AMERICAN JOURNAL OF INDUSTRIAL MEDICINE 53:1128–1134 (2010)

Job Stress and Healthy Behavior Among MaleJapanese Office Workers

Kyoko Nomura, MD, MPH, DMSc,� Mutsuhiro Nakao, MD, Shinobu Tsurugano, MD,Takeaki Takeuchi, MD, Mariko Inoue, PhD, Yasuko Shinozaki, MD, and Eiji Yano, MD

Background Lifestyle modification in healthy workers is challenging. We aim toinvestigate associations between job stress and healthy behavior change among workers.Methods This cross-sectional study investigated 1,183 Japanese male white-collarworkers in 2008 during health checkups for Metabolic Syndrome. Healthy behaviorincluded either a calorie-focused diet or regular exercise. Job stress was measured by JobContent Questionnaire based on the job demands-control model and tension-anxiety andanger-hostility scales on the Profile of Mood States.Results Healthy behaviors were confirmed in 54% of study subjects. Multivariate logisticmodel showed that healthy behaviors were positively associated with a higher degree ofwork control and negatively associated with greater work demand. Work control andsupport were negatively correlated with tension-anxiety and depression, whereas workdemand and strain were positively correlated with these two emotion domains (allP’s< 0.0001).Conclusions It is suggested that addressing job stress is of clinical importance to promotehealthy behaviors. Am. J. Ind. Med. 53:1128–1134, 2010. � 2010 Wiley-Liss, Inc.

KEY WORDS: healthy behavior; health promotion at workplace; job stress; lifestylemodification; Metabolic Syndrome

INTRODUCTION

According to the National Nutrition Survey conducted in

2007, approximately 9 million people in Japan have

Metabolic Syndrome (MetS), and another 10 million people

are at risk of developing MetS [The Ministry of Health

Labour and Welfare Office for Life-style Related Diseases

Control Health Service Bureau, 2007]. In April 2008, the

Japanese Ministry of Health, Labour and Welfare enforced a

specific health checkup to identify patients with or persons at

high-risk for MetS. The concept of the MetS is that the

concurrence of three or more of its components determines

the risk of cardiovascular disease and diabetes [Klein et al.,

2007] and for its prevention, lifestyle modification is of

clinical importance.

Among the MetS, obesity is a major risk factor, and there

the merits of weight loss are widely recognized: weight

reduction (5–10% of body weight) leads to improved

insulin sensitivity [McAuley et al., 2002], prevents type 2

diabetes [Knowler et al., 2002], and decreases cardiovascular

risk factors [Ko et al., 2007]. A meta-analysis [Franz et al.,

2007] investigating several interventions for obesity showed

that low-calorie diet and exercise are the most effective

interventions. However, the promotion of healthy behavior

remains challenging because many factors affect the

decision-making of healthy behavior change, including the

surrounding environment [Jeffery et al., 1991], individual

socioeconomic [Petrovici and Ritson, 2006], psychological

� 2010Wiley-Liss, Inc.

Department of Hygiene and Public Health, Teikyo University School of Medicine, Tokyo,Japan

Contract grant sponsor: Ministry of Education, Culture, Sports, Science & Technology ofJapan; Contract grant number: 21590666.

*Correspondence to: Dr. Kyoko Nomura, Department of Hygiene and Public Health,TeikyoUniversity School of Medicine, 2-11-1Kaga, Itabashi-ku,Tokyo173-8605, Japan.E-mail: kyoko@med.teikyo-u.ac.jp

Accepted 20 April 2010DOI 10.1002/ajim.20859. Published online 1June 2010 in Wiley Online Library

(wileyonlinelibrary.com).

[Nishitani and Sakakibara, 2006], and physical status [Ko

and Chan, 2008], and health beliefs [Beeney and Dunn, 1990;

Dunn et al., 1990; Polly, 1992; Wooldridge et al., 1992;

Gordon-Larsen, 2001].

