Analysis of heart rate variability during emergency flight simulator missions in fighter pilots

Title: Analysis of heart rate variability during emergency flight simulator missions 1 in fighter pilots 2 ABSTRACT: 3 Introduction: The management of emergency situations in the different 4 simulated flight segments can entail a workload that could affect the performance 5 of military pilots. The aim was to analyse the modifications in neurovegetative 6 balance (using Heart Rate Variability - HRV) of professional fighter pilots 7 attending learning/training sessions in emergency situations in a flight simulator. 8 Methods: A total of eighteen pilots from the Spanish Air and Space Force were 9 included. HRV was recorded simultaneously during diverse simulated emergency 10 situations in three different flight segments: take-off, in-flight, and landing. 11 Results: The comparison between take-off and in-flight revealed a statistically 12 significant increase (p<0.05) of pNN50, rMSSD, SD1, SD2 and a statistically 13 significant decrease (p<0.000) in Stress Score (SS) and S/PS ratio. Between 14 flight and landing a statistically significant increase (p<0.05) in Mean HR, Min HR, 15 Max HR, SS, and S/PS was shown while experiencing a significant decrease 16 (p<0.000) in pNN50, rMSSD, and SD2. Finally, between take-off and landing, the 17 variables which showed significant changes (p<0.05), these changes being a 18 significant increase, were Mean HR, Min HR, Max HR, rMSSD, SD1, and SD2. 19 SS and S/PS ratios showed a statistically significant decrease (p<0.000). 20 Conclusions: An emergency situation in a flight simulator manoeuvre produced 21 an anticipatory anxiety response in pilots, demonstrated by low heart rate 22 variability, which increased during the flight and decreased in the landing 23 segment of the flight. 24 25 Keywords: Military pilots; Flight simulator; Emergency mission; Heart-rate 26 variability; Autonomic Nervous System. 27 28 Key messages: 29 What is already known on this topic 30 • To improve the flight performance and safety of fighter pilots, it seems 31 necessary to assess the psychophysiological parameters related to mental 32 workload during emergency situations in a flight simulator. 33 What this study adds 34 • Anticipatory anxiety response was observed during three flight segments 35 (take-off, in-flight, and landing), with no significant differences between 36 student and instructor pilots. 37 • The results indicate that an emergency situation in a flight simulator 38 manoeuvre produced a low heart rate variability, which increased during 39 the flight and returned down in the landing phase of the flight. 40 How this study might affect military practice or policy 41 • The emergency situations recreated at separate times during the flight 42 simulation seem to be useful in the training of fighter pilots. 43 44 45 1. INTRODUCTION 46 Air combat is one of the most exacting challenges in human performance. The 47 perceptual, physiological, and physical demands are extreme and require 48 exhaustive training and preparation1,2. Flight simulation is a fundamental tool, 49 which underpins fighter pilot training3. Flight simulators allow progressive 50 development of the skills required for flying and in-flight decision-making4. The 51 final objective is to train the pilot into a flight state without restrictions through 52 training based on the control of cognitive demands5. 53 Furthermore, the pilot trained in simulated contexts confronts highly dynamic and 54 threatening environments (e.g., adverse weather and climatological conditions). 55 Therefore, the pilot requires a high level of attention to the different instruments 56 of the system6. This fact has led NATO (North Atlantic Treaty Organization) to 57 propose the necessity of assessing the physical condition of pilots5,7. Regarding 58 this, there are recent studies that study the response to psychophysiological 59 demands in both real and simulated environments1,8–10. 60 Specifically, on the use of flight simulators, the management of emergency 61 situations in the different flight segments can entail a workload which could affect 62 the performance of both civil aviation and military pilots10–13. To improve flight 63 performance and safety10–12, it seems necessary to assess the 64 psychophysiological parameters related to the mental workload of the pilots14. 