The 1% rule has been introduced as a practical model for aeromedical risk acceptance to airworthiness risk. It has been incorporated into European Joint Aviation Authorities’ guidance, then endorsed by International Civil Aviation Organization as threshold of decision. Although it has served as an important benchmark in aeromedical judgment, the underlying assumptions for initial setting have changed, making it necessary to revise the 1% rule. Specifically, the risk of heart disease has been excessively emphasized, which could lead to unintended losses of experienced older pilots. On the other hand, as pilots are now working to older ages, issues such as brain diseases are becoming more relevant, making additional tests for asymptomatic cerebral infarctions necessary. Maintaining strict standards alone does not necessarily contribute to aviation safety. More flexible criteria are needed depending on the working environment in aviation, and for example, standards such as the 2% or 5% rule could be introduced.

Purpose: The diagnosis and management of cardiovascular diseases are important for pilots, as well as the assessment of workload. Heart rate variability (HRV) can be evaluated from electrocardiography (ECG) signals during flight phases to assess the activation of the autonomic nervous system. Methods: In this study, continuous ECG activity was recorded of one pilot who flied as a pilot flying during a 4-hour long round trip using wearable ECG machine and was analyzed with MATLAB (R2020b ver. 9.9, The Mathworks Inc.). Total flight was divided into five phases: preflight, take off, cruise, landing, and postflight. Results: Mean heart rate (HR) was lowest in the postflight phase (76 bpm), and highest in the landing phase (86 bpm). Landing phase showed the highest values in standard deviation of NN interval (59.3 ms), triangular index (11.7), and triangular interpolation of NN interval (195 ms), while the postflight phase had highest root mean square of successive difference (20.5 ms) and proportion of successive RR interval (3.4 ms). As for frequency-domain metrics, the landing phase had the highest lowfrequency/high-frequency ratio of 5.33. Among the non-linear HRV measures, the landing phase presented the lowest SD1/SD2 ratio (0.15). Conclusion We observed the relative increase of mean HR and change of HRV in the landing phase, indicating elevated sympathetic nervous tone. Further studies should be considered to evaluate specific changes of ECG signals in flight phases and confirm the clinical use of the MATLAB signal analysis tools.

Purpose: The diagnosis and management of cardiovascular diseases are important for pilots, as well as the assessment of workload. Heart rate variability (HRV) can be evaluated from electrocardiography (ECG) signals during flight phases to assess the activation of the autonomic nervous system. Methods: In this study, continuous ECG activity was recorded of one pilot who flied as a pilot flying during a 4-hour long round trip using wearable ECG machine and was analyzed with MATLAB (R2020b ver. 9.9, The Mathworks Inc.). Total flight was divided into five phases: preflight, take off, cruise, landing, and postflight. Results: Mean heart rate (HR) was lowest in the postflight phase (76 bpm), and highest in the landing phase (86 bpm). Landing phase showed the highest values in standard deviation of NN interval (59.3 ms), triangular index (11.7), and triangular interpolation of NN interval (195 ms), while the postflight phase had highest root mean square of successive difference (20.5 ms) and proportion of successive RR interval (3.4 ms). As for frequency-domain metrics, the landing phase had the highest lowfrequency/high-frequency ratio of 5.33. Among the non-linear HRV measures, the landing phase presented the lowest SD1/SD2 ratio (0.15). Conclusion We observed the relative increase of mean HR and change of HRV in the landing phase, indicating elevated sympathetic nervous tone. Further studies should be considered to evaluate specific changes of ECG signals in flight phases and confirm the clinical use of the MATLAB signal analysis tools.

Many of aviation accidents are caused by human-related factors. Despite advancements in various fields related to aviation accidents caused by humanrelated factors continue. There are many unknown areas affecting aviation safety in the human factors of aviation workers, and the main task of aerospace medicine is to ensure aviation safety by appropriately managing these. Recently, with the development of the medical and aviation industries, related environments are rapidly developing and changing. In detail, it is necessary to modernize the aeromedical evaluation standards for air crews in line with international standards. As Korea is also pursuing space development, research on the space environment has also become necessary. Aviation medicine is an essential field that manages human factors in the field of aviation safety, and considering the public nature of aviation-related work, the nation's initial investment in the field of aerospace medicine must be a priority. To this end, it is necessary to establish a state-run aviation medical center and manage it under state leadership.

Purpose: The diagnosis and management of cardiovascular diseases are important for pilots, as well as the assessment of workload. Heart rate variability (HRV) can be evaluated from electrocardiography (ECG) signals during flight phases to assess the activation of the autonomic nervous system. Methods: In this study, continuous ECG activity was recorded of one pilot who flied as a pilot flying during a 4-hour long round trip using wearable ECG machine and was analyzed with MATLAB (R2020b ver. 9.9, The Mathworks Inc.). Total flight was divided into five phases: preflight, take off, cruise, landing, and postflight. Results: Mean heart rate (HR) was lowest in the postflight phase (76 bpm), and highest in the landing phase (86 bpm). Landing phase showed the highest values in standard deviation of NN interval (59.3 ms), triangular index (11.7), and triangular interpolation of NN interval (195 ms), while the postflight phase had highest root mean square of successive difference (20.5 ms) and proportion of successive RR interval (3.4 ms). As for frequency-domain metrics, the landing phase had the highest lowfrequency/high-frequency ratio of 5.33. Among the non-linear HRV measures, the landing phase presented the lowest SD1/SD2 ratio (0.15). Conclusion We observed the relative increase of mean HR and change of HRV in the landing phase, indicating elevated sympathetic nervous tone. Further studies should be considered to evaluate specific changes of ECG signals in flight phases and confirm the clinical use of the MATLAB signal analysis tools.

