The effect of sleepiness in situation awareness: A scoping review

Abstract

BACKGROUND:

Situational awareness is the acquisition of information from elements present in the work environment, the perception of the meaning of this information, and the prediction of future working conditions. Sleepiness and fatigue can influence an individual’s ability to reach situation awareness, decision-making, and performance on a task.

OBJECTIVE:

This scoping review examines methods used to assess situational awareness, fatigue, sleepiness, and their interrelationships.

METHODS

A systematic search of online databases was conducted to identify experimental, peer-reviewed articles published in English between 2017 and 2022. A total of 29 publications were selected for analysis.

RESULTS:

The selected studies originated from various countries, primarily in the northern hemisphere. Health and automotive engineering were the academic categories with the highest publications. The studies employed objective and subjective methods to assess situational awareness, fatigue, and sleepiness.

CONCLUSIONS:

Most studies reported a decline in situational awareness during fatigue and sleepiness conditions, although one study did not find this association. Future research should focus on employing objective methods to analyze cognitive factors, increasing sample sizes, and conducting testing in real-world situations.

Keywords

Awareness sleepiness fatigue safety ergonomics decision making

1 Introduction

Situational awareness (SA) is a factor that can contribute to human error and is directly associated with the activity’s success [1]. SA encompasses awareness of the current environment and categorizes it into three levels: perception (Level 1), comprehension (Level 2), and projection (Level 3). Level 1 relates to the perception of information from the environment acquired by any of the five senses. Level 2 interprets the information perceived in Level 1 and its importance to the situation. Level 3 involves anticipating how the perceived information will impact future events. Considering the three levels, Level 1 is the most important, as it is the basis for achieving the other levels [2]. It is important to note that SA serves as the basis for decision-making rather than being the decision-making process itself [3]. Stanton et al. conclude that “loss of situational awareness is correlated with poor system performance. People who have lost situational awareness may be slower to detect problems with the system they are controlling as well as requiring additional time to diagnose problems and conduct remedial activities when they are finally detected [4].”

SA demands high levels of cognitive functioning. However, sleep and fatigue can hinder achieving SA levels and impact decision-making and activity performance [5]. Sleep is an essential physiological process that allows the body to renew its physical and mental capacities, enabling the individual to reach higher levels of awareness. On the other hand, individuals who experience insufficient or poor-quality sleep may struggle to restore their self-control abilities, leading to unsafe behaviors [3]. Sleep loss, a reduction in the hours of sleep from the recommended levels, may occur due to sleep deprivation, restriction, or disruption [5]. Sleep deprivation can reduce alertness, attention, concentration, memory, and decision-making. Functions such as working and long-term memory, visual attention, and decision-making are necessary for achieving higher levels of SA [6].

Fatigue and sleepiness are usually used as synonymous [7]. According to the International Civil Aviation Organization (ICAO), 2019, fatigue is “ a physiological state of reduced mental or physical performance capability resulting from sleep loss, extended wakefulness, circadian phase, and/or workload (mental and/or physical activity) that can impair a person’s alertness and ability to perform safety related operational duties [8].” Thus, sleepiness is one of the causes of fatigue. Other factors that contribute to the development of mental fatigue are working time and mental workload [9]. Fatigue can decrease attention and vigilance, delay cognitive processing, alter safety awareness, and influence the ability to achieve SA levels [6, 10]. As stress and fatigue increase, there is a decrease in SA levels, which increases unsafe behavior and the risk of accidents [11, 12].

Sleep loss, sleep deprivation, and fatigue are potential risks to health and safety. Therefore, more information about the relationships and consequences of sleepiness and fatigue on situational awareness is needed. Thus, this study aimed to perform a scoping review to systematically map studies that experimentally assess sleepiness, fatigue, and situational awareness. The study evaluated the main methodologies employed in previous research to assess sleepiness, fatigue, and situational awareness, while also identifying gaps in knowledge.

2 Methods

This study conducted a scoping review of the published scientific literature on situational awareness, fatigue, and sleepiness. The study design adhered to the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [13]. This study is exempt from Institutional Review Board approval as a literature review.

This scoping review incorporates full articles published in peer-reviewed journals, written in English, and published from 2017 until 2022. The selection process involved utilizing keywords such as “situation awareness” or “situational awareness” in conjunction with “fatigue” or “sleep.” The review included experimental studies that objectively or subjectively assessed the relationship between situational awareness and fatigue or situational awareness and sleepiness. Excluded from the review were review articles, abstracts, and studies that solely evaluated situational awareness or fatigue and situational awareness or sleepiness.

Three databases, namely Scopus, Web of Science, and PubMed were used to conduct the article search. The survey was conducted between October 25 and November 3, 2022. All the articles were compiled into a table, and the duplicates were removed. Initially, the articles were sorted based on their title and abstract, and subsequently, the full texts were examined to determine whether they met the inclusion and exclusion criteria. The two authors collaborated in conducting the search and article screening process for this scoping review. They employed a systematic approach, utilizing pre-established inclusion and exclusion criteria to identify relevant articles. In instances of disagreement regarding study selection and data extraction, consensus was achieved through thoughtful discussions between the authors. Only articles that received agreement from both authors were included in this review. This cooperative approach guaranteed a thorough and meticulous screening of pertinent publications for review.

The data charting process involved extracting relevant information from the included sources of evidence and organizing it into a structured data summary table. This table included details such as the publication year, country, journal, academic categories, population, methods used to assess situational awareness, fatigue, sleepiness, strengths, and weaknesses, as well as the results and limitations of each study. Following the data charting, the studies were categorized based on the methods employed to evaluate situational awareness, fatigue, and sleepiness, specifically objective and subjective methods.

