Association Between Health Behaviors and Mental Health Among Airline Pilots

Abstract

Background
Lifestyle behaviors including physical activity, sleep, nutrition, smoking, and alcohol consumption are independently associated with health, yet the relationship between these behaviors and mental health has not been explored among airline pilots. The aim of this study was to measure the association between health behaviors and mental health.

Methods
A cross‐sectional study was conducted among 502 airline pilots. The primary outcome measure was the mental component score (MCS), derived from the Short Form Health Survey 12v2. We collected information regarding age, sex, ethnicity, height, body mass, alcohol consumption, tobacco smoking status, moderate‐to‐vigorous physical activity (MVPA), fruit and vegetable intake, and sleep duration.

Results
After controlling for demographic and anthropometric parameters, MVPA, fruit and vegetable intake, and sleep duration were positively correlated with MCS (p ≤ 0.001), and alcohol consumption and tobacco smoking were negatively correlated with MCS (p ≤ 0.001). Multiple linear regression analyses revealed alcohol consumption was the strongest predictor of MCS (β = −0.308, p ≤ 0.001), followed by smoking (β = −0.236, p ≤ 0.001), MVPA (β = 0.233, p ≤ 0.001), sleep (β = 0.148, p ≤ 0.001), and fruit and vegetable intake (β = 0.097, p = 0.003).

Conclusion
The results suggest that greater physical activity, sleep duration, and fruit and vegetable intake are associated with better mental health. Meanwhile, excessive alcohol consumption and tobacco smoking undermine mental health status.

Full text

Lifestyle Medicine
ORIGINAL ARTICLE
Association Between Health Behaviors and Mental Health
Among Airline Pilots
Daniel Wilson1,2Matthew Driller3Ben Johnston4Nicholas Gill1,5
1Te Huataki Waiora School of Health, The University of Waikato, Hamilton, New Zealand 2Faculty of Health, Education and Environment, Toi Ohomai
Institute of Technology, Tauranga, New Zealand 3Sport, Performance, and Nutrition Research Group, School of Allied Health, Human Services and Sport, La
Trobe University, Melbourne, Australia 4Aviation and Occupational Health Unit, Air New Zealand, Auckland, New Zealand 5New Zealand Rugby,
Wellington, New Zealand
Correspondence: Daniel Wilson (daniel.wilson@toiohomai.ac.nz)
Received: 28 July 2024 Revised: 15 September 2024 Accepted: 25 September 2024
Funding: The authors received no specific funding for this work.
Keywords: alcohol | nutrition | physical activity | sleep | smoking
ABSTRACT
Background: Lifestyle behaviors including physical activity, sleep, nutrition, smoking, and alcohol consumption are indepen-
dently associated with health, yet the relationship between these behaviors and mental health has not been explored among airline
pilots. The aim of this study was to measure the association between health behaviors and mental health.
Methods: A cross-sectional study was conducted among 502 airline pilots. The primary outcome measure was the mental
component score (MCS), derived from the Short Form Health Survey 12v2. We collected information regarding age, sex, ethnicity,
height, body mass, alcohol consumption, tobacco smoking status, moderate-to-vigorous physical activity (MVPA), fruit and
vegetable intake, and sleep duration.
Results: After controlling for demographic and anthropometric parameters, MVPA, fruit and vegetable intake, and sleep duration
were positively correlated with MCS (p≤0.001), and alcohol consumption and tobacco smoking were negatively correlated with
MCS (p≤0.001). Multiple linear regression analyses revealed alcohol consumption was the strongest predictor of MCS (β=−0.308,
p≤0.001), followed by smoking (β=−0.236, p≤0.001), MVPA (β=0.233, p≤0.001), sleep (β=0.148, p≤0.001), and fruit and
vegetable intake (β=0.097, p=0.003).
