INTRODUCTION

Aviation safety is a fundamental global concern that requires quality assurance at every stage of the operational chain. The increasing technological complexity of modern aircraft has heightened reliance on human performance and cognitive reliability [1]. Automated systems continue to advance through the integration of artificial intelligence, machine learning, and advanced sensors; nevertheless, critical decision-making, complex problem-solving, and precision maintenance tasks remain dependent on human cognitive capacity, physical readiness, and psychological well-being [2,3].

Airport operations depend on many essential roles, including aircraft maintenance personnel (AMP), who help ensure aircraft airworthiness and safety. Maintenance activities performed by trained and certified personnel are vital to keeping aircraft flightworthy and safe to operate [4,5]. Indonesia’s geography, which requires 24-hour flight operations to connect thousands of islands, creates substantial demand for AMP [6]. More generally, proper maintenance procedures are a prerequisite for ensuring the safety of passengers, crew, and high-value assets. At the core of these procedures is a series of activities designed to maintain the integrity and function of aircraft systems [7]. Unfortunately, the welfare of AMP is often overlooked compared with that of other professions in the aviation industry.

Recent findings from the United States indicate that 52.9% of aircraft maintenance workers experience fatigue, with 12.2% reporting extreme fatigue that may endanger flight safety. This prevalence far exceeds that in the general population and underscores the urgent need for comprehensive intervention. More concerning, 46.8% of AMP experience abnormal sleepiness, and 42.6% have high workload scores requiring longer recovery time. This condition impacts individual worker health and is directly associated with the risk of human error, which has been implicated in 79% of fatal aviation accidents. Furthermore, a comprehensive study involving 312 AMP in Portugal and Brazil identified a complex risk profile. Workers aged 36–50 years showed the highest fatigue levels, and night-shift workers also experienced substantial fatigue. These findings indicate that demographic and operational factors interact in complex ways to influence fatigue. Correlation analysis further showed that fatigue does not occur in isolation but instead exists within a broader network of related variables. Meanwhile, quality of life was significantly negatively correlated with fatigue (r=−0.731), indicating that greater fatigue was associated with lower quality of life [8,9].

Environmental factors further exacerbate individual vulnerability. Aircraft maintenance environments typically involve high noise levels (80–120 dB), extreme temperatures (−40 to +60°C, depending on location and season), chemical exposure (fuel, solvents, and lubricants), physical demands (lifting, climbing, and awkward postures), and time pressure related to aircraft turnaround requirements. The combination of environmental stressors and organizational demands creates a cumulative burden that may exceed individual adaptive capacity [10].

To maintain flight punctuality, AMP often work rotating shifts that provide 24-hour operational coverage, which may increase their susceptibility to fatigue. Contributing factors include excessive workload, which can require them to work long hours at a rapid pace without adequate rest, and uneven task distribution, which creates psychological pressure. The accumulation of physical and mental fatigue not only disrupts productivity, safety, and worker health but also increases the risk of human error, which has been identified as a major cause of air accidents, rather than machine failure [8,11–13].

Excessive and unbalanced workload is a key contributor to fatigue. Workload, defined as the set of activities that must be completed within a given time, becomes problematic when its volume and intensity exceed the worker’s capacity. An imbalance between task demands and the worker’s ability, competence, and available time reduces performance and contributes to work stress. Work stress is a detrimental psychological and physiological response to this mismatch, ultimately endangering mental and physical well-being [14,15].

This study has strategic implications for global aviation safety. The Industry 4.0 era demands integration of human factors and technological advancement. With human factors implicated in 84% of serious accidents and 94% of fatal accidents [16], a deeper understanding of fatigue and work stress is essential. Quality of life affects not only well-being but also reliability and accuracy in performing critical aircraft maintenance tasks. Because every maintenance error can compromise the safety of hundreds of passengers, this study has substantial societal relevance.

Sultan Hasanuddin International Airport in Makassar, the main gateway and aviation hub in eastern Indonesia, plays a critical role in maintaining the continuity and safety of the national connectivity network. In addition, the airport has been encouraged to develop into one of the major maintenance, repair, and overhaul centers in eastern Indonesia. Its busy operations place substantial pressure on all personnel, especially aircraft maintenance technicians. Therefore, understanding the experiences of maintenance personnel in this dynamic and strategically important airport environment is highly relevant from both the perspective of operational safety and that of decent working conditions in accordance with Indonesian labor regulations.

