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Effort–reward imbalance at work and risk of sleep disturbances. Cross-sectional and prospective results from the Danish Work Environment Cohort Study
Journal of Psychosomatic Research 66 (2009) 75 – 83
Effort–reward imbalance at work and risk of sleep disturbances. Cross-sectional and prospective results from the Danish Work Environment Cohort Study☆ Reiner Rugulies a,b,⁎, Malene Norborg b , Tilde Sand Sørensen b , Lisbeth E. Knudsen b , Hermann Burr a a
National Research Centre for the Working Environment, Copenhagen, Denmark b Institute of Public Health, University of Copenhagen, Denmark
Received 12 October 2007; received in revised form 10 April 2008; accepted 6 May 2008
Abstract Objectives: This study aimed to analyze if adverse psychosocial working conditions, defined by the model of effort–reward imbalance (ERI), increase the risk of sleep disturbances in the Danish workforce. Methods: Analyses were conducted both crosssectionally and prospectively in a representative sample of Danish employees. The cross-sectional sample included 2614 participants (50% women) aged 18–59 years, of whom 263 had sleep disturbances. Of the 2351 participants initially free of sleep disturbances, 304 (12.9%) developed sleep disturbances during the 5-year follow-up. Data were analyzed with gender-stratified, multivariate logistic and linear regression analyses, adjusted for numerous covariates. Results: Cross-sectionally, a 1 S.D. increase in the ERI ratio was associated with sleep disturbances among both
men [odds ratio (OR)=1.65, 95% confidence interval (CI)=1.20– 2.27] and women (OR=1.82, 95% CI=1.46–2.28). In the prospective analysis, a 1 S.D. increase of the ERI ratio at baseline predicted the onset of sleep disturbances among men (OR=1.39, 95% CI=1.03–1.87) but not among women (OR=0.97, 95% CI=0.76–1.24). Conclusion: Among men, ERI is a risk factor for the development of sleep disturbances in the Danish workforce. Among women, an association between ERI and sleep disturbances was restricted to the cross-sectional sample. Improving psychosocial working conditions might reduce the risk of sleep disturbances and subsequently also help to prevent clinical disorders related to sleep disturbances. © 2009 Elsevier Inc. All rights reserved.
Keywords: Insomnia; Longitudinal study; Occupational health; Psychosocial; Sleep impairment; Stress
Introduction Sleep disturbances are associated with a wide range of health problems and diseases. People suffering from sleep disturbances have lower self-rated health, report more physical and mental health complains, and have more sickness absence [1–3]. Moreover, a meta-analysis of 10 ☆
The study was conducted at the National Research Centre for the Working Environment, Copenhagen, Denmark. ⁎ Corresponding author. National Research Centre for the Working Environment, Lersø Parkallé 105, DK-2100 Copenhagen, Denmark. Tel.: +45 39 16 52 18; fax: +45 39 16 52 01. E-mail address: [email protected] (R. Rugulies). 0022-3999/09/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.jpsychores.2008.05.005
case–control and cohort studies revealed that sleep disturbance was a risk factor for incident coronary heart disease (CHD) [4]. Other studies have shown that sleep disturbances were prospectively associated with the incidence of diabetes [5,6], obesity [7], and depression [8]. An experimental study demonstrated that restricting participants to 4 hours sleep per night over a period of 6 days resulted into decreased glucose tolerance, an increase in cortisol concentration, and heightened activity of the sympathetic nervous system [9]. The contribution of working conditions to sleep disturbances has been investigated in numerous studies (for an extensive review, see Ref. [1]). In particular, shift work and nonstandard working hours have been identified as important risk factors [1,10–12]. Sleep disturbances were also
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associated with adverse psychosocial working conditions, such as high quantitative workload, low decision latitude, or role conflicts [1,13–18]. However, with a few exceptions [17,18], the vast majority of research studies on psychosocial working conditions and sleep are cross-sectional in design and, therefore, limited in drawing causal inference. In recent years, the model of effort–reward imbalance (ERI) at work has emerged as a new theoretical approach for conceptualizing health-hazardous psychosocial working conditions. The model posits that a “high cost/low gain” situation at work, in which individuals spent high effort while receiving low rewards (in terms of monetary gratification, career opportunities, esteem, respect, and job security), elicits severe psychological distress, which consequently affects both mental and physical health. It is further assumed that ERI has, in particular, adverse health consequences, when it co-occurs with a motivational disposition called “work-related overcommitment” [19,20]. The ERI model has been tested foremost with regard to cardiovascular disease [21,22] but has recently also been used in research on other health outcomes [23–26]. Crosssectional multivariate analyses revealed a statistically significant association between ERI and sleep disturbances in two Japanese studies and one Swedish study [27–29], whereas a study in Germany did not find a statistically significant relation in the final multivariate model [30]. To our knowledge, the impact of ERI on sleep disturbances has not been analyzed in a prospective study or within a national workforce yet. This article aims to fill this research gap by studying ERI and risk of sleep disturbances in a 5-year follow-up analysis of a representative sample of the Danish workforce.
Methods Study design and population The Danish Work Environment Cohort Study (DWECS) was established in 1990 and is a longitudinal study to assess sociodemographic factors, work environment characteristics, health behaviors, and health status in the Danish working population [31]. In the DWECS 2000 survey, 11,437 randomly selected Danish residents were approached, of whom 8583 participated in the survey (response rate: 75%). Data were collected by means of telephone interviews. Among the respondents, 5292 were gainfully employed at the time of the survey. We excluded 527 respondents [those who were in job training, those who were employed under special working conditions (e.g., “modified duty due to illness”), those who worked less than 20 hours per week, and those who were 60 years or older at the time of the baseline survey], resulting in a sample of 4765 respondents. Of those, 3300 responded to the follow-up survey in 2005 (follow-up response rate: 69%). The follow-up survey was conducted with different survey methods, including telephone inter-
views and self-administered questionnaires on paper and on the Internet. Among the respondents, 2779 were still employed at the time of the follow-up and, therefore, eligible for the analyses. We excluded 165 respondents who had a missing value on any variable included in the multivariate analyses, yielding a final cross-sectional sample of 2614 employees. Of those, 263 (10.1%) had sleep disturbances at baseline. Among the 2351 respondents initially free of sleep disturbances (the prospective cohort), 304 (12.9%) reported sleep disturbances at follow-up. Measurement of sleep disturbances Sleep disturbances were measured with two questions: (a) “During the last 4 weeks, to what extent did you have trouble falling asleep?” and (b) “During the last 4 weeks, to what extent did you wake up and have trouble falling asleep again, when you should have been sleeping?” Response categories for both questions were (1) At no time, (2) A little of the time, (3) Some of the time, (4) A good part of the time, (5) Most of the time, and (6) All of the time. The scores for the two questions were added up, resulting in a sum score ranging from 2 to 12 points. Sleep disturbances were assessed when respondents scored 6 or more points on the sum score. In other words, respondents were categorized as having sleep disturbances when one of the following three conditions was met: •
They had answered all of the time or most of the time to at least one question. • They had answered a good part of the time to one question and at least a little of the time to the other question. • They had answered some of the time to both questions. The decision on the cutoff point was made by the research team, after a discussion on the interpretation of the response categories. The decision was made a priori, that is, before we started with the data analysis. Because we used items and cutoff points for sleep disturbances that had not been validated previously, we analyzed if our measure of sleep disturbances at baseline predicted poor self-rated health at follow-up, a health endpoint that includes sleep disturbance in its symptomatology [1]. We found that sleep disturbances in 2000 strongly predicted poor self-rated health in 2005, indicating the predictive validity of the sleep disturbance measure. Measurement of ERI Because DWECS did not include the original ERI questionnaire, we assessed effort and reward with proxy measures. The exact wording of the items and the response categories for each item are presented in Table 1. DWECS did not contain items that could be used to approximate work-related overcommitment, and consequently, we did not include this construct.
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Table 1 Items used to measure effort and rewards in the study Items:
Response categories and their value labels:
1. Effort ef1: Is your work unevenly distributed so it piles up? ef2: How often do you not have time to complete all your work tasks? ef3: Do you have to work overtime? ef4: Do you have to work very fast?
1=Never/almost never, 2=Rarely, 3=Sometimes, 4=Often, 5=Always 1=Never/almost never, 2=Rarely, 3=Sometimes, 4=Often, 5=Always 1=Never/almost never, 2=Rarely, 3=Sometimes, 4=Often, 5=Always 1=Never/hardly ever, 2=Seldom, 3=Sometimes, 4=Often, 5=Always
2. Reward 2.1 Financial and status reward rw1: Have you good prospects for the future in your job? rw2: How would you assess your salary with regard to your effort and your qualification?