Conclusion: The results suggest that greater physical activity, sleep duration, and fruit and vegetable intake are associated with
better mental health. Meanwhile, excessive alcohol consumption and tobacco smoking undermine mental health status.
1 Introduction
Mental ill-health substantially contributes to the health system
burden globally [1], which may be mitigated through the
attainment of modifiable lifestyle behaviors including sufficient
physical activity [2], a healthy dietary pattern [3], adequate sleep
[4], limiting alcohol consumption [5], and avoidance of smoking
tobacco [6]. Indeed, a recent nationally representative survey
among adults in China demonstrated collective improvements
in lifestyle behaviors were associated with reduced rates of
depression, anxiety, loneliness, and perceived pressure [7]. Since
the Germanwings 9525 disaster in 2015, the issue of supporting
and managing mental health among airline pilots has gained
increased attention [8]. Circadian disruption, compounded by
suboptimal work–life balance and stressful work conditions are
occupational characteristics frequently reported among airline
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly
cited.
© 2024 The Author(s). Lifestyle Medicine published by John Wiley & Sons Ltd.
Lifestyle Medicine, 2024; 5:e70003
https://doi.org/10.1002/lim2.70003 1of9

pilots, which are associated with elevated psychological stress
and fatigue [9–11].
A diverse occupational analysis among the general population
revealed factors including high-risk occupations, excessive
work demands, low social support, and lack of control over
work demands were associated with higher odds of developing
psychological distress compared with dichotomous occupations
[12]. Noteworthily, it is evident these similar adverse occupational
characteristics are often associated with the role of being an
airline pilot [13–15]. Given the low probability of imminent alter-
ations in the occupational demands associated with the role of
an airline pilot, it is advisable to devise and implement strategies
aimed at enhancing health and wellness within this professional
group. Such initiatives could serve to mitigate occupational risk
factors, thereby promoting an environment conducive to flight
safety. This approach aligns with a preventive healthcare perspec-
tive, emphasizing the importance of addressing potential health
concerns before they escalate into significant issues, ultimately
contributing to the overall safety and efficiency of aviation opera-
tions. Accordingly, there is a need for enhanced implementation
of preventive policies and practices in aeromedical regulation
that target underlying risk factors associated with mental health.
The work of Hoffman and colleagues has raised awareness that
airline pilots often engage in healthcare avoidance behavior
due to the fear of losing their aeromedical license [16, 17].
Consequently, it is evident that there are barriers experienced by
airline pilots to seeking and engaging with healthcare support to
address underlying mental ill-health.
Recent findings suggest there is a notable proportion of air-
line pilots experiencing depressive symptoms [17] and excessive
fatigue [9]. Among the literature, there is heterogeneity in the
prevalence of mental health risk factors among airline pilots. A
recent systematic review reported a 21% (20.8−21.6 CI) prevalence
of mild depression among airline pilots [9]. Further, a web-based
survey reported a prevalence of 57% for meeting their threshold
for mild depression [15], whereas another systematic review
reported that the prevalence of depression ranged from 1.9% to
12.6% [8]. Regardless of the heterogeneity of existing research
methods and depression classification thresholds, collectively,
present empirical evidence suggests a notable proportion of air-
line pilots experiencing suboptimal mental health at comparable
rates to that of the general population [8].
Among the general population, a large body of evidence suggests
that lifestyle behaviors physical activity, nutrition, sleep, alcohol
consumption, and smoking are independently associated with
mental health, yet the direct relationship of each behavior with
mental health has not been sufficiently explored among airline
pilots. Recent reports from clinical trials suggest improvements
in health behaviors are associated with elevated perceived mental
health, improved cardiometabolic fitness, and decreased fatigue
[18–23]. Further, diet management and physical exercise have
been reported as the most prevalent coping mechanisms for
work-related stress among airline pilots [15]. Evidence suggests
collectively improving multiple health behaviors may have a pos-
itive effect on mental health [24], yet further research is required
to examine the independent relationship between discrete health
behaviors and mental health.
