Causes and effects of fatigue in experienced military aircrew

Fatigue Perception Among Military Aircrew


Miller & Melfi carried out a research to determine how aircrews perceived fatigue countermeasures , whether they had been fatigued in flight, what might have caused fatigue in cockpit, and whether they had been fatigued in flight, what might have caused fatigue in the cockpit, and whether they would have benefited from medication during crew rest and while to counteract the effects of fatigue. The research survey is done on 162 military aircrew and this article provides further descriptive statistic done on the original results.

Although the survey is carried out using a 5 point likert scale, the results were downgraded from ordinal to nominal scale with the responses from Strongly agree and Agree combining into a "positive" category and the responses from Strongly disagree and disagree combining to form a "negative" category. That will make 3 categories in positive, neutral and negative.

These following hypothesis are tested in each individual question

Hypothesis 1 – The aircrew had experienced at least one episode of unintended sleep while flying in their crew position on their aircraft
Hypothesis 2 – The aircrew had experienced at least one episode of performance degrading effect of fatigue while flying in their crew position
Hypothesis 3 – The aircrew Perceive that improper scheduling is the main cause for their in-flight fatigue
Hypothesis 4 – The aircrew have not received sufficient training and education on the different countermeasure that combat fatigue
Hypothesis 5 – The aircrew would benefit from medication to aid in wakefulness during flight to help combat the effects of fatigue during an operational mission
Hypothesis 6 – The aircrew would benefit from medication to assist them in adjusting their circadian rhythm, to combat the effects of fatigue
Hypothesis 7 – The aircrew quality of sleeping quarters contributed to incidents of in flight fatigue

Interpretation

Hypothesis 1 is the assumption if the aircrew had experienced fatigue during flight. A positive experience will mean that the aircrew had encountered fatigue in flight during his/her duty.
Hypothesis 2 is the assumption if fatigue had affected the performances of the aircrew. A positive experience will mean fatigue had affected the performance of the aircrew.
Hypothesis 3 is assumption if management is the cause of the aircrew fatigue. A positive experience will mean that management is the cause of the aircrew fatigue.
Hypothesis 4 is the assumption if management had help aircrew reduce or combat fatigue. A positive experience will mean that aircrew thinks that management had helped them reduce or combat fatigue.
Hypothesis 5 and 6 are assumptions if medications can help aircrew reduce or combat fatigue. A positive experience will mean that aircrew thinks medication has a prominent role in reducing or combating fatigue.
Hypothesis 7 is assumptions if quality of sleep is the cause of the aircrew fatigue. A positive experience will mean that quality of sleep is the cause of the aircrew fatigue.

Illustration 1: positive, neutral and negative experience are converted into scores 1, 2 and 3 respectively. Mean and Standard deviation were than tabulated from the scores. Hypothesis 2 had the smallest mean at 1.09 which translate to most aircrew having a positive experience with fatigue and had at least 1 experience of performance degradation due to fatigue. Hypothesis 5 had the highest mean at 2.12 which translate to most aircrew are neutral to the belief that medication can help them combat the effects of fatigue. The standard deviation of Hypothesis 2 is the smallest at 0.38 which also means that the score do not vary too much away from the mean giving us the idea that most aircrew had the positive experience while hypothesis 5 had a larger standard deviation at 0.81 which also means that the aircrew perception that medication can aid wakefulness in flight is pretty much spread up evenly between the 3 experience therefore larger variation.

Illustration 1: Frequency of Response, Mean and Standard deviation
Hypothesis Positive(1) Neutral(2) Negative(3) Mean Std Dev
Hypothesis 1 106 3 53 1.67 0.94
Hypothesis 2 153 4 5 1.09 0.38
Hypothesis 3 106 20 36 1.57 0.83
Hypothesis 4 139 13 10 1.20 0.54
Hypothesis 5 44 55 63 2.12 0.81
Hypothesis 6 65 41 56 1.94 0.87
Hypothesis 7 108 20 34 1.54 0.82

Illustration 2: The correlation coefficient measures the strength between 2 sets of variable, in this instance we compare Hypothesis 3 and 7 which assumes if management and quality of sleep is the cause of fatigue respectively with hypothesis 2 which assume the if the aircrew had encountered fatigue. The results show strong positive correlation for both comparison indicating that if an aircrew had encountered fatigue then most likely he/she thinks management or quality of sleep can be the cause.