In this study, we examined a group of Japanesewhite-collar

workers at one company, a relatively homogeneous population

with regard to socioeconomic background. We took a multi-

disciplinary approach to investigate predictors of healthy

behavior change: in particular, we focused on job stress because

it may be more preventable than individual lifestyle factors,

and occupational health staff can more easily intervene in

prevention with workers than in individual lifestyle [Noblet

and Lamontagne, 2006]. Thus, our purpose was to assess an

association between job stress and healthy behavior, adjusting

for age, physical illnesses, lifestyle, and health beliefs.

STUDY POPULATION AND METHODS

Participants

This investigation was conducted in April 2008 as a part

of annual health checkup for MetS. The checkup recipients

were white-collar office workers at a Japanese enterprise

that deals with agricultural machinery and cast-steel products.

We recruited all employees to participate in this study.

Among 1,429 employees who worked at the headquarters in

metropolitan Tokyo, 1,364 agreed to provide written informed

consent and answered self-administered questionnaires

(response rate 98%). After excluding 181 female workers

because of a relatively small sample size, the participants for

analyses became 1,183 male workers (19–68 years, mean age

44 years). This study was performed in accordance with

the World Medical Association Declaration of Helsinki

2000 revision, and the protocol was approved by the Ethics

Committee of Teikyo University School of Medicine and the

Safety Committee of Labour of the company.

Healthy Behavior

Healthy behavior included a calorie-focused diet and

regular exercise that were initiated or/and maintained during

1 month prior to the investigation. Participants were asked,

‘‘Have you initiated or/and maintained the following health

behaviors for at least one month prior to the investigation?’’

The behaviors included a calorie-focused diet or regular

exercise equivalent of a 1-hr walk at least once weekly (yes/

no). Healthy behaviors were confirmed if workers responded

positively to the items (i.e., a calorie-focused diet, regular

exercise, or both).

Metabolic Syndrome (MetS)

The criteria for MetS used in this study were those

proposed by the International Diabetes Federation [Alberti

et al., 2006]. Referring to a consensus statement proposed by

the Association for Weight Management and Obesity

Prevention [Klein et al., 2007], the following risk factors

were defined:

* obesity: A body-mass index (BMI) �25 kg/m2;

* hypertension: Either a systolic blood pressure

�130 mmHg or a diastolic blood pressure �85 mmHg;

* hypertriglycemia: Serum triglyceride levels �150 mg/dl;

* low high-density lipoprotein (HDL) cholesterol: Serum

HDL cholesterol levels <40 mg/dl;

* impaired glucose metabolism: Either fasting blood

glucose �100 mg/dl, hemoglobin A1c �6.4%, or regu-

larly taking medication for diabetes mellitus.

MetS was defined as positive when any three of these five

criteria were met.

Job Stress Indices

Job stress was measured by the Japanese version of the

Job Content Questionnaire (JCQ). The reliability and validity

of the JCQ are considered excellent for assessing job stress

among Japanese employees [Kawakami and Fujigaki, 1996].

The parameter of job demand is conceptualized by the speed

in completing work, the degree of difficulty of the work, the

amount of work, and the time allowed to complete the work

and conflicting demands (Cronbach’s a¼ 0.67); job control

is measured by two theoretically distinct subdimensions

of decision latitude, namely skill discretion (a¼ 0.68) and

decision authority (a¼ 0.69); and social support includes

support from supervisors (a¼ 0.90) and coworkers

(a¼ 0.80). Items were scored on a four-point Likert-type

scale using anchors of 1 for ‘‘agree’’ and 4 for ‘‘disagree.’’

Job strain was estimated by scores for job demand divided by

job control’s scale. These four parameters of job demand, job

control, job strain, and social support were used as job stress

indices.

The psychological responses were assessed by using the

reliable and valid measurement tool [Yokoyama et al., 1990],

Profile of Mood States (POMS), which identifies one’s mood

states with regard to tension-anxiety and depression.