3 Results

The search of three databases had initially identified 298 papers. After removing the duplicates total of 138 were analyzed. Out of the total articles reviewed, 109 articles were excluded for the following reasons: 74 articles did not evaluate situational awareness, fatigue, or sleepiness; 18 articles were not experimental research; 3 articles were not full-length articles, and five articles were unable to be retrieved. As a result, 29 articles met the eligibility criteria for the systematic study. Figure 1 provides a visual representation of the research flow diagram.

Fig. 1

Flow diagram.

The 29 papers included in the study were conducted in 21 countries. Among them, four articles originated from the USA [5 , 14–16]. Five countries contributed two articles each: Germany, [7, 17], India, [18, 19], Korea, [20, 21] Ireland, [22, 23] and Italy [24, 25]. The rest of the countries made one paper each: Australia [26], Belgium [27], Canada [28], China [29], Denmark [30], France [31], Ghana [32], Indonesia [6], Iran [3], New Zealand [33], Norway [11], Philippines [34], Puerto Rico [35], Slovakia [36], and the United Kingdom [37].

The study classified the 29 articles into ten academic categories. Figure 2 illustrates the distribution of these categories. Among the articles, eight were related to Health [16 , 36], while seven assessed automotive engineering [6 , 29]. Four articles focused on Aeronautics [22 , 35], two articles on maritime transport [11, 27], and one article on aerospace [5]. Additionally, two articles were related to agriculture [15, 37], two articles to industrial organization [3, 25], one article to computer engineering [19], one article to robotics [14], and one article to transport engineering [28].

Fig. 2

Distribution of academic categories.

The papers included in the range had varying population sizes, ranging from 4 to 601. Like the academic categories, the population also varied. Health studies analyzed physicians [31–33 , 36], nurses [26 , 36], paramedics [36], and soccer student-athletes [16]. The population of automotive engineering was composed of truck drivers [7] and common drivers [17 , 29], as well as students [6]. In the aeronautics category, air traffic management officers [34], and commercial airline pilots were assessed in the aeronautics category [23]. The analyzed aerospace population consisted of volunteers trained on SPHERE [5] and maritime engineering employees from the maritime industry [27]. In the agricultural sector, the study included farmers, agricultural stakeholders [37], and professional loggers [15]. The population of the industrial organization comprised workers from various industries [3, 25]. In Robotics, university students were surveyed [14] and in transport engineering, locomotive crew [28]. Some papers did not specify the population analyzed, referring to them as non-pilot volunteers [22], participants [24], and working individuals [19]. Two articles analyzed teams of professionals, such as health teams managing newborns [30], and the National Transportation Safety Board (NTSB) [35].

This study focused on three main categories: Situation awareness, Fatigue, and Sleepiness. The evaluation method of each category was analyzed in all papers. Each category’s evaluation method was analyzed in all papers, categorized as objective or subjective. Objective evaluation of situation awareness utilized the following method subcategories: Behavior observation [17 , 37], coded archival research [35], eye-tracking glasses [7 , 18], and quantitative analysis of situation awareness [5 , 34]. To assess subjective situation awareness, the method subcategories were: questionnaires [3 , 36], and semi-structured interviews [15 , 27].

The method subcategories for evaluating objective fatigue included behavior observation [16 , 37], coded archival research [35], psychomotor vigilance task (PVT) [22 , 33], and physiological sensors [11, 14]. Subjective fatigue, on the other hand, was assessed through questionnaires [3 , 36], and semi-structured interviews [15 , 27].

The objective methods subcategories o analyze sleepiness were with physiological sensors [5 , 23]. The subjective sleepiness method subcategories were questionnaires [3 , 33], and sleep logs [5, 23].

For data collection, ten studies used simulators [5 , 33], and two studies used scenarios [31, 37]. For sleepiness and fatigue assessment, seven studies subjected participants to sleep deprivation [6 , 33].

The authors, country of origin, academic categories, population, and methods of the studies are presented in Table 1. The quality of the selected articles in the review was assessed based on the strengths and weaknesses of the methodology employed in each article. Table 2 represents this evaluation.

Table 1

Characteristics of the study

Author(s)	Country	Academic categories	Population	Method subcategories
Aram et al. (2022)	Ghana	Health	Healthcare Works	Questionnaires
Bej et al. (2021)	India	Computer engineering	Working individuals	Behavior observation
Bongo et al. (2022)	Philippines	Aeronautics	Air traffic management officers	Questionnaires, quantitative analysis of situation awareness
Brogaard et al. (2022)	Denmark	Health	Health teams managing newborn	Behavior observation
Castritius et al. (2021)	Germany	Automotive engineering	Truck drivers	Questionnaires, physiological sensor
Falco et al. (2021)	Italy	Health	Participants	Questionnaires
Hannaford et al. (2021)	Belgium	Maritime transport	Employs of the maritime industry	Semi-structured interview
Hopko et al. (2021)	USA	Robotics	University students	Questionnaires, physiological sensor
Kim et al. (2022)	Korea	Automotive engineering	Drivers	Behavior observation, physiological sensor
Mahajan et al. (2021)	India	Automotive engineering	Drivers	Questionnaires, physiological sensor
Mariani et al. (2019)	Italy	Industrial organization	Workers from industry	Questionnaires
Mohammadfam et al. (2021)	Iran	Industrial organization	Workers from industry	Questionnaires
Monteiro et al. (2020)	Norway	Maritime transport	Volunteers	Questionnaires, physiological sensor
Myers et al. (2017)	New Zealand	Health	Physicians	Questionnaires, Behavior observation, PVT
Neuschwander et al. (2017)	France	Health	Physicians	Questionnaires, Behavior observation
Newman et al. (2018)	USA	Agriculture	Professional logging	Semi-structured interview
Niu et al. (2022)	China	Automotive engineering	Drivers	Questionnaires, behavior observation
O’Hagan et al. (2018)	Ireland	Aeronautics	Non-pilot volunteers	Questionnaires, PVT
O’Hagan et al. (2020)	Ireland	Aeronautics	Commercial airline pilots	Questionnaires, quantitative analysis of situation awareness, physiological sensors, PVT, sleep log
Peddle et al. (2019)	Australia	Health	Nurses	Semi-structured interview
Rudin-Brown et al. (2017)	Canada.	Transport engineering	Locomotive crew	Behavior observation
Schneider et al. (2018)	USA	Aerospace	Volunteers trained on SPHERE	Quantitative analysis of situation awareness, physiological sensors, sleep log
Sedlár (2021)	Slovakia	Health	Emergency Medical Services (physicians, paramedics, ambulance drivers, and nurses)	Questionnaires
Shin et al. (2022)	Korea	Automotive engineering	Drivers	Questionnaires
Shipherd et al. (2018)	USA	Health	Soccer student-athletes	Quantitative analysis of situation awareness, Behavior observation
Tone et al. (2022)	United Kingdom	Agriculture	Farmers and agricultural stakeholders	Behavior observation
Velazquez (2018)	Puerto Rico	Aeronautics	NTSB (National Transportation Safety Board)	Behavior observation
Vogelpohl et al. (2019)	Germany	Automotive engineering	Drivers	Questionnaires, behavior observation
Wijayanto et al. (2020)	Indonesia	Automotive engineering	Male students	Questionnaires, physiological sensors, quantitative analysis of situation awareness