Ongoing understanding of the connections among factors
influencing health risks may inform the development of
interventions and policies aimed at effectively reducing mental
health issues among airline pilots. Consequently, the primary
objective of this research is to explore the correlation between
lifestyle health behaviors and mental health status among
airline pilots. Additionally, this study aims to ascertain whether
adherence to a greater number of health behavior guidelines
correlates with improved mental health outcomes. This
investigation will contribute to the body of knowledge necessary
for the development of targeted strategies aimed at enhancing
the well-being and operational efficiency of this professional
group.
2Materials and Methods
2.1 Design
A cross-sectional study was conducted to investigate the asso-
ciation between lifestyle health behaviors and mental health
among airline pilots. This study examined health risk variables:
age, sex, ethnicity, body mass, body mass index (BMI), physical
activity levels, sleep duration, fruit and vegetable intake, alcohol
consumption, smoking status, and perceived mental health. The
study was approved by the Human Research Ethics Committee
of the University of Waikato in New Zealand (reference number
2019#35).
2.2 Participants
Five hundred two airline pilots volunteered to participate in the
study from an international airline. The aviation regulatory body
relevant to this population was the Civil Aviation Authority of
New Zealand. Airline pilots were invited to participate in the
study at the time of completing their routine aviation medical
examinations at the airlines’ occupational medicine clinic.
Invitations were delivered electronically via email prior to their
appointment and verbally by clinic staff at the time of their
appointment. Further, posters were placed at the arrival desk
and in the waiting room. Data were collected over a 12-month
period between January 2021 to January 2022 to provide all
pilots the opportunity to participate. Participants consisted of
a combination of short-haul and long-haul fleet types (n=
259 and 243, respectively). Inclusion criteria involved pilots
having a valid commercial flying license and currently being
employed on a full-time basis with active duty. All participants
provided written informed consent to participate in the
study.
2.3 Instruments/Outcome Measures
Height was measured with a SECA 206 height measure and body
mass using SECA 813 electronic flat scales (SECA, Hamburg,
Germany). For the measurement of body mass, scales were
positioned on a flat, even surface. Participants were fully clothed
and were instructed to stand at the center of the scales, arms
loosely hanging by their sides, and weight evenly distributed on
both feet. Two weight readings were recorded with a precision
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of 0.1 kg. In cases where the two measurements displayed a
difference exceeding 1%, a third measurement was taken. The
final recorded weight for each participant was determined by
averaging the two measurements. Body mass and height values
were utilized to determine BMI (kg/m2)
An online survey delivered through Qualtrics software (Qualtrics,
Provo, UT, USA) was utilized to measure self-report measures.
Participants completed the survey in the waiting room prior
to their aviation medical appointment via an iPad (Apple, CA,
USA). To ensure anonymity and dataset blinding throughout
the data analysis process, participants were assigned a unique
identification code on their informed consent form and instructed
to use this code, rather than their name, when completing the
electronic survey.
Health-related quality of life was measured with the Short
Health Form 12v2 (SF-12v2), a short version of the SF-36 that
has demonstrated a high correlation with SF-36 scores [25]
and has been widely used in previous cohort studies including
airline pilots [10]. The 12-item questionnaire produces a mental
component summary score (MCS), which has shown good test–
retest reliability and convergent validity in detecting changes
in mental health over time in adults [26]. Further, the MCS has
demonstrated acceptable capability for detecting both active
and recent depressive disorders among the general population
[27]. Scoring of the SF-12v2 was carried out in accordance
with standard summary scoring methods [28]. Accordingly,
the scales associated with the questionnaire response were
aggregated into the MCS, which produces a value on a scale
of 0–100, with higher numbers indicating better mental
health.
Moderate-to-vigorous physical activity (MVPA) was measured
utilizing the International Physical Activity Questionnaire Short
Form (IPAQ-SF) [29]. A volume of ≥150 min moderate intensity
or 75 min vigorous intensity, or an equivalent combination per
week was set as the criteria for attainment of guidelines for health.