Illustration 2: Correlation of Hypothesis
Comparison Positive Neutral Negative Correlation Coefficient
Hypothesis 2 Vs Hypothesis 3 153/106 4/20 5/36 0.9856
Hypothesis 2 Vs Hypothesis 7 153/108 4/20 5/34 0.9898

Illustration 3: Significance testing on various hypothesis is done using a 90% confidence interval two tailed CHI square test, the 90% confidence interval means if the p value is smaller then 0.1 then we will accept the hypothesis as significant.

Illustration 3: Significance Test : Chi Square
Hypothesis Significance level P-Value Interpretation
Hypothesis 1 90%(1-0.90) = 0.1 p < 0.001 Very highly Significant
Hypothesis 2 90%(1-0.90) = 0.1 p < 0.001 Very highly Significant
Hypothesis 3 90%(1-0.90) = 0.1 p < 0.001 Very highly Significant
Hypothesis 4 90%(1-0.90) = 0.1 p < 0.001 Very highly Significant
Hypothesis 5 90%(1-0.90) = 0.1 p < 0.10 Significant
Hypothesis 6 90%(1-0.90) = 0.1 p > 0.10 Non-Significant
Hypothesis 7 90%(1-0.90) = 0.1 p < 0.001 Very highly Significant

Study Scope

This investigation is to determine how perceive fatigue and how it will affect them and it is carried out on the population of interest which are the military aircrews. These aircrew are subjected to different kinds of workload and stress level and therefore results cannot be generalize to the commercial aircrews.

Methods

Research Approach

Self-report survey which are designed to test seven different hypothesis on aircrew fatigue in flight operation.

Sample

The population of interest are the United States Air Force (USAF) active duty pilots and navigators from veteran aircrews to the junior aircrews. The breakdown of them are provided in the table below.

Rank USAF Pilot USAF Navigator
Lieutenant 15.7% 17.3%
Captain 35.7% 24.3%
Major 24.9% 26.2%
Lt Colonel 16.8% 24.5%
Colonel 5.7% 6.8%
General 1.1% 0.1%

Variables

A 5 points likert scale is being used to gauge the aircrew perception on fatigue from strongly agree to strong strong disagree however the results were downgraded from ordinal to nominal scale with the responses from Strongly agree and Agree combining into a "positive" category and the responses from Strongly disagree and disagree combining to form a "negative" category. That will make 3 categories in positive, neutral and negative.

The following hypothesis is being tested

Hypothesis 1 (Question 7) – The aircrew had experienced at least one episode of unintended sleep while flying in their crew position on their aircraft
Hypothesis 2 (Question 9) – The aircrew had experienced at least one episode of performance degrading effect of fatigue while flying in their crew position
Hypothesis 3 (Question 10) – The aircrew Perceive that improper scheduling is the main cause for their in-flight fatigue
Hypothesis 4 (Question 12) – The aircrew have not received sufficient training and education on the different countermeasure that combat fatigue
Hypothesis 5 (Question 15) – The aircrew would benefit from medication to aid in wakefulness during flight to help combat the effects of fatigue during an operational mission
Hypothesis 6 (Question 16) – The aircrew would benefit from medication to assist them in adjusting their circadian rhythm, to combat the effects of fatigue
Hypothesis 7 (Question 17) – The aircrew quality of sleeping quarters contributed to incidents of in flight fatigue

The rest of the questions were used to determine demographics and open ended questions and were not used in our meta-analysis.

Data Analysis

The original article only uses percentage to express the collected data which after a Chi-Squared test is carried out on the nominal data with a 90% confidence level to determine the 2 tailed statistical significance. Our meta-analysis, uses other descriptive statistics like dispersion , central tendency and correlation.

References

1.Paul C.Cozby & Scott C.Bates (2012).methods in behavioural research. Understanding research results : Statistical Inference. Chapter 13. pages 262 - 285.
2.James C.Miller & Mary L.Melfi (2006). Causes and effects of fatigue in experienced military aircrew. retrieved from http://www.dtic.mil/cgi-bin/GetTRDoc?Location=U2&doc=GetTRDoc.pdf&AD=ADA462989

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