Purpose: The diagnosis and management of cardiovascular diseases are important for pilots, as well as the assessment of workload. Heart rate variability (HRV) can be evaluated from electrocardiography (ECG) signals during flight phases to assess the activation of the autonomic nervous system. Methods: In this study, continuous ECG activity was recorded of one pilot who flied as a pilot flying during a 4-hour long round trip using wearable ECG machine and was analyzed with MATLAB (R2020b ver. 9.9, The Mathworks Inc.). Total flight was divided into five phases: preflight, take off, cruise, landing, and postflight. Results: Mean heart rate (HR) was lowest in the postflight phase (76 bpm), and highest in the landing phase (86 bpm). Landing phase showed the highest values in standard deviation of NN interval (59.3 ms), triangular index (11.7), and triangular interpolation of NN interval (195 ms), while the postflight phase had highest root mean square of successive difference (20.5 ms) and proportion of successive RR interval (3.4 ms). As for frequency-domain metrics, the landing phase had the highest lowfrequency/high-frequency ratio of 5.33. Among the non-linear HRV measures, the landing phase presented the lowest SD1/SD2 ratio (0.15). Conclusion We observed the relative increase of mean HR and change of HRV in the landing phase, indicating elevated sympathetic nervous tone. Further studies should be considered to evaluate specific changes of ECG signals in flight phases and confirm the clinical use of the MATLAB signal analysis tools.

Purpose: The diagnosis and management of cardiovascular diseases are important for pilots, as well as the assessment of workload. Heart rate variability (HRV) can be evaluated from electrocardiography (ECG) signals during flight phases to assess the activation of the autonomic nervous system. Methods: In this study, continuous ECG activity was recorded of one pilot who flied as a pilot flying during a 4-hour long round trip using wearable ECG machine and was analyzed with MATLAB (R2020b ver. 9.9, The Mathworks Inc.). Total flight was divided into five phases: preflight, take off, cruise, landing, and postflight. Results: Mean heart rate (HR) was lowest in the postflight phase (76 bpm), and highest in the landing phase (86 bpm). Landing phase showed the highest values in standard deviation of NN interval (59.3 ms), triangular index (11.7), and triangular interpolation of NN interval (195 ms), while the postflight phase had highest root mean square of successive difference (20.5 ms) and proportion of successive RR interval (3.4 ms). As for frequency-domain metrics, the landing phase had the highest lowfrequency/high-frequency ratio of 5.33. Among the non-linear HRV measures, the landing phase presented the lowest SD1/SD2 ratio (0.15). Conclusion We observed the relative increase of mean HR and change of HRV in the landing phase, indicating elevated sympathetic nervous tone. Further studies should be considered to evaluate specific changes of ECG signals in flight phases and confirm the clinical use of the MATLAB signal analysis tools.

Purpose: The medical certification for pilots is required by the aviation safety authority based with the aviation safety act. Methods: In this study, aeromedical decision results according to aviation medical examination of the years from 2013 to 2015 were reviewed by retrospective analysis. Results: Data from a cumulative total of 19,459 cases were screened. Of these, 15% were determined to be fit with condition. Among them, 259 were diagnosed with cancer. The ratios by cancer type are as follows: the most common cancer was thyroid cancer (39.7%), followed by gastric cancer (12.1%), kidney cancer (7.9%), colorectal cancer (7.9%), skin cancer (7.9%), prostate cancer (7.5%), lymphoma/leukemia (5.9%), lung cancer (5.0%), head and neck cancer (2.5%), and other cancers (2.5%). Conclusion The number of aircrew with cancer tends to increase slightly every year.The most common cause of thyroid cancer seems to be due to excessive diagnostic activity.

Purpose: The medical certification for pilots is required by the aviation safety authority based with the aviation safety act. Methods: In this study, medical examination items of the year of 2021 performed during the aviation medical examination in Korea were reviewed by retrospective analysis. Results: Data from a total of 27 hospitals were collected. The number of inspection items ranged from a minimum of 22 items to a maximum of 57 items, showing a difference of more than double. Data from tertiary general hospitals and large airlines showed a tendency to perform more items of test compared to clinic-level institutions. The ratios for each inspection item are as follows: 1) 100% inspection items: urine test, ophthalmic test (except fundus test), fasting blood sugar, general blood test (complete blood count), electrocardiogram, electoencephalography, chest posterior anterior, pure tone audiometry, and total cholesterol; 2) 65%–74% inspection items: triglyceride, low-density lipoprotein-cholesterol, high-density lipoprotein-cholesterol, blood urea nitrogen/creatinine, and hepatitis B surface antigen; 3) 57% inspection items: hepatitis B surface antibody, human immunodeficiency virus, and venereal disease research laboratory; 4) 50% inspection items: urate and fundus examination. Conclusion There have been many changes in the medical field over the past 40 years, but in terms of examination items, the aviation physical examination does not reflect the flow of change. Over the past years, the number of pilots has increased significantly, the flight environment has changed, and medical examination methods have been developed and diversified. In accordance with this, it is necessary to change the test items.