2.2 Esteem reward rw3: Is your work recognized and appreciated by management? rw4: How often do you receive help and support from your colleagues? rw5: How often do you receive help and support from your closest supervisor? 2.3 Job security reward rw6: Are you worried about becoming unemployed? rw7: Are you worried about being transferred to another job against your will?
Effort was measured with four items on quantitative demands derived from the Copenhagen Psychosocial Questionnaire [32]. All items had five response categories ranging from 1=never/almost never to 5=always; that is, higher values indicated more effort. The effort score was built by summing the items up, resulting into a potential score ranging from 4 to 20 points. The Cronbach's α was .63. Rewards were measured with seven items, including two items on “financial/status reward,” three items on “esteem reward,” and two items on “job security reward.” The items had a different number of response categories (see Table 1) but were scored in such a way that, for all items, higher values indicated more rewards. The reward score was built by summing the items up, resulting into a potential score ranging from 7 to 27 points. The Cronbach's α was .53. Following an established procedure from the literature [20], we constructed an ERI ratio with the effort score in the nominator and the reward score in the denominator. Hence, higher values of the ratio expressed a higher level of imbalance between high effort and low reward. The mean ERI ratio at baseline was 0.54 (S.D.=0.18) in the crosssectional sample and 0.53 (S.D.=0.18) in the prospective cohort. For the purpose of the analyses, we divided the ERI ratio into quartiles. Measurement of covariates As covariates, we recorded gender, age, occupational grade, cohabitation, age of youngest child living with the respondent, smoking, alcohol consumption, leisure time
1=To a very slight degree, 2=To a slight degree, 3=To some degree, 4=To a high degree, 5=To a very high degree 1=Too high/Appropriate, 2=A little bit too low, 3=Much too low (Note: Too high and appropriate were separate response categories in the questionnaire but were combined for the analyses, because only very few participants responded too high) 1=To a very slight degree, 2=To a slight degree, 3=To some degree, 4=To a high degree, 5=To a very high degree 1=Never/almost never, 2=Rarely, 3=Sometimes, 4=Often, 5=Always. Not applicable: Do not have colleagues 1=Never/almost never, 2=Rarely, 3=Sometimes, 4=Often, 5=Always. Not applicable: Do not have a supervisor 1=Yes, 2=No 1=Yes, 2=No
physical activity, body mass index (BMI), self-rated health, sickness absence days last year, weekly working hours, and work time arrangement. We also included a variable, indicating the type of survey method at follow-up. Occupational grade was defined based on employment grade, job title, and education, yielding five categories: I: Executives and/or having a university degree; II: Middle managers and/or having more than 3 to 4 years of vocational education; III: Other white collar workers; IV: Skilled blue collar workers; V: Semi- or unskilled blue collar workers. Alcohol consumption was dichotomized in “no or moderate consumption” versus “heavy consumption,” with heavy consumption defined as drinking more than two (for women) or three (for men) units per day. A unit was defined as one small bottle of beer (33 cl), one glass of wine, or one shot of liquor. Leisure time physical activity was assessed with the question “When you should describe your leisure time physical activity in the last year, including commuting to or from work, to what group do you belong?” with four response categories corresponding to sedentary, light, moderate, and strenuous physical activity. BMI was calculated based on self-reported height and weight and categorized into “underweight” (b18.5), “normal weight” (18.5–24.9), “overweight” (25–30), and “obese” (N30). Self-rated health was measured with the question “In general, how would you rate your health?” [33]. We dichotomized responses into two categories: (1) “good health,” which included the responses very good and good, and (2) “reduced health,” which included the responses fair, poor, and very poor. Number of sickness absence days during the last year was based on self-report and was divided
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into four groups: “no sickness absence,” “1–5 days of absence,” “6–15 days,” and “16 or more days.” Weekly working hours were categorized in accordance with the Danish standard working hours into the categories “parttime work” (20–29 h), “full-time work” (30–37 h), “moderate overtime work” (38–45 h), and “heavy overtime work” (46 h or more). Work time arrangement was categorized into “daytime work,” “morning or afternoon work,” “night work,” “shift work,” and “irregular working hours.” To control for the effects of the type of survey method on reporting of sleep disturbances, we included a variable indicating by which method (telephone interview, paper questionnaire, Internet questionnaire) data were collected at follow-up. Statistical analysis All analyses were calculated with the statistical software program STATA 8.0 for Windows. We used multivariate logistic regression analyses to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the effect of ERI on risk of sleep disturbances. We first conducted a cross-sectional analysis on the association between ERI and the prevalence of sleep disturbances measured at baseline among all 2614 participants, adjusted for all covariates. Next, we analyzed the impact of ERI, measured at baseline, on the incidence of sleep disturbances, measured at the 5-year follow-up, among the 2351 participants, who were free of sleep disturbances at baseline. The prospective analyses were stratified by gender and were incrementally adjusted for type of survey method at follow-up, age, occupational grade, cohabitation, and age of youngest child at home (Model 1); smoking, alcohol consumption, physical activity, and BMI (Model 2); selfrated health and sickness absence days (Model 3); and weekly working hours and work time arrangement (Model 4). In addition, we used multivariate linear regression analyses to calculate the association between the continuous ERI ratio at baseline and the continuous sleep disturbance score at followup. This analysis was conducted with all 2614 participants and was adjusted for the sleep disturbance score at baseline and all covariates. The interpretation of this analysis, however, has to be done with caution, because the sleep disturbance score was not normally distributed and, therefore, did not formally fulfill the statistical requirement for a linear regression analysis. Results Study population characteristics and cross-sectional association with prevalence of sleep disturbances at baseline Table 2 shows the characteristics of the study population and baseline associations with prevalence of sleep disturbances. Among the 2614 respondents, 1318 were women
(50.4%) and mean age was 40 years (S.D.=9 years). The majority of the participants were white collar workers (72%), and most worked during daytime (83%). At baseline, 263 (10.1%) participants reported sleep disturbances. When all variables were adjusted for each other, female gender, reduced self-rated health, high number of sickness absence days, irregular working hours, and high ERI showed statistically significant associations with prevalence of sleep disturbances. Moderate and strenuous leisure time physical activity were protective against sleep disturbances. Respondents in the highest ERI quartile showed an OR of 2.26 (95% CI=1.56–3.27, Pb.001) for sleep disturbances (Table 2). Stratifying the sample by gender resulted into ORs of 1.81 (95% CI=0.93–3.52, P=.08) and 2.78 (95% CI=1.74–4.44, Pb.001) for men and women, respectively (data not shown in Table 2). When we used the ERI score as a continuous variable, we found that a 1 S.D. increment of the ERI score was associated with an increased risk of sleep disturbances among both men (OR=1.65, 95% CI=1.20–2.27, P=.002) and women (OR=1.82, 95% CI=1.46–2.28, Pb.001, data not shown in Table 2). Prospective associations between ERI at baseline and 5-year incidence of sleep disturbances Of the 2351 participants initially free of sleep disturbances, 304 (12.9%) developed sleep disturbances during the 5-year follow-up. Table 3 shows the prospective association between ERI in 2000 and incident sleep disturbances in 2005 among men. ERI predicted incident sleep disturbances in all models. In the fully adjusted model, the highest ERI quartile showed an OR of 2.06 (95% CI=1.14–3.74, P=.02). A 1 S.D. increment of the ERI ratio predicted a 39% (P=.03) increased risk of incident sleep disturbances in the full model. Of the covariates, reduced self-rated health (OR=2.13, 95% CI=1.27–3.58) and working at night (OR=3.79, 95% CI=1.09–13.15) predicted sleep disturbances among men, after adjustment for ERI quartiles and all other covariates. Moderate overtime work had a protective effect (OR=0.63, 95% CI=0.40–1.00, data not shown in Table 3). Among women, ERI in 2000 was unrelated to incident sleep disturbances in 2005 in all four models (Table 4). Of the covariates, smoking (OR=1.44, 95% CI=1.01–2.06), reduced self-rated health (OR=2.38, 95% CI=1.42–3.98), and working at night (OR=3.09, 95% CI=1.22–7.83) predicted sleep disturbances among women, after adjustment for ERI quartiles and all other covariates (data not shown in Table 4). Prospective associations between the ERI score and the sleep disturbance score Table 5 shows the association between the continuous ERI ratio at baseline and the continuous sleep disturbance
