The Association of Sleep Duration and Sleep Quality With Depression and Anxiety Among Chinese Commercial Pilots

Abstract

Background: Sleep problems are known as risk factors for depression and anxiety, but research on this subject with commercial pilots is limited. This study aimed to explore the effects of sleep problems on depressive and anxiety symptoms among Chinese commercial pilots.
Methods: Adults who participated in the baseline assessment of the Civil Aviation Health Cohort of China between December 2022 and March 2023 formed the study sample. Depressive and anxiety symptoms and sleep quality were assessed using standardized scales. Sleep duration was measured with standardized questions. Logistic regression and restricted cubic splines (RCSs) were used to analyze the association between sleep problems and depression/anxiety symptoms.
Results: A total of 7055 pilots were included in this study. The overall prevalence of depression and anxiety among pilots was 23.3% (n = 1642; 95% confidence interval [CI] = 22.3%–24.3%) and 17.0% (n = 1196; 95% CI = 16.1–17.8%), respectively. Logistic regression analyses revealed that short sleep duration (<7 h) was significantly associated with a higher risk of depression (odds ratio [OR] = 2.491; p <0.001) and anxiety (OR = 2.555; p <0.001), while poor sleep quality was also associated with a higher risk of depression (OR = 7.297; p <0.001) and anxiety (OR = 7.469; p <0.001). After adjusting for confounders, there was an inverse, J-shaped nonlinear relationship between sleep duration and both depression (inflection point: 7.64 h) and anxiety (inflection point: 7.48 h). Similarly, a J-shaped nonlinear relationship was found between sleep quality and depression/anxiety with an inflection point of Pittsburgh Sleep Quality Index (PSQI) = 4 points for both. The major limitation of the study was that causal relationships between variables could not be inferred due to the cross-sectional study design.
Conclusion: This study found that depression and anxiety were common among Chinese commercial pilots. Insufficient length and poor quality of sleep were associated with an increased risk of depression and anxiety. Implementing targeted strategies to improve sleep patterns is crucial for reducing the risk of depression and anxiety in this population.

Full text

Research Article
The Association of Sleep Duration and Sleep Quality With
Depression and Anxiety Among Chinese Commercial Pilots
Pan Chen ,
1,2
He-Li Sun ,
1,2
Yuan Feng ,
3
Qinge Zhang ,
3
Tong Leong Si ,
1,2
Zhaohui Su ,
4
Teris Cheung ,
5
Gabor S. Ungvari ,
6,7
Erliang Zhang ,
8
Minzhi Chen,
8
Jie Zhang,
8
Lin Zhang,
9
Bin Ren,
9
Qingqing Jin,
9
Robert D. Smith ,
1,2
Mi Xiang ,
10
and
Yu-Tao Xiang
1,2
1
Unit of Psychiatry, Department of Public Health and Medicinal Administration, Institute of Translational Medicine,
Faculty of Health Sciences, University of Macau, Macao SAR, China
2
Centre for Cognitive and Brain Sciences, University of Macau, Macao SAR, China
3
Beijing Key Laboratory of Mental Disorders,
National Clinical Research Center for Mental Disorders and National Center for Mental Disorders,
Beijing Anding Hospital, Capital Medical University, Beijing, China
4
School of Public Health, Southeast University, Nanjing, China
5
School of Nursing, Hong Kong Polytechnic University, Hong Kong SAR, China
6
Section of Psychiatry, University of Notre Dame Australia, Fremantle, Australia
7
Division of Psychiatry, School of Medicine, University of Western Australia, Perth, Australia
8
School of Public Health, Shanghai Jiao Tong University, Shanghai, China
9
CAAC East China Aviation Personnel Medical Appraisal Center, Shanghai 200336, China
10
Hainan Branch, Shanghai Children’s Medical Center, School of Medicine, Sanya and School of Public Health,
Shanghai Jiao Tong University, Shanghai, China
Correspondence should be addressed to Mi Xiang; xiang-sjtu@hotmail.com and Yu-Tao Xiang; xyutly@gmail.com
Received 22 May 2024; Accepted 28 October 2024
Academic Editor: Sizhi Ai
Copyright ©2024 Pan Chen et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background: Sleep problems are known as risk factors for depression and anxiety, but research on this subject with commercial pilots is
limited. This study aimed to explore the effects of sleep problems on depressive and anxiety symptoms among Chinese commercial pilots.
