Introduction
This study aims to examine pilots’ alertness level post-COVID-19 pandemic using the Boeing alertness model scale, which employs a questionnaire. Alertness in this context means the ability to complete a particular task within a specific timeframe. To ensure high accuracy, the data collected post-flight by the BAM scale will be compared to information given by pilots in a questionnaire. The critical issue is whether or not the recordings are similar. Since the pandemic impacted pilots differently, it is important to compare data with which BAM was created vs. data collected from the pilots.
It is essential to highlight that the BAM scale employs the Crew Alert Program (CAP). Rostering departments use this airline application to calculate pilots’ alertness levels. It uses information such as changes in time zones, work schedules, and composition of the crew members. Simultaneously, the questionnaire checks the alertness level using the same variables. For this research work, the alertness level in pilots will be established with similar scales employed by the BAM tool to conduct quantitative analysis using IBM SPSS Statistics predictive analytic software.
The purpose of adding a new variable to the CAP system with the assistance of the BAM system developer is to strengthen the study by considering variables other than weariness that can impact a pilot’s alertness. The researchers can incorporate more factors that can help better anticipate when a pilot is at risk of declining cognitive performance by collaborating with the BAM developer (Wilson et al., 2021). This will make it possible to create a more thorough CAP system that considers a more extensive range of variables that may influence pilot awareness, ultimately improving flight safety.
The CAP employs a single optimum model for each pilot; thus, the approach differs. The software uses one model that enables generalizability since evaluating every pilot’s tiredness is difficult as each has a different level of exhaustion. The Karolinska Sleepiness Scale and the Common Alertness Scale are used in the algorithm to generate forecasts of alertness (Jahanpour et al., 2020).
Therefore, putting a 1500 value as the standard in the CAS to perform this research work and compare BAM’s predictions with actual circumstances is essential. Then, the obtained outcomes will be compared and contrasted with the program’s conclusions after the data provided in the questionnaire. The primary independent variable in this study will be the effect of the pandemic on pilots’ attentiveness. Others are the time of day, the length of the trip, the number of hours worked in the previous week, and the age and experience of the pilots.
It is crucial to find additional factors, in addition to weariness, that can affect a pilot’s alertness to strengthen the study’s validity. For instance, according to available literature, numerous variables, such as caffeine use, age, circadian rhythm, workload, and stress, might affect a pilot’s level of alertness. According to research, pilots who get enough sleep are more attentive and commit fewer mistakes (Jahanpour et al., 2020). Although too much caffeine can have adverse effects, studies have shown that consuming it can help with alertness and cognitive function (Jahanpour et al., 2020). Another factor to consider is age, as older pilots may see a loss in cognitive ability and alertness.
Disruptions to the circadian rhythm can also cause exhaustion and a decline in attentiveness. Long-distance travelers who frequently encounter jet lag may find it challenging to stay alert. Workload and stress can affect a pilot’s alertness since they both hurt cognitive function and tire them. By considering these other parameters, the study can gain a more complete knowledge of the elements that affect pilot alertness levels (Morgul et al., 2021). This information can help create more potent methods to reduce fatigue and raise aircraft safety.
Background Information
The number of flights keeps increasing from year to year. In 2004, there were more than twenty million flights conducted by the global airline sector, while in 2020, the number had risen to about forty million (Jahanpour et al., 2020). International domestic and global airline flights will be about twenty-two million in 2021 (Morgul et al., 2021). After a constant upward trend until 2020, the pandemic happened and caused a 3.2% annual growth to a steep drop of around 40% as of February 2021 (Morgul et al., 2021). According to Rienks et al. (2022), in 2020 and 2021, fatigue levels among pilots had risen due to the widespread misinformation about COVID-19. Nevertheless, due to the immense workload as the schedule has been interrupted by the pandemic, the aviation sector workforce encounters a higher risk of being exhausted.
This research aims to determine if Etihad Airways’ BAM system yields the same results as those provided by the crew and pilots. It is crucial to notice the increased mental fatigue level since it may result in mistakes and severe damage (Koh et al., 2020). Awareness of the elevated mental fatigue level is essential because it increases the risk of making errors and causing significant damage (Nižetić, 2020). However, other alertness strategies and neuroscientific instruments exist, suggesting the potential for various accurate fatigue measures (Koh et al., 2020). Thus, this research aims to evaluate the BAM’s correctness to assess the connection between its output and the actual level of pilots’ alertness in Etihad Airways company.
