Skip Navigation



Occupational Medicine Advance Access published online on February 23, 2007
Occupational Medicine, doi:10.1093/occmed/kqm006
This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
57/4/262    most recent
kqm006v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Yamazaki, S.
Right arrow Articles by Ueshima, H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Yamazaki, S.
Right arrow Articles by Ueshima, H.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2007. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Lifestyle and work predictors of fatigue in Japanese manufacturing workers

Shin Yamazaki1, Shunichi Fukuhara1, Yoshimi Suzukamo1, Satoshi Morita1, Tomonori Okamura2, Taichiro Tanaka2 and Hirotsugu Ueshima2

1 Department of Epidemiology and Healthcare Research, Graduate School of Medicine and Public Health, Kyoto University, Yoshida-Konoecho, Sakyo-ku, Kyoto 606-8501, Japan
2 Department of Health Science, Shiga University of Medical Science, Otsu, Japan

Correspondence to: Shin Yamazaki, Department of Epidemiology and Healthcare Research, Graduate School of Medicine and Public Health, Kyoto University, Yoshida-Konoecho, Sakyo-ku, Kyoto 606-8501, Japan. Tel: +81 75 753 4645; fax: +81 75 753 4644; e-mail: syam{at}pbh.med.kyoto-u.ac.jp


   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 Conflicts of interest
 References
 
Background Fatigue is one of the most common symptoms encountered in medical practice. However, little is known about the causal relationship between change in lifestyle and fatigue.

Aim To help prevent fatigue-related disorders, we investigated the association between changes in lifestyle and fatigue among employees.

Methods We studied data sets from the High-risk and Population Strategy for Occupational Health Promotion study for employees at 10 workplaces in Japan. The baseline survey was done in 1999 and the follow-up survey in 2003 via a questionnaire which examined lifestyle and fatigue variables using the vitality domain scale of the SF-36 Health Survey. The lifestyle factors focused on were diet, smoking and alcohol habits and working conditions. Four-year changes in lifestyle that predicted the vitality domain score in the follow-up survey were examined by analysis of covariance

Results Of the 6284 participants in the baseline survey, 4507 replied to the follow-up survey, of whom 3498, with a mean age of 37 (SD 18) years, returned valid responses. A low vitality score at follow-up was predicted by a change in lifestyle factors such as an increase in overtime work, change to non-sedentary work and increased frequency of eating between meals (P < 0.01, P < 0.01 and P = 0.02, respectively).

Conclusion Fatigue in salaried workers as measured by the vitality domain of the SF-36 is predicted by an increase in overtime work, change to non-sedentary work and an increase in the frequency of eating between meals.

Keywords      Fatigue; lifestyle; quality of life; vitality


   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 Conflicts of interest
 References
 
Fatigue is one of the most common symptoms encountered in medical practice [1–3]. In a cross-sectional study of samples of active working populations in 15 European countries, 5–56% of employees reported fatigue at work [4], while recent cohort studies have reported prevalences of 18–29% [5,6] and a 1-year cumulative incidence of prolonged fatigue of 12% [6]. Fatigue is commonly reported in general population studies also (14–22%), albeit that these generally differ in their definition of fatigue as well as in study area/country or cultural background among subjects [3,7]. As a gradual and cumulative process, fatigue reflects a decrement in vigilance and a decreased capacity to perform, along with subjective states associated with this decreased performance. Further, fatigue represents a general psychophysiological phenomenon that diminishes the ability of individuals to perform a particular task by altering alertness and vigilance, and includes the changes in motivational and subjective states that occur during this transition. As a consequence, there is a decrease in competence and in willingness to develop or maintain goal-directed behavior aimed at adequate performance. Fatigue can also be a key indicator of a variety of physical and mental health disorders.

Although frequent, fatigue is not always trivial. Little attention has been paid to its epidemiology, perhaps because it is non-specific, rarely directly fatal, and difficult to define and measure [8,9]. The difficulty of measuring fatigue is due to not only its subjectivity in meaning and experience but also its multidimensional, heterogeneous nature [3,10]. Moreover, there is no standard way to assess fatigue [3,11]. Previous cross-sectional studies have shown that fatigue, including prolonged fatigue, is associated with age, gender, marital status, ethnic identification, socioeconomic status and work status [6,12]. Moreover, a recent cross-sectional study showed that alcohol drinkers rated their levels of fatigue as measured by the SF-36 Health Survey (SF-36) as good in comparison with non-drinkers [13].

