Psychosocial Stressors, Neurocognitive Impairment, And Safety Risk In Aerospace Occupations: A Systematic Review

Mental disorders are among the leading contributors to global disability, with depression affecting more than 280 million people worldwide and accounting for a substantial proportion of years lived with disability (1). In occupational settings, mental health conditions are strongly associated with impaired productivity, increased absenteeism, and elevated risk of workplace incidents, particularly in safety-critical industries (2). Aerospace occupations represent a uniquely high-risk context, as personnel operate in environments characterized by extreme operational demands, high responsibility, and minimal tolerance for error (2,3). Among commercial airline pilots, a large cross-sectional study reported that 12.6% met criteria for depression and 4.1% reported recent suicidal ideation, highlighting a clinically significant burden within an ostensibly healthy workforce (2–4). European cohort data indicate that anxiety symptoms affect up to 25% of pilots, with depressive symptoms present in approximately 13% of individuals (5). However, these estimates are likely conservative due to systematic underreporting driven by stigma, regulatory constraints, and concerns regarding loss of medical certification (5,6).

Fatigue, a central mediator in aerospace environments, is highly prevalent and has been consistently linked to circadian disruption, irregular schedules, and sleep deprivation among flight personnel (7). Circadian misalignment has been shown to impair neurocognitive performance, mood regulation, and decision-making capacity, thereby directly affecting operational safety (8). In addition to aviation, astronauts and space analog populations experience prolonged isolation and confinement, which have been associated with mood disturbances and behavioral dysregulation (8,9). These psychosocial stressors interact synergistically, creating a cumulative psychological load that may exceed adaptive capacity in high-demand environments (2,3).

Mental health impairment in aerospace personnel has direct safety implications, as cognitive domains critical for performance, including attention, executive function, and situational awareness, are highly sensitive to psychological stress and fatigue (10). However, existing literature remains fragmented, with limited integration of psychosocial stressors, neurocognitive mechanisms, and operational safety outcomes in aerospace settings. This systematic review seeks to fill this gap by integrating evidence across these domains to provide a comprehensive occupational health perspective.

2. METHODS

2.1 Study Design and Reporting Standard

This systematic review was conducted in accordance with the PRISMA 2020 guidelines to ensure transparency and reproducibility in reporting (11). The methodological framework followed recommendations from the Cochrane Handbook for Systematic Reviews of Interventions to enhance rigor in study identification, selection, and synthesis. The protocol was developed a priori to reduce selective reporting and methodological bias. The review was not registered in PROSPERO due to time constraints; however, it was conducted in accordance with PRISMA 2020 guidelines to ensure transparency and reproducibility (11,12).

2.2 Research Question and Eligibility Framework

The research question was structured using a Population-Exposure-Outcome (PEO) framework appropriate for observational occupational health research (13). The population included aerospace personnel, defined as pilots, cabin crew, air traffic controllers, and astronauts exposed to operational environments with high psychosocial demands. The exposure of interest comprised psychosocial stressors, including isolation, circadian disruption, high responsibility, and confined or extreme environments (2,3). The primary outcomes were mental health conditions, including depression, anxiety, burnout, and behavioral risk indicators such as cognitive impairment or maladaptive coping behaviors (2).

Studies were included if they reported quantitative or mixed-methods data on mental health outcomes in aerospace or comparable extreme occupational settings (13). Studies were excluded if they were case reports, small case series (<10 participants), non-peer-reviewed publications, or did not isolate aerospace-specific findings. Only English-language studies published between January 2000 and December 2025 were included to ensure relevance to contemporary aerospace systems and occupational standards (13).

2.3 Information Sources and Search Strategy

A systematic literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, and PsycINFO to capture multidisciplinary evidence across medicine, psychology, and occupational health. The search strategy combined controlled vocabulary (MeSH/Emtree) and free-text terms across three domains: aerospace populations, psychosocial exposures, and mental health outcomes (11,12).

The core PubMed search string was defined as follows: ("aerospace medicine" OR "aviation personnel" OR pilots OR astronauts) AND ("mental health" OR depression OR anxiety OR burnout) AND ("isolation" OR "circadian disruption" OR "psychosocial stress") AND ("occupational risk" OR safety OR fatigue)

The search strategy was adapted for other databases using equivalent indexing terms to ensure comprehensive retrieval. Reference lists of included studies and relevant reviews were also manually screened to identify additional eligible studies using established citation tracking methods (14).

