Insufficient sleep and fitness to drive in shift workers: A systematic literature review

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Insufficient sleep and fitness to drive in shift workers: A systematic literature review Melissa Knotta, , Sherrilene Classenb, Sarah Krasniuka, Marisa Tippettc, Liliana Alvarezd ⁎

a

Faculty of Health Sciences, Western University, London, Ontario, Canada Department of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, USA c Western Libraries, Western University, London, Ontario, Canada d School of Occupational Therapy, Faculty of Health Sciences, Western University, London, Ontario, Canada b

ARTICLE INFO

ABSTRACT

Keywords: Sleep deprivation Cognition Shift work Driving Systematic literature review

Background: Insufficient sleep, < 6.5 h per night, majorly affects shift workers, placing them at higher risk for motor vehicle crash related injury or fatality. While systematic reviews (SLRs) examine the effects of insufficient sleep and driving, to date, no SLR focuses on driver fitness or performance in shift workers. Objectives: Determine the class of evidence (Class I-highest to Class IV-lowest), and level of confidence (Level Ahigh, to Level U-insufficient) in the determinants of driver fitness and performance in shift workers. Next, consider evidence-based recommendations for clinical practice, research, and policy. Methods: A protocol was registered on PROSPERO (#CRD42018052905) using an established SLR methodology: a comprehensive electronic database search, study selection, data extraction, critical appraisal, analysis, and interpretation using published guidelines. Results: Searches identified 1226 unique records with 11(2 on-road, 9 simulator) meeting final inclusion criteria. Class III to IV evidence identified that exposure to overnight shift work possibly predicts (Level C confidence) drivers at risk for adverse on-road outcomes and likely predicts (Level B) drivers at risk for adverse driving simulator outcomes. Higher ratings of subjective sleepiness and extended time driving possibly predict (Level C) drivers at risk for adverse driving simulator outcomes. Conclusions: This study demonstrates a low to moderate level of confidence in the determinants of driving in shift workers. A critical need exists for gold-standard on-road assessments integrating complex driving environments representative of real-world demands, targeting tactical and strategic outcomes in a broad spectrum of shift workers.

1. Introduction 1.1. Background Shift work comprises 15–28% of employment in North America, with varying prevalence by occupation (Bureau of Labor Statistics, 2005; Williams, 2008). Employees with the highest prevalence for shift work include those in sales and service, health care, accommodation and protective services (Bureau of Labor Statistics, 2005; Williams, 2008). Insufficient sleep, defined as < 6.5 h of sleep per night,

negatively impacts driving, and is a primary complaint affecting up to three-quarters of shift workers (Åkerstedt, 1988; Watson et al., 2015). Of American drivers, 42% reported insufficient sleep weekly, with 30% engaging in drowsy driving in the past month (American Automobile Association Foundation for Traffic Safety, 2017), and up to 20% of North American drivers reported nodding off or falling asleep while driving in the past year (Tefft, 2010; Marcoux et al., 2012). Insufficient sleep, is known to result in neurocognitive and neurobehavioral deficits, including episodes of hypovigilance; slowed reaction time; and impaired memory, attention, executive function, and/or risk

Abbreviations: AAN, american academy of neurology; CDE, comprehensive driving evaluation; DP, driving performance; DRS, driving rehabilitation specialist; EEG, electroencephalogram; EOG, electrooculography; FTD, fitness to drive; IROG, infrared reflectance oculography; KSS, karolinska sleepiness scale; MVC, motor vehicle crash; PRISMA, preferred reporting items for systematic reviews and meta-analyses; PVT, psychomotor vigilance task; SDLP, standard deviation of lateral position; SLR, systematic literature review ⁎ Corresponding author at: i-Mobile Research Lab, Elborn College, 1201 Western Road, Western University, London, Ontario, N6G 1H1, Canada. E-mail addresses: [email protected] (M. Knott), [email protected] (S. Classen), [email protected] (S. Krasniuk), [email protected] (M. Tippett), [email protected] (L. Alvarez). https://doi.org/10.1016/j.aap.2019.07.010 Received 7 June 2019; Accepted 15 July 2019 0001-4575/ © 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Melissa Knott, et al., Accident Analysis and Prevention, https://doi.org/10.1016/j.aap.2019.07.010

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perception—all which negatively impact driving (Watson et al., 2015; Banks and Dinges, 2011; Czeisler et al., 2016; Bonnet, 2011; Durmer and Dinges, 2005; Roehrs et al., 2019; Goel et al., 2009; Transport Canada, 2011; MacLean, 2016). Tasks like driving, that require rapid response times dictated by the environment or sustained attention in monotonous conditions uniquely strain these cognitive functions and may result in critical errors, negatively impacting fitness to drive (Bonnet, 2011; Durmer and Dinges, 2005; MacLean, 2016). Fitness to drive requires both the physical and mental capacity to smoothly operate a motor vehicle under all environmental and traffic conditions (Brouwer and Ponds, 1994). The gold standard for assessing fitness to drive is the comprehensive driving evaluation (CDE), which includes an off-road and on-road assessment administered by a driver rehabilitation specialist (DRS) (Transportation Research Board, 2016; Classen et al., 2017a; Di Stefano and Macdonald, 2005). Nevertheless, the CDE may not always be readily available – it is costly, time consuming, and there are limited numbers of DRS available. Although they represent real-life driving experiences (but are not reality itself), driving simulators can provide insight into a driver’s performance and behaviors (Di Stefano and Macdonald, 2005). As described by Michon’s Model of Driving Behavior, driving behavior is comprised of three levels: the most complex, strategic (e.g., applying executive functions for route planning or adapting to changing road conditions); the next, tactical (e.g., maneuvers to negotiate on-road situations, such as avoiding obstacles or making turns), and the least complex, operational (e.g., interaction with vehicle equipment, such as reaction time to brake or steer (Michon, 1985). Taken together, errors in the strategic, tactical, and operational levels result in observable errors in driving behaviors both on-road, as well as on driving simulators. The impact of insufficient sleep and driver sleepiness on on-road and driving simulator outcomes have been examined in a number of systematic reviews (SLRs) to date (Czeisler et al., 2016; Connor et al., 2001; Bioulac et al., 2017; Soleimanloo et al., 2017; Schreier et al., 2018). Two SLRs investigated the influence of insufficient sleep on motor vehicle crashes (MVC) and injuries in drivers with sleep disorders (Connor et al., 2001; Bioulac et al., 2017). In 2001, Connor and colleagues concluded that moderate evidence exists pertaining to sleep apnea as a causal factor in MVC in adult drivers; while weak to insufficient evidence exists for insufficient sleep/driver sleepiness for MVC in the general population (Connor et al., 2001). More recently, in 2017 Bioulac and colleagues conducted a meta-analysis and concluded that increased subjective driver sleepiness resulted in an increased crash involvement with a pooled odds ratio of 2.51 [95% CI 1.87; 3.39] (Bioulac et al., 2017). An expert panel convened by (Czeisler et al., 2016) concluded that drivers are not fit to drive with < 2 h of sleep/ night, while most healthy adults would likely be impaired with 3–5 hours of sleep/night. Consensus was not reached in the 3–5 hour range due to research study limitations, individual variability in response to insufficient sleep, and limited control of confounding variables (Czeisler et al., 2016). Meanwhile, Solemianoo, White, and Garcia-Hansen (Soleimanloo et al., 2017) determined that young drivers demonstrated deteriorating driving performance with both acute sleep loss of ≥2 h/night, and chronic sleep loss of 1–4 hours/night (Soleimanloo et al., 2017). Finally, Schrier, Banks, and Mathis (Schreier et al., 2018) concluded moderate associations between simulator and on-road results in drivers experiencing sleepiness. However, predictions of real-world individual crash involvement were unreliable, possibly due to methodological limitations. Overall, the evidence suggests that though drivers with sleep disorders may be more vulnerable for crashes, healthy drivers with insufficient sleep or sleepiness also face an increased risk of crashes (Czeisler et al., 2016; Connor et al., 2001; Bioulac et al., 2017; Schreier et al., 2018). However, to our knowledge, no systematic review to date has synthesized and appraised the quality of the evidence on the on-road fitness to drive and simulated driving performance of shift workers.