The Alcohol Use Disorders Test (AUDIT), a 10-item questionnaire
that covers three aspects of alcohol use: alcohol consumption,
dependence, and adverse consequences was used to measure
alcohol consumption [30]. A score of <8 was used to classify
nonharmful alcohol consumption and achievement of guidelines
for health [31]. Smoking status was determined by self-report
of whether the individual is currently a tobacco smoker, with
a response of no classed as achieving health guidelines. Fruit
and vegetable intake was assessed using two questions derived
from the New Zealand Health Survey to ascertain on average,
over the past week how many servings of fruit and vegetables
they had consumed per day [32]. Sleep duration was recorded
via one question derived from the Pittsburgh Sleep Quality Index
[33], which asked how much sleep the respondent usually got
in a 24-h period, during the last month. Responses from this
question were used to identify the proportion of participants who
achieved health guidelines of ≥7 h of sleep per night [34]. To
generate subgroups within the sample population corresponding
to the number of health behavior guidelines achieved, a score
of 1 was allocated for each health behavior guideline participant
achieved. The associated summation was utilized to allocate
participants into groups of achieving 0, 1, 2, 3, 4, or 5 health
behavior guidelines.
2.4 Statistical Analysis
The statistical procedures were conducted utilizing SPSS software
(version 29.0. IBM Corp. Armonk, NY). The Shapiro–Wilk test
and its histograms, Q–Q plots, and box plots were analyzed
for data distribution normality. Levene’s test was used to test
homogeneity of variance. The chi-square test was utilized
for comparing categorical variables. Descriptive statistics were
computed stratified by population quartiles according to the MCS
score. Continuous descriptive data were compared via analysis
of variance (ANOVA) for variables satisfying assumptions of
normality and homogeneity and the Kruskal–Wallis test was
used for variables not meeting the assumptions for parametric
analysis. Bonferroni post hoc analysis was utilized for between
the quartile group comparison for parametric analysis variables
and the Dunn–Bonferroni was used for nonparametric analysis
variables. To compare MCS for individuals who adhered to 0, 1,
2, 3, 4, or 5 health behavior guidelines, an ANOVA was utilized,
controlling for sex, age, ethnicity, fleet type, and BMI.
Partial Pearson correlation coefficients were estimated for exam-
ination of relationships between MCS and all health behavior
parameters, controlling for sex, age, ethnicity, fleet type, and BMI.
A multiple regression model was conducted to examine the rela-
tionship between MCS and health behaviors, incorporating BMI
and age. The interaction effect of sex, ethnicity, height, and fleet
type was initially incorporated in analyses, yet none were signifi-
cant, nor did they change the significance level of predictors, thus
they were omitted from the final analysis and discussion. Multi-
collinearity was assessed using the variance inflation factor, with
a value greater than 5 indicating a multicollinearity issue [35]. All
pvalues were two-tailed, and the αlevelwassetatlessthan0.05.
3Results
3.1 Characteristics of the Study Population
Descriptive data of the population stratified by MCS quartiles are
presented in Table 1. Five hundred two airline pilots voluntarily
participated in the study, which represents approximately 33% of
the population within the airline [10]. Ethnicity within the pop-
ulation was predominantly white Caucasian, followed by Indian,
M¯
aori, and Asian (97.6%, 1.4%, 0.6%, and 0.4%, respectively). The
sex demographic of the population was heavily skewed towards
males (92%). Collectively, these demographic traits are congruent
with previous reports among this occupational group [10].
The proportion of airline pilots achieving health guidelines for
MVPA, sleep, AUDIT, smoking, and fruit and vegetable intake
were 52%, 67%, 89%, 95%, and 34%, respectively. No significant
differences were observed between sex for health behavior
guideline attainment, except the proportion of females achieving
guidelines for fruit and vegetable intake where greater than
males (50% and 32%, respectively; p≤0.05).
