Introduction
Problem Statement
I have met the Manager of Safety in Etihad Airways, and he explained to me that Etihad had been using BAM for almost two years. Since upgrading their Fatigue risk management system to the Crew Alert Program (CAP), the have noticed a dramatic improvement in crew scheduling, amount of rest between flights. Most importantly, the number of fatigue reported from pilots has signifcatly reduced, meaning that pilots are less fatigued than before. The purpose of this study is to examine the relationship between what the Boeing alertness model (BAM) gives and what the pilots fill in as their actual level of alertness. The main question here is whether these results (given by BAM and pilots themselves) are similar to each other or not. Pilots’ fatigue level will be measured through the questionnaire (Appendix A) after each performed flight and then compared with the results given by the BAM. It should be highlighted that the BAM uses a program called Crew Alert Program (CAP), which can be downloaded on a computer or as an application on the mobile phone. The BAM considers such indicators as work schedule, time zone changes, crew composition, take-off and landing waypoints, and commuting (Civil Aviation Safety Authority, 2014). At the same time, in the created questionnaire, the level of alertness is measured through the same variables.
For this study, the level alertness in pilots is measured with the same scales used by the BAM to perform quantitative analysis using SPSS version 28 (CITE). However, the approach is different because the program Crew Alert uses one optimum model that is used for all pilots. Thus, as every pilot has a different fatigue level, it is impossible to consider every pilot fatigue assessment. That is why, Crew Alert, as every other program, uses one optimum model that can be generalized to all the pilots.
The BAM program calculates predictions over the alertness using Karolinska Sleepiness Scale (KSS) and the Common Alertness Scale (CAS). Thus, to conduct the study and compare BAM’s predictions with the real state of affairs, 1500 value will be put in the CAS as a threshold. It is possible to use 0 CAS value instead, but Etihad Airways uses 1500 CAS value as a margin, not the minimum (which is 0); thus, in this study also, the value of 1500 will be used. After this, received outcomes will be compared with the conclusions made by the program after the real data, gained with the help of the questionnaire filled after the flight, being put as an input.
Background
Every year the number of flights performed is only increasing. In 2004, there were 23.8 million flights performed by the global airline industry, whereas in 2020 (pre-pandemic period), this number has increased to 40.3 million (Mazareanu, 2012). Respectively, the fatigue reports are increasing, especially since COVID-19 started to spread around the world and intervene in workers of various industries (Wilson et al., 2021). This is true because of the anxiety produced by the news and assumptions about the symptoms and infection’s consequences created over the pandemic. Moreover, the pandemic has led to additional work stress factors, causing an increase in fatigue levels. These factors include the inability for a crew to leave outstation hotels, unstable schedule, flight cancelations, last-minute additional flying, country entry restrictions, increased crew testing requirements, and others.
This study aims to determine whether the system used now by Etihad Airways, which is the BAM, suggests an output similar to the actual mental state of the pilots and crew members. It is vital to detect the increased level of mental fatigue in time because it may lead to pilots’ mistakes and serious damages or even catastrophes (Dawson et al., 2017; ICAO, 2015). Thus, BAM focus on different variables, crew schedule planning, and other details related to work and possible tiredness, predicting the dangerous level of fatigue (Bam, 2020; David-Cooper, 2019). However, other alertness models and neuroscientific tools exist, such as BAM, CAS, FAID, FRI, SAFE, and SWP, meaning that there is a variety of valid measures for fatigue (Papanikou et al., 2020). Therefore, this study is meant to evaluate the accuracy of the Boeing model to examine the relationship between BAM’s outputs and the actual level of pilots’ alertnesss in Etihad Airways company.
The Research Question and Hypotheses
The primary research question that will guide this study is the following: “Does the Crew Alert program give accurate predictions of the level of alertness in airline pilots?” The main idea of this research question lies in the examination whether the program can be so accurate that the results can be generalized? The feasibility of this research question will be discussed in the Chapter 3 of this paper. Regarding the research hypothesis of this study, there will be no significant difference between the program-generated (CAP) prediction alertness level and the actual real-life alertness level.
