Tossing and turning – insomnia in relation to occupational stress, rumination, fatigue, and well-being

Michiel Aj Kompier,Toon W Taris,Marc Van Veldhoven

doi:10.5271/sjweh.3263

Abstract

This study of a large and heterogeneous sample of 5210 daytime employees was designed to shed more light on the work effort-recovery mechanism by examining the cross-sectional relations between subjective sleep quality and (i) psychosocial work characteristics, (ii) work-related rumination, (iii) fatigue after work, and (iv) affective well-being at work and work pleasure. We used the Dutch Questionnaire on the Experience and Evaluation of Work and created three sleep quality groups (low, low-to-intermediate, and high quality). Group differences were studied through analysis of variance (ANOVA). To examine the relations among the study variables in more detail, we also conducted four sets of stepwise regression analyses. In all the analyses, we corrected for age, level of education, and gender. A series of (M)ANOVA provided strong evidence for a relation between sleep quality and adverse work characteristics and work-related rumination. Furthermore, poor sleepers reported higher levels of fatigue after work, and poor sleep quality was related to both lower affective well-being during work and work pleasure. Regression analyses revealed that sleep quality was the strongest statistical predictor of after-work fatigue and affective well-being at work, and high levels of work rumination constituted the strongest statistical predictor of sleep complaints. As this study showed strong relations between sleep quality, occupational stress, fatigue, perseverative cognitions, and work motivation, it supports effort-recovery theory. Interventions should aim to prevent a disbalance between effort and recovery.

Highlights

Background variablesWe recorded age, sex, and level of education (with six levels: 1=primary education, not completed (0.3%); 2=primary education, completed (4%); 3=lower vocational training completed, or higher secondary education, not completed (18.8%); 4=higher secondary education completed (31.2%); 5=higher education/college degree (31.4%); and 6=higher education/university degree (14.3%)
In the case of prolonged or repeated exposure to stressful work characteristics combined with insufficient recovery and coping possibilities, a cumulative process may start in which psychophysiological reactions that initially were adaptive and reversible are sustained and in the long run may result in subsequent adverse health
Within effort–recovery theory and comparable theoretical approaches, such as allostatic load theory [4,5,6] and the cognitive activation theory of stress [7], recovery is a process of psycho-physiological unwinding that is the opposite of the activation of the sympathetic-adrenal-medullary system and the hypothalamic-pituitary-adrenal system during effort expenditure, under stressful conditions [3]

Summary

Introduction

Background variablesWe recorded age (in years), sex, and level of education (with six levels: 1=primary education, not completed (0.3%); 2=primary education, completed (4%); 3=lower vocational training completed, or higher secondary education, not completed (18.8%); 4=higher secondary education (nearly) completed (31.2%); 5=higher education/college degree (31.4%); and 6=higher education/university degree (14.3%). We tested between-group differences among the four clusters of study variables using a 3 (sleep quality: low versus intermediate versus high quality) × 2 (gender: male versus female) × 3 (level of education: low versus intermediate versus high) × 3 (age: young versus intermediate versus old) multivariate analysis of variance (MANOVA). Due to the large sample size, we employed an alpha level of 0.01 rather than 0.05 to ensure that statistically significant differences between the means of groups were practically relevant. As the assumption that the variance of all criterion variables was homogeneous across all sleep quality groups could not be maintained, we employed Tamhane’s range test for these comparisons. We computed Cohen’s D for all comparisons (ie, high versus intermediate sleep quality; high versus low sleep quality; and intermediate versus low sleep quality groups) as an indication of effect size. Following Cohen [32], we distinguished among small (0.8) effect sizes

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