Methods: Adults who participated in the baseline assessment of the Civil Aviation Health Cohort of China between December
2022 and March 2023 formed the study sample. Depressive and anxiety symptoms and sleep quality were assessed using
standardized scales. Sleep duration was measured with standardized questions. Logistic regression and restricted cubic splines
(RCSs) were used to analyze the association between sleep problems and depression/anxiety symptoms.
Results: A total of 7055 pilots were included in this study. The overall prevalence of depression and anxiety among pilots was 23.3%
(n=1642; 95%confidence interval [CI] =22.3%–24.3%) and 17.0%(n=1196; 95%CI =16.1–17.8%), respectively. Logistic regres-
sion analyses revealed that short sleep duration (<7 h) was significantly associated with a higher risk of depression (odds ratio
[OR] =2.491; p<0:001) and anxiety (OR =2.555; p<0:001), while poor sleep quality was also associated with a higher risk of
depression (OR =7.297; p<0:001) and anxiety (OR =7.469; p<0:001). After adjusting for confounders, there was an inverse,
J-shaped nonlinear relationship between sleep duration and both depression (inflection point: 7.64 h) and anxiety (inflection point:
7.48 h). Similarly, a J-shaped nonlinear relationship was found between sleep quality and depression/anxiety with an inflection
point of Pittsburgh Sleep Quality Index (PSQI) =4 points for both. The major limitation of the study was that causal relationships
between variables could not be inferred due to the cross-sectional study design.
Wiley
Depression and Anxiety
Volume 2024, Article ID 9920975, 11 pages
https://doi.org/10.1155/da/9920975

Conclusion: This study found that depression and anxiety were common among Chinese commercial pilots. Insufficient length
and poor quality of sleep were associated with an increased risk of depression and anxiety. Implementing targeted strategies to
improve sleep patterns is crucial for reducing the risk of depression and anxiety in this population.
Keywords: anxiety; commercial pilots; depression; sleep duration; sleep quality
1. Introduction
The mental health of special occupational populations has
gained increased attention, particularly among those closely
associated with public safety, such as pilots, healthcare work-
ers, police officers, and bus drivers. Air crashes caused by
human factors have alerted the public about the need to pay
attention to the mental health of pilots. For instance, in the
case of “Germanwings Flight 4U 9525,”all 150 people on
board died tragically due to deliberate maneuvering by the first
officer, who had a relapse of depression [1]. Another one, in
the case of “JetBlue Flight 191,”the first officer timely recog-
nized the suspicious behavior of the captain who appeared to
be having a panic attack, thus ensuring the safety of the pas-
sengers [2, 3]. Depressive symptoms and anxiety symptoms
were commonly found in pilots [4, 5]. A review concluded that
the global prevalence of depression among pilots ranged from
1.9%to 12.6%[6]. A Chinese survey reported that 26.2%of pilots
experienced anxiety symptoms [5]. High workload, extended
duty hours [7], sudden air crash events (i.e., China Eastern
Airlines Flight 5735) [8], and the emergent public health issues
(i.e., the COVID-19 pandemic) in recent years as other stressors
may have contributed to elevated risk of depression and anxiety
[9, 10], which could lead to a number of adverse health outcomes
and poor quality of life (QOL) [2]. However, to date, only a few
studies have investigated the prevalence of depression and anxi-
ety among commercial pilots.
Sleep problems have also been a great concern among
pilots, which are not only manifested as symptoms but also
acted as risk factors for mental health problems. Both the
quantity and quality of sleep were crucial for maintaining
normal or productive work and personal lives [11], especially
for people who have heavy workloads and shift work [12].
Previous surveys found high levels of fatigue, sleep problems,
and mental health issues among both short- and long-haul
pilots [13, 14]. A previous study on pilots showed that up to
half of them were at risk of developing insomnia [15], which
would jeopardize aviation safety. Abnormal sleep duration
(i.e., either less or more sleep) could increase the risk of
accidental injury and death [16, 17] and affect QOL and
cognitive function [18, 19]. Moreover, poor sleep quality is
associated with increased risk of various adverse physical and
psychological health outcomes such as obesity, cardiovascular
diseases, depression, anxiety, and even suicidality [20–22].