Table 2

Paper’s strengths and weaknesses

Paper	Strengths	Weaknesses
[3]	The study demonstrates positive aspects such as a representative sample, standardized questionnaires, appropriate statistical analysis, and consideration of multiple variables.	The study has limitations. It has a cross-sectional design, which limits the ability to establish causal relationships. Self-report questionnaires introduce potential bias, and the exclusion of incomplete questionnaires may affect sample representativeness.
[5]	The article provides detailed information about the sample characteristics. It thoroughly describes the equipment used, specifically the SPHERES satellites and their components, enabling readers to comprehend the technical aspects and potentially replicate or expand upon the experiment.	The study had a small sample size, with only 8 participants completing the protocol. Demographic diversity was limited, and more details needed to be provided regarding the statistical analysis methods employed.
[6]	The study employed a carefully selected sample of participants, considering age, driving experience, and sleep patterns. A driving simulator was utilized to replicate a realistic driving environment.	The study had a limited sample size of fifteen participants and exclusively focused on male university students, which may restrict the generalizability of the results to other populations.
[7]	The article describes the study equipment, including the truck platooning system, eye tracker, and subjective sleep measures. The methodology is clearly explained, outlining the procedures and tests conducted. Appropriate statistical analyses, such as paired t-tests and ANOVA, are employed to enhance result validity.	The study’s reliance on a small sample size of ten truck drivers raises concerns about the generalizability of the findings. Technical issues and test cancellations further restrict the results’ representativeness and the conclusions’ strength due to excluded data.
[11]	A proposed sensor framework combines various physiological sensors to assess mental fatigue during maritime operations. It uses sensor fusion techniques, incorporates established measurement techniques, and enables real-time assessment for risk evaluation and safety measures.	The small sample size and limited diversity may reduce generalizability. The scenario-based experiment may not accurately replicate maritime conditions, and the shorter duration limits the assessment of prolonged fatigue.
[14]	The article demonstrates a comprehensive approach by providing participant recruitment details, addressing ethical considerations, thoroughly describing the experimental task and measures, and utilizing statistical analysis methods to ensure the results’ reliability and significance.	Small sample size (N = 16) limits generalizability and increases Type II error risk. The restriction of participants solely from Texas A&M University hampers the broader applicability of the findings. Diverse backgrounds or multiple institutions would enhance external validity.
[15]	The study used a mixed-methods approach, collecting interview and survey data simultaneously. In-depth interviews were conducted to explore participants’ knowledge and experiences, and the interviews were recorded and transcribed for accurate data capture.	Study findings were limited in generalizability, focused on specific programs, and small sample size. Reliance on self-reporting introduces bias, including social desirability bias. Survey data analysis needs more statistical tests or advanced analysis techniques, limiting interpretation depth.
[16]	The comprehensive program targeted various mental skills for male and female soccer players. It used realistic assessments to simulate game situations, improving the transferability of skills to actual matches.	Gender-specific assessments hindered gender comparison. Limited training duration may not capture long-term effects. Reliance on self-reported measures and objective performance measures would enhance assessment.
[17]	The article presents a well-designed study on driver fatigue during automated driving, utilizing a driving simulator and combining objective and subjective measures. Rater training enhances fatigue ratings, and the sample of 60 participants increases generalizability.	The study’s findings may have limited generalizability to real-world driving conditions due to the study’s location and simulator usage. Replication studies in diverse settings and with varied populations would enhance the external validity of the findings.
[18]	The study used a validated driving simulator with a detailed description. Participants were selected based on specific criteria. Well-described experimental setup, including route and environment. Integration of objective and subjective measures for comprehensive findings.	The study’s sample size was small, with only twenty-four participants. Acknowledging the potential differences between driving in a simulator and real-world driving experiences on the roads is essential.
[19]	The study successfully developed a prototype of the proposed system. A visual representation of the prototype system, depicting the client, repeater, and server components, was presented, enhancing the understanding of the system’s setup and configuration.	The prototype has a limited Wi-Fi range of around 50 m. Primarily focuses on alertness detection, excluding fatigue detection and remote monitoring.
[20]	This innovative study investigates take-over requests (TORs) during wake-up from sleep, filling a gap in existing research. The robust experimental design reveals differences in TORs between sleep and wakefulness, offering valuable insights into human-machine interaction in autonomous vehicles.	The study acknowledges the limitations of the sleep-based scenario, including the unverified method to induce sleep and the exclusion of EEG data due to motion artifacts. Further investigation is needed to validate the sleep simulation, explore its comparability to actual sleep, and gain insights into sleep stage classification and driver cognitive load.
[21]	This study utilizes the Kano model to assess autonomous driving acceptance, collecting comprehensive data and presenting clear results.	This study acknowledges the lack of consideration for external factors influencing autonomous driving acceptance. Recommends exploring relationships between acceptance elements, user characteristics, and acceptance levels for deeper insights.
[22]	The article provides detailed participant information. It describes the measures and instruments used in the study, emphasizing their validity and reliability. The study adopts a repeated measures crossover design to minimize individual differences. It presents the statistical analysis and results, including p-values, adding rigor to the study.	The study’s limited sample size of only seven male participants restricts the applicability of the findings and reduces the study’s statistical strength. Additionally, the lack of diversity in the sample, consisting exclusively of male individuals from a specific academic background, undermines the ability to generalize the results to broader populations.
