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Citation: Wilson, D.; Driller, M.;
Winwood, P.; Clissold, T.; Johnston,
B.; Gill, N. The Effectiveness of a
Combined Healthy Eating, Physical
Activity, and Sleep Hygiene Lifestyle
Intervention on Health and Fitness of
Overweight Airline Pilots: A
Controlled Trial. Nutrients 2022,14,
1988. https://doi.org/10.3390/
nu14091988
Academic Editor: Maria Izquierdo-
Pulido
Received: 18 April 2022
Accepted: 6 May 2022
Published: 9 May 2022
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nutrients
Article
The Effectiveness of a Combined Healthy Eating, Physical
Activity, and Sleep Hygiene Lifestyle Intervention on Health
and Fitness of Overweight Airline Pilots: A Controlled Trial
Daniel Wilson 1,2 ,* , Matthew Driller 3, Paul Winwood 2,4 , Tracey Clissold 2, Ben Johnston 5
and Nicholas Gill 1,6
1Te Huataki Waiora School of Health, The University of Waikato, Hamilton 3216, New Zealand;
nicholas.gill@waikato.ac.nz
2Faculty of Health, Education and Environment, Toi Ohomai Institute of Technology,
Tauranga 3112, New Zealand; paul.winwood@toiohomai.ac.nz (P.W.); tracey.clissold@toiohomai.ac.nz (T.C.)
3Sport and Exercise Science, School of Allied Health, Human Services and Sport, La Trobe University,
Melbourne 3086, Australia; m.driller@latrobe.edu.au
4Sports Performance Research Institute New Zealand, Auckland University of Technology,
Auckland 1010, New Zealand
5Aviation and Occupational Health Unit, Air New Zealand, Auckland 1142, New Zealand;
ben.johnston@otago.ac.nz
6New Zealand Rugby, Wellington 6011, New Zealand
*Correspondence: daniel.wilson@toiohomai.ac.nz; Tel.: +64-7557-6035
Abstract:
(1) Background: The aim of this study was to evaluate the effectiveness of a three-
component nutrition, sleep, and physical activity (PA) program on cardiorespiratory fitness, body
composition, and health behaviors in overweight airline pilots. (2) Methods: A parallel group study
was conducted amongst 125 airline pilots. The intervention group participated in a 16-week per-
sonalized healthy eating, sleep hygiene, and PA program. Outcome measures of objective health
(maximal oxygen consumption (VO
2max
), body mass, skinfolds, girths, blood pressure, resting heart
rate, push-ups, plank hold) and self-reported health (weekly PA, sleep quality and duration, fruit
and vegetable intake, and self-rated health) were collected at baseline and post-intervention. The
wait-list control completed the same assessments. (3) Results: Significant group main effects in favor
of the intervention group were found for all outcome measures (p< 0.001) except for weekly walking
(p= 0.163). All objective health measures significantly improved in the intervention group when
compared to the control group (p< 0.001, d= 0.41–1.04). Self-report measures (moderate-to-vigorous
PA, sleep quality and duration, fruit and vegetable intake, and self-rated health) significantly in-
creased in the intervention group when compared to the control group (p< 0.001, d= 1.00–2.69).
(4) Conclusion: Our findings demonstrate that a personalized 16-week healthy eating, PA, and sleep
hygiene intervention can elicit significant short-term improvements in physical and mental health
outcomes among overweight airline pilots. Further research is required to examine whether the
observed effects are maintained longitudinally.
Keywords:
weight loss; nutrition; fruit and vegetable intake; aerobic capacity; moderate-to-vigorous
physical activity; lifestyle medicine
1. Introduction
Adverse health outcomes promoted by occupational demands of airline pilots includ-
ing shift and irregular work schedules, circadian disruption, sedentary activity, and high
fatigue [
1
] may be mitigated through attainment of health guidelines for lifestyle behaviors:
healthy diet, physical activity (PA), and sleep [
2
,
3
]. Non-communicable diseases (NCDs)
including cardiovascular disease (CVD), stroke, type 2 diabetes, and their major risk factors
are among leading causes of mortality and morbidity worldwide [
4
]. The presence of
Nutrients 2022,14, 1988. https://doi.org/10.3390/nu14091988 https://www.mdpi.com/journal/nutrients
Nutrients 2022,14, 1988 2 of 15
modifiable behavioral NCD risk factors including obesity, hypertension, physical inactivity,
low cardiorespiratory fitness, unhealthy dietary patterns, short sleep, depression, high
perceived stress levels, and high fatigue are each associated with adverse outcomes to
acute and chronic health [
4
–
6
]. Obesity is a complex, widespread, yet modifiable NCD risk
factor that poses a significant public health threat [
7
]. The obesity prevalence worldwide
was estimated as 13% in 2015, which is nearly double the prevalence from 1980 [
7
]. In
2020, 67% of male airline pilots in New Zealand were classified as overweight or obese
with hypertension affecting 27% of the population [
8
]. Moreover, this study reported the
prevalence of insufficient fruit and vegetable intake, physical inactivity, and <7 h sleep per
night among airline pilots as 68%, 48%, 33.5%, respectively [8].
The global economic burden associated with NCDs is estimated as $47 trillion between
2010 and 2030 [
4
]. Previous research has demonstrated evidence of significantly reduced
longitudinal health care cost utilization following diet and exercise lifestyle interventions [
9
].