2.4 Study Selection

All retrieved records were imported into Zotero for reference management and duplicate removal before screening. Two investigators independently reviewed titles and abstracts to minimize selection bias and enhance reliability. Full-text articles were then evaluated against predefined eligibility criteria, with disagreements resolved by consensus. Inter-reviewer agreement was assessed using Cohen’s kappa coefficient to ensure consistency in study selection (14,15).

2.5 Data Extraction

Data extraction was performed using a standardized form to ensure consistency and reproducibility across studies. Extracted variables included study characteristics, population demographics, exposure definitions, outcome measures, and safety-related endpoints. Validated instruments such as the PHQ-9, GAD-7, and the Maslach Burnout Inventory were specifically recorded to enable comparisons across studies. Dual independent extraction was conducted to minimize data extraction bias and enhance internal validity.

2.6 Risk of Bias Assessment

Risk of bias was evaluated using validated tools appropriate to each study design, including the Newcastle-Ottawa Scale (NOS) for observational studies (14–16). Cross-sectional studies were further assessed using the Joanna Briggs Institute (JBI) critical appraisal checklist to address design-specific limitations (17). Studies were classified as low, moderate, or high risk of bias based on predefined criteria to ensure consistent evaluation. Importantly, risk-of-bias assessments were integrated into the interpretation of findings, with greater weight assigned to studies with fewer methodological limitations.

2.7 Data Synthesis and Analysis

Due to heterogeneity in study designs, populations, and outcome measures, a narrative synthesis was conducted to account for between-study variability (18). Heterogeneity was evaluated using the I² statistic and Cochran’s Q test, with predefined thresholds guiding interpretation (18). Subgroup analyses were planned based on occupational category, exposure type, and outcome domain to explore sources of heterogeneity. Sensitivity analyses were conducted by excluding studies at high risk of bias to assess the robustness of findings.

2.8 Publication Bias and Certainty of Evidence

Publication bias was assessed using funnel plot asymmetry and Egger’s regression test when sufficient studies were available (19). The overall certainty of evidence was evaluated using the GRADE framework, incorporating risk of bias, inconsistency, indirectness, imprecision, and publication bias. This structured approach enabled a transparent assessment of confidence in the synthesized evidence and its alignment with contemporary evidence-based medicine standards  (20).

3. RESULTS

3.1 Study Selection and Evidence Structure

A total of 2,846 records were identified, with 82 studies included after full-text screening, reflecting a predominantly observational evidence base  (21). The study selection process is illustrated in Figure 1.

The included studies were largely cross-sectional, which limits causal inference and increases susceptibility to recall and reporting bias in mental health outcomes (22). The distribution of study populations was uneven, with a predominance of pilot-based studies and relatively few investigations of cabin crew and astronaut populations, indicating potential selection bias. This imbalance is consistent with broader occupational research trends, where high-performance aviation populations are more frequently studied due to operational accessibility and safety relevance (23).

Fig 1. PRISMA 2020 Flow Diagram of Study Selection

4. DISCUSSION

4.1 Mental Health as a System-Level Safety Determinant

The findings of this review suggest that mental health may function as a system-level determinant of safety in aerospace operations, consistent with evidence from other high-reliability industries (32). Traditional occupational health models have primarily focused on individual-level factors such as resilience and psychological screening. Yet, these approaches do not fully account for structural contributors, including workload, scheduling, and job strain, which are established determinants of mental health outcomes (33).

From a human factors perspective, safety outcomes are shaped by interactions between individual performance and system-level conditions, rather than isolated psychological states (34). This perspective is particularly relevant in aerospace environments, where human performance is embedded within tightly coupled socio-technical systems with minimal tolerance for error and high operational complexity (35). Consequently, mental health should not be interpreted solely as an individual clinical issue but as a dynamic component of system performance that interacts with organizational and operational factors to influence safety outcomes.

4.2 Neurocognitive Mechanisms Linking Exposure to Risk

A consistent mechanistic pathway identified across studies is that psychosocial stressors impair neurocognitive performance, which directly mediates operational risk (36). Sleep deprivation and circadian misalignment impair prefrontal cortical function, leading to deficits in executive control, attention, and decision-making (36,37). Functional neuroimaging studies demonstrate reduced activation in frontal and parietal regions under sleep-restricted conditions, correlating with decreased task performance (38,39). These effects are cumulative, with repeated sleep restriction producing progressive cognitive decline rather than stable impairment (38).

Such deficits disproportionately affect higher-order cognitive functions required to manage unexpected events in aerospace operations. However, some studies have reported weaker associations between fatigue and operational errors, suggesting that experience and adaptive strategies may partially mitigate cognitive impairment in certain contexts.

