Perceived Fitness Protects against Stress

Perceived Fitness Protects against Stress

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J Occup Health 2013; 55: 376–384
J Occup Health, Vol. 55, 2013
Journal of
Occupational Health
Perceived Fitness Protects against Stress-based Mental Health Impairments among Police Officers Who Report Good Sleep
Markus Gerber1, Micheal Kellmann2, Catherine Elliot1, Tim Hartmann1,
Serge Brand1,3, Edith Holsboer-Trachsler3 and Uwe Pühse1
Institute of Exercise and Health Sciences, University of Basel, Switzerland, 2Faculty of Sport Science, Ruhr-University
Bochum, Germany and 3Psychiatric Hospital of the University of Basel, Center for Affective, Stress and Sleep Disorders,
Switzerland
1
Abstract: Perceived Fitness Protects against
Stress-based Mental Health Impairments among
Police Officers Who Report Good Sleep: Markus
G ERBER , et al. Institute of Exercise and Health
Sciences, University of Basel, Switzerland—Objectives: This study examined a cognitive stress-moderation model that posits that the harmful effects of chronic
stress are decreased in police officers who perceive
high levels of physical fitness. It also determined
whether the stress-buffering effect of perceived fitness
is influenced by officers’ self-reported sleep. Methods:
A total of 460 police officers (n=116 females, n=344
males, mean age: M=40.7; SD=9.7) rated their physical
fitness and completed a battery of self-report stress,
mental health, and sleep questionnaires. Three-way
analyses of covariance were performed to examine
whether officers’ self-reported mental health status
depends on the interaction between stress, perceived
fitness and sleep. Results: Highly stressed officers
perceived lower mental health and fitness and were
overrepresented in the group of poor sleepers. Officers
with high fitness self-reports revealed increased mental
health and reported good sleep. In contrast, poor sleepers scored lower on the mental health index. High stress
was more closely related to low mental health among
poor sleepers. Most importantly, perceived fitness
revealed a stress-buffering effect, but only among officers who reported good sleep. Conclusions: High
perceived fitness and good sleep operate as stress
resilience resources among police officers. The findings
suggest that multimodal programs including stress
management, sleep hygiene and fitness training are
essential components of workplace health promotion in
the police force.
(J Occup Health 2013; 55: 376–384)
Received Feb 12, 2013; Accepted Jun 26, 2013
Published online in J-STAGE Jul 26, 2013
Correspondence to: M. Gerber, Institute of Exercise and Health
Sciences, University of Basel, Switzerland (e-mail: markus.
[email protected])
Key words: Insomnia, Mental health, Moderator,
Multiple risk factors, Short-form health survey (SF-12),
Stress buffer
People are at risk of becoming ill when they are
exposed to stress1). Factors such as longer working
hours, heavier workloads, more robust managerial
styles, less job control and increased pressure influence the experiences of employees at work and, in
turn, affect their health and well-being2). According
to Badura and Hehlmann3), organizations are experiencing pressure to work flexibly and efficiently in
globalized markets, which requires healthy and motivated personnel to cope with these challenges. On
the other hand, exposure to occupational stress is
associated with both physical impairments and mental
health problems4). These complications can negatively
impact work productivity as well as worker health5).
During recent decades, police work has undergone
considerable adjustment6). On the one hand, statistics
show a dramatic increase in violent crime in some
Western countries such as Switzerland7). On the other
hand, the role of the police has gradually shifted
towards community policing. Thus, police offers not
only enforce laws and investigate crimes but also
provide proactive social services. With these additional functions, the police encounter increased scrutiny
on how they use their power. Modern policing has
therefore become a balancing act with officers “caught
between the increasing threat of violence on our
streets, high public demands and a mounting focus on
police efficiency and probity”8).
Among health interventionists, there is general
consensus that promoting physically active lifestyles
and cardiorespiratory fitness (CRF) are important
parts of occupational health promotion9). Low CRF is
associated with decreased job performance, increased
absenteeism rates and increased risk for future cardio-
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Markus GERBER, et al.: Perceived Fitness, Stress and Sleep
vascular disorders10).