3.2 Association Between Mental Health, Health
Behaviors, and Demographic Variables
The correlation coefficient relationship between mental health,
health behaviors, and demographic variables is displayed in
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TABLE 1 Demographic and health risk characteristics among airline pilots, stratified by mental health score quartiles.
First quartile
(n=126)
Second quartile
(n=125)
Third quartile
(n=126)
Fourth quartile
(n=125)
Sex (female/male) 6/120 10/115 15/111 11/114
Short haul (n)56647166
Long haul (n)70615559
Age (years) 47.1 (11.2) 47.2 (9.5) 44.8 (10.0) 44.1 (11.1)
Height (cm) 179.8 (6.5) 179.2 (6.5) 179.4 (8.4) 178.9 (7.1)
Body mass (kg) 89.6 (14.2)3,4** 90.0 (13.1)3,4** 81.2 (11.4)1,2 ** 81.2 (13.2)1,2**
BMI (kg/m2) 27.7 (4.1)3,4** 28.0 (3.2)3,4 ** 25.2 (2.9)1,2** 25.3 (3.3)1,2 **
MCS (score) 39.6 (5.4)2,3,4** 49.0 (1.6)1,3,4** 53.7 (1.1)1,2,4** 58.3 (1.8)1,2,3**
Fruit and vegetables
(servings/day)
3.1 (1.6)3,4** 3.4 (1.2)3,4 ** 4.1 (1.5)1,2** 4.3 (1.9)1,2 **
AUDIT (score) 5.5 (2.8)2,3,4** 3.4 (2.1)1,4 ** 3.2 (1.7)1** 4* 2.4 (1.2)1,2** 3*
Sleep (h) 6.7 (0.6)3,4** 6.7 (0.5)3,4 ** 7.2 (0.6)1,2** 4*7.5 (0.6)1,2 ** 3*
MVPA (minutes/week) 93.0 (52.4)2*3,4** 117.6 (58.0)1*3,4 ** 161.7 (67.8)1,2,4** 207.7 (70.3)1,2,3 **
Smoking (n,%) 27 (21%)2,3,4** 0 (0%)1** 0 (0%)1** 0 (0%)1**
Note: The data are presented by marginal estimated mean with standard deviation or counts and percentages in parentheses. A superscript number identifies a
significant relationship within categories from the post hoc analysis, 1 represents the leftmost category, 2 denotes the second category from the left, and so forth.
Abbreviations: AUDIT, alcohol use disorders identification test; BMI, body mass index; MCS, Mental Component Score; MVPA, moderate-to-vigorous physical
activity; n, sample size.
*Indicates statistically significance group difference at level p<0.05.
**Indicates p<0.001.
TABLE 2 Partial correlation coefficients between health behavior variables and mental health, controlling for ethnicity, age, sex, BMI, and fleet
type.
F&V AUDIT Sleep MVPA Smoking MCS
F&V −0.15** 0.17** 0.22** −0.17** 0.27**
AUDIT −0.34** −0.38** 0.46** −0.58**
Sleep 0.46** −0.20** 0.43**
MVPA −0.25** 0.50**
Smoking −0.49**
MCS
Abbreviations: AUDIT, alcohol use disorders identification test; F&V, fruit and vegetable servings; MCS, Mental Component Score; MVPA, moderate-to-vigorous
physical activity.
*Indicates statistical significance at p<0.05.
**indicates significance at p<0.001.
Table 2. All health behaviors and MCS were significantly cor-
related (p≤0.001). Age was significantly correlated with all
variables (p≤0.05) except fruit and vegetable intake and
smoking status. The coefficients of all variables in the multiple
regression analysis are summarized in Table 3. The model
significantly explained variances for MCS (R2=0.56, p≤
0.001). As illustrated in Figure 1, higher values for MVPA,
fruit and vegetable intake, sleep, and lower values for AUDIT
score were associated with better mental health (p≤0.001).