[23]	The study provided detailed participant information, used validated measures, and employed a combination of objective and subjective measures to understand cognitive functioning.	The study’s findings may need more generalizability due to the small sample size and specific participant characteristics. Additionally, potential confounding factors like caffeine intake should have been addressed.
[24]	The article features a comprehensive study design with robust sampling and data collection methods. The adequate sample size of 358 participants enhances generalizability and statistical power.	Limited generalizability due to a specific focus on Italian workers; caution is needed when extending findings. Primarily relies on multiple regression analyses; exploring alternative techniques could enhance validity and uncover additional insights.
[25]	The research follows a rigorous methodology with structured data collection and statistical analysis. It includes a sizable sample of 269 employees from three chemical plants, representing different roles, to comprehensively understand NTS in that context.	Due to its specific context, the research may not apply to other industries or settings. The study’s reliance on self-report measures could introduce biases, and objective measures would improve validity.
[26]	The study employed structured focus groups and interviews to collect qualitative data, which were then analyzed using systematic framework analysis. The findings revealed valuable insights into developing non-technical skills (NTS) through interactions with virtual patients.	The study focused on qualitative data to gain insights into participants’ perceptions and experiences. However, the need for quantitative data limits the objectivity of the findings. The study was conducted in specific nursing schools, which may affect the generalizability of the results to other settings.
[27]	The study utilizes multiple qualitative methodologies, including a multiple-choice survey and individual semi-structured interviews. The study considers the reliability and validity of the persons interviewed, ensuring that the data collected is trustworthy and credible.	The study’s limited generalizability due to the specific period (Feb-Apr 2019) requires further research for broader applicability. Despite efforts to mitigate bias through rating scales and qualitative analysis, survey responses’ subjectivity is acknowledged.
[28]	This paper assesses locomotive voice and video recording (LVVR) systems. A representative sample of recordings collected under different conditions ensures real-world applicability. The assessment evaluates voice-only, video-only, and combined systems, revealing their strengths and weaknesses.	The assessment identified system compatibility limitations, hindering the evaluation of crew usage and interaction with controls.
[29]	The study uses a driving simulator for controlled experiments and real-world scenarios. Objective and subjective measures assess driver fatigue. Fatigue detection system alerts fatigued drivers with voice signals. Goal: prevent accidents and enhance driver safety.	The study lacks a control group to compare and understand sleep deprivation effects. Limited generalizability due to specific driving simulators and young driver focus. Findings may not fully apply to real-world driving or other demographics.
[30]	The study used real-life video recordings from multiple hospitals and structured assessments to evaluate team performance in newborn management.	Possible selection bias due to teams declining participation if they perceived their performance as suboptimal. Lack of blinding between assessors may introduce bias.
[31]	The participants were randomized into sleep-deprived and rested groups, reducing bias and improving the reliability of the comparison. The participants’ non-technical skills were evaluated by independent senior anaesthetists blinded to the group allocation, minimizing bias and enhancing the study’s validity.	The study’s findings may not generalize to other populations, indicating further research needs. The subjective assessment of non-technical skills and the reliance on self-reporting for sleep deprivation could be strengthened by incorporating objective measures.
[32]	Detailed study area description provided. The quantitative cross-sectional design was used with an adapted questionnaire. A pilot study was conducted for questionnaire enhancement. Data were analyzed using Stata SE 15.0, including descriptive analysis, chi-square tests, and binary logistic regression.	Improvement areas: Lack of non-respondent information limits generalizability. Need for an explanation of the work resources scale. No details on the data collection procedure, raising concerns about reliability and validity.
[33]	This study utilized a high-fidelity air ambulance simulator to mimic real-world scenarios closely. Objective and subjective measures were used to assess participant performance and fatigue comprehensively. Blinded assessment of video recordings increased reliability.	This study had a small sample size of 19 physicians, limiting the statistical power and generalizability of the findings. The study’s specific setting in a New Zealand tertiary teaching hospital may also restrict its applicability to other simulation environments or healthcare contexts.
[34]	The article’s methodology is well-defined, employing multiple measurement instruments for a comprehensive assessment. The research findings are effectively presented, offering insights into relationships and their implications for further understanding.	The study’s limitations include a small sample size (N = 13), limiting generalizability to all air traffic controllers. The specific context of a Philippine air traffic control unit raises concerns about the universal applicability of the findings.
[35]	The study uses archival research methods to analyze a large dataset of aviation accident reports, focusing on flight crew errors. Complete data collection and qualitative analysis techniques are employed to identify behavioral traps in flight crew accidents.	The study’s limitations include a restricted time frame, potential biases in data collection, and a lack of quantitative analysis.
[36]	The study utilized a comprehensive data collection approach with seven translated questionnaires, thoroughly assessing safety incident involvement among EMS crew members. The inclusion of a sample size of 131 enhanced both reliability and generalizability. Appropriate statistical analyses examined relationships and tested hypotheses.	The study employed questionnaire measures lacking validation for Slovaks or EMS crew members, potentially compromising their appropriateness. Subjective questionnaire measures are prone to biases and underreporting, indicating the necessity for addressing these effects.
[37]	This study has implemented an effective recruitment strategy to ensure a diverse participant pool from the UK and Ireland. By including a large sample size (111), the study aims to enhance the generalizability of its findings. The researchers have chosen to use online questionnaires through SNAP Survey Software, allowing for efficient data collection while ensuring participant anonymity.	The study had a low response rate of 10%, which raises concerns about sample representativeness and non-response bias. The findings may not be generalizable beyond the United Kingdom and Ireland.