In contrast, high levels of CRF have proved to
reduce cardiovascular risk markers and mortality11).
Previous research also demonstrated close links
between CRF and mental health. For instance, studies showed that high CRF protects against the development of depressive symptoms12), and that high CRF
is associated with reduced symptoms of occupational
burnout13). In summary, these findings suggest that
psychosocial stress and physical fitness have opposite
effects on both physical and mental health parameters.
Field studies have shown that regular participation
in exercise activities can mitigate the adverse health
outcomes associated with high stress exposure14).
Nevertheless, research is scarce regarding the ability
of increased physical fitness to attenuate the relationship between perceived stress and impaired health in
the adult workforce.
The present investigation expands previous stressbuffer research by placing a specific focus on the
study of perceived physical fitness. This new focus
is warranted because in previous studies, self-rated
fitness was more closely related to mental health and
daily stress than objective fitness measures15). This
suggests that some of the positive emotional results
associated with exercise may occur because of the
psychological gains from the experience of trying to
get fit or believing that one is fit rather than from
an increase in aerobic physical fitness16). Moreover,
perceived fitness proved to be more closely associated
with quality of sleep than self-reported exercise17).
Lastly, perceived fitness explained significant variance
in psychological and physiological stress reactivity,
even more than that attributable to estimated aerobic fitness18). In conclusion, these findings suggest
that perceived fitness is an important health resource,
and that subjective perceptions of fitness are at least
equally important as objective aerobic fitness estimates
to predict mental health outcomes13).
Given the literature presented above, the first aim
of the present study was to test a cognitive stressmoderation model positing that stress impairs individuals’ mental health status and that perceived fitness
attenuates this relationship. The second aim was
to determine whether the stress-buffering potential
of perceived fitness is associated with sleep. This
aspect is relevant because there is a dearth of research
regarding fitness as a resilience resource among individuals with multiple risk factors. Sleep was selected
as an additional risk factor because it emerged in
previous research that there is a close relationship
between stress and sleep among police officers19) and
because physical exercise is positively associated with
quality of sleep20).
The following eight hypotheses will be investigated
in the present study. Hypotheses 1−3: Increased
stress is associated with decreased mental health
levels21), whereas high perceived fitness22) and good
sleep23) are associated with increased mental health.
Hypotheses 4−6: We further expected a positive association between high stress and poor sleep24), and we
assumed that high perceived fitness is related to lower
stress25) and good sleep17). Hypothesis 7: We also
hypothesized that high perceived fitness attenuates
the negative relationship between stress and mental
health14). Hypothesis 8: Nevertheless, we assumed
that the strongest stress-buffering effect occurs with
simultaneously reported high stress and poor sleep.
Hypothesis 8 was derived from the finding that poor
and deprived sleep have tremendous negative effects
on performance and well-being26). Poor sleepers have
limited stress-coping capacities and are therefore at
risk for further stress increases 27). This may also
support research showing that good sleep can bolster
stress recovery28).
Materials and Methods
Participants
The sample consisted of 460 police officers working in an urban area in the German-speaking part
of Northwestern Switzerland. The mean age was
40.7 years (SD=9.7 years). Respondents reported
an average of 13.7 (SD=9.0) years of service. The
sample included personnel from all managerial
levels, with 55% (n=251) being shift-workers. Men
comprised 75% (n=344) of the sample and women
25% (n=116). The majority of the officers were
married (56%, n=256) and did not have a university
degree (89.6%; n=412).
Procedure
A battery of validated questionnaires was sent to all
police officers. In an accompanying letter, the officers
were informed about the purpose of the study, the
voluntary and anonymous nature of participation and
the confidentiality of the information. Participants
provided written informed consent. The questionnaire
took approximately 20−25 minutes to complete. The
completed questionnaires were sent back directly to
the researchers in a prepaid envelope. The response
rate was 48%. The gender distribution (25% females)
represented the general police force (21% females)
(Chi2=2.00, p=ns). The study was conducted in accordance with the ethical principles of the Declaration of
Helsinki.