Relevantly, airline pilots undergo annual or biannual medical examinations, results of
which influence flight certification status [
10
]. Ongoing health care costs associated with
the presence of NCDs and their risk factors present economic implications for aviation
medical care [4,10].
Better health status is generally associated with enhanced productivity and work per-
formance [
11
]. In the context of commercial aviation, pilot work performance is imperative
to flight operation safety. As established in the International Civil Aviation Organiza-
tion’s Annex 1, aviation medicine providers are required to implement appropriate health
promotion for license holders (pilots) to reduce future medical risks to flight safety [
10
].
Thus, interventions that promote positive health of pilots, mitigate health risk factors for
NCDs, and reduce longitudinal health care costs of employees are of importance to aviation
medicine, health practices, and policies.
Limited studies have investigated the efficacy of health promotion interventions
among airline pilots, and no studies to date have reported on cardiorespiratory fitness
or body fat percentage among this occupational group [
1
]. Based on the findings of our
recent preliminary research [
2
,
12
], we found a personalized three-component healthy
eating, sleep hygiene, and PA intervention produced favorable outcomes in subjective
health and reductions in body mass and blood pressure among airline pilots. Utilizing a
different sample of pilots, the aim of the present study was to evaluate the effects of a three-
component healthy eating, sleep hygiene, and PA program on cardiorespiratory fitness,
body composition, and health behaviors in overweight airline pilots. It was hypothesized
that the intervention group would have significantly greater improvements in physical
fitness, body composition and health behaviors compared to the wait-list control group at
four months.
2. Materials and Methods
2.1. Design
A parallel controlled study (intervention and control) with pre- and post-testing
was conducted to evaluate the effectiveness of a personalized three-component, 16-week
lifestyle intervention for enhancing subjective and objective health indices in airline pilots.
This study was approved by the Human Research Ethics Committee of the University of
Waikato in New Zealand; reference number 2020#07. The trial protocol is registered at The
Australian New Zealand Clinical Trials Registry (ACTRN12622000233729).
2.2. Participants
The participants comprised of self-selected airline pilots who were recruited from a
large international airline in New Zealand. Invitations to participate in the study were
distributed to all airline pilots within the company through internal communication net-
works. Group allocation was determined by a first in, first serve basis due to intervention
implementation capacity. Accordingly, pilots who expressed interest to participate in the
study early and satisfied the eligibility criteria were allocated to the intervention group
Nutrients 2022,14, 1988 3 of 15
(n= 86) and subsequent enrolments that exceeded initial capacity were allocated to the
wait-list control (n= 80). Participants involved pilots from short-haul (regional flights) and
long-haul (international flights) rosters. The participants allocated to the wait-list control
group received no intervention and were invited to participate in the intervention after the
study period.
Potentially eligible pilots who volunteered to participate were screened according
to the following eligibility criteria: (a) aged >18 years, (b) pilots with a valid commercial
flying license, (c) working on a full-time basis, (d) having a body mass index (BMI) of
≥
25
(overweight), and (e) a resting blood pressure of >120/80 (systolic/diastolic). Pilots were
excluded if medical clearance was deemed necessary prior to engagement in a PA program
after completion of the 2020 Physical Activity Readiness Questionnaire for Everyone (PAR-
Q+) [13].
Informed consent was obtained from participants prior to commencement of partici-
pation in the study and participants were notified that they were permitted to withdraw at
any time during the study if they wish to do so. To encourage data blinding and anonymity
during data analysis, participants were allocated a unique identifier code on their informed
consent form and were instructed to input this into their online health survey in lieu of
their name.
2.3. Intervention
At baseline the intervention group completed an individual face-to-face 60-min consul-
tation session with an experienced health coach practitioner located at the airline occupa-
tional health facility, followed by provision of a personalized health program. Participants
also received weekly educational content emails throughout the intervention and a mid-
intervention follow-up phone call with a health coach to discuss progress and support
adherence. Health coaching advice delivered to pilots was evidence-based and derived
from experts in the fields of dietetics, physical activity, and sleep science.
For extended details of the procedures associated with the three-component inter-
vention, readers are referred to the study of Wilson and colleagues [
12
]. In brief, the
intervention incorporated seven behavior change techniques (BCT) including collaborative
goal setting, action planning, problem solving, information about health consequences,
self-monitoring, feedback on behaviors, and reviewing of outcomes. The intervention
utilized 35 participant interactions: including two face-to-face consultations (baseline and
post-intervention), one mid-intervention telephone call, 16 weekly emails, and 16 weekly
self-monitoring surveys.
Between the participant and health coach, personalized collaborative outcome, process,
and performance goals [
12
] were established at baseline for (a) sleep hygiene, (b) healthy
eating, and (c) PA. Healthy eating goals were defined based on a healthy eating resource
(see Appendix A, adapted from Beeken and colleagues [
14
] with amendments derived from
Cena and Calder [
15
]). Sleep goals were set based on a Sleep Hygiene Checklist (see Ap-
pendix B) which was derived from previous sleep hygiene and stimulus control studies [
12
].
Physical activity prescription goals were established based on assessment of individual
barriers and facilitators to physical activity, implementation of the frequency, intensity,
time, and type principles [
16
], and progression to fulfillment of sufficient moderate-to-
vigorous-intensity physical activity (MVPA) to meet World Health Organization health
guidelines [
17
] according to individual capabilities. Sufficient physical activity was de-
fined as
≥
150 min moderate-intensity, or
≥
75 min vigorous-intensity, or an equivalent
combination MVPA per week [17].