Sleep problems, depression, and anxiety could interact
with each other [23]; therefore, understanding their relation-
ships was crucial for developing preventive or treatment strat-
egies and allocating health resources. To date, studies have
examined the relationship between sleep problems and
depression and anxiety across various populations, such as
children and adolescents, older adults, university students,
and the general population [20, 23–27]. Previous studies
reported inconsistent findings, which may be partly due to
different sampling methods and differing sociocultural con-
texts. Several studies on the linear relationship between sleep
problems and levels of depression and anxiety have reported
a negative association between shorter sleep duration and
depression and anxiety [28, 29]. However, recent evidence
has revealed a nonlinear relationship between sleep distur-
bances and depression and anxiety [24, 26, 30, 31]. For
instance, a study conducted in older Chinese adults found a
U-shaped relationship between nighttime sleep duration and
depression, as well as a J-shaped relationship between sleep
quality and depression [32]. These findings suggest that both
inadequate and excessive sleep duration, along with poor
sleep quality, elevate the risk of developing depression. Con-
versely, some studies have demonstrated a reverse J-shaped,
nonlinear relationship between sleep duration and depres-
sion, indicating that longer sleep duration is associated with
a decreased risk of depressive symptoms [31, 33]. However,
larger-scale studies specifically on pilots are lacking.
The relationship between sleep problems and depression
and anxiety among Chinese pilots is still unclear. Thus, this
study aimed to (1) investigate the prevalence of depression
and anxiety among Chinese commercial pilots and (2)
explore the specific relationship of sleep duration and quality
with depression and anxiety in the same population. Given
previous findings [4, 34], we hypothesized that depression
and anxiety among Chinese commercial pilots would be com-
mon and there would be nonlinear relationships between
sleep problems and depression and anxiety.
2. Methods
2.1. Study Design and Study Population. This study was based
on the baseline assessment of the Civil Aviation Health
Cohort of China (CAHCC), a national survey of the physical
and mental health of crew members. The survey was con-
ducted online between December 2022 and March 2023,
involving 10 Chinese commercial airlines. The inclusion cri-
teria were as follows: (1) age 18 years or above; (2) employed
as pilots in the participating commercial airlines during the
study period; and (3) voluntary participation in the study.
There were no specific exclusion criteria. The study was
approved by the Ethics Committee of Civil Aviation Shang-
hai Hospital (No. 2021-7). All participants provided elec-
tronic written informed consent.
2.2. Measures
2.2.1. General Characteristics. Sociodemographic and clinical
characteristics of the participants were collected, including
age, gender, education level, living status, perceived income
2 Depression and Anxiety

level, employment as pilots (years), global QOL, and COVID-
19-related information (i.e., history of COVID-19 infection,
being close contact or suspected close contact, and having
quarantined during the COVID-19 pandemic). The global
QOL was measured by summing the first two items of the
self-reported World Health Organization Quality of Life Brief
Version (WHOQOL-BREF) [35–37]. A higher total score
indicated a higher QOL.
2.2.2. Sleep Duration and Quality. Following previous research
[38], sleep duration was measured using three standardized
questions for three conditions: “During the past month, on
days with early morning flights, how many hours did you
actually sleep per day?”;“During the past month, on days
with late evening flights, how many hours did you actually
sleep per day?”; and “During the past month, on days with rest
days, how many hours did you actually sleep per day? (Note:
Actual sleep duration may be shorter than the number of
hours you spend in bed).”The sleep duration (hours) for
each individual was defined as the average daily sleep duration
across three situations: on days with early morning flights, on
days with late evening flights, and on days with rest days.
According to the recommendation from the National Sleep
Foundation [39, 40], 7–9 h were considered “normal sleep
duration,”while less than 7 h and more than 9 h were consid-
ered “short”and “long”sleep durations, respectively.
Sleep quality was assessed using the validated Chinese
version of the self-reported Pittsburgh Sleep Quality Index
(PSQI) [41–43]. The PSQI consists of 19 items, covering
seven components: subjective sleep quality, sleep latency,
sleep duration, habitual sleep efficiency, sleep disturbances,
use of sleeping medications, and daytime dysfunction. Each
component was scored from 0 to 3; thus, the total score
ranged from 0 to 21, with higher scores indicating poorer
sleep quality. Following previous studies [44, 45], the total
score of PSQI greater than 5 was considered indicative of
“poor sleep quality.”
2.2.3. Depressive and Anxiety Symptoms. Depression was
assessed using the validated Chinese version of the self-reported
Patient Health Questionnaire 9-item (PHQ-9) [46, 47]. Each
item was rated on a 4-point Likert scale from 0 (“notatall”)
to 3 (“nearly every day”). The total scores of the PHQ-9 ranged
from 0 to 27. A cutoff value of 5 was considered as “having a
depressive syndrome (depression hereafter)”[48, 49].
