Content uploaded by Daniel Wilson
Author content
All content in this area was uploaded by Daniel Wilson on Apr 18, 2022
Content may be subject to copyright.
Int. J. Environ. Res. Public Health 2022, 19, 4848. https://doi.org/10.3390/ijerph19084848 www.mdpi.com/journal/ijerph
Review
The Prevalence of Cardiometabolic Health Risk Factors among
Airline Pilots: A Systematic Review
Daniel Wilson 1,2, Matthew Driller 3,*, Ben Johnston 4 and Nicholas Gill 1,5
1 Te Huataki Waiora School of Health, The University of Waikato, Hamilton 3216, New Zealand;
daniel.wilson@toiohomai.ac.nz (D.W.); nicholas.gill@waikato.ac.nz (N.G.)
2 Faculty of Health, Education and Environment, Toi Ohomai Institute of Technology,
Tauranga 3112, New Zealand
3 Sport and Exercise Science, School of Allied Health, Human Services and Sport, La Trobe University,
Melbourne 3086, Australia
4 Aviation and Occupational Health Unit, Air New Zealand, Auckland 1142, New Zealand;
ben.johnston@otago.ac.nz
5 New Zealand Rugby, Wellington 6011, New Zealand
* Correspondence: m.driller@latrobe.edu.au
Abstract: Background: The occupational demands of professional airline pilots such as shift work,
work schedule irregularities, sleep disruption, fatigue, physical inactivity, and psychological stress
may promote adverse outcomes to cardiometabolic health. This review investigates the prevalence
of cardiometabolic health risk factors for airline pilots. Methods: An electronic search was
conducted utilizing PubMed, MEDLINE (via OvidSP), CINAHL, PsycINFO, SPORTDiscus,
CENTRAL, and Web of Science for publications between 1990 and February 2022. The
methodological quality of included studies was assessed using two quality assessment tools for
cross-sectional and clinical trial studies. The prevalence of physiological, behavioral, and
psychological risk factors was reported using descriptive analysis. Results: A total of 48 studies
derived from 20 different countries, reviewing a total pooled sample of 36,958 airline pilots.
Compared with general population estimates, pilots had a similar prevalence for health risk factors,
yet higher sleep duration, lower smoking and obesity rates, less physical activity, and a higher
overall rate of body mass index >25. Conclusions: The research reported substantial prevalence
>50% for overweight and obesity, insufficient physical activity, elevated fatigue, and regular alcohol
intake among pilots. However, the heterogeneity in methodology and the lack of quality and
quantity in the current literature limit the strength of conclusions that can be established. Enhanced
monitoring and future research are essential to inform aviation health practices and policies
(Systematic Review Registration: PROSPERO CRD42022308287).
Keywords: aviation medicine; occupational health; morbidity; noncommunicable disease risk; risk
factors; modifiable risk
1. Introduction
Cardiometabolic noncommunicable diseases (NCDs) such as cardiovascular disease
(CVD), stroke, type 2 diabetes (T2D), and their primary risk factors are a leading public
health concern that produce significant and growing economic costs globally [1]. The
leading cause of mortality worldwide is CVD [2], which has been reported as the most
frequent cause of permanent groundings among Korean airline pilots [3]. Cardiovascular
and cerebrovascular incidents have also been reported among the most prevalent causes
of flight incapacitation in the United Kingdom [4].
Airline pilots experience unique occupational demands which may promote adverse
outcomes to cardiometabolic health, including shift work, work schedule irregularities,
sleep disruption, fatigue, the sedentary nature of the job, and stress demands associated
Citation: Wilson, D.; Driller, M.;
Johnston, B.; Gill, N. The Prevalence
of Cardiometabolic Health Risk
Factors among Airline Pilots: A
Systematic Review. Int. J. Environ.
Res. Public Health 2022, 19, 4848.
https://doi.org/10.3390/
ijerph19084848
Academic Editor: Paul B.
Tchounwou
Received: 20 March 2022
Accepted: 12 April 2022
Published: 16 April 2022
Publisher’s Note: MDPI stays
neutral with regard to jurisdictional
claims in published maps and
institutional affiliations.
Copyright: © 2022 by the author.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(https://creativecommons.org/license
s/by/4.0/).
Int. J. Environ. Res. Public Health 2022, 19, 4848 2 of 26
with flight safety [3,5–8]. Cardiometabolic diseases are associated with numerous
modifiable risk factors across physiological, behavioral, and psychological domains [1].
Central and systemic obesity, hypertension, dyslipidemia, hyperglycemia, insulin
resistance, and adipose dysfunction are among prevalent physical risk factors associated
with increased risk of CVD and T2D [1,9]. Modifiable behavioral risk factors [10] such as
unhealthy diet, physical inactivity, excessive alcohol consumption, and tobacco smoking,
along with psychological risk factors including high fatigue [11] and depression [12], are
each independently established as risk factors for cardiometabolic diseases.
To date, no systematic reviews have been published pertaining to the evaluation of
modifiable health risk factor prevalence among airline pilots. Estimations of health risk
prevalence are important for monitoring of trends and to inform risk reduction
interventions; hence, the aim of the current review was to critically analyze the global
literature to quantify the prevalence of modifiable cardiometabolic health risk factors
among commercial airline pilots. The findings from this review may be valuable to inform
aviation health practices and policies for supporting pilot health, enhancing flight
operation safety, and identifying deficiencies within the literature base to inform future
research.
2. Materials and Methods
2.1. Protocol
This systematic review was conducted according to the guidelines of the Preferred
Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) [13].
The protocol of this systematic review was registered with the International Prospective
Register of Systematic Reviews (PROSPERO, CRD42022308287).
2.2. Literature Search
An electronic search was conducted utilizing PubMed, MEDLINE (via OvidSP),
CINAHL, PsycINFO, SPORTDiscus, CENTRAL, and Web of Science. A broad search
strategy was implemented to gather literature published between 1 January 1990 and 28
February 2022. Key terms incorporated in the search string were relating to airline pilots
and cardiometabolic health risk prevalence (see Table 1). The search was limited to peer-
reviewed publications in English. Eligible publications were extracted, and their reference
lists were manually checked for potentially relevant studies. The reference lists of existing
review articles pertaining to aviation medicine were also cross-checked for relevant
articles.
Table 1. Search terms blocks were combined for text and word search in PubMed and adapted to
the remaining databases: 1 and 2; 1 and 3; 1, 2, and 3.
1. Airline Pilots 2. Cardiometabolic Risk Markers 3. MeSH
Pilots OR “airline pilot*” OR
“commercial pilot*” OR
“professional pilot*” OR
“civil pilot*” OR “civilian
pilot*” OR “aviation pilot*”
OR “commercial airline*” OR
aircrew OR “cockpit crew*”
NOT military* NOT army
NOT “pilot study” NOT
piloted NOT “pilot project”
NOT “pilot research”
“Health risk*” OR “risk factor*” OR cardiometabolic OR cardio-
metabolic OR cardiovascular OR “cardiometabolic risk” OR
“metabolic syndrome” OR “syndrome x” OR diabetes OR
hypertension OR weight OR overweight OR obesity OR “body
composition” OR adiposity OR “physical act
ivity” OR exercise OR
sleep OR circadian OR apnoea OR apnea OR nutrition OR diet OR
eating OR fruit* OR vegetable* OR stress OR lipids OR cholesterol
OR glucose OR insulin OR “insulin resistance” OR “insulin
sensitivity” OR “waist circumference” OR fat OR
“blood pressure”
OR hypertension OR “C-reactive protein” OR “inflammatory
markers” OR inflammation OR “microvascular dysfunction” OR
fatigue OR medical OR depression OR stress OR distress OR
MeSH terms: “risk
factors” [mesh] OR
“health risk
behaviors” [mesh]
OR “health status
indicators” [mesh]
OR “risk
assessment” [mesh]
Int. J. Environ. Res. Public Health 2022, 19, 4848 3 of 26
anxiety OR alcohol OR smok* OR microalbumin* OR “endothelial
dysfunction”
Note: * indicates use of truncation.
2.3. Eligibility Criteria
Publications were identified for inclusion on the basis of population, literature type,
publication date, and cardiometabolic health risk eligibility. The population criteria for
inclusion were fixed-wing pilots (airline, commercial, civilian), and no restrictions were
placed on fleet type (short-haul, long-haul, mixed-fleet). Articles were excluded if they
included pilots with <1 year experience of being a pilot, as well as those who worked part-
time, were a helicopter pilot, or worked in noncivil aviation roles (Air Force, military,
army, or private), and if they were published before 1990. Literature sources that met
inclusion were peer-reviewed original articles (retrospective, prospective, cross-sectional,
case–control, cohort, and experimental), and other sources were excluded, e.g., literature
reviews, commentaries, and editorials. To be eligible for inclusion, publications had to
report on at least one of the following cardiometabolic health risk markers: blood pressure
(BP), body composition (body mass, body mass index [BMI], waist circumference, waist-
to-hip ratio, body fat percentage, lean mass percentage, visceral adiposity), glycemic
control (fasting or postprandial glucose, HbA1c), insulin (fasting, insulin sensitivity, or
insulin resistance), inflammation (C-reactive protein, inflammatory markers), blood lipid
panel (total cholesterol (TC), low-density lipoprotein (LDL) cholesterol, high-density
lipoprotein (HDL) cholesterol, and triglycerides (TG)), microalbuminuria, endothelial or
microvascular dysfunction, alcohol consumption, smoking, dietary behaviors (fruit and
vegetable intake, high-energy-dense intake, high-saturated-fat intake, high sugar intake,
or low-fiber), physical activity (sedentary behavior, moderate-to-vigorous physical
activity (MVPA), or daily steps), cardiorespiratory fitness (submaximal or maximal
oxygen consumption [VO2]), sleep (hours per night, sleep quality), psychosocial stress
(stress, depression, anxiety, and fatigue), and self-rated health.
To avoid including studies involving work duty-induced inflation of cardiometabolic
risk prevalence, studies pertaining to outcome measures recorded preceding (<24 h),
during, or acutely following (<48 h) long-haul flights were excluded. Where available,
nonflight duty baseline data from these studies were utilized. Studies reporting data
exclusively on pilot subpopulations (e.g., diabetic or obese pilots) were excluded. A hand
search of recent issues of prominent aviation journals was conducted to screen for any
recently published articles that were not yet indexed and apparent on the systematic
search.
2.4. Screening Process
The lead author conducted the initial literature search, and results were downloaded
and imported into Endnote citation software (Endnote x9, Clarivate Analytics,
Philadelphia, PA, USA) for collation and duplicate removal. Thereafter, articles were
exported to Microsoft Excel (Microsoft® Excel version 16.54) for further removal of
duplicates and subsequent eligibility screening. Initial title and abstract screening was
conducted by one author and cross-checked by a second reviewer. Subsequently,
potentially eligible articles from the initial screening progressed to full-text evaluation of
eligibility for inclusion. Discrepancies in outcomes between the reviewers were resolved
via discussion and consultation with a third reviewer.
2.5. Methodological Quality Assessment
The methodological quality of publications included for this review was
independently assessed by two reviewers. The risk-of-bias quality assessment checklist
(adapted from Hoy and colleagues [14]) was utilized for evaluation of cross-sectional
studies, which consisted of four external validity items and six internal validity items.
Int. J. Environ. Res. Public Health 2022, 19, 4848 4 of 26
Clinical trials were evaluated utilizing the risk-of-bias tool from Cochrane [15]. The
summative quality assessment for each publication was expressed as being of low quality
(high risk of bias), moderate quality (high risk of bias), or high quality (low risk of bias).