To our knowledge, however, little is known about the causal relationship between change in lifestyle and fatigue. Here, we examined the association between change in lifestyle and fatigue among a large group of salaried workers at 10 workplaces in Japan.


   Methods
Top
 Abstract
 Introduction
 Methods
Results
 Discussion
 Conflicts of interest
 References
 
The data used were obtained from the High-risk and Population Strategy for Occupational Health Promotion (HIPOP-OHP) study, a 4-year trial funded by the Japanese Ministry of Health, Labour and Welfare aimed at preventing cardiovascular disease at the workplace. The study has been described in detail elsewhere [14]. Briefly, it was a 4-year trial begun in Japan in 1999 with the aim of promoting health in the workplace by two different intervention approaches, particularly at the prevention of high-risk conditions associated with cardiovascular disease. The intervention areas were diet, physical activity and smoking cessation. Twelve workplaces with a total of 7226 participants were assigned to intervention and control groups of six workplaces each.

The present study was conducted on a population of blue-collar or manual workers and hence data from two workplaces employing mainly white-collar workers in the HIPOP-OHP study were excluded. The baseline survey in 1999 and 4-year follow-up survey (follow-up survey) in 2003 were used. Lifestyle, subjective fatigue and subject characteristics were measured with a questionnaire containing items on sex, age, height, weight, number of times a physician was consulted in the preceding year, number of days absent from work in the preceding year, marital status, residential status, chronic comorbid conditions, working status, dietary habits, smoking habit, drinking habit and the SF-36. With respect to working status, we asked about shift-work status, hours of overtime worked and degree of sedentary work. With respect to dietary status, the questionnaire asked how many times per week the subject had a nutritionally balanced breakfast, lunch and dinner, with nutritionally balanced defined as including rice, bread or noodles (carbohydrates); fish, meat or soybean (protein) and fruits and vegetables (vitamins and fiber). We also measured the daily frequency of eating between meals.

The SF-36 is a generic health-related quality of life instrument based on a conceptual model consisting of physical and mental health constructs which is designed to measure perceived health status and daily functioning. It contains 36 items clustered into the eight domains of physical functioning, role-physical functioning, bodily pain, general health perception, vitality, social functioning, role-emotional functioning and mental health. The SF-36 has been translated into Japanese and validated for the Japanese general population [15,16]. The vitality domain of the SF-36 measures subjective general fatigue or energy using the following four questions: How much of the time during the last month did you: (i) feel full of pep? (ii) have a lot of energy? (iii) feel worn out? and (iv) feel tired? For each question of vitality, the subject was asked to choose one of the following responses: all the time (1 point), most of the time (2 points), some of the time (3 points), a small part of the time (4 points) and none of the time (5 points). Because items (i) and (ii) ask about positive feelings, their scoring is reversed. The score for vitality is computed by summing the scores on each question item and then transforming the raw scores to a 0- to 100-point scale, with a lower score indicating poorer energy.

We restricted subjects for analysis to respondents to both the baseline and fourth follow-up survey. The two data sets were first analyzed independently in a cross-sectional analysis. Analysis of covariance was used to estimate adjusted mean differences among the groups divided by lifestyle for the vitality domain of the SF-36. In this model, the dependent variable was vitality and the independent variables were age, gender, body mass index, number of times a physician was consulted in the preceding year, number of days absent from work in the preceding year, marital status, residential status, shift-work status, hours of overtime worked, sedentary work status, nutritionally balanced meal for breakfast (times a week), nutritionally balanced meal for lunch (times a week), nutritionally balanced meal for dinner (times a week), eating between meals (snack eating), smoking status and daily alcohol consumption.

The data set was then analyzed longitudinally. The dependent variable in this analysis was the vitality domain score of the SF-36 measured in the fourth follow-up survey and the independent variables as predictors were 4-year changes in lifestyle, such as eating, smoking and drinking habits and work status. Predictors were categorized as follows: change in smoking status (no change, cessation and new smoker); change in drinking habit (no change, increase and decrease); change in nutritionally balanced breakfast, lunch and dinner (no change, increase and decrease); change in eating between meals (no change, increase and decrease); change in overtime working hours (no change, increase and decrease); change in shift-work status (no change, day shift and night shift) and change in sedentary work (no change, sedentary change and non-sedentary change). The sedentary change group included those who did other than sedentary work in the baseline survey and who changed to sedentary work in the follow-up survey. The non-sedentary change group included those who did sedentary work in the baseline survey and who changed to other than sedentary work. Analysis of covariance was used to examine associations between the vitality domain score at follow-up and 4-year changes in lifestyle (for example, with respect to change in smoking status, the three groups were as follows: no change, cessation and new smoker) and the vitality domain score at follow-up, adjusted for age, sex, workplace, body mass index, number of times a physician was consulted in the preceding year, number of days absent from work in the preceding year, lifestyle in the baseline survey and vitality domain score in the baseline survey. In this analysis, we used Bonferroni's test for post hoc comparisons.