Lifestyles

Lifestyle factors were investigated for smoking habits

and alcohol intake. Smoking status was treated as a binary

variable: current smokers and nonsmokers. Alcohol con-

sumption was classified into three groups based on

the weekly frequency of consumption: every day, sometimes,

and never. Workers in the ‘‘every day’’ group were further

divided into a binary categorization based on the volume of

ethanol consumed: low to moderate (<46 g ethanol/day) and

large (�46 g ethanol/day).

Job Stress and Healthy Behavior 1129

Health Beliefs

Health beliefs we investigated included knowledge

about MetS, perception of the threat of MetS, and self-

responsibility for health.

To evaluate knowledge about MetS, participants were

asked, ‘‘Which of the following do you think is the ultimate

purpose of specific checkups for MetS?’’ Four alternative

responses included the prevention of obesity, diabetes,

cancer, cardiovascular diseases; a fifth option was ‘‘do not

know.’’ Prevention of diabetes and cardiovascular diseases

were taken as a measure of knowledge about MetS based on

the definition of MetS proposed by International Diabetes

Association [Alberti et al., 2006].

Concerning the perception of the MetS threat, we

hypothesized that people would seek preventive health

behaviors if they viewed having MetS as threatening.

Participants were asked, ‘‘Do you think it would be serious

if you were to develop or already have MetS? (yes/no/do not

know).’’

Self-responsibility for health was defined as positive if

respondents perceived that they were responsible for their

own health condition. Participants were asked, ‘‘Do you

think your health condition is determined by your own

responsibility? (yes/no/do not know).’’ A ‘‘no’’ answer was

assumed to indicate that participants perceived that their

health condition is greatly influenced by factors other than

themselves.

Data Analyses

Differences in age, MetS indicators, job stress indices,

psychological responses, lifestyles, and health beliefs were

compared between workers who reported healthy behavior

and those who did not. For group comparisons, either a t-test

or Wilcoxon’s rank sum test was used for continuous

variables depending on their distribution, and a chi-square

test was used for categorical variables. Spearman’s correla-

tion coefficients were calculated between job stress indices

and psychological responses. A logistic regression model

was then used to study predictors of healthy behavior. Both

crude and age-adjusted odds ratios (ORs) of healthy behavior

were calculated along with the 95% confidence intervals

(95% CI). Age and job stress indices were treated as

continuous independent variables in a logistic regression

analysis because the outcome of healthy behavior appeared

to increase with age and job control, and to decrease with

job demand. The ORs reflected an increase in the odds of

healthy behavior per 10-year age increase and a 10-point

score increase in job stress indices. Model selection

was performed by stepwise methods (entry significance

level¼ 0.3, staying significance level¼ 0.35); on the basis

of the results, explanatory variables were selected for

multivariate models.

Analyses were conducted using SAS Version 8.12 for

Windows. All tests were two-tailed, with the significance

level set at 5%.

RESULTS

Table I shows the subjects’ characteristics according to

healthy behaviors. Of the total sample, 638 participants

(54%) initiated or/and maintained healthy behaviors: regular

exercise (n¼ 432, 37%) and a calorie-focused diet (n¼ 336,

29%). The prevalence of MetS was 17%, and obesity was the

most prevalent condition among the five MetS components

(n¼ 181; 92%). Compared to those without such behaviors,

workers with healthy behaviors were more likely to be

older, have hypertension, report a higher degree of job

control; report a lower level of job demand, job strain,

tension-anxiety, and depression; be current nonsmokers;

have accurate MetS-related knowledge; perceive the threat

of MetS; and have a sense of responsibility for their own

health.

Table II shows Spearman’s correlation coefficients

between job stress indices and psychological responses.