2.4. Outcome Measures
Objective measures of health (maximal oxygen consumption (VO
2max
), body mass,
skinfolds, girths, blood pressure, resting heart rate, pushups, plank hold) and self-report
measures (weekly PA, sleep quality and duration, fruit and vegetable intake, and self-rated
health) were collected at baseline and 4 months (post-intervention). Self-report measures
Nutrients 2022,14, 1988 4 of 15
(weekly MVPA, sleep duration, fruit, and vegetable intake) were also collected weekly to
monitor intervention adherence via an online survey delivered through Qualtrics software
(Qualtrics, Provo, UT, USA).
Participants were instructed to avoid large quantities of food, stimulants such as caf-
feine, and strenuous exercise 4 h prior to measurement of physiological outcome measures.
Outcome measurement protocols for body mass, blood pressure, and subjective health
have been previously described in detail [
2
,
12
]. In brief, at the start of the consultation
session, participants completed an electronic questionnaire via an iPad (Apple, California,
CA, USA) to provide data for self-report measures. Using standardized methods previously
described [
2
], resting heart rate was measured utilizing a Rossmax pulse oximeter SB220
(Rossmax Taipei, Taiwan, China), height was recorded with SECA 206 height measures,
body mass was measured with SECA 813 electronic scales (SECA, Hamburg, Germany),
and blood pressure was measured with an OMRON HEM-757 device (Omron Corporation,
Kyoto, Japan).
Skinfold measurements were collected following standardized procedures of the Inter-
national Society for the Advancement of Kinanthropometry (ISAK) [
18
]. The skinfold sum
was determined by measurements obtained for eight locations: biceps, triceps, subscapular,
abdominal, supraspinale, iliac crest, mid-thigh, and medial calf. All skinfold measurements
were taken from the right side of the body twice, with a third measurement taken if the
difference between recordings were greater than 4%. The anthropometrical technical errors
were under the recommended limits [
18
] for all final recorded measurements. Skinfold
measurements were conducted by an accredited ISAK anthropometrist, using Harpenden
calipers (British Indicators, Hertfordshire, UK) which were sufficiently calibrated as per the
manufacturers’ guidelines. Body fat percentage was derived from skinfold assessments and
was calculated using updated sex and ethnicity specific equations reported elsewhere [
19
].
Girth measurements for the waist and hip locations were measured with a thin-line metric
tape measure (Lufkin; Apex Tool Group, Sparks, MD, USA) congruent with standardized
technique [20].
Push-ups and the plank isometric hold were utilized as assessments of musculoskeletal
fitness, using previously reported standardized methods [
21
,
22
]. For push-ups, the hand
release technique was utilized, where participants were instructed to keep their torso tight
so that the shoulders, hips, knees, and ankles were aligned throughout the range of motion.
At the bottom position, the hands were lifted from the floor between each push-up. Push-up
cadence was coordinated by a metronome and participants completed maximum full range
of motion repetitions until the onset of failure to maintain correct form [
21
]. The basic plank
isometric hold technique was utilized, consisting of the participant holding a prone bridge
position supported by their feet and forearms. Elbows were below the shoulders with the
forearms and fingers extending forward. The neck was maintained in a neutral position
so that the body remained straight from the head to the heels. Time was recorded from
initiation of the position until the loss of the plank position [22].
For quantification of aerobic fitness, estimated VO
2max
was obtained by participants
performing a previously validated [
23
,
24
] 3-min aerobic test (3mAT) on a Wattbike (Wood-
way USA, Waukesha, WI, USA) electro-magnetically and air-braked cycle ergometer. Par-
ticipants were given a full explanation of the protocol, safety procedures, the Wattbike
seat and handle were fitted appropriately for the participant, who was also fitted with a
Polar H10 heart rate strap (Polar Electro, Kempele, Finland). Full details on the procedure
have been detailed elsewhere [
23
]. Participants completed a 10-min warmup consisting of
self-paced cycling at 70–90 rpm with two 6-s sprints within that timeframe, as suggested by
the manufacturer. The goal of the 3mAT was to maintain the highest power output possible
for 3 full minutes. Verbal encouragement was provided, and participants were allowed to
adjust the resistance and pedal cadence as needed throughout the test. Each participant’s
customized setup was noted, and the same procedures were carried out for the retest at
4 months.
Nutrients 2022,14, 1988 5 of 15
Prior to baseline testing, the Wattbike was calibrated by the manufacturer, and a
between session reliability assessment was conducted with the Wattbike utilizing a con-
venience sample of seven untrained airline pilots (aged = 42
±
12 years, body mass =
80
±
11 kg, height = 173
±
4 cm, mean
±
standard deviation (SD), 5 males, 2 females).
Following standardized procedures [
23
,
24
], participants of the reliability trial performed
the 3mAT twice separated by >48 h between assessments. For measurement of estimated
VO
2max
, the reliability trial produced a coefficient of variation (CV) of 4.3% and an intraclass
correlation coefficient (ICC) of 0.98 (0.90–0.99), denoting acceptable CV [
25
] and excellent
ICC reliability [26].
Self-report measures (PA, sleep quality and duration, fruit and vegetable intake, and
self-rated health) have been previously described in detail [
12
]. In brief, self-rated health
(physical and mental) were measured utilizing the Short Health Form 12v2 (SF-12v2) [27].