1.2. Objectives This SLR synthesizes the literature pertaining to the effects of insufficient sleep and sleepiness on the driver fitness and driver performance of shift workers. We aim to determine the quality of evidence supporting the determinants of on-road and simulator driving outcomes in shift workers with insufficient sleep; and the level of confidence in such determinants. Finally, we identify evidence-based recommendations for clinical practice, research, and policy. 2. Methods Researchers were granted a letter of exemption from the Institutions’ Research Ethics Board to complete this SLR. A team of five researchers, including two PhD Candidates, two PhD-level rehabilitation science researchers and occupational therapists, and a university research librarian conducted this SLR following an established sevenstep methodology (Cooper and Hedges, 2009) and the PRISMA reporting guidelines (Moher et al., 2009). A detailed study protocol was registered on PROSPERO (#CRD42018052905) (Knott et al., 2018a) and published in a peer-reviewed journal (Knott et al., 2018b) adhering to the PRISMA-P reporting guidelines (Moher et al., 2015). A brief overview of the methods used to locate and select studies, collect data, appraise critically, analyze and present data, and interpret results are outlined below. For concision, full details of the search strategy, and the operationalized predictor and outcome variables of interest, and information dissemination are solely in the published protocol (Knott et al., 2018b). 2.1. Study selection The targeted search strategy employed keywords and subject headings for concepts of insufficient sleep, shiftwork, and driving, focusing on six databases: Scopus, Embase, PsycINFO, PubMed, Cumulative Index of Nursing and Allied Health Literature (CINAHL), and ProQuest Nursing and Allied Health. Duplicate titles were removed using reference management software. This SLR examined shift workers working non-daytime shifts and who experienced insufficient sleep, i.e., ≤6.5 h of sleep per 24 h. Comparators include healthy adults with usual sleep, who may be nonshift workers, or shift workers assessed on a rest day acting as their own controls. Outcomes include measures of vehicle output metrics, driving behaviors, or summary metrics. Studies were included if they were English-language, peer-reviewed quantitative research in on-road or driving simulator studies with adults 16–65 years who are employed in shift work, indicating insufficient sleep. Studies were excluded if they were reviews, employed qualitative methods, focused on psychometrics properties, or were pharmacological or clinical interventions addressing the impact of insufficient sleep. Title and abstract screening was conducted independently by pairs of researchers (N = 4), with inter-rater reliability calculated using Cohen’s kappa statistic (κ). Any discrepancies for inclusion/exclusion were reviewed and consensus achieved before undertaking full-text review to verify eligibility. Footnote chasing was conducted to identify further relevant studies for further title and abstract screening (White, 2009; Rothstein and Hopewell, 2009). 2.2. Data collection Articles meeting inclusion criteria then underwent independent data extraction by pairs of researchers (N = 4) using the Systematic Process for Investigating and Describing Evidence-based Research (SPIDER), (Classen et al., 2008) with permission from the lead author of this tool. Predictor and outcome variables of interest were determined a priori, and were operationalized within the SLR Protocol manuscript (Knott et al., 2018b). Predictor variables of interest included shift work type, 2

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(Gronseth et al., 2017). Per the AAN guidelines, a higher risk of bias indicates a higher risk of systematic error which may affect the accuracy of the study outcome. Specific criteria for each class of evidence is outlined in Supplementary Table 1.

Table 1 SLR Search Returns based on keywords and MeSH headings as per study protocol. Database

Total

Scopus Embase PsycINFO PubMed CINAHL ProQuest Nursing & Allied Health Total returns Unique returns Footnote chasing Total records screened

384 487 154 349 51 248 1673 887 339 1226

2.4. Results interpretation Following critical appraisal of individual studies with the AAN guidelines (Gronseth et al., 2017), researchers analyzed the level of confidence in the predictors of adverse fitness to drive or driving performance outcomes (hereafter referred to as adverse driving outcomes). The level of confidence ranged from Level A, highest level of confidence (e.g., highly likely to predict drivers at risk for adverse driving outcomes) to level U, insufficient evidence (Gronseth et al., 2017) to predict drivers at risk for adverse outcomes. The criteria for the level of confidence ratings is outlined in Supplementary Table 1. First, an anchor is assigned for the level of confidence, from: Level A, B, C or U. Following this, if warranted, the level of confidence can be adjusted upwards by one level for determinants with a large effect size, dose-response relationship, or direction of bias compared to the direction of effect (Gronseth et al., 2017). The level of confidence can also downgraded by 1–2 levels depending on the number and severity of concerns regarding consistency of results between studies, study power, generalizability, or plausibility (Gronseth et al., 2017). Due to the heterogeneity of the driving constructs, and the heterogeneity of predictor and outcome variables, authors conducted a qualitative synthesis versus a meta-analysis of results (Cooper and Hedges, 2009; Gronseth et al., 2017; Classen and Alvarez, 2016). The syntheses of participant demographics and study methods are presented first, followed by the critical appraisal of the class of evidence and then the level of confidence.

Abbreviations: CINAHL = Cumulative Index of Nursing and Allied Health Literature.

e.g., afternoon or overnight shift; shift length in hours; amount of sleep obtained by shift workers in hours; subjective sleepiness, e.g., Epworth Sleepiness Scale; and/or objective sleepiness, e.g., Psychomotor Vigilance Task. Outcome variables of interest included vehicle output metrics, e.g., standard deviation of lateral position, speed; driving behaviors, e.g., lane maintenance, visual scanning; and summary metrics, e.g., total number of errors, pass/fail outcomes. 2.3. Critical appraisal Paired reviewers independently and critically appraised studies using the American Academy of Neurology guidelines (Gronseth et al., 2017), with permission granted from the AAN. The class of evidence for each study was rated from the highest quality with the lowest risk of bias (Class I) to the lowest quality with the highest risk of bias (Class IV)

Fig. 1. PRISMA Flow Diagram. The PRISMA flow diagram tracks the total number of articles obtained in searches, and tracks articles included or excluded at each stage, with reasons identified (Moher et al., 2009). 3

4

United States

Liang, Horrey, Howard, Lee, Anderson, Shreeve, O’Brien, and Czeisler. (2017)

United States

Lee, Howard, Horrey, Liang, Anderson, Shreeve, O’Brien, & Czeisler. (2016)

On-Road Studies

Authors (Year) Location

Table 2 Data Summary table.

Secondary data analysis of a withinsubjects crossover study (Lee, et al., 2016), to develop prediction models for drowsy driving events in shift workers driving on-road following one night shift versus following a night sleep.

Within-subjects quasi-experimental crossover study examining in-vehicle driving on a closed road-course following one night shift versus following a night sleep.

Study Design, Purpose

III

III

Class of Evidence

M/F: 7/9 Mean Age: 47.8 ± 14.8 Age Range: 19-65 Health Status: unknown. Group screen: 31% of group high risk for sleep apnea.

N=16, unknown occupations

Predictors for Models: 1. Driver Factors (general, individualized)

1. Night shift 2. Night sleep

Prediction of performance degradation (Lane Crossing Events)

Emergency braking maneuvers

2. IED 3. PERCLOS 4. BLINKD 5. Positive AVR 6. JDS EEG Micro sleep episodes KSS (in drive, q15m) Condition

Drives Terminated Early

Ocular measures via EOG and IROG 1. Slow eye movement

Group screen: 31% high risk for sleep apnea.

Near Crash Events

2. Night sleep

Mean Age: 47.8 ± 14.8 Age Range: 19-65 Health Status: unknown.

(crossings)

Lane departure

Outcome Variables

1. Night shift

Condition

Predictor Variables

M/F: 7/9

N=16, unknown occupations

Participant Demographics

Models using driver factors and ocular factors (above) significantly better than general models without driver factors. No significant difference for including driving performance measures as predictors.

AUC: 0.82 ± 0.08 Sensitivity: 0.36 ± 0.22 Specificity: 0.98 ± 0.02

Lane crossing prediction model:

Time on task: all events (emergency braking, near-crash, driving terminations) occurred after > 45 minutes of driving post shift.

Condition: night shift significant for lane departure both straight and curved sections (p < 0.0001), Emergency braking maneuvers (p = 0.0088), Nearcrash events (p = 0.0088), Driving terminations (p = 0.0034).

Main Results

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Possible confounders: caffeine/ stimulant use; alerting effect of stopping for KSS (q15m); passenger in car; 5/16 participants identified as high risk sleep apnea; no healthrelated exclusion criteria. Control group sleep range as low as 5h. Low power.

Possible confounders: caffeine/ stimulant use; alerting effect of stopping for KSS (q15m); passenger in car; 5/16 participants identified as high risk sleep apnea; no healthrelated exclusion criteria. Control group sleep range as low as 5h, which may be impaired. Low power.

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Secondary data analysis of a withinsubjects crossover design (Åkerstedt, et al. 2005) to estimate relative risk of having driving simulator crash, accident, or incident by subjective sleepiness levels (KSS).