Previous studies have often relied on analytical methods that cannot adequately address complex multivariable relationships. Therefore, this study aimed to develop a comprehensive model to analyze the relationships among fatigue, work stress, and quality of life in AMP at Sultan Hasanuddin International Airport, Makassar. The findings are expected to provide a foundation for evidence-based intervention strategies both to enhance aviation safety and to promote the holistic well-being of these essential aircraft maintenance professionals.

METHODS

Design and Sampling

This study used a cross-sectional design in which data were collected at a single point in time to analyze the relationships among variables. The study was conducted at the maintenance, repair, and overhaul facility at Sultan Hasanuddin International Airport, Makassar. Participants were recruited from two aircraft maintenance companies operating within the same facility. Because both companies shared similar operational characteristics and no organizational comparison was performed in the structural equation modeling (SEM) analysis, data from both companies were combined for the purposes of this study. The study population comprised all AMP working at the airport, totaling 145 individuals according to available administrative data. To determine the sample, this study applied a simple random sampling technique in which respondents were selected randomly from the population list so that each individual had an equal chance of selection. The population list was obtained from the airport’s administrative records. From this list, a simple random sample was drawn using a random number generator in Microsoft Excel (Microsoft, Redmond, WA, USA) to select 106 participants. The technicians corresponding to the selected numbers were then invited to participate, subject to the inclusion criteria of voluntary participation and active employment as maintenance technicians. The sample size was calculated using the Lemeshow formula with a 95% confidence level and a 10% margin of error, yielding a minimum required sample of 106 personnel [17]. Thus, 106 randomly selected technicians who met the eligibility criteria constituted the final study sample.

Instruments

The instruments used in this study were standardized measures widely applied in occupational health and safety research. Mental workload was assessed using the NASA Task Load Index (NASA-TLX), which consists of six dimensions: mental demand, physical demand, temporal demand, performance, effort, and frustration. Each dimension is scored on a 0–20 Likert scale, after which a total score is calculated [18]. Feelings of fatigue were assessed using the Work Fatigue Feeling Measurement Questionnaire. This instrument was developed by Setyawati et al. [19] in 1994 and has been tested for validity and reliability. It measures subjective feelings of work-related fatigue experienced by workers.

Physical workload was measured objectively using an oximeter, with pulse rate serving as an indicator of physiological response to workload. The pulse-related variables measured included resting pulse rate (the average pulse rate before work begins), working pulse rate (the average pulse rate during activity), and working pulse (the difference between working pulse rate and resting pulse rate). Accordingly, measurements were taken twice: once while the worker was active to obtain the working pulse and once while the worker was resting or not performing activities to obtain the resting pulse. Work stress was assessed using the validated stress subscale of the Depression Anxiety Stress Scale-21 (DASS-21), which consists of 7 items [10,20], followed by objective measurement using a Cocoro Meter device. Thus, work stress was measured both subjectively and objectively. A Cocoro Meter measures stress objectively using amylase enzyme activity as an indicator. When an individual experiences stress, the body stimulates the autonomic nervous system, particularly the sympathetic-adrenomedullary system, triggering the release of norepinephrine and adrenaline; it also activates the hypothalamic-pituitary-adrenal axis, which stimulates cortisol release from the adrenal glands. The salivary glands are directly connected to the sympathetic nervous system. When norepinephrine levels increase in response to stress, the salivary glands respond by increasing salivary amylase secretion. This response is rapid, usually occurring within 1–5 minutes, and is faster than the increase in cortisol, which typically takes 20–30 minutes. The Cocoro Meter is non-invasive, rapid, and practical. In addition, this monitor can distinguish between eustress and distress through changes in enzyme activity over time, making it an effective method for assessing psychological stress and work stress in real time [21–23]. The measurement categories were as follows [24]: normal (0–30 KU/L), moderate (31–45 KU/L), and severe (≥46 KU/L). This measurement was also conducted while respondents were resting.

Quality of life was measured using the abbreviated World Health Organization Quality of Life questionnaire [25]. This instrument consists of 26 questions covering 4 domains: physical health, psychological health, social relationships, and environment, with responses scored on a 1–5 Likert scale. Respondent characteristics were categorized as follows: (1) age, >35 years old or ≤35 years old [26]; (2) length of service, >10 years or ≤10 years [27]; and (3) working hours, categorized as within standard working-hour limits if they did not exceed normal working hours (8 hr/day or 40 hr/wk) and as excessive/nonstandard if they exceeded those limits [28].