Anxiety was assessed using the validated Chinese version
of the self-reported General Anxiety Disorder scale 7-item
(GAD-7) [50, 51]. Each item was rated on a 4-point Likert
scale from “0”(not at all) to “3”(nearly every day). The total
scores of the GAD-7 (0 to 21) ranged from 0 to 21. A cutoff
value of 5 was considered as “having an anxiety syndrome
(anxiety hereafter)”[49, 51] .
2.3. Data Analysis
2.3.1. Univariate and Multivariate Analyses. All analyses
were performed using R version 4.3.2 [52]. The normality
of distribution for continuous variables was tested using the
Kolmogorov–Smirnov test. To compare the demographic
and clinical characteristics between the depression and
nondepression groups and between the anxiety and nonan-
xiety groups, independent sample t-tests, Mann–Whitney U
tests, and Chi-square tests were employed, as appropriate.
Binary logistic regression analyses with the “enter”method
were performed to examine the independent association of
sleep variables (sleep duration/quality as independent vari-
ables) with depression or anxiety (dependent variables) after
adjusting for confounders. Variables with significant group dif-
ferences in univariate analyses (p<0:05) were considered as
potential confounders of having depression or anxiety. Adjusted
odds ratios (ORs) and 95%confidence intervals (CIs) were
calculated to estimate the strength of the associations. Statistical
significance was set at p<0:05 for all analyses (two-tailed).
Restricted cubic spline (RCS) is a commonly used approach
for describing the dose–response relationship between con-
tinuous exposure and outcomes when a nonlinear correlation
is anticipated [53]. RCS curves were fitted with four knots to
further explore the potential nonlinear relationship between
sleep variables (sleep duration/quality) and depression and
anxiety. Four knots were positioned at the 5th, 35th, 65th,
and 95th percentiles of the sleep duration (i.e., 6.0, 7.3, 8.0,
and 9.0 h) and the PSQI score (0, 3, 5, and 10 points) [54, 55].
Apvalue of <0.05 indicated a nonlinear relationship.
3. Results
3.1. Sociodemographic Characteristics. Of the 8640 pilots
invited to participate in this study, 7918 agreed to participate
and completed the CAHCC survey assessment, giving a
participation rate of 91.6%. Eventually, 7055 pilots met the
study entry criteria and were included in the analysis.
Participants’demographic and clinical characteristics are
showninTable1.Themeanageofthesamplewas34.1
(standard deviation [SD] =6.94) years. Most pilots (n=4958;
70.3%) had a history of COVID-19 infection, and over one-third
(n=2664; 37.8%) were quarantined during the COVID-19
pandemic. The sleep duration ranged from 4 to 13 h (mean =
7.4, SD =0.92), and the mean PSQI score was 4.5 (SD =2.87).
3.2. Prevalence of Depression and Anxiety. The overall prev-
alence of depression (PHQ-9 total score ≥5) and anxiety
(GAD-7 total score ≥5) among pilots was 23.3%(n=1642;
95%CI =22.3%–24.3%) and 17.0%(n=1196; 95%CI =
16.1%–17.8%), respectively. The total score of the PHQ-9
and GAD-7 in the whole sample was 2.69 (SD =4.036) and
1.78 (SD =3.245), respectively.
3.3. Associations Between Sleep Duration, Sleep Quality, and
Depression and Anxiety. In univariate analyses (Table 1),
participants with depression were more likely to have a short
sleep duration (<7 h; 35.4%vs. 15.2%;p<0:001) and poor
sleep quality (PSQI >5; 69.4%vs. 22.1%;p<0:001) com-
pared to the nondepression group. Participants with anxiety
were more likely to have a short sleep duration (<7 h; 37.5%
vs. 17.2%;p<0:001) and poor sleep quality (PSQI >5; 73.2%
vs. 24.9%;p<0:001) compared to the nonanxiety group. Par-
ticipants with depression (p<0:001) or anxiety (p<0:001)
were more likely to report a lower QOL.
Depression and Anxiety 3

TABLE 1: Demographic and clinical characteristics with respect to depressive and anxiety symptoms among pilots.