Ingre, Åkerstedt, Peters, Anund, Kecklund, & Pickles. (2006b)

Sweden

Secondary data analysis of a withinsubjects crossover design (Åkerstedt, et al. 2005) to estimate associations between subjective sleepiness, blink duration, and lane drifting measured via driving simulator to evaluate individual differences.

Within-subjects quasi-experimental crossover study examining driving performance following working one night shift versus following one day off.

Study Design, Purpose

Ingre, Åkerstedt, Peters, Anund & Kecklund. (2006a)

Sweden

Driving Simulator Studies Åkerstedt et al. (2005)

Authors (Year) Location

Table 2 (continued)

III

III

III

Class of Evidence

5 KSS (in drive, q5m) EOG (BLINKD) Condition

Health Status: unknown N=10, mixed hospital, energy plant, newspaper

1. Night shift 2. Night sleep

M/F: 5/5 Mean Age: 37 ± 12

Condition

Incident

TTC Steering angle SDLP

EOG (BLINKD) N=10, mixed hospital, energy plant, newspaper

Lateral position SDLP

Prior work-sleep hours KSS (in drive, q5m)

Health Status: unknown

Accidents Speed

Incidents

Outcome Variables

1. Night shift 2. Night sleep

Condition

2. Driving performance (SLDP, SD steering, mean steering wheel angle, not during lane crossing event). 3. Ocular Measures (EOG measures: AVR, JDS, PERCLOS).

Predictor Variables

M/F: 5/5 Mean Age: 37 ± 12

N=10, mixed hospital, energy plant, newspaper

Participant Demographics

Condition: night shift greater events (incident, accident, crash) (p = 0.010)

BLINKD increases significantly predict SDLP in a linear manner (X2 = 49, df = 1, p < 0.001). Curvilinear relationship for BLINKD and SDLP shows larger increases in SDLP (X2 = 8.78, df = 1, p= < 0.003) at higher BLINKD (ICC = 0.30).

KSS increases significantly predict SDLP in a linear manner (X2 = 38 df = 1, p < 0.001). Curvilinear relationship for KSS and SDLP shows larger increases in SDLP (X2 = 11, df = 1, p < 0.001) at higher KSS (ICC = 0.49).

Condition, Sex: No significant changes for lane position or SD Speed.

Time on task: Significant for SDLP (F = 3.7, p < 0.01). No significant interaction of condition by time on SDLP.

Condition: Night shift greater Incidents (z = 2.8, p < 0.01), TTC (z = 2.2, p < 0.05), SDLP (F = 6.0, p < 0.05).

Main Results

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Possible confounders: caffeine use and napping not controlled, no health-related exclusion criteria.

Possible confounders: caffeine use and napping not controlled, no health-related exclusion criteria.

Possible confounders: caffeine use and napping not controlled, no health-related exclusion criteria.

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De Valck, Quanten, Berckmans & Cluydts. (2007)

United States

Driving Simulator Studies Ware, Risser, Manser, & Karlson. (2006)

Sweden

Authors (Year) Location

Table 2 (continued)

Two-group quasi-experimental counterbalanced design examining driving performance in chemical plant technicians following regular rotating shifts (day, afternoon, night) comparing fast-forward and slowbackward shift rotation schedules.

Within-subjects crossover design examining driving performance of physicians following a night on-call versus a night of sleep.

Study Design, Purpose

III

III

Class of Evidence

KSS (in drive, q5m)

Health Status: unknown

VAS-F (pre, post)

Health Status: No significant medical history or sleep disorder. No medication affecting CNS. N=36, chemical plant technicians

Sleep Quality, Time ESS (pre)

Male: 29.4 ± 4.3 Female: 28.7 ± 2.4

Gender Epoch (6 x10 min) Shift

1. Night call 2. Night sleep

M/F: 12/7 Mean Age:

Condition

2. Night sleep

Mean Age: 37 ± 12

N=19 MD Resident, student

1. Night shift

Predictor Variables

M/F: 5/5

Participant Demographics

SD Speed SDLP

Crash

SDLP SD Speed

Crash

Accident

Outcome Variables

Shift type predicted SDLP (F2,68 = 5.79, p < 0.01), night shift greater than afternoon shift. No significant effect on SD Speed or Accident Liability.

Shift type: No main effect of shift type on lane position. No main effect of call, time, or gender on SD Speed.

Epoch: SDLP increased with time on task F5,85= 2.53).

Condition (post shift) by sex interaction. Night shift men had greater: SDLP (F1,17 = 7.96, p = 0.012); Crashes (U = 17.50). No interaction effects for night sleep (p = 0.96).

Time on task: greater events (p = 0.009)With KSS included in model, neither condition nor time on task remained predictive of events. KSS increases resulted in greater RR of events with greater RR at higher levels of KSS. RR (95% CI) for events between KSS = 5 (neither sleep nor alert), KSS = 7 (sleepy but no effort to remain awake), KSS = 9 (very sleepy, fighting sleep, difficulty staying awake) (Åkerstedt & Gillberg, 1990) KSS 5-7, RR = 6.365 (3.719 10.893) KSS 7-9, RR = 29.12 (11.246 75.390) KSS 5-9, RR = 185.3 (42.073 816.4) Random effects: Significant variability of accident tendency (p < 0.001) and condition (p = 0.002), estimate 79% of accident variability is stable across night shift or night sleep conditions (ICC = 0.795).

Main Results

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Possible confounders: caffeine use differs by shift (night, morning > afternoon) Workers self-selected to shift rotation patterns.

Possible confounders: caffeine use differed by condition (night shift > night sleep).

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United States

Talusan, Long, Halim, Guliani, Carroll, & Reach. (2014)

United States

Waggoner, Grant, Van Dongen, Belenky, & Vila. (2012)

Belgium

Authors (Year) Location

Table 2 (continued)

Pre-test post-test within-subjects design examining driving performance in physicians pre-shift versus post-shift for 28-hour traditional call shifts and night float shifts.

Within-subjects crossover design examining driving performance in police officers following five consecutive night shifts versus following 3 days off.

Study Design, Purpose

IV

III

Class of Evidence

MD Service:

Health Status: no lower extremity injury

2. Internal ESS (pre)

1. Orthopedic

1. Traditional, 28h 2. Night float, 14h

M/F: n/a Age range: PGY 1-4

KSS (pre, post) Condition:

PVT-lapses (pre, post)

Health Status: Fit to work. Group screens: overall high fatigue (ESS), low sleep quality (PQSI), high sleep apnea risk (MAPS-Q). N=58, MD residents

1. Post 5-night shifts 2. Post 3 days off

M/F: 27/2 Mean Age: 37 ± 6.3

N=29, police

3. Overnight

Health Status: no medication affecting CNS Shift Rotation 1. Fast-Forward 2. Slow-Backward SSS (pre, post) Condition

1. Day 2. Afternoon

Predictor Variables

M/F: 36/0 Mean Age: 42.4 ± 1.3

Participant Demographics

Brake reaction time

SDLP

Accident Liability

Outcome Variables

MD Service: Orthopedic MD increased BRT pre- to post-shift (p = 0.007), No change for Internal MD pre- vs post- shift (p = 0.76).

Shift - Night Float: No change night-float pre- vs post- shift (p = 0.65).

Shift – Traditional Call: increased BRT for post-traditional call shift, vs pre-call (p = 0.01).

Time on Task: SDLP increased with time on task driving (F1,78 = 10.43, p = 0.002), but was nonsignificant interaction condition by session.

Condition: Post night shift increased SDLP (F1,78 = 6.78, p = 0.011).

Shift Rotation: No significant effect on SD Speed or Accident Liability.

Main Results

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Possible confounders: caffeine, napping, number of nights worked in a row. Possible experiment-wide error (overall Physician BRT > post-surgery patient BRT). No between groups analysis.

Two post-shift drives with crashes were excluded from analysis.

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Huffmeyer, Moncrief, Tasjian, Kleiman, Scalzo, Cox, & Nemergut. (2016)

United States

Driving Simulator Studies James & Vila. (2015)

Authors (Year) Location

Table 2 (continued)

III

III

Four-group quasi-experimental between-subjects design examining non-operational post-shift driving performance of police officers after working five consecutive static shifts (days, power, swing, graves) versus after three days off.

Within-subjects quasi-experimental design examining driving performance of physicians after working six night shifts consecutively versus after a night of sleep.