Consistent with the Grades of Recommendation, Assessment, Development, and
Evaluation and Cochrane approaches, total scores for the cross-sectional study assessment
were grouped as the following thresholds: very high risk of bias (0–4 points), high risk of
bias (5–6 points), or low risk of bias (7–10 points). Clinical trials were rated as ‘high’, ‘low’,
or ‘unclear’ for seven items: random sequence generation, allocation concealment,
blinding of participants and personnel, blinding of outcome assessment, incomplete
outcome data, selective outcome reporting, and outcome-specific evaluations of risk of
bias.
2.6. Data Extraction
The study country, aim, design, participant characteristics, outcomes of interest, and
instruments for included publications were extracted (Appendix A). If necessary,
additional publication information was sought from trial registries, article supplementary
materials, or direct contact with article authors. Study data were independently extracted
and coded by one reviewer, and a second reviewer independently extracted and coded
20% of the included studies for process cross-evaluation. Any discrepancies between
reviewers were resolved via discussion and consultation with a third reviewer if
necessary. For clinical trials included in our analysis, descriptive data were extracted from
their reported baseline data, and post-intervention data were not included. For between-
group studies that only reported subgroup descriptive statistics (e.g., interventional and
control), we computed the combined population mean using Cochran’s formula [16].
2.7. Analysis of Data
The prevalence of cardiometabolic health risks was reported using descriptive
analysis. Available data were sought and extracted from included publications for
descriptive analysis, including one or multiple of the following available statistical
metrics: mean descriptive statistics, prevalence proportions, incidence rates, standardized
incidence ratios, prevalence ratios, odds ratios, risk ratios, or scoring outcomes derived
from relevant self-report instruments. The meta-analysis estimates for proportions and
descriptive statistics for cardiometabolic health risk factors were calculated by weighing
the studies according to their sample size within pooled samples. A 95% confidence
interval was presented alongside pooled prevalence statistics. Meta-analyses were not
conducted for some cardiometabolic risk factors due to a low number of studies reporting
the parameter of interest (n < 4) or due to methodological heterogeneity. Data were
entered into an Excel spreadsheet (Microsoft, Seattle, WA, USA) and then imported into
statistical software SPSS v28 for Windows (IBM, New York, NY, USA), where meta-
analysis interpretation was performed.
3. Results
3.1. Study Selection
The search strategy produced 6138 unique results, 107 of which were deemed
potentially eligible at primary screening. After full-text reviews, 48 passed eligibility
evaluation for inclusion. A PRISMA flowchart depicting stages of the selection process is
illustrated in Figure 1.
Int. J. Environ. Res. Public Health 2022, 19, 4848 5 of 26
Figure 1. PRISMA flow diagram. PRISMA, Preferred Reporting Items for Systematic Reviews and
Meta-Analyses.
3.2. Study Characteristics
The 48 studies involved a total of 36,958 participants, included in 46 cross-sectional
studies and three clinical trials (Figure 2). The characteristics of the included studies are
summarized in Appendix A. Across all studies, males represented 96% of participants.
The mean age of participants was 40 ± 11 years according to 35/48 studies which reported
the mean age. The most prevalent age range reported in the remaining studies was 35–45
years. Twenty-five studies reported self-report subjective data, 14 utilized a combination
of self-report subjective and objective data, and five reported only objective data. The
included studies were conducted in 20 different countries or regions, including Brazil
(five), China (five), New Zealand (four), Finland (three), Indonesia (three), Sweden (three),
Int. J. Environ. Res. Public Health 2022, 19, 4848 6 of 26
the United Kingdom (three), the United States (three), Korea (two), the Netherlands (two),
Portugal (two), and one study each from Arab states, Australia, Europe, Germany, India,
Oceania, Saudi Arabia, Spain, and Thailand. Four studies involved participants from
numerous countries.
Figure 2. Cardiometabolic risk markers and airline pilot outcome summary for each study.
3.3. Quality of Reviewed Articles
The results of the risk-of-bias assessment are displayed in Tables 2 and 3. Of the 48
publications included in the review, four were considered of low methodological quality
with a high risk of bias and 13 were considered of high methodological quality with a low
risk of bias. Weak external validity was apparent for most cross-sectional studies, with a
paucity of random sampling (n = 39) and high nonresponse bias (n = 33) as leading factors.
Lacking reliability and validity of outcome measures (n = 17) and inappropriate observed
prevalence period (n = 14) were prominent factors of poor internal validity among cross-
sectional studies. The three clinical trials reviewed ranged from low to moderate quality,
all exhibiting high risk of bias for allocation concealment and blinding of participants.
Int. J. Environ. Res. Public Health 2022, 19, 4848 7 of 26
Table 2. Methodological quality scores of cross-sectional studies.
Author (Year) External Validity Internal Validity
1 2 3 4 5 6 7 8 9 10 Quality
Åkerstedt et al. (2021) [17] N N N Y Y Y Y Y Y Y (3) High
Albermann et al. (2020) [18] Y Y N Y Y Y N Y Y Y (2) High
Alhejaili et al. (2021) [19] N N N N Y Y Y Y Y Y (4) Med
Aljurf et al. (2018) [20] Y N N N Y Y Y Y Y Y (3) High
Alonso-Rodríguez and Medina-Font (2012) [21] Y Y N Y Y Y Y N Y Y (2) High
Ariani et al. (2017) [22] N N N N N Y N Y Y Y (6) Med
Bhat et al. (2019) [23] Y Y N N Y Y Y Y Y Y (2) High
Bostock and Steptoe (2012) [24] Y N N N Y Y N Y Y Y (4) Med
Cahill et al. (2021) [25] N N N N Y Y Y Y N Y (5) Med
Chairina et al. (2018) [26] N N N N Y N N N Y Y (7) Low
Chen et al. (2016) [27] Y N N N Y Y Y Y N Y (4) Med
Feijó et al. (2012) [28] Y Y N N Y Y Y Y Y Y (2) High
Flynn-Evans et al. (2018) [29] N N N Y Y Y N Y Y Y (4) Med
Guo et al. (2017) [30] Y N N N Y Y Y Y N Y (4) Med
Han et al. (2020) [31] N N N N Y Y Y Y N Y (5) Med
Houston et al. (2010) [32] Y Y Y Y Y Y Y Y Y N (1) High
Huang et al. (2012) [33] N Y N Y Y Y N N N Y (5) Med
Jackson and Earl (2006) [34] N N N N Y Y N Y N Y (6) Med
Lamp et al. (2019) [35] N N N N Y Y Y Y N Y (5) Med
Li et al. (2021) [36] N N N N Y Y Y Y Y Y (4) Med
Lindgren et al. (2012) [37] Y Y N N Y Y N Y N Y (4) Med
Liu et al. (2021) [38] N Y N Y Y Y N Y N Y (4) Med
Marqueze et al. (2017) [39] Y Y N N Y Y Y Y Y Y (2) High
O’Hagen et al. (2016) [40] Y N N N Y Y N Y Y Y (4) Med
Palmeira et al. (2016) [41] Y Y Y N Y Y N Y Y Y (2) High
Pellegrino and Marqueze (2018) [42] N N N N Y Y Y Y Y Y (4) Med
Pellegrino et al. (2018) [43] N N N N Y Y Y Y Y Y (4) Med
Prombumroong et al. (2011) [44] N N N N Y Y Y Y Y Y (4) Med
Qiang et al. (2004) [45] N Y N Y Y Y Y Y Y Y (2) High
Reis et al. (2013) [46]; Reis et al. (2016) [47] Y N N N Y Y N Y Y Y (4) Med
Roach et al. (2012) [48] N N N N Y Y Y Y N Y (5) Med
Runeson-Broberg and Lindgren (2013) [49] Y Y N N Y Y N Y N Y (4) Med
Sallinen et al. (2017) [50] Y N Y Y Y N Y N N N (5) Med
Sallinen et al. (2020) [51] Y N N Y Y N Y N Y N (5) Med
Sallinen et al. (2021) [52] N N N N Y Y N Y Y Y (5) Med
Signal et al. (2014) [53] N N N N Y Y Y Y Y Y (4) Med
Sykes et al. (2012) [6] Y Y N N Y Y N Y Y Y (3) High
Venus and Holtforth (2021) [54] N N N N Y Y Y Y Y Y (4) Med
Widyahening (2007) [55] N N N N Y N N Y N Y (7) Low
Wilson et al. (2022) [56] Y Y Y N Y Y Y Y Y Y (1) High
Wirawan et al. (2013) [57] Y Y N N Y Y N N N Y (5) Med
Wu et al. (2016a) [58] N N N N Y Y Y Y Y Y (4) Med
Wu et al. (2016b) [59] Y N N N Y Y Y Y Y Y (3) High
Note: High = high quality (low risk of bias); Low = low quality (high risk of bias); Med = medium quality
(moderate risk of bias); N, no; Y, yes; 1—Was the study’s target population a close representation of the
national population in relation to relevant variables, age, sex, and occupation? 2—Was the sampling
frame a true or close representation of the target population? 3—Was some form of random selection
used to select the sample OR was a census undertaken? 4—Was the likelihood of nonresponse bias
minimal? 5—Were data collected directly from the subjects (as opposed to a proxy)? 6—Was an
acceptable case definition used in the study? 7—Was the study instrument that measured the parameter
of interest (e.g., prevalence of lower-back pain) shown to have reliability and validity (if necessary)? 8—
Was the same mode of data collection used for all subjects? 9—Was the length of the shortest prevalence
period for the parameter of interest appropriate? 10—Were the numerator(s) and denominator(s) for the
parameter of interest appropriate?
Int. J. Environ. Res. Public Health 2022, 19, 4848 8 of 26
Table 3. Risk-of-bias assessment of clinical trials.
Author (Year) 1 2 3 4 5 6 7
Choi and Kim 2013 [3] High
High
High
High
Unclea
r Low Low
Van Drongelen et al. 2014 [60]; Van
Drongelen et al. 2016 [61] Low High
High
High
Low Low High
Wilson et al. 2021 [62] High
High
High
Low Low Low Low
Note: 1 = random sequence; 2 = allocation concealment; 3 = blinding of participants; 4 = blinding of
outcomes; 5 = incomplete outcome data; 6 = selective reporting; 7 = other; High = high risk of bias;
Low = low risk of bias; Unclear = not possible to rate risk of bias.
3.4. Physiological Cardiometabolic Risk Factors among Pilots
Twenty-eight studies investigated physiological cardiometabolic risk factors. From
the 22 studies reporting BMI, 12 were objectively measured and 10 were based on self-
report data. The overall objectively measured BMI (n = 20,279) pooled mean was 26.1 ± 3.0
kg/m2 and the overall subjective BMI (n = 3710) pooled mean was 24.7 ± 3.1 kg/m2. For
females, one study (n = 661) reported an objectively measured BMI of 23.9 kg/m2 (20.0–
27.7), and another (n = 32) reported a subjective BMI as 22.7 kg/m2.
Eleven studies investigated the prevalence of overweight and obesity; five (n =
19,171) were objectively measured and six (n = 3309) were based on self-reporting from
participants. The pooled mean for objective measures of overweight and obesity were
47.5% (47.4–47.5%) and 11.6% (11.6–11.7%), respectively. One study reported obesity only,
revealing a prevalence of 20% [6]. The pooled mean for subjective measures of overweight
and obesity was 43.6% (43.3–43.9%) and 12.4% (11.9–12.9%), respectively. The overall
pooled prevalence of overweight plus obesity was 59.1% (59.0–59.2%) for objective
measures and 56.0% (55.5–56.5%) for subjective measures. The combined pooled
prevalence from subjective and objective measures for overweight, obesity, and
overweight plus obesity was 46.8% (46.7–46.9), 11.7% (11.6–11.8%), and 58.6% (58.5–
58.7%), respectively. One study [32] (n = 661) reported the prevalence of overweight and
obesity for females as 28% and 6%, respectively. The prevalence of metabolic syndrome
was reported by two studies, ranging from 15% [21] to 38% [27]. Furthermore, these
studies reported objectively measured central obesity (>102 cm) prevalence as 18% [21]
and 64% [27]. Only one study investigated C-reactive protein levels, reporting a mean hs-
CRP serum level of 1.68 ± 1.79 (mg/L) [21].