Potential Significance and Generalizability of the Study
- Potential generalizability of results. This study aims at making a significant contribution not only to the academic community but also to the real world and to improve the safeness of performed flights in which the BAM is used, including more than 45 companies worldwide (Civil Aviation Safety Authority, 2014). As the actual information about the pilots’ psychological conditions will be provided, the results and conclusions are expected to be as accurate as possible. Moreover, regarding the external validity of this study, it is possible to generalize the results to all the airlines that use the Boeing Alertness Model to predict dangerous levels of mental fatigue of pilots. This is true because the sample will be representative for all the pilots. In Etihad Airways, there are more than 100 different nationalities in the pilot community. One can be sure that by using a random sample, there will be many different nationalities in the sample population. Moreover, there are 45 airlines that use BAM. For these two reasons, it is very easy for the study to be generalized to all Male populations aged between 25 and 55. Specifically, it can be generalized to all the Male pilot populations aged between 25 and 55 in Etihad Airways.
- Rationale, implications, benefits of the research. The rationale for this study pertains to the potential benefits of alertness prediction among pilots and crew members are increasing work efficiency and decreasing a possible threat. Previous studies on the topic regarding pilots’ fatigue provided a ground for this research. In other words, many studies uncovered underlying causes of mental fatigue: lack of sleep, anxiety, workload, and others (David-Cooper, 2019; O’Hagan, 2018). For instance, Şenol and Acar (2020) conducted research studying the causes of Air France Flight 447 Crash in 2009 and came up with a conclusion that “the captain might have had problems with fatigue management before the flight” (p. 196). Thus, the study will be based on prior research, aiming to extend the existing knowledge regarding the prevention of high levels of fatigue (Oliver et al., 2017). Moreover, there are different ways to prevent the increase in fatigue, such as crew resource management, that includes training, leadership, and other tools (Bennett, 2019; Gelmi, 2019). In case this research demonstrates that the Crew Alert Program makes accurate predictions, the implications for this study may have crucial effects on the work of most of the world airlines and the situation over the safeness overall. Therefore, the benefits of this research exceed the contribution to the academic community and extend to the prevention of actual accidents and catastrophes.
- Limitations and delimitations. The sample chosen for this study includes pilots whose age is between 25 and 55 years, all of whom are men. Thus, it is hard to determine whether there is a possibility to generalize the study results to female pilots. Moreover, there is a limitation in that all questionnaires will be filled out by the participants themselves. There is also an issue of potential dishonesty with self-reporting, but anonymity helps to combat this. Finally, the limitations of this study are that it can only be beneficial to some airlines, which are the 45 airlines that use BAM. It cannot be helpful to all airlines because different airlines use different ways to measure fatigue. The delimitations of this study includes anonymity and the sample consisting of male pilots only, the age of which is between is 25 and 55 years old (those are the limitations that the research put for this study).
Literature Review
The Definition of Fatigue in Aviation
Before analyzing and researching fatigue in aviation, one should be acknowledged with the general definition of fatigue. ICAO (2015) defined fatigue as 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 perform safety-related operational duties” (p. xiii). Thus, although the aviation industry provides one of the safest ways to travel, pilots’ and crew members’ fatigue can cause accidents and incidents (ICAO, 2015). Moreover, the most threatening feature of crew member fatigue is that it is inevitable because of the all-day operations and brain activity. Therefore, some approaches to address and manage this psychological state should be introduced and established. ICAO (2015) provides an overview of the various approaches to fatigue management in the aviation industry, emphasizing the role of such fatigue grounds as the need for adequate sleep, daily rhythms, workloads.
Some scholars emphasize not only the lack of sleep as a contributing factor to fatigue and its possible consequences but also timing and quality of sleep itself. For instance, it was claimed that the “amount, timing, and quality of sleep each day (sleep/wake schedule)” is crucial in preventing fatigue (Bendak & Rashid, 2020, p. 2). Moreover, such a variable as operations made in multiple time zones is also considered while defining fatigue in pilots and crew members (Bendak and Rashid, 2020). Lee and Kim (2018) also concluded that aviation workers’ night schedules often fail to provide adequate time for sleep, making the crew members feel more tired during night shifts. Consequently, pilots and others may experience mental or physical decline or the rest of the defects, which define fatigue (Lee & Kim, 2018). It cannot be claimed that the listed above crucial factors of such a psychological state oppose the definition suggested by ICAO (2019). On the contrary, such research as Lee and Kim (2018) only complements the model definition of fatigue in aviation.