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Table 2 Study population characteristics and cross-sectional associations with prevalence of sleep disturbances at baseline among 2614 Danish employees Exposed
Gender Male Female Age (years) b 30 30–39 40–49 50–59 Occupational grade Executive/university degree Middle manager/further vocational education Other white collar worker Skilled blue collar worker Semi- or unskilled blue collar worker Married or cohabitating Yes No Children living at home No children Youngest child 2 years or younger Youngest child 3 to 6 years old Youngest child 7 years or older Smoking Current nonsmoker Current smoker Alcohol consumption No or moderate consumption Heavy consumption Physical activity Sedentary Light activity Moderate activity Strenuous activity BMI Underweight (b 18.5) Normal weight (18.5–24.5) Overweight (25–30) Obese (N30) Self-rated health Very good or good health Fair, poor, or very poor health Sickness absence days last year 0 1–5 6–15 16+ Weekly working hours 20–29 30–37 38–45 46+ Work time arrangement Daytime work Morning or afternoon work Night work Shift work Irregular working hours
Sleep disturbances
Multivariate ORs
n
%
n
%
OR
95% CI
1296 1318
49.6 50.4
99 164
7.6 12.4
1 1.87
Reference 1.37–2.54
382 839 875 518
14.6 32.1 33.5 19.8
35 80 81 67
9.2 9.5 9.3 12.9
1 0.98 0.89 1.37
Reference 0.62–1.56 0.55–1.44 0.85–2.21
468 455 949 348 394
17.9 17.4 36.3 13.3 15.1
45 45 99 39 35
9.6 9.9 10.4 11.2 8.9
1 0.76 0.91 1.18 0.75
Reference 0.47–1.21 0.61–1.37 0.71–1.95 0.45–1.25
2122 492
81.2 18.8
206 57
9.7 11.6
1 1.14
Reference 0.81–1.61
1114 293 380 827
42.6 11.2 14.5 31.6
120 22 41 80
10.8 7.5 10.8 9.7
1 0.85 1.16 1.00
Reference 0.50–1.46 0.74–1.83 0.70–1.42
1709 905
65.4 34.6
158 105
9.3 11.6
1 1.18
Reference 0.89–1.57
2487 127
95.1 4.9
243 20
9.8 15.8
1 1.44
Reference 0.84–2.48
369 1112 871 262
14.1 42.5 33.3 10.0
52 114 79 18
14.1 10.3 9.1 6.9
1 0.72 0.67 0.53
Reference 0.49–1.04 0.45–0.99 0.30–0.96
46 1546 811 193
1.8 59.8 31.0 7.4
9 152 82 20
19.6 9.7 10.1 10.4
1.89 1 1.14 1.00
0.86–4.13 Reference 0.84–1.54 0.59–1.70
2318 296
88.7 11.3
209 54
9.0 18.2
1 1.70
Reference 1.18–2.45
851 1130 437 196
32.6 43.2 16.7 7.5
71 96 61 35
8.3 8.5 14.0 17.9
1 1.01 1.54 1.95
Reference 0.73–1.42 1.05–2.27 1.21–3.15
144 1632 588 250
5.5 62.4 22.5 9.6
15 176 50 22
10.4 10.8 8.5 8.8
0.74 1 0.84 0.80
0.40–1.36 Reference 0.59–1.21 0.49–1.33
2173 57 45 122 217
83.1 2.2 1.7 4.7 8.3
200 8 7 18 30
9.2 14.0 15.6 14.8 13.8
1 1.84 1.89 1.67 1.79
Reference 0.81–4.20 0.78–4.59 0.96–2.91 1.16–2.77
(continued on next page)
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Table 2 (continued) Exposed
ERI ratio divided into quartiles Low ERI Medium-low ERI Medium-high ERI High ERI
Sleep disturbances
Multivariate ORs
n
%
n
%
OR
95% CI
661 659 656 638
25.3 25.2 25.1 24.4
54 37 68 104
8.2 5.6 10.4 16.3
1 0.67 1.43 2.26
Reference 0.43–1.04 0.97–2.11 1.56–3.27
Multivariate ORs: All variables are adjusted for each other. ORs and CIs with P values b .05 are in boldface. Note that we recalculated the analyses based on a new ERI score and that, therefore, effect estimates in all tables are slightly different from the first submission.
score at follow-up, after adjustment for sleep disturbance score at baseline and for all covariates. The ERI ratio was associated with a higher sleep disturbance score among men (P=.05), but not among women (P=.80), when adjusted for all covariates.
Discussion ERI was cross-sectionally associated with prevalence of sleep disturbances in both men and women after adjustment for numerous covariates in this representative sample of the Danish workforce. When associations were analyzed prospectively, we found for men that the highest ERI quartile predicted a twofold increased risk of incident sleep disturbances and that the continuous ERI ratio at baseline was significantly associated with sleep disturbance score at follow-up. Among women, ERI was not prospectively related to sleep disturbances. Associations between ERI and prevalence of sleep disturbances have been previously analyzed in crosssectional studies. Kudielka et al. reported that employees with sleep problems had higher levels of ERI in a study with 616 men and 93 women from an aeroplane manufacturing plant and a commercial medical laboratory in Germany. However, these associations vanished in the multivariate analyses [30]. In a Swedish study, Fahlén et al. [29] showed that high ERI was associated with sleep
disturbances in 789 men and 214 women from different occupations. In Japan, Utsugi et al. [27] found high ERI to be associated with both insomnia and short sleep (less than 6 h per day) in 6997 men and 1733 women working at local governments and a transit company. In another Japanese study, Ota et al. [28] showed that high ERI was associated with insomnia in 983 male and 98 female workers in a corporate group of electric products. Our study adds to the evidence from these previous studies that ERI is crosssectionally associated with the prevalence of sleep disturbances in both men and women. As far as we are aware, prospective associations between ERI and sleep disturbances have not been studied before. The prospective associations between ERI and sleep disturbances in the present study corroborate the crosssectional findings from previous studies that ERI has an adverse effect on sleep among men. Among women, however, ERI was associated with sleep disturbances only in the cross-sectional, but not in the prospective, analysis. There are different explanations for this discrepancy. On the one hand, it is possible that among women, ERI just does not affect sleep disturbances and that the positive association at baseline was due to reverse causation, that is, that sleep disturbances have caused women to perceive their work environment as more adverse. On the other hand, it is possible that during the relatively long follow-up period of 5 years, working conditions have changed for many participants and that these changes were more pronounced
Table 3 Prospective associations between ERI in 2000 and incident sleep disturbances in 2005 among 1197 employed men in the DWECS
ERI ratio divided into quartiles Low ERI Medium-low ERI Medium-high ERI High ERI ERI ratio continuous (1 S.D. increase)
Exposed
Sleep disturbances
Model 1
n
n
%
OR
95% CI
OR
95% CI
OR
95% CI
OR
95% CI
266 305 321 305 1197
21 35 31 43 130
7.9 11.5 9.7 14.1 10.9
1 1.43 1.23 2.02 1.36
Reference 0.80–2.55 0.68–2.22 1.15–3.55 1.03–1.81
1 1.34 1.21 1.95 1.36
Reference 0.74–2.39 0.67–2.18 1.10–3.44 1.02–1.81
1 1.33 1.18 1.84 1.31
Reference 0.74–2.39 0.65–2.14 1.03–3.26 0.98–1.75
1 1.44 1.34 2.06 1.39
Reference 0.80–2.61 0.73–2.47 1.14–3.74 1.03–1.87
Model 2
Model 3
Model 4
Logistic regression analysis: Model 1 is adjusted for survey method (telephone survey, questionnaire, or Internet survey at follow-up), age, occupational grade, married/cohabitating, and age of youngest child at home; Model 2 is further adjusted for smoking, alcohol consumption, physical activity, and BMI; Model 3 is further adjusted for self-rated health and sickness absence days; Model 4 is further adjusted for weekly working hours and work time arrangement.