   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 Conflicts of interest
 References
 
The 10 workplaces included a total of 6284 participants, of whom 4507 (72%) responded to the fourth follow-up survey. Of the 4507, we analyzed data for the 3498 who completed the questionnaire correctly, of whom 2875 (80%) were male. In the baseline survey, mean age was 37.3 (range: 19–64) years in men and 36.5 (range: 19–56) years in women. Subject characteristics and mean vitality scores are shown in Table 1. The proportion of smokers was 48% among the 3498 baseline subjects and 43% in the follow-up survey.


View this table:
[in this window]
[in a new window]

  		Table 1. Demographic characteristics and lifestyles of subjects and their vitality scores in the SF-36 at baseline and follow-up survey

 
In the baseline survey, vitality was associated with age, number of times a physician was consulted in the preceding year, number of days absent from work in the preceding year, hours of overtime worked, sedentary work, nutritionally balanced meals and workplace (Table 2). In the fourth follow-up survey, vitality was associated with all these, and additionally with sex and eating between meals (Table 2). In this analysis, we observed a negative correlation between the number of times a physician was consulted in the preceding year and vitality in both cross-sectional analyses. That is, for every increase in the number of consultations with a physician per year between the baseline and follow-up surveys, vitality score decreased to 0.21 (P < 0.001) and 0.18 (P < 0.001) points, respectively. Similar associations were observed between vitality and the number of days absent from work in the preceding year.


View this table:
[in this window]
[in a new window]

  		Table 2. Results of analysis of covariance to examine associations between vitality and independent variables among 3498 subjects (cross-sectional analysis)

 
Further, we also analyzed the data set longitudinally. Table 3 shows the distribution of changes in working status, dietary habit, drinking habit and smoking habit in the 4 years. Results showed that vitality scores in the follow-up survey were lower than at baseline in subjects who increased the number of hours of overtime worked, changed to non-sedentary work or increased the frequency of eating between meals than in those who decreased overtime work, changed to sedentary work or decreased the frequency of eating between meals, respectively (P < 0.001, P = 0.001 and P = 0.017, respectively) (Figure 1). These significant associations remained when the data were examined for men only. In contrast, the only association found in vitality for women was a change in sedentary work.


View this table:
[in this window]
[in a new window]

  		Table 3. Change in lifestyle and mean vitality score in the SF-36 at baseline and follow-up survey

 

Figure 1
View larger version (9K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Figure 1. Association between 4-year changes in lifestyle and vitality domain score measured in the follow-up survey, adjusted for age, sex, workplace, body mass index, number of times a physician was consulted in the preceding year, number of days absent from work in the preceding year, lifestyle in the baseline survey and vitality domain score in the baseline survey. (a) The association between overtime work hours and vitality. (b) The association between change in sedentary work and vitality. (c) The association between change in the frequency of eating between meals and vitality. *P < 0.05, **P < 0.001.

 

   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
Conflicts of interest
 References
 
This study shows that fatigue in salaried workers as measured by the vitality domain of the SF-36 is predicted by an increase in overtime work, change to non-sedentary work and an increase in the frequency of eating between meals.

The results of cross-sectional analysis showed that vitality is associated with age, sex, number of days absent from work in the preceding year, number of times a physician was consulted in the preceding year, sedentary work status, hours of overtime worked, frequency of nutritionally balanced meals, frequency of eating between meals and workplace. On longitudinal analysis, the vitality score was predicted by a change in the number of hours of overtime worked, change in sedentary work status and change in the frequency of eating between meals. To our knowledge, this is the first study to longitudinally examine the association between lifestyle and fatigue. Interpretation of the results was facilitated by its conduct as a large population survey in a relatively homogenous population, consisting mainly of manufacturing and related company employees.

With respect to the association between vitality and change in the frequency of eating between meals, a three-point difference in vitality score was seen between the increased versus the decreased frequency groups. Crude analysis showed a two-point difference between the group which did not consult a physician in the preceding year versus that which had one to seven consultations per year (Table 1). Further, the adjusted cross-sectional analysis also showed a significant association between vitality and the number of times a physician was consulted in the preceding year, decreasing by 0.21 points (at baseline survey) and 0.18 points (at follow-up survey) for every single increment (Table 2). Although we were unable to determine causal relationships for consultation with a physician in the preceding year and vitality in the cross-sectional analysis, the findings suggested that a small change in vitality score in the general population likely has a large impact on overall health. Further, a small difference in the vitality score likely represents an important variable in the formulation of public health policies.