Class of Evidence

Study Design, Purpose

N=29, physician

SDLP Collisions Steering angle

SD Speed

4. Graves Sleep Time 1. Day sleep

2. Night sleep Sleep Time (pre) Wake Time (pre) Sleep 72h PVT 1. RT (pre, post) 2. Lapses (pre, post) KSS (pre) Condition

Pedal reaction time (braking latency) Lane departure

1. Days

Health Status: unknown

Acceleration Braking

Speed

Outcome Variables

2. Power 3. Swing

1. Post 5 shifts 2. Post 3 days off Shift

Condition

Predictor Variables

M/F: 78/8 Mean Age: 40.2 ± 7.9 Age Range: 28-58

N=78, police

Participant Demographics

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Post-night shift open-road: Increased SD speed (F = 19.98,p < 0.001), SDLP (F = 5.72, p = 0.03), SD throttle (F = 24.02, p < 0.001), SD steering (p = 0.01), collisions (p = 0.04). Postshift obstacle avoidance: greater SD Speed (F = 4.73,p = 0.04), SD throttle (F = 4.81, p = 0.04).

PVT-lapses predicted increased collisions (F1,151 = 5.33, p = 0.022), increased SDLP (F1,150 = 25.86, p = 0.001), braking latency (F1,146 = 6.48, p = 0.012). Largest effect of for Graves shift for SDLP (F1,141 = 25.86, p < 0.001)

PVT-RT predicted increased collisions (F1,151 = 14.10, p < 0.001), lane deviation (F1,150 = 50.83, p < 0.001), lane position (F1,151 = 31.48, p < 0.001), braking latency (F1,143 = 2.72, p = 0.032). Largest effect for Graves shift on SDLP (F1,151 = 31.33, p < 0.001).

KSS scores predicted increased collisions (F1,150 = 4.80, p = 0.030), lane deviations (F1,149 = 4.10, p = 0.045), braking latency (F1,145 = 9.62, p = 0.02).

Condition, Shift: Post-shift Night shifts (Swing, Graves) greater SDLP (F1,150= 4.4, p = 0.038), than those working days shifts (days, power). No significant difference for Afternoon shift drives by condition.

Main Results

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Possible simulator learning effect: night shift condition always tested first. Possible confounder: no control for caffeine use (low levels reported), no health-related exclusion criteria.

Groups differ in age, with older officers working day shifts (e.g., Day, Power) and younger officers working night shifts (e.g., Swing, Grave). Possible confounders: no health-related exclusion criteria.

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Collisions

SD Throttle SD Steering

Abbreviations: AVR = positive amplitude-velocity ratio; BLINKD = eye blink duration; EEG = Electroencepholgram;, EOG = Electrooculogram; ESS = Epworth Sleepiness Scale; IED = interevent duration; JDS = John’s Drowsiness Score; KSS, Karolinska Sleepiness Scale, PERCLOS = Percentage of eye closure; PVT-RT = Psychomotor Vigilance Task – Reaction Time, PVT-Lapses (> 0.5 s)= Psychomotor Vigilance Task – Attention Lapses, RR = Relative Risk SDLP = standard deviation of lane position; SSS = Stanford Sleepiness Scale; TTC = time to line crossing, VAS = Visual Analogue Scale.

1. Night shift 2. Night sleep ESS (pre/post) M/F: 23/6 Mean Age: 29.8 ± n/a Health Status: unknown United States

PVT (pre) Sleepiness & driving questionnaire Epoch (1x10min, 2-4 x15min)

Predictor Variables Authors (Year) Location

Table 2 (continued)

Study Design, Purpose

Class of Evidence

Participant Demographics

SDLP

Outcome Variables

Main Results

Epoch: Open Road: Increased SD steering (F = 8.04, p < 0.001) and SDLP (F = 11.35, p < 0.001), SD throttle (F=6.46, p < 0.001), collisions (p = 0.010). Obstacle avoidance: increased SD throttle (F = 3.81, p = 0.03).

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3. Results 3.1. Locate and select studies Table 1 shows database searches identified 1673 records and footnote chasing further identified 339 records. Following duplicate removal, 1226 unique records remained for further examination. Fig. 1 depicts the subsequent title and abstract screening, with 21 studies progressing to full-text review. Inter-rater reliability calculations demonstrated substantial agreement between the lead author (MK) and reviewers (SK, MT, LA), with agreement on 1205/1226 titles and abstracts (98.3 percent agreement, κ = 0.659, p < 0.001) (Portney and Watkins, 2009). Discrepancies were reviewed, with 100% consensus reached for inclusion and exclusion before proceeding to fulltext review. After the full-text review, only 11 studies (2 on-road, 9 driving simulator) remained for data extraction and critical appraisal. The search returns demonstrate that the search strategy was very sensitive to maximize study recall and minimize the chance of missing relevant records (White, 2009). 3.1.1. Synthesis of study participant demographics 3.1.1.1. On-Road studies. Participants in the two on-road studies were comprised of one sample with 7 males and 9 females (N = 16, 56% female, Mage = 47.8 ± 14.8 years, range 19–65) (Liang et al., 2017; Lee et al., 2016). Table 2, summarizes the study data. 31% of participants were at high risk for sleep apnea. Inclusion criteria required visual acuity standards of (corrected to) normal visual acuity (Liang et al., 2017; Lee et al., 2016). 3.1.1.2. Driving simulator studies. Participants in the 9 simulator studies (Ntotal = 269) included 28 females (11%, age range 24–61) (Åkerstedt et al., 2005; Ingre et al., 2006a, b; Ware et al., 2006; De Valck et al., 2007; Waggoner et al., 2012; James and Vila, 2015; Huffmeyer et al., 2016). One study (n = 58) did not report sex and used a proxy for age (post-graduate-year of medical residency) (Talusan et al., 2014). Employment included full-time police (n = 107) (Waggoner et al., 2012; James and Vila, 2015), physicians (n = 106) (Ware et al., 2006; Huffmeyer et al., 2016; Talusan et al., 2014), chemical plant technicians (n = 36) (De Valck et al., 2007), and mixed groups (n = 10).(Åkerstedt et al., 2005; Ingre et al., 2006a, b). Five studies did not specify participant health status or health-related exclusion criteria (Åkerstedt et al., 2005; Ingre et al., 2006a, b; James and Vila, 2015; Huffmeyer et al., 2016). Four reported participant health status as ‘fit to work’ (Waggoner et al., 2012), no lower extremity injury (Talusan et al., 2014), no significant medical/sleep disorder (Ware et al., 2006), or no medications acting on the central nervous system (Ware et al., 2006; De Valck et al., 2007). Researchers used standardized self-report assessments of daytime sleepiness and sleep apnea risk factors, and identified higher than average levels of subjective daytime sleepiness and sleep apnea risk compared to the compared to the general population (Ware et al., 2006; Waggoner et al., 2012). 3.1.2. Synthesis of study methods 3.1.2.1. Driving assessment characteristics 3.1.2.1.1. On-Road studies. On-road studies were conducted in the United States (USA) from 2016 to 2017 (Liang et al., 2017; Lee et al., 2016). Both studies employed a small sample size (N = 16) and used a within-subjects crossover design. Participants were assessed following their regular night shift, or while serving as their own control after regular sleep on a day off. Data was collected in an instrumented minivan, and lane crossing errors were observed by an investigator. The assessment lasted 120 min in clear or overcast conditions on a 2-lane 0.8 km (0.5 mi) closed-loop track, with no other traffic, obstacles, or traffic controls. Participants stopped every 15 min to complete a subjective sleepiness measure (Liang et al., 2017; Lee et al., 2016). 9