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Table 4 Prospective associations between ERI in 2000 and incident sleep disturbances in 2005 among 1154 employed women in the DWECS
ERI ratio divided into quartiles Low ERI Medium-low ERI Medium-high ERI High ERI ERI ratio continuous (1 S.D. increase)
Exposed
Sleep disturbances
Model 1
n
n
%
OR
95% CI
OR
95% CI
OR
95% CI
OR
95% CI
341 317 267 229 1154
50 56 33 35 174
14.7 17.7 12.4 15.3 15.1
1 1.31 0.81 1.07 1.00
Reference 0.86–1.99 0.50–1.31 0.66–1.73 0.79–1.28
1 1.33 0.83 1.08 1.01
Reference 0.87–2.03 0.51–1.35 0.67–1.76 0.79–1.29
1 1.30 0.83 0.96 0.96
Reference 0.85–2.01 0.51–1.35 0.59–1.58 0.76–1.23
1 1.33 0.87 0.98 0.97
Reference 0.86–2.06 0.53–1.43 0.59–1.63 0.76–1.24
Model 2
Model 3
Model 4
Logistic regression analysis: Model 1 is adjusted for survey method (telephone survey, questionnaire, or Internet survey at follow-up), age, occupational grade, married or cohabitating, and age of youngest child at home; Model 2 is further adjusted for smoking, alcohol consumption, physical activity, and BMI; Model 3 is further adjusted for self-rated health and sickness absence days; Model 4 is further adjusted for weekly working hours and work time arrangement.
among women. If this was the case, then there would have been more misclassification of exposure among women, which consequently would have resulted into diminished associations between exposure measured in 2000 and endpoint measured in 2005. We do not know which explanation is the right one, but these findings clearly call for further research on potential gender-specific effects of ERI in prospective analyses.
case, statistical adjustment for poor health would have been appropriate control for confounding, whereas in the second case, this would have been inappropriate overadjustment for an intermediate step in a causal pathway. As it turned out in the analyses, adjustment for indicators of poor health (adding self-rated health and number of sickness absence days in Model 3) as well as adjustments for the other covariates affected risk estimates only to a minor extent. This indicates that the association between ERI and sleep disturbances was largely independent of these other factors.
Methodological considerations Analyses in the present study were incrementally adjusted for numerous covariates, including sociodemographic factors, occupational grade, health-related behaviors, health status at baseline, weekly working hour, and work time arrangement. These covariates had been found to be associated with ERI and/or sleep disturbances in previous studies and, therefore, might have been confounder for the statistical association between ERI and sleep disturbances [1,23–25]. On the other hand, some of these covariates could also have been intermediate steps in a pathway leading from ERI to sleep disturbances. For example, it is possible that poor health has caused both the perception of high ERI and the onset of sleep disturbances, but it is also possible that ERI has caused poor health in the first step and then poor health has caused sleep disturbances in the second step. In the first
Strengths and weaknesses of the study To our knowledge, this is the first time that the impact of ERI on risk of incident sleep disturbances has been analyzed with a longitudinal design and with a representative sample of a national workforce. Due to the prospective nature of the study, temporal order between exposure and outcome variable was established, which is an important criterion for determination of causality. In cross-sectional studies, statistical associations between ERI and sleep disturbances might equally indicate that ERI had causally influenced the development of sleep disturbances or that sleep disturbances had caused reduced workability, which subsequently caused employees to perceive the work environment as more adverse.
Table 5 Prospective associations between the continuous ERI ratio in 2000 and the continuous sleep disturbance score in 2005 among 1296 employed men and 1318 employed women in the DWECS Model 1
Model 2
Model 3
Model 4
ERI ratio at baseline
B
S.E.
P
B
S.E.
P
B
S.E.
P
B
S.E.
P
Men Women
.59 .10
.30 .29
.05 .73
.59 .13
.30 .29
.05 .65
.48 −.01
.30 .29
.11 .96
.59 −.07
.30 .29
.05 .80
Linear regression analysis: Model 1 is adjusted for sleep disturbance score at baseline, survey method (telephone survey, questionnaire, or Internet survey at follow-up), age, occupational grade, married or cohabitating, and age of youngest child at home; Model 2 is further adjusted for smoking, alcohol consumption, physical activity, and BMI; Model 3 is further adjusted for self-rated health and sickness absence days; Model 4 is further adjusted for weekly working hours and work time arrangement. B, regression coefficient; S.E., standard error.
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Another strength of the study is the use of a representative sample, which allows generalizing the findings for the Danish workforce. Moreover, analyses were adjusted for a wide range of potential confounders, allowing us to demonstrate that the effect of ERI was largely independent of other risk factors for sleep disturbances. A weakness of this study is the measurement of sleep disturbances by only two items and the absence of an established cutoff point for the sum score. Although we made the decision about the cutoff point before we started with the data analysis, we would have preferred to use an established cutoff point from the literature. We also lacked information about the use of sleeping pills. This has to be acknowledged as a weakness, because respondents who had used sleeping pills should have been scored as cases of sleep disturbances, even if their score on the sleep disturbance scale was below the cutoff point. With regard to the assessment of ERI, we had to use proxy measures, because the original ERI questionnaire was not used in DWECS. As Bourbonnais [34] has recently pointed out, proxy measures of ERI might result into an incomplete assessment of the construct and a misclassification of the exposure, which consequently might lead to either an overor an underestimation of the association between ERI and health outcomes. Although we cannot rule out that this also applies to our study, it has to be noted that we were able to measure all three subdimensions of the reward construct, which, we believe, indicates the comprehensive nature of our ERI assessment. The Cronbach's α of the reward score was only modest. Although Cronbach's α is a popular estimate for reliability, the assumptions behind the approach are not often discussed. Cronbach's α may overestimate reliability if some items show local dependency, for example, if they are too similar in content or wording [35]. On the other hand, for scales that are multidimensional or contain items that are causal indicators, Cronbach's α may underestimate reliability [36]. Since both the effort and the reward scale contain causal indicator items and since the reward scale also consists of three subdimensions, Cronbach's α may underestimate reliability for these scales. As delineated in the Introduction, it is assumed in the ERI model that the health-hazardous effects of ERI are heightened, when it co-occurs with a motivational disposition called “work-related overcommitment.” Of the four abovementioned cross-sectional studies on ERI and sleep disturbances, only Fahlén et al. have tested for an interaction effect between ERI and overcommitment. They found no interaction effect among men and a weak and statistically nonsignificant interaction effect among women [29]. It would have been interesting to conduct analyses on a potential interaction effect also in our study, but this was not possible, because DWECS did not include Siegrist's workrelated overcommitment scale [20] or items that could have been used to approximate this construct.
Conclusion and recommendation for further research We conclude that ERI is a risk factor for the onset of sleep disturbances among men in the Danish workforce. We recommend further prospective studies on this association, which should include more elaborate measures of sleep disturbances, for example, the Karolinska Sleep Questionnaire [37]. Such studies can also investigate whether ERI has stronger or weaker associations with specific types of sleep disturbances, for example, repeated awakenings, premature awakenings, or nightmares. We further recommend that future studies measure psychosocial work environment and sleep disturbances more often (e.g., once a year) to better capture changes in ERI and time of onset of sleep disturbances. This seems to be particularly important for today's work environment that is characterized by frequent organizational changes and reconstructions [38,39]. There is good scientific evidence that ERI is a risk factor for the development of CHD and maybe also for depression [21,22,26,40]. For both disorders, a causal role of sleep disturbances has also been discussed [4,8]. It would be very interesting if future studies could investigate if sleep disturbances are an intermediate step in the causal pathway leading from ERI to CHD and ERI to depression. If this would be the case, improving psychosocial working conditions might not only help reduce the risk of sleep disturbances but also prevent chronic disorders of high public health impact. Acknowledgments This study was, in part, conducted as a project for obtaining a Bachelor of Science degree in Public Health at the University of Copenhagen by two of the authors (M.N. and T.S.S.). Data analyses were conducted at the National Research Centre for the Working Environment, Denmark, and were partly funded by a grant (5-2006-04) from the Danish Working Environment Research Fund. We thank Jakob B. Bjørner, PhD, for his insightful comments on an earlier draft of this article and Dorthe Johansen, BSc, for her valuable help with the data analysis. The study has been notified to and registered by the Danish Data Protection Agency (Datatilsynet, see http:// www.datatilsynet.dk/english for details). According to Danish law, questionnaire- and register-based studies do not need approval from the Danish National Committee on Biomedical Research Ethics (Den Centrale Videnskabetiske komité, see http://www.cvk.im.dk/cvk/site.aspx?p=119 for details). References [1] Åkerstedt T. Psychosocial stress and impaired sleep. Scand J Work Environ Health 2006;32:493–501. [2] Edéll-Gustafsson UM, Kritz EI, Bogren IK. Self-reported sleep quality, strain and health in relation to perceived working conditions in females. Scand J Caring Sci 2002;16:179–87.