Worldwide restrictions caused by the pandemic led to a rise in general anxiety. Numerous locations required citizens to continue the practices used before COVID-19 and during the COVID-19 pandemic (Wilson et al., 2021). The most frequent precautions are respirator use, hand washing, avoiding crowded areas, and social seclusion (Morgul et al., 2021). With the safety protocols in place, despite being in 2023, the anxiety levels in pilots seem to be high due to fear of being exposed to the coronavirus. Whereas airlines have implemented different safety measures to minimize the danger of transmission, the strict rules have made pilots assume they are always at risk of exposure at work.
The fear among pilots continues to impact their anxiety levels significantly. The COVID-19 pandemic has increased stress and workload for pilots as they try to adjust to a situation they thought would be for only a while (MacIntyre et al., 2021). The situation is said to have affected the pilots’ performance and decision-making capacity (Jang, 2020). Additionally, it has been observed that the societal support for pilots that existed before and during the pandemic has decreased since the pandemic (Tam, 2020). Determining their degree of alertness following the pandemic is crucial.
By adding fresh factors that affect pilot alertness, the current study is an extension of the initial BAM study on pilot weariness. The current one intends to uncover additional factors that can affect pilot alertness, including sleep quality, circadian rhythms, workload, and ambient factors, in contrast to the original BAM study, which primarily focused on the effects of fatigue on pilot performance. The study aims to provide a more thorough knowledge of the various aspects contributing to pilot attentiveness and improving flight safety by including these extra variables.
Preliminary Research Questions and Hypotheses
The main research question is: Does the CAP accurately forecast how alert airline pilots will be? The main reason behind this question is to examine if the program can be accurate to the extent the results are generalized. The hypothesis is that no significant difference exists between the program-generated prediction and real-life alertness levels. Other questions include:
- Is there any difference between the Captains’ and First Officers’ overall results?
- Do specific variables, such as workload or the time of day, impact the reliability of the forecasts made by the CAP?
- How do different flight types, including short-haul versus long-haul flights, affect the accuracy of the CAP?
Methodology
Population and Sample
The study aims to analyze the effectiveness of the Boeing alertness model when used on pilots. Due to this, the target population comprises captains and first officers employed by BAM-using airlines. As previously indicated, it would be hard to contact every pilot at the more than 45 airlines that utilize BAM (Jeppesen Fatigue Risk Management, 2019). The available population consists of male pilots employed by Etihad Airways, which consented to provide the data for this study project and conduct a survey among its crew members.
The accessible population consists of 210 pilots, of which 105 are captains, and the others are first officer ranks, males aged 25 to 55 years. This is significant since, between 2002 and 2020, the average age in private and public organizations stayed between 45 and 46. The individuals in the chosen sample are of a career-average age (MacIntyre et al., 2021). As a result, the sample was selected to represent the general population as possible. For example, evaluating participants of different ages and ethnicities will enable generalizations (MacIntyre et al., 2021). G*Power analysis will be employed to determine the minimum sample size.
The measures ANOVA within factor will be employed as a statistical test, where F is assumed as the test family. The assumed effect size is 0.25, which indicates a medium effect size. Regarding the analysis conditions, the size of the alpha error probability is 0.05, and the 1- β error probability is 0.80, meaning that the outcomes of this test might be regarded as correct with 80% probability.
With such inputs, the a priori power analysis indicates a minimum sample size of 34 participants is needed. In the airline that offers data, about one thousand pilots fly regularly. Therefore, rather than employing the 34 proposed samples, it should be decided to use 210. The benefit would lessen the error margin and raise the possibility of discovering statistical significance. In addition, it will eliminate the danger of mortality for internal validity.
Instruments
The schedule for the randomly chosen pilots will remain unchanged as they will have a fixed roster. After every flight, they must log in online using a link to the survey. They must answer all the questions before submitting the questionnaire. The primary mental factors contributing to pilot fatigue include time awake, sleep debt, the number of sectors, and the amount of secondary duty time (McCauley et al., 2021). Its measurement by the Boeing alertness model, which uses the Karolinska Sleepiness 9-point scale, is accurate and trustworthy (McCauley et al., 2021). This is true since each of the nine points has a precise definition, with 1 denoting a state of total alertness and alertness and nine denoting total exhaustion and inability to operate.
The Common Alertness Scale ranges from 0 to 10,000, where 0 is the most minor alert condition and, thus, the highest danger of fatigue, which is the default output of BAM. The application may convert the CAS to the KSS and analyze it as an instrument. For this research work, a minimum of 1500 in CAS will be employed at the start of every flight, and the system is anticipated to forecast the level of alertness for that flight. When pilots complete the questionnaire, the answers will be placed in the CAP to check the results.
The question concerns whether or not the outcomes will resemble the ones forecasted with the program. Thus, the instrument is considered valid to answer the research question (McCauley et al., 2021). The Karolinska Sleepiness Scale focuses on self-reported sleepiness prediction to show the instrument’s appropriateness. Concerning the validity of the survey, it is vital to note that the participants will be immediately after a flight completes the forms.