The International Physical Activity Questionnaire Short Form (IPAQ) was utilized to
quantify self-report MVPA [
28
]. Self-report subjective sleep quality and duration were
measured with the Pittsburgh Sleep Quality Index (PSQI) [
29
]. Daily fruit and vegetable
intake were measured using dietary recall questions derived from the New Zealand Health
Survey [28].
2.5. Statistical Analyses
G-Power software was utilized to calculate sample size required to detect a clinically
significant change in primary outcome measures of
≥
5% weight loss and a change of
3.5 mL/kg/min for VO
2max
[
30
]. Our sample size power calculation suggested 65 pilots
were required in each group to achieve 90% power and a 5% significance criterion to detect
relevant differences between the intervention and wait-list control groups. To account for
20% dropout observed in a similar study [2], our target sample size was 156.
Statistical Package for the Social Sciences (SPSS, version 28; IBM Corp., Armonk, NY,
USA) was utilized for all analyses. Listwise deletion (i.e., entire case record removal) was
applied if individual datasets had missing values or for participants who did not complete
post-tests. Stem and leaf plots were inspected to ascertain whether there were any outliers
in the data for each variable. A Shapiro–Wilk test (p> 0.05) and its histograms, Q–Q
plots, and box plots were analyzed for the normality of data distribution for all variables.
Levene’s test was used to test homogeneity of variance.
Independent t-tests were utilized to calculate whether any significant differences
existed between groups at baseline. For categorical variables (long haul and short haul) the
Chi square test was used. Between group analysis of pre-test and post-test were assessed
using paired t-tests and analysis of covariance (ANCOVA) (respectively). To control for
baseline differences between groups, baseline data were included as a covariate in the
ANCOVA [
31
], in addition to inclusion of age and sex. Effect sizes were calculated using
Cohen’s d to quantify between-group effects from pre-test to post-test. Effect size thresholds
were set at >1.2, >0.6, >0.2, and <0.2, which were classified as large, moderate, small, and
trivial, respectively [32]. The αlevel was set at a p value of less than 0.05.
3. Results
3.1. Characteristics of the Study Population
Two-hundred twelve airline pilots were considered for eligibility and 148 were re-
cruited to participate (Figure 1). Of them, 84% (n= 125) of recruits provided data for both
timepoints, which comprised a combination of short-haul and long-haul rosters (n= 60
and 65, respectively). The dropout rates from baseline to post-intervention were 12% (time
commitment n= 5; ceased employment n= 3; testing not fully completed n= 1) and 19%
(time commitment n= 8; ceased employment n= 4) for the intervention and wait-list control
groups, respectively. As displayed in Table 1, at baseline both groups demonstrated similar
characteristics for most health parameters, yet the wait-list control group had lower SBP
(t(123) = 1.191, p= 0.03, d= 0.39) and lower MAP (t(123) = 2.113, p= 0.03, d= 0.38). No
significant differences were observed between groups for sex and fleet type.
Nutrients 2022,14, 1988 6 of 15
Figure 1. Flow diagram of participant recruitment and data collection.
Nutrients 2022,14, 1988 7 of 15
Table 1. Baseline characteristics of participants.
Parameters All subjects (n= 125) Intervention (n= 67) Control (n= 58)
Sex (female/male) 12/113 6/61 6/52
Age (years) 44.5 ±10.7 43.7 ±10.0 45.6 ±11.4
Short haul (n) 60 34 26
Long haul (n) 65 33 32
Height (cm) 178.4 ±7.4 179.2 ±6.9 177.4 ±7.8
Systolic BP (mmHg) 132.3 ±5.6 133.3 ±6.0 131.1 ±4.9 *
Diastolic BP (mmHg) 85.6 ±3.8 86.0 ±3.9 85.0 ±3.6
MAP (mmHg) 101.1 ±3.8 101.8 ±3.8 100.4 ±3.6 *
Pulse (bpm) 66.9 ±6.6 67.4 ±6.1 66.4 ±7.2
Body mass (kg) 90.5 ±9.2 91.1 ±8.0 89.8 ±10.5
BMI (kg/m2)28.4 ±2.0 28.3 ±1.7 28.5 ±3.4
Skinfold sum ×8 sites (mm) 136.5 ±24.1 138.3 ±17.7 134.4 ±29.9
Bodyfat (%) 24.3 ±3.6 24.7 ±3.2 23.9 ±4.0
Waist girth (cm) 96.6 ±7.6 97.8 ±8.1 95.2 ±6.8
Waist to hip ratio 0.93 ±0.07 0.94 ±0.07 0.93 ±0.08
VO2max (mL/kg/min) 36.3 ±5.4 35.6 ±5.8 37.0 ±4.8
Push-ups (repetitions) 17.2 ±7.3 16.4 ±6.8 18.1 ±7.7
Plank hold (s) 79.7 ±24.7 77.2 ±25.5 82.5 ±23.7
Walking per week (min) 73.8 ±42.5 70.5 ±32.2 77.7 ±52.0
MVPA per week (min) 141.8 ±41.1 138.0 ±41.6 146.2 ±40.3
Fruit intake (serve/day) 1.3 ±0.7 1.5 ±0.8 1.0 ±0.6
Vegetable intake (serve/day) 2.0 ±0.7 1.8 ±0.7 2.4 ±0.5
F&V intake (serve/day) 3.3 ±0.7 3.3 ±0.7 3.4 ±0.7
Sleep per day (h) 7.0 ±0.5 7.0 ±0.4 7.0 ±0.6
Global PSQI (score) 6.3 ±2.1 6.4 ±2.2 6.1 ±1.9
MCS-12 (score) 48.9 ±4.6 48.6 ±5.8 49.3 ±2.8
PCS-12 (score) 46.7 ±3.4 46.3 ±3.8 47.2 ±2.8
Note: Mean
±
SD reported for all subjects, intervention and control. Abbreviations: SD = Standard deviation; BMI
= body mass index; VO
2max
= maximal oxygen consumption; BP = blood pressure; MAP = mean arterial pressure;
MVPA = moderate-to-vigorous physical activity; F&V = fruit and vegetable intake; MCS-12 = Short Health Form
12v2 mental component summary scale; PCS-12 = Short Health Form 12v2 physical health component summary
scale; PSQI = Pittsburgh Sleep Quality Index. * Indicates statistical significance (p< 0.05).