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Effort–reward imbalance at work and risk of sleep disturbances. Cross-sectional and prospective results from the Danish Work Environment Cohort Study☆ Reiner Rugulies a,b,⁎, Malene Norborg b , Tilde Sand Sørensen b , Lisbeth E. Knudsen b , Hermann Burr a a
National Research Centre for the Working Environment, Copenhagen, Denmark b Institute of Public Health, University of Copenhagen, Denmark
Received 12 October 2007; received in revised form 10 April 2008; accepted 6 May 2008
Abstract Objectives: This study aimed to analyze if adverse psychosocial working conditions, defined by the model of effort–reward imbalance (ERI), increase the risk of sleep disturbances in the Danish workforce. Methods: Analyses were conducted both crosssectionally and prospectively in a representative sample of Danish employees. The cross-sectional sample included 2614 participants (50% women) aged 18–59 years, of whom 263 had sleep disturbances. Of the 2351 participants initially free of sleep disturbances, 304 (12.9%) developed sleep disturbances during the 5-year follow-up. Data were analyzed with gender-stratified, multivariate logistic and linear regression analyses, adjusted for numerous covariates. Results: Cross-sectionally, a 1 S.D. increase in the ERI ratio was associated with sleep disturbances among both
men [odds ratio (OR)=1.65, 95% confidence interval (CI)=1.20– 2.27] and women (OR=1.82, 95% CI=1.46–2.28). In the prospective analysis, a 1 S.D. increase of the ERI ratio at baseline predicted the onset of sleep disturbances among men (OR=1.39, 95% CI=1.03–1.87) but not among women (OR=0.97, 95% CI=0.76–1.24). Conclusion: Among men, ERI is a risk factor for the development of sleep disturbances in the Danish workforce. Among women, an association between ERI and sleep disturbances was restricted to the cross-sectional sample. Improving psychosocial working conditions might reduce the risk of sleep disturbances and subsequently also help to prevent clinical disorders related to sleep disturbances. © 2009 Elsevier Inc. All rights reserved.
Keywords: Insomnia; Longitudinal study; Occupational health; Psychosocial; Sleep impairment; Stress
Introduction Sleep disturbances are associated with a wide range of health problems and diseases. People suffering from sleep disturbances have lower self-rated health, report more physical and mental health complains, and have more sickness absence [1–3]. Moreover, a meta-analysis of 10 ☆
The study was conducted at the National Research Centre for the Working Environment, Copenhagen, Denmark. ⁎ Corresponding author. National Research Centre for the Working Environment, Lersø Parkallé 105, DK-2100 Copenhagen, Denmark. Tel.: +45 39 16 52 18; fax: +45 39 16 52 01. E-mail address: [email protected] (R. Rugulies). 0022-3999/09/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.jpsychores.2008.05.005
case–control and cohort studies revealed that sleep disturbance was a risk factor for incident coronary heart disease (CHD) [4]. Other studies have shown that sleep disturbances were prospectively associated with the incidence of diabetes [5,6], obesity [7], and depression [8]. An experimental study demonstrated that restricting participants to 4 hours sleep per night over a period of 6 days resulted into decreased glucose tolerance, an increase in cortisol concentration, and heightened activity of the sympathetic nervous system [9]. The contribution of working conditions to sleep disturbances has been investigated in numerous studies (for an extensive review, see Ref. [1]). In particular, shift work and nonstandard working hours have been identified as important risk factors [1,10–12]. Sleep disturbances were also
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associated with adverse psychosocial working conditions, such as high quantitative workload, low decision latitude, or role conflicts [1,13–18]. However, with a few exceptions [17,18], the vast majority of research studies on psychosocial working conditions and sleep are cross-sectional in design and, therefore, limited in drawing causal inference. In recent years, the model of effort–reward imbalance (ERI) at work has emerged as a new theoretical approach for conceptualizing health-hazardous psychosocial working conditions. The model posits that a “high cost/low gain” situation at work, in which individuals spent high effort while receiving low rewards (in terms of monetary gratification, career opportunities, esteem, respect, and job security), elicits severe psychological distress, which consequently affects both mental and physical health. It is further assumed that ERI has, in particular, adverse health consequences, when it co-occurs with a motivational disposition called “work-related overcommitment” [19,20]. The ERI model has been tested foremost with regard to cardiovascular disease [21,22] but has recently also been used in research on other health outcomes [23–26]. Crosssectional multivariate analyses revealed a statistically significant association between ERI and sleep disturbances in two Japanese studies and one Swedish study [27–29], whereas a study in Germany did not find a statistically significant relation in the final multivariate model [30]. To our knowledge, the impact of ERI on sleep disturbances has not been analyzed in a prospective study or within a national workforce yet. This article aims to fill this research gap by studying ERI and risk of sleep disturbances in a 5-year follow-up analysis of a representative sample of the Danish workforce.
Methods Study design and population The Danish Work Environment Cohort Study (DWECS) was established in 1990 and is a longitudinal study to assess sociodemographic factors, work environment characteristics, health behaviors, and health status in the Danish working population [31]. In the DWECS 2000 survey, 11,437 randomly selected Danish residents were approached, of whom 8583 participated in the survey (response rate: 75%). Data were collected by means of telephone interviews. Among the respondents, 5292 were gainfully employed at the time of the survey. We excluded 527 respondents [those who were in job training, those who were employed under special working conditions (e.g., “modified duty due to illness”), those who worked less than 20 hours per week, and those who were 60 years or older at the time of the baseline survey], resulting in a sample of 4765 respondents. Of those, 3300 responded to the follow-up survey in 2005 (follow-up response rate: 69%). The follow-up survey was conducted with different survey methods, including telephone inter-
views and self-administered questionnaires on paper and on the Internet. Among the respondents, 2779 were still employed at the time of the follow-up and, therefore, eligible for the analyses. We excluded 165 respondents who had a missing value on any variable included in the multivariate analyses, yielding a final cross-sectional sample of 2614 employees. Of those, 263 (10.1%) had sleep disturbances at baseline. Among the 2351 respondents initially free of sleep disturbances (the prospective cohort), 304 (12.9%) reported sleep disturbances at follow-up. Measurement of sleep disturbances Sleep disturbances were measured with two questions: (a) “During the last 4 weeks, to what extent did you have trouble falling asleep?” and (b) “During the last 4 weeks, to what extent did you wake up and have trouble falling asleep again, when you should have been sleeping?” Response categories for both questions were (1) At no time, (2) A little of the time, (3) Some of the time, (4) A good part of the time, (5) Most of the time, and (6) All of the time. The scores for the two questions were added up, resulting in a sum score ranging from 2 to 12 points. Sleep disturbances were assessed when respondents scored 6 or more points on the sum score. In other words, respondents were categorized as having sleep disturbances when one of the following three conditions was met: •
They had answered all of the time or most of the time to at least one question. • They had answered a good part of the time to one question and at least a little of the time to the other question. • They had answered some of the time to both questions. The decision on the cutoff point was made by the research team, after a discussion on the interpretation of the response categories. The decision was made a priori, that is, before we started with the data analysis. Because we used items and cutoff points for sleep disturbances that had not been validated previously, we analyzed if our measure of sleep disturbances at baseline predicted poor self-rated health at follow-up, a health endpoint that includes sleep disturbance in its symptomatology [1]. We found that sleep disturbances in 2000 strongly predicted poor self-rated health in 2005, indicating the predictive validity of the sleep disturbance measure. Measurement of ERI Because DWECS did not include the original ERI questionnaire, we assessed effort and reward with proxy measures. The exact wording of the items and the response categories for each item are presented in Table 1. DWECS did not contain items that could be used to approximate work-related overcommitment, and consequently, we did not include this construct.
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Table 1 Items used to measure effort and rewards in the study Items:
Response categories and their value labels:
1. Effort ef1: Is your work unevenly distributed so it piles up? ef2: How often do you not have time to complete all your work tasks? ef3: Do you have to work overtime? ef4: Do you have to work very fast?
1=Never/almost never, 2=Rarely, 3=Sometimes, 4=Often, 5=Always 1=Never/almost never, 2=Rarely, 3=Sometimes, 4=Often, 5=Always 1=Never/almost never, 2=Rarely, 3=Sometimes, 4=Often, 5=Always 1=Never/hardly ever, 2=Seldom, 3=Sometimes, 4=Often, 5=Always
2. Reward 2.1 Financial and status reward rw1: Have you good prospects for the future in your job? rw2: How would you assess your salary with regard to your effort and your qualification?