Measures
1) Control variables
Information was acquired about gender, age, body
mass index (BMI: body weight in kg/body height
378
in m2), highest level of education and shift/non-shift
work schedules. Participants also reported the amount
of cigarettes smoked per day on a 5-point Likert scale
ranging from 0 (none) to 4 (>20 cigarettes/day) and
weekly alcohol consumption (0−7 days).
2) Chronic stress
The Chronic Stress Screening Scale (SSCS) of the
Trier Inventory for the Assessment of Chronic Stress
(TICS) was administered to assess chronic stress29).
The SSCS includes three items related to work overload (e.g., “too many tasks at the same time”), one
item related to excessive social stress (“I perceive
the responsibility for other people as a burden.”), two
items related to excessive demands at work (e.g., “I
do not perform as well as others expect me to do.”),
two items related to lack of social recognition (e.g.,
“Although I give my best, I do not get enough recognition for what I am doing.”), and four items related
to chronic worries (e.g., “I cannot suppress my worrying thoughts.”). Validity and internal consistency of
this instrument have been previously established 29).
For instance, the SCSS was strongly correlated with
the Life Experiences Survey, the Perceived Stress
Scale and the Perceived Stress Questionnaire 29) .
Answers were given on a 5-point Likert scale ranging
from 1 (never) to 5 (very often), with higher scores
indicating higher stress. As for all measures (see
Table 1), the internal consistency of the SSCS was
above the critical value of ≥0.70.
3) Sleep
Participants completed the Insomnia Severity Index
(ISI)30) to measure sleep complaints. The seven items
of the ISI are answered on a 5-point rating scale from
0 (not at all) to 4 (very much). These items refer,
in part, to DSM-IV criteria for insomnia by measuring difficulties falling asleep, difficulties maintaining
sleep, early morning awakening, increased daytime
sleepiness, low daytime performance, low satisfaction
with sleep and worrying about sleep. Higher scores
indicate more sleep complaints. Reliability and validity of the ISI has been established in prior research31).
4) Perceived fitness
Perceived physical fitness was measured with one
item ranging from 1 (very poor fitness) to 10 (excellent fitness)15). Concretely, participants were asked to
respond to the following item: “Overall, how would
you rate your physical fitness?” This measure has
previously been demonstrated to be a valid indicator of perceived fitness, as it was highly correlated
with the 12-item Perceived Physical Fitness scale32).
Moreover, this measure has proved to be correlated
with measures of objective physical fitness, perceived
well-being and sleep17, 32). Nevertheless, the fact that
the measure was only weakly associated with estimated VO2max indicates that perceived fitness and
J Occup Health, Vol. 55, 2013
objectively assessed fitness are distinct constructs.
5) Mental health
Mental health was measured with the psychological composite score of the 12-Item Short-Form Health
Survey (SF-12), a frequently used measure for general
population research. The SF-12 is capable of discriminating between participants with and without chronic
conditions. The discriminatory power of the SF-12
is comparable to that of the 36-Item Short-Form
Health Survey (SF-36)33). The composite score for
the mental health subscale was obtained by weighting
each item as described in the SF-12 manual34), with
higher scores reflecting better mental health.
Statistical analyses
Three independent ANCOVAs were performed
to compare groups of officers with differing stress,
fitness and sleep levels using mental health as the
outcome (Hypotheses 1−3). To classify police officers
into groups with low, moderate and high fitness levels,
we used categories derived from a previous study
with university students17). The cut-off scores were
0−5=low perceived fitness (n=186, 41%), 6−8=moderate perceived fitness (n=167, 37%) and 9−10=high
perceived fitness (n=103, 23%); self-rated fitness level
was missing for 4 officers. According to Morin et
al.31), the total scores of the Insomnia Severity Index
should be interpreted as follows: absence of insomnia (0−7), subthreshold insomnia (8−14), moderate
insomnia (15−21) and severe insomnia (22−28). To
ensure that the categorization reflected a meaningful
criterion and that each cell consisted of at least 10
participants in the three-way ANCOVA, participants
were classified into two groups: 0−14=“good” sleepers (n=281, 61%) and ≥15=“poor” sleepers (n=179,
39%). With regard to self-reported stress, no given
standards existed29). To avoid a median split, we used
the tertiles to obtain groups with approximately equal
sizes. The cut-off scores used in the present study
were: 0−9=low stress (n=155, 34%), 10−15=moderate
stress (n=161, 35%) and 16−48=high stress (n=142,
31%); self-rated stress data were missing for 2 officers. Chi2-tests were carried out to determine how
stress, perceived fitness and sleep were interrelated
(Hypotheses 4−6). A multifactorial ANCOVA was
performed with the SF-12 Mental Health subscale
as the outcome variable. This calculation examined
whether the relationship between stress and health
was moderated by perceived fitness (Hypothesis 7),
and whether this moderation is associated with sleep
(Hypothesis 8). Descriptive statistics, Cronbach’s
alpha coefficients and ANCOVAs were computed with
SPSS 21 (IBM Corp., Armonk, NY, USA) for Mac®.
