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Research concerning the association between stress at work and body mass index (BMI) has mainly focused on two models (ie, job demand-control and effort-reward imbalance) as predictors and mostly been cross-sectional. The aim of our study is to extend previous research in two ways. First, social stressors - in the sense of social conflict and animosities at work - were included as an independent variable, arguing that they should be an especially promising predictor as they reflect a "social-evaluative threat". Second, a longitudinal design was employed with a two-year follow-up. In addition, the variables specified by the job demand-control model and the effort-reward imbalance model were assessed as well. Participants comprised 72 employees (52 men, 20 women) from a Swiss service provider. Multiple regression analyses were used to predict BMI two years later with social stressors, effort-reward imbalance, demands, control, and the interaction of demands and control. Baseline BMI was controlled so that the dependent variable reflects the change in BMI over two years. Regression analyses revealed control and social stressors to be statistically significant predictors of follow-up BMI, while effort-reward imbalance was marginally significant. The results underscore the importance of social stressors and job control as predictors of stress-related impaired health.
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Scand J Work Environ Health 2011, vol 37, no 1 45
Original article
Scand J Work Environ Health 2011;37(1):45–53
Does stress at work make you gain weight? A two-year longitudinal study
Martial Berset, PhD,1, 2 Norbert K Semmer, PhD,1, 2 Achim Elfering, PhD,1, 2 Nicola Jacobshagen, PhD,1, 2
Laurenz L Meier, PhD 1,2
Berset M, Semmer NK, Elfering A, Jacobshagen N, Meier LL. Does stress at work make you gain weight? A two-
year longitudinal study. Scand J Work Environ Health. 2011;37(1):45–53.
Objectives Research concerning the association between stress at work and body mass index (BMI) has mainly
focused on two models (ie, job demand–control and effort–reward imbalance) as predictors and mostly been
cross-sectional. The aim of our study is to extend previous research in two ways. First, social stressors – in the
sense of social conflict and animosities at work – were included as an independent variable, arguing that they
should be an especially promising predictor as they reflect a “social-evaluative threat”. Second, a longitudinal
design was employed with a two-year follow-up. In addition, the variables specified by the job demand–control
model and the effort–reward imbalance model were assessed as well.
Methods Participants comprised 72 employees (52 men, 20 women) from a Swiss service provider. Multiple
regression analyses were used to predict BMI two years later with social stressors, effort–reward imbalance,
demands, control, and the interaction of demands and control. Baseline BMI was controlled so that the dependent
variable reflects the change in BMI over two years.
Results Regression analyses revealed control and social stressors to be statistically significant predictors of
follow-up BMI, while effort–reward imbalance was marginally significant.
Conclusions The results underscore the importance of social stressors and job control as predictors of stress-
related impaired health.
Key terms body mass index; control; demand; effort–reward imbalance; job demand–control model; social stressor.
1 Swiss National Center of Competence in Research on “Affective Sciences”, Geneva, Switzerland
2 Department of Psychology, University of Bern, Bern, Switzerland
Correspondence to: Martial Berset, Department of Psychology, University of Bern, Muesmattstrasse 45, 3000 Bern 9, Switzerland. [E-mail:
martial.berset@psy.unibe.ch].
This study focused on the impact of working conditions
on the body mass index (BMI). Working conditions
investigated include job demands and job control, which
are derived from the job demand–control model, as well
as effort–reward imbalance and social stressors.
The BMI is an important indicator of health. It is
predictive of many health outcomes, such as diabetes
(1) and cardiovascular disease (2, 3). In addition, it is
an important driver of costs in the workplace through
direct medical costs as well as indirect costs, such as
absenteeism or workplace injuries (4). Given the impor-
tance of BMI for individual health and organizational
costs, it seems crucial to identify variables that predict
BMI and to understand mediating mechanisms linking
stress at work and body weight. Knowing these variables
can increase our understanding of the development of
diseases that are related to body weight, and it can help
to develop preventive measures.
Stress at work has repeatedly been considered a
possible factor in the development of obesity (5, 6).
Although we do not specifically examine the mecha-
nisms linking stress at work and BMI in our study, we
will briefly sketch the most important ones in order to
explain the rationale behind our work.
One way in which stress can be transferred into a
higher body weight is via the hormone cortisol, which
is an important hormone in the regulation of the human
stress response (7). Cortisol leads to an accumulation of
fat in abdominal tissue and inhibits sex and growth hor-
mone secretion, which would counteract this accumula-
tion (5). Therefore, being exposed to stress should lead
to an accumulation of fat via elevated cortisol levels.