65 Previous studies have investigated the performance of fighter and attack pilots in 66 emergency situations within the flight simulator10,13. These studies highlight that 67 physiological characteristics, such as Heart Rate Variability (HRV), can be 68 predictors of the behaviour and reaction of pilots when assessing and dealing 69 with risk during the resolution of different emergency scenarios during combat 70 flight13,15. 71 HRV is one of the principal parameters to evaluate the interactions between 72 sympathetic and parasympathetic mediators in the sinus and atrioventricular 73 nodes, defined as neurovegetative or autonomic balance16. It is applied 74 noninvasively in clinical studies as well as for research purposes16,17. Bearing in 75 mind the existing limitations to measure physiological changes in flight situations, 76 HRV recording could help in monitoring the workload of a military pilot training 77 task, without creating intrusions or disturbances in the development of flight 78 missions9,10,13,18. 79 Likewise, previous authors have analysed the autonomic response of fighter 80 pilots in simulated environments during attack and defence missions1,8. 81 Nevertheless, to the authors’ knowledge, there are few studies that record 82 parameters related to autonomic balance in emergency situations during 83 simulated flight. Then, this study aimed to analyse the modifications in 84 neurovegetative balance (using HRV) of professional military pilots during 85 emergency situations in a flight simulator. 86 2. MATERIALS AND METHODS 87 2.1 Design 88 An observational and correlational study was conducted to compare parameters 89 related to the state and physical performance of the cervical spine among military 90 pilots (instructor pilots versus student pilots). The study was performed following 91 the Strengthening the Reporting of Observational Studies in Epidemiology 92 (STROBE) statement19. This study was registered on ClinicalTrials.gov (NCT 93 number: 04487899). 94 2.2 Participants 95 An initial, potentially eligible sample of 26 pilots, male and female adult volunteers 96 were recruited. The sample consisted of instructors or student pilots assigned, at 97 the time of assessment, to the district ALA 23 of the Talavera la Real Air Base of 98 the Ministry of Defense of the Government of Spain (Badajoz). Finally, the sample 99 consisted of 18 pilots (students and instructors) (Figure 1). They were between 100 21 and 34 years old (M=26; SD= 7.3). The recruitment period was from the 1st of 101 November 2019 to the 31st of January 2020. Figure 1 provides a flow chart of 102 subject recruitment during the study. 103 The participants carried out their practices/training in a flight simulator (Northrop 104 F-5 Model, Indra©, Spain) which reproduce different combat missions10. Pilots 105 had at least one year of experience in the flight simulator. First, they started with 106 cockpit habituation procedures, progressing from simple manoeuvres such as 107 take-offs and landings to combat missions. 108 The mission conducted consisted of resolving emergency situations during three 109 different flight segments (take-off, in-flight, and landing). The emergencies were 110 the following: i) immediate action, in which the pilot had to resolve the failure of 111 the aircraft with previously memorised procedures and ii) follow-up, where the 112 pilot must analyse the situation by a checklist. These emergencies were 113 distributed as follows: a) Take-off: immediate action, consisting of engine failure; 114 b) In-flight: Follow-up manoeuvres characterised by the recovery of abnormal 115 positions; c) Landing: immediate action and follow-up. It consists of a loss of 116 spatial situation, with a tendency of slowing down and flying up. The pilot had to 117 perform a checklist reading and later analysis to land the aircraft, controlling the 118 landing traffic. In conclusion, three different emergency situations, one for each 119 flight segment, were simulated. 120 The local ethics committee (University of Extremadura) approved the study 121 (register number: 54/2020), which followed all the principles outlined in the 122 Declaration of Helsinki. All subjects signed an informed written consent to 123 participate in this study. 124 2.3 Assessment 125 2.3.1 Outcome Measures of HRV 126 To calculate the autonomous balance, the HRV method based on the Poincaré 127 plot was used20,21. This software has proven to be extremely valid and capable of 128 recording non-linear trends that are frequently present on R-R intervals of 129 record21. 130 Time domain variables: 131 - MeanHR (bpm): It corresponds to the interval between two beats (R peaks 132 on the ECG). 