3.2. Intervention Adherence
For the intervention group, compliance was measured mid-intervention for health
behaviors, including self-report weekly MVPA, daily fruit and vegetable intake and average
sleep duration per night. Sixty-four (97%) were achieving
≥
5 serves of fruit and vegetables
per day, 94% reported sleeping
≥
7 h sleep per night, and 97% were obtaining
≥
150 MVPA
(min) per week. Comparatively, 36% of the wait-list control group were achieving
≥5 serves
of fruit and vegetables per day, 71% were sleeping
≥
7 h per night, and 53% were obtaining
≥150 MVPA (min) per week.
3.3. Body Mass, Skinfolds, Waist Girth, Bodyfat Percentage, Blood Pressure and Pulse
Significant group main effects (p< 0.001) in favor of the intervention group were
found for all variables. Small to large effect size differences were observed from baseline
to post-intervention (Table 2). The within-group analysis revealed that the intervention
elicited significant improvements (p< 0.001) in all measures at post-intervention associ-
ated with moderate to large effect sizes (Table 2; Figure 2). The wait-list control group
reported a significantly lower body mass (t(57) = 2.538, p= 0.014, d = 0.33) and reduced
waist girth (
t(57) = 2.358
,p= 0.022, d= 0.31), yet no significant changes were observed in
other measures.
Nutrients 2022,14, 1988 8 of 15
Table 2.
Changes in objective and self-report health measures from baseline to post-intervention at
4-months.
Intervention Control ANCOVA
(Group Main
Effects)
Between Group ES
(n= 67) (n= 58)
Time
(Months) M SD
Follow Up
Change
(95% CI)
M SD
Follow Up
Change
(95% CI)
p d
Body mass
(kg)
0 91.1 8.0 89.8 10.5 0.14, Trivial
4 85.6 7.7 5.5 (4.8–6.1) 89.4 85.6
0.4 (0.1–0.7)
<0.001 −0.41, Small
BMI (kg/m2)0 28.3 1.7 28.5 3.4 0.08, Trivial
4 26.7 1.6 1.7 (1.5–1.9) 28.4 2.4
0.1 (0.0–0.2)
<0.001 −0.86, Moderate
Systolic BP
(mmHg)
0 133.3 6.0 131.1 4.9 0.39, Small
4 125.2 5.8 8.1 (7.3–8.9) 132.5 5.9
1.3 (0.1–2.8)
<0.001 −1.25, Large
Diastolic BP
(mmHg)
0 86.0 3.9 85.0 3.6 0.27, Small
4 80.8 5.4 5.2 (4.2–6.2) 84.8 4.7
0.2 (0.9–1.4)
<0.001 −0.77, Moderate
MAP
(mmHg)
0 101.8 3.8 100.4 3.6 0.38, Small
4 95.6 5.0 6.2 (5.4–6.9) 100.7 4.7
0.3 (0.8–1.4)
<0.001 −1.04, Moderate
Pulse (bpm) 0 67.4 6.1 66.4 7.2 0.15, Trivial
4 61.0 6.5 6.3 (4.8–7.8) 67.0 8.8
0.6 (1.0–2.2)
<0.001 −0.78, Moderate
Skinfold sum
(mm)
0 138.3 17.7 134.4 29.9 0.16, Trivial
4 110.1 14.5 28.2 (26–30.5) 133.0 29.8
1.5 (0.5–3.4)
<0.001 −1.00, Moderate
Bodyfat (%) 0 24.7 3.2 23.9 4.0 0.21, Small
4 21.0 2.8 3.6 (3.3–4.0) 23.7 4.1
0.2 (0.1–0.4)
<0.001 −0.79, Moderate
Waist (cm) 0 97.8 8.1 95.2 6.8 0.35, Small
4 91.8 7.9 6.0 (5.3–6.8) 94.3 6.9
1.0 (0.1–1.8)
<0.001 −0.34, Small
Waist to hip
ratio
0 0.94 0.07 0.93 0.08 0.09, Trivial
4 0.90 0.07 0.03 (0.02–0.04) 0.92 0.07
0.1 (0.0–0.2)
<0.001 −0.22, Small
VO2max
(mL/kg/min)
0 35.6 5.8 37.0 4.8 −0.26, Small
4 40.2 5.9 4.5 (4.0–5.0) 37.3 5.1
0.2 (0.1–0.6)
<0.001 0.52, Small
Push-ups
(repetitions)
0 16.4 6.8 18.1 7.7 −0.22, Small
4 24.3 7.1 7.8 (6.5–9.1) 19.9 8.1
1.9 (1.2–2.6)
<0.001 0.57, Small
Plank hold (s)
0 77.2 25.5 82.5 23.7 −0.21, Small
4 120.0 39.6 42.8 (34.4–51.3) 92.1 32.1 9.5
(3.8–15.1) <0.001 0.77, Moderate
Hours slept
(h/day)
0 7.0 0.4 7.0 0.6 −0.17, Trivial
4 7.6 0.5 0.7 (0.6–0.8) 7.1 0.5
0.1 (0.0–0.2)
<0.001 1.00, Moderate
PSQI Global
(score)
0 6.4 2.2 6.1 1.9 0.14, Trivial
4 4.0 1.3 2.4 (2.0–2.8) 5.8 1.8
0.3 (0.1–0.5)
<0.001 −1.16, Moderate
IPAQ-walk
(min)
0 70.5 32.2 77.7 52.0 −0.17, Trivial
4 97.0 30.0 26.5 (18.1–34.9) 95.4 49.0 17.8
(8.0–27.6) 0.163 0.04, Trivial
IPAQ-MVPA
(min)
0 138.0 41.6 146.2 40.3 −0.20, Small