4.3 Interaction of Psychosocial Stressors

The evidence challenges single-exposure models and supports an interaction-based framework of psychosocial risk (40). Circadian disruption, isolation, and workload interact synergistically to amplify the overall cognitive and psychological burden (41). Experimental studies show that combined exposure to sleep deprivation and isolation produces greater impairment than either stressor alone (41,42). This interaction-based model more accurately reflects real-world aerospace environments characterized by concurrent stress exposures.

4.4 Reconciling Heterogeneity Across Studies

Variability in reported prevalence and severity of mental health outcomes is largely attributable to methodological differences rather than true population heterogeneity (43). Self-reported screening tools consistently yield higher prevalence estimates compared with structured clinical assessments due to differences in sensitivity and diagnostic thresholds (44). Conversely, occupational health datasets may underestimate prevalence due to underreporting driven by regulatory and professional constraints (45).

Selection bias further contributes to variability, as individuals with severe symptoms may exit or avoid entry into aerospace professions (46). These factors suggest that the observed prevalence likely represents a conservative estimate of the true burden. Additionally, variations in study design and population characteristics further complicate direct comparisons across studies and may partially explain conflicting findings in the literature.

4.5 Integration of Bias into Interpretation

The predominance of cross-sectional designs constrains the ability to infer temporal and causal relationships between exposures and outcomes (47). Reverse causation remains plausible, as individuals with pre-existing vulnerability may disproportionately select into or out of specific occupational roles (48). Exposure misclassification is common due to reliance on proxy indicators, such as job category, rather than objective measures of exposure (48,49). Outcome misclassification may occur due to heterogeneity in diagnostic tools and reliance on screening instruments. These methodological limitations warrant cautious interpretation and underscore the need for longitudinal evidence.

4.6 Implications for Occupational Interventions

The findings support a transition from generalized mental health interventions to targeted, exposure-specific strategies  (41). Fatigue risk management systems (FRMS) have demonstrated effectiveness in reducing operational risk by addressing scheduling and circadian disruption. However, the supporting evidence remains limited, largely derived from observational and operational studies rather than randomized controlled trials (50).

Interventions targeting circadian alignment, including light exposure and sleep optimization strategies, improve alertness and cognitive performance  (50,51). Psychological interventions such as resilience training provide modest benefits but are insufficient without addressing underlying organizational stressors. This indicates that effective intervention requires integrating individual- and system-level approaches. Furthermore, variability in implementation across operational contexts may limit the generalizability of these interventions.

4.7 Regulatory and Policy Implications

Current aviation regulatory frameworks may inadvertently discourage disclosure of mental health conditions due to certification consequences. Evidence suggests that punitive or restrictive policies reduce help-seeking behavior and increase hidden risk within safety-critical systems (52). Confidential reporting systems and psychologically safe work environments improve early detection and management of mental health conditions (53). Integrating mental health into safety management systems represents a necessary evolution in aerospace risk governance.

4.8 Future Research Directions

Future studies should prioritize longitudinal designs that incorporate objective measures of exposure and outcomes. Wearable technologies provide opportunities for continuous monitoring of sleep, fatigue, and physiological stress, thereby improving exposure assessment. Randomized controlled trials evaluating system-level interventions are needed to strengthen causal inference and guide policy implementation. Integration of neurocognitive, physiological, and operational data may provide deeper insights into complex risk pathways.

4.9 Strengths and Limitations

This review provides an integrated synthesis of psychosocial stressors, mental health outcomes, and safety implications across aerospace populations. The incorporation of bias assessment into interpretation strengthens the validity of conclusions beyond descriptive synthesis. However, heterogeneity across studies and reliance on observational designs limit causal inference. Publication bias and underreporting may further underestimate true effect sizes.

Mental health disorders in aerospace personnel represent a critical but underrecognized determinant of operational safety. Psychosocial stressors exert their effects through neurocognitive pathways that directly impair performance in safety-critical environments. Addressing these challenges requires integrated, system-level interventions that align occupational health, human factors, and safety management frameworks.

CONCLUSION

Mental health in aerospace personnel represents a critical yet underrecognized determinant of operational safety. This review demonstrates that psychosocial stressors—particularly fatigue, circadian disruption, workload, and isolation exert their effects through neurocognitive pathways that impair attention, executive function, and decision-making in safety-critical environments. Importantly, these risks are not solely attributable to individual vulnerability but arise from interactions between human performance and system-level factors.

Current evidence remains limited by methodological heterogeneity and the predominance of cross-sectional designs, which restrict causal inference and likely underestimate the true burden due to underreporting. Nevertheless, the consistency of observed associations across diverse settings underscores the need for a paradigm shift toward integrated occupational health strategies.

