ABSTRACT
Psychosocial working conditions have been linked to mental health outcomes, but their association with well-being is poorly studied. We aimed to investigate the association between psychosocial working conditions and well-being before retirement, and to explore the role of gender and leisure activities in the association. From the Swedish National Study on Aging and Care in Kungsholmen, 598 community dwellers aged 60–65 years were included in the cross-sectional study. Lifelong occupational history was obtained through an interview. Job demands and job control in the longest-held occupation were graded with job exposure matrices. Psychosocial working conditions were classified into high strain (high demands, low control), low strain (low demands, high control), passive job (low demands, low control), and active job (high demands, high control). Well-being was assessed with the 10-item version of positive and negative affect schedule, and scored using confirmatory factor analysis. Engagement in leisure activities was categorized as low, moderate, and high. Data were analyzed using linear regression. Both high job control and high job demands were dose-dependently associated with higher well-being. Overall, compared to active jobs, passive jobs were associated with lower well-being (β −0.19, 95% CI −0.35 to −0.02, P = 0.028). Passive (β −0.28, 95% CI −0.51 to −0.04, P = 0.020) and high strain (β −0.31, 95% CI −0.52 to −0.10, P = 0.004) jobs were associated with lower well-being in men, but not in women. The association between passive jobs and well-being was attenuated by high leisure activities, while the association between high strain and well-being was magnified by low leisure activities. In conclusion, negative psychosocial working conditions are associated with poor well-being, especially in men. Leisure activities may modulate the association. Our study highlights that promoting favorable working conditions can be a target to improve well-being among employees and active participation in leisure activities is encouraged to cope with work-related stress for better well-being.
1. Introduction
Well-being is an important indicator for measuring the quality of life of older adults. Poor well-being has been related to adverse health outcomes, including cardiovascular diseases, mental disorders, disability, and even premature death (Diener & Chan, Citation2011; Kubzansky et al., Citation2018). Furthermore, according to the World Health Organization, poor psychological well-being is one of the most prominent causes of reduced job involvement and absenteeism from the workplace (Schultz et al., Citation2012). Workers experiencing reduced well-being may be less productive and make decisions of lower quality, which therefore diminishes overall contributions to society (Lanciano & Zammuner, Citation2014). Consequently, a greater burden will be posed on individuals, families, and societies. Work is one of the activities that occupy a great deal of time in one’s adult life, which possibly makes it a relevant determinant of well-being, even in later career. With retirement age gradually increasing, the impact of working conditions on well-being later in life will most likely become even stronger and needs a special attention.
Psychosocial working conditions result from the interaction between psychological and social factors in the work environment, that is, the way how workers perceive and react to their surroundings (Bujacz et al., Citation2018; Vargas et al., Citation2014). One of the most frequently applied models to assess psychosocial working conditions is the job demand-control model (Karasek, Citation1979), which ponders two dimensions: job control and job demands, and subsequently conceptualizes four work scenarios: high strain (high demands/low control), low strain (low demands/high control), passive job (low demands/low control), and active job (high demands/high control). There is a growing body of evidence linking psychosocial working conditions to several health outcomes in later life (Jones et al., Citation2013; Pan et al., Citation2017, Citation2019), especially mental health, such as depression (Almroth et al., Citation2021), anxiety (Too et al., Citation2020), and well-being (Häusser et al., Citation2010; Schütte et al., Citation2014). However, most studies used self-reported psychosocial working conditions, which could lead to bias in the association with well-being. In addition, women and men are usually exposed to different types of working environments, and there exists a sex difference in the perception of stress in general even when they work in the same sector and profession. Thus, it is plausible that women and men differ with regard to the problems caused by work stress.
Involvement in leisure activities including physical, mental, and social activities could be a key factor of healthy aging, and its beneficial effects on well-being have been widely accepted (Michèle et al., Citation2019). Nevertheless, there is a lack of understanding of whether active engagement in leisure activities may help to improve well-being among workers with adverse psychosocial working conditions. In this study, we aimed to 1) examine the association of work-related stress during working life with poor well-being before retirement, 2) investigate whether the association between psychosocial working conditions and well-being differs in men and women, and 3) assess whether leisure activities may modulate the association between psychosocial working conditions and well-being.