Four studies (n = 16,327) reported the prevalence of hypertension (BP ≥ 140/90
mmHg) from objective measurement as 29% [32], 28% [27], 26% [56], and 11% [23], with a
pooled prevalence of 27.6% (27.5–27.7%). Furthermore, one study (n = 303) reported the
prevalence of elevated BP (≥130/85 mmHg) as 38% [21]. Derived from four studies
[3,27,56,57], the objectively measured pooled mean systolic blood pressure (SBP) was 126
± 14 mmHg, and the objectively measured pooled mean diastolic blood pressure (DBP)
was 79 ± 9 mmHg. The prevalence of self-reported known hypertension of participants in
three studies was 13% [20], 7% [38], and 6% [57]. One study reported the prevalence of
objective hypertension for females as 14% [32].
HDL cholesterol and triglycerides were reported in four studies [3,27,33,57] (n =
1640), revealing pooled means of 1.3 ± 0.9 mmol/L and 19 ± 1.6 mmol/L, respectively.
Additionally, three studies reported the prevalence of low HDL as 8% [21], 46% [27], and
57% [26] and of elevated triglycerides as 24% [21], 28% [27], and 29% [26]. The pooled
mean of three studies [3,33,57] (n = 1337) reporting TC was 5.3 ± 1.0 mmol/L, and an LDL
cholesterol mean of 3.3 ± 0.9 mmol/L was derived from two studies [3,33] (n = 742). The
prevalence of self-reported known dyslipidemia of participants in two studies was 10%
[57] and 19% [38]. Only two studies investigated hyperglycemia, reporting the prevalence
as 31% [21] (≥100mg/dL) and 30% [27] (≥5.6 mmol/L).
Int. J. Environ. Res. Public Health 2022, 19, 4848 9 of 26
3.5. Behavioral Cardiometabolic Risk Factors among Pilots
Thirty-one studies included the evaluation of behavioral cardiometabolic risk factors.
Alcohol intake was investigated in 10 samples of airline pilots
[6,24,27,33,36,38,39,46,47,57,60,61]; one study utilized a validated questionnaire [36], and
five studies [27,33,36,38,39] (n = 2538) ascertained “regular alcohol intake” on the basis of
a participant self-recall question, producing a pooled prevalence of 52% (51.3–53.1).
Twelve studies [6,26,27,29,32,33,36,37,39,49,57,60] (n = 19,116) reported smoking
prevalence, yet no studies evaluated quantity or frequency of smoking. The pooled
prevalence was 9.4% (9.3–9.5%). One study reported the prevalence of smoking for
females as 6% [32].
From the 20 studies evaluating sleep, seven studies objectively measured sleep hours
with actigraphy (n = 1764) [29,35,48,50–53,59], and six used self-recall methods (n = 2224)
[17,24,39,56,59,62]. The pooled means for objective and self-recall sleep hours per night
were 7.2 ± 1.1 and 7.0 ± 0.6, respectively. Three studies reported the prevalence of <6 h of
sleep per night as 23% [43], 20% [61], and 22% [20]. Furthermore, other studies reported
that <6 h of sleep per night was associated with obesity [41] and poor sleep quality [42]
within participants. The prevalence of excessive sleepiness assessed by the Epworth
Sleepiness Scale (score ≥10) was reported by five studies [19,20,39,42,46], exhibiting a
pooled prevalence of 44.5% (44.1—44.8%). Among four studies reporting high obstructive
sleep apnea (OSA) risk ascertained from the Berlin Questionnaire, the prevalence was 5%
[19], 20% [39], 21% [42], and 29% [20], providing a pooled mean of 21.4% (21.3–21.5%).
The prevalence of self-reported insufficient physical activity (<150 min MVPA per
week) was reported in five studies (n = 2233) providing a pooled prevalence of 51.5%
(51.3–51.7%) [22,26,42,56,62]. Additionally, <150 min MVPA per week was found to be
associated with obesity in one study which reported a prevalence ratio of 1.08 (0.98–1.19)
[41]. One study reported the mean days per week of moderate physical activity and
strenuous physical activity as 3.3 ± 1.9 and 2.0 ± 1.4, respectively. Another study reported
the mean walking minutes and MVPA minutes per week as 110 ± 117 and 145 ± 72,
respectively.
Three studies (n = 955) reported the prevalence of subjective insufficient daily fruit
intake as 33% (<200 g/day) [38], 60% [56], and 65% (<2 servings/day) [62] and of
insufficient daily vegetable intake as 19% (<300 g/day) [38], 47% [62], and 48% [56] (<3
servings/day). From these studies, two reported the prevalence of combined insufficient
fruit and vegetable intake as 68% [56] and 84% [62]. One study reported the mean number
of snacks per duty as 4 ± 3 [60].
3.6. Psychological Cardiometabolic Risk Factors among Pilots
Sixteen studies included an evaluation of psychological cardiometabolic risk factors.
Among 10 studies investigating the prevalence of psychological fatigue, four studies (n =
2987) utilized the Fatigue Severity Scale (FSS), two of which reported a psychological
fatigue prevalence (FSS ≥ 4 mean score) of 77% [54] and 89% [46,47]. Another two studies
reported the severe psychological fatigue prevalence (FSS ≥ 36 total score) as 33% [19] and
68% [20]. The prevalence of elevated psychological fatigue in the remaining studies (n =
2719) was reported as 5% [58], 27% [42,43], 30% [60,61], and 75% [34], each produced with
different methodology.
Seven studies subjectively measured the prevalence of depression, with a pooled
mean of 21% (20.8–21.6) for mild depression derived from five studies [19,25,30,54,58] (n
= 3411) utilizing the Patient Health Questionnaire (PHQ-9; score ≥10). One study reported
a depression prevalence of 35% [20] according to the Hospital Anxiety and Depression
Scale (score >8), whereas another study reported depression or anxiety within the last 12
months as 54.4% [40]. One study reported mild depression (PHQ-9 score ≥10) prevalence
in females as 11% [58]. Two studies (n = 2527) reported the prevalence of mild anxiety
derived from the Generalized Anxiety Disorder-7 (GAD-7; score > 10) scale, noting 4%
Int. J. Environ. Res. Public Health 2022, 19, 4848 10 of 26
[30] and 7% [54]. The prevalence of below-average or poor subjective self-rated health was
reported in three studies (n = 1282) as 8% [38], 25% [56], and 39% [22], each derived from
different methodology.
4. Discussion
To our knowledge, this is the first comprehensive synthesis of published research
pertaining to physiological, behavioral, and psychological cardiometabolic health risk
factors among this unique occupational group. Our findings provide stakeholders
including aviation medical professionals, policymakers, researchers, clinicians, and
occupational health authorities with a scientific synthesis of the magnitude of prevalence
of cardiometabolic health risk factors among commercial pilots. These findings may be
beneficial to inform developments in aviation health practices and policies to support pilot
health and wellness, to mitigate risks of occupational morbidity, medical conditions
causing loss of license, and medical incapacity, and to support flight safety [5].
Findings from the review suggest similar health risk factor prevalence to the general
population, yet higher sleep duration, less physical activity, lower smoking rates, higher
regular alcohol consumption, less obesity, and a higher overall rate of body mass index
>25 among pilots. We discovered, within the literature reviewed, a dominance of self-
reported data, with most studies exhibiting moderate to high risk of methodological bias.
Indeed, there are limited high-quality studies within the field, warranting the need for
future research to address the gaps within and strengthen the body of knowledge.
4.1. Prevalence of Physiological Cardiometabolic Risk Factors among Pilots
As described by the International Civil Aviation Organization (ICAO) aviation
medical regulations, cardiometabolic health risk data are acquired routinely during
aviation medical examinations for pilots >35 years old for CVD risk assessment, which
include BMI, BP, resting heart rate, blood lipids, and HbA1c [5]. In 2015, the global
prevalence of overweight, obesity, and overweight plus obesity in the general population
was reported as 38.7%, 16.4%, and 55.1%, respectively [63]. This general population
estimate is relative to the country, age, and sex characteristics represented in the present
review of studies conducted among pilots. Past research has reported a lower prevalence
of overweight and obesity in pilots compared to the general population [6,64,65]. Indeed,
the present review found that pilots had a 4.7% lower prevalence of obesity than the
general population [63]. As obesity is a major risk factor for diseases such as CVD and
T2D [66], the lower rate of obesity within pilots may promote a lower pilot population
cardiometabolic disease relative risk compared to the general population.
Interestingly, with overweight and obesity pooled together, we discovered that pilots
had an overall 3.5% higher rate of overweight plus obesity compared to the general
population (58.6% and 55.1%, respectively). This finding suggests that past reports of
lower rates of overweight and obesity within pilots [6,64,65] compared to the general
population may be archaic, and future research should investigate the underlying causal
mechanisms that contribute to overweight and obesity rates among pilots. A noteworthy
consideration for interpretation of this information is the lack of random sampling and
potential response bias within studies on pilots compared to the general population,
which adds a notable limitation to the validity of prevalence comparisons between
populations.
Most countries represented within the present review were high-income countries.
In 2010, the global hypertension prevalence was estimated as 31.1% (30.0–32.2%), while
that among high-income countries was estimated as 28.5% (27.3–29.7%) [67]. We found
four studies reporting the prevalence of hypertension within pilots, ranging from 11% to
29% [23,27,32,56], with a pooled prevalence slightly lower than the general population at
27.6%. Airline pilots undergo regular medical examinations evaluating BP [5], and this
regular active monitoring may promote the observed lower hypertension prevalence.
However, one study reported a 38% [21] prevalence of pilots exhibiting elevated BP
Int. J. Environ. Res. Public Health 2022, 19, 4848 11 of 26
(≥130/85 mmHg); thus, it would be valuable for future epidemiological research to report
the prevalence of elevated BP, accompanying hypertension rates in order to better inform
researchers on the distribution of BP ranges across the pilot population.
Prospective epidemiological studies have consistently reported that unfavorable
blood lipid profiles are associated with increased incidence of metabolic syndrome (MS)
and NCDs such as CVD [68]. We found three studies reporting the prevalence of markers
of dyslipidemia in pilots, with prevalence of low HDL ranging from 8% to 57% and of
elevated TG ranging from 24% to 29% [21,26,27], whereas MS prevalence ranged from
15% [21] to 38% [27]. These evident variances in prevalence rates based on the small
sample of studies (n = 3) [21,26,27] illustrate the need for further research to be conducted
regarding quantification of these risk factors in pilots to reach valid inferences of their
prevalence.
The global prevalence of diabetes is rising, and it has been estimated that 49.7% of
people living with diabetes are undiagnosed [69]. Investigation of impaired glucose
tolerance and hyperglycemia is scarce in the airline pilot literature, with only two studies
reporting the prevalence of hyperglycemia (30.4–31.3%) [21,27] and scant research
reporting on the prevalence of elevated HbA1c, which is the leading diagnostic criteria
for T2D [69]. This dearth of information may be attributable to past barriers for diabetic
pilots to operate commercials flights due to the risk of incapacitation from hypoglycemia
while flying, yet recent advances in insulin therapies, monitoring techniques, and modes
of administration have given rise to policy developments reducing barriers for diabetic
pilots to operate commercial flights [70]. Seemingly, there is a need for more research
attention on glycemic control and identifying the prevalence of elevated risk markers for
T2D among airline pilots.