A possible explanation for the association of eating between meals and fatigue is that fatigue results from the overconsumption of sugar. Lactate is largely considered a dead-end waste product of glycolysis in the presence of hypoxia, the primary cause of the oxygen debt following exercise, a major cause of muscle fatigue and a key factor in acidosis-induced tissue damage [17]. Blood lactate concentration is one index of muscle fatigue. A previous study has shown that blood lactate concentration at the point of fatigue is always lower after a diet low in carbohydrates and higher after a diet high in carbohydrates than after a normal diet. Even when the duration of the exercise task is kept constant, blood lactate concentration is higher after exercise on a high-carbohydrate diet than on a low-carbohydrate diet [17]. We therefore speculate that because eating between meals mainly involves snacks or sweets, it results in the overconsumption of sugar or carbohydrate, causing fatigue.

Eating between meals has been considered as a means of coping with stress. Takeda et al. [18] pointed out that night-eating syndrome has been found to occur during periods of stress and is associated with poor results at attempts to lose weight. Conversely, however, they also pointed out that eating is thought to be suppressed during stress due to the anorectic effects of corticotrophin-releasing hormone, and is increased during recovery from stress due to the appetite-stimulating effects of residual cortisol [18]. Lowe and Kral [19] noted that an adequate explanation for stress-induced eating in restrained eaters remains elusive. Further studies are needed to examine the effects of stress on the association between eating between meals and fatigue.

Previous cross-sectional studies of fatigue have shown associations with age, gender, marital status, ethnic identification, drinking status, socioeconomic status and work status [6,12]. The present findings add an increase in the frequency of eating between meals as a predictor of fatigue. With respect to drinking status, a previous cross-sectional study using baseline data from the HIPOP-OHP study showed that alcohol drinkers rated their fatigue as better than non-drinkers [13]. In our study, however, we did not observe an association between alcohol drinking and vitality. A number of reasons for this apparent discrepancy can be identified: first, subjects in the previous study were restricted to men; second, alcohol drinking was categorized into five categories (from 1 day/week to daily) and third, the vitality domain score was changed to a binary variable and the association was examined using a logistic regression model. Chronic alcoholics lack slow-wave sleep and have decreased amounts of rapid eye movement sleep as an acute response to alcohol [20]. Alcohol may have a negative impact on fatigue as it disturbs quality of sleep. The association between alcohol and vitality may therefore require further analysis.

Several limitations of this study can be identified. First, we did not collect data on socioeconomic factors, such as annual household income or position within the company. Further, we also did not collect data on working status at home. Second, because we defined fatigue in this study as the vitality domain of the SF-36 rather than the multidimensionality of fatigue [3,10], we were unable to examine associations between lifestyle and other dimensions of fatigue. Moreover, we could not distinguish fatigue from prolonged fatigue. While fatigue occurring as a normal, everyday phenomenon is usually relieved by a period of rest, prolonged fatigue is not easily reversible in the short term. Further studies are needed to examine the association between lifestyle and prolonged fatigue.

Our study showed that fatigue in salaried workers as measured by the vitality domain of the SF-36 is predicted by an increase in overtime work, change to non-sedentary work and an increase in the frequency of eating between meals. Further studies are needed to examine the association between lifestyle and prolonged fatigue.


Key points

  • Fatigue is predicted by an increase in overtime work and change to non-sedentary work in Japanese manufacturing workers.
  • In addition to these work environments, fatigue is also predicted by an increase in the frequency of eating between meals.

 


   Conflicts of interest
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conflicts of interest
References
 
None declared.