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3.1.2.1.2. Driving simulator studies. Simulator studies were conducted in the USA (Ware et al., 2006; Waggoner et al., 2012; James and Vila, 2015; Huffmeyer et al., 2016; Talusan et al., 2014), Sweden (Åkerstedt et al., 2005; Ingre et al., 2006a, b), and Belgium (De Valck et al., 2007), from 2005 to 2017. Sample sizes ranged from 10 (Åkerstedt et al., 2005; Ingre et al., 2006a, b) to 78 (James and Vila, 2015). Studies included within-subjects crossover, (Åkerstedt et al., 2005; Ware et al., 2006; Waggoner et al., 2012; Huffmeyer et al., 2016) pre-test, post-test within-subjects, (Talusan et al., 2014) and two-group or four-group between-subjects designs(De Valck et al., 2007; James and Vila, 2015). Shift workers were assessed following one (Åkerstedt et al., 2005; Ware et al., 2006; Talusan et al., 2014) or multiple work shifts(De Valck et al., 2007; Waggoner et al., 2012; James and Vila, 2015; Huffmeyer et al., 2016). Participants acted as their own controls, either pre-shift shift (Talusan et al., 2014), or following one (Åkerstedt et al., 2005; Ware et al., 2006; Huffmeyer et al., 2016), or more nights of sleep(De Valck et al., 2007; James and Vila, 2015). Simulator assessments ranged from brief repeated 10-second tasks assessing brake reaction time (Talusan et al., 2014) to simulated drives lasting 30 min (De Valck et al., 2007; James and Vila, 2015) to 120 min (Åkerstedt et al., 2005; Ingre et al., 2006a, b) assessing a variety of vehicle output metrics or driving behaviors. Driving conditions were described as ideal daytime, (Åkerstedt et al., 2005; Ingre et al., 2006a, b; James and Vila, 2015), or not described (Ware et al., 2006; Waggoner et al., 2012; Huffmeyer et al., 2016). Six studies used fixed-base highfidelity simulators (Ware et al., 2006; De Valck et al., 2007; Waggoner et al., 2012; James and Vila, 2015; Huffmeyer et al., 2016; Talusan et al., 2014) while three used high-fidelity moving-base models (Åkerstedt et al., 2005; Ingre et al., 2006a, b). Most scenarios involved a 2-lane rural highway, with or without road bends or traffic controls (Åkerstedt et al., 2005; James and Vila, 2015), using constant highway speeds of 45 mph (Huffmeyer et al., 2016) to 62 mph (De Valck et al., 2007). Other virtual environments included a closedloop 2-mile oval track (Huffmeyer et al., 2016), and a green light to red light change on a driving simulator display to determine brake reaction time(Talusan et al., 2014). Traffic density ranged from none (James and Vila, 2015, 2015; Huffmeyer et al., 2016; Talusan et al., 2014) to sparse (Åkerstedt et al., 2005; Waggoner et al., 2012), with rare use of obstacles, pedestrians, or dogs (James and Vila, 2015; Huffmeyer et al., 2016). Overall, the on-road studies were most recent, and were conducted in the past 2 years in the USA, while simulator studies were conducted over the past 13 years in the USA and Europe. All studies used participants as their own controls and assessed both post-shift and post-sleep conditions. Assessments lasted 2 h or less and were typically conducted in low-demand monotonous highway or track scenarios. As such, current evidence generalizes to shift worker driving outcomes observed in low complexity rural highway environments in low-traffic with ideal daytime conditions.

2006a, b; James and Vila, 2015). Objective measures of sleepiness included EOG (Ingre et al., 2006a) and PVT(James and Vila, 2015). Additional predictors included time on task(Åkerstedt et al., 2005; Ingre et al., 2006b; Ware et al., 2006; Waggoner et al., 2012; Huffmeyer et al., 2016), sex (Ware et al., 2006), and physician specialty (Talusan et al., 2014). While sleep-wake data was often collected, it was only reported as secondary outcomes. Overall, all on-road and simulator studies examined overnight shift work, while few simulator studies considered other alternate shifts. Measures of sleep-wake data were not evaluated as predictor variables, and measures of subjective and objective sleepiness were infrequently used to predict driving performance outcomes. As such, multiple studies provide evidence for certain predictors such as overnight shift work. However, many predictors are only examined in one or two studies, which limit the level of confidence associated with these determinants. 3.1.2.3. Outcome variables 3.1.2.3.1. On-Road studies. One vehicle output metric, the standard deviation of lateral position (SDLP) was reported in both studies (Liang et al., 2017; Lee et al., 2016). Three driving errors were assessed including lane maintenance, near-crash events, and drives terminated due to driver failure to maintain vehicle control. Overall, the outcome variables reported in on-road studies consistently demonstrated significant adverse changes in driving outcomes such as lane crossing events. 3.1.2.3.2. Driving simulator studies. Vehicle output metrics were frequently examined as outlined in Fig. 3, with fourteen variables reported. Notably, researchers used varying definitions for metrics pertaining to lane and roadway departures and collisions. Two studies used collisions defined as striking objects (e.g., random obstacles, pedestrians) (James and Vila, 2015; Huffmeyer et al., 2016), while the remainder defined a ‘crash’ or ‘accident’ as varying degrees of lane or roadway departure, without striking an object (Åkerstedt et al., 2005; Ingre et al., 2006a, b; Ware et al., 2006). Consistently, adverse outcomes for on-road and simulated driving were identified via measures of SDLP (Åkerstedt et al., 2005; Ingre et al., 2006a; Ware et al., 2006; De Valck et al., 2007; Waggoner et al., 2012; James and Vila, 2015; Huffmeyer et al., 2016), braking latency (James and Vila, 2015; Talusan et al., 2014), lane departure (Åkerstedt et al., 2005; Ingre et al., 2006b; James and Vila, 2015), roadway departure (Ingre et al., 2006b; Ware et al., 2006), SD steering (Huffmeyer et al., 2016), and SD throttle (Huffmeyer et al., 2016), frequency of collisions with objects (James and Vila, 2015; Huffmeyer et al., 2016), and time to line crossing (Åkerstedt et al., 2005). In the context of Michon’s Model of Driving Behavior (Michon, 1985), outcomes reported in this SLR primarily comprise the least complex level of driving behavior – operational outcomes such as SDLP, SD of speed and throttle. Few studies considered tactical level outcomes, such as avoiding obstacles, emergency braking maneuvers, or lane maintenance errors. Presently, no study examined the highest-level strategic driving behaviors.

3.1.2.2. Predictor variables 3.1.2.2.1. On-Road studies. Overnight shift data indicated an average shift length of 8.3 ± 4.1 h (Liang et al., 2017; Lee et al., 2016), though did not specify whether participants worked static or rotating shift schedules. Shift length and sleep-wake data were reported via descriptive statistics. Subjective sleepiness was assessed within the drive every 15 min via the Karolinska Sleepiness Scale (KSS). Measures of objective sleepiness were collected continuously via electrooculography (EOG), infrared reflectance oculography (IROG), and electroencephalogram (EEG) (Liang et al., 2017; Lee et al., 2016). 3.1.2.2.2. Driving simulator studies. As outlined in Fig. 2, all studies examined overnight shift work, while remaining shift types, shift length, or rotation of shift schedules were rarely documented. Subjective sleepiness scales were frequently used for screening or descriptive statistics – just three studies examined subjective sleepiness (i.e., KSS), as a predictor of driving outcomes (Ingre et al.,

3.2. Critical appraisal 3.2.1. Class of evidence Through critical appraisal, we determined the class of evidence for each included study. The assessment was based on study-level indicators of quality per the AAN Guidelines, as outlined in Supplementary Table 1 (Gronseth et al., 2017). The researchers independently critically appraised each study and achieved 100% agreement across all studies. 3.2.1.1. On-Road studies. Both on-road studies were appraised as Class III evidence for predicting shift worker’s driving outcomes in an on-road assessment (Liang et al., 2017; Lee et al., 2016). These two studies used a narrow spectrum of participants, each with a small sample size 10

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Fig. 2. Predictor Variables Reported in On-Road and Driving Simulator Studies. Abbreviations: KSS = Karolinska Sleepiness Scale, EEG = Electroencephalogram; EOG = Electrooculogram; IROG = Infrared oculography. PVT-RT = Psychomotor Vigilance Task – Reaction Time, PVT-Lapses (> 0.5 s)= Psychomotor Vigilance Task – Attention Lapses. Figure 2 Summarizes the frequency with which predictor variables were reported by shift type and shift-related factors, and measures of subjective and objective sleepiness in both on-road and driving simulator studies.

(N = 16). The outcome measures, i.e., errors in lane maintenance, nearcrash events, and SDLP were assessed on a closed-track assessment versus gold standard on-road assessment conducted by a DRS. As such, the highest possible rating is Class III, indicating a moderately high risk of bias (Gronseth et al., 2017).

adverse on-road and simulator driving outcomes. For each determinant, the anchor level will be first described, which is a Level A, B, C or U. Subsequently, as outlined in the methods, any criteria that may upgrade or downgrade this anchor rating are considered and applied as warranted (Gronseth et al., 2017).

3.2.1.2. Driving simulator studies. The nine driving simulator studies were assessed as Class III (Åkerstedt et al., 2005; Ingre et al., 2006a, b; Ware et al., 2006; De Valck et al., 2007; Waggoner et al., 2012; James and Vila, 2015; Huffmeyer et al., 2016) and Class IV (Talusan et al., 2014) for predicting shift workers’ driving outcomes on a driving simulator. While driving performance on a simulator represents real-life driving, it is a standard assessment versus gold standard CDE. As such, the highest rating possible is Class III. One study was assessed as Class IV (Talusan et al., 2014) because the driving simulator assessment was comprised of a brake reaction time to a light changing from green to red on the simulator display versus within a virtual driving environment context required for a standard assessment.