4 210.3 44.3 72.4 (60.0–84.8) 156.9 46.4 10.8
(5.0–16.5) <0.001 1.18, Moderate
F&V Intake
(serve/day)
0 3.3 0.7 3.4 0.7 −0.17, Trivial
4 6.9 1.3 3.6 (3.3–4.0) 3.8 0.9
0.4 (0.1–0.7)
<0.001 2.69, Large
PCS-12
(score)
0 46.3 3.8 47.2 2.8 −0.28, Small
4 51.5 3.4 5.2 (4.4–5.9) 47.9 2.8
0.7 (0.3–1.1)
<0.001 1.14, Moderate
MCS-12
(score)
0 48.6 5.8 49.3 2.8 −0.15, Trivial
4 53.3 3.6 4.7 (3.7–5.8) 49.5 2.9
0.2 (0.2–0.7)
<0.001 1.15, Moderate
Note: Mean
±
SD reported for all participants, intervention and control. Abbreviations: M = mean;
SD = standard
deviation; CI = Confidence interval; ES = effect size; BMI = body mass index. BP = blood pressure.
MAP = mean
arterial pressure. MVPA = moderate-to-vigorous physical activity. PSQI = Pittsburgh Sleep Quality Index.
IPAQ = International
Physical Activity Questionnaire. F&V = fruit and vegetable intake. PCS-12 = Short Health
Form 12v2 physical component summary score. MCS-12 = Short Health Form 12v2 mental component sum-
mary score.
Nutrients 2022,14, 1988 9 of 15
Figure 2.
Mean values for health outcomes across time (baseline and 4-months), showing 95%
confidence intervals ((
a
), bodyweight; (
b
), VO
2max
; (
c
), Mean Arterial Pressure; (
d
), Skinfolds; (
e
), Fruit
and Vegetable Intake; (
f
), Weekly MVPA Minutes; (
g
), Sleep Hours; (
h
), MCS-12). Abbreviations:
VO
2max
= maximal oxygen consumption; MVPA = moderate-to-vigorous physical activity; MCS-12
= Short Health Form 12v2 mental component summary score. Notes: * indicates moderate within
group effect size from baseline to 4-months. ** indicates large within group effect size from baseline to
4-months.
Nutrients 2022,14, 1988 10 of 15
3.4. VO2max, Pushups and Plank Hold
Significant group main effects were found for all measures (p< 0.001) in favor of the
intervention group. The within-group analysis reported significantly greater improved
changes from baseline to post-intervention for all physical performance measures in the
intervention group (p< 0.001), associated with large effect sizes (Table 2; Figure 2). In
contrast, the wait-list control group significantly increased push-ups (t(57) = 5.323,
p< 0.001
,
d= 0.69) and plank hold (t(57) = 3.365, p= 0.001, d= 0.44), yet no significant change was
observed for VO2max.
3.5. Health Behaviors and Self-Rated Health
Significant group main effects in favor of the intervention group were found for all
self-report health measures (p< 0.001) except for weekly walking minutes (p= 0.163). The
within-group analysis reported significantly greater improved health changes from baseline
to post-intervention for all self-report health measures in the intervention group (p< 0.001),
associated with moderate to large effect sizes (see Table 2; Figure 2). Further, the wait-list
control group significantly improved weekly walking, weekly MVPA, global PSQI score,
and Short Health Form 12v2 physical component summary scale score (PCS-12,
p< 0.001
),
enhanced fruit and vegetable intake (p= 0.008), and increased sleep hours (
p= 0.020
).
The significant changes observed within the wait-list control group from baseline to post-
intervention were associated with trivial to small effect sizes (see Table 2).
4. Discussion
To our knowledge, this study is the first clinical trial that has explored the effects of a
lifestyle intervention on physical fitness and body composition measures among airline
pilots. This study aimed to promote enhancement in cardiorespiratory and musculoskeletal
fitness, body composition, and health behaviors through a personalized intervention on
healthy eating, sleep hygiene, and PA.