2. Materials and methods
2.1. Study population
Participants of this study were identified from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K). SNAC-K is an ongoing, population-based longitudinal study undertaken in the urban district, Kungsholmen, in Stockholm, Sweden (Lagergren et al., Citation2004). Between March 2001 and June 2004, a total of 3363 adults aged 60 years and older living in Kungsholmen were invited to the baseline examination. Of the 3363 participants, 739 were ≤65 years and working. We chose 65 years as a cut-off to obtain a greater proportion of people approaching retirement and reduce selection bias and confounding bias introduced by age. Additionally, we excluded 62 with missing data on occupation and 79 with incomplete data on well-being, leaving 598 participants in the current study (Supplementary Figure S1).
All phases of the SNAC-K study were approved by the Karolinska Institutet Ethics Committee and the Regional Ethics Review Board in Stockholm, Sweden. Written informed consent was obtained from all participants or their next of kin.
2.2. Data collection
Data collection of SNAC-K followed a structured protocol at baseline and all follow-ups (available at http://www.snac-k.se/). Physicians conducted clinical examinations; nurses collected information on age, sex, education, smoking, alcohol consumption, the occupation of participants’ father, occupational characteristics, and leisure activity, as well as measured anthropometrics (i.e. height and weight). Education was recorded as elementary, high school, and university according to the highest degree achieved. Smoking status was recorded as never, former, and current smoking. Alcohol consumption was classified as none or occasional, light-to-moderate, and heavy drinking (Breslow et al., Citation2013; Lu et al., Citation2019). Current engagement in leisure activities (including mental, social, and physical activities, see Section 2.5 for more details) was categorized as low, moderate, or high. The occupation of participants’ father was used to assess early-life socioeconomic status (SES) in accordance with three groups: manual, intermediate, and professional (Karp et al., Citation2004). Occupational characteristics (i.e. blue- and white-collar workers) were also considered (Rydwik et al., Citation2013). The information on early-life strain was collected through the self-administered questionnaire on the question: Did you experience financial difficulties in family while growing up. Body mass index (BMI) was calculated as weight in kilogram divided by the square of height in meter. Chronic medical conditions were ascertained by clinical examination, medication use, medical history, laboratory data, linkage to inpatient and outpatient care data from the Swedish National Patient Registry, and/or through proxy interviews (Calderón-Larrañaga et al., Citation2017).
2.3. Assessment of psychosocial working conditions
Information on the longest-held jobs throughout participants’ professional lives, including employers, job titles, tasks, and time spans, were collected through nurses’ interviews at baseline (Lagergren et al., Citation2004). Each occupation was coded in accordance with the three-digit Nordic Occupation Classification Codes (Sweden, Citation1989). Levels of job control and job demands at work were evaluated by a validated psychosocial job exposure matrix (Fredlund et al., Citation2000) constructed based on data from the Swedish Work Environment Survey, for men and women separately. The items measuring job control and demands were identified by factor analyses. The average score of each job dimension was transformed to vary between 0 and 10, with higher scores indicating higher levels.
For the operationalization of demand-control status in SNAC-K, job control and demands in the jobs were dichotomized into high and low using the median values from the job exposure matrix, respectively. Further, occupations were classified into four scenarios based on the demand-control combinations: high job strain (high demands, low control), low job strain (low demands, high control), passive job (low demands, low control), and active job (high demands, high control). The five most prevalent occupations in the SNAC-K cohort in each of the categories were listed in Supplementary Table S1.
2.4. Assessment of well-being
The 10-item version of positive and negative affect schedule (PANAS) was used to measure emotional components of well-being. Participants at baseline were asked to report their positive and negative affect during the last 12 months. The mood adjectives included in PANAS positive affect (PANAS-PA) were active, enthusiastic, alert, inspired, and determined. PANAS negative affect (PANAS-NA) included distressed, scared, upset, nervous, and afraid. Response options included not at all, a little, somewhat, quite a bit, and very much, which were coded from one to five.
We generated the well-being score using confirmatory factor analysis with a two-level structure. On the first level, we generated latent factors of PANAS-PA and PANAS-NA scores using the original scores of 10 items of mood adjectives. The standardized individual loading of latent factors ranged 0.50–0.87 for PANAS-PA, and 0.52–0.82 for PANAS-NA. On the second level, we generated the latent score of well-being based on its correlation with latent factors of PANAS-PA and PANAS-NA scores (). A higher score of the latent factor of well-being indicated better well-being.
Figure 1. Confirmatory factor analysis to generate the latent variable of well-being based on the 10-item of PANAS.
