89 %
81.5 % (56-95)
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46,47
46
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78 % 51
95 % 40
89 %
89 %
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41
55 % 49
78 %
78 %
89 %
78 % (55-95)
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Near-misses and/or accidents
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CARA decision
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Road position
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Table 4. Median of CCAT scores of studies reporting on the corresponding item (and range, if more than one study was available). Abbreviations: CARA: Centre for fitness to drive evaluation and car adaptations, TRIP: Test ride for investigating practical fitness to drive.
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