2.2 Esteem reward rw3: Is your work recognized and appreciated by management? rw4: How often do you receive help and support from your colleagues? rw5: How often do you receive help and support from your closest supervisor? 2.3 Job security reward rw6: Are you worried about becoming unemployed? rw7: Are you worried about being transferred to another job against your will?
Effort was measured with four items on quantitative demands derived from the Copenhagen Psychosocial Questionnaire [32]. All items had five response categories ranging from 1=never/almost never to 5=always; that is, higher values indicated more effort. The effort score was built by summing the items up, resulting into a potential score ranging from 4 to 20 points. The Cronbach's α was .63. Rewards were measured with seven items, including two items on “financial/status reward,” three items on “esteem reward,” and two items on “job security reward.” The items had a different number of response categories (see Table 1) but were scored in such a way that, for all items, higher values indicated more rewards. The reward score was built by summing the items up, resulting into a potential score ranging from 7 to 27 points. The Cronbach's α was .53. Following an established procedure from the literature [20], we constructed an ERI ratio with the effort score in the nominator and the reward score in the denominator. Hence, higher values of the ratio expressed a higher level of imbalance between high effort and low reward. The mean ERI ratio at baseline was 0.54 (S.D.=0.18) in the crosssectional sample and 0.53 (S.D.=0.18) in the prospective cohort. For the purpose of the analyses, we divided the ERI ratio into quartiles. Measurement of covariates As covariates, we recorded gender, age, occupational grade, cohabitation, age of youngest child living with the respondent, smoking, alcohol consumption, leisure time
1=To a very slight degree, 2=To a slight degree, 3=To some degree, 4=To a high degree, 5=To a very high degree 1=Too high/Appropriate, 2=A little bit too low, 3=Much too low (Note: Too high and appropriate were separate response categories in the questionnaire but were combined for the analyses, because only very few participants responded too high) 1=To a very slight degree, 2=To a slight degree, 3=To some degree, 4=To a high degree, 5=To a very high degree 1=Never/almost never, 2=Rarely, 3=Sometimes, 4=Often, 5=Always. Not applicable: Do not have colleagues 1=Never/almost never, 2=Rarely, 3=Sometimes, 4=Often, 5=Always. Not applicable: Do not have a supervisor 1=Yes, 2=No 1=Yes, 2=No
physical activity, body mass index (BMI), self-rated health, sickness absence days last year, weekly working hours, and work time arrangement. We also included a variable, indicating the type of survey method at follow-up. Occupational grade was defined based on employment grade, job title, and education, yielding five categories: I: Executives and/or having a university degree; II: Middle managers and/or having more than 3 to 4 years of vocational education; III: Other white collar workers; IV: Skilled blue collar workers; V: Semi- or unskilled blue collar workers. Alcohol consumption was dichotomized in “no or moderate consumption” versus “heavy consumption,” with heavy consumption defined as drinking more than two (for women) or three (for men) units per day. A unit was defined as one small bottle of beer (33 cl), one glass of wine, or one shot of liquor. Leisure time physical activity was assessed with the question “When you should describe your leisure time physical activity in the last year, including commuting to or from work, to what group do you belong?” with four response categories corresponding to sedentary, light, moderate, and strenuous physical activity. BMI was calculated based on self-reported height and weight and categorized into “underweight” (b18.5), “normal weight” (18.5–24.9), “overweight” (25–30), and “obese” (N30). Self-rated health was measured with the question “In general, how would you rate your health?” [33]. We dichotomized responses into two categories: (1) “good health,” which included the responses very good and good, and (2) “reduced health,” which included the responses fair, poor, and very poor. Number of sickness absence days during the last year was based on self-report and was divided
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into four groups: “no sickness absence,” “1–5 days of absence,” “6–15 days,” and “16 or more days.” Weekly working hours were categorized in accordance with the Danish standard working hours into the categories “parttime work” (20–29 h), “full-time work” (30–37 h), “moderate overtime work” (38–45 h), and “heavy overtime work” (46 h or more). Work time arrangement was categorized into “daytime work,” “morning or afternoon work,” “night work,” “shift work,” and “irregular working hours.” To control for the effects of the type of survey method on reporting of sleep disturbances, we included a variable indicating by which method (telephone interview, paper questionnaire, Internet questionnaire) data were collected at follow-up. Statistical analysis All analyses were calculated with the statistical software program STATA 8.0 for Windows. We used multivariate logistic regression analyses to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the effect of ERI on risk of sleep disturbances. We first conducted a cross-sectional analysis on the association between ERI and the prevalence of sleep disturbances measured at baseline among all 2614 participants, adjusted for all covariates. Next, we analyzed the impact of ERI, measured at baseline, on the incidence of sleep disturbances, measured at the 5-year follow-up, among the 2351 participants, who were free of sleep disturbances at baseline. The prospective analyses were stratified by gender and were incrementally adjusted for type of survey method at follow-up, age, occupational grade, cohabitation, and age of youngest child at home (Model 1); smoking, alcohol consumption, physical activity, and BMI (Model 2); selfrated health and sickness absence days (Model 3); and weekly working hours and work time arrangement (Model 4). In addition, we used multivariate linear regression analyses to calculate the association between the continuous ERI ratio at baseline and the continuous sleep disturbance score at followup. This analysis was conducted with all 2614 participants and was adjusted for the sleep disturbance score at baseline and all covariates. The interpretation of this analysis, however, has to be done with caution, because the sleep disturbance score was not normally distributed and, therefore, did not formally fulfill the statistical requirement for a linear regression analysis. Results Study population characteristics and cross-sectional association with prevalence of sleep disturbances at baseline Table 2 shows the characteristics of the study population and baseline associations with prevalence of sleep disturbances. Among the 2614 respondents, 1318 were women
(50.4%) and mean age was 40 years (S.D.=9 years). The majority of the participants were white collar workers (72%), and most worked during daytime (83%). At baseline, 263 (10.1%) participants reported sleep disturbances. When all variables were adjusted for each other, female gender, reduced self-rated health, high number of sickness absence days, irregular working hours, and high ERI showed statistically significant associations with prevalence of sleep disturbances. Moderate and strenuous leisure time physical activity were protective against sleep disturbances. Respondents in the highest ERI quartile showed an OR of 2.26 (95% CI=1.56–3.27, Pb.001) for sleep disturbances (Table 2). Stratifying the sample by gender resulted into ORs of 1.81 (95% CI=0.93–3.52, P=.08) and 2.78 (95% CI=1.74–4.44, Pb.001) for men and women, respectively (data not shown in Table 2). When we used the ERI score as a continuous variable, we found that a 1 S.D. increment of the ERI score was associated with an increased risk of sleep disturbances among both men (OR=1.65, 95% CI=1.20–2.27, P=.002) and women (OR=1.82, 95% CI=1.46–2.28, Pb.001, data not shown in Table 2). Prospective associations between ERI at baseline and 5-year incidence of sleep disturbances Of the 2351 participants initially free of sleep disturbances, 304 (12.9%) developed sleep disturbances during the 5-year follow-up. Table 3 shows the prospective association between ERI in 2000 and incident sleep disturbances in 2005 among men. ERI predicted incident sleep disturbances in all models. In the fully adjusted model, the highest ERI quartile showed an OR of 2.06 (95% CI=1.14–3.74, P=.02). A 1 S.D. increment of the ERI ratio predicted a 39% (P=.03) increased risk of incident sleep disturbances in the full model. Of the covariates, reduced self-rated health (OR=2.13, 95% CI=1.27–3.58) and working at night (OR=3.79, 95% CI=1.09–13.15) predicted sleep disturbances among men, after adjustment for ERI quartiles and all other covariates. Moderate overtime work had a protective effect (OR=0.63, 95% CI=0.40–1.00, data not shown in Table 3). Among women, ERI in 2000 was unrelated to incident sleep disturbances in 2005 in all four models (Table 4). Of the covariates, smoking (OR=1.44, 95% CI=1.01–2.06), reduced self-rated health (OR=2.38, 95% CI=1.42–3.98), and working at night (OR=3.09, 95% CI=1.22–7.83) predicted sleep disturbances among women, after adjustment for ERI quartiles and all other covariates (data not shown in Table 4). Prospective associations between the ERI score and the sleep disturbance score Table 5 shows the association between the continuous ERI ratio at baseline and the continuous sleep disturbance