Procedures
The research will use a quantitative design since there are targets to analyze forms from 210 pilots completed after each flight. The data will be collected using the questionnaire, seeking insights about the participants’ fatigue levels after the flight. Therefore, this study will employ the survey technique, which constitutes the scientific sampling method with a designed questionnaire to determine a given population’s traits through statistical methods. The Karolinska Sleepiness 9-point scale, utilized by the Crew Alertness program to assess alertness, is the primary tool employed by the questionnaire to measure weariness. It is a scale of 1 to 9, with 1 denoting full alertness and nine denoting total exhaustion.
Even though the quantitative design has some advantages, a mixed-methods approach would enable a deeper understanding of how aviation workers view the BAM system. The perception of the BAM system in actual circumstances, including elements that could reduce the system’s efficiency, including workload and diversions, could be better understood by incorporating qualitative data from pilots and rostering schedules. In addition, it would offer a more thorough examination of how pilots engage with the system and how this impacts their overall performance.
Conducting semi-structured interviews with pilots and rostering schedules is one way to adopt a mixed-methods approach. To detect common themes and patterns, it would be possible for researchers to ask participants open-ended questions about their experiences using the BAM system (MacIntyre et al., 2021). This type of questioning can also be used to collect data for the CAP. The quantitative information gathered through the survey approach may then be triangulated with the qualitative information to create a more complete picture of the effects of the BAM system on aviation workers.
It is vital to discuss the validity and reliability of the survey method and its sources of invalidity. Validity evaluates how well a questionnaire captures the desired variable. The comprehensive survey is reliable since it measures weariness using the KSS, a standard academic technique. Simultaneously, reliability considers how far the questions produce similar results each time the instrument is repeatedly asked in the same circumstances. This means that reliability represents the reproductivity of the study. Regarding the procedures, the study’s test criteria is a repeated measure ANOVA.
In addition, it is essential to stress the study’s complete anonymity. No identifying information will be published, such as names and other personal data. A signed Institutional Review Board (IRB) approval and the airline’s approval will be utilized to survey the working pilots. Informed consent, which includes items such as the to withdraw at any time, clear directions, and anonymity, will be signed before the beginning of the research. For the survey sample, Etihad Airways 210 pilots will be randomly chosen. Then, participants must sign every necessary document, such as informed consent. The information will be gathered by a Google Forms program, guaranteeing that every question must be completed when creating the form.
Data Analysis
Data from the questionnaires given to the pilots will be gathered after each flight. After the information is collected, those participants who completed every questionnaire will be evaluated and further processed. It isn’t easy to get research that analyzes if the alertness model forecasts the same outcomes as they happen. The repeated measure ANOVA will be utilized within the factor (Statista, 2019). Thus, this process and other possible ones are proper for the study question and numerical information gathered through the questionnaires.
Significance
Fatigue is a widespread condition among pilots that poses a substantial hazard to flight safety. The aviation sector has created numerous solutions to this issue, such as the Boeing Alertness Model (BAM), which forecasts pilots’ risky levels of mental weariness. However, the BAM tool’s capability to predict pilot tiredness levels is currently being studied (McCauley et al., 2021). By offering insightful data on the pilots’ mental health, the proposed study seeks to solve this problem and considerably improve flight safety.
The possibility of generalizability is one of this study’s essential contributions to the aviation sector. The results can be applied to other airlines as the sample population will represent all pilots using the BAM tool. Since more than a hundred different countries are represented among the pilots, it is likely that other nations will be represented among the sample population (McCauley et al., 2021).
The BAM tool is used by 45 airlines, which makes it simpler to extrapolate the study’s findings to male populations aged 25 to 55 (McCauley et al., 2021). This includes all male pilots at Etihad Airways in the proposed age range. The study’s possible advantages include boosting productivity and lowering the dangers pilots confront due to psychological exhaustion. The leading causes of mental weariness include insufficient sleep, anxiety, and workload.
Conclusion
To advance previous studies, this study focuses on what is known about preventing excessive levels of pilot tiredness. Crew Resource Management, which incorporates leadership and training, is one strategy to prevent rising levels of weariness (Statista, 2019). Suppose the study reveals that the CAP generates reliable projections. In that case, the ramifications may have a considerable impact on most airlines’ operations and overall aviation safety.
Benefits of the study go beyond academia and include preventing tragedies in the real world (Statista, 2019). Pilot weariness has been the cause of multiple events in the aviation sector, which have resulted in significant financial and human losses. Therefore, it is impossible to overestimate the study’s potential contribution to improving flight safety. Airlines can take preventative action to stop catastrophic catastrophes by accurately reporting pilot fatigue levels.
References
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