The mean scores and standard deviations of the job stress

indices were 32� 5.4 for job demand, 33� 5.2 for skill

discretion, 36� 5.8 for decision authority, 12� 2.1 for

supervisor support, and 12� 1.6 for coworker support.

Compared to Japanese workers in a JCQ study by

Kawakami and Fujigaki [1996], our study subjects appeared

to have relatively higher job control and social support at

work and a moderate degree of job demand. The mean

and standard deviation of scores on POMS tension-

anxiety and depression were 10.8 and 5.8, and 6.8 and 8.2,

respectively. These scores corresponded to the 47th and

44th percentiles of the general population (male). No workers

had scores higher than the 90th percentile in the two

dimensions. Tension-anxiety was positively correlated with

depression, job demand, and job strain, and was negatively

correlated with job support (all P’s< 0.0001). Depression

was positively correlated with job demand and strain and

negatively correlated with job control and support (all

P’s< 0.0001).

Table III shows the adjusted ORs and 95% CI of

healthy behaviors by stepwise logistic regression models.

After adjusting for age, lifestyle, and health beliefs, healthy

behaviors were positively associated with a higher degree of

job control (OR 1.22, 95% CI: 1.04–1.43) and negatively

associated with higher job demand (OR 0.69, 95% CI: 0.53–

0.89). Other significant contributors were older age (OR

1.22, 95% CI: 1.07–1.40), current smoking (OR 0.61, 95%

CI: 0.47–0.80), occasional drinking (OR 1.60, 95% CI:

1.14–2.27), affirmative perception of self-responsibility

for health (OR 2.21, 95% CI: 1.59–3.07), and negative

perception self-responsibility for health (OR 0.49, 95% CI:

0.29–0.83).

1130 Nomura et al.

DISCUSSION

We investigated the relationship between job stress and

healthy behaviors. Our results demonstrated that workers

who did not exhibit healthy behaviors were more likely to

have higher job demand and strain, whereas those who had

healthy behaviors were more likely to have a higher degree of

job control even after adjusting for age, MetS, lifestyle, and

health beliefs. Participants in the former group were more

likely to have higher tension-anxiety and depression,

TABLE I. Subjects’ Characteristics According to Healthy Behaviors

Variables

Healthy behaviorsa

P-value

(þ) n¼ 638 (�) n¼ 545

N % N %

Age, years (mean� SD) 45 �11 43 �10 0.006Metabolic Syndrome (MetS)b 107 17 90 17 0.906Constituents of MetS

BMI�25 kg/m2 278 44 210 39 0.079SBP�130 mmHg or DBP�85 mmHg 228 36 157 29 0.015HDL<40 mg/dl 33 5 43 8 0.057TG�150 mg/dl 155 24 143 26 0.443Fasting glucose�120 mg/dl or HbA1c�6.4% 142 22 102 19 0.134

Job stress indices (mean, SD)Job demand 31.6 5.5 32.6 5.1 0.001Job control 70.1 8.4 68.9 7.9 0.009Job support 23.5 3.3 23.2 3.0 0.210Job strainc 0.45 0.09 0.48 0.08 <0.0001

Psychological responsed (mean, SD)Tension-anxiety 10 6 11 6 0.014Depression 6 7 8 9 0.003

LifestylesTobacco <0.0001Current smokers 204 33 246 47Alcohol intake 0.009None 103 17 118 23Sometimes 291 47 201 39Everyday<Ethanol 46 g 149 24 119 23�Ethanol 46 g 81 13 83 16

Health beliefsKnowledge of MetSe 0.012

Correct 449 71 340 64Incorrect 165 26 162 30

Perception of the severity of MetSe <0.001Severely perceived 493 78 356 67Not severely perceived 61 10 78 15

Self-responsibility for health <0.0001Positively perceived 514 81 307 58Negatively perceived 29 5 94 18

aHealthy behaviors were either regular exercise habits or calorie-focused diet or both.bMetabolic Syndrome (MetS) is defined if subjects had three or more number of individual risk factors including obesity, hypertension, impaired glucose metabolism and low HDLcholesterol and hypertriglycemia.cJob strain is computed by job demand/control.dBased on Profile of Mood States.eThe rest was categorized as ‘‘do not know’’ group.