133 - pNN50 (%): Percentage of consecutive RR intervals that differ by more 134 than 50 ms from each other. A high value of pNN50 provides valuable 135 information about high spontaneous heart rate (HR)22. 136 - Root Mean Square of the Successive Differences (rMSSD) (ms): The 137 square root of the average of the sum of the differences squared between 138 normal adjacent. It shows the degree of activation of the Parasympathetic 139 Nervous System on the cardiovascular system. This parameter reports the 140 short-term variations of the RR intervals. It is directly associated with short-141 term variability22,23. 142 - Min HR and Max HR (bpm): They indicate the minimum and maximum 143 heart rate, respectively, using N beats. 144 Frequency domain variables: 145 - Low-Frequency power (LF) (ms2): Situated between 0.04 and 0.15 Hz. 146 In long-term recordings, it provides us with more information about the 147 activity of the sympathetic nervous system (SNS)22. 148 - High-Frequency power (HF) (ms2): They are located between 0.15 and 149 0.4 Hz. HF is clearly related to the activity of the parasympathetic nervous 150 system (PNS) activity and has a relaxation-related effect on HR22. 151 - Low/high-Frequency ratio (LF/HF): From the low-frequency and high-152 frequency ratios of the HRV spectral analysis results we can estimate the 153 vagal (related to relaxation and HF) and sympathetic (related to stress and 154 LF) influence. Thus, we can estimate sympathetic-vagal balance22. 155 156 Non-linear variables: 157 - SD1 (ms): Sensitivity of short-term variability of the non-linear range of the 158 HRV. It is considered an indicator of parasympathetic activity20. 159 - SD2 (ms): Long-term variability of the non-linear range of the HRV. It is a 160 diameter from the Poincaré plot which indicates the degree of longitudinal 161 dispersion. It is thought to reflect long-term changes in RR intervals and it 162 is considered an inverse indicator of parasympathetic activity20. 163 - Stress Score (SS) (ms): It is an index described by Naranjo-Orellana et 164 al.20 to facilitate the physiological interpretation of the Poincaré plot. It is 165 expressed as the inverse of SD2 diameter multiplied by 1000.It is 166 considered directly proportional to the sympathetic activity in the sinus 167 node. 168 - Sympathetic/parasympathetic ratio (S/PS): It is also described by 169 Naranjo-Orellana et al.20, S/PS is expressed as the quotient of SS and 170 SD1. It is considered to reflect autonomic balance - that is, the relationship 171 between sympathetic and parasympathetic activity. 172 The recording of the parameters indicated corresponds to the emergency 173 situations described above. Data collection was made in three different flight 174 segments: 175 a) Take-off: it goes from reading the checklist to the resolution of the engine 176 fault. 177 b) In-flight: during manoeuvres, characterised by the recovery of abnormal 178 positions. 179 c) Landing: where the pilot performs the landing manoeuvre, with the loss of 180 spatial situation. 181 2.4 Experiment Hardware 182 The flight simulator OFS (operational flight simulator (Northrop F-5 Model, 183 Indra©, Spain) in which the study was developed provides synthetic training to 184 improve the basic flight proficiency of F5B pilots. It also improves the skills 185 necessary to accustom them to advanced flight conditions, navigation, and 186 missions in the presence of ground-to-air threats4,10. The simulator is designed 187 and equipped with visual, auditory, and mechanical interfaces to simulate realistic 188 flight and combat conditions, as well as scenarios for operations in varying 189 environmental and atmospheric conditions12 (Figure 2). 190 To study HRV and the Autonomic Nervous System (ANS) we used First Beat 191 Bodyguard 2 ® (Firstbeat Technologies, Jyväskylä, Finland): it is a reliable RR 192 recording device for shortand long-term measurements24. It is capable of 193 recording and analysing physiological variables of the human body. It works by 194 connecting it directly to the skin placing two electrodes on the chest and then 195 starts recording data automatically. First Beat Bodyguard 2® can be used during 196 exercise and sleep. The data obtained from these measurements can be directly 197 downloaded to the Firstbeat Uploader® software through its USB port. All RR 198 interval series were imported into the Kubios software package (University of 199 Eastern Finland, Kuopio, Finland). 