3.1 Situation awareness assessment

Situational awareness involves obtaining existing information in the work environment, perceiving the meaning of this information, and predicting future working conditions [3]. The methods used to assess situational awareness in the included articles were categorized as objective and subjective. Table 3 presents the methods employed in each article.

Table 3
Situation awareness assessment

Measure Method subcategories Method Authors

Situation awareness Behavior observation Anesthetists’ non-technical skills Neuschwander et al. (2017); Myers et al. (2017)

Go/no-go scenario Tone et al. (2022)

Observation of facial, behavioral, and eye-based indicators Niu et al. (2022); Vogelpohl et al. (2018); Kim et al. (2022)

Records the real-time data Bej et al. (2021); Rudin-Brown et al. (2017)

Global Assessment of Team Performance checklist (GAOTP) Brogaard et al. (2022)

Coded archival research Velazquez (2018)

Objective measures Physiological sensors Eye-tracking glasses Mahajan et al. (2021); Castritius et al. (2021); Monteiro et al. (2020)

Quantitative analysis of situation awareness Situational awareness questions O’Hagan et al. (2020); Schneider et al. (2018); Shipherd et al. (2018)

SPAM Bongo et al. (2022)

Quantitative analysis of situation awareness (QASA) Wijayanto et al. (2020)

Situation awareness Questionnaires SASHA Bongo et al. (2022)

Subjective measure L Situation Awareness Rating Technique (SART) O’Hagan et al. (2018); Hopko et al. (2021)

Kano questionnaire Shin et al. (2022)

Likert-scale questionnaires Aram et al. (2022); Mohammadfam et al. (2021)

Safety at Work (SAPH@W) Falco et al. (2021)

Work situation awareness scale Sedlár (2021)

NTSC-Q Mariani et al. (2019)

Semi-structured interviews Newman et al. (2018); Peddle et al. (2019); Hannaford et al. (2021)

Measure	Method subcategories	Method	Authors
Situation awareness	Behavior observation	Anesthetists’ non-technical skills	Neuschwander et al. (2017); Myers et al. (2017)
		Go/no-go scenario	Tone et al. (2022)
		Observation of facial, behavioral, and eye-based indicators	Niu et al. (2022); Vogelpohl et al. (2018); Kim et al. (2022)
		Records the real-time data	Bej et al. (2021); Rudin-Brown et al. (2017)
		Global Assessment of Team Performance checklist (GAOTP)	Brogaard et al. (2022)
		Coded archival research	Velazquez (2018)
Objective measures	Physiological sensors	Eye-tracking glasses	Mahajan et al. (2021); Castritius et al. (2021); Monteiro et al. (2020)
	Quantitative analysis of situation awareness	Situational awareness questions	O’Hagan et al. (2020); Schneider et al. (2018); Shipherd et al. (2018)
		SPAM	Bongo et al. (2022)
		Quantitative analysis of situation awareness (QASA)	Wijayanto et al. (2020)
Situation awareness	Questionnaires	SASHA	Bongo et al. (2022)
Subjective measure L		Situation Awareness Rating Technique (SART)	O’Hagan et al. (2018); Hopko et al. (2021)
		Kano questionnaire	Shin et al. (2022)
		Likert-scale questionnaires	Aram et al. (2022); Mohammadfam et al. (2021)
		Safety at Work (SAPH@W)	Falco et al. (2021)
		Work situation awareness scale	Sedlár (2021)
		NTSC-Q	Mariani et al. (2019)
	Semi-structured interviews		Newman et al. (2018); Peddle et al. (2019); Hannaford et al. (2021)

Anesthetists’ Non-Technical Skills (ANTS) employ a graded scale with four categories to assess performance, which two anesthetists observe via video [31]. AeroNOTS is an adaptation of ANTS, assessing clinical performance in situation awareness, task management, decision-making, and teamwork [33].

The go/no-go scenario involves participants choosing to continue or halt a task related to cattle handling, explaining their reasoning and risk management strategies [37].

Observation of facial, ocular, and behavioral indicators was used to evaluate drivers’ behavior in automated driving, including reaction times, glancing behavior, and responses to risky situations [17 , 29].

Data was recorded in real-time to assess security guards’ alertness levels and locomotive crew members’ response to train control signals [19, 28].

Video analysis and the Global Assessment of Team Performance (GAOTP) checklist assessed labor non-technical skills and team performance [30].

Coded archival research analyzed crew behavior using air accident files to evaluate situational awareness. Phrases like “I do not know why” and “I do not know what is going on” are examples of not being aware [35].

The eye-tracking glasses have a camera to record what the individual sees and infrared cameras that evaluate the driver’s pupils to detect the gaze. The eye-tracking glasses measured situational awareness through glance behavior [7 , 18].

Situational Awareness Questions: SA was assessed by asking questions over 5 seconds at specific points during the flight. Awareness of speed, location, altitude, and TCAS (Traffic Avoidance Collision System) static images were evaluated [23]. In another study, SA questions included the perception of each SPHERES’ initial and final position, movement, and fuel consumption [5]. During practice, student-athletes were required to close their eyes, indicate other players’ positions, and estimate the distance between them. SA was measured by assessing the actual and estimated distance between athletes [16].

Situational awareness was assessed through speed, location, perception, and estimation questions in various scenarios [5 , 23].