A further mediating mechanism refers to changes in
eating behavior. Eating may alleviate feelings of stress
by making one feel better (8). Consequently, scales on
coping with stress often include items like “eating more”
46 Scand J Work Environ Health 2011, vol 37, no 1
Work stress as a predictor of body weight
(9). The same applies to increased alcohol consumption
(10). Tsutsumi et al (11) not only found an association
of job strain with higher consumption of alcohol but also
with lower consumption of vegetables. Furthermore,
Hellerstedt & Jeffery (12) found that job demands were
associated with higher fat intake among American men.
Such unhealthy eating and drinking behavior is likely
to be connected to stress because stress tends to weaken
self-regulatory capacity (13). To the extent that eating
healthy (or refraining from over-eating) requires self-
control, people may eat more and less healthy when
stressed.
Besides food intake, physical activity is an important
factor that influences weight. Exercising, however, also
requires self-control, which tends to suffer under stress.
Therefore, people should tend to exercise less when
stressed, a situation which has, indeed, been found (14).
Impaired sleep may be a further mechanism how
stress at work affects body weight. It has, for example,
been found that short sleep duration is associated with
a higher BMI (15, 16). An important cause of impaired
sleep is stress (17). Therefore, it is plausible to assume
that impaired sleep is a mediating mechanism between
stress at work and body weight.
Stress at work and body mass index
Empirical studies investigating the association between
stress at work and BMI have mainly concentrated on
two models of stress: the job demand–control (18)
and the effort–reward imbalance (19) models. We will
briefly discuss pertinent research concerning these two
models and BMI and present arguments why incorporat-
ing social stressors should be promising when trying to
predict BMI.
The job demand–control model and BMI
The job demand–control model postulates an inter-
action between job demands and control. According
to this model, job control should help employees to
cope with the demands and thus attenuate their effects;
conversely, the combination of high demands and low
control implies a “high-strain” job, which should be
experienced as especially stressful. Although an asso-
ciation between high strain and BMI seems plausible
theoretically, this association has not been substanti-
ated empirically so far. Overgaard et al (20) reviewed
ten cross-sectional studies investigating the association
between body weight and the variables specified by the
job demand–control model. They concluded that the
literature is not supportive of such an association. This
conclusion holds for high-strain jobs as well as for the
constituent variables (demands, control). The review
by Siegrist & Rödel (21) draws a similar conclusion.
However, if only prospective studies in this review (21)
are considered, the job demand–control model receives
some support in four out of six studies, although, for the
most part, only main effects of either demands or control
emerged as significant predictors.
The effort–reward imbalance model and BMI
The effort–reward imbalance model (19) suggests that a
perceived imbalance between the effort invested in one’s
work and the rewards received (such as money, career
opportunities, support) induces increased susceptibility to
illness (22). This model has also been investigated with
regard to BMI, although much less than the job demand–
control model. Kivimäki et al (23) found a positive
association between effort–reward imbalance and BMI
in a prospective study. Kouvonen et al (24) found similar
results in a cross-sectional study. In contrast Vrijkotte et
al (25) could not show any association between effort–
reward imbalance and BMI in a cross-sectional analysis.
Thus, there is weak support for an association between
effort–reward imbalance and BMI, but the number of
studies is still too small to draw firm conclusions.
Social stressors
One reason for the lack of empirical evidence for an
association between work stress and BMI so far might
lie in the small range of predictors that have been
investigated. Specifically, we argue that social stress-
ors, which have largely been neglected in this context,
should be especially promising. Social stressors, as
described by Dormann & Zapf (26) relate to “social
animosities, conflicts with co-workers and supervisors,
unfair behavior, and a negative group climate”. Social
stressors are a direct threat to an individual’s need to
belong, which is related to negative affect (27) and self-
esteem (28). Therefore, social stressors are supposed to
be experienced as being very unpleasant.
Social stressors have, indeed, been shown to predict
strain (26, 29). Further results supporting the importance
of social stressors come from a meta-analysis by Dicker-
son & Kemeny (30). They showed that social-evaluative
elements (“social-evaluative threat”) emerged as one of
two stressful characteristics of tasks capable of eliciting
cortisol responses (the second one being uncontrollabil-
ity). Considering the importance of cortisol with respect
to body weight, such results, therefore, strongly suggest
that social stressors should not be neglected in this area.