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Table 2 Study population characteristics and cross-sectional associations with prevalence of sleep disturbances at baseline among 2614 Danish employees Exposed
Gender Male Female Age (years) b 30 30–39 40–49 50–59 Occupational grade Executive/university degree Middle manager/further vocational education Other white collar worker Skilled blue collar worker Semi- or unskilled blue collar worker Married or cohabitating Yes No Children living at home No children Youngest child 2 years or younger Youngest child 3 to 6 years old Youngest child 7 years or older Smoking Current nonsmoker Current smoker Alcohol consumption No or moderate consumption Heavy consumption Physical activity Sedentary Light activity Moderate activity Strenuous activity BMI Underweight (b 18.5) Normal weight (18.5–24.5) Overweight (25–30) Obese (N30) Self-rated health Very good or good health Fair, poor, or very poor health Sickness absence days last year 0 1–5 6–15 16+ Weekly working hours 20–29 30–37 38–45 46+ Work time arrangement Daytime work Morning or afternoon work Night work Shift work Irregular working hours
Sleep disturbances
Multivariate ORs
n
%
n
%
OR
95% CI
1296 1318
49.6 50.4
99 164
7.6 12.4
1 1.87
Reference 1.37–2.54
382 839 875 518
14.6 32.1 33.5 19.8
35 80 81 67
9.2 9.5 9.3 12.9
1 0.98 0.89 1.37
Reference 0.62–1.56 0.55–1.44 0.85–2.21
468 455 949 348 394
17.9 17.4 36.3 13.3 15.1
45 45 99 39 35
9.6 9.9 10.4 11.2 8.9
1 0.76 0.91 1.18 0.75
Reference 0.47–1.21 0.61–1.37 0.71–1.95 0.45–1.25
2122 492
81.2 18.8
206 57
9.7 11.6
1 1.14
Reference 0.81–1.61
1114 293 380 827
42.6 11.2 14.5 31.6
120 22 41 80
10.8 7.5 10.8 9.7
1 0.85 1.16 1.00
Reference 0.50–1.46 0.74–1.83 0.70–1.42
1709 905
65.4 34.6
158 105
9.3 11.6
1 1.18
Reference 0.89–1.57
2487 127
95.1 4.9
243 20
9.8 15.8
1 1.44
Reference 0.84–2.48
369 1112 871 262
14.1 42.5 33.3 10.0
52 114 79 18
14.1 10.3 9.1 6.9
1 0.72 0.67 0.53
Reference 0.49–1.04 0.45–0.99 0.30–0.96
46 1546 811 193
1.8 59.8 31.0 7.4
9 152 82 20
19.6 9.7 10.1 10.4
1.89 1 1.14 1.00
0.86–4.13 Reference 0.84–1.54 0.59–1.70
2318 296
88.7 11.3
209 54
9.0 18.2
1 1.70
Reference 1.18–2.45
851 1130 437 196
32.6 43.2 16.7 7.5
71 96 61 35
8.3 8.5 14.0 17.9
1 1.01 1.54 1.95
Reference 0.73–1.42 1.05–2.27 1.21–3.15
144 1632 588 250
5.5 62.4 22.5 9.6
15 176 50 22
10.4 10.8 8.5 8.8
0.74 1 0.84 0.80
0.40–1.36 Reference 0.59–1.21 0.49–1.33
2173 57 45 122 217
83.1 2.2 1.7 4.7 8.3
200 8 7 18 30
9.2 14.0 15.6 14.8 13.8
1 1.84 1.89 1.67 1.79
Reference 0.81–4.20 0.78–4.59 0.96–2.91 1.16–2.77
(continued on next page)
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R. Rugulies et al. / Journal of Psychosomatic Research 66 (2009) 75–83
Table 2 (continued) Exposed
ERI ratio divided into quartiles Low ERI Medium-low ERI Medium-high ERI High ERI
Sleep disturbances
Multivariate ORs
n
%
n
%
OR
95% CI
661 659 656 638
25.3 25.2 25.1 24.4
54 37 68 104
8.2 5.6 10.4 16.3
1 0.67 1.43 2.26
Reference 0.43–1.04 0.97–2.11 1.56–3.27
Multivariate ORs: All variables are adjusted for each other. ORs and CIs with P values b .05 are in boldface. Note that we recalculated the analyses based on a new ERI score and that, therefore, effect estimates in all tables are slightly different from the first submission.
score at follow-up, after adjustment for sleep disturbance score at baseline and for all covariates. The ERI ratio was associated with a higher sleep disturbance score among men (P=.05), but not among women (P=.80), when adjusted for all covariates.
Discussion ERI was cross-sectionally associated with prevalence of sleep disturbances in both men and women after adjustment for numerous covariates in this representative sample of the Danish workforce. When associations were analyzed prospectively, we found for men that the highest ERI quartile predicted a twofold increased risk of incident sleep disturbances and that the continuous ERI ratio at baseline was significantly associated with sleep disturbance score at follow-up. Among women, ERI was not prospectively related to sleep disturbances. Associations between ERI and prevalence of sleep disturbances have been previously analyzed in crosssectional studies. Kudielka et al. reported that employees with sleep problems had higher levels of ERI in a study with 616 men and 93 women from an aeroplane manufacturing plant and a commercial medical laboratory in Germany. However, these associations vanished in the multivariate analyses [30]. In a Swedish study, Fahlén et al. [29] showed that high ERI was associated with sleep
disturbances in 789 men and 214 women from different occupations. In Japan, Utsugi et al. [27] found high ERI to be associated with both insomnia and short sleep (less than 6 h per day) in 6997 men and 1733 women working at local governments and a transit company. In another Japanese study, Ota et al. [28] showed that high ERI was associated with insomnia in 983 male and 98 female workers in a corporate group of electric products. Our study adds to the evidence from these previous studies that ERI is crosssectionally associated with the prevalence of sleep disturbances in both men and women. As far as we are aware, prospective associations between ERI and sleep disturbances have not been studied before. The prospective associations between ERI and sleep disturbances in the present study corroborate the crosssectional findings from previous studies that ERI has an adverse effect on sleep among men. Among women, however, ERI was associated with sleep disturbances only in the cross-sectional, but not in the prospective, analysis. There are different explanations for this discrepancy. On the one hand, it is possible that among women, ERI just does not affect sleep disturbances and that the positive association at baseline was due to reverse causation, that is, that sleep disturbances have caused women to perceive their work environment as more adverse. On the other hand, it is possible that during the relatively long follow-up period of 5 years, working conditions have changed for many participants and that these changes were more pronounced
Table 3 Prospective associations between ERI in 2000 and incident sleep disturbances in 2005 among 1197 employed men in the DWECS
ERI ratio divided into quartiles Low ERI Medium-low ERI Medium-high ERI High ERI ERI ratio continuous (1 S.D. increase)
Exposed
Sleep disturbances
Model 1
n
n
%
OR
95% CI
OR
95% CI
OR
95% CI
OR
95% CI
266 305 321 305 1197
21 35 31 43 130
7.9 11.5 9.7 14.1 10.9
1 1.43 1.23 2.02 1.36
Reference 0.80–2.55 0.68–2.22 1.15–3.55 1.03–1.81
1 1.34 1.21 1.95 1.36
Reference 0.74–2.39 0.67–2.18 1.10–3.44 1.02–1.81
1 1.33 1.18 1.84 1.31
Reference 0.74–2.39 0.65–2.14 1.03–3.26 0.98–1.75
1 1.44 1.34 2.06 1.39
Reference 0.80–2.61 0.73–2.47 1.14–3.74 1.03–1.87
Model 2
Model 3
Model 4
Logistic regression analysis: Model 1 is adjusted for survey method (telephone survey, questionnaire, or Internet survey at follow-up), age, occupational grade, married/cohabitating, and age of youngest child at home; Model 2 is further adjusted for smoking, alcohol consumption, physical activity, and BMI; Model 3 is further adjusted for self-rated health and sickness absence days; Model 4 is further adjusted for weekly working hours and work time arrangement.