Previous Studies about Fatigue in Aviation
Different studies about fatigue in aviation have already been conducted, and a summary can be made concerning the suggested methods to manage the discussed psychological state. To begin with, Alcéu (2015) analyzed planned pilots’ flights and their psychological state during work. The study revealed the high-risk areas during the early morning flights, late evening, and days with more than four sectors. In such risk areas, the predictions regarding safety were not followed, and the pilots’ estimation of the fatigue level was higher than it should be. Thus, based on the schedule analysis, this study suggests a new approach to managing workload in fatigue and reconsidering the safety settings’ management. The focus should be on the group with the highest duty hours, making sure to monitor their roaster all year round to mitigate risk and increase the level of safety (Alcéu, 2015). Moreover, Alcéu (2015) recommends that future researchers should conduct a 365-days study to see the correlations in the increase of fatigue level and winters or summers, meaning the most extreme times in a year.
Previous studies on the damaging consequences of fatigue in the work of pilots also provided an understanding that this psychological state can lead to severe accidents, mostly in activities demanding the concentration of attention. Impairment of critical skills and functions is one of the main consequences of cases when pilots cannot concentrate their attention, have poor memory, slow response, and mood changes (Hobbs et al., 2018; Stokes & Kite, 2017). According to Hobbs et al. (2018), “The National Transportation Safety Board (NTSB) has estimated that fatigue is a contributing factor in approximately 20% of major transport accidents” (p. 14). Moreover, the agency included the decrease of fatigue-related accidents in all transport modalities to the list of the first ten safety priorities (Hobbs et al., 2018). This research conducted surveys of bar pilots and analyzed their dispatch records for one year. It is an important notion that marine pilots have similar high-risk navigating jobs to airline pilots, and therefore, the fatigue itself may have a similar impact. Thus, the Hobbs et al. (2018) emphasized the necessity of accepting basic safety management measures. Such measures include minimizing night shifts and providing the personnel with at least 36 consecutive hours to rest in a 14-days working period. Summing all above, there are various studies about fatigue in aviation and other related industries, so it is crucial to review the measures converted into a variable by different scholars.
The Ways to Measure Fatigue in Airline Pilots
In their literature review and meta-analysis, Jerman and Meško (2018) discussed the various instruments that measure fatigue among pilots. Several scales have been used: FAI test (a fatigue assessment instrument), a standing-position balance test, CFF test (critical flicker fusion frequency test), subjective rating of sleepiness and fatigue, and many others (Jerman & Meško, 2018). The most crucial notion here is that all the instruments mentioned above are in terms of what they measure and focus on various sides of the psychological state under analysis. However, if some airline companies want to implement the safety management policies recommended in the paragraphs above, they can choose those discussed by Jerman and Meško (2018). Moreover, their literature analysis provides an overview of the tendencies followed by scholars in this research field. Regarding tendencies, even though Jerman and Mesko (2018) conducted a deep analysis of the existing literature, they did not provide readers with recommendations or outline advantages of the discussed instruments to measure pilots’ fatigue.
The work about fatigue measurement through questionnaires should be discussed in this section. Bourgeois-Bougrine et al. (2003) conducted a study that measured fatigue among 739 pilots from short and long-haul flights using a questionnaire. The results of these self-reported manifestations analysis revealed that night flights and jet lag were the most critical factors that generated this psychological state (Bourgeois-Bougrine et al., 2003). Long periods of awakening and poor quality of sleep, which is a factor that was emphasized by various scholars (Bendak & Rashid, 2020), also increase the level of pilots’ fatigue (Bourgeois-Bougrine et al., 2003). Therefore, the measurement of fatigue through questionnaires can be considered an option because it allows conclusions that other academic community representatives confirm.
Fatigue in the COVID-19 Pandemic
The COVID-19 pandemic caused many restrictions on populations worldwide, which led to an increase in overall anxiety. The most common measures introduced to cope with infection were avoiding public areas, hand hygiene, wearing masks, and social distancing. MacIntyre et al. (2021) conducted a cross-sectional survey of preventive behaviors in adults (people more than 18 years old) in five cities in Australia, the UK, and the USA. According to MacIntyre et al. (2021), “pandemic fatigue was more common in younger people” (p. 199). Although aircraft crew contain younger and older members, a study should be conducted researching how pandemic restrictions influenced the growth of fatigue in pilots’ behaviors and the general population.