Age and BMI were not significantly correlated with mental
health. The variance inflation factors were all within an accept-
able range (less than 2) and did not indicate multicollinearity
issues.
3.3 Adherence to Health Behavior Guidelines
and Mental Health
After multivariable adjustment for ethnicity, age, sex, BMI, and
fleet type, a significant main effect for a group (either adhering
to 0, 1, 2, 3, 4, and 5 health behavior guidelines) was observed (p
≤0.001). Significant differences in MCS were observed between
all health behavior guideline groups (p≤0.001), where MCS
improved with increasing adherence to health behavior guide-
lines (see Figure 2). The MCS for individuals who adhered to 0
(n=16), 1 (n=30), 2 (n=81), 3 (n=120),4(n=139), and 5 (n=
116) health behavior guidelines was 32 ±3, 39 ±7.7, 46 ±5, 49 ±
5, 53 ±5, and 56 ±3, respectively.
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TABLE 3 Results of multiple regression analysis between demo-
graphic and health behavior variables and mental health.
Mental health score
BSEβpvalue
BMI −0.109 0.069 −0.052 0.114
F&V 0.447 0.149 0.097 0.003
AUDIT −0.995 0.120 −0.308 <0.001
Sleep 1.704 0.412 0.148 <0.001
MVPA 0.023 0.004 0.233 <0.001
Smoking −7.889 1.149 −0.236 <0.001
Age −0.014 0.022 −0.019 0.553
Abbreviations: AUDIT, alcohol use disorders identification test; B, unstan-
dardized coefficient; BMI, body mass index; F&V, fruit, and vegetable
servings; MVPA, moderate-to-vigorous physical activity; SE, standard error; β,
standardized coefficient.
4 Discussion
This cross-sectional study is the first to quantify the associations
of lifestyle behaviors with mental health related quality of life
among airline pilots. Consistent with previous research among
the general population, higher physical activity levels, adequate
sleep, healthful dietary behaviors, limiting alcohol, and avoidance
of tobacco smoking were associated with enhanced mental health
[2–6]. We found each behavior investigated was significantly
and independently associated with mental health status and an
accumulation effect was observed, where attainment of health
guidelines for a higher amount of health behaviors was associated
with significantly better mental health. Our findings contribute to
elucidating the relationship between lifestyle behaviors and men-
tal health status among airline pilots. Impaired mental health,
such as mild depressive symptoms, may negatively influence
cognitive function among airline pilots [36], which is critical to
flight operation safety. Thus, understanding factors that underpin
mental health among airline pilots has broad implications for the
aviation industry and may inform future policies and strategies to
promote mental well-being among this occupational group.
Although comparable literature concerning airline pilots is lim-
ited, congruent with earlier investigations among the general
population, our findings demonstrated that achievement of more
health behavior guidelines was associated with better health
outcomes. Among a general population cohort in Ireland, clusters
of healthy lifestyle behavior patterns including smoking and
alcohol avoidance, achievement of physical activity guidelines,
and strong adherence to the Dietary Approaches to Stop Hyper-
tension dietary pattern reported the highest levels of energy
vitality, lowest levels of psychological stress, and the highest
self-rated quality of life [24]. Within a United States–based
prospective cohort analysis, all-cause mortality risk was over
three times higher in those who adhered to 0 health behavior
guidelines (i.e., smoking, alcohol, physical activity, and fruit
and vegetable intake) compared with those who achieved all
four guidelines [37]. Interestingly, in the present study, we also
included sleep, and those who achieved all five health behavior
guidelines had an approximate 1.75 times greater average mental
health score compared with those who achieved no guidelines.
Collectively, available data suggest a synergistic effect between
health behaviors, and that accumulation of multiple guidelines
promotes more influential impacts on mental health.