Variables Total Depressive symptoms (PHQ-9 ≥5) Univariable analysis Anxiety symptoms (GAD-7 ≥5) Univariable analysis
Yes (n=1642) No (n=5413) Yes (n=1196) No (n=5859)
n(%)n(%)n(%)χ2pn(%)n(%)χ2p
Male 6978 (98.9) 1631 (99.3) 5347 (98.8) 3.032 0.082 1191 (99.6) 5787 (98.8) 5.321 0.021
College and above 6914 (98.0) 1619 (98.6) 5295 (97.8) 3.518 0.061 1182 (98.8) 5732 (97.8) 4.545 0.033
Living with others 5617 (79.6) 1265 (77.0) 4352 (80.4) 8.553 0.003 923 (77.2) 4694 (80.1) 5.118 0.024
Satisfied with income level 3750 (53.2) 612 (37.3) 3138 (58.0) 215.960 <0.001 416 (34.8) 3334 (56.9) 194.310 <0.001
History of COVID-19 infection 4958 (70.3) 1220 (74.3) 3738 (69.1) 16.333 <0.001 870 (72.7) 4088 (69.8) 4.052 0.044
Identified or suspected as a close contact during
the COVID-19 pandemic 942 (13.4) 273 (16.6) 669 (12.4) 19.459 <0.001 212 (17.7) 730 (12.5) 23.357 <0.001
Being quarantined during the COVID-19
pandemic 2,664 (37.8) 713 (43.4) 1951 (36.0) 28.881 <0.001 528 (44.1) 2136 (36.5) 24.669 <0.001
Sleep duration
<7 h 1457 (20.7) 581 (35.4) 876 (16.2) 290.410∗<0.001 449 (37.5) 1008 (17.2) 252.100∗<0.001
7–9 h 5394 (76.5) 1038 (63.2) 4356 (80.5) ——726 (60.7) 4668 (79.7) ——
>9 h 204 (2.9) 23 (1.4) 181 (3.3) ——21 (1.8) 183 (3.1) ——
Poor sleep quality (PSQI >5) 2,334 (33.1) 1,140 (69.4) 1,194 (22.1) 1274.800 <0.001 875 (73.2) 1459 (24.9) 1042.700 <0.001
Mean (SD) Mean (SD) Mean (SD) ZpMean (SD) Mean (SD) t/Z p
Age 34.1 (6.94) 34.0 (6.81) 34.1 (6.97) −0.360 0.719 33.9 (6.63) 34.1 (7.00) −0.185 0.854
BMI 23.8 (2.33) 23.9 (2.45) 23.7 (2.29) −3.515 <0.001 23.9 (2.40) 23.7 (2.32) −2.067 0.039
Work years 9.2 (7.53) 9.1 (7.35) 9.2 (7.58) −0.653 0.514 9.2 (7.10) 9.1 (7.61) −1.593 0.111
QOL 7.1 (1.45) 6.1 (1.26) 7.4 (1.36) −32.362 <0.001 6.0 (1.32) 7.3 (1.37) −28.985 <0.001
Sleep duration (h) 7.4 (0.92) 7.1 (0.98) 7.6 (0.87) −17.638 <0.001 7.0 (0.98) 7.5 (0.88) −16.794 <0.001
Sleep quality (PSQI total score) 4.5 (2.87) 7.1 (2.85) 3.8 (2.39) —<0.001 7.3 (2.92) 4.0 (2.50) −34.375 <0.001
Note: Bold values: <0.05.
Abbreviations: BMI, body mass index; GAD-7, General Anxiety Disorder 7-item; PHQ-9, Patient Health Questionnaire 9-item; PSQI, Pittsburgh Sleep Quality Index; SD, standard deviation; QOL, quality of life.
∗degree of freedom =2, others =1.
4 Depression and Anxiety

TABLE 2: Results of logistic regression analyses between sleep problems and depressive/anxiety symptoms in pilots.
Variables Depressive symptoms
a
Anxiety symptoms
b
pOR 95%CI pOR 95%CI
Sleep duration
<7 h (short) <0.001 2.491 2.190–2.832 <0.001 2.555 2.221–2.938
7–9 h (normal, reference group) —— — —— —
>9 h (long) 0.008 0.548 0.342–0.836 0.223 0.750 0.459–1.165
Sleep quality
Good (PSQI ≤5, reference group) —— — —— —
Poor (PSQI >5) <0.001 7.297 6.444–8.273 <0.001 7.469 6.479–8.627
Note: Bold values signifies p<0:05.