Moreover, Morgul et al. (2021) questioned 4,700 people to define factors influencing the psychological fatigue in Istanbul, Turkey. Only 35.9% were declared to be psychologically normal (Morgul et al., 2021); “age, educational level, occupational status, place of residence and number of family members” impact the mental fatigue of a person (Morgul et al., 2021, p. 128). The critical role here is knowledge about the COVID-19: what consequences it has, how many infected people are there in the city/state, what are the symptoms, etc.
The way people are coping with their life in crises affects their psychological condition. Morgul et al. (2021) argued that: normal participants generally showed more positive attitudes than the fatigued in believing that COVID-19 will finally be controlled, satisfaction with preventive measures taken by the authorities, reporting suspected cases with symptoms, and trusting that Turkey can overcome the COVID-19 pandemic (p. 128).
In other words, beliefs affect the mental fatigue of a person. In addition to that, Teng et al. (2020) claimed that being overwhelmed with news regarding the pandemic and other significant infections or crises may lead to increased fear, anxiety, and fatigue. This overwhelming feeling can be referred to as messaging fatigue, which means being tired because of permanent exposure to similarly themed information (Koh et al., 2020). In their research, Kim and So (2018) show that the reaction to this information overload can negatively affect message fatigue. Moreover, psychological fatigue can be tied to information so that a person starts to fear missing something out (Dhir et al., 2018). Thus, pilots and crew members, who are also taking the risk of getting infected on the job and always overloaded with the COVID-19 news, may have negative beliefs regarding the pandemic, resulting in the psychological state of fatigue.
Activities Causing Fatigue and How to Reduce Fatigue with the Boeing Alertness Model
Bunting (2016) examined relationships between the fatigue level in pilots and crew members with solutions such as napping, spontaneous episodes for sleep, and reports for duties. As Bunting (2016) concluded, napping for a short period, usually less than 30 minutes, could help reduce the levels of psychological fatigue. Other scholars, Sivasankari and Karthika (2014), suggested the usage of the iPhone application Crew Alert. According to Sivasankari and Karthika (2014), “the application is intended for use by pilots as a tool for assessment, logging, and reporting of fatigue, to increase safety in the air” (p. 269). In other words, in addition to a short nap, crew members have been advised to manage fatigue by tracking their sleep patterns and work shifts. However, there are other ways to reduce psychological fatigue, such as the Boeing Alertness Model (BAM).
According to Alcéu (2015), modern alertness models for aircrews, such as the Boeing Alertness Model (BAM), have increased previous models’ complexity and outcome reliability by adding extra variables, generating a more precise prediction of psychological fatigue, and therefore, producing better risk management. The BAM also has one of the highest numbers of applications because of the accurate predictions and wide range parameters (Alcéu, 2015). The predictive biomathematical fatigue the Boing Alertness Model (BAM) used as an output the Karolinska Sleepiness Scale (KSS) and the Samn-Perelli 7-point fatigue scale (SPF) (Jahanpour et al., 2020). The former is a 9-points scal (Shahid et al., 2012), and the latter is a 7-point scale, both starting from 1, which means “fully alert, wide awake” to 7 or 9, meaning “completely exhausted, unable to function effectively” (Samn, & Perelli, 1982). One can claim that the BAM measurement scale should be deliberately accurate to be applied in aviation because of deviation in terms of defined thresholds from other existing scales.
Mental fatigue decreases with the help of the Boeing Alertness Model. By conducting schedule analysis with the BAM, Alcéu (2015) concluded that it is risky to have early morning and late evening flights. Hellerström et al. (2010) presented a methodology (based on the BAM analysis) to improve the efficiency of the prescriptive rules to enhance alertness while maintaining or improving pilots’ and crew members’ productivity. Therefore, one can claim that with the help of the Boeing Alertness Model, scholars can draw some recommendations regarding the safest time of flights and rules that will help detect specific mental conditions.