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Markus GERBER, et al.: Perceived Fitness, Stress and Sleep
Results
were considered to be covariates during hypotheses
testing for Hypotheses 1−3 and 7−8.
Descriptive statistics and sociodemographic influences
Table 1 reports the descriptive statistics for stress,
perceived fitness, sleep and mental health. Compared
with male officers, women reported lower mental
health (F (1,449)=5.27, p<0.01, η 2=0.02). No significant gender effects were found for perceived stress,
sleep complaints and perceived fitness. With increasing age, participants reported slightly higher mental
health (r=0.11, p<0.05) and lower perceived fitness
(r=−0.22, p<0.001). Age was related to neither stress
nor sleep complaints. Shift workers reported more
sleep complaints (F (1,459)=4.53, p<0.01 η 2=0.02) and
increased fitness (F (1,455)=13.94, p<0.001, η 2=0.03).
BMI was significantly associated with mental health (r=
−0.15, p<0.01) and perceived physical fitness (r=−0.38,
p<0.001), whereas no significant relationships existed
with stress and sleep. Level of education, smoking
and alcohol consumption were not associated with any
of the study variables. Thus, since gender, age, BMI
and shift status were significantly associated with at
least one of the main study variables, these factors
Hypothesis 1−3. Group differences in mental health
status based on participants’ stress, perceived fitness,
and sleep
Table 2 shows that after controlling for gender, age,
BMI and shift status, there were significant differences in the mental health of officers with varying
stress levels (Hypothesis 1). Bonferroni post hoc
tests revealed significant differences between all
groups (p<0.001) and that higher stress scores related
to decreased mental health scores. A significant
main effect was also observed for perceived fitness
(Hypothesis 2). The findings delineate that officers
with high perceived fitness levels had the highest
mental health scores.
Finally, a significant group difference in mental
health existed between participants with good and
poor sleep (Hypothesis 3). Thus, participants with
moderate to severe insomnia scores reported lower
mental health than counterparts with low scores
(Table 2). In summary, the results support the first
Table 1. Descriptive statistics for study variables
Perceived stress (SSCS)
Sleep complaints (ISI)
Mental health (SF-12)
Perceived fitness
N
M
SD
Range
Kurtosis
Skewness
α
458
460
450
456
12.97
14.02
50.60
6.00
7.07
4.82
8.40
1.83
0−42
7−31
21.62−62.13
1−10
0.77
0.69
1.25
−0.12
0.74
0.93
−1.27
−0.34
0.89
0.79
0.79
—
Variations in the number of cases depend on missing values for the various scales. SSCS=chronic stress
screening scale. ISI=Insomnia Severity Index. SF-12=12-item short-form health survey.
Table 2. Group differences in mental health between participants with differing
stress levels, perceived fitness and quality of sleep
SF12-Mental health
Stress
Low
Moderate
High
Perceived fitness
Low
Moderate
High
Sleep
Poor
Good
M
SD
F
p
η2
55.67
51.35
44.30
4.01
6.91
9.40
101.59
<0.001
0.315
49.97
50.27
52.10
9.19
8.04
7.36
5.67
<0.01
0.025
46.60
53.19
9.52
6.39
85.16
<0.001
0.161
BMI=Body Mass Index. All analyses controlled for gender, age, BMI and shift status. SF-12=12-item short-form health survey.