For most outcome measures, in support of our initial hypothesis the controlled trial
revealed significantly higher improvements in the intervention group compared to the wait-
list control group. Our findings suggest that a face-to-face health assessment alone with no
provision of an intervention may promote small short-term effects for improvements in
health behaviors and weight management among airline pilots. Furthermore, the provision
of a personalized multicomponent lifestyle intervention may facilitate moderate to large
short-term effects for promoting healthy changes in physical fitness, body composition,
and health behaviors among airline pilots.
These findings are important for health care professionals and researchers to provide
insight regarding the efficacy of lifestyle interventions for promoting health, and to inform
practices relating to disease prevention, health promotion, and public health policymaking.
Furthermore, in relation to the limited literature base pertaining to three-component sleep,
nutrition, and PA interventions and the insufficient depth of health behavior intervention
research among airline pilots, our findings provide novel contributions to this field.
Excessive adiposity is evidently associated with higher all-cause mortality and ele-
vated risk of cardiometabolic NCDs [
33
]. Counteractively, clinically significant improve-
ments in NCD risk factors have been reported with as little as 2–3% of weight loss among
those with high BMI [
34
]. A meta-analysis of 59 lifestyle weight loss interventions reported
a pooled mean weight loss range of 5–8.5 kg (5–9% body mass) within the initial six months,
and among studies exceeding 48 months a mean weight loss range of 3–6 kg (3–6% body
mass) [
35
]. Comparatively, in our intervention group we observed 6% weight loss and
1.6 reduction in BMI at four months. Weight loss and BMI alone as assessments of body
composition change are inherently limited due to their inability to precisely measure central
adiposity, fat distribution, bone density, and lean mass [36].
In the present study we assessed additional body composition metrics with girth and
skinfold measures. Waist circumference has been reported as being strongly associated with
all-cause and cardiovascular mortality, with or without adjustment for BMI [
36
]. Further,
Nutrients 2022,14, 1988 11 of 15
skinfold thickness has been reported as a better predictor of body fatness compared to
BMI [
37
]. We found the intervention elicited a decrease of 6 cm waist circumference and
28 mm skinfold thickness sum reduction, which were associated with an overall 3.7%
reduction in predicted body fat percentage and a decrease of 8.1 mmHg for systolic blood
pressure (SBP). These findings are consistent, yet of higher magnitude than a previous meta-
analysis which reported exercise training programs were associated with pooled mean
reductions of 5.1 mmHg SBP and 2.2 cm waist girth [
38
]. This study also reported that
reductions in blood pressure (BP) and waist circumference were associated with reduced
high-density lipoprotein (HDL) cholesterol and metabolic syndrome risk reduction [
38
].
Thus, interventions which induce these adaptations are of importance for risk reduction of
these well-established NCD risk factors [4].
To our knowledge, our study is the first to report on objective measures of cardiores-
piratory capacity among airline pilots. Prospective cohort research suggests exercise
capacity is an authoritative predictor of mortality among adults, and an increase of 1 MET
(3.5 mL/kg/min) is associated with a 12% CVD risk reduction [
30
]. A meta-analysis of
aerobic exercise training interventions among adults (aged 41
±
5 y) reported a pooled
mean increase in VO
2max
of 3.5 mL/kg/min (1.9–5.2, 95% confidence interval (CI)), asso-
ciated with a moderate effect size of 0.6 [
39
]. In comparison, we observed an increase of
4.5 mL/kg/min within our intervention group, associated with a large effect size which ex-
ceeds previously suggested thresholds for clinical relevance [
26
]. However, future research
is required to determine whether these acute adaptations are longitudinally maintained
after the brief 16-week intervention.
The intervention promoted significant positive health outcomes for health behaviors
and self-rated health, associated with moderate to large effect sizes. Sleep duration in-
creased by 0.6 h in the intervention group, which is a lower magnitude compared with a
recent meta-analysis of behavioral interventions to extend sleep length, which reported
a pooled increase of 0.8 h per night (0.28–1.31, 95% CI) [
40
]. In part, this variance may be
related to the different nature of interventions, where the present intervention targeted
multiple-behavior modification for nutrition, sleep, and PA simultaneously, compared
with the individual component focus in other studies (i.e., targeting sleep modification
alone) [40].
For weekly MVPA we found the intervention elicited an increase of 72 min/week,
which is notably higher than a previous meta-analysis which reported a mean increase of
24 min/week from PA interventions implemented in primary care settings [
41
]. Similarly, a
meta-analysis of behavior interventions to increase fruit and vegetable intake reported a
pooled mean increase of 1.1 servings per day [
42
], which was a lower magnitude of change
compared to the increase of 3.6 servings following the present intervention. Notably, a
meta-analysis of effective BCTs for promoting PA and healthy eating in overweight and
obese adults highlighted the use of goal setting and self-monitoring of behavior as strong
predictors of positive short and long-term health behavior change [
43
]. Congruently, our
intervention implemented these components in addition to five other BCTs, which may
have contributed to the observed effect sizes of change.
Strengths and Limitations
A strength of this study is our findings add valuable contribution to a small global
literature base pertaining to interventions that include components for each healthy eating,
PA, and sleep hygiene. The magnitude of effect sizes for positive health change observed in
the intervention may be at least partly attributable to; (a) the implementation of seven BCTs
including collaborative goal setting, (b) the personalized multiple-component nutrition, PA
and sleep approach, (c) the multimodal intra-intervention communication administered
via face-to-face consultations, a telephone call, and regular educational emails, and (d) the
potential underlying motivation of airline pilots to improve their health to maintain their
aviation medical license.