3.3.1.1. On-Road studies. With consistent findings in two Class III studies, exposure to night shift work (Liang et al., 2017; Lee et al., 2016) possibly predicts (Level C), shift worker drivers at risk for adverse driving outcomes. Criteria to upgrade or downgrade the level do not apply. There is insufficient evidence (Level U) for the remaining variables (i.e., time on task and objective measures of sleepiness via EOG), as outlined in Table 3. Overall, there is a low level of confidence in the evidence underlying on-road driving outcomes in shift workers. 3.3.1.2. Driving simulator studies. Based on consistent findings in seven Class III studies (Åkerstedt et al., 2005; Ingre et al., 2006b; Ware et al., 2006; De Valck et al., 2007; Waggoner et al., 2012; James and Vila, 2015; Huffmeyer et al., 2016), and one inconsistent Class IV study (Talusan et al., 2014), exposure to overnight shift work possibly predicts (Level C) drivers at risk for adverse driving outcomes. Criteria to downgrade the level do not apply. Criteria to upgrade the level apply due to the direction of bias (versus effect) in 4/7 studies (Ware et al., 2006; De Valck et al., 2007; Waggoner et al., 2012; James and Vila,

3.3. Interpret results 3.3.1. Level of confidence Following critical appraisal, researchers determined the level of confidence for each predictor variable found to be significant for

Fig. 3. Frequency of Vehicle Output Metrics Reported in On-Road and Driving Simulator Studies. Abbreviations: SD = standard deviation. Figure 3 Summarizes the frequency and type of vehicle output metrics data used as outcome variables in on-road and driving simulator studies. 11

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No significant difference for Afternoon shift drives by condition.

Increased SDLP (F1,150 = 4.4, p = 0.038).

Increased BRT post-call (p = 0.01).

No significant effect on SD speed or accident liability.

Swing Shift

28 Hour Shift (Traditional Call)

Shift Rotation (Fast Forward, Slow Backward)

No significant change in BRT pre-shift to post-shift (p = 0.65).

Increased SDLP (F2,68 = 5.79, p < 0.01).

Men had increased: SDLP (F1,17 = 7.96, p = 0.012); crashes (U = 17.50). Non-significant for mean lane position, SD speed. Increased incidents (z = 2.8, p < 0.01), SDLP (F = 6.0, p < 0.05), reduced TTC (z = 2.2, p < 0.05). No significant changes in mean lane position, SD speed. Increased events (incident, accident, crash) (p = 0.010).

Increased SDLP (F1,150 = 4.4, p = 0.038).

Afternoon Shift

Driving Simulator Overnight Shift

Open-road: Increased SD speed (F = 19.98, p < 0.001), SDLP (F = 5.72, p = 0.03), SD throttle (F = 24.02, p < 0.001), SD steering (p = 0.01), collisions (p = 0.04). Post-shift obstacle avoidance: greater SD Speed (F = 4.73, p = 0.04), SD throttle (F = 4.81, p = 0.04). Increased SDLP (F1,78 = 6.78, p = 0.011).

Lane crossing prediction model: performance degradation in lane crossing events. AUC: 0.82 ± 0.08, sensitivity: 0.36 ± 0.22, specificity: 0.98 ± 0.02.

EOG Measures (AVR, JDS, PERCLOS)

Time on Task

Increased emergency braking maneuvers (p = 0.0088), Near-crash events (p = 0.0088), Driving terminations (p = 0.0034), lane departure both straight and curved sections (p < 0.0001) Lane crossing prediction model: performance degradation in lane crossing events. AUC: 0.82 ± 0.08, sensitivity: 0.36 ± 0.22, specificity: 0.98 ± 0.02. All events (emergency braking, near-crash, driving terminations) occurred after > 45 minutes of driving post-shift.

Dependent Variable(s) Results

Overnight Shift

On Road

Independent Variable

Table 3 Data Interpretation Table.

III

Waggoner, et al. (2012) James and Vila (2015) Ware, et al. (2006)

De Valck, et al. (2007)

Talusan, et al. (2014)

James and Vila (2015)

III

Ingre, et al. (2006b) De Valck, et al. (2007) Talusan, et al. (2014) James and Vila (2015)

III

IV

III

III

IV

III

III

Åkerstedt, et al. (2005)

III

III

III

III

Huffmeyer, et al. (2016)

Liang, et al. (2017)

III

III

Liang, et al.(2017) Lee, et al. (2016)

III

Class

Lee, et al. (2016)

Study

U, Insufficient evidence to predict drivers at risk for adverse driving outcomes U, Insufficient evidence to predict drivers at risk for adverse driving outcomes U, Insufficient evidence to predict drivers at risk for adverse driving outcomes U, Insufficient evidence to predict drivers at risk for adverse driving outcomes

Anchor: Level CUpgraded to: Level B, Likely predicts drivers at risk for adverse driving outcomes

U, Insufficient evidence to predict drivers at risk for adverse driving outcomes U, Insufficient evidence to predict drivers at risk for adverse driving outcomes

C, Possibly predicts drivers at risk for adverse driving outcomes

Level of Confidence, Conclusion Statement

Not applicable

Not applicable

Not applicable

Not applicable

(continued on next page)

Direction of bias (versus effect) in majority (4/7) of studies (Ingre et al., 2006a, b; Ware et al., 2006; De Valck et al., 2007). Level of confidence upgraded from Level C to Level B.

Not applicable

Not applicable

Not applicable

Upgrading/Downgrading

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Open Road: Increased SD steering (F = 8.04, p < 0.001) and SDLP (F = 11.35, p < 0.001), SD throttle (F = 6.46, p < 0.001), collisions (p = 0.010). Obstacle avoidance: increased SD throttle (F = 3.81, p = 0.03). Increased SDLP (F1,78 = 10.43, p = 0.002). Non-significant interaction condition by session. Increased SDLP (F5,85 = 2.53). Increased SDLP (F = 3.7, p < 0.01). No significant interaction of condition by time on SDLP. No significant changes for lane position or SD Speed. Increased events (incidents, accidents, crashes) (p = 0.009).

KSS increases significantly predict SDLAT in a linear manner (χ2= 11, df = 1, p < 0.001). Curvilinear relationship between KSS and SDLAT indicated with larger increases in SDLP at higher KSS levels (ICC = 0.49). Increased RR for events with KSS > 5: KSS 5-7, RR = 6.365 (3.719 - 10.893). KSS 7-9, RR = 29.12 (11.246 - 75.390). KSS 5-9, RR = 185.3 (42.073 - 816.4). Increased collisions (F1,150 = 4.80, p = 0.030), lane deviations (F1,149 = 4.10, p = 0.045), Braking latency (F1,145 = 9.62, p = 0.02). Increased collisions (F1,151 = 14.10, p < 0.001), lane deviation (F1,150 = 50.83, p < 0.001), lane position (F1,151 = 31.48, p < 0.001), braking latency (F1,143 = 2.72, p = 0.032). Largest effect for Graves shift on SDLP (F1,151 = 31.33, p < 0.001). Increased collisions (F1,151 = 5.33, p = 0.022), increased SDLP (F1,150 = 25.86, p = 0.001), braking latency (F1,146 = 6.48, p = 0.012). Largest effect of for Graves shift for SDLP (F1,141 = 25.86, p < 0.001). Increased SDLP in a linear manner (χ = 49, df = 1, p < 0.001). Curvilinear relationship for BLINKD and SDLP shows larger increases in SDLP (χ = 8.78, df = 1, p < 0.003) at higher BLINKD (ICC = 0.30). Condition by sex interaction. Increased SDLP for men (F1,17 = 7.96, p = 0.012), crashes (U = 17.50).

Time on Task

KSS (Within drive)

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Orthopedic: increased BRT pre- to post-shift (p = 0.007).

Internal Medicine: no significant change post-shift (p = 0.76).