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J Occup Health, Vol. 55, 2013
three hypotheses, indicating that mental health is associated with the stress level, perceived fitness and sleep
quality of police officers.
Hypothesis 4−6. How are stress, perceived fitness
and sleep interrelated?
To test whether high stress was associated with
poor sleep (Hypothesis 4), we examined if poor sleepers were overrepresented among participants with
moderate and high stress levels. Table 3 validated
this assumption by identifying a linear relationship
between self-reported stress and quality of sleep.
Further, Table 3 shows that perceived fitness was
associated with perceived stress (Hypothesis 5).
Individuals who reported high stress scores were more
likely to report low, rather than high, fitness levels.
Although the findings pointed in the expected
direction, no significant association existed between
perceived fitness and sleep (Hypothesis 6).
In summary, the results accord with our second
set of hypotheses stating that self-reported stress,
perceived fitness and sleep are interrelated among
police officers. Contrary to our hypothesis, however,
no significant association occurred between perceived
fitness and sleep.
Hypothesis 7−8. Does perceived fitness protect
against stress-related mental health impairments, and
is the stress-buffering effect related to participants’
sleep?
Table 4 summarizes the findings of the multifactorial ANCOVA with stress, perceived fitness and sleep
as fixed factors. After controlling for gender, age,
BMI and shift status, two significant main effects
were found for stress and sleep. The effect size for
stress was high (20.7% of explained variance), whereas the one for sleep was weak to moderate (7.2% of
explained variance). Figure 1 corroborates that participants who reported high stress and perceived poor
sleep had lower mental health scores.
Additionally, a significant two-way interaction was
observed between stress and sleep, but not between
the other variables. As illustrated in Fig. 1, participants with high perceived stress self-reported higher
mental health if they perceived good versus poor
sleep. However, the differences between good and
poor sleepers are of minor magnitude under low and
moderate stress conditions. In contrast, there was no
support for Hypothesis 7, which posited that perceived
fitness moderates the relationship between stress
and mental health, independent of sleep. Rather,
Table 4 suggests that the stress-buffering potential of
perceived fitness is associated with participants’ sleep.
When good sleep was reported, high perceived fitness
protected against low mental health (Fig. 2). Thus,
among good sleepers, participants with high perceived
fitness levels had relatively high mental health scores
despite reporting high stress. This was not the case
among participants with low or moderate fitness
perceptions. Among poor sleepers, mental health
linearly decreases with increasing stress, independent
of perceived fitness.
In summary, our data showed no support for
perceived fitness as a direct stress-buffer (Hypothesis
7). Our findings suggested, however, that the stressbuffering potential of perceived fitness is related
to sleep quality. Instead of finding a fitness-based
stress-buffering effect among participants who reported multiple risk factors (Hypothesis 8: high stress and
poor sleep), our data unravelled that perceived fitness
protected against impaired mental health only among
participants who reported good sleep.
Discussion
The key findings of the present study are that
high stress was more closely related to low mental
health among poor sleepers and that perceived fitness
protected against stress-related mental health impair-
Table 3. Group differences in stress, sleep and fitness between participants with differing stress levels, perceived fitness and quality of sleep
Sleep
Fitness
Poor
n
Stress
Low
Moderate
High
Sleep
Good
Poor
Good
%
n
Low
%
Chi2=60.95, p<0.001, C=0.34
30
19.4
125
80.6
59
36.6
102
63.4
90
63.4
52
36.6
n
54
65
66
104
82
Moderate
%
n
%
High
n
Chi2=11.13, p<0.05, C=0.16
35.3
55
35.9
44
40.6
55
34.4
40
46.8
56
39.7
19
Chi2=4.01, p=ns
37.3
106
38.0
69
46.3
61
34.5
34
%
28.8
25.0
13.5
24.7
19.2
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Markus GERBER, et al.: Perceived Fitness, Stress and Sleep
Table 4. Group differences in mental health depending on participants’ stress, perceived fitness
and sleep
SF-12 Mental health
Model 1:
Unadjusted
F
Gender
Age
BMI
Shift status
Stress
Fitness
Sleep
Stress × Fitness
Stress × Sleep
Fitness × Sleep
Stress × Fitness × Sleep
p
55.53
0.10
29.30
0.20
5.51
2.49
4.40
<0.001
ns
<0.001
ns
<0.01
ns
<0.01
Model 2:
Controlled for gender, age,
BMI and shift status
η2
F
0.206
—
0.064
—
0.025
—
0.040
0.06
6.28
10.72
0.75
55.20
1.66
32.59
0.08
4.87
1.99
3.19
p
ns
<0.05
<0.01
ns
<0.001
ns
<0.001
ns
<0.01
ns
<0.05
η2
—
0.015
0.025
—
0.207
—
0.072
—
0.023
—
0.029
BMI=Body Mass Index. SF-12=12-item short-form health survey.