R. Rugulies et al. / Journal of Psychosomatic Research 66 (2009) 75–83
81
Table 4 Prospective associations between ERI in 2000 and incident sleep disturbances in 2005 among 1154 employed women in the DWECS
ERI ratio divided into quartiles Low ERI Medium-low ERI Medium-high ERI High ERI ERI ratio continuous (1 S.D. increase)
Exposed
Sleep disturbances
Model 1
n
n
%
OR
95% CI
OR
95% CI
OR
95% CI
OR
95% CI
341 317 267 229 1154
50 56 33 35 174
14.7 17.7 12.4 15.3 15.1
1 1.31 0.81 1.07 1.00
Reference 0.86–1.99 0.50–1.31 0.66–1.73 0.79–1.28
1 1.33 0.83 1.08 1.01
Reference 0.87–2.03 0.51–1.35 0.67–1.76 0.79–1.29
1 1.30 0.83 0.96 0.96
Reference 0.85–2.01 0.51–1.35 0.59–1.58 0.76–1.23
1 1.33 0.87 0.98 0.97
Reference 0.86–2.06 0.53–1.43 0.59–1.63 0.76–1.24
Model 2
Model 3
Model 4
Logistic regression analysis: Model 1 is adjusted for survey method (telephone survey, questionnaire, or Internet survey at follow-up), age, occupational grade, married or cohabitating, and age of youngest child at home; Model 2 is further adjusted for smoking, alcohol consumption, physical activity, and BMI; Model 3 is further adjusted for self-rated health and sickness absence days; Model 4 is further adjusted for weekly working hours and work time arrangement.
among women. If this was the case, then there would have been more misclassification of exposure among women, which consequently would have resulted into diminished associations between exposure measured in 2000 and endpoint measured in 2005. We do not know which explanation is the right one, but these findings clearly call for further research on potential gender-specific effects of ERI in prospective analyses.
case, statistical adjustment for poor health would have been appropriate control for confounding, whereas in the second case, this would have been inappropriate overadjustment for an intermediate step in a causal pathway. As it turned out in the analyses, adjustment for indicators of poor health (adding self-rated health and number of sickness absence days in Model 3) as well as adjustments for the other covariates affected risk estimates only to a minor extent. This indicates that the association between ERI and sleep disturbances was largely independent of these other factors.
Methodological considerations Analyses in the present study were incrementally adjusted for numerous covariates, including sociodemographic factors, occupational grade, health-related behaviors, health status at baseline, weekly working hour, and work time arrangement. These covariates had been found to be associated with ERI and/or sleep disturbances in previous studies and, therefore, might have been confounder for the statistical association between ERI and sleep disturbances [1,23–25]. On the other hand, some of these covariates could also have been intermediate steps in a pathway leading from ERI to sleep disturbances. For example, it is possible that poor health has caused both the perception of high ERI and the onset of sleep disturbances, but it is also possible that ERI has caused poor health in the first step and then poor health has caused sleep disturbances in the second step. In the first
Strengths and weaknesses of the study To our knowledge, this is the first time that the impact of ERI on risk of incident sleep disturbances has been analyzed with a longitudinal design and with a representative sample of a national workforce. Due to the prospective nature of the study, temporal order between exposure and outcome variable was established, which is an important criterion for determination of causality. In cross-sectional studies, statistical associations between ERI and sleep disturbances might equally indicate that ERI had causally influenced the development of sleep disturbances or that sleep disturbances had caused reduced workability, which subsequently caused employees to perceive the work environment as more adverse.
Table 5 Prospective associations between the continuous ERI ratio in 2000 and the continuous sleep disturbance score in 2005 among 1296 employed men and 1318 employed women in the DWECS Model 1
Model 2
Model 3
Model 4
ERI ratio at baseline
B
S.E.
P
B
S.E.
P
B
S.E.
P
B
S.E.
P
Men Women
.59 .10
.30 .29
.05 .73
.59 .13
.30 .29
.05 .65
.48 −.01
.30 .29
.11 .96
.59 −.07
.30 .29
.05 .80
Linear regression analysis: Model 1 is adjusted for sleep disturbance score at baseline, survey method (telephone survey, questionnaire, or Internet survey at follow-up), age, occupational grade, married or cohabitating, and age of youngest child at home; Model 2 is further adjusted for smoking, alcohol consumption, physical activity, and BMI; Model 3 is further adjusted for self-rated health and sickness absence days; Model 4 is further adjusted for weekly working hours and work time arrangement. B, regression coefficient; S.E., standard error.
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R. Rugulies et al. / Journal of Psychosomatic Research 66 (2009) 75–83
Another strength of the study is the use of a representative sample, which allows generalizing the findings for the Danish workforce. Moreover, analyses were adjusted for a wide range of potential confounders, allowing us to demonstrate that the effect of ERI was largely independent of other risk factors for sleep disturbances. A weakness of this study is the measurement of sleep disturbances by only two items and the absence of an established cutoff point for the sum score. Although we made the decision about the cutoff point before we started with the data analysis, we would have preferred to use an established cutoff point from the literature. We also lacked information about the use of sleeping pills. This has to be acknowledged as a weakness, because respondents who had used sleeping pills should have been scored as cases of sleep disturbances, even if their score on the sleep disturbance scale was below the cutoff point. With regard to the assessment of ERI, we had to use proxy measures, because the original ERI questionnaire was not used in DWECS. As Bourbonnais [34] has recently pointed out, proxy measures of ERI might result into an incomplete assessment of the construct and a misclassification of the exposure, which consequently might lead to either an overor an underestimation of the association between ERI and health outcomes. Although we cannot rule out that this also applies to our study, it has to be noted that we were able to measure all three subdimensions of the reward construct, which, we believe, indicates the comprehensive nature of our ERI assessment. The Cronbach's α of the reward score was only modest. Although Cronbach's α is a popular estimate for reliability, the assumptions behind the approach are not often discussed. Cronbach's α may overestimate reliability if some items show local dependency, for example, if they are too similar in content or wording [35]. On the other hand, for scales that are multidimensional or contain items that are causal indicators, Cronbach's α may underestimate reliability [36]. Since both the effort and the reward scale contain causal indicator items and since the reward scale also consists of three subdimensions, Cronbach's α may underestimate reliability for these scales. As delineated in the Introduction, it is assumed in the ERI model that the health-hazardous effects of ERI are heightened, when it co-occurs with a motivational disposition called “work-related overcommitment.” Of the four abovementioned cross-sectional studies on ERI and sleep disturbances, only Fahlén et al. have tested for an interaction effect between ERI and overcommitment. They found no interaction effect among men and a weak and statistically nonsignificant interaction effect among women [29]. It would have been interesting to conduct analyses on a potential interaction effect also in our study, but this was not possible, because DWECS did not include Siegrist's workrelated overcommitment scale [20] or items that could have been used to approximate this construct.
Conclusion and recommendation for further research We conclude that ERI is a risk factor for the onset of sleep disturbances among men in the Danish workforce. We recommend further prospective studies on this association, which should include more elaborate measures of sleep disturbances, for example, the Karolinska Sleep Questionnaire [37]. Such studies can also investigate whether ERI has stronger or weaker associations with specific types of sleep disturbances, for example, repeated awakenings, premature awakenings, or nightmares. We further recommend that future studies measure psychosocial work environment and sleep disturbances more often (e.g., once a year) to better capture changes in ERI and time of onset of sleep disturbances. This seems to be particularly important for today's work environment that is characterized by frequent organizational changes and reconstructions [38,39]. There is good scientific evidence that ERI is a risk factor for the development of CHD and maybe also for depression [21,22,26,40]. For both disorders, a causal role of sleep disturbances has also been discussed [4,8]. It would be very interesting if future studies could investigate if sleep disturbances are an intermediate step in the causal pathway leading from ERI to CHD and ERI to depression. If this would be the case, improving psychosocial working conditions might not only help reduce the risk of sleep disturbances but also prevent chronic disorders of high public health impact. Acknowledgments This study was, in part, conducted as a project for obtaining a Bachelor of Science degree in Public Health at the University of Copenhagen by two of the authors (M.N. and T.S.S.). Data analyses were conducted at the National Research Centre for the Working Environment, Denmark, and were partly funded by a grant (5-2006-04) from the Danish Working Environment Research Fund. We thank Jakob B. Bjørner, PhD, for his insightful comments on an earlier draft of this article and Dorthe Johansen, BSc, for her valuable help with the data analysis. The study has been notified to and registered by the Danish Data Protection Agency (Datatilsynet, see http:// www.datatilsynet.dk/english for details). According to Danish law, questionnaire- and register-based studies do not need approval from the Danish National Committee on Biomedical Research Ethics (Den Centrale Videnskabetiske komité, see http://www.cvk.im.dk/cvk/site.aspx?p=119 for details). References [1] Åkerstedt T. Psychosocial stress and impaired sleep. Scand J Work Environ Health 2006;32:493–501. [2] Edéll-Gustafsson UM, Kritz EI, Bogren IK. Self-reported sleep quality, strain and health in relation to perceived working conditions in females. Scand J Caring Sci 2002;16:179–87.
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