In summary, it is obvious from the literature review that pandemic restrictions, such as appeared because of the COVID-19, influence a lot on the growth of fatigue in pilots’ behaviors as well as the general population. Moreover, using the BAM measurement scale is one of the most reliable ways to research this topic. Thus, the influence of various crises (such as pandemics) on the increase of the level of fatigue should be researched.
Methodology
The research question, which is the following: “Does the Crew Alert program give accurate predictions of the level of alertness in airline pilots?” is concise and feasible. This is true. because the Etihad Airways has decided to provide the data of the crew alert program and sponsor the whole research. Therefore, having the data and possibility to evaluate the level of alertness among pilots by the created questionnaire, the study is meant to have reliable outcomes and applicable conclusions and recommendations regarding the BAM. There are two statistical hypotheses for this study: null hypothesis and alternative one. To begin with; the H0 is the following: there will be no significant difference between the program-generated (CAP) alertness and the actual real-life crew alertness. H1 suggests: there will be a significant difference between the BAM score and the actual score of pilots’ psychological fatigue level.
Population and Sample
This study targets the analysis of BAM effectiveness on pilots. That is why the target population for this research consists of captains and first officers working in airlines using the Boeing Alertness Model. As mentioned previously, more than 45 airlines worldwide use the BAM, and it would not be possible to reach all the pilots at these airlines (Civil Aviation Safety Authority, 2014). As Etihad Airways agreed to provide the data for this study and conduct a survey with its crew members, the accessible population consists of male pilots working in this airline.
Concerning major characteristics of the chosen sample, it regards participants’ occupation, age, and sex. The sample is 210 pilots, which consist of 105 Captain rank and 105 First officer rank. All these participants are men of the age between 25 and 55. This is highly important because, according to Salas (2021), the average age of pilots working in the USA in both commercial and private companies remained approximately the same from 2002 to 2020 and was 45-46 years old. In other words, the chosen sample includes pilots whose age is average in their profession. Thus, the sample selection was performed to achieve the highest representativeness of the accessible population. For instance, the analysis of participants of diverse ages (from 25 to 55) and ethincity (100 different nationalities) will allow generalizations for all the mentioned professions and ages.
In particular, the minimum sample size was determined using G*Power analysis (Anderson et al., 2017). Using the following input, F is the test family, The repeated measures ANOVA within factor is used a statistical test, the effect size is 0.25 which indicated a medium effect size.. Concerning the conditions of this analysis, the size of alpha error probability is 0.05, and the 1-b error probability is 0.80, meaning that the results of this test might be regarded as accurate with 80% probability. With these inputs, the a priori power analysis suggests that a minimum total sample size of 34 participants is required: 17 Captains and 17 First officers.
In the airline that provides data for this study, there are approximately 1,000 pilots, who perform flights regularly. For this reason, the decision was made to take a larger sample than what the program suggested (34 participants), The sample size for this study will be a total 210 pilots, 105 Captains and a 105 First officers. One advantage of having a larger sample size is that it increases the chances of finding statistical significance and reduce the margin error.. more importantly, it will eliminate the threat of mortality for the internal validity. Therefore, the same 210 pilots are used with two variables: the first is the output of the Crew Alert program alertness score which is the prediction alertness level score, and the second is the pilots filling in the output from the questionnaire alertness score which is the actual level of alertness score.
Instruments
The randomly chosen pilots will have a fixed roaster, which means that their full month schedule will not be changed. After every flight, they will need to log in online using an URL that takes them to survey (using survey monkey) and fill the questionnaire. Moreover, they will be required to answer all the questions (in order to avoid empty spaces) to be able to submit.
Pilots’ fatigue includes such main psychological factors as time awake, time of the day, prior sleep debt, and secondary as the duty time and number of sectors. Its measurement through the Karolinska Sleepiness 9-points Scale, used by the Boeing Alertness Model, is reliable and efficient (Naeeri et al., 2021). This is true because of the clear definitions of all 9 points, where 1 means “fully alert, wide awake” and 9 stands for the condition in which a person is “completely exhausted, unable to function effectively” (Naeeri et al., 2021). According to Jeppesen Fatigue Risk Management (2021), “The default output of BAM is alertness expressed on the Common Alertness Scale (CAS) that ranges from 0 to 10,000 where 0 is the least alert state and thereby the highest fatigue risk” (p. 1). Moreover, it was mentioned that “The CAS scale is directly anchor-ed to the Karolinska Sleepiness Scale (KSS) in a way that CAS 0 = KSS 9 and CAS 10,000 = KSS 1” (Jeppesen Fatigue Risk Management, 2021, p. 1). This means that CAS as an instrument used to predict fatigue is easily converted to the KSS and, thus, analyzed by the program.