We observed a significant negative relationship between mental
health status and both alcohol consumption and tobacco smok-
ing, consistent with previous research [6, 38]. Excessive alcohol
consumption and smoking are independently well-established
risk factors for all-cause mortality and adverse physical and men-
tal health [6, 39]. Alcohol consumption expressed as the AUDIT
score was the strongest individual predictor for mental health
status among the investigated lifestyle behaviors. Indeed, among
the general population, those with mild-to-moderate depressive
symptoms have demonstrated substantially higher consumption
and problems related to alcohol use [40]. Notably, the prevalence
of hazardous drinking (AUDIT >8) among airline pilots was
markedly lower than that reported among general population
estimates (11% and 22%, respectively) [41]. However, some pilots
may be inherently biased to misrepresent true alcohol intake to
healthcare professionals or researchers due to existing aviation
alcohol policies which mandate a minimum of 8 h between alco-
hol consumption and flight duty, with strict legal limits on blood
and breath alcohol levels [9, 42]. As perceived psychological stress
and negative affect have been associated with excessive alcohol
consumption patterns and often precede initiation of smoking
[43, 44], evidence-based strategies that facilitate improvements in
underlying determinants of these behaviors would be a valuable
direction for future research among this occupational population.
Circadian disruption is an inherent risk for airline pilots due
to occupational characteristics including shift work, changing
work schedules, extended duty periods, and traveling across
time zones [45]. Poor sleep is associated with impaired daytime
functioning and increased mental health problems in the general
population [4]. Among pilots, poor sleep has been expressed as
a primary form of work-related stress [15] and has been linked
to elevated psychological stress and fatigue, dysregulation of
other health behaviors, and poses risk to flight safety [9, 45]. A
recent study reported poor sleep quality among airline pilots was
associated with an adverse profile of plasma biomarkers reflective
of cardiometabolic risk [46]. We observed a prevalence of 33%
insufficient sleep (<7 h) among airline pilots, which is 6% higher
than country-matched general population sample estimates [10],
yet only 2% higher than a larger United States–based study [47].
Consequently, as one third of airline pilots reported insufficient
habitual sleep, and previous research suggests that self-report
sleep duration is often over-reported by pilots [48], there is a need
for enhanced implementation of behavioral strategies to enhance
sleep health of airline pilots to mitigate risks associated with the
nonmodifiable demands of the job.
Sedentary behavior and insufficient physical activity are associ-
ated with adverse effects on mental health [2, 49]. Work demands
of airline pilots are predominantly sedentary in nature [45], and a
recent estimate of the prevalence of insufficient physical activity
among airline pilots globally was 51.5% [9]. Correspondingly, 48%
of our participants did not achieve MVPA guidelines. Achieve-
ment of physical activity guidelines has been associated with
enhanced physical health, including lower rates of obesity and
hypertension among airline pilots [10, 50]. However, to date, there
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FIGURE 1 Association between health behaviors and mental health among airline pilots.
Smoking was omitted due to being an ordinal variable. AUDIT, alcohol use disorders identification test; MCS, Mental Component Score; MVPA,
moderate-to-vigorous physical activity.
FIGURE 2 Mental health and the number of health behaviors (i.e., physical activity, fruit and vegetables, sleep, alcohol, smoking) achieving
guidelines for health. MCS =Mental Component Score. Health guideline criteria: Physical activity =≥150 min moderate-to-vigorous physical activity
min per week; Fruit and vegetables =≥5 servings per day; Sleep =≥7 h duration per night; Alcohol =Alcohol Use Disorders Identification Test score
<8; Smoking =Nonsmoker.
6of9 Lifestyle Medicine,2024

has been a lack of investigation regarding the association between
physical activity guideline achievement and mental health. In
our analyses, physical activity demonstrated the second strongest
correlation with mental health status. Further, higher MVPA was
significantly associated with lower rates of smoking and excessive
alcohol use, higher sleep duration, and more fruit and vegetable
consumption. As there is evidence to support the notion that
sufficient physical activity may overcome the deleterious health
effects of sedentary time [51], research attention is warranted to
investigate barriers and facilitators to engagement with physical
activity among airline pilots and to establish evidence-based
strategies to increase MVPA in this population.