To our knowledge, however, there is only one study (6)
that has investigated the relationship between social
aspects (ie, conflicts) of the workplace and BMI and
which, surprisingly, found no association. However, in
that study the measure of conflict was dichotomized,
using a rather low threshold, which resulted in almost
Scand J Work Environ Health 2011, vol 37, no 1 47
Berset et al
half of the participants being considered as being exposed
to social conflict, treating people with rather low levels
of conflict on the same level as people with rather high
levels of conflict. Assessing social stressors with a con-
tinuous variable may well lead to different results. In
any case, with only one study, it would be premature to
conclude that social stressors are not predictive of BMI.
Altogether, theoretical considerations and empirical evi-
dence for the role of social stressors in predicting strain
(including cortisol) responses strongly suggest social
stressors to be promising for predicting weight change.
With this study, we would like to contribute to the
research on BMI in several ways. First, we want to
assess the predictive power of the two most prominent
models in occupational stress research, namely those
of job demand–control and effort–reward imbalance.
Second, we want to extend previous research by inves-
tigating the predictive power of social stressors. Third,
we want to strengthen the explanatory value of our
results by employing a longitudinal design with a time
lag of two years. By controlling for baseline BMI, the
remaining variance reflects the relative change in BMI
during these two years, which we then try to predict with
baseline working conditions. Testing all predictors in a
single model would imply an unfortunate ratio of predic-
tors to participants and problems with multicolinearity,
since the predictors are considerably correlated (see
table 1). Therefore, we tested each model separately and
then entered those predictors that were significant in any
of these three analyses into a final model.
Hypotheses
As argued, it is theoretically plausible to assume asso-
ciations between demands, control, and their interaction
with BMI, with empirical evidence being weak in cross-
sectional studies, but somewhat stronger in longitudinal
studies. Confirming evidence relates mostly to individ-
ual variables of the model, that is, demands and control.
These findings are in line with the evidence on the job
demand–control model in general (31). We, therefore,
expect BMI at time 2 to be associated positively with
demands at time 1 (hypothesis 1.1) and negatively with
control at time 1 (hypothesis 1.2).
Theoretically, an effect of effort–reward imbalance
on body weight seems very plausible as well. Empiri-
cally, there is some support for this association, but it
is based on only few studies so far. Given the trend in
the few existing studies, and the strong support for the
model in general (32), we expect a positive association
between effort–reward imbalance at time 1 and BMI at
time 2 (hypothesis 2).
Concerning social stressors, there is not much evi-
dence with respect to BMI. Based on our theoretical
arguments and the empirical evidence for the importance
of social stressors in general, we expect a positive asso-
ciation of social stressors at time 1 with BMI at time 2
(hypothesis 3).
Methods
Participants and design
Employees from three departments of a large Swiss ser-
vice provider were asked to fill in a questionnaire at two
times (2003 and 2005), with a time-lag of two years. The
study was presented at general meetings of the depart-
ments, with about 245 individuals present. A total of 147
participants filled out the first questionnaire, correspond-
ing to a response rate of approximatively 65%. Two years
later, those 114 participants who were still working in the
same organization were asked to participate again. Of
those, 76 (66%: 56 men, 20 women) agreed and filled out
the follow-up questionnaire. The sample includes blue-
collar workers (eg, people transporting goods) and white-
collar workers (eg, secretaries, accountants). The mean
age was 41.89 years [standard deviation (SD) = 9.13].
Due to missing data and list-wise deletion, the sample
for the various analyses varies between N=68 and N=72.
Measures
Job demand–control model. Karasek (18) conceptual-
ized job demands rather broadly, including “workload
demands, conflicts or other stressors” (p287). We follow
this tradition of a broader definition and used a short
self-report version of the Instrument for Stress Oriented
Task Analysis (ISTA) (33) to measure demands. It
assesses five task stressors with four items each. Time
pressure (eg, “How often do you have to work faster
than normal in order to complete your work?”), con-
centration demands (eg, “How often must you remem-
ber many things simultaneously?”), uncertainty (eg,
“How often do you receive unclear instructions?”), and
work interruptions (eg, “How often are you interrupted
by other colleagues during the course of your work
activity?”) were assessed with a 5-point Likert format,
reflecting either intensity or frequency. The format of
the fifth scale, performance constraints, was slightly
different: two workplaces with opposing characteristics
were described (for example “must spend a lot of time
in order to get informa tion and/or materials to pursue
his/her work activity” versus “always has the necessary
information and/or materials at his/her disposal”); par-
ticipants had to rate, on a 5-point Likert scale, to which
of those two workplaces their own is most similar.