Abbreviations: BMI, body mass index; CI, confidence interval; OR, odds ratio; PSQI, Pittsburgh Sleep Quality Index.
a
Adjusted for BMI, living status, income level, history of COVID-19 infection, identified as a close contact or suspected close contact, being quarantined.
b
Adjusted for gender, education, living status, BMI, income level, history of COVID-19 infection, identified as a close contact or suspected close contact, being quarantined.
Depression and Anxiety 5

As shown in Table 2 and Figure 1, after adjusting for
confounders, logistic regression analyses indicated that com-
pared to the normal sleep duration group (7–9 h), partici-
pants with short sleep duration (<7 h) had a significantly
higher risk of depression (OR =2.491; p<0:001) and anxiety
(OR =2.555; p<0:001). Compared to the normal sleep dura-
tion group (7–9 h), however, only those with a long sleep
duration (>9 h) had a significantly lower risk of depression
Sleep duration
<7 h
>9 h
Sleep quality
Poor
2.491 (2.190 2.832)
(Models 1 and 2)
(Models 3 and 4)
0.548 (0.342 0.836)
7.297 (6.444 8.273)
<0.001
0.008
<0.001
2.555 (2.221 2.938)
0.750 (0.459 1.165)
7.469 (6.479 8.627)
<0.001
0.224
<0.001
02468 02468
Variables OR (95% CI) pDepression risk OR (95% CI) pAnxiety risk
FIGURE 1: Logistic regression models between sleep duration, sleep quality, and depressive and anxiety symptoms in pilots. CI, confidence
interval; OR, odds ratio.
0
1
2
3
4
5
6
7
8
9
10
5678910
OR (95% CI)
Depression risk
Sleep duration
ðaÞ
0
10
20
30
40
50
60
70
80
90
100
0246 1012148
Sleep quality
OR (95% CI)
Depression risk
ðbÞ
FIGURE 2: Nonlinear association of sleep duration (a), sleep quality (b), and depression. CI, confidence interval; OR, odds ratio.
0
1
2
3
4
5
6
7
8
9
10
5678910
OR (95% CI)
Anxiety risk
Sleep duration
ðaÞ
0
10
20
30
40
50
60
70
80
0 2 4 6 10 12 148
Sleep quality
Anxiety risk
OR (95% CI)
ðbÞ
FIGURE 3: Nonlinear association of sleep duration (a), sleep quality (b), and anxiety. CI, confidence interval; OR, odds ratio.
6 Depression and Anxiety

(OR =0.548; p¼0:008), while no significant association between
long sleep duration and risk for anxiety was found (OR =
0.750;p¼0:223). In addition, participants with poor sleep quality
(PSQI >5) had seven times higher risk of depression (OR =
7.297; p<0:001) and anxiety (OR =7.469; p<0:001) compared
to those with good sleep quality.
3.4. J-Shaped Nonlinear Relationship Between Sleep Duration/
Quality and Depression and Anxiety. As shown in Figures 2
and 3, after adjusting for confounders, there was an inverse J-
shaped nonlinear relationship between sleep duration and
depression with an inflection point (OR =1) of 7.64 h. Simi-
larly, an inverse J-shaped nonlinear relationship between sleep
duration and anxiety was found with an inflection point of
7.48 h. In addition, J-shaped nonlinear relationship between
sleep quality and depression and anxiety was also found, with
an inflection point of PSQI =4points(OR=1) for both. The p
values for all nonlinearity values were less than 0.001.
4. Discussion
To the best of our knowledge, this was the first large-scale
study that investigated the prevalence of depression and anx-
iety among Chinese commercial pilots and explored the non-
linear relationship of sleep duration and sleep quality with
depression and anxiety in this population. The main findings
of thisstudy were that depression and anxiety were common
among Chinese commercial pilots. Commercial pilots with
depression and anxiety were also more likely to have lower
sleep duration and poorer sleep quality compared to those
without these sleep disturbances.
4.1. High Prevalence of Depression and Anxiety Among Pilots.
The prevalence of depression (PHQ-9 total score ≥5) and anxi-
ety (GAD-7 total score ≥5) among pilots was 23.3%(95%CI =
22.3%–24.3%) and 17.0%(95%CI =16.1%–17.8%), respec-
tively. These rates indicated an elevated or comparable risk
compared to the Chinese general population, with rates of
17.0%for depression and 18.0%for anxiety during the
COVID-19 pandemic [56]. When compared to the findings
in the Chinese general population prior to the COVID-19
pandemic (depression: 17.9%;anxiety:11.0%), pilots appeared
to be at higher risk of both depression and anxiety [57, 58].