4.2. Prevalence of Behavioral Cardiometabolic Risk Factors among Pilots
From the 31 studies we found reporting on behavioral risk factors, sleep was the most
frequently reported risk factor (n = 21). Sleep disruption is an inherent risk for pilots as
occupational characteristics such as extended duty periods, work schedules, crossing time
zones, and sleep restrictions cause perturbance of sleep routine consistency [71]. Recent
research has indicated that sleep difficulty is frequently expressed as a primary source of
work induced stress among pilots [25]. The present review found the mean pooled sleep
hours per night to range from 7.0 h to 7.1 h, indicating that the population mean falls
within the lower range of sleep guidelines for health in adults, which has been reported
as the attainment of 7–9 h [72]. We found three studies reporting the prevalence of short
sleep (<6 h) ranging from 20% to 23% [20,59,61], which is comparably lower than a USA-
based study noting a prevalence of ≤6 h sleep as 29% among the general population in
2012 [73]. Indeed, due to the influence of fatigue on flight safety, pilots are often subject
to fatigue management training via aviation medical management [71,74], facilitating the
implementation of adaptive coping strategies to mitigate fatigue which may support the
attainment of sleep guidelines within the population.
Past research has reported lower sex- and age-adjusted prevalence for smoking and
higher levels of physical exercise among aircrews compared to the general population
[65]. Indeed, we found a pooled smoking prevalence of 9% among pilots, which is
considerably lower than a 2015 prevalence estimate of 25% for smoking among the global
male general population [75]. Interestingly, for physical activity, we found a pooled
prevalence of 51.5% for insufficient physical activity among pilots, which is markedly
higher than a recent global prevalence estimate of 32% (30–33%) in 2016 among the
general population within high-income countries [76]. However, our findings were only
derived from four studies using self-recall data and small samples, making comparisons
with the general population of scant validity. Future research utilizing objective outcome
measures is important to further evaluate the accuracy of current findings.
Alcohol use is a leading risk factor for global disease burden, with the global
prevalence of current drinking estimated as 47% in 2017 [77]. According to five studies we
Int. J. Environ. Res. Public Health 2022, 19, 4848 12 of 26
found, the pooled mean for regular alcohol intake among pilots was 52%. However, the
lack of quantity and the low-quality methodology among studies minimize the validity of
prevalence estimation. Furthermore, pilots may be inherently biased to misrepresent their
true alcohol intake to aviation medical professionals or researchers due to aviation
regulations prohibiting alcohol consumption within 8 h of acting as a crew member and
existing alcohol testing mandates [78].
Dietary behaviors are a leading risk factor for obesity and cardiometabolic diseases
such as CVD and T2D [79]. Previous studies have conveyed occupational factors such as
inconsistent mealtimes, physical inactivity on duty, suboptimal airport and airline
catering options, and shift work as factors that may be detrimental to healthy dietary
patterns among pilots [6,7,21]. There is a dearth of literature pertaining to the
quantification of dietary behaviors among pilots, with only two studies identified in this
review reporting the prevalence of pilots who were not achieving daily fruit and vegetable
intake guidelines of ≥5 servings ranging from 68–84% [56,62]. Although lacking validity,
this estimate is comparable to the estimated global prevalence estimate of 79% derived
from the World Health Survey 2002–2004 [80], relative to the country and sex
characteristics represented in the present review.
4.3. Prevalence of Psychological Cardiometabolic Risk Factors among Pilots
High levels of psychological fatigue are associated with elevated risk of CVD and
excess mortality within the general population [11] and are detrimental to a pilot’s ability
to safely operate the aircraft or perform safety-related duties [5]. We found the prevalence
of elevated psychological fatigue to range from 5% to 77% [19,42,43,46,47,54,58,60,61] And
of severe psychological fatigue to range from 33% to 68% [19,20]. The heterogeneity of
methodology among studies within pilots inhibits valid comparisons with the general
population. Nonetheless, with numerous studies reporting noteworthy rates of elevated
psychological fatigue during nonduty periods, this warrants further research regarding
the development of innovative interventions to better facilitate fatigue mitigation in this
occupational group.
Major depressive disorder is associated with elevated cardiometabolic risk factors
and poor health outcomes [12]. Depression and mental health issues among pilots have
been proposed as contributing factors in numerous flight incidents resulting in mass
casualties [40]. Thus, psychological risk factors are pertinent to pilot health and wellness
and, in turn, flight operation safety. We discovered a pooled mean of 21% for mild
depression [19,25,30,54,58] among pilots. Comparatively, a prevalence of 21% for mild
depression was reported within a general population sample using congruent
methodology, delineating a similar prevalence between populations.
4.4. Study Strengths
To the authors’ knowledge there is no published scientific synthesis of
cardiometabolic health risk factor data for airline pilots. The studies included in the
systematic review were derived from 20 different countries from around the globe.
Therefore, the findings are not localized to a certain region and are relevant data
pertaining to the global airline pilot population. This review revealed insights that diverge
from previous assumptions regarding cardiometabolic health among airline pilots, thus
providing useful data which may inform public health practice and the development of
targeted initiatives to support occupational health and safety.
4.5. Study Limitations
As this review sought to identify baseline nonwork duty-related prevalence of
cardiometabolic health risk factors, we did not examine risk factor quantification during
or immediately following flight duty periods, as these work characteristics often elicit
acute inflated risk prevalence for factors such as psychological fatigue, sleep disruption,
Int. J. Environ. Res. Public Health 2022, 19, 4848 13 of 26
and other psychological distress-related parameters [39,52]. Thus, the present review did
not capture the magnitude of work duty-induced perturbations to behavioral and
psychological cardiometabolic health risks.
Furthermore, as we sought to identify the prevalence of cardiometabolic health risks
among the overall airline pilot population, we did not stratify outcomes by fleet division,
such as short-haul, long-haul, or mixed-fleet. The comparison of health risk prevalence
between fleet divisions may be an appropriate scope for a future systematic review, which
would add to the literature for understanding the magnitude of health risk difference
between pilot rosters.
Due to the heterogeneity of publication dates among the literature featured in our
review, the global general population prevalence comparison studies utilized may not
optimally align with timepoints from studies among pilots and should be considered by
readers with our presented population comparisons. Additionally, the heterogeneity of
measurements of cardiometabolic parameters among the airline pilot studies reviewed
and the general population estimates should be considered in the interpretation of our
findings.
Lastly, the low quantity of robust studies limits the generalizability of the current
findings reported within the literature. Future high-quality epidemiological research
utilizing validated measurements will be valuable to increase the probability of attaining
reliable conclusions pertaining to the health risk prevalence within the pilot population.
To provide further meaningful insight into pilot cardiometabolic health risk and to
address gaps in the literature, research attention pertaining to the assessment of glycemic
control (i.e., HbA1c) and blood lipids, objectively measured health behaviors (dietary
behaviors, physical activity, alcohol intake), and wider assessment of depressive
symptoms among the airline pilot population would provide valuable contributions to
advance the body of knowledge.
5. Conclusions
The findings of this review provide synthesis on the prevalence and magnitude of
cardiometabolic health risk factors among airline pilots. A wide range of prevalence rates
were reported for many investigated health risk parameters in the literature, with
pervasiveness of overweight and obesity, insufficient physical activity, elevated
psychological fatigue, insufficient fruit and vegetable intake, and regular alcohol
consumption among pilots. The inherent bias, dominance of self-report data, and
heterogeneity of methodology mean that it was not possible to establish strong
conclusions. Future research utilizing objective measures and robust random sampling
strategies are advocated to strengthen the validity of prevalence estimates and enhance
the generalizability of findings.
Author Contributions: D.W. and N.G. participated in the conceptualization and design of the work,
data collection and analysis, interpretation of the results, and manuscript writing; M.D. and B.J.
contributed to the data analysis, interpretation of the results, and manuscript writing. All authors
read and agreed to the published version of the manuscript. All authors have read and agreed to
the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflicts of interests.
Int. J. Environ. Res. Public Health 2022, 19, 4848 14 of 26
Appendix A
Summary of characteristics of studies that investigated cardiometabolic health risk parameters among airline pilots.
Table A1. Summary of characteristics of studies that investigated cardiometabolic health risk parameters among airline pilots.
Study (Year) Aim of Study Design; Data;
Country
Sample;
% Male;
Age Key Findings Instruments
Åkerstedt et al.
(2021) [17]
Investigate
associations among
schedule, fatigue,
and sleep
Cross-sectional;
subjective;
Sweden
n
= 89; 76%; 38 ± 9
years
Fatigue (KSS) = 4.2 ± 1.8; sleep = 6.8 ± 1.4 h; sleep,
duty
time, and early starts are important predictors
of fatigue in the 24 h window and that the number
of very early starts and short sleep have cumulative
effects on fatigue across a 7 day work period
Karolinska Sleepiness Scale; sleep duration
self-report
Albermann et
al. (2020) [18]
Evaluate the
prevalence of lower-
back pain compared
with the general
population
Cross-sectional;
subjective;
Germany
n
= 698; 92%; 40 ± 9
years
BMI = 24.4 ± 2.7; overweight = 35% and obesity =
3.2%; chronic lower-back pain = 83%; time spent
sitting during work = 90%
BMI (self-report); Oswestry Lower-
Back Pain
Disability Index
Alhejaili et al.
(2021) [19]
Evaluate the
presence of
obstructive sleep
apnea in pilots
Cross-sectional;
subjective and
objective; Saudi
Arabia
n
= 39; 100%; 43 ±
10 years
BMI = 24.5 ± 2.4; insomnia prevalence (AIS ≥6) =
31%; high risk of obstructive apnea = 5%; abnormal
sleepiness = 23%; mild depression = 26%; moderate
severity depression = 10%; suboptimal sleep quality
= 39%; severe fatigue = 33%
; VAFS abnormal fatigue
= 23%
BMI; Athens insomnia scale (AIS); Berlin
Questionnaire; Epworth Sleepiness Scale
(ESS); Pittsburgh Sleep Quality Index (PSQI);
Fatigue Severity Scale (FSS); Visual Analog
Fatigue Scale (VAFS); Patient Health
Questionnaire (PHQ-9)
Aljurf et al.
(2018) [20]
Evaluate the
prevalence of
fatigue, depression,
sleepiness, and the
risk of obstructive
sleep apnea
Cross-sectional;
subjective; Arab
states
n
= 328; 99%; 41 ±
10 years
BMI = 27.6 ± 5.0; BMI ≥30 = 24%; Sleep <6 h = 22%;
known hypertension = 13%; severe fatigue (FSS ≥36)
= 68%; reported mistakes being made in the cockpit
because of fatigue = 67%; ESS excessive sleepiness =
34%; high risk of OSA = 29%; depression (HADS ≥8)
= 35%
Fatigue Severity Scale (FSS); Berlin
Questionnaire; Epworth Sleepiness Scale
(ESS); Hospital Anxiety and Depression Scale
(HADS)
Alonso-
Rodríguez and
Medina-Font
(2012) [21]
Evaluate C-
reactive
protein levels and
the prevalence of
metabolic syndrome
Cross-sectional;
objective; Spain
n = 1009; 100%;
42
± 11 years
elevated BP = 38%; hyperglycemia = 31%; elevated
serum triglycerides = 24%; abdominal obesity =
18%; low HDL cholesterol = 8%; hs-CRP serum
levels = 1.7 ± 1.8 mg/L; high hs-CRP incidence
Venous blood test;
waist circumference; blood
pressure; not all instruments specified
Int. J. Environ. Res. Public Health 2022, 19, 4848 15 of 26
increased with age; metabolic syndrome (MS)
prevalence = 15%; MS in pilots <35 years old = 4%;
MS in pilots 35–
50 years old = 14%; MS in pilots >50
years old = 29%; hs-
CRP was significantly higher in
pilots with MS than those without MS
Ariani et al.
(2016) [22]
Evaluate physical
exercise habits and
associated factors
Cross-sectional;
subjective;
Indonesia
n = 332; 100%; 20-
29 years = 39%, 30–
39 years = 23%, 40–
49 years = 21%, 50–
65 years = 16%
<150 MVPA min per week = 56%; overweight = 28%
and obesity = 53%; central obesity = 46%; low or
average SLS score (≤24) = 39%
BMI (self-report); Satisfaction with Life
Scale
(SLS); not all instruments specified
Bhat et al.