For this study, a minimum of 1,500 in CAS will be used at the beginning of each flight, and the system is expected to predict the level of alertness for that flight. When pilots conduct the survey, filling out the questionnaire, the answers will be put into CAP to see what results it would suggest. The question is whether the results will be exactly the same as predicted with the 1500 CAS input or worse. Therefore, this instrument is considered to be valid to answer the stated research question. According to McCauley et al. (2021), the Karolinska Sleepiness Scale focuses on subjective sleepiness prediction based on self-reports. This notion is important because it demonstrates the appropriateness of this instrument to this study: captains and first officers will fill out the questionnaires on their own. Concerning the validity of the questionnaire, it also should be pointed out that selected crew members will fill out the forms by themselves after each flight.
Procedures
This study is meant to have a fully quantitative research design, inferential statistics, analyzing the collected with the help of survey data with SPSS Version 28 (cite). This design is the most appropriate because there is an aim to analyze forms from 210 people filled out after each performed flight. Thus, the number of questionnaires is very high, and to calculate the average values and find relationships in numerical data, and quantitative methods are required to be applied.
To begin with, the data will be collected using the questionnaire (Appendix A), aiming at the evaluation of captains and first officers’ actual level of fatigue after the flight. Thus, this study will use the survey method, which “encompasses the use of scientific sampling method with a designed questionnaire to measure a given population’s characteristics through the utilization of statistical methods” (Apuke, 2017, p. 43). This form focuses on fatigue measurement through the Karolinska Sleepiness 9-points Scale, used by the Crew Alertness program to measure alertness. It is a 9 point scale, in which 1 means “fully alert, wide awake” and 9 stands for the condition in which a person is “completely exhausted, unable to function.” Participants will be instructed about how to fill this survey out in directions on the questionnaire, so that everyone will have the same perception of the scale.
Concerning the sources of invalidity of this survey method, reliability and validity should be discussed. According to Jang (2020), validity looks at the extent to which a survey questionnaire measures the targeted variable. In this regard, the elaborated survey is valid because it measures the fatigue level with the KSS, which is a common practice in the academic community. At the same time, “reliability considers the extent to which the questions used in a survey instrument consistently elicit the same results each time it is asked in the same situation on repeated occasions” (Jang, 2020, para. 12). In other words, reliability stands for the reproductiveness of the research. As the questionnaire itself, having numerical scales, is provided with this study, this research is expected to be reproducible if taking the same conditions. However, there is a crucial point about the trustworthiness of crew members’ answers. Typical self-report issues are always a concern of trustworthiness, but this will be compensated by promised confidentiality. Regarding the procedures themselves, the test criteria for this study is a repeated measure ANOVA.
Moreover, it is crucial to emphasize the full anonymity of this study, and participants will be acknowledged about this also. No names and other personal information of participated pilots will be published and used for purposes different from the research ones. There will be used a signed Institutional Review Board (IRB) approval and the approval of the airline regarding the ability to conduct a survey with the working pilots. Informed consent, including clear directions, option to withdraw (opt-out) at any time, and anonymity (or confidentiality) will also be signed before the start of the study.
Pilots working in Etihad Airways will be recruited randomly for the survey sample. Then, participants will be required to sign all the necessary documents, including informed consent. The data will be collected by a Google Forms program, ensuring that all questions are needed to be completed at the stage of creating the form.
Data Analysis
Data received from the filled-out questionnaires will be collected immediately after each flight. After the data collection, those participants who completed all the questionnaires will be analyzed (those, who failed to complete some, will be excluded from the survey); and further processed. It is hard to find a study that analyses if the alertness model predicts the same results as it happens in reality. The repeated measure ANOVA will be used within factor. Therefore, this procedure and other possible ones are appropriate for this research question and numerical data collected through the questionnaires.