200 2.5. Statistical Analysis 201 A descriptive analysis was conducted of all the quantitative variables under study 202 according to the group to which they belong, obtaining for each variable the 203 mean, standard deviation, median IQR (interquartile range), and maximum and 204 minimum values. To compare between groups, either Student's t-test,Welch's 205 test (under normality), or the non-parametric Mann-Whitney-Wilcoxon test (when 206 normality was not acceptable) was applied. Afterward, the mean values of all 207 parameters for the three flight segments were compared using the Student’s t-208 test (under normality) or the non-parametric Wilcoxon signed-rank test (when 209 normality was not admissible). Previously, normality tests were performed using 210 the Shapiro-Wilk test. 211 4. DISCUSSION 282 The aim of this study was to analyse modifications in the neurovegetative balance 283 of professional military pilots during an emergency situation in a flight simulator. 284 The main findings of the study were the statistically significant changes (p<0.05) 285 observed between the different flight segments recorded for most of the variables 286 measured. The association between increased sympathetic activity and 287 decreased HRV, as well as between increased parasympathetic activity and 288 increased HRV, has previously been noted20,21. Thus, HRV reflects ANS control 289 over the cardiovascular system. Against this background, the reported results 290 indicate changes in the autonomic balance resulting from the emergencies 291 depicted in the simulated scenario. 292 Between take-off and in-flight segments characterised by increased 293 parasympathetic activity and decreased sympathetic activity, changes were 294 observed. In contrast, between in-flight and landing there was a progressive 295 increase in parameters related to sympathetic activity and a decrease in 296 parasympathetic activity. 297 The results observed showed statistically significant decreases in SS and S/PS 298 ratio and a significant increase in SD2 between take-off and the rest of the flight 299 segments. This indicates that take-off is the segment in which pilots show the 300 highest level of stress. This is a common preparatory response observed in 301 eliciting contexts. This anticipatory anxiogenic response prepares the subject to 302 face any hazards and uncertainties that can compromise his integrity25. It could 303 also be related to the large cognitive demand (attentional level and concentration) 304 required for the take-off segment. It produces a higher level of stress in the pilots 305 due to the need of maintaining a constant rate of ascent while maintaining eye, 306 hand, and leg coordination11. 307 In the in-flight segment, in which the aircraft reaches the necessary altitude 308 stabilises itself and enters into the cruise phase13, statistically significant 309 increases in SD1, rMSSD, and HF power were found, as well as a significant 310 decrease in SS and S/PS ratio. It is clear that the most demanding segments of 311 the flight, in which the sensation of danger is greater, are take-off and landing. 312 These are the flight segments in which any minimal failure can become a 313 catastrophic accident. However, during the flight you can try to correct the 314 problems detected and even if the failure is catastrophic, the pilot can be ejected. 315 Previous studies such as Mansikka18 or Mohanavelu11 have explained that in the 316 less demanding flight segments, the pilot's perceived workload decreases and 317 can be represented as Figure 3 shows. In any case, as it is a longer segment in 318 time, the pilot has more time to relax before the next flight segment13. Alaimo et 319 al.30, in a similar study, observed an increase in parasympathetic activity through 320 SD1 and rMSSD after the completion of the take-off manoeuvre.). 321 On landing we found a decrease in SD1, rMSSD and HF power parameters, 322 being this modification related to a decrease in parasympathetic activity. In this 323 segment, we found lower levels of takeoff stress, despite experiencing an 324 emergency situation. Previous studies have shown that the mental workload on 325 landings is lower than in other segments of the flight27. This is because landings 326 are a routine manoeuvre performed so many times that the effort done is 327 underestimated by pilots13. 