(2019) [23]
Examine the
prevalence of
hypertension and
obesity and their
relationship
Cross-sectional;
objective; India
n
=1185; 89%; 35 ±
14 years
Overweight = 39% and obesity = 7%;
hypertension =
11% BMI; blood pressure
Bostock and
Steptoe (2012)
[24]
Investigate work
schedule influence
on diurnal cortisol
rhythm
Cross-sectional;
subjective; the
United Kingdom
n = 30; 100%; 20–
29
years = 15%, 30–
39
years = 41%, 40–
49
years = 30%, 50–
65
years = 15%
BMI = 25.6 ± 2.5; sleep = 8.2 ± 1 h; consumed alcohol
on nonwork days = 52%; exercised >10 min on
nonwork days = 28%
BMI (self-report); not all instruments specified
Cahill et al.
(2021) [25]
Investigate the
relationship among
work-
related stress,
wellbeing, and
coping mechanisms
Cross-sectional;
subjective;
international
n = 821; 88%; <25
years = 5%, 25–
35
years = 27%, 36–
45
years = 31%, 46–
55
years = 26%, 56–
65
years = 12%
Mild depression = 40%; moderate-severity
depression = 4%; severe depression = 2%; regular
exercise (≥3 times per week) = 25%; perceived
regular sleep difficulties = 81%; regular work stress
digestive symptoms = 59%; regular work stress
induced psychosocial distress = 37%
Patient Health Questionnaire-9 (PHQ-9);
Oldenburg Burnout
Chairina et al.
(2018) [26]
Identify the risk
factors associated
with dyslipidemia
Cross-sectional;
subjective and
objective;
Indonesia
n
= 128; 100%; not
reported
Overweight = 20% and obesity = 65%; <150
MVPA
min per week = 71%; inappropriate or excessive
food intake = 66%; smoking = 45%; dyslipidemia =
62%; elevated TG = 29%; elevated LDL = 47%; low
HDL = 57%
Instruments not specified
Chen et al.
(2016) [27]
Evaluate metabolic
syndrome and
Cross-sectional;
objective; China
n
= 303; 100%; 35 ±
8 years
BMI = 23.6 ± 2.6; smoking = 33%; regular alcohol
drinker = 20%; metabolic syndrome =3 8%; elevated
Venous blood test; saliva test; periodontal
examination; blood pressure; waist
Int. J. Environ. Res. Public Health 2022, 19, 4848 16 of 26
periodontal disease
status
waist circumference = 64%, 87.6 ± 8.5 (cm); low
HDL-C levels = 46%, 1.2 ± 1.9 (mmol/L); elevated
fasting plasma glucose = 30%, 5.4 ± 0.6 (mmol/L);
high systolic BP = 28%, 124 ± 11 (mmHg); elevated
TG levels = 28%, 1.5 ± 0.8 (mmol/L); high diastolic
pressure = 17%, 79 ± 7 (mmHg)
circumference; BMI; Community Periodontal
Index
Choi and Kim
(2013) [3]
Evaluate the effects
of physical
examination and
diet consultation on
risk factors for CVD
Clinical trial;
subjective and
objective; Korea
n = 326; 100%; 30–
39 years = 47%, 40–
49 years = 33%, 50–
59 years = 20%
TC >220 mg/dL = 18%; TC (mg/dL) = 236 ± 13; HDL
(mg/dL) = 51 ± 11; LDL (mg/dL) = 155 ± 16; TG
(mg/dL) = 154 ± 81; BMI = 24.5 ± 2.1; weight (kg) =
73 ± 8;
SBP (mmHg) = 118 ± 12; DBP (mmHg) = 76 ±
9
BMI; venous blood test; blood pressure
Feijó et al.
(2012) [28]
Evaluate the
prevalence of
common mental
disorders and
related factors
Cross-sectional;
subjective; Brazil
n = 807; 92%; 46
years
Regular physical activity practice = 61%; common
mental disorders = 7% Self-Reporting Questionnaire—20 items
Flynn-
Evans et
al. (2018) [29]
Investigate work
schedule effects on
neurobehavioral
performance and
sleep
Cross-sectional;
subjective and
objective; USA
n
= 44; 91%; 31 ± 7
years
BMI = 24.2 ± 2.6; sleep = 6.8 ± 0.9 h; sleep latency
18%; sleep efficiency = 83%; smoking habit = 5%
Sleep diary; Psychomotor Vigilance Task;
Samn–Perelli fatigue scale; actigraphy
Guo et al.
(2017) [30]
Investigate the
effects of emotional
intelligence on
depression and
anxiety
Cross-sectional;
subjective; China
n
= 319; 100%; 31 ±
6 years
Mild depression = 24%; moderate depression = 1%;
mild anxiety = 4%; moderate anxiety = 0.3%
Trait Meta Mood Scale; Proactive Coping
Scale; The Patient Health Questionnaire
(PHQ-9); Generalized Anxiety Disorder-7
(GAD-7)
Han et al.
(2021) [31]
Investigate the
occurrence of
obstructive sleep
apnea
Cross-sectional;
subjective and
objective; Korea
n =
103; 100%; 44 ±
8 years
BMI = 24.6 ± 2.1; neck circumference = 38 ± 2 (cm);
OSA high risk = 32%
BMI; Epworth Sleepiness Scale (ESS); Berlin
questionnaire; neck circumference;
polysomnography; apnea–hypopnea index;
oxygen desaturation index; respiratory
disturbance index
Houston et al.
(2010) [32]
Identify the 10 year
absolute CVD risk
Cross-sectional;
subjective and
n = 14,379; 95%;
not reported
BMI = 26.0 (male) and 23.9 (female); overweight =
47% (male) and 28% (female); obesity = 12% (male)
BMI; blood pressure; not all instruments
specified
Int. J. Environ. Res. Public Health 2022, 19, 4848 17 of 26
of pilots using a
cardiovascular
disease risk
prediction model
objective; the
United Kingdom
and 6% (female); smoking = 8% (male) and 6%
(female); hypertension = 29% (male) and 14%
(female); population 10 year absolute CVD risk =
8% ± 7%; 10 year absolute CVD risk >20% (high
risk) was 9% for males and 0% for females
Huang et al.
(2013) [33]
Evaluate
distribution of
APOE gene
polymorphism,
dyslipidemia, and
overweight
Cross-sectional;
subjective and
objective; China
n
= 416; 100%; 39 ±
11 years
BMI = 24.2 ± 2.5; fasting glucose = 5.2 ± 0.6
(mmol/L); smoking = 54%; regular alcohol intake =
32%; total cholesterol = 4.6 ± 0.9 (mmol/L); LDL
(mmol/L) = 2.8 ± 0.8;
HDL = 1.3 ± 0.3 (mmol/L); TG =
1.6 ± 0.9 (mmol/L)
BMI; venous blood test; not all instruments
specified
Jackson and
Earl (2006) [34]
Evaluate fatigue
prevalence
Cross-sectional;
subjective; the
United Kingdom
n
= 162; 94%; 38 ± 9
years, range 21–
59
years
Global CFS fatigue score = 18 ± 5; severe fatigue on
the CFS = 75%; “fatigue worse than 2 years ago” =
81%; “feel tired with impaired judgement while
flying?” = 80%; “concerned with the level of fatigue
you experience?” = 78%
Chronic Fatigue Scale (CFS)
Lamp et al.
(2019) [35]
Evaluate sleep
timing and duration
Cross-sectional;
subjective and
objective; USA
n
= 92; 84%; 51 ± 9
years Sleep = 8.2 ± 1.7h Actigraphy
Li et al. (2021)
[36]
Investigate the
prevalence of
functional
gastrointestinal
disorders and
associated triggers
Cross-sectional;
subjective; China
n
= 212; 100%; 34 ±
7 years
BMI = 23.8 ± 2.4, range 19–
29; regular alcohol = 31%;
smoking = 49%; functional gastrointestinal disorder
prevalence = 39%
BMI (self-report); semi-quantitative food
frequency questionnaire (SQFFQ)
Lindgren et al.
(2012) [37]
Investigate
associations among
digestive symptoms
and diet, insomnia,
and lifestyle factors
Cross-sectional;
subjective;
Sweden
n = 354; 91%; 49 ±
6
years
Male BMI = 25.2; female BMI = 22.7; overall
overweight = 41% and obesity = 4%; smoking = 5%
BMI (self-report); not all instruments specified
Liu et al. (2021)
[38]
Investigate health-
related quality of
Cross-sectional;
subjective; China
n
= 373; 100%; 35 ±
8 years
BMI = 23.8 ± 2.2; hypertension = 7%; dyslipidemia =
19%; overweight = 46% and obesity = 3%; smoking =
39%; regular alcohol intake = 38%; physical activity
BMI (self-report); WHOQOL-BREF; not all
instruments specified
Int. J. Environ. Res. Public Health 2022, 19, 4848 18 of 26
life and its related
factors
days per week = 2 (range 1–3); vegetable intake
≤300 g per day = 19%; fruit intake ≤200
g per day =
33%; self-rated health (very poor or poor) = 13%;
self-rated quality of life (very poor or poor) = 8%;
self-
rated energy and fatigue (very poor or poor) =
6%
Marqueze et al.
(2017) [39]
Evaluate factors
associated with
unintentional sleep
at work of airline
pilots
Cross-sectional;
subjective; Brazil
n
= 1234; 97%; 39 ±
10 years
Smoking = 7%; regular alcohol = 75%; moderate
alcohol intake = 24%; harmful use of alcohol = 1%;
sleep 6.9 ± 1.2 h; unintentional sleep while on duty
= 58%; sleep quality “fairly or very bad” = 11%;
OSA high risk = 20%; excessive sleepiness = 42%
Alcohol Use Disorders Identification Test;
Karolinska Sleep Questionnaire; Berlin
questionnaire; Epworth Sleepiness Scale;
Work Ability Index
O’
Hagan et al.
(2017) [40]
Investigate the
differences in self-
reported depression
or anxiety
Cross-sectional;
subjective;
Europe
n = 701; 95%; not
reported
Depression or anxiety in the past 12 months
prevalence = 54%; working >41 h per week, sleep
disruption, elevated fatigue, and being female were
factors associated with higher probability of
reporting feeling depressed or anxious in the last 12
months
Internally validated questionnaire
Palmeira et al.
(2016) [41]
Identify the
prevalence and
associated factors of
overweight and
obesity
Cross-sectional;
subjective; Brazil
n
= 1198; 100%; 39
± 10 years, range
21–67 years
Overweight = 54% and obesity = 15%; factors
associated with obesity included ≤150 min of
weekly physical
activity, ≤6 h of sleep during days
off, sleepiness, and time of being a pilot were
associated with obesity
BMI (self-report); Karolinska Sleep
Questionnaire
Pellegrino and
Marqueze
(2019) [42];
Pellegrino et al.
(2019) [43]
Investigate the
association of work
organization and
sleep aspects with
work ability
Cross-sectional;
subjective; Brazil
n
=1234; 97%; 39 ±
10 years
<150 MVPA min per week = 50%; perceived
insufficient sleep = 32%; excessive sleepiness = 43%;
perceived of high fatigue = 27%; OSA high risk =
21%; poor sleep quality = 48%; poor sleep quality
was associated with shift characteristics, being
insufficiently physically active, and sleeping <6 h
on days off.
Karolinska Sleepiness Scale; Berlin
questionnaire; Epworth Sleepiness Scale;
Yoshitake
questionnaire; Work Ability Index;
Job Stress Scale; Need for Recovery Scale
Prombumroon
g et al. (2011)
[44]
Evaluate the
prevalence of lower-
back pain and
associated factors
Cross-sectional;
subjective;
Thailand
n
= 684; 100%; 40 ±
10 years
BMI = 24.3 ± 2.8; no regular exercise = 64%; lower-
back pain in the last 12 months = 56%
BMI (self-
report); Job Content Questionnaire
Thai version (JCQ Thai version); Nordic
questionnaire for lower-back pain
Int. J. Environ. Res. Public Health 2022, 19, 4848 19 of 26
Qiang et al.