Nutrients 2022,14, 1988 12 of 15
Potential limitations of this study need to be considered in the interpretation of our
findings. Firstly, pilots voluntarily participated in the study via self-selection. Thus, those
who enrolled may have exhibited higher readiness and motivation for health behavior
change than the general population, which may limit the generalizability of our findings.
Secondly, for feasibility of implementation and to minimize participant burden, self-report
measures for health behaviors were utilized which inherently possess inferior validity to
more invasive objective methods. Accordingly, future research, including measures such as
a food frequency questionnaire or photo meal logging for dietary behaviors and actigraphy
coupled with heart rate monitoring (e.g., smart watches) for PA and sleep monitoring,
would be valuable contributions to increase the validity of findings. Third, although the sex
characteristics of our sample are congruent with the general airline pilot population [
8
], the
lack of female participants limits the generalizability of our findings to female populations.
Thus, future research should evaluate the effects of the intervention among an ample
sample size of females. Finally, the intervention was delivered by an experienced health
coach, which presents a barrier to intervention adoption at scale. Future research should
evaluate the delivery of interventions using similar procedures via cost-effective and
scalable methods, such as online modes of delivery (i.e., smartphone application).
5. Conclusions
The personalized 16-week healthy eating, sleep hygiene, and PA intervention im-
plemented in this study elicited significant positive changes associated with moderate
to large effects sizes in all main outcome measures at four months follow-up, relative
to the wait-list control group. Our findings suggest that the achievement of these three
guidelines promotes physical and mental health among overweight airline pilots and these
outcomes may be transferrable to other populations. However, there is a need for future
research to examine whether the observed effects are longitudinally maintained following
the intervention.
Author Contributions:
D.W. and N.G. participated in conceptualization of the study and data
collection; D.W., M.D., B.J., P.W., T.C. and N.G. contributed to the design of the study, data analysis,
interpretation of the results, and manuscript writing. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement:
This study was approved by the Human Research Ethics
Committee of the University of Waikato in New Zealand; reference number 2020#07. The trial protocol
is registered at The Australian New Zealand Clinical Trials Registry (ACTRN12622000233729).
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: Not applicable.
Acknowledgments:
The authors wish to thank the pilots for providing their time to voluntarily
participate in this study.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
TOP TEN TIPS FOR HEALTHY EATING
A system of habits to support a healthier you.
1. Emphasize whole foods
Choose unprocessed natural foods.
As food processing increases, nutrient density decreases. The more ingredients that
are listed on a food, the more processed the food will likely be.
2. Reduce sugar where possible
Nutrients 2022,14, 1988 13 of 15
Limit foods with added sugar (cookies, cakes, sugar sweetened beverages etc.) where
you can. Read labels to avoid hidden sugars (sauces, cereals, dairy products etc.). Aim for
less than 10% of daily energy from sugar or under 5% for better health.
3. Eat a rainbow of foods
Eat a variety of fruit and vegetables each day. Try those with rich colors of red, blue,
green and orange. The more color in your day the more antioxidant, vitamins and minerals
you will be getting.
4. Reduce white
Try to avoid the energy dense white foods like pasta, rice, bread, and potato. Use
MyFitnessPal to understand other options. For example, 2 cups of broccoli with a curry is a
healthier meal and some would say tastier than 2 cups of rice, and far fewer calories! Also
consider having more vegetables that grow above the ground than those that grow below
the ground.
5. Eat lean protein with each meal
Protein foods such as lean meat, chicken, fish, eggs, low fat dairy foods, bean, nuts,
seeds, legumes and lentils aid in muscle repair and support lean body mass.
6. Caution with your portions
Do not heap food on your plate (except vegetables). Use the hand portion sizing guide
to make good meal size decisions. Think twice before having second helpings.
7. Eat slowly and mindfully
Set aside adequate time for your meal so you’re not rushed and chew your food well.
While eating try to avoid watching TV or eating on the go. Pay attention to your food. Eat
until you feel 80% full.
8. Think about your drinks
Drink 2 L of fluids a day. Choose mainly water. Unsweetened fruit juice contains
natural sugar so limit to one glass a day (200 mL/one third pint). Alcohol is high in calories;
limit to one unit a day for women and two for men.
9. Choose good fats
Choose fats that enhance your recovery and immune system not those that break it
down. Some good sources of are nuts and seeds, nut butters, avocado, fatty fish, olive oil,
and flaxseed oils.
10.
Setup your healthy environment
If a food is in your house or possession, either you or someone you love, will eat it.
If you remove the temptation of unhealthy foods from your surroundings and add more
healthy options, you will set yourself up for success.
Appendix B
Sleep Hygiene Strategies for Enhancing Sleep YES Achieving NOT Achieving
1. Sleep at least 7 h
2. Sleep routine or depower hour
3. Regular sleep and wake time
4. Dim lights near bedtime and turn off electronics >30 min before bed
5. Avoid sleep disruptors 4–6 h before bed e.g., caffeine, large meals, alcohol
6. Have a dark, cool, quiet sleep environment
7. Exercise every day, not too close to bedtime
8. Use the bedroom only for sleeping and intimacy
9. Do a brain dump on paper before bed
10. Early morning light exposure
Nutrients 2022,14, 1988 14 of 15
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