To estimate the reliability of such a composite of
scales consisting of subscales that are inter-correlated
48 Scand J Work Environ Health 2011, vol 37, no 1
Work stress as a predictor of body weight
but do not represent a homogeneous construct, a com-
posite score proposed by Nunnally & Bernstein (34) is
appropriate. The composite score was rYY = 0.85.
Job control was measured with a 6-item scale that
covers time control (eg, “To what degree is it possible
for you to set your own working pace?”) and method
control (eg, “Can you decide on which way to carry out
your work?”) with 3 items each [5-point Likert scale,
ISTA (33)]. Internal consistency (Cronbach’s alpha)
was α=0.91.
Effort-reward imbalance. Effort–reward imbalance
was measured with a 6-item scale by van Yperen (35)
adapted by Jansen (36). Van Yperen (35) calls the scale
“exchange relationship with the organization”, which
encompasses the idea of equity between an employee’s
investments and the rewards he or she gets. In contrast
to the instrument by Siegrist (37), this scale does not
calculate a ratio between efforts and reward, but directly
envelopes the imbalance of efforts and rewards in single
items. Examples are: “The rewards you receive are not
proportional to your investments” and “You give a great
deal of time and attention to the organization, but get
very little appreciation”. Cronbach’s alpha was α=0.92.
Social stressors. Social stressors were measured with
an 8-item scale developed by Frese & Zapf (38). The
items had to be rated on a 5-point Likert scale ranging
from “strongly disagree” to “strongly agree”. The items
are: (i) “My supervisor always assigns the pleasant
tasks to particular people”, (ii) “One has to pay for the
mistakes of others”, (iii) “With some colleagues there
is often conflict”, (iv) “I have to work with people who
can’t take a joke”, (v) “My supervisor pushes all the
time”, (vi) “When an error occurs, the supervisor always
blames us but never himself”, (vii) “Around here, one
gets reprimanded even for little things”, (viii) “Some
colleagues interrupt the regular work rhythm repeatedly”
(39). Cronbach’s alpha was α=0.75.
Body mass index and control variables. Self-reported
data provided weight and height of the participants for
the calculation of BMI. Since the BMI is dependent on
age and gender (40), these two variables were entered
as control variables into the analyses. Social class has
also been found to be inversely related to being over-
weight (41). We, therefore, controlled for educational
attainment, which can be considered a proxy for social
class. We asked individuals about their highest gradua-
tion level achieved and clustered the answers into three
groups: basic school graduation, apprenticeship, and
higher education. Because this is an ordinal variable, we
used Spearman’s Rho to calculate correlations and built
dummy variables to control for educational attainment
in regression analyses.
Statistical analysis
First, correlations between all predictors, controls,
and the criterion were calculated. Second, to test each
model individually, three prospective multiple regres-
sion analyses were computed. The control variables
and baseline BMI (time 1) were entered in the first
step, the predictors specified by the respective stress
model (job demand–control, effort–reward imbal-
ance, social stressors), measured at the baseline, were
entered in the second step. With respect to the job
demand–control model, the interaction term followed
in step 3. The variables for calculating the interaction
were centered at their mean. Entering baseline BMI
as a control variable had the consequence that the
individual stability of the body weight was partialed
out of the dependent variable. Therefore, the depen-
dent variable then represented the deviation of BMI
from the value that was to be expected based on its
baseline value.
In a second step, a trimmed model, consisting of
the predictors that were significant in any of the initial
analyses, was calculated.
Results
Correlations
Means, SD, and intercorrelations are shown in table 1.
Mean BMI did not change from time 1 to time 2. As
expected, BMI at baseline correlated very highly with
BMI at follow-up (r=0.92, P<0.01), indicating consid-
erable stability. Gender correlated negatively with BMI
at baseline (r=-0.33, P<0.01) and follow-up (r=-0.25,
P<0.05), indicating that men had a higher BMI than
women. No association was found for age.
Associations of the stressors with follow-up BMI
were positive and significant throughout. Demands
(r=0.31, P<0.05) and social stressors (r=0.24, P<0.05)
were also correlated positively with baseline BMI;
however, effort–reward imbalance was not. Correla-
tions between stressors and BMI were higher for BMI
two years later than at baseline, possibly indicating
that the influence of stress on body weight is a delayed
one.
Women experienced less job demands (r=-0.29,
P<0.05) and less job control (r=-0.31, P<0.05) than
men. Educational attainment correlated positively
with demands and control (r=0.29, P<0.05; r=0.36,
P<0.01) but not with BMI. Intercorrelations between
the different stressors were positive. Job control was
negatively correlated with effort–reward imbalance but
not associated with BMI.