(2004) [45]
Evaluate the
association of body
mass index with
cardiovascular
disease
Cross-sectional
and prospective;
subjective and
objective; USA
n =3019; 100%;
range 45–54 years
BMI = 27.2 ± 3.4; overweight = 55% and obesity =
7%; pilots who were overweight and obese had 6%
and 22% higher CVD risk, respectively
BMI; blood pressure
Reis et al.
(2013) [46
]; Reis
et al. (2016) [47]
Evaluate the
prevalence of
fatigue and compare
the differences
among fatigue,
sleep, and labor
specificities
Cross-sectional;
subjective;
Portugal
n
= 456; 97%; 39 ± 8
years
Total fatigue prevalence (FSS ≥4) = 89%; JSS ≥4 =
35.0%; excessive sleepiness = 59%; alcohol intake >3
times per week = 1%
Internally validated questionnaire; Fatigue
Severity Scale (FSS); Epworth Sleepiness Scale
(ESS); Jenkins Sleep Scale (JSS)
Roach et al.
(2012) [48]
Evaluate the impact
of work schedule on
the sleep and fatigue
Cross-sectional;
subjective and
objective;
Australia
n
= 19; 100%; 54 ± 2
years BMI = 25.0 ± 2.4; sleep hours = 7.2 h Samn–Perelli Fatigue Checklist; actigraphy;
not all instruments specified
Runeson-
Broberg and
Lindgren (2014)
[49]
Assess the
prevalence of
musculoskeletal
symptoms
Cross-sectional;
subjective;
Sweden
n = 354; 91%; 31-
40
years = 9%, 41–
50
years = 61%, 51–
60
years = 29%, 61+
years = 2%
Overweight = 41% and obesity = 4%; smokers = 5%
BMI (self-report); Nordic questionnaire for
analyzing musculoskeletal symptoms
Sallinen et al.
(2017) [50];
Sallinen et al.
(2021) [52]
Evaluate and
compare sleep
patterns,
sleepiness,
and management
strategies
Cross-sectional;
subjective and
objective; Finland
n
= 477; 100%; 43 ±
7 years BMI = 25.1 ± 2.9; sleep = 7 h 27 min ± 51 min
Actigraphy; Karolinska Sleepiness Scale; BMI
(self-report)
Sallinen et al.
(2020) [51]
Compare sleepiness
ratings of airline
pilot and truck
drivers
Cross-sectional;
subjective and
objective; Finland
n
= 33; 100%; 44 ± 7
years
Sleep = 7 h 48 min ± 56 min; BMI = 25.6 ± 3.6; KSS =
4.0
Actigraphy; Karolinska Sleepiness Scale; BMI
(self-report); not all instruments specified
Signal et al.
(2014) [53]
Evaluate the uptake
and effectiveness of
Cross-sectional;
objective; New
Zealand
n = 52; 100%; 55
years Sleep hours = 7.0 ± 1.2 h; sleep efficiency = 88 ± 5%
Actigraphy
Int. J. Environ. Res. Public Health 2022, 19, 4848 20 of 26
fatigue mitigation
guidance material
Sykes et al.
(2012) [6]
Compare the
prevalence of
medical conditions
and risk factors with
the general
population
Cross-sectional;
subjective and
objective; New
Zealand
n = 595; 97%; not
reported
BMI = 27.1; obesity prevalence = 20%; smoking =
2%; alcoholic drink per week = 5.4 Instruments not specified
Van Drongelen
et al. (2014)
[60]; Van
Drongelen et al.
(2017) [61]
Investigate the
effects of an
mHealth
intervention to
mitigate fatigue and
determine risk
factors for fatigue
Clinical trial;
subjective; the
Netherlands
n
= 502; 93%; 41 ± 8
years
BMI = 24.1 ± 2.3; alcohol intake several days per
week = 67%; smoking = 11.2%; CIS = 62 ± 22;
moderate physical activity (days p/w) = 3.3 ± 1.9;
strenuous physical activity (days p/w) = 2.0 ± 1.4;
number of snacks per duty = 4.6 ± 3.6; sleep quality
(1–20 scale) = 7.5 ± 3.9; sleep duration <6 h = 20%;
health perception (1–5 scale, higher value denotes
better health) = 3.4 ± 0.8; CIS fatigue prevalence =
30%
BMI (self-report); Checklist Individual
Strength (CIS); Need for Recovery scale;
Dutch Questionnaire on the Experience and
Evaluation of Work; Jenkins Sleep Scale;
Pittsburgh Sleep Quality Index; Short Form
36-item (SF-36) Health Survey
Venus and
Holtforth
(2021) [54]
Evaluate work
schedule effects on
fatigue risks on
flight duty, stress,
sleep problems,
fatigue severity,
wellbeing, and
mental health
Cross-sectional;
subjective;
International
n
= 406; 92%; 41 ±
11 years
PHQ stress = 5.0 ± 3.5; WHO5 PR (wellbeing) = 55 ±
20; PHQ-8 = 5.7 ± 4.4; SRQ-20 (common mental
disorders) = 3.9 ± 4.0; Fatigue Severity Scale = 4.5 ±
1.0; Jenkins Sleep Scale = 2.0 ± 1.1; high fatigue =
33% and severe fatigue = 42%; PHQ8 ≥10 = 19%;
GAD-7 = 3.9 ± 3.8; GAD7 ≥10 = 7.2%
Fatigue Severity Scale; Jenkins Sleep Scale;
WHO5; Self-Reporting Questionnaire-20
(SRQ20); Patient Health Questionnaire (PHQ-
8); Generalized Anxiety Disorder-7 (GAD-7)
Widyahening
(2007) [55]
Identify the effect of
work stressors and
other factors on
mental–emotional
disturbances among
airline pilots
Cross-sectional;
subjective;
Indonesia
n
= 109; 100%; <40
years = 65%, >40
years = 56%
Mental–
emotional disturbance = 39%; poor physical
conditions, high work stressors, and household
tension were associated with mental–emotional
disturbance
Symptom Checklist 90 (SCL90) questionnaire
Int. J. Environ. Res. Public Health 2022, 19, 4848 21 of 26
Wilson et al.
(2021) [62]
Evaluate the efficacy
of an intervention
for enhancing health
behaviors
Clinical trial;
subjective; New
Zealand
n
= 79; 82%; 42 ± 12
years
Sleep = 7.2 ± 0.5 h; PSQI global score = 5.4 ± 2.7;
weekly walking min = 110 ± 117; weekly MVPA
min = 145 ± 72; <150 MVPA min per week = 49%;
fruit and vegetable intake (servings/day) = 3.6 ± 0.9;
<2 fruit (servings/day) = 65%; <3 vegetables
(servings/day) = 47%; <5 fruit and vegetables
(servings/day) = 84%; physical health score (SF-
12v2) = 48 ± 7; mental health score (SF-12v2) = 51 ± 5
Pittsburgh Sleep Quality Index (PSQI);
International Physical Activity Questionnaire
(IPAQ) Short Form; Short Health Form 12v2
(SF-12v2); dietary recall
Wilson et al.
(2022) [56]
Explore the
prevalence and
distribution of
health risk factors in
airline pilots and
compare these with
the general
population
Cross-sectional;
subjective and
objective; New
Zealand
n
= 504; 91%; 46 ±
10 years
BMI = 26
.6; overweight = 51%; obesity = 16%; SBP =
131 ± 13; DBP = 81 ± 9; hypertension = 27%; sleep <7
h = 34%; sleep = 7 h 11 min; weekly MVPA = 141 ±
87; insufficient physical activity = 48%; physical
health score (SF-
12v2) = 47 ± 6; mental health score
(SF-12v2) = 49 ± 8; fruit and vegetable intake
(servings/day) = 3.7 ± 1.7; <2 fruit (servings/day) =
60%; <3 vegetables (servings/day) = 48%; <5 fruit
and vegetables (servings/day) = 68%; poor or fair
self-rated health = 25%
International Physical
Activity Questionnaire
(IPAQ) Short Form; Short Health Form 12v2
(SF-12v2); dietary recall
Wirawan et al.
(2013) [57]
Investigate the
prevalence of
excessive CVD risk
score
Cross-sectional;
subjective and
objective; Oceania
n
= 595; 100%; 46 ±
12 years
BMI = 26.5 ± 4.0; smoking = 2%; alcohol
consumption 5 ± 6 u/week; known hypertension =
6%; SBP = 128 ± 15; DBP = 78 ± 10; hyperlipidemia
history = 10%; TC = 5.3 ± 1.1; HDL = 1.3 ± 0.5; TG =
1.1 ± 0.8; cholesterol–HDL ratio = 3.9 ± 1.4; pilots
who were found to have 5 year CVD risk score of
10–15% or higher = 3.5%
Instruments not specified
Wu et al.
(2016a) [58]
Investigate the
prevalence of
depression
Cross-sectional;
subjective;
international
n
= 1826; 86%; not
reported
13% of males and 11% of females met depression
threshold; 4.1% reported suicidal thoughts within
the past two weeks; 5% reported experiencing
fatigue daily
Patient Health Questionnaire 9 (PHQ-9)
Wu et al.
(2016b) [59]
Characterize sleep
behaviors
Cross-sectional;
objective;
international
n = 332; 100%; 52
years, range 23–
64
years
Sleep = 7.6 h (self-report) and 6.8 h (objective);
sleep
≤6 h = 23%; sleep >9 h = 1% Actigraphy and self-report
Int. J. Environ. Res. Public Health 2022, 19, 4848 22 of 26
Note: AIS = Athens Insomnia Scale; BMI = body mass index; BP = blood pressure; CIS = Checklist Individual Strength; CRP = C-reactive protein; CSF = Chronic
Fatigue Scale; CVD = cardiovascular disease; DBP = diastolic blood pressure; ESS = Epworth Sleepiness Scale; FSS = Fatigue Severity Scale; GAD-7 = Generalized
Anxiety Disorder-7; HADS = Hospital Anxiety and Depression Scale; HDL = high-density lipoprotein; IPAQ = International Physical Activity Questionnaire; JSS =
Jenkins Sleep Scale; KSS = Karolinska Sleepiness Scale; LDL = low-density lipoprotein; mmHg = millimeters of mercury; MS = metabolic syndrome; MVPA =
moderate-to-vigorous physical activity; OSA = obstructive sleep apnea; PHQ-8 = Patient Health Questionnaire 8; PHQ-9 = Patient Health Questionnaire 9; PSQI =
Pittsburgh Sleep Quality Index; SBP = systolic blood pressure; SCL90 = Symptom Checklist 90; SF-36 = Short Form 36-item Health Survey; SLS = Satisfaction with
Life Scale; SRQ20 = Self-Reporting Questionnaire-20; TG = triglycerides; VAFS = Visual Analog Fatigue Scale; WHOQOL-BREF = World Health Organization
Quality of Life Brief Form.
Int. J. Environ. Res. Public Health 2022, 19, 4848 22 of 26
References
1. Miranda, J.J.; Barrientos-Gutiérrez, T.; Corvalan, C.; Hyder, A.A.; Lazo-Porras, M.; Oni, T.; Wells, J.C.K. Understanding the rise
of cardiometabolic diseases in low- and middle-income countries. Nat. Med. 2019, 25, 1667–1679. https://doi.org/10.1038/s41591-
019-0644-7.
2. Townsend, N.; Wilson, L.; Bhatnagar, P.; Wickramasinghe, K.; Rayner, M.; Nichols, M. Cardiovascular disease in Europe:
Epidemiological update 2016. Eur. Heart J. 2016, 37, 3232–3245. https://doi.org/10.1093/eurheartj/ehw334.