These findings differed from those reported in previous
studies on pilots. For example, an online survey conducted in
over 50 countries found that 12.6%of pilots suffered from
moderate or severe depression (PHQ-9 ≥10) [59]. Another
study in China found that the prevalence of anxiety among
pilots was 26.2%, as measured with the Zung Self-Assessment
Scale for Anxiety (SAS) with a cutoff value of 50 [5]. Similar
surveys were conducted in Australia (17.2%with depression
and 7.8%with anxiety) and the European Aviation Safety
Agency (EASA) (18.0%with depression and 8.5%with anxi-
ety), measured using the PHQ-8 (a score of ≥10 indicating
depression) and GAD-7 (a score of ≥10 indicating anxiety;
the GAD-7 total score was 3.94 Æ3.63 in Australia and 3.76 Æ
3.76 in EASA) [14]. These inconsistent findings in both the
prevalence and severity of depression and anxiety may be
related to the differences in measurement criteria, sample
sizes, and geographic regions with different sociocultural
backgrounds [60]. Wu et al. [58] found that pilots from coun-
tries dominated by Western cultural traditions tended to have
a lower prevalence of depression. The high prevalence of
depression and anxiety in pilots can be attributed to multiple
reasons, such as occupational stress (i.e., high workload and
shift work) [7], unhealthy lifestyle [61], low income level [5],
and adverse working or life experiences (e.g., substance abuse
and verbal or sexual abuse) [6]. For example, there is evidence
that pilots with longer hours of duty were more likely to
report feeling depressed or anxious [4]. Another study found
that Chinese airline pilots were paid much less than their
foreign counterparts. This imbalance between high workload
and lower income level could increase the level of negative
emotions and occupational stress leading to anxiety [5].
4.2. J-Shaped Relationship Between Sleep Duration and
Depression and Anxiety. Consistent with a previous finding
in adolescents [62], this study revealed the nonlinear rela-
tionships between sleep duration and depression and anxiety
among Chinese pilots after adjusting for confounders. The
RCS results further indicated that shorter sleep duration was
the risk factor for depression (<7.64 h) and anxiety (<7.48 h).
Insufficient sleep was a recognized risk factor for both phys-
ical and mental conditions such as increased incidence of
cardiovascular diseases, obesity and related metabolic syn-
drome, cancer, cognitive deficit, mood dysregulation, irrita-
bility, depression, anxiety, and even suicide [63–65]. For
airline pilots, the leading consequence of insufficient sleep
could be fatigue, decreasing concentration or alertness dur-
ing duty, which may trigger and further elevate the risk of
depression and anxiety [6, 30, 66]. Additionally, from a
biological perspective, there is compelling evidence that
the association between sleep and mental problems, such
as depression and anxiety, was mediated by inflammation
[67]. Sleep deprivation can lead to proinflammatory state
[67, 68], such as increased sensitivity of inflammatory cyto-
kines and the change in the level of brain-derived neuro-
trophic factor (BDNF) [69, 70]. Inflammation is a predictor
of depression and anxiety [71–73]. Thus, sleep disturbances
have been identified as a significant vulnerability factor to
increase the risk of mental disturbances in the presence of
inflammation [40, 74].
The present study also found that long sleep duration
(>9 h) remained a protective factor for depression in pilots,
as indicated by the logistic regression analysis. This is differ-
ent from previous findings that found that excessive sleep
increased the risk of mental health problems [18, 62, 75].
Previous studies proposed several potential mechanisms for
the findings that long sleep duration could increase the risk
of mental health disturbances [24, 62]. For example, exces-
sive sleep may be associated with increased sleep fragmenta-
tion and reduced physical activities, leading to lower energy
and vitality as well as mood dysregulation [76–78]. Further-
more, excessive sleep may be a consequence of stress or
stressful events, while stress-coping deficits may be a driver
of the relationship between sleep and depression [79]. The
inconsistency between the current and previous findings
Depression and Anxiety 7

may be related to the different distributions of sleep dura-
tion across the samples; in this study, the proportion of long
sleepers was low (2.9%), whereas in the study of Chinese
older adults aged ≥65 years, it was 8.2%[75]. Other reasons
included different confounders adjusted for [32] as well as
inconsistent definitions of long sleep duration [32, 62] and
the age of participants [24].