Statistical Analysis

The analysis was performed in several stages: editing, coding, data entry, cleaning, and tabulation. The main analysis used partial least squares structural equation modeling (PLS-SEM) with SmartPLS 3 software (SmartPLS GmbH, Bönningstedt, Germany) [29]. SPSS version 23 (IBM Corp., Armonk, NY, USA) was also used for descriptive analysis of respondent characteristics and study variable scores. In addition, numerical data were assessed for normality using the Shapiro-Wilk test and visualized with a Q-Q plot [30]. The diagnostic performance of the Cocoro Meter, compared with that of the DASS-21 for measuring work stress, was further evaluated using receiver operating characteristic (ROC) curve analysis to assess sensitivity, specificity, and area under the curve (AUC) [31].

Ethics Statement

This study received ethical approval from the Ethics Commission of the Faculty of Public Health, Hasanuddin University, under protocol No. 21525062121 and letter No. 923/UN4.14.1/TP.01.02/2025.

RESULTS

Participant characteristics are detailed in Table 1. Among the 106 AMP, there was a predominance of younger respondents (≤35 years, 64.2%), those with a diploma or bachelor’s degree (65.1%), and those who were married (73.6%). Half (50.0%) had more than 10 years of work experience, and most reported working hours exceeding standard limits (73.6%). All respondents reported some degree of fatigue, with 38.7% categorized as fatigued and 20.8% as severely fatigued. Mental workload was most commonly moderate (42.5%), whereas physical workload was generally light (61.3%). Based on the DASS-21, 44.3% had normal stress levels, 37.7% had moderate stress levels, and 17.9% had high stress levels; the Cocoro Meter findings indicated lower stress levels, with 79.2% categorized as normal. Overall, most respondents reported a moderate quality of life (57.5%), followed by low (34.9%) and high (7.5%) quality of life (Table 1).

ROC curve analysis (Figure 1) was conducted to evaluate the ability of the Cocoro Meter to detect work stress using the DASS-21 as the reference standard. The analysis showed an AUC value of 0.68, indicating fair discriminatory ability. These findings suggest that the Cocoro Meter may be useful as a rapid preliminary screening instrument for work stress, although its diagnostic performance remains limited for comprehensive psychological assessment. Normality testing using a Q-Q plot (Figure 2) showed that most variables did not fully follow a normal distribution; in particular, physical workload, fatigue, and work stress deviated substantially from the diagonal line. This finding supported the use of PLS-SEM (SmartPLS) for further analysis, as this method is appropriate for non-normally distributed data and complex models with latent variables. The SmartPLS analysis (Table 2 and Figure 3) showed that physical workload (β=0.229, p=0.018) and work experience (β=0.277, p=0.007) had significant effects on fatigue, whereas age and mental workload were not significant. For work stress, only age exerted a significant positive effect (β=0.371, p=0.001). The most prominent finding was that quality of life was negatively associated with fatigue (β=−0.344, p=0.002) and work stress (β=−0.385, p<0.001), whereas the other variables showed no significant relationships.

DISCUSSION

This study aimed to develop and test a comprehensive model analyzing the relationships among workload, age, work experience, fatigue, work stress, and quality of life among AMP at Sultan Hasanuddin International Airport, Makassar. Using a PLS-SEM approach, the analysis yielded several key findings. First, physical workload and work experience were identified as significant direct predictors of fatigue. In contrast, mental workload and age did not show significant direct effects on fatigue. Second, older age emerged as the only significant predictor of work stress. Third, both fatigue and work stress had significant negative direct effects on quality of life, making them the dominant mediating variables in the model. No direct paths from the exogenous variables (workload and demographic characteristics) to quality of life were significant, highlighting the central mediating role of these psychological conditions.

The finding that physical workload was significantly associated with fatigue is consistent with the existing literature on AMP and other physically demanding professions [32]. Aircraft maintenance work often involves manual labor, awkward postures, and exposure to challenging environmental conditions, all of which can deplete physical energy reserves and contribute to fatigue [8,10,33]. These results underscore the persistent physical demands of this profession and support the need for targeted ergonomic interventions.

The positive relationship between work experience and fatigue is a notable finding. Although more experienced workers might be expected to develop better coping mechanisms, the results of this study suggest the opposite: long-term exposure to job demands and shift-work patterns may contribute to accumulated fatigue. This observation aligns with the concept of allostatic load, in which cumulative physiological wear and tear resulting from chronic work stress manifests as greater fatigue over time [34].