3. Choi, Y.Y.; Kim, K.Y. Effects of physical examination and diet consultation on serum cholesterol and health-behavior in the
Korean pilots employed in commercial airline. Ind. Health 2013, 51, 603–611. https://doi.org/10.2486/indhealth.2012-0027.
4. Evans, S.; Radcliffe, S.A. The annual incapacitation rate of commercial pilots. Aviat. Space Environ. Med. 2012, 83, 42–49.
https://doi.org/10.3357/asem.3134.2012.
5. International Civial Aviation Authority. Manual of Civil Aviation Medicine, 3rd ed.; International Civial Aviation Authority:
Quebec, Canada, 2012; p. 580.
6. Sykes, A.J.; Larsen, P.D.; Griffiths, R.F.; Aldington, S. A Study of Airline Pilot Morbidity. Aviat. Space Environ. Med. 2012, 83,
1001–1005. https://doi.org/10.3357/asem.3380.2012.
7. Wilson, D.; Driller, M.; Winwood, P.; Johnston, B.; Gill, N. The Effects of a Brief Lifestyle Intervention on the Health of
Overweight Airline Pilots during COVID-19: A 12-Month Follow-Up Study. Nutrients 2021, 13, 4288.
https://doi.org/10.3390/nu13124288.
8. Coombes, C.; Whale, A.; Hunter, R.; Christie, N. Sleepiness on the flight deck: Reported rates of occurrence and predicted
fatigue risk exposure associated with UK airline pilot work schedules. Saf. Sci. 2020, 129, 104833.
https://doi.org/10.1016/j.ssci.2020.104833.
9. Eckel, R.H.; Grundy, S.M.; Zimmet, P.Z. The metabolic syndrome. Lancet 2005, 365, 1415–1428. https://doi.org/10.1016/S0140-
6736(05)66378-7.
10. Marmot, M.; Bell, R. Social determinants and non-communicable diseases: Time for integrated action. BMJ 2019, 364, l251.
https://doi.org/10.1136/bmj.l251.
11. Basu, N.; Yang, X.; Luben, R.N.; Whibley, D.; Macfarlane, G.J.; Wareham, N.J.; Khaw, K.-T.; Myint, P.K. Fatigue is associated
with excess mortality in the general population: Results from the EPIC-Norfolk study. BMC Med. 2016, 14, 122.
https://doi.org/10.1186/s12916-016-0662-y.
12. Lasserre, A.M.; Strippoli, M.P.F.; Glaus, J.; Gholam-Rezaee, M.; Vandeleur, C.L.; Castelao, E.; Marques-Vidal, P.; Waeber, G.;
Vollenweider, P.; Preisig, M. Prospective associations of depression subtypes with cardio-metabolic risk factors in the general
population. Mol. Psychiatry 2017, 22, 1026–1034. https://doi.org/10.1038/mp.2016.178.
13. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.;
Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, n71.
https://doi.org/10.1136/bmj.n71.
14. Hoy, D.; Brooks, P.; Woolf, A.; Blyth, F.; March, L.; Bain, C.; Baker, P.; Smith, E.; Buchbinder, R. Assessing risk of bias in
prevalence studies: Modification of an existing tool and evidence of interrater agreement. J. Clin. Epidemiol. 2012, 65, 934–939.
https://doi.org/10.1016/j.jclinepi.2011.11.014.
15. Sterne, J.A.C.; Savović, J.; Page, M.J.; Elbers, R.G.; Blencowe, N.S.; Boutron, I.; Cates, C.J.; Cheng, H.-Y.; Corbett, M.S.; Eldridge,
S.M.; et al. RoB 2: A revised tool for assessing risk of bias in randomised trials. BMJ 2019, 366, l4898.
https://doi.org/10.1136/bmj.l4898.
16. Cochran, W.G. The Combination of Estimates from Different Experiments. Biometrics 1954, 10, 101–129.
https://doi.org/10.2307/3001666.
17. Akerstedt, T.; Klemets, T.; Karlsson, D.; Habel, H.; Widman, L.; Sallinen, M. Acute and cumulative effects of scheduling on
aircrew fatigue in ultra-short-haul operations. J. Sleep Res. 2021, 30, e13305. https://doi.org/10.1111/jsr.13305.
18. Albermann, M.; Lehmann, M.; Eiche, C.; Schmidt, J.; Prottengeier, J. Low Back Pain in Commercial Airline Pilots. Aerosp. Med.
Hum. Perform. 2020, 91, 940–947. https://doi.org/10.3357/amhp.5656.2020.
19. Alhejaili, F.; Hafez, A.; Wali, S.; Alshumrani, R.; Alzehairi, A.M.; Balkhyour, M.; Pandi-Perumal, S.R. Prevalence of Obstructive
Sleep Apnea Among Saudi Pilots. Nat. Sci. Sleep 2021, 13, 537–545. https://doi.org/10.2147/nss.S299382.
20. Aljurf, T.M.; Olaish, A.H.; BaHammam, A.S. Assessment of sleepiness, fatigue, and depression among Gulf Cooperation
Council commercial airline pilots. Sleep Breath. 2018, 22, 411–419. https://doi.org/10.1007/s11325-017-1565-7.
21. Alonso-Rodriguez, C.; Medina-Font, J. High Sensitivity C-Reactive Protein in Airline Pilots with Metabolic Syndrome. Aviat.
Space Environ. Med. 2012, 83, 504–508. https://doi.org/10.3357/asem.3004.2012.
22. Ariani, C.; Soemarko, D.S.; Yuliawati, I.; Basuki, B. Flight hours of unplanned flight and other risk factors affecting exercise
habit among commercial pilots in Indonesia. Health Sci. J. Indones. 2016, 8, 36–42. https://doi.org/10.22435/hsji.v8i1.5341.36-42.
23. Bhat, K.G.; Verma, N.; Pant, P.; Marwaha, M.P.S. Hypertension and obesity among civil aviation pilots. Aerosp. Med. Hum.
Perform. 2019, 90, 703–708. https://doi.org/10.3357/AMHP.5374.2019.
24. Bostock, S.; Steptoe, A. Influences of early shift work on the diurnal cortisol rhythm, mood and sleep: Within-subject variation
in male airline pilots. Psychoneuroendocrinology 2013, 38, 533–541. https://doi.org/10.1016/j.psyneuen.2012.07.012.
Int. J. Environ. Res. Public Health 2022, 19, 4848 23 of 26
25. Cahill, J.; Cullen, P.; Anwer, S.; Wilson, S.; Gaynor, K. Pilot Work Related Stress (WRS), Effects on Wellbeing and Mental Health,
and Coping Methods. Int. J. Aerosp. Psychol. 2021, 31, 87–109. https://doi.org/10.1080/24721840.2020.1858714.
26. Chairina, N.; Werdhani, R.A.; Gathmyr, D. Association of total flight hours with lipid blood profiles among civilian pilots in
Indonesia. J. Phys. Conf. Ser. 2018, 1073, 042012. https://doi.org/10.1088/1742-6596/1073/4/042012.
27. Chen, X.; Xie, L.; Liu, Y.; Chen, D.J.; Yu, Q.; Gan, X.Q.; Yu, H.Y. Metabolic Syndrome and Periodontal Disease Among Civilian
Pilots. Aerosp. Med. Hum. Perform. 2016, 87, 1016–1020. https://doi.org/10.3357/amhp.4654.2016.
28. Feijo, D.; Luiz, R.R.; Camara, V.M. Common Mental Disorders Among Civil Aviation Pilots. Aviat. Space Environ. Med. 2012, 83,
509–513. https://doi.org/10.3357/asem.31.85.2012.
29. Flynn-Evans, E.E.; Arsintescu, L.; Gregory, K.; Mulligan, J.; Nowinski, J.; Feary, M. Sleep and neurobehavioral performance
vary by work start time during non-traditional day shifts. Sleep Health 2018, 4, 476–484. https://doi.org/10.1016/j.sleh.2018.08.002.
30. Guo, Y.N.; Ji, M.; You, X.Q.; Huang, J. Protective Effects of Emotional Intelligence and Proactive Coping on Civil Pilots’ Mental
Health. Aerosp. Med. Hum. Perform. 2017, 88, 858–865. https://doi.org/10.3357/amhp.4799.2017.
31. Han, S.H.; Lee, G.Y.; Hyun, W.; Kim, Y.; Jang, J.S. Obstructive sleep apnea in airline pilots during daytime sleep following
overnight flights. J. Sleep Res. 2021, 30, e13375. https://doi.org/10.1111/jsr.13375.
32. Houston, S.; Mitchell, S.; Evans, S. Application of a Cardiovascular Disease Risk Prediction Model Among Commercial Pilots.
Aviat. Space Environ. Med. 2010, 81, 768–773. https://doi.org/10.3357/asem.2748.2010.
33. Huang, H.; Liu, J.; Feng, Y.J.; Chen, W.R. The distribution of apolipoprotein E gene polymorphism in Chinese civil aircrews,
and a possible risk factor to their overweight and dyslipidemia is cumulative flight time. Clin. Chim. Acta 2013, 416, 36–40.
https://doi.org/10.1016/j.cca.2012.10.049.
34. Jackson, C.A.; Earl, L. Prevalence of fatigue among commercial pilots. Occup. Med.-Oxf. 2006, 56, 263–268.
https://doi.org/10.1093/occmed/kql021.
35. Lamp, A.; McCullough, D.; Chen, J.M.C.; Brown, R.E.; Belenky, G. Pilot Sleep in Long-Range and Ultra-Long-Range Commercial
Flights. Aerosp. Med. Hum. Perform. 2019, 90, 109–115. https://doi.org/10.3357/amhp.5117.2019.
36. Li, C.; Xu, J.R.; Yin, D.W.; Zhang, Y.H.; Shan, D.Z.; Jiang, X.; Shang, L. Prevalence and trigger factors of functional
gastrointestinal disorders among male civil pilots in China. Sci. Rep. 2021, 11, 2021. https://doi.org/10.1038/s41598-021-81825-0.
37. Lindgren, T.; Runeson, R.; Wahlstedt, K.; Wleslander, G.; Dammstrom, B.G.; Norback, D. Digestive Functional Symptoms
Among Commercial Pilots in Relation to Diet, Insomnia, and lifestyle Factors. Aviat. Space Environ. Med. 2012, 83, 872–878.
https://doi.org/10.3357/asem.3309.2012.
38. Liu, T.B.; Qiu, B.; Zhang, C.Y.; Deng, M.Z.; Liang, Z.H.; Qi, Y.M. Health-related quality of life in pilots of a Chinese commercial
airline. Arch. Environ. Occup. Health 2021, 76, 511–517. https://doi.org/10.1080/19338244.2020.1863765.
39. Marqueze, E.C.; Nicola, A.C.B.; Diniz, D.; Fischer, F.M. Working hours associated with unintentional sleep at work among
airline pilots. Rev. De Saude Publica 2017, 51, 61. https://doi.org/10.1590/s1518-8787.2017051006329.
40. O’Hagan, A.D.; Issartel, J.; Nevill, A.; Warrington, G. Flying Into Depression: Pilot’s Sleep and Fatigue Experiences Can Explain
Differences in Perceived Depression and Anxiety Associated With Duty Hours. Workplace Health Saf. 2017, 65, 109–117.
https://doi.org/10.1177/2165079916659506.
41. Palmeira, M.L.D.; Marqueze, E.C. Excess weight in regular aviation pilots associated with work and sleep characteristics. Sleep
Sci. 2016, 9, 266–271. https://doi.org/10.1016/j.slsci.2016.12.001.
42. Pellegrino, P.; Marqueze, E.C. Aspects of work and sleep associated with work ability in regular aviation pilots. Rev. De Saude
Publica 2019, 53, 31. https://doi.org/10.11606/S1518-8787.2019053000345.