Physician Specialty (Orthopedic)

Physician Specialty (Internal Medicine)

Sex (Male)

EOG (BLINKD)

PVT-Lapses (Pre-drive)

PVT-RT (Pre-drive)

KSS (Pre-drive)

Dependent Variable(s) Results

Independent Variable

Table 3 (continued)

Talusan, et al. (2014)

Talusan, et al. (2014)

Ware, et al. (2006)

Ingre, et al. (2006a)

James and Vila (2015)

James and Vila (2015)

James and Vila (2015)

Ingre, et al. (2006b)

Ingre, et al (2006b) Ingre, et al. (2006a)

III

Waggoner, et al. (2012) Ware, et al. (2006) (Åkerstedt, et al. (2005)

IV

IV

III

III

III

III

III

III

III

III

III III

III

Class

Huffmeyer, et al. (2016)

Study

U, Insufficient evidence to predict drivers at risk for adverse driving outcomes U, Insufficient evidence to predict drivers at risk for adverse driving outcomes U, Insufficient evidence to predict drivers at risk for adverse driving outcomes

U, Insufficient evidence to predict drivers at risk for adverse driving outcomes

U, Insufficient evidence to predict drivers at risk for adverse driving outcomes

U, Insufficient evidence to predict drivers at risk for adverse driving outcomes U, Insufficient evidence to predict drivers at risk for adverse driving outcomes

C, Possibly predicts drivers at risk for adverse driving outcomes

Level of Confidence, Conclusion Statement C, Possibly predicts drivers at risk for adverse driving outcomes

Not applicable

Not applicable

Not applicable

Not applicable

Not applicable

Not applicable

Not applicable

Not applicable

Direction of bias (versus effect) in minority (2/5) of studies (Ingre et al., 2006b; Ware et al., 2006).Upgrading not applicable.

Upgrading/Downgrading

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4.2. Methods

2015) suggesting the effect of overnight shifts may be greater than the data indicate (Gronseth et al., 2017; Guyatt et al., 2011). In four studies, the direction of bias was toward minimizing adverse driving outcomes. Such minimizing decision were excluding crash-involved drives from analyses (Waggoner et al., 2012), allowing greater caffeine use in the night shift condition (Ware et al., 2006; De Valck et al., 2007), or including younger age overnight shift workers (James and Vila, 2015). Yet, despite these, results remained significant. Accordingly, the level of confidence in the evidence for overnight shift work is upgraded to Level B. Therefore, exposure to overnight shift work likely predicts (Level B) drivers at risk for adverse driving outcomes. With two consistent Class III studies (Ingre et al., 2006a, b), higher ratings of subjective sleepiness measured within-drive via KSS possibly predicts (Level C) drivers at risk for adverse driving outcomes. Criteria to upgrade or downgrade the level do not apply. With five consistent Class III studies (Åkerstedt et al., 2005; Ingre et al., 2006b; Ware et al., 2006; Waggoner et al., 2012; Huffmeyer et al., 2016), extended time on task possibly predicts (Level C) drivers at risk for adverse driving outcomes. Criteria to upgrade the level apply to only 2/5 studies (Ware et al., 2006; Waggoner et al., 2012), resulting in no upgrade. Despite the Level C recommendation and despite small sample sizes, this is a significant finding, since five separate studies consistently identified this outcome. Due to limited findings in single Class III and Class IV studies, there is insufficient evidence for the remaining determinants outlined in Table 3. Overall, a moderate level of confidence exists in exposure to night shift work, and a low level of confidence exists in measures of subjective sleepiness and extended time on task to predict shift workers at risk for adverse driving outcomes. There is a very low level of confidence in the remaining determinants (i.e., shift types other than overnight shifts; direction of shift rotation schedule; pre-drive subjective sleepiness via KSS; objective measures of sleepiness via PVT-RT, PVT-lapses, or EOG; sex; or physician specialty), due to insufficient evidence required to make recommendations.

Current evidence examines shift worker driving outcomes following exposure to a range of shift work types, assessed via low-complexity, low-stimulation driving environments at highway speeds, with no-tosparse traffic or obstacles. Assessments did not require drivers to adapt to changing environments (e.g., suburban, urban, highway), road conditions (e.g., traffic density), or hazards. Adapting to changing driving environments, conditions, and/or reacting to hazards may be reasonably required for shift workers who commute to/from work, or who drive throughout the course of their employment. Therefore, the generalizability of the current evidence for the determinants identified is limited to the conditions under study. High-speed highway scenarios are representative of the driving environments where sleep-related crashes most frequently occur (Filtness et al., 2017), and are designed to evoke sleepiness, and challenge vigilance and attention (Durmer and Dinges, 2005; Klauer et al., 2006). However, these scenarios may not sufficiently challenge other areas of neurocognitive deficits arising from insufficient sleep compared to complex environments demanding rapid perception, judgement, and reaction time to execute driving maneuvers. For example, inattention may result in failing to recognize an upcoming turn, resulting in turning late and encroaching on other traffic, while poor judgement may result in a driver failing to accept an appropriate gap to safely make a turn across traffic, resulting in a near-miss event or MVC (Filtness et al., 2017). While the majority of sleep-related MVC occur on highways at higher speeds, approximately 41% of sleep-related MVC occur in low-speed (< 60 km/h) urban environments, characterized by complex demands in high-density traffic (Filtness et al., 2017). Moreover, sleep-related crashes on low-speed roads involve increased frequency of rear-end or head-on collisions, and frequently occur at intersections or traffic controls (Filtness et al., 2017). Thus, consistent with recent literature, a critical need exists to examine complex driving environments and conditions since these have not been studied to date in this population, and represent a substantial portion of sleep-related crash events (Soleimanloo et al., 2017; Filtness et al., 2017; Liu et al., 2009). Subjective and objective measures of sleepiness were rarely used to predict driving outcomes. However, when used, subjective and objective measures of sleepiness were identified as significant predictors of adverse driving outcomes. For example, subjective sleepiness measured via KSS significantly predicted collisions, lane and roadway departure events, SDLP, and braking latency (Ingre et al., 2006a, b). Additionally, objective sleepiness measured via PVT-RT and PVT-Lapses significantly predicted collisions, SDLP, and braking latency (James and Vila, 2015). Finally, research conducted in non-shift worker groups of young adult and adult drivers show measures of subjective and objective sleepiness and sleep-wake data predict adverse driving outcomes (Czeisler et al., 2016; Bioulac et al., 2017; Soleimanloo et al., 2017). Thus, future research may examine subjective and objective measures of sleepiness, as well as sleep-wake data in shift workers. Outcome measures reported in this SLR focused on objective vehicle output metrics, and rarely reported driving behavior errors. Both driving behavior errors and summary metrics are commonly reported in studies examining driver fitness and performance in medically at-risk populations (Transportation Research Board, 2016). Consistently, adverse on-road and driving simulator outcomes were identified via measures of braking latency, lane positioning (e.g., SDLP), and lane and roadway departure. These effects are consistent with research identifying lane departure crashes as seven times more likely to involve driver sleepiness, versus other types of crashes (Tefft, 2010). Due to the low-complexity driving environments used to evoke sleepiness, current driving simulator scenario designs afford limited opportunity for drivers to engage in driving maneuvers, such as changing lanes or navigating turns. In future, high-complexity environments representative of real-life driving may provide greater opportunity to evaluate the

4. Discussion This SLR synthesized and appraised the level of evidence and confidence in the predictors of driver fitness and performance in shift workers with insufficient sleep. Overall, this SLR found a small number of on-road (n = 2) and driving simulator (n = 9) studies, with studylevel methodological limitations, which resulted in an overall lowmoderate level of confidence in predicting adverse on-road and driving simulator outcomes in shift workers. As such, study findings demonstrate a pressing need for additional research in this at-risk population. 4.1. Demographics Study demographics indicate that gender, age and occupation were not representative of the workforce. Women were significantly underrepresented, yet comprise 43% of full-time and 69% of part-time North American shift workers (Bureau of Labor Statistics, 2005; Williams, 2008). Drivers > 61 or < 24 years old were under- or un-represented; yet notably, drivers < 24 are identified as particularly vulnerable to the effects of insufficient sleep (Soleimanloo et al., 2017; Czeisler, 2009). A barrier to young adults participating may be that most inclusion criteria indicated full-time employment, while young adults are more likely to obtain part-time/casual employment (Statistics Canada, 2018). Moreover, several occupational groups with prevalent shift work (e.g., sales, service, other healthcare) are not represented in this SLR. Thus, future research may consider engaging more representative participant groups pertaining to age, gender, and employment.