328 In contrast to the rest of the sympathetic variables, we notice that LF power 329 increases in the take-off-in-flight comparison, whereas it decreases between in-330 flight and landing. Recent studies suggest that the HRV power spectrum, 331 including its LF component, is mainly determined by the parasympathetic 332 system28. 333 The results obtained seem consistent with the findings reported by previous 334 studies concerning HRV and workloads in emergency situations9,10,13,29. 335 Dahlstrom and Nahlinder used Mean HR to identify mentally demanding 336 segments and manoeuvres in emergency situations. In particular, the takeoff 337 segment with engine failure and the approach, landing and touchdown segments. 338 Besides, Mohanavelu et al.11,13 measured workload in four situations by playing 339 with visibility and task execution. In the take-off and landing segments, increases 340 in sympathetic activity were observed through SD2 and S/PS ratio parameters 341 Other authors have assessed workload in other scenarios different than 342 emergency situations, like regular flight manoeuvres9,18 or attack and defence 343 missions8. Santos-Villafaina et al.8 used HRV measurement during take-off 344 landing segments in attack missions in flight simulators and workload indicators 345 to influence pilot performance variations. 346 Cao et al12 studied the psychophysiological response of commercial pilots during 347 the execution of flight manoeuvres of varying complexity in the flight simulator 348 using rMSSD and HF/LF ratio variables. Their results are similar to ours, 349 experiencing an increase in sympathetic activity during take-off, approach, and 350 landing manoeuvres. Moreover, they compare their results with normative values 351 in the population, concluding that commercial pilots show higher sympathetic 352 activation levels than the general population. Compared with our results, the 353 fighter and attack pilots in our study also show increased sympathetic activity30. 354 355 Limitations of the study 356 The limitations of this study are as follows: Firstly, a real flight mission tends to 357 be more physically and cognitively demanding (more mental work), which can be 358 extrapolated to a lower performance than expected in the simulator9. Secondly, 359 pilots perform these flight simulator exercises as part of their activity routines, at 360 least once a week. This could limit the surprise factor that sometimes hampers 361 simulated environments10. The severe limitations and restrictions on measuring 362 physiological changes during real flight must be considered. Lastly, this study 363 presents a short sample. Bearing in mind the results, further studies should 364 analyse specifically take-off and landing segments in larger sample sizes. 365 366 Practical implications 367 The take-off segment produced a response of anticipatory anxiety that was 368 reduced during in-flight and increased again during the landing segment, 369 suggesting higher stress during take-off and landing in comparison to the in-flight 370 segment. 371 HRV seems useful to monitor the stress response of pilots under training in a 372 simulator environment. It could be valuable to measure response to interventions 373 intended to manage stress and workload during flight emergencies. 374 375 CONCLUSIONS 376 There were changes in the neurovegetative balance (using HRV) in professional 377 military pilots during emergency situations in a flight simulator. The analysed 378 changes in the neurovegetative balance showed differences in the take-off-in-379 flight segments (with a tendency for the sympathetic activity to be significantly 380 reduced during the segment) and flight-landing segment (with a tendency for the 381 sympathetic activity to be significantly increased during the segment) in 382 professional military pilots during the emergency situations in a flight simulator. 383 384 Funding: This research received no funding. 385 Conflict of interest: The authors declare no conflict of interest. 386 REFERENCES 387 1. Hormeño-Holgado AJ, Clemente-Suárez VJ. Effect of different combat jet 388 manoeuvres in the psychophysiological response of professional pilots. Physiol 389 Behav. 2019;208:112559. 390 doi:10.1016/j.physbeh.2019.112559 391 2. Bustamante-Sánchez Á, Clemente-Suárez VJ. Psychophysiological 392 response to disorientation training in different aircraft pilots. 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Comparison of Mean HR, pNN50, rMSSD, LF power, HF power, SD1, 517 SD2, Stress Score (SS) and sympathetic-parasympathetic ratio (S/PS ratio) 518 between Take-off, In-flight and Landing. (*) p < 0.05; (**) p<0.001. 519