43. Pellegrino, P.; Moreno, C.R.D.; Marqueze, E.C. Aspects of work organization and reduced sleep quality of airline pilots. Sleep
Sci. 2019, 12, 43–48. https://doi.org/10.5935/1984-0063.20190053.
44. Prombumroong, J.; Janwantanakul, P.; Pensri, P. Prevalence of and Biopsychosocial Factors Associated with Low Back Pain in
Commercial Airline Pilots. Aviat. Space Environ. Med. 2011, 82, 879–884. https://doi.org/10.3357/asem.3044.2011.
45. Qiang, Y.; Li, G.; Rebok, G.W.; Baker, S.P. Body mass index and cardiovascular disease in a birth cohort of commuter air carrier
and air taxi pilots. Ann. Epidemiol. 2005, 15, 247–252. https://doi.org/10.1016/j.annepidem.2004.08.002.
46. Reis, C.; Mestre, C.; Canhao, H. Prevalence of Fatigue in a Group of Airline Pilots. Aviat. Space Environ. Med. 2013, 84, 828–833.
https://doi.org/10.3357/asem.3548.2013.
47. Reis, C.; Mestre, C.; Canhao, H.; Gradwell, D.; Paiva, T. Sleep and Fatigue Differences in the Two Most Common Types of
Commercial Flight Operations. Aerosp. Med. Hum. Perform. 2016, 87, 811–815. https://doi.org/10.3357/amhp.4629.2016.
48. Roach, G.D.; Petrilli, R.M.A.; Dawson, D.; Lamond, N. Impact of Layover Length on Sleep, Subjective Fatigue Levels, and
Sustained Attention of Long-Haul Airline Pilots. Chronobiol. Int. 2012, 29, 580–586. https://doi.org/10.3109/07420528.2012.675222.
49. Runeson-Broberg, R.; Lindgren, T.; Norback, D. Musculoskeletal symptoms and psychosocial work environment, among
Swedish commercial pilots. Int. Arch. Occup. Environ. Health 2014, 87, 685–693. https://doi.org/10.1007/s00420-013-0911-8.
50. Sallinen, M.; Sihvola, M.; Puttonen, S.; Ketola, K.; Tuori, A.; Harma, M.; Kecklund, G.; Akerstedt, T. Sleep, alertness and alertness
management among commercial airline pilots on short-haul and long-haul flights. Accid. Anal. Prev. 2017, 98, 320–329.
https://doi.org/10.1016/j.aap.2016.10.029.
51. Sallinen, M.; Pylkkonen, M.; Puttonen, S.; Sihvola, M.; Akerstedt, T. Are long-haul truck drivers unusually alert? A comparison
with long-haul airline pilots. Accid. Anal. Prev. 2020, 137, 105442. https://doi.org/10.1016/j.aap.2020.105442.
52. Sallinen, M.; Onninen, J.; Ketola, K.; Puttonen, S.; Tuori, A.; Virkkala, J.; Akerstedt, T. Self-reported reasons for on-duty
sleepiness among commercial airline pilots. Chronobiol. Int. 2021, 38, 1308–1318. https://doi.org/10.1080/07420528.2021.1927071.
Int. J. Environ. Res. Public Health 2022, 19, 4848 24 of 26
53. Signal, T.L.; Mulrine, H.M.; van den Berg, M.J.; Smith, A.A.; Gander, P.H.; Serfontein, W. Mitigating and monitoring flight crew
fatigue on a westward ultra-long-range flight. Aviat. Space Environ. Med. 2014, 85, 1199–1208.
https://doi.org/10.3357/ASEM.4034.2014.
54. Venus, M.; Holtforth, M.G. Short and Long Haul Pilots Rosters, Stress, Sleep Problems, Fatigue, Mental Health, and Well-Being.
Aerosp. Med. Hum. Perform. 2021, 92, 786–797. https://doi.org/10.3357/AMHP.5812.2021.
55. Widyahening, I.S. High level of work stressors increase the risk of mental-emotional disturbances among airline pilots. Med. J.
Indones. 2007, 16, 117–121. https://doi.org/10.13181/mji.v16i2.267.
56. Wilson, D.; Driller, M.; Johnston, B.; Gill, N. The prevalence and distribution of health risk factors in airline pilots: A cross-
sectional comparison with the general population. Aust. N. Z. J. Public Health 2022. https://doi.org/10.1111/1753-6405.13231.
57. Wirawan, I.M.A.; Aldington, S.; Griffiths, R.F.; Ellis, C.J.; Larsen, P.D. Cardiovascular Investigations of Airline Pilots with
Excessive Cardiovascular Risk. Aviat. Space Environ. Med. 2013, 84, 608–612. https://doi.org/10.3357/asem.3465.2013.
58. Wu, A.C.; Donnelly-McLay, D.; Weisskopf, M.G.; McNeely, E.; Betancourt, T.S.; Allen, J.G. Airplane pilot mental health and
suicidal thoughts: A cross-sectional descriptive study via anonymous web-based survey. Environ. Health A Glob. Access Sci.
Source 2016, 15, 121–121. https://doi.org/10.1186/s12940-016-0200-6.
59. Wu, L.J.; Gander, P.H.; van den Berg, M.J.; Signal, T.L. Estimating long-haul airline pilots’ at-home baseline sleep duration. Sleep
Health 2016, 2, 143–145. https://doi.org/10.1016/j.sleh.2016.02.004.
60. van Drongelen, A.; Boot, C.R.L.; Hlobil, H.; Twisk, J.W.R.; Smid, T.; van der Beek, A.J. Evaluation of an mHealth intervention
aiming to improve health-related behavior and sleep and reduce fatigue among airline pilots. Scand. J. Work Environ. Health
2014, 40, 557–568. https://doi.org/10.5271/sjweh.3447.
61. van Drongelen, A.; Boot, C.R.L.; Hlobil, H.; Smid, T.; van der Beek, A.J. Risk factors for fatigue among airline pilots. Int. Arch.
Occup. Environ. Health 2017, 90, 39–47. https://doi.org/10.1007/s00420-016-1170-2.
62. Wilson, D.; Driller, M.; Ben, J.; Gill, N. The effectiveness of a 17-week lifestyle intervention on health behaviors among airline
pilots during COVID-19. J. Sport Health Sci. 2021, 10, 333–340. https://doi.org/10.1016/j.jshs.2020.11.007.
63. GBD 2015 Obesity Collaborators. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N. Engl. J. Med. 2017,
377, 13–27. https://doi.org/10.1056/NEJMoa1614362.
64. De Stavola, B.L.; Pizzi, C.; Clemens, F.; Evans, S.A.; Evans, A.D.; Silva, I.D. Cause-specific mortality in professional flight crew
and air traffic control officers: Findings from two UK population-based cohorts of over 20,000 subjects. Int. Arch. Occup. Environ.
Health 2012, 85, 283–293. https://doi.org/10.1007/s00420-011-0660-5.
65. Pizzi, C.; Evans, S.A.; De Stavola, B.L.; Evans, A.; Clemens, F.; Silva, I.D. Lifestyle of UK commercial aircrews relative to air
traffic controllers and the general population. Aviat. Space Environ. Med. 2008, 79, 964–974.
https://doi.org/10.3357/asem.2315.2008.
66. Janssen, I.; Katzmarzyk, P.T.; Ross, R. Body mass index, waist circumference, and health risk: Evidence in support of current
National Institutes of Health guidelines. Arch. Intern. Med. 2002, 162, 2074–2079. https://doi.org/10.1001/archinte.162.18.2074.
67. Mills, K.T.; Bundy, J.D.; Kelly, T.N.; Reed, J.E.; Kearney, P.M.; Reynolds, K.; Chen, J.; He, J. Global Disparities of Hypertension
Prevalence and Control. Circulation 2016, 134, 441–450, doi:doi:10.1161/CIRCULATIONAHA.115.018912.
68. Kopin, L.; Lowenstein, C.J. Dyslipidemia. Ann. Intern. Med. 2017, 167, ITC81–ITC96. https://doi.org/10.7326/AITC201712050.
69. Cho, N.H.; Shaw, J.E.; Karuranga, S.; Huang, Y.; da Rocha Fernandes, J.D.; Ohlrogge, A.W.; Malanda, B. IDF Diabetes Atlas:
Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res. Clin. Pract. 2018, 138, 271–281.
https://doi.org/10.1016/j.diabres.2018.02.023.
70. Russell-Jones, D.L.; Hutchison, E.J.; Roberts, G.A. Pilots flying with insulin-treated diabetes. Diabetes Obes. Metab. 2021, 23, 1439–
1444. https://doi.org/10.1111/dom.14375.
71. Wingelaar-Jagt, Y.Q.; Wingelaar, T.T.; Riedel, W.J.; Ramaekers, J.G. Fatigue in Aviation: Safety Risks, Preventive Strategies and
Pharmacological Interventions. Front. Physiol. 2021, 12, 712628. https://doi.org/10.3389/fphys.2021.712628.
72. Hirshkowitz, M.; Whiton, K.; Albert, S.M.; Alessi, C.; Bruni, O.; DonCarlos, L.; Hazen, N.; Herman, J.; Adams Hillard, P.J.; Katz,
E.S.; et al. National Sleep Foundation’s updated sleep duration recommendations: Final report. Sleep Health 2015, 1, 233–243.
https://doi.org/10.1016/j.sleh.2015.10.004.
73. Ford, E.S.; Cunningham, T.J.; Croft, J.B. Trends in Self-Reported Sleep Duration among US Adults from 1985 to 2012. Sleep 2015,
38, 829–832. https://doi.org/10.5665/sleep.4684.
74. Petrie, K.J.; Powell, D.; Broadbent, E. Fatigue self-management strategies and reported fatigue in international pilots. Ergonomics
2004, 47, 461–468. https://doi.org/10.1080/0014013031000085653.
75. Reitsma, M.B.; Fullman, N.; Ng, M.; Salama, J.S.; Abajobir, A.; Abate, K.H.; Abbafati, C.; Abera, S.F.; Abraham, B.; Abyu, G.Y.;
et al. Smoking prevalence and attributable disease burden in 195 countries and territories, 1990–2015: A systematic analysis
from the Global Burden of Disease Study 2015. Lancet 2017, 389, 1885–1906. https://doi.org/10.1016/S0140-6736(17)30819-X.
76. Guthold, R.; Stevens, G.A.; Riley, L.M.; Bull, F.C. Worldwide trends in insufficient physical activity from 2001 to 2016: A pooled
analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob. Health 2018, 6, e1077–e1086.
https://doi.org/10.1016/S2214-109X(18)30357-7.
77. Manthey, J.; Shield, K.D.; Rylett, M.; Hasan, O.S.M.; Probst, C.; Rehm, J. Global alcohol exposure between 1990 and 2017 and
forecasts until 2030: A modelling study. Lancet 2019, 393, 2493–2502. https://doi.org/10.1016/S0140-6736(18)32744-2.
78. Kraus, C.K.; Li, G. Pilot alcohol violations reported in U.S. newspapers, 1990-2006. Aviat. Space Environ. Med. 2006, 77, 1288–
1290.
Int. J. Environ. Res. Public Health 2022, 19, 4848 25 of 26
79. Aune, D.; Giovannucci, E.; Boffetta, P.; Fadnes, L.T.; Keum, N.; Norat, T.; Greenwood, D.C.; Riboli, E.; Vatten, L.J.; Tonstad, S.
Fruit and vegetable intake and the risk of cardiovascular disease, total cancer and all-cause mortality—A systematic review and
dose-response meta-analysis of prospective studies. Int. J. Epidemiol. 2017, 46, 1029–1056. https://doi.org/10.1093/ije/dyw319.
80. Murphy, M.M.; Barraj, L.M.; Spungen, J.H.; Herman, D.R.; Randolph, R.K. Global assessment of select phytonutrient intakes by
level of fruit and vegetable consumption. Br. J. Nutr. 2014, 112, 1004–1018. https://doi.org/10.1017/S0007114514001937.