4.3. J-Shaped Relationship Between Sleep Quality and Depression
and Anxiety. Similar nonlinear relationships were also observed
between sleep quality and depression and anxiety in this study. A
PSQI score of >4wasidentified as a risk factor for both depres-
sion and anxiety. Our findings are consistent with previous stud-
ies, indicating that individuals with poor sleep quality were more
likely to be depressed or anxious than those with good sleep
quality [21, 80, 81]. Sleep quality refers to individuals’satisfaction
with all aspects of their sleep, which is affected by multiple
factors, including physiological (e.g., age, gender, and circadian
rhythm), psychological (e.g., stress, anxiety, and depression), and
environmental factors (e.g., noise) [22]. Previous research on
pilots found that distressing shifts were associated with difficulty
of falling asleep and that low social support and high workloads
were associated with subjective poor sleep quality [82]. Pilots
often fly early and late shifts, which could frequently change
the sleep rhythms. Circadian rest–activity rhythm distur-
bances are associated with higher risk of mental health dis-
turbances such as depression and anxiety [83] and could
even lead to suicide and self-injury [84].Additionally, there is
an overlap between the neural mechanisms of emotional reg-
ulation and sleep regulation; thus, impaired sleep quality
could disrupt emotional regulation and increases the risk of
depressive and anxiety symptoms [85].
4.4. Strengths and Limitations. The strengths of this study
included its large sample size andthemulticenterstudydesign.
In addition, the high participation rate (91.6%) and the use of
standardized outcome measures enabled the direct comparison
of the results with those of other populations. However, several
limitations need to be noted. First, causal relationships between
variables could not be inferred due to the cross-sectional study
design. Second, the use of self-reported measurements may
result in recall bias. Third, although a few confounders were
adjusted for to explore the independent relationships between
sleep and depression and anxiety, there was still a risk to
residual confounding, such as personality traits [86], physical
comorbidities [87], and adverse childhood experiences [88].
5. Conclusions
Depression and anxiety were common among Chinese com-
mercial pilots and were significantly associated with sleep
duration and sleep quality in this study. Insufficient sleep,
along with poor sleep quality, was linked to an increased risk
of depression and anxiety. These findings underscore the
importance of managing airline pilots’sleep disturbances
and promote their psychological well-being. This study has
practical implications. Both sleep length and quality play
important roles in developing mental health disturbances,
especially depression and anxiety. Thus, implementing
targeted interventional strategies to improve sleep patterns
is crucial for reducing the risk of mental health problems and
mitigating the potential negative impact on the psychological
health of this population. The interventional strategies include
arranging more convenient duty schedules for pilots to ensure
adequate rest breaks, thereby promoting mental and physical
recovery after stressful workdays; providing training on proper
sleep hygiene practices (e.g., regular sleep schedule, suitable
physical activities, and conducive sleep environment); increas-
ing supportfor preventive measures (e.g., regular health check-
ups); and implementing mobile health (mHealth) intervention
via mobile technologies and Internet-based cognitive behavior
therapy [59, 89–91].
Data Availability Statement
The datasets presented in this article are not readily available
because the Ethics Committee of Civil Aviation Shanghai
Hospital (No. 2021-7) that approved the study prohibits the
authors from making publicly available the research dataset of
clinical studies. Requests to access the datasets should be
directed to xyutly@gmail.com.
Conflicts of Interest
The authors declare no conflicts of interest.
Author Contributions
Study design: Pan Chen, Yuan Feng, Qinge Zhang, Mi Xiang,
and Yu-Tao Xiang. Data collection, analysis, and interpreta-
tion: Pan Chen, He-Li Sun, Tong Leong Si, Zhaohui Su, Teris
Cheung, Gabor S. Ungvari, Erliang Zhang, Minzhi Chen, Jie
Zhang, Lin Zhang, Bin Ren, and Qingqing Jin. Drafting of
the manuscript: Pan Chen and Yu-Tao Xiang. Critical revi-
sion of the manuscript: Robert D. Smith. Approval of the
final version for publication: all coauthors. Pan Chen and
He-Li Sun contributed equally to this work.
Funding
This project is funded by the National Natural Science Foun-
dation of China (Grant 71804110), the Shanghai Science and
Technology Development Funds (Grant 21QA1405300), the
Science Foundation of Ministry of Education of China
(Grant 22YJAZH116), and the University of Macau (Grants
MYRG2019-00066-FHS and MYRG2022-00187-FHS).
Acknowledgments
The authors are grateful to all participants and clinicians
involved in this study.
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