2.5. Assessment of leisure activities
Participants were asked which of a list of 26 predefined activities they engaged in and how often they had engaged in them over the past 12 months (Supplementary Table S2). Response alternative for physical activities was daily, weekly, monthly, less frequently, or never. Based on previous studies (Rizzuto et al., Citation2017; Wang et al., Citation2002), the activities were categorized as physical, mental, or social.
Physical activities were those for which the predominant component was light to vigorous physical exercise. Engagement in physical activity was coded as 0 (performed <1 time/week), 1 (performed 1 time/week), or 2 (performed ≥2 time/week). Mental activities included those activities that were predominantly cognitive in nature and required little to no social engagement. Engagement in mental activity was coded as 0 (≤1 activity), 1 (2–3 activities), or 2 (≥4 activities). Social activities included those with social interactions. Engagement in social activities was coded as 0 (no activity), 1 (1 activity), or 2 (≥2 activities).
Finally, the engagement level in each of the three types of activities was summed into a continuous variable ranging between 0 and 6, which was used as a leisure activities index that categorized participation in leisure activities as low (score 0–1), moderate (score 2–3), or high (score 4–6) (Rizzuto et al., Citation2017).
2.6. Statistical analysis
Characteristics between participants with different demand-control categories were compared using chi-square (χ2) for categorical variables or one-way analysis of variance for continuous variables. Linear regression was applied to examine the association of job demand-control status (active jobs as reference) with well-being score. We performed two models: 1) the basic-adjusted model including gender and education and 2) the multi-adjusted model additionally including job characteristics, alcohol consumption, and leisure activities. These covariates were chosen because of their statistically significant associations with psychosocial working conditions. Statistical interactions of psychosocial working conditions with 1) gender and 2) leisure activity engagement were tested separately by incorporating the two factors (i.e. demand-control status and gender or leisure activity engagement) and their cross-product term in the same models. We performed stratified analyses by 1) gender and 2) leisure activity engagement to aid the interpretation of the interactions, with an adjustment for multiple variables except the stratified factor.
Statistical tests were two-tailed and p-values <0.05 were considered statistically significant. All analyses were performed using Stata SE 15.0 (StataCorp LP., College Station, Texas, U.S.A.).
3. Results
3.1. Characteristics of the study population
All participants were aged 60–65 years at baseline, and 55.9% were female. Among all participants, 436 (72.9%) had active, 59 (9.8%) had high strain, 66 (11.0%) had low strain, and 37 (6.2%) had passive jobs. Men were more likely to be in passive and active jobs than in low and high strain jobs. People who had active jobs were more likely to have higher education, white-collar occupation, and light-to-moderate alcohol drinking, and to engage in high leisure activities compared to the other three working conditions. In contrast, people who had passive jobs were more likely to have lower education, blue-collar occupation, no or occasional alcohol drinking, and low engagement in leisure activities compared to active jobs. However, there is no significant difference in terms of smoking, father’s occupation, early-life financial strain, BMI, or the number of chronic diseases ().
Table 1. Baseline characteristics of the study population by demand-control status of the longest-held job.
3.2. Association of job control, demands, and demand-control status with well-being
In the linear regression analysis, as continuous variables, both high job control, and job demands were dose-dependently associated with higher well-being, in both basic- and multi-adjusted models. The multi-adjusted β coefficients of job control and job demands in relation to well-being were 0.06 (95% confidence interval [CI] 0.02 to 0.09, P = 0.004), and 0.07 (95% CI 0.004 to 0.14, P = 0.038), respectively.
When job control or job demands were dichotomized, compared to the high level of job control or job demands, both low control and low demands were significantly associated with a lower score of well-being in the basic-adjusted linear regression. In the multi-adjusted linear regression, the association between low job control and well-being remained significant (β −0.11, 95% CI −0.22 to −0.003, P = 0.043), but the association between low demands and well-being became non-significant although the tendency remained similar to the results in the basic-adjusted model (β −0.08, 95% CI −0.18 to 0.03, P = 0.146).
Compared to people with active jobs, those with passive jobs had a lower score of well-being (β −0.19, 95% CI −0.35 to −0.02, P = 0.028) in the multi-adjusted linear regression. Despite the trend, the association of low or high strain jobs with well-being was not statistically significant (both P > 0.05) ().
Table 2. β coefficients and 95% confidence intervals (CIs) for the relation of job control, demands, and demand-control status to well-being.
3.3. Effect modification of gender and leisure activities
We found that both passive job (β −0.28, 95% CI −0.51 to −0.04, P = 0.020) and high strain job (β −0.31, 95% CI −0.52 to −0.10, P = 0.004), but not low strain job (β −0.06, 95% CI −0.29 to 0.16, P = 0.581), were related to a lower well-being score in men. However, such associations were not significant in women. There was a significant interaction between high strain and gender on well-being (P < 0.05) ( and Supplementary Table S3).