Job Stress and Healthy Behavior 1131

TABLE II. Spearman Correlation Coefficient Between Psychological Responses and Job Stress Indices

Psychological responses Job stress indices

Tension-anxiety Depression Demand Control Support Strain

Psychological responsesTension-Anxiety 1.000 *** *** 0.187 *** ***Depression 0.723 1.000 *** *** *** ***

Job stress indicesJob demand 0.284 0.142 1.000 *** 0.361 ***Job control �0.039 �0.117 0.216 1.000 *** ***Job support �0.150 �0.284 0.027 0.292 1.000 ***Job straina 0.296 0.210 0.758 �0.393 �0.162 1.000

Based on Spearman’s correlation coefficients.***P< 0.0001.aJob strain is computed by job demand/control.

TABLE III. Adjusted Odds Ratio (OR) and 95% Confidence Interval (CI) of Healthy Behaviors (n ¼1,114)

Variables

Healthy behaviors (þ)a

P-valueAdjusted ORb

95%CI

Lower Upper

Ageb 1.22 1.07 1.40 0.004Metabolic Syndrome (MetS)c 0.96 0.68 1.35 0.799Job stress indicesb

Job demand 0.69 0.53 0.89 0.004Job control 1.22 1.04 1.43 0.017Job support F F F F

LifestylesTobacco 0.000

Current smokers vs. nonsmokers 0.61 0.47 0.80Alcohol intake (vs. none) 0.045

Sometimes 1.60 1.14 2.27Everyday<Ethanol 46 g 1.16 0.78 1.73�Ethanol 46 g 1.05 0.67 1.65

Health beliefsPerception of the severity of MetSd 0.325

Severely perceived 1.27 0.89 1.84Not severely perceived 0.92 0.56 1.50

Self-responsibility for health <0.0001Affirmatively perceived 2.21 1.59 3.07Negatively perceived 0.49 0.29 0.83

Only selected variables by stepwise logistic models are reported.aHealthy behaviors were either regular exercise habits or calorie-focused diet or both.bOdds ratios reflected an increase in the odds of healthy behaviors per10-year increase in age and10 scores increase in Job stressindices.cMetabolic Syndrome (MetS) is defined if subjects had three or more number of individual risk factors including obesity,hypertension, impaired glucose metabolism and low HDL cholesterol and hypertriglycemia.dWith reference of ‘‘do not know’’ group.

1132 Nomura et al.

and their job stress indices were highly correlated with

tension-anxiety and depression. Thus, the results of this study

suggested that workers who were under higher job demand

and strain were less likely to exhibit healthy behaviors,

possibly because of emotional strain. We discuss our results

in light of their strengths and limitations of this study and

compare them with previous research findings.

Previously, a Japanese study [Ishizaki et al., 2008]

followed 3,571 workers for 6 years and reported that high job

strain is a risk factor for increased abdominal obesity.

Another Japanese study [Nishitani and Sakakibara, 2006]

reported that obese workers tended to have a higher degree

of anxiety, possibly due to high job strain, which might

have affected eating behaviors and contributed to obesity.

Although these two previous studies inferred that psycho-

logical stress at work may play a role to induce obesity

through the emotional strain, our additional analyses failed to

find such a direct relationship; emotion domains as well as

job stress indices were not significantly different between

obese workers and nonobese workers. A possible explanation

of the differences between the results of the previous study

[Nishitani and Sakakibara, 2006] and those of the present

study may be the characteristics of study samples; more than

half of their subjects were blue-collar workers whereas all

subjects in the present study were white-collar workers. In

addition, the job demands in our study subjects were not as

high as those faced by Japanese employees in the computer

company used as a reference in Japanese JCQ studies

[Kawakami and Fujigaki, 1996].