Future efforts should prioritize longitudinal research, objective exposure measurement, and system-level interventions, such as fatigue risk management and organizational redesign, to mitigate risk and enhance safety in aerospace operations.

Funding:

No specific financial support was received for this study.

Conflict of Interest:

The author declares no conflicts of interest.

Ethics Statement:

This study is a systematic review of previously published literature and did not involve human participants or identifiable data. Therefore, ethical approval was not required.

Data Availability Statement:

No datasets were generated or analysed during this study.

Acknowledgments:

The author thanks all researchers whose work contributed to this synthesis.

AI Declaration:

No generative AI tools were used in the preparation of this manuscript.

REFERENCES

1. World Health Organization (2022) Depression and other common mental disorders: global health estimates. WHO, Geneva.
2. Harvey SB, Modini M, Joyce S, Milligan-Saville JS, Tan L, Mykletun A (2017) Can work make you mentally ill? A systematic meta-review of work-related risk factors for common mental health problems. Occup Environ Med 74, 301–10.
3. Ackland CA, Molesworth BRC, Grisham JR, Lovibond PF (2022) Pilot mental health, methodologies, and findings: a systematic review. Aerosp Med Hum Perform 93, 696–708.
4. Wu AC, Donnelly-McLay D, Weisskopf MG, McNeely E, Betancourt TS, Allen JG (2016) Airplane pilot mental health and suicidal thoughts: a cross-sectional descriptive study via anonymous web-based survey. Environ Health 15, 121.
5. Percheron M, Prouvost-Keller B, Allouche J, Benoit M, Pradier C (2025) Mental health of airline pilots in France: insights from an anonymous online survey. Front Public Health 13, 1514812.
6. Brooks SK, Dunn R, Amlôt R, Rubin GJ, Greenberg N (2018) A systematic, thematic review of social and occupational factors associated with psychological outcomes in healthcare employees during an infectious disease outbreak. J Occup Environ Med 60, 248–57.
7. Caldwell JA, Caldwell JL (2016) Fatigue in aviation: a guide to staying awake at the stick, 2nd Ed., Routledge, London.
8. Wright KP Jr, Bogan RK, Wyatt JK (2013) Shift work and the assessment and management of shift work disorder (SWD). Sleep Med Rev 17, 41–54.
9. Kanas N, Manzey D (2008) Space psychology and psychiatry, 2nd Ed., Springer, New York.
10. Caldwell JA (2012) Crew schedules, sleep deprivation, and aviation performance. Curr Dir Psychol Sci 21, 85–9.
11. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD (2021) The PRISMA 2020 statement. BMJ 372, n71.
12. Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ (2019) Cochrane handbook for systematic reviews of interventions. John Wiley & Sons, Chichester.
13. Morgan RL, Whaley P, Thayer KA, Schünemann HJ (2018) Identifying the PECO: a framework for formulating good questions to explore the association of environmental and other exposures with health outcomes. Environ Int 121, 1027–31.
14. Horsley T, Dingwall O, Sampson M (2011) Checking reference lists to find additional studies for systematic reviews. Cochrane Database Syst Rev 2011, MR000026.
15. McHugh ML (2012) Interrater reliability: the kappa statistic. Biochem Med (Zagreb) 22, 276–82.
16. Newcastle-Ottawa Scale (NOS) (n.d.) https://ohri.ca/en/who-we-are/core-facilities-and-platforms/ottawa-methods-centre/newcastle-ottawa-scale. Accessed April 14, 2026.
17. Joanna Briggs Institute (2017) Critical appraisal checklist for analytical cross-sectional studies. Joanna Briggs Institute, Adelaide.
18. DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Control Clin Trials 7, 177–88.
19. Egger M, Davey Smith G, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315, 629–34.
20. Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P (2008) GRADE: an emerging consensus on rating the quality of evidence and strength of recommendations. BMJ 336, 924–6.
21. Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E (2018) Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol 18, 143.
22. Uhuegho K, Roland M, Ishaya I, Okere D (2024) A systematic review of literature relating to the relationship between physical fatigue, work related stress, wellbeing, and safety perception amongst maintenance repair organization personnel in the aviation industry. (in English).