Budget and Time Schedule
The cost planned for implementing this study is 15,000 AED, which is equivalent to $4,000. This budget includes the researcher’s time spent on the data analysis. A month will be spent for the IRB approval. Then, the timeline associated with the data collection study is two months. Finally, another two months will be dedicated to the data analysis, writing, and reviewing the manuscript by the airline before the publication. To begin with, IRB approval will be the issue starting in January 2022. Then, data will be collected in February-March 2022. Moreover, data is expected to be analyzed and conclusions prepared within a two-month period, meaning that in June 2022, there will be completed and reviewed research on the effectiveness of the BAM program to predict pilots’ fatigue.
References
Alcéu, P. M. D. M. (2015). Managing fatigue in a regional aircraft operator: fatigue and workload on multi-segment operations (Master’s thesis).
Anderson, S. F., Kelley, K., & Maxwell, S. E. (2017). Sample-size planning for more accurate statistical power: A method adjusting sample effect sizes for publication bias and uncertainty. Psychological science, 28(11), 1547-1562.
Apuke, O. D. (2017). Quantitative research methods: A synopsis approach. Kuwait Chapter of Arabian Journal of Business and Management Review, 33(5471), 1-8.
Bam, B. (2020). Framework for identifying fatigue characteristics of aviation cabin crew’s risk management systems (Master’s thesis). Retrieved from LUT University (10638).
Bendak, S., & Rashid, H. S. (2020). Fatigue in aviation: A systematic review of the literature. International Journal of Industrial Ergonomics, 76.
Bennett, S. A. (2019). The training and practice of crew resource management: recommendations from an inductive in vivo study of the flight deck. Ergonomics, 62(2), 219-232.
Bourgeois-Bougrine, S., Carbon, P., Gounelle, C., Mollard, R., & Coblentz, A. (2003). Perceived fatigue for short-and long-haul flights: A survey of 739 airline pilots. Aviation, Space, and Environmental Medicine, 74(10), 1072-1077.
Bunting, T. P. (2016). Fatigue mitigation effects of en-route napping on commercial airline pilots flying international routes. The University of North Dakota.
Civil Aviation Safety Authority. (2014). Biomathematical Fatigue Models Guidance Document. CASA.
David-Cooper, R. (2019). Pilot fatigue—a study on the effectiveness of flight and duty time regulations for professional pilots in Canada. Revue québécoise de droit international, 117-146.
Dawson, D., Darwent, D. & Gregory D. R. (2017). How should a bio-mathematical model be used within a fatigue risk management system to determine whether or not a working time arrangement is safe? Accident Analysis and Prevention 99, 469-473.
Dhir, A., Yossatorn, Y., Kaur, P., & Chen, S. (2018). Online social media fatigue and psychological wellbeing—A study of compulsive use, fear of missing out, fatigue, anxiety and depression. International Journal of Information Management, 40, 141-152.
Gelmi, T. (2019). Breakthrough: What Cabin Crew Can Teach You about Leadership, Teamwork and Customer Contact. iUniverse.
Hellerström, D., Eriksson, E., Romig, E., & Klemets, T. (2010). Flight time limitations and fatigue risk management: A comparison of three regulatory approaches. Boeing Flight Safety Foundation.
Hobbs, A., Gregory, K., Parke, B., Pradhan, S., Caddick, Z., Bathurst, N., Flynn-Evans, E. (2018). San Francisco bar pilot fatigue study. NASA Technical Report ASA. San Jose State University.
ICAO. (2015). Fatigue risk management systems manual for operators (2nd ed.). Montréal.
Jahanpour, E. S., Berberian, B., Imbert, J. P., & Roy, R. N. (2020). Cognitive fatigue assessment in operational settings: a review and UAS implications. IFAC-PapersOnLine, 53(5), 330-337.
Jang, Y. (2020). Survey data: Reliability and validity? Are they interchangeable? Web.
Jeppesen Fatigue Risk Management. (2021). BAM Prediction Power. Jeppesen.
Jerman, A., & Meško, M. (2018). How to measure fatigue among pilots? Zbornik Veleučilišta u Rijeci, 6(1), 13-22.