Scand J Work Environ Health 2011, vol 37, no 1 49
Berset et al
Regression analysis
Tables 2–4 show the results for the regression analysis
pertaining to each stress model. Baseline BMI was by
far the strongest predictor of BMI at follow-up. Con-
cerning the variables of the job demand–control model,
only control emerged as a significant predictor, and the
coefficient was negative, as expected (table 2). Thus,
control, while not correlated with baseline and follow-
up BMI, did predict relative change in BMI. Neither
the main effect for demands nor the interaction between
demands and control were significant predictors of
follow-up BMI.
Effort–reward imbalance did not predict BMI to a
statistically significant amount, although it was margin-
ally significant (P=0.08; table 3). Social stressors did,
however, emerge as a significant predictor (table 4).
Since control and social stressors emerged as sig-
nificant predictors of BMI when the underlying models
were tested individually, we ran a further regression
analysis in which we entered control and social stress-
ors in step 2, after demographics and baseline BMI. As
shown in table 5, both could explain a significant amount
of variance over and above baseline BMI.
Discussion
The most important result of these analyses is that social
stressors and job control predicted relative change in
BMI over a period of two years. We expected social
stressors to be especially important because they imply
being offended and treated with disrespect. Thus, they
represent a threat to one’s self-worth. Feeling accepted
by others is a basic human need; therefore, a social
situation involving negative evaluations and offending
treatment are likely to be experienced as highly stressful
(42). As outlined in the introduction, being stressed can
then lead to a gain in weight through several pathways,
Table 1. Descriptive statistics and correlations among the study variables. All are Pearson correlation coefficients except for correlations with
educational attainment, which are Spearman’s Rho; N=68. [SD=standard deviation; BMI=body mass index; ERI=effort–reward imbalance.]
Variables Mean SD BMI
time 1
BMI
time 2
Demands
time 1
Control
time 1
ERI
time 1
Social stressors
time 1
Age Gender a
BMI time 1 24.45 3.20 · · · · · · · ·
BMI time 2 24.45 3.14 0.92 b· · · · · · ·
Demands time 1 3.07 0.44 0.31 b0.36 b· · · · · ·
Control time 1 3.51 1.00 0.17 0.01 0.04 · · · · ·
ERI time 1 3.25 1.29 0.18 0.25 c0.48 b-0.24 c· · · ·
Social stressors time 1 2.05 0.56 0.24 c0.35 b0.41 b-0.21 d0.60 b· · ·
Age 41.89 9.13 0.06 0.02 0.11 0.08 -0.17 -0.07 · ·
Gender a0.28 0.45 -0.33 b-0.25 c-0.29 c-0.31 c-0.16 -0.19 -0.17 ·
Educational attainment · · 0.06 -0.01 0.29 c0.36 b-0.18 -0.07 0.01 -0.24 c
a 0=male, 1=female. Listwise deletion.
b P<0.01 (2-tailed).
c P<0.05 (2-tailed).
d P<0.10 (2-tailed).
Table 2. Summary of multiple regression analysis for the job demand–control model predicting follow-up body mass index (BMI); N=68.
[B=unstandardized regression coefficient. SE B=standard error of unstandardized regression coefficient. β=standardized regression
coefficient. R2=change in explained variance. R2=explained variance.]
Variables B (final) SE B β (final) R2R2
Step 1 · · · · 0.85
BMI time 1 0.94 0.05 0.94 a· ·
Education dummy I -0.61 0.53 -0.06 · ·
Education dummy II 0.29 0.36 0.04 · ·
Age 0.00 0.02 -0.01 · ·
Gender b0.42 0.38 0.06 · ·
Step 2 · · · 0.02 a0.87
Demands time 1 0.47 0.37 0.07 · ·
Control time 1 -0.45 0.16 -0.14 a· ·
Step 3 · · · 0.00 0.87
Demands × control time 1 -0.28 0.34 -0.04 · ·
a P<0.01 (2-tailed)
b 0=male, 1=female.
50 Scand J Work Environ Health 2011, vol 37, no 1
Work stress as a predictor of body weight
Table 3. Summary of multiple regression analysis for the effort–rewards imbalance model predicting follow-up body mass index (BMI);
N=70. [B=unstandardized regression coefficient. SE B=standard error of unstandardized regression coefficient. β=standardized regres-
sion coefficient. R2=change in explained variance. R2=explained variance.]