SPAM involves measuring SA through online questions administered randomly to participants [34]. QASA employs a freezing technique, challenging participants to assess their car’s speed, compare it with the vehicle ahead, and estimate the time required to reach it [6]. SASHA is a questionnaire designed to gauge SA in aircrews [34]. SART is a subjective tool developed for assessing SA in pilots, consisting of attention supply, attention demand, and task comprehension items [14].

The Kano Questionnaire incorporates functional and dysfunctional questions to categorize acceptance elements into different dimensions [21]. Likert scale questionnaires assess SA in work environments, employing scales to measure factors like awareness, event recording, and response to COVID-19 risks [3, 32]. The SAPH@W questionnaire adopts a ten-point scale to evaluate SA by addressing specific risks in participants’ work environments [24].

The work situation awareness scale comprises 20 items and utilizes factor analysis to identify subscales related to SA [36]. The NTSC-Q questionnaire employs behavioral indicators such as risk identification and situational monitoring to assess SA [25].

Lastly, semi-structured interviews provide qualitative insights into SA, with participants sharing experiences and perceptions through graded responses and open-ended questions. These varied methods and instruments contribute to a comprehensive understanding of SA across different domains, enabling researchers and practitioners to enhance awareness and decision-making processes [15 , 27].

3.2 Fatigue assessment

Fatigue is characterized as a decrease in cognitive and physical performance resulting from prolonged exertion, sleep deprivation, and interruption of the internal clock [25]. Measures for assessing fatigue in the articles were divided into objective and subjective. Table 4 shows the method utilized in each paper.

Table 4
Fatigue assessment

Measure Method subcategories Method Authors

Fatigue Behavior observation Go/no-go scenario Tone et al. (2022)

Objective measures Observation of facial, behavioral, and eye-based indicators Niu et al. (2022); Vogelpohl et al. (2018); Bej et al. (2021); Rudin-Brown et al. (2017)

Global Assessment of Team Performance checklist (GAOTP) Brogaard et al. (2022)

Coded archival research Velazquez (2018)

Performance under pressure and fatigue Shipherd et al. (2018)

Physiological sensors Electrocardiogram (ECG) Hopko et al. (2021); Monteiro et al. (2020)

Electromyogram (EMG) Monteiro et al. (2020)

Body temperature sensor Monteiro et al. (2020)

Psychomotor vigilance task O’Hagan et al. (2018); O’Hagan et al. (2020); Myers et al. (2017)

Fatigue Questionnaires Fatigue Scale-14 (FS-14) Niu et al. (2022)

Subjective measure Fatigue level experienced Hopko et al. (2021)

Fatigue Severity Scale (FSS) O’Hagan et al. (2018)

Samn-Perelli fatigue scale Bongo et al. (2022); O’Hagan et al. (2020); Myers et al. (2017)

Kano questionnaire Shin et al. (2022)

Maslach burnout inventory General survey Aram et al. (2022)

Safety at Work (SAPH@W) Falco et al. (2021)

Fatigue assessment scale Sedlár (2021)

NTSC-Q Mariani et al. (2019)

Occupational Fatigue/Exhaustion Recovery (OFER) Mohammadfam et al. (2021); Myers et al. (2017)

Semi-structured interviews Newman et al. (2018); Peddle et al. (2019); Hannaford et al. (2021)

Measure	Method subcategories	Method	Authors
Fatigue	Behavior observation	Go/no-go scenario	Tone et al. (2022)
Objective measures		Observation of facial, behavioral, and eye-based indicators	Niu et al. (2022); Vogelpohl et al. (2018); Bej et al. (2021); Rudin-Brown et al. (2017)
		Global Assessment of Team Performance checklist (GAOTP)	Brogaard et al. (2022)
		Coded archival research	Velazquez (2018)
		Performance under pressure and fatigue	Shipherd et al. (2018)
	Physiological sensors	Electrocardiogram (ECG)	Hopko et al. (2021); Monteiro et al. (2020)
		Electromyogram (EMG)	Monteiro et al. (2020)
		Body temperature sensor	Monteiro et al. (2020)
	Psychomotor vigilance task		O’Hagan et al. (2018); O’Hagan et al. (2020); Myers et al. (2017)
Fatigue	Questionnaires	Fatigue Scale-14 (FS-14)	Niu et al. (2022)
Subjective measure		Fatigue level experienced	Hopko et al. (2021)
		Fatigue Severity Scale (FSS)	O’Hagan et al. (2018)
		Samn-Perelli fatigue scale	Bongo et al. (2022); O’Hagan et al. (2020); Myers et al. (2017)
		Kano questionnaire	Shin et al. (2022)
		Maslach burnout inventory General survey	Aram et al. (2022)
		Safety at Work (SAPH@W)	Falco et al. (2021)
		Fatigue assessment scale	Sedlár (2021)
		NTSC-Q	Mariani et al. (2019)
		Occupational Fatigue/Exhaustion Recovery (OFER)	Mohammadfam et al. (2021); Myers et al. (2017)
	Semi-structured interviews		Newman et al. (2018); Peddle et al. (2019); Hannaford et al. (2021)

In the go/no-go scenario, participants are exposed to scenarios involving cattle handling. They are required to decide whether to proceed with the task, explain their choice, and outline their risk management strategies [37].

Observation of facial, ocular, and behavioral indicators involves analyzing facial points to detect blinking and examining signs of fatigue, such as prolonged eyelid closure, yawning, and facial rubbing [17 , 29].

The Global Assessment of Team Performance Checklist (GAOTP) is a validated tool to assess team performance in non-technical skills. It considers various factors such as the team’s environment, the voices of team members, and indications of stress or fatigue [30].

In coded archival research, researchers analyze air accident files to evaluate crew behavior and identify indications of sleep deprivation, shift work, speech errors, or yawning [35].

During performance under pressure and fatigue, athletes exert themselves physically and mentally in activities like speed and precision competitions, which can result in fatigue [16].