Kim, S., & So, J. (2018). How message fatigue toward health messages leads to ineffective persuasive outcomes: examining the mediating roles of reactance and inattention. Journal of Health Communication, 23(1), 109-116.
Koh, P. K. K., Chan, L. L., & Tan, E. K. (2020). Messaging fatigue and desensitisation to information during pandemic. Archives of Medical Research, 51(7), 716-717.
Lee, S., & Kim, J.K. (2018). Factors contributing to the risk of airline pilot fatigue. Air Transport Management, 67, 197–207.
MacIntyre, C. R., Nguyen, P. Y., Chughtai, A. A., Trent, M., Gerber, B., Steinhofel, K., & Seale, H. (2021). Mask use, risk-mitigation behaviours and pandemic fatigue during the COVID-19 pandemic in five cities in Australia, the UK and USA: A cross-sectional survey. International Journal of Infectious Diseases, 106, 199-207.
Mazareanu, E. (2021). Number of flights performed by the global airline industry from 2004 to 2022. Statista.
McCauley, M. E., McCauley, P., Riedy, S. M., Banks, S., Ecker, A. J., Kalachev, L. V., Rangan, S., Dinges, D. F. & Van Dongen, H. P. (2021). Fatigue risk management based on self-reported fatigue: expanding a biomathematical model of fatigue-related performance deficits to also predict subjective sleepiness. Transportation research part F: traffic psychology and behaviour, 79, 94-106.
Morgul, E., Bener, A., Atak, M., Akyel, S., Aktaş, S., Bhugra, D., Ventriglio, A., & Jordan, T. R. (2021). COVID-19 pandemic and psychological fatigue in Turkey. The International Journal of Social Psychiatry, 67(2), 128.
Naeeri, S., Kang, Z., Mandal, S., & Kim, K. (2021). Multimodal Analysis of Eye Movements and Fatigue in a Simulated Glass Cockpit Environment. Aerospace, 8(10), 283.
O’Hagan, A. D. (2018). Sleep loss and fatigue among commercial airline pilots (Doctoral Dissertation). Retrieved from Dublin City University.
Oliver, N., Calvard, T., & Potočnik, K. (2017). Cognition, technology, and organizational limits: Lessons from the Air France 447 disaster. Organization Science, 28(4), 729-743.
Papanikou, M. C., Frantzidis, C. A., Nikolaidou, A., Plomariti, C., Karagianni, M., Nigdelis, V., Karkala, A., Nday, C., Ntakakis, G., Krachtis, A., Bamidis, P. D & Kourtidou-Papadeli, C. (2020). Neuroscientific tools in the cockpit: towards a meaningful decision support system for fatigue risk management. In MATEC Web of Conferences. EDP Sciences.
Salas, E. B. (2021). Average age of active pilots in the United States from 2002 to 2020. Statista.
Samn, S.W. and Perelli, L.P. (1982). Estimating aircrew fatigue: a technique with application to airlift operations. School of Aerospace Medicine.
Şenol, S., & Acar, A. B. (2020). Crew Resource Management in Aviation: The Analysis of the Air France Flight 447 Crash. Journal of Organizational Behavior Review, 2(2), 172-200.
Shahid, A., Wilkinson, K., Marcu, S., & Shapiro, C.M. (2012). Karolinska Sleepiness Scale (KSS). Springer New York, 209–210.
Sivasankari, A., & Karthika N. (2014). Fatugue risk management using mobile application. International Journal of Computer Science and Information Technology Research, 2(3), 269-279.
Stokes, A. F., & Kite, K. (2017). Flight stress: Stress, fatigue and performance in aviation. Routledge.
Teng, Z., Wei, Z., Qiu, Y., Tan, Y., Chen, J., Tang, H., Wu, H., Wu, R., & Huang, J. (2020). Psychological status and fatigue of frontline staff two months after the COVID-19 pandemic outbreak in China: A cross-sectional study. Journal of Affective Disorders, 275, 247-252.
Wilson, D., Driller, M., Johnston, B., & Gill, N. (2021). The effectiveness of a 17-week lifestyle intervention on health behaviors among airline pilots during COVID-19. Journal of Sport and Health Science, 10(3), 333-340.