Variables B (final) SE B β (final) R2R2
Step 1 · · · · 0.85
BMI time 1 0.93 0.05 0.94 a· ·
Education dummy I -0.71 0.55 -0.06 · ·
Education dummy II 0.26 0.36 0.04 · ·
Age 0.00 0.02 0.00 · ·
Gender b 0.70 0.37 0.10 c· ·
Step 2 · · · 0.01 c0.86
Effort–reward imbalance time 1 0.22 0.12 0.09 c· ·
a P <0.01 (2-tailed).
b 0=male, 1=female.
c P <0 .10 (2-tailed).
Table 4. Summary of multiple regression analysis with social stressors predicting follow-up body mass index (BMI); N=70.
[B=unstandardized regression coefficient. SE B=standard error of unstandardized regression coefficient. β=standardized regression
coefficient. R2=change in explained variance. R2=explained variance.]
Variables B (final) SE B β (final) R2R2
Step 1 · · · · 0.85
BMI time 1 0.92 0.05 0.93 · ·
Education dummy I -0.80 0.52 -0.07 · ·
Education dummy II 0.14 0.33 0.02 · ·
Age 0.00 0.02 -0.01 · ·
Gender a0.71 0.35 0.10 b· ·
Step 2 · · · 0.02 c0.87
Social stressors time 1 0.75 0.26 0.14 c· ·
a 0=male, 1=female.
b P<0.05 (2-tailed).
c P<0.01 (2-tailed).
such as heightened cortisol levels, changed eating pat-
terns, impaired sleep etc.
In our first hypothesis, we expected main effects for
demands and control. Only control showed a signifi-
cant effect in the regression analyses, however. Several
aspects about these results are noteworthy. First, in line
with previous research, no interaction was found. Sec-
ond, regarding the two variables specified by the model,
existing research suggests that the effects of control are
more consistent than those of demands (43). Our results
confirm this picture. Interestingly, control was signifi-
cant in the multiple regression analysis only. Detailed
analyses revealed that it became significant once the
baseline value of BMI was controlled. Thus, while not
associated with BMI as such, control is associated with
(relative) change in BMI.
With respect to our second hypothesis, the weak
prediction by effort–reward imbalance is difficult to
explain. Effort–reward imbalance indicates a lack of
reciprocity and fairness. As such, it is conceptually
rather close to social stressors, and likely to reflect
a “social-evaluative threat”, at least to some degree.
The conceptual overlap can be seen in the correlation
between effort–reward imbalance and social stressors,
which is rather high (r=0.60). There is one difference,
however, that may be important: social stressors refer
to events that are characterized by tension, offense,
conflict, neglect, lack of appreciation, etc. Social stress-
ors, therefore, represent experiences where many of
the mechanisms involved in weight issues come into
play (eg, increases in cortisol, coping by changes in
eating behavior, and breakdown of self-control). By
contrast, effort–reward imbalance may not primarily
reflect specific events but rather a more general evalua-
tion of one’s work situation that results when reflecting
about one’s work. However, such explanations must
remain speculative at this point. At the same time, it
should be mentioned that our sample size is not very
large. Effort–reward imbalance showing a marginally
significant effect may therefore indicate a lack of power
rather than a failure of effort–reward imbalance to pre-
dict BMI, and a significant effect may well occur with
larger samples (44). Thus, while our results support an
association between BMI and social stressors as well as
control, the effects of effort–reward imbalance should
not be dismissed based on these results.
Scand J Work Environ Health 2011, vol 37, no 1 51
Berset et al
Referring to our third hypothesis, one could argue
that the 2% variance explained by social stressors (see
table 4) is sparse. It is noteworthy, however, that this
effect occurs despite a considerable stability in BMI.
With a standardized regression coefficient of 0.92,
there is not much variance left to be explained by other
variables. Thus, the effect of social stressors seems quite
remarkable. The practical relevance of social stressors is
reflected in the unstandardized regression coefficient of
0.75 (see table 4). This implies that an increase in one
point on the 5-point social stressors scale is associated
with an increase in predicted BMI of 0.75 points two
years later. Given that the difference of one point in BMI
is related to a 4–5% increase in risk of coronary heart
disease mortality (45), this effect of social stressors on
BMI is not trivial. Note that this effect is obtained with
social stressors at work, which constitutes only a subset
of social stressors people experience in life.