Insomnia
Severity
Index
60
“Good” sleep
“Poor” sleep
SF-12 Mental Health
●
55
■
●
50
■
●
45
■
40
Low stress
Moderate stress
High stress
TICS Screening Scale for Chronic Stress
Fig. 1. Interaction effects of stress and sleep on officers’ mental health. SF-12=12 item short-form health survey.
ments only among those who reported good sleep.
Eight hypotheses were formulated in total, and
these will now be discussed in sequence. This study
confirmed the first hypothesis: Stress is associated
with impaired self-perceived mental health. This
coincides with prior research regarding the relationship between stress and mental health among police
officers21). The effect size points towards a strong
link between self-reported stress and mental health.
Consequently, stress management is a vital issue
among police officers, especially since police work is
considered more stressful than other professions21).
Support was also found for the second hypothesis:
High levels of perceived fitness were associated with
increased mental health 22). This parallels previous
studies showing that high aerobic fitness protects
against symptoms of burnout and depression12, 13).
Our results also support a significant link between
perceived fitness and mental health indicators13, 32).
Our third hypothesis was maintained. It proposed
that sleep problems were related to lower perceived
mental health. Analogous to prior research23, 31), officers with sleep complaints reported lower mental
health scores. Poor sleep may affect health through
multiple behavioral, physiological and neurobiological
changes35). For instance, sleep loss has been associated with reduced work performance26) and changes in
HPA axis activity36).
The fourth hypothesis was sustained: High stress
was associated with increased sleep complaints24).
This relationship was attributed to both cognitive
factors as well as neurophysiological changes35). For
example, stress exposure may impede sleep onset by
increasing cognitive and somatic arousal. In particular, one study posits that intrusive thoughts associated with past stress could elicit worries about being
unable to fall asleep and the potential consequences
of a sleep deficit37).
To reinforce Hypothesis 5, perceived fitness was
significantly associated with self-reported stress. This
is congruent with field studies showing that regular
exercise is associated with reduced levels of stress25).
It further confirms laboratory studies that found
382
“Good” sleep
Perceived
fitness
60
Low fitness
Moderate fitness
High fitness
●
SF-12 Mental Health
●
55
■
●
■
●
●
50
●
■
45
40
Low stress
Moderate stress
High stress
TICS Screening Scale for Chronic Stress
“Poor” sleep
Perceived
fitness
60
SF-12 Mental Health
reduced stress reactivity among participants with
high exercise or fitness levels38). Moreover, this finding accords with the notion that self-rated fitness is
negatively related to daily stress15). Also, perceived
fitness explains significant variance in the psychological and physiological reactivity to laboratory stress
beyond that attributable to estimated aerobic physical
fitness18, 32). Nevertheless, it must be highlighted that
our study could not show that perceived fitness is a
better explanatory variable than objectively assessed
fitness, as objective fitness was not assessed.
Our sixth hypothesis was not confirmed. High
perceived fitness was not associated with good sleep.
This is at odds with a Swiss college student study,
which indicated that the relationship between exercise
and sleep might be a “mental affair” that is more
related to the perceptions of one’s exercise involvement than the behavior itself17). The diverging findings may be attributable to the fact that a previously
established cut-off was used in the present study
to distinguish between “good” and “poor” sleepers,
whereas in the college student study, sleep was used
as a continuous variable.