A common assumption among many health professio-

nals is that providing knowledge is essential to promote

lifestyle changes. In this study, 68% of our subjects possessed

accurate knowledge of MetS. However, such knowledge was

not a significant predictor of healthy behavior after adjusting

for potential confounding factors. Previous studies [Beeney

and Dunn, 1990; Dunn et al., 1990; Gordon-Larsen, 2001]

generally agreed that illness-specific knowledge did not

facilitate attitude change, although the majority of study

subjects possessed accurate knowledge.

With regard to the perception of the severity of diseases,

the results have been inconsistent in the literature. Although a

few studies reported a positive effect on glucose control

among diabetic subjects [Polly, 1992; Wooldridge et al.,

1992], some studies failed to show such effects on exercise

behavior among nondiabetic populations [Noland and

Feldman, 1985; O’Connell et al., 1985; Biddle and

Ashford, 1988]. Thus, the perceived severity of disease

might vary depending on the outcome of interest, and the

presence of illness requires careful interpretation of our

results.

By contrast, self-responsibility for health was found to

be a significant and independent predictor of healthy

behaviors, even after adjusting for potential confounders.

Initiating and adhering to individual healthy behavior has

been considered challenging because it was thought to be a

matter of individual lifestyle modification. Consequently,

discussions in this area often concern individual responsi-

bility. Hence, our results are reasonable. Self-responsibility

for health suggests that causality is assigned to one’s own

responsibility. Thus, we presume that if workers perceived

self-responsibility negatively, they likely attributed their

health condition to others (e.g., their wives or the work

environment) according to a literature [Rotter, 1966].

Previously, two studies using measures of locus of control

[Saltzer, 1978; Nir and Neumann, 1995] reported that

internal control is one of the powerful predictors of

healthy behavior. However, two studies [Molinari and

Khanna, 1981; Sandler and Lakey, 1982] suggested that the

self-perception of personal control may be vulnerable to

psychological distress. In this regard, we assessed job

stress and showed the significance of self-responsibility for

health even after adjusting for psychological stress at work.

Our study thus suggests that an internal attribution for one’s

own health plays a pivotal role in facilitating healthy

behaviors.

The strengths of this study are that we used a relatively

large sample of subjects who were similar in ethnicity and

sociodemographic characteristics, and we assessed multi-

disciplinary factors that affect healthy behavior. On the other

hand, our study had several study limitations. First, the

outcome of this study was measured by self-reporting,

healthy behavior for a relatively short period of time (i.e., the

most recent 1 month) and thus does not guarantee adherence.

In this regard, our study just identified what would contribute

to the decision-making of healthy behavior change. Second,

there may be several other factors that we could not

investigate in this study, but that may be associated with

healthy behaviors. For example, if a person works very late,

he or she may be less likely to have extra time for exercise or

low-calorie cooking. Thus, length of workday or amount of

overtime as well as social support from family or spouse (or

alternatively, marital status) may be also important factors

that should be warranted in future studies.

This study suggested the association between job stress

and healthy behaviors among workers. This means that

lifestyle modification is no longer an individual issue but

the mission of occupational health professionals. Various

psychological interventions such as stress management may

be helpful in decreasing the level of perceived job strain.

Alternatively, occupational health practitioners might be

able to improve workers’ degree of job control by cooperat-

ing with the supervisors of workers experiencing job

stress. In addition, while efforts to eradicate smoking

should be continued, the perception of self-responsibility

for health should be intensified among workers by providing

a health education program that has previously been

directed at simply providing information about health and

illness.

Job Stress and Healthy Behavior 1133

ACKNOWLEDGMENTS

This research was supported in part by Grant-in-Aid for

Scientific Research (C) No. 21590666 (2008) from the

Ministry of Education, Culture, Sports, Science & Techno-

logy of Japan.

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