This study found that older employees tended to have higher levels of work stress. Older workers may experience greater stress due to the perceived physical burden of the job on an aging body [35], concerns about maintaining performance standards, or greater family responsibilities [36]. Previous research has also noted substantial work pressure among senior personnel in critical safety roles. In such roles, these workers may experience greater pressure because they bear primary responsibility for safety and decision-making [37].

The non-significant effect of mental workload on stress was unexpected, given that AMP perform complex cognitive tasks. One possible explanation is that mental demands are perceived as an inherent and manageable part of the job, especially when workers are supported by personal resources such as resilience or job control [38]. In some cases, mental demands may even have a protective effect against cognitive decline when accompanied by adequate job control [39]. By contrast, physical workload and age-related pressure may be less controllable stressors. High physical workload has consistently been associated with an increased risk of physical health problems, such as cardiovascular disease and musculoskeletal disorders, as well as chronic stress, especially among older workers. Moreover, physical workload often cannot be offset by psychological coping strategies alone, which may lead to feelings of helplessness [40].

Fundamentally, this model indicates a strong negative association of fatigue and work stress with quality of life. Fatigue may deplete the personal resources needed to engage positively with work and personal life, whereas work stress directly impairs psychological well-being. Over time, chronic workplace stress may further erode personal resources such as energy, motivation, and the capacity to engage positively with work and personal life, while also contributing to long-term physical and psychological health problems [38].

The findings of this study contribute theoretically to the development of models of work stress and fatigue. The results indicate that physical workload and work experience were significantly associated with fatigue, whereas age was associated with work stress. These findings support the interaction of individual factors (age and work experience) and job factors (mental and physical workload) in shaping worker well-being. From a practical perspective, the findings are also relevant to aviation safety and human resource management. Accordingly, companies should prioritize ergonomic and administrative interventions, such as providing mechanical aids, optimizing the workspace, and implementing structured breaks, to reduce physical workload.

Although the methodology was robust, this study has several limitations. The cross-sectional design limits causal inference; therefore, longitudinal studies are needed to track the development of fatigue and work stress over time. In addition, the use of self-report instruments such as the DASS-21 may have introduced perception bias. Future research should include broader study settings, more balanced samples, and the integration of objective measurements with mixed quantitative-qualitative approaches. Nevertheless, this study makes a meaningful contribution to understanding the mechanisms of work stress and fatigue among aircraft maintenance technicians and may support the development of more comprehensive models in the future.

Based on these results, fatigue and work stress appear to be the key factors associated with reduced quality of life among AMP. Companies should proactively reduce physical workload through ergonomic and administrative interventions, such as providing mechanical aids and structured rest schedules. In parallel, targeted support may be especially beneficial for more experienced and older workers (>35 years), who appear to be more vulnerable to the accumulation of fatigue and work stress. Ultimately, investing in the well-being of this workforce is not only a matter of productivity but also a fundamental component of sustainable aviation safety.

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

This study was funded by the Institute for Research and Community Service (LPPM), Hasanuddin University (Thematic Research Group, 2025 with contract No. 00518/UN4.22/PT.01.03/2025).

Acknowledgements

The authors are grateful to the Institute for Research and Community Service (LPPM) and the Faculty of Public Health, Department of Occupational Safety and Health, for their contributions to the success of this study. The authors also thank the aircraft maintenance personnel from PT X and PT Y for their support.

Author Contributions

Conceptualization: Saleh LM, Syafitri NM. Data curation: Russeng SS, Yusbud M, Kim TG. Formal analysis: Saleh LM, Syafitri NM. Funding acquisition: Saleh LM. Methodology: Saleh LM, Russeng SS, Syafitri NM, Kim TG. Project administration: Zainuddin FA, Tenri AAKU. Visualization: Syafitri NM. Writing – original draft: Saleh LM, Russeng SS, Syafitri NM. Writing – review & editing: Saleh LM, Russeng SS, Syafitri NM, Zainuddin FA, Tenri AAKU.

Figure 1

Receiver operating characteristic (ROC) curve of the Cocoro Meter for work stress detection using the Depression Anxiety Stress Scale-21 as the reference standard. AUC, area under the curve.

Figure 2

Q–Q plot of variables (A: duration of work; B: mental workload; C: age; D: physical workload; E: feelings of fatigue; F: work stress, and G: quality of life) indicates that the data are not normally distributed.

Figure 3

Result of the research model.

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