We controlled for age, gender, and education, the
last of which we used as a proxy for social class. Nev-
ertheless, one can never rule out that the effects found
are due to other confounding variables that we did not
measure. However, such confounding factors would in
all likelihood have influenced BMI already at baseline;
since that variable is controlled for in our longitudinal
study, we are confident that social stressors and control
are, indeed, predictors of relative change in BMI.
It also seems conceivable that social stressors not
only predict BMI, but that BMI, at the same time, leads
to more social stressors. For example, it may be that
overweight individuals are more frequent victims of
bullying. This would then suggest a negative spiral, in
which weight and stress reinforce each other. We tested
this reverse causation hypothesis, but BMI was not pre-
dictive of social stressors two years later, however (more
detailed results can be obtained from the first author).
Individuals may react differently with respect to
their eating behavior when suffering from chronic stress
(8). Assuming that these differences accumulate under
chronic stress, they should also be reflected in dif-
ferences in baseline BMI (2). As a consequence, we
tested if the baseline BMI interacts with the working
conditions in predicting follow-up BMI. There was a
significant interaction between baseline BMI and job
control. Individuals high in baseline BMI benefited
from job control as they lost weight, whereas there was
no change in BMI for individuals low in baseline BMI.
No interactions were found between baseline BMI and
job stressors. Therefore, there is some indication for dif-
ferential effects of working conditions on body weight,
but the evidence is far from conclusive.
Strengths and limitations
This study has two strong points. First, several different
aspects of working conditions were investigated within
the same study. Second, the longitudinal design makes
a stronger case for a causal role of social stressors than
a cross-sectional design would have permitted.
A limitation of our study is the rather small sample
size, implying limited power. The low power makes the
effect of social stressors even more remarkable, but the
effect of effort–reward imbalance may have been under-
estimated because of the small sample size. Another
consequence of the small sample size is a somewhat
limited generalizability of our results. Further research
and replication is, therefore, needed.
Another limitation is the use of self-reported data for
weight and height. This might have induced a bias, due
to social desirability. However, a study by Donaldson
& Grant-Vallone (46) suggested that self-reported data
of height and weight are quite accurate. Furthermore,
Boström & Diderichsen (47) argue that self-reported
measures of BMI should have little effect on analyses, as
long as BMI is used as a continuous and not a categori-
cal variable. To the extent that a bias does nevertheless
Table 5. Summary of multiple regression analysis with control and social stressors predicting follow-up body mass index (BMI) (trimmed
model); N=70. [B=unstandardized regression coefficient. SE B=standard error of unstandardized regression coefficient. β=standardized
regression coefficient. R2=change in explained variance. R2=explained variance.]
Variables B (final) SE B β (final) R2R2
Step 1 · · · · 0.85
BMI time 1 0.93 0.05 0.94 a · ·
Education dummy I -0.70 0.51 -0.06 · ·
Education dummy II 0.33 0.34 0.05 · ·
Age 0.00 0.02 -0.01 · ·
Gender b0.52 0.35 0.08 · ·
Step 2 · · · 0.03 a0.88
Control time 1 -0.33 0.16 -0.11 c· ·
Social stressors time 1 0.60 0.27 0.11 c · ·
a P<0.01 (2-tailed).
b 0=male, 1=female.
c P <0 .05 (2-tailed).
52 Scand J Work Environ Health 2011, vol 37, no 1
Work stress as a predictor of body weight
exist, it is likely that this is a bias towards the mean,
in that overweight people tend to underestimate and
underweight people to overestimate their weight (48).
This would result in effects being underestimated, rather
than overestimated, as the bias would imply a restricted
range of values. We, therefore, are quite confident that
our self-report measure did not result in a serious distor-
tion of results.
Concluding remarks
According to our results, social stressors and job control
are important predictors of BMI. They should, therefore,
receive more attention in future research. Besides rep-
lication, future research should also focus more on the
mechanisms involved, such as metabolic mechanisms, as
for example the role of cortisol, changes in eating behav-
ior, physical activity, and disturbed sleep. Furthermore,
future research should include social stressors in private
life in addition to social stressors at work.
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Received for publication: 26 February 2010
... No interaction was found between ERI and sex. Berset et al (2011) 31 examined 70 Swiss service providers and found a suggestive, albeit statistically nonsignificant association (P < .1) between ERI at baseline and risk of higher BMI at 2-year follow-up. Modification by sex was not tested. ...