Electrocardiogram (ECG) signals are collected to analyze heart rate variability (HRV) features, reflecting the impact of fatigue on parasympathetic activity [11, 14]. Electromyogram (EMG) is employed to study muscle contraction variations during operational activities, which tend to decrease in the presence of fatigue [11]. Additionally, body temperature sensors monitor fluctuations related to the circadian rhythm, indicating fatigue [11].

The Psychomotor Vigilance Task (PVT) involves participants performing a task that assesses attention and fatigue by quickly responding to visual stimuli on a computer screen. Mean reaction time (MRT) and attention lapses are analyzed [22 , 33].

The Fatigue Scale-14 (FS-14) is employed to subjectively evaluate individual fatigue levels and monitor fatigue cases in epidemiological studies [29]. The Fatigue level experienced participants assess their level of fatigue on a scale ranging from low to high after completing an activity [14]. The Fatigue Severity Scale (FSS) is a practical tool known for its high internal consistency and good test-retest reliability, used to measure subjective fatigue [14]. Additionally, the Samn-Perelli Fatigue Scale, consisting of seven items, specifically targets fatigue in military air transport [23 , 33].

The Kano questionnaire evaluates acceptance elements and categorizes them into distinct dimensions [21]. The Maslach Burnout Inventory General Survey involves participants rating their responses on a five-point scale to assess emotional exhaustion experienced in the workplace [32]. The Safety at Work (SAPH@W) questionnaire employs a ten-point scale to gauge the effectiveness of strategies for managing physical fatigue and related factors [24]. A subjective measure of fatigue, the fatigue assessment scale consists of ten items that are graded on a five-point scale [36]. The NTSC-Q questionnaire assesses behavioral indicators of fatigue, coping strategies, and sources of stress [25]. Lastly, the Occupational Fatigue/Exhaustion Recovery (OFER) scale employs a six-point Likert scale to evaluate various types of fatigue, including severe, chronic, and shift work fatigue [3, 33].

In semi-structured interviews, participants share their experiences of fatigue by responding to questions and rating their responses on a five-point scale. They also have the opportunity to provide open-ended answers [15, 27]. These interviews aim to identify and explore fatigue categories, such as competing demands, fatigue, and stress [26].

3.3 Sleepiness assessment

Sleep loss can lead to reduced performance, learning, and short-term working memory, leading to increased accidents and errors [5]. Table 5 shows the method utilized in each paper.

Table 5
Sleepiness assessment

Measure Method subcategories Method Authors

Sleepiness Physiological sensors Eye-tracking glasses Mahajan et al. (2021)

Objective measures Actigraphy O’Hagan et al. (2020); Schneider et al. (2018); Wijayanto et al. (2020)

Electroencephalography (EEG) Kim et al. (2022)

Galvanic skin response (GSR) Kim et al. (2022)

Electrocardiogram (ECG) Kim et al. (2022)

Sleepiness Questionnaires Karolinska sleepiness scale (KSS) Mahajan et al. (2021); Wijayanto et al. (2020); Myers et al. (2017); Castritius et al. (2021); Vogelpohl et al. (2018); Neuschwander et al. (2017); Monteiro et al. (2020)

Subjective measure Epworth sleepiness scale Castritius et al. (2021); Mohammadfam et al. (2021); Myers et al. (2017)

Pittsburgh Sleep Quality Index (PSQI) Myers et al. (2017)

Sleep log Schneider et al. (2018); O’Hagan et al. (2020)

Measure	Method subcategories	Method	Authors
Sleepiness	Physiological sensors	Eye-tracking glasses	Mahajan et al. (2021)
Objective measures		Actigraphy	O’Hagan et al. (2020); Schneider et al. (2018); Wijayanto et al. (2020)
		Electroencephalography (EEG)	Kim et al. (2022)
		Galvanic skin response (GSR)	Kim et al. (2022)
		Electrocardiogram (ECG)	Kim et al. (2022)
Sleepiness	Questionnaires	Karolinska sleepiness scale (KSS)	Mahajan et al. (2021); Wijayanto et al. (2020); Myers et al. (2017); Castritius et al. (2021); Vogelpohl et al. (2018); Neuschwander et al. (2017); Monteiro et al. (2020)
Subjective measure		Epworth sleepiness scale	Castritius et al. (2021); Mohammadfam et al. (2021); Myers et al. (2017)
		Pittsburgh Sleep Quality Index (PSQI)	Myers et al. (2017)
	Sleep log		Schneider et al. (2018); O’Hagan et al. (2020)

Actigraphy is a wearable device like a smartwatch or a smart ring that assesses daily sleep patterns, total sleep time, sleep efficiency, and wake frequency after sleep onset [5 , 23].

Eye-tracking glasses provide objective measures of alertness, including pupil diameter, blink frequency, and blink duration [18]. Electroencephalography (EEG) detects brain electrical waves to differentiate sleep stages and measure cognitive workload [20]. Galvanic skin response (GSR) evaluates sweating as an indicator of participant arousal [20]. Electrocardiogram (ECG) signals assess heart rate variation and participant excitement/alertness [20].

The Karolinska sleepiness scale (KSS) and Epworth sleepiness scale (ESS) are subjective scales that rate sleepiness levels [3 , 33]. The Pittsburgh Sleep Quality Index (PSQI) is a subjective questionnaire assessing sleep quality and disturbances over a month [33].

Sleep logs involve participants keeping records of their sleep hours and quality [5, 23].

4 Discussion

The present scoping review focused on analyzing experimental studies on situation awareness, fatigue, and sleepiness. These studies were conducted in 21 countries, with the highest production observed in the USA. Most studies were conducted in the Northern Hemisphere, and none were conducted in South America.