Figure 2. Association between demand-control status and well-being by sex.
In the stratified analysis by leisure activity, the association between passive job and well-being attenuated in people with high leisure activities (β −0.10, 95% CI −0.46 to 0.27, P = 0.600), and the association between high strain jobs and well-being was present only in people with low leisure activities (β −0.55, 95% CI −0.94 to −0.16, P = 0.006). The interaction between passive jobs and high leisure activities, as well as high strain and low leisure activities, was significant (both P < 0.05). ( and Supplementary Table S4).
Figure 3. Association between demand-control status and well-being by leisure activity engagement.
3.4. Supplementary analysis
In sensitivity analyses, similar results to those from the initial analysis were obtained when we excluded: 1) those who had more than four chronic diseases (n = 65) to minimize the potential bias due to poor health conditions and 2) those who had low education (n = 29) to limit possible information bias. In addition, our results were robust when we used psychosocial working conditions of the latest job to reduce recall bias.
4. Discussion
In this community-based study of workers aged 60–65 years, we found that 1) both high job control and high job demands were associated with higher well-being; 2) compared to people with active jobs, those with passive jobs had lower well-being; 3) in men, but not in women, both passive jobs and high strain jobs were associated with poor well-being; and 4) the association between passive jobs and well-being was attenuated by high leisure activities, while the association between high strain and well-being was magnified by low leisure activities.
Several previous studies have shown a similar positive relationship between job control or demands and well-being (Häusser et al., Citation2010; Van der Doef & Maes, Citation1999). Active jobs that are high on both demands and decision latitude will challenge an employee and allow them to develop new skills and may subsequently increase their well-being. In addition, control is predicted to attenuate the negative impact of job demands on well-being (Van der Doef & Maes, Citation1999), and thus in jobs low on demands and high on controls (low strain jobs) the occurrence of adverse reaction might be rather unlikely (Häusser et al., Citation2010). Jobs that are low on both demands and decision latitude are considered as passive jobs, and will not provide opportunities for growth or the development of new skills. Workers in passive jobs might have difficulties in finding self-identity or living up to expectations from the social context, and they also face an increased risk of neuropsychiatric disorders including depression (Almroth et al., Citation2021), alcohol-related illnesses (Almroth et al., Citation2022), and cognitive decline (K. Y. Pan et al., Citation2019), which in turn affect multiple systems and eventually well-being. Therefore, on the organizational level, measures aiming to minimize stress in workers can emphasize restricting workload to a reasonable quantity and reinforcing workers’ control over decision-making and skill development. Working conditions are essential determinants of mental health and promoting favorable working conditions can be a target to improve well-being among employees (Leka et al., Citation2011; Ljungblad et al., Citation2014), especially in the context of late retirement.
Currently, gender differences in psychosocial work stress have not been clearly specified due to the different roles in life and at work between men and women. In the present study, we observed a gender difference in the association between job demand-control status and well-being. The findings that passive jobs and high strain work environments were associated with decreased well-being in men, but not in women, are in line with some previous studies suggesting that high strain jobs have a greater impact on psychological well-being among men (Van der Doef & Maes, Citation1999; Vermeulen & Mustard, Citation2000). High strain jobs are considered the most risky type of job which have been linked to fatigue, anxiety, depression, and physical illness (Stansfeld & Candy, Citation2006). One of the plausible biological mechanisms that might underlie the association between high strain and poor well-being is related to chronic stress. The dynamic regulatory process in the body that is the key to the maintenance of homeostasis can fail to operate within an adaptive limit due to constant stimulation of sustained or repeated stress. The cost of this chronic energy mobilization and allostatic load can ultimately lead to the dysregulation of multiple systems in the body and increase vulnerability to diseases leading to poor well-being (McEwen & Stellar, Citation1993). On the other hand, women and men tend to react differently to stress. Biologically, female sex hormones attenuate the sympathoadrenal and hypothalamic-pituitary-adrenal responsiveness that regulate the state of steady internal conditions. This may result in sluggish cortisol feedback on the brain and less or delayed containment of the stress response (Verma et al., Citation2011). Men are more likely to occupy higher positions and are usually expected to have a successful career by society, and thus having high strain jobs may cause additional stress (Buss et al., Citation2020). It is noteworthy that both high strain jobs and passive jobs consist of low control, and therefore the importance of a sense of control and improving psychosocial conditions at work is emphasized, especially for men.