Finally, no support was found for Hypothesis 7,
which assumed a direct fitness-based stress-buffer
effect. Scientists argued that an active leisure style
can operate as a buffer against stress to maintain
physical and mental health by providing feelings
of self-determination, social support or a mindset
of mental toughness39). A recent review, however,
showed that the stress-buffer hypothesis associated with exercise and fitness was only supported in
about half of the existing studies14). Perhaps these
inconsistencies are due to extraneous factors such as
impaired sleep. This study reveals, for the first time,
that perceived fitness operates as a stress-resilience
resource among participants who perceive good sleep
(Hypothesis 8). This finding alludes to the notion that
high stress exposure combined with poor sleep is too
heavy a burden for a single protective factor (perceived
fitness) to counteract the negative corollaries of stress.
Furthermore, the serious consequences of multiple
risk factor exposure are well known from childhood
psychopathology. For instance, a study showed that
for each additional risk factor, the odds of a psychiatric diagnosis increased by 20% among children with
depressed mothers40).
This research extends into broader areas. It dovetails with studies showing that the risk for the metabolic syndrome is substantially increased among individuals who accumulate several risk factors41). In an
occupational setting, this underlines the importance of
multimodal interventions that (i) focus on more than
one risk factor and (ii) are tailored towards employee
needs and may include stress management, sleep
J Occup Health, Vol. 55, 2013
55
Low fitness
Moderate fitness
High fitness
●
●
■
●
50
■
45
●
■
40
●
Low stress
Moderate stress
High stress
TICS Screening Scale for Chronic Stress
Fig. 2. Interaction effects of stress and perceived fitness on
officers’ mental health, separately for “Good” sleepers
and “Poor” sleepers. SF-12=12 item short-form health
survey.
hygiene and fitness training, for example. Importantly,
a recent pilot study showed that such a multiple risk
factor intervention is feasible within the healthcare
system and was agreeable with participants42).
The present study expands extant research by
focusing on perceived fitness as a stress-resilience
resource and testing the stress-buffering hypothesis for
two combined risk factors. Even though fitness was
assessed via self-report, perceived fitness was correlated with objectively measured fitness and self-reported
leisure-time exercise in prior research43). Moreover,
the relatively large sample size allowed calculation
of three-way interactions between stress, fitness and
sleep. Finally, it is noteworthy that this study emphasized chronic perceived stress, since Berg et al.21)
argued that police officers are better prepared to face
operational stressors, whereas the skills to manage
organizational stressors are less developed.
Notwithstanding the strengths of our study, the
present investigation has four major limitations. First,
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Markus GERBER, et al.: Perceived Fitness, Stress and Sleep
the cross-sectional design does not allow a causal
interpretation of the data. Second, all variables were
assessed with subjective self-report measures. Third,
the response rate was below 50 percent. However,
this response rate is comparable to other published
police force studies21). Fourth, we did not control for
psychiatric disorders or the intake of psychopharmacological substances.
Conclusions and Practical Implications
From an applied point of view, the results demonstrate that stress prevention, coping skills training
and sleep hygiene should be incorporated into workplace health promotion programs of police departments. Additionally, in a police setting, good fitness
is important since, high agility and physical readiness
are fundamental in dealing with unpredictable and
violent situations. Nevertheless, our findings suggest
that fitness training alone is insufficiently effective for
all employees but that individually tailored treatments
focusing on more than one risk factor may exude
more beneficial outcomes.
Acknowledgments: The study was financially
supported by the Kantonspolizei Basel-Stadt (KP-BS),
Switzerland. The KP-BS had no influence concerning
data collection, data entry, data analyses, interpretation
of the data, writing and submission of the manuscript
or the journal chosen for possible publication. We
thank Claudia Zuber for her contribution to the data
collection and processing. We also thank all participants for their contribution of their valuable time and
effort to the study.
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Conflicts of interest: All authors declare that there
are no conflicts of interest.
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