Regarding academic categories, eight studies were related to Health, and five specifically evaluated non-technical skills (NTS). NTS encompasses social and mental skills crucial for effective and safe task performance [38]. These skills include leadership, situational awareness, communication, teamwork, decision-making, and coping with stress and fatigue [30]. NTS are considered tacit rather than technical skills and play a fundamental role in incident control. They find application in various work situations as they are more general and less specific to a particular domain than technical skills [25]. Failures in NTS have been associated with accidents across different work environments [37]. Moreover, NTS can be influenced by fatigue [33]. The three main factors for coping with fatigue are recognizing the antecedents of fatigue, identifying the consequences of fatigue, and implementing coping strategies [25].

Seven articles were devoted to studying automotive engineering, focusing on automated driving. Automated driving may increase driver sleepiness and reduce situational awareness [7]. The introduction of automated driving systems allows drivers to relinquish active control, which, in turn, can result in reduced alertness. Notably, one of the primary challenges associated with highly automated vehicles revolves around ensuring that drivers can promptly assume control when necessary, taking into account considerations of situational awareness and safety [18]. In the context of automated driving, specific environmental conditions or road characteristics, such as heavy rain or roadworks, can adversely affect the performance of sensors and object recognition, thereby necessitating driver intervention [17]. Consequently, when drivers are required to regain control, it becomes crucial for them to allocate sufficient time for reestablishing situational awareness and physically preparing themselves [29].

4.1 Situation awareness and fatigue/sleepiness

Sleepiness and fatigue are common among the population studied in the papers, yet their impact on performance remains uncertain. The relationship between sleep loss, situational awareness, and non-technical skills (NTS) remains unknown [31]. Sleep loss can influence an individual’s ability to achieve optimal levels of situational awareness, decision-making, and task performance. Moreover, individuals may overestimate their situational awareness when experiencing sleep loss or food deprivation compared to expert assessments [5].

Several papers have shown that fatigue and sleepiness impact situational awareness. Mohammadfam et al. revealed a negative correlation between fatigue and situational awareness, where an increase in sleepiness results in a decline in situational awareness. Myers et al. demonstrated that tiredness decreases non-technical performance and situational awareness compared to well-rested individuals. Wijayanto et al. confirmed that sleep-deprived driving performance is significantly impaired due to reduced situational awareness. Vogelpohl et al. identified that drivers experiencing fatigue have difficulty achieving situational awareness and may face challenges in take-over requests situations. Bongo et al. provided evidence of an inverse relationship between fatigue and workload, which alters situational awareness. Moreover, fatigue is associated with various concerns related to the visual display terminal. Lastly, Sedlár demonstrated that higher stress levels and fatigue in the workplace lead to poorer situational awareness and reduced cognitive flexibility.

Although one paper does not demonstrate a significant impact of fatigue or sleepiness on situational awareness, O’Hagan et al. found no significant changes in situational awareness after 24 hours of sleep deprivation. The study utilized subjective methods to assess situational awareness. It is important to note that subjective measures may only reflect the participant’s perceived consciousness during the activity rather than their actual level of consciousness.

4.2 Studies limitations and gaps

The reviewed studies had limitations, including small sample sizes, disparities between simulated and real environments, and reliance on subjective analysis. Some studies also faced limitations related to subjective measures of cognitive abilities. It is important to note that findings from simulated conditions may not fully represent actual performance, and the configuration of driving simulators might have influenced the development of fatigue. Additionally, the absence of force feedback in the simulated environment and the closer monitoring in eye-tracking studies could affect participants’ behavior.

The study identified several gaps in the existing literature. Firstly, there is a need for more research conducted in South America to address the geographical gap in understanding situational awareness, fatigue, and sleepiness. Secondly, employing more objective methods to assess these factors is crucial, as objective measures provide more accurate and reliable data. Moreover, the limited sample sizes in many studies restrict the generalizability of the findings. Lastly, future research should include real-world testing to validate the results and ensure their practical relevance.

Future research should focus on utilizing objective measures, increasing sample sizes, conducting real-world testing, and further exploring the cognitive aspects of situational awareness, fatigue, and sleepiness. Addressing these gaps will contribute to a more comprehensive understanding of these factors and their impact on safety and performance in various domains.

This study had certain limitations. Firstly, while some studies have established a connection between fatigue, drowsiness, and situational awareness, further research is required to better establish this association in specific articles. Secondly, although some studies reported the analysis method used, providing additional explanations regarding the employed methodology would be beneficial.

5 Conclusion

This scoping review aimed to assess experimental studies focusing on situational awareness, fatigue, and sleepiness, highlighting several significant findings, and identifying critical gaps in the existing literature.

The results of the examined studies consistently indicated a reduction in situational awareness under conditions of fatigue and sleepiness, except for one study that did not establish this connection. This implies that both fatigue and sleepiness can have a substantial impact on an individual’s capacity to attain and sustain situational awareness, ultimately influencing their decision-making and task performance. Nevertheless, there is a need for further research to incorporate objective methodologies for the analysis of cognitive factors related to situational awareness.

While the reviewed studies offered valuable insights, they also unveiled several gaps. These gaps encompass the absence of research conducted in South America, the limited application of objective methods to evaluate situational awareness, fatigue, and sleepiness, the relatively small sample sizes within many of the reviewed studies, and the prevalence of studies conducted in simulated environments. These factors underscore the necessity for future research to prioritize studies in diverse geographical regions, the adoption of objective measures, the enlargement of sample sizes, the investigation of real-world scenarios, and a deeper exploration of the cognitive aspects related to situational awareness, fatigue, and sleepiness.

Addressing these gaps will enable researchers and practitioners to develop a more comprehensive understanding of situational awareness, fatigue, and sleepiness. This understanding will facilitate the development of effective interventions and strategies to enhance performance and safety across various domains, thereby improving the well-being and productivity of individuals in both professional and everyday life contexts.

Ethical approval

This study, as a literature review, is exempt from Institutional Review Board approval.

Informed consent

This study, as a literature review, is exempt from informed consent.

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

Acknowledgments

The authors have no acknowledgments.

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior –Brasil (CAPES) –Finance Code 001.

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