Adherence to fruit and vegetable intake guidelines is associated
with reduced all-cause mortality and increasing evidence sug-
gests a positive effect on mental health [52]. Our findings revealed
a significant positive association between fruit and vegetable
intake and mental health, yet only 34% of pilots achieved the
recommended guideline of ≥5 servings of fruit and vegetables
per day. These findings are consistent with past research which
reported approximately one third of pilots and one quarter of
the general population globally fail to achieve this guideline
[9]. Indeed, barriers to adherence to healthy dietary behaviors
have been reported among airline pilots, including unhealthy
environmental food availability and inconsistent meal timing
opportunities [15, 45]. Our findings provide preliminary insights
that dietary factors may influence mental health among pilots,
yet future research is needed to characterize nutrition compre-
hensively, such as utilization of dietary intake logging to evaluate
nutrition composition and validated food frequency question-
naires to evaluate the association between dietary patterns and
health outcomes.
4.1 Limitations and Future Research
There are inherent limitations related to this study that should
be considered in the interpretation of our findings. Although
the sample size attained represents approximately 33% of the
population, participation was voluntary and those who chose to
participate may have been more engaged in personal health than
those who did not participate. As data were collected in the post-
COVID-19 pandemic era, the associated unique circumstances
and stressors on the aviation industry may have affected out-
comes. All participants were from a single airline, which may
introduce sampling bias when generalizing to the global airline
pilot population. Data were collected within the airlines’ aviation
medicine clinic; therefore, participants may have altered their
responses due to concerns about their employment. However,
efforts were made to mitigate this by informing participants that
the study was external to their employment medical data and
survey responses were anonymized using unique identifier codes.
For feasibility, health behavior outcomes were measured via
self-report instruments, which likely yield lower accuracy than
comparative gold standard objective measures, which would be
valuable for future research to utilize (i.e., comprehensive diet
records and actigraphy). In this study, fruit and vegetable intake
was measured as a marker for dietary behaviors; however, there
are many other components that contribute to healthful nutrition
[45], which should be incorporated into future research.
For the outcome measure MCS, there are no well-established
cut points among the literature for a score-based threshold for
psychological distress, depression, or mental ill-health. There-
fore, we conducted a within-population quartile-based analysis
to evaluate mental health status relative to the sample. Future
research investigating the association between health behaviors
and mental health, using mental health outcome measures that
incorporate well-defined cut points for mental ill-health and
depression. Consequently, this would enable the calculation
of associated odds ratios for the relationship between health
behaviors and mental ill-health, which would complement our
research aim and findings.
The findings from this study are correlational in nature, and
the scientific body of knowledge would benefit from future
research investigating the cause-and-effect relationship between
discrete health behaviors and mental health status. Further,
future research should investigate broader subdomains of mental
ill-health, such as mood regulation and severity of anxiety and
depressive symptoms.
5 Conclusion
This is the first study to quantify the relationship between health
behaviors and mental health status among airline pilots. We
found alcohol consumption, physical activity, sleep duration, fruit
and vegetable intake, and tobacco smoking were significantly
and independently associated with mental health status. Fur-
ther, an accumulation effect was observed, where attainment
of health guidelines for more health behaviors was associated
with significantly better mental health. These findings highlight
the importance of addressing modifiable lifestyle behaviors in
policies and practices to support mental health among airline
pilots.
Acknowledgments
The authors wish to thank the pilots for providing their time to participate
in this study.
Conflicts of Interest
The authors declare no conflicts of interest.
Data Availability Statement
The data that support the findings of this study are available from the
corresponding author upon reasonable request.
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