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operational, tactical, and strategic driving behaviors of Michon’s model (Michon, 1985). The current evidence is primarily situated in the operational level of behavior (Michon, 1985). Presently, there is limited evidence pertaining to tactical level outcomes, and no published evidence pertaining to strategic driving behavior in shift workers. Further, since insufficient sleep affects a range of cognitive functions, including higher-level executive functions and memory, driving assessments should also evaluate strategic level driving behaviors (Campos et al., 2017). In order to study higher level driving behaviors, assessments employing more complex environments and conditions are required. Thus, consistent with recent literature, a need exists for increased complexity in driving environments and outcome measures (Soleimanloo et al., 2017; Liu et al., 2009). Due to methodological limitations, current evidence is limited to Class III and IV, consistent with prior SLRs (Soleimanloo et al., 2017; Schreier et al., 2018). Pertaining to study design, there are benefits and drawbacks to the methodological approaches employed in current evidence. Use of within-subjects designs reduce the number of participants required and provide the best equivalence of individual subject characteristics by reducing variability, such that changes in performance most likely resulting from the effect of the condition (Portney and Watkins, 2009; Holmes, 2018). However, a disadvantage in the context of insufficient sleep remains: it is unclear in most studies whether participants obtained sufficient sleep between conditions to washout the effects of sleep debt and resulting cognitive deficits. Shift workers experience chronic sleep disturbances and biological dysregulation, and observable changes in cognition may take significant time to resolve (Cheng and Drake, 2016). Thus, the results of this SLR may underestimate the magnitude of the effects of insufficient sleep on shift worker driving outcomes, compared to non-shift workers with regular sleep. Future prospective observational studies may be improved through examining larger sample sizes of representative shift workers, exposed to a range of shift types, using an independent control group. Second, improved clarity and consistency in reporting participant health status, health-related criteria, and possible confounders for sleepiness levels is required, since those with certain medical or sleep disorders, or medications may be differentially vulnerable to the effects of insufficient sleep (Czeisler, 2009). Finally, consistent use of terminology for terms such as collisions and lane or roadway departures will improve conceptual clarity and the ability of researchers to compare outcomes and disseminate research (Classen et al., 2017b).

representative participants, including: younger and older workers, females, part-time workers, and individuals employed in high-shiftwork prevalence occupations. Class I and II studies require a broad spectrum of participants, via larger sample sizes, a variety of shift work types, and independent control groups. Additional research to further examine the impact of predictors with Level U insufficient evidence includes alternate shift types (e.g., 24+ hour, afternoon, or swing shifts); subjective and objective measures of sleepiness (e.g., pre-drive KSS, PVT; within drive EOG, EEG), sleep-wake measures (e.g., sleep logs, actigraphy), and sex. Finally, a critical need exists for on-road CDEs in real-world driving demands faced by shift workers. Future research would benefit by expanding driving simulator scenarios beyond highway environments into more complex environments and conditions. Outcome measures evaluating higher-level strategic and tactical driving behaviors are required to more fully understand the overall impact on driver fitness and performance in shift workers. Predictors of simulated driving performance, including time on task and subjective sleepiness may be further evaluated in on-road assessments given the variability between individuals and between simulator and on-road assessments (Ingre et al., 2006a, b; Van Dongen et al., 2004). Researchers may include more extensive medical history information and clear reporting of health status and health-related exclusion criteria. Finally, employing consistent terminology in future research will improve conceptual clarity and enhance researchers’ abilities to communicate research results effectively with stakeholders such as employers, engineers, policy makers (Classen et al., 2017b). 4.5. Recommendations for policy Overnight shift work likely predicts drivers at risk for adverse driving outcomes, and as such, prudent actions by employers and institutions would include instituting education programs and instituting best-practice sleep promotion strategies to minimize insufficient sleep and sleepiness in shift workers. The results of this SLR identify a dearth of research into shift worker driving outcomes, despite these workers comprising a prevalent and at-risk group of drivers who may place themselves and other road users at risk on a daily basis, resulting in preventable crashes and fatalities. Additional high-quality on-road studies are critical to improve evidence-informed prevention strategies and critical decision making to mitigate adverse effects of insufficient sleep on road safety. 4.6. Limitations

4.3. Recommendations for clinical practice

Authors did not assess meta-biases, grey literature (e.g., government reports, crash databases, book chapters, etc.), and research published in languages other than English. Thus, relevant studies may have been missed, and publication bias may exist (Shamseer et al., 2015). The objective of this SLR was to examine shift workers’ driving; in doing so, authors sought only studies with actual shift workers assessed following their typical work schedule to maximize ecological validity. However, this search strategy resulted in a very low inclusion rate (< 1%). In future SLRs, relaxing criteria to include laboratory-based simulated shift work protocols may increase the number of studies analyzed to expand on existing data.

Overall, limited recommendations can be made for clinical practice due to lack of on-road studies and the overall low-to-moderate level of confidence (Levels B, C, U) in the determinants predictive of adverse driving outcomes in shift workers. Based on Level B confidence in simulator studies and Level C confidence in on-road studies, clinicians should consider educating shift workers that exposure to night shift work likely predicts drivers at risk for adverse driving outcomes. Based on Level C confidence in simulator studies, clinicians may consider educating shift workers that extended time on task¸ and high ratings of subjective sleepiness on the KSS, possibly predict drivers at risk for adverse driving outcomes. However, caution is recommended since subjective ratings of sleepiness may vary significantly between persons, and between simulator and on-road assessments (Ingre et al., 2006a, b; Van Dongen et al., 2004).

4.7. Strengths This SLR followed an established protocol that is both registered (Knott et al., 2018a) and published (Knott et al., 2018b). This protocol was based on an established methodology (Cooper and Hedges, 2009) and criteria for critical appraisal and data analysis and interpretation (Gronseth et al., 2017). The purposeful search strategy was designed with a research librarian to maximize study recall and minimized chances of missing relevant records. Inter-rater reliability demonstrated substantial agreement during study selection. Data was extracted using

4.4. Recommendations for research To improve the class of evidence and the level of confidence in the determinants underlying driving outcomes in shift workers, gaps in the existing literature may be addressed for both on-road and simulator studies (Gronseth et al., 2017). Overall, future research may target 15

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a data extraction tool with demonstrated reliability and vaility (Classen et al., 2008). Finally, results were reported using the PRISMA guidelines (Moher et al., 2009).

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5. Conclusions This SLR synthesized and critically appraised the evidence underlying the determinants of driving outcomes in shift workers with insufficient sleep. Researchers identified that exposure to overnight shift work possibly predicts drivers at risk for adverse fitness to drive outcomes. Additionally, researchers identified four determinants underlying driving performance in shift workers, including: exposure to overnight shift work, high subjective ratings of sleepiness, and extended time on task, as possibly or likely predicting drivers at risk for adverse driving outcomes. However, there is an overall low-to-moderate level of confidence in the evidence. In future, additional Class I and II studies will improve the level of confidence in the literature by assessing a representative spectrum of shift workers via gold-standard on-road CDE encompassing a range of real-world driving conditions. Additional large scale observational and ecologically relevant studies are vital to reduce the public health burden of road traffic crashes, injuries, and fatalities in shift workers and those with whom they share the road. Contributions MK is the Guarantor. All authors contributed to the development of the search strategy. MK and SC developed the selection criteria, critical appraisal. SC provided methodological and content area expertise. MK, SK, MT, LA were reviewers. All authors read, provided feedback and approved the final manuscript. Funding No funding was received for the completion of this systematic review. Declaration of Competing Interest None. Acknowledgements Infrastructure and support provided by the i-Mobile Research Lab, Western University, London, ON, Canada, and the Institute for Mobility, Activity and Participation, University of Florida, Gainesville, FL, USA. Thank you to Stuart Fogel, PhD and Trish Tucker, PhD for feedback on a preliminary version of the manuscript. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.aap.2019.07.010. References Åkerstedt, T., 1988. Sleepiness as a consequence of shift work. Sleep 11 (1), 17–34. Åkerstedt, T., Peters, B., Anund, A., Kecklund, G., 2005. Impaired alertness and performance driving home from the nightshift: a driving simulator study. J Sleep Res. 14, 17–20. https://doi.org/10.1111/j.1365-2869.2004.00437.x. American Automobile Association Foundation for Traffic Safety, 2017. Traffic Safety Culture Index. Washington, DC. pp. 2018. Banks, S., Dinges, D.F., 2011. Chronic sleep deprivation. In: Kryger, M.H., Roth, T., Dement, W.C. (Eds.), Principles and Practice of Sleep Medicine, 5th ed. Elsevier Saunders, St. Louis, MO. Bioulac, S., Micoulaud-Franchi, J.A., Arnaud, M., et al., 2017. Risk of motor vehicle accidents related to sleepiness at the wheel: a systematic review and meta-analysis. Sleep. 40 (10), 1–10. https://doi.org/10.1093/sleep/zsx134. Bonnet, M.H., 2011. Acute sleep deprivation. In: Kryger, M.H., Roth, T., Dement, W.C. (Eds.), Principles and Practice of Sleep Medicine, 5th ed. Elsevier Saunders, St. Louis,

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