... Previous studies have reported associations between ERI and increased BMI over a 10-year period in 812 Finnish workers from the metal industry 30 and a small statistically insignificant association between ERI and increased BMI in a small sample (n = 70) of Swiss service providers. 31 These previously reported associations were not supported in our data. However, comparing our findings with findings of the previous studies should be done with caution due to differences in the operationalization of ERI, study populations, and sample sizes. ...
... However, comparing our findings with findings of the previous studies should be done with caution due to differences in the operationalization of ERI, study populations, and sample sizes. For example, our sample size was more than 10 times larger than the previous studies, 30,31 likely yielding more precise estimates. ...
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... Much of the previous work stress and obesity research focused on the demand-control model (Karasek, 1979;Berset et al., 2011;Solovieva et al., 2013) or the ERI model (Siegrist, 1996;Berset et al., 2011;Solovieva et al., 2013). Some authors reported positive associations between psychosocial job stressors and BMI (Kouvonen et al., 2005;Fernandez et al., 2010) and obesity (Kuper and Marmot, 2003;Fernandez et al., 2010) whereas other authors reported no such associations (Landsbergis et al., 1998;Brisson et al., 2000; ...
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... 41 Estressores sociais, os quais referem-se à ocorrência de conflitos ou inimizades no ambiente de trabalho, e controle no trabalho, o que diz respeito à capacidade de autonomia ou tomada de decisões no contexto do trabalho, estiveram associados de forma positiva e significante com mudanças no IMC ao longo de dois anos. 42 Intensas situações de estresse no trabalho mostraram-se associadas ao comer de forma exagerada, pôde-se notar isso em trabalhadores japoneses com níveis altos de IMC. 43 O estresse apresenta-se como evento natural e próprio de ocorrer entre os seres humanos. ...
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... In the workplace, high demands were associated with increased BMI in both men and women (Kivimaki et al., 2006). Similarly, loss of control and authority over decision making reported association with obesity in both genders (Berset et al., 2011). Significant findings were found about role of job strain that is defined as low control accompanied by psychological demands among female gender (Eek and Ostergren, 2009). ...
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... The relationship between MetS and low mental health might be mediated through indirect effects on health behavior as well as direct effects on the stress system, including the HPA axis, the autonomic nervous system, and immune system [17,43,44]. Crosssectional [39,45] and longitudinal studies [38,46] have shown higher work stress is associated with higher risk of MetS. In a study about associations among obstructive sleep apnea syndrome, MetS, and mental health [40], early-stage obstructive sleep apnea was associated with worsening of psychological conditions. ...
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Introduction: Paramedics play a vital role in the healthcare system by providing professional support in situations of direct threat to patient health and life. They experience numerous difficulties during their work, which result in occupational stress. During the COVID-19 pandemic, their work has become even more demanding. The aim of the current study was to examine the role of resilience in the subjective experience of stress among paramedics during the COVID-19 pandemic. Materials and methods: The study was carried out in two phases, in October-November 2019 (N = 75) and in May-June 2020 (N = 84), using the Sense of Stress Questionnaire (Skala Poczucia Stresu) and the Resilience Scale (Skala Pomiaru Prężności). Results: Paramedics exhibited higher intrapsychic stress before the COVID-19 pandemic. Tolerance of failure and treating life as a challenge were higher during the pandemic, in contrast to optimism and the ability to mobilize in difficult situations. Paramedics who were in contact with patients with COVID-19 experienced higher stress. Perseverance and determination, openness to new experiences and sense of humor, as well as competences and tolerance of negative emotions were revealed to play a key part in mitigating subjectively experienced stress. Conclusion: Paramedics’ subjectively experienced stress was lower during the COVID-19 pandemic. Paramedics who were in direct contact with patients with COVID-19 experienced higher stress. They had sufficient psychological resources, in the form of resilience (perseverance and determination, openness to new experiences, sense of humor, and competences and tolerance of negative emotions), which allowed them to cope with the situation of the COVID-19 pandemic.
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The present study focused on the distinction between two equity considerations in “people” jobs: the relationship with the recipients with whom one works and the exchange relationship with the organization for which one works. The sample consisted of nurses who were employed in one particular nursing home for mentally retarded patients. The results show that perceived inequity in both types of relationships is linked to emotional exhaustion and reduced personal accomplishment. As expected, individual differences in communal orientation differentiate when nurses feel inequitably treated in their relationships with their patients. Moreover, the results suggest that low communally oriented nurses restore equity in their relationships with patients by withdrawing emotionally (depersonalization). In contrast, nurses who felt inequitably treated in the employee-employer relationship reported a higher intent to quit. No direct link between burnout symptoms and the intention to quit was observed.
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