   References
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conflicts of interest
 References
 

  1. Bates DW, Schmitt W, Buchwald D, et al. (1993) Prevalence of fatigue and chronic fatigue syndrome in a primary care practice. Arch Intern Med 153:2759–2765.[Abstract/Free Full Text]

  2. David A, Pelosi A, McDonald E, et al. (1990) Tired, weak, or in need of rest: fatigue among general practice attenders. Br Med J 301:1199–1202.[Abstract/Free Full Text]

  3. Lewis G and Wessely S. (1992) The epidemiology of fatigue: more questions than answers. J Epidemiol Community Health 46:92–97.[Free Full Text]

  4. Benavides FG, Benach J, Diez-Roux AV, Roman C. (2000) How do types of employment relate to health indicators? Findings from the second European survey on working conditions. J Epidemiol Community Health 54:494–501.[Abstract/Free Full Text]

  5. Jansen NW, van Amelsvoort LG, Kristensen TS, van den Brandt PA, Kant IJ. (2003) Work schedules and fatigue: a prospective cohort study. Occup Environ Med 60:Suppl. 1, i47–i53.[Abstract/Free Full Text]

  6. Kant IJ, Bultmann U, Schroer KA, Beurskens AJ, Van Amelsvoort LG, Swaen GM. (2003) An epidemiological approach to study fatigue in the working population: the Maastricht cohort study. Occup Environ Med 60:Suppl. 1, i32–i39.[Abstract/Free Full Text]

  7. Loge JH, Ekeberg O, Kaasa S. (1998) Fatigue in the general Norwegian population: normative data and associations. J Psychosom Res 45:53–65.[CrossRef][Web of Science][Medline]

  8. Kroenke K, Wood DR, Mangelsdorff AD, Meier NJ, Powell JB. (1988) Chronic fatigue in primary care. Prevalence, patient characteristics, and outcome. J Am Med Assoc 260:929–934.[Abstract/Free Full Text]

  9. Wessely S, Chalder T, Hirsch S, Wallace P, Wright D. (1997) The prevalence and morbidity of chronic fatigue and chronic fatigue syndrome: a prospective primary care study. Am J Public Health 87:1449–1455.[Abstract/Free Full Text]

  10. Smets EM, Garssen B, Bonke B, De Haes JC. (1995) The multidimensional fatigue inventory (MFI) psychometric qualities of an instrument to assess fatigue. J Psychosom Res 39:315–325.[CrossRef][Web of Science][Medline]

  11. De Vries J, Michielsen HJ, Van Heck GL. (2003) Assessment of fatigue among working people: a comparison of six questionnaires. Occup Environ Med 60:Suppl. 1, i10–i15.[Abstract/Free Full Text]

  12. Taylor RR, Jason LA, Jahn SC. (2003) Chronic fatigue and sociodemographic characteristics as predictors of psychiatric disorders in a community-based sample. Psychosom Med 65:896–901.[Abstract/Free Full Text]

  13. Saito I, Okamura T, Fukuhara S, et al. (2005) A cross-sectional study of alcohol drinking and health-related quality of life among male workers in Japan. J Occup Health 47:496–503.[CrossRef][Web of Science][Medline]

  14. Okamura T, Tanaka T, Babazono A, et al. (2004) The high-risk and population strategy for occupational health promotion (HIPOP-OHP) study: study design and cardiovascular risk factors at the baseline survey. J Hum Hypertens 18:475–485.[CrossRef][Web of Science][Medline]

  15. Fukuhara S, Bito S, Green J, Hsiao A, Kurokawa K. (1998) Translation, adaptation, and validation of the SF-36 Health Survey for use in Japan. J Clin Epidemiol 51:1037–1044.[CrossRef][Web of Science][Medline]

  16. Fukuhara S, Ware JE Jr, Kosinski M, Wada S, Gandek B. (1998) Psychometric and clinical tests of validity of the Japanese SF-36 Health Survey. J Clin Epidemiol 51:1045–1053.[CrossRef][Web of Science][Medline]

  17. Maughan RJ, Greenhaff PL, Leiper JB, Ball D, Lambert CP, Gleeson M. (1997) Diet composition and the performance of high-intensity exercise. J Sports Sci 15:265–275.[CrossRef][Web of Science][Medline]

  18. Takeda E, Terao J, Nakaya Y, et al. (2004) Stress control and human nutrition. J Med Invest 51:139–145.[CrossRef][Medline]

  19. Lowe MR and Kral TV. (2006) Stress-induced eating in restrained eaters may not be caused by stress or restraint. Appetite 46:16–21.[CrossRef][Web of Science][Medline]

  20. Czeisler CA, Winkelman JW, Richardson GS. (2005) Sleep disorders. In Kasper DL, Braunwald E, Fauci AS, Hauser SL, Longo DL, Jameson JL (Eds.). Harrison's Principles of Internal Medicine 16th edn (McGraw-Hill, New York) pp. 153–162.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?



This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
57/4/262    most recent
kqm006v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Yamazaki, S.
Right arrow Articles by Ueshima, H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Yamazaki, S.
Right arrow Articles by Ueshima, H.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?