The significance of daily stress has led researchers to examine various resources that may help people cope with stress (Baqutayan, Citation2015). One of the potential coping resources is leisure time activities including physical, mental, and social activities, since leisure time availability and activities have been related to psychological stress and well-being (Michèle et al., Citation2019; Zuzanek et al., Citation1998). One previous study has revealed that leisure time could partially remedy the damage by high daily stress on positive affect (Qian et al., Citation2014). To date, however, there has been a lack of research on the beneficial effect of leisure activities on well-being in relation to work stress. In this study, we found that the association between psychosocial working conditions and well-being was modified by leisure activity engagement. Specifically, the association between high strain and poorer well-being was magnified by low leisure activity engagement, while the association between passive job and poorer well-being appeared to be attenuated by high leisure activity engagement. This highlights the importance of leisure activities for buffering the detrimental effect of boredom and lack of development in passive jobs on well-being. Thus, active engagement in leisure activities should be encouraged after work. In the context of the compensating role of leisure activities, an emerging theory is that exercise enhances several growth factors, such as brain-derived neurotrophic factor and insulin-like growth factor (Hamer et al., Citation2012), while social isolation and lack of mental stimulation lead to lower levels of these factors (Branchi et al., Citation2006; Fox et al., Citation2014; Zaletel et al., Citation2017), which could contribute to the protective and therapeutic effects of leisure activities on mental health. Leisure activities may act as a buffer against exaggerated or sustained stress responses and inflammation, and the lack of these activities may amplify the effect of work stress on well-being.
The main strength of this study lies in the use of community-based data, including occupational information throughout working life. Therefore, the risk accumulation is evaluated using the longest work experience which is prior to the outcome assessment, and thus the temporality of the observed association is clear. Additionally, individual predispositions that might preselect participants into occupations, such as education and early-life SES, are also available. The following limitations should be considered. First, the original data in the present study were collected 20 years ago, and thus the socioeconomic, cultural, and occupational contexts might be different from the situation today. Therefore, future longitudinal studies are warranted to confirm our results. In addition, participants of the SNAC-K were from the urban district in Stockholm and generally highly educated, for which caution is needed when generalizing our findings to other older working populations living in rural areas or having lower socioeconomic status. Second, retrospective recall of occupational experience could lead to misclassification, which, however, is likely to be non-differential. Thus, the misclassification could lead to an underestimation of the observed association. The sensitivity analysis that uses the current occupation may have addressed this issue. Third, the reliance on psychosocial job exposure matrices does not consider variabilities in individuals’ perception of working conditions or job characteristics within occupations. However, self-reporting bias would have been reduced by this approach. Fourth, despite the common use of PANAS for the measure of well-being, concern has been expressed that PANAS does not capture low positive affect concepts such as fatigue. Fifth, the associations of psychosocial working conditions and leisure activity engagement with well-being are cross-sectional; thus, we cannot make any causal inference about the association observed and cannot evaluate long-term changes in well-being. Sixth, only the occupation of participants’ father, but not parental occupation, was used to evaluate early-life SES due to unavailable information on the mother’s occupation. Nevertheless, early-life financial strain could partially reflect early-life SES. Finally, our results from the stratified analyses should be interpreted with caution considering the small number of subjects in the strata, especially in participants with low leisure activities.
In conclusion, low job control, low job demands, and passive jobs are related to poorer well-being before retirement. In men, high strain jobs are also associated with poorer well-being. The association between psychosocial working conditions and well-being can be moderated by leisure activities. Our findings have implications in identifying workplace scenarios that are detrimental to well-being later in one’s career to implement early intervention strategies, such as improving work environments, especially for men. Workers should be encouraged to engage in leisure time activities to maintain well-being when they face stress from work scenarios.
Author contribution
KP contributed to the conception and design of the study. YZ and WY contributed to drafting of the manuscript. YZ did the analyses with support from WY. WX obtained funding. KP and WX were responsible for the integrity of the data and the accuracy of the data analysis. All authors contributed to interpretation of data, critical revision of the manuscript for important intellectual content, and final approval of the version to be published. All authors had full access to the data in the study and had final responsibility for the decision to submit for publication. YZ and WY contributed equally as first authors.
Role of the funder/sponsor
The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Acknowledgments
We thank the SNAC-K participants and the SNAC-K Group for their collaboration in data collection and management.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Additional information
Funding
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