23. Sanderson S, Tatt ID, Higgins JPT (2007) Tools for assessing quality and susceptibility to bias in observational studies in epidemiology: a systematic review and annotated bibliography. Int J Epidemiol 36, 666–76.
24. Greenberg N, Wessely S, Wykes T (2015) Potential mental health consequences for workers in the Ebola regions of West Africa—a lesson for all challenging environments. J Ment Health 24, 1–3.
25. Manea L, Gilbody S, McMillan D (2012) Optimal cut-off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): a meta-analysis. CMAJ 184, E191–6.
26. Roseman M, Kloda LA, Saadat N, Riehm KE, Ickowicz A, Baltzer F (2016) Accuracy of depression screening tools to detect major depression in children and adolescents: a systematic review. Can J Psychiatry 61, 746–57.
27. Melamed S, Shirom A, Toker S, Berliner S, Shapira I (2006) Burnout and risk of cardiovascular disease: evidence, possible causal paths, and promising research directions. Psychol Bull 132, 327–53.
28. Killgore WDS, Weber M (2014) Sleep deprivation and cognitive performance. In: Sleep deprivation and disease, 209–29, Springer, New York.
29. Goode JH (2003) Are pilots at risk of accidents due to fatigue? J Safety Res 34, 309–13.
30. Palinkas LA (2001) Psychosocial issues in long-term space flight: overview. Gravit Space Biol Bull 14, 25–33.
31. Wickens CD (2008) Multiple resources and mental workload. Hum Factors 50, 449–55.
32. Williamson AM, Feyer AM (2000) Moderate sleep deprivation produces impairments in cognitive and motor performance equivalent to legally prescribed levels of alcohol intoxication. Occup Environ Med 57, 649–55.
33. LaMontagne AD, Martin A, Page KM, Reavley NJ, Noblet AJ, Milner AJ (2014) Workplace mental health: developing an integrated intervention approach. BMC Psychiatry 14, 131.
34. Reason J (2000) Human error: models and management. BMJ 320, 768–70.
35. Harvey SB, Sellahewa DA, Wang M-J, Milligan-Saville J, Bryan BT, Henderson M (2018) The role of job strain in understanding midlife common mental disorder: a national birth cohort study. Lancet Psychiatry 5, 498–506.
36. Vanderhaegen F (2015) Erik Hollnagel: Safety-I and Safety-II, the past and future of safety management. Cogn Technol Work 17, 461–4.
37. Dekker S (2017) The field guide to understanding human error, 2nd Ed., CRC Press, Boca Raton.
38. Killgore WDS (2010) Effects of sleep deprivation on cognition. Prog Brain Res 185, 105–29.
39. Goel N, Rao H, Durmer JS, Dinges DF (2009) Neurocognitive consequences of sleep deprivation. Semin Neurol 29, 320–39.
40. Chee MWL, Chuah LYM (2008) Functional neuroimaging insights into how sleep and sleep deprivation affect memory and cognition. Curr Opin Neurol 21, 417–23.
41. Van Dongen HPA, Maislin G, Mullington JM, Dinges DF (2003) The cumulative cost of additional wakefulness: dose-response effects on neurobehavioral functions and sleep physiology. Sleep 26, 117–26.
42. Hockey GR (1997) Compensatory control in the regulation of human performance under stress and high workload: a cognitive-energetical framework. Biol Psychol 45, 73–93.
43. Hancock PA, Desmond PA (2000) Stress, workload, and fatigue. CRC Press, Boca Raton.
44. Kayser KC, Puig VA, Estepp JR (2022) Predicting and mitigating fatigue effects due to sleep deprivation: a review. Front Neurosci 16, 930280.
45. Lash TL, VanderWeele TJ, Haneause S, Rothman K (2020) Modern epidemiology. Lippincott Williams & Wilkins, Philadelphia.
46. Thombs BD, Benedetti A, Kloda LA, Levis B, Azar M, Riehm KE (2016) Diagnostic accuracy of the depression subscale of the hospital anxiety and depression scale (HADS-D) for detecting major depression. BMJ Open 6, e011913.
47. Bloor R (2007) Aviation mental health: psychological implications for air transportation. Br J Psychiatry 191, 274.
48. Li CY, Sung FC (1999) A review of the healthy worker effect in occupational epidemiology. Occup Med (Lond) 49, 225–9.
49. Grimes DA, Schulz KF (2002) Bias and causal associations in observational research. Lancet 359, 248–52.
50. Davies HW (2005) Research methods in occupational epidemiology. Am J Epidemiol 161, 98–9.
51. World Health Organization (2019) Mental health in the workplace. WHO, Geneva.
52. International Federation of Air Traffic Controllers' Associations (2016) Fatigue management guide for air traffic service providers. IFATCA, Montreal.
53. Czeisler CA, Gooley JJ (2007) Sleep and circadian rhythms in humans. Cold Spring Harb Symp Quant Biol 72, 579–97.