scholarly journals Perceived Stress among Different Occupational Groups and the Interaction with Sedentary Behaviour

2019 ◽  
Vol 16 (23) ◽  
pp. 4595 ◽  
Author(s):  
Dėdelė ◽  
Miškinytė ◽  
Andrušaitytė ◽  
Bartkutė
Keyword(s):  
Physical Activity ◽  
Sedentary Behaviour ◽  
Perceived Stress ◽  
White Collar ◽  
Individual Characteristics ◽  
Blue Collar ◽  
Full Time ◽  
Occupational Groups ◽  
Blue Collar Workers ◽  
The Impact

Sedentary lifestyle and low physical activity are associated with health issues, including both physical and mental health, non-communicable diseases, overweight, obesity and reduced quality of life. This study investigated differences in physical activity and other individual factors among different occupational groups, highlighting the impact of sedentary behaviour on perceived stress by occupation. Cross-sectional study included 571 full-time workers of Kaunas city, Lithuania. The outcome of this study was assessment of perceived stress. Time spent sedentary per day, occupation and other individual characteristics were self-reported using questionnaires. Two main occupational groups were analysed: white-collar and blue-collar workers. Multivariate logistic regression was used to assess the impact of sedentary behaviour on perceived stress among different occupational groups. The prevalence of high sedentary behaviour was 21.7 and 16.8 % among white-collar and blue-collar workers, respectively. Blue-collar workers had a higher risk of high perceived stress (OR 1.55, 95% CI 1.05–2.29) compared to white-collar workers; however, sedentary time did not have any impact on high perceived stress level. Meanwhile, white-collar male (OR 4.34, 95% CI 1.46–12.95) and white-collar female (OR 3.26, 95% CI 1.23–8.65) workers who spend more than three hours per day sedentary had a greater risk of high levels of perceived stress. These findings indicate sedentary behaviour effect on perceived stress among two occupational groups—white-collar and blue-collar workers—and other important factors associated with perceived stress.

Socioeconomic Differences in Patient Survival Are Increasing for Acute Myeloid Leukemia and Multiple Myeloma in Sweden

2009 ◽  
Vol 27 (12) ◽  
pp. 2073-2080 ◽  
Author(s):  
Sigurdur Yngvi Kristinsson ◽  
Åsa Rangert Derolf ◽  
Gustaf Edgren ◽  
Paul W. Dickman ◽  
Magnus Björkholm
Keyword(s):  
Multiple Myeloma ◽  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
White Collar ◽  
Blue Collar ◽  
Lower Mortality ◽  
Blue Collar Workers ◽  
White Collar Workers ◽  
The Impact ◽  
Acute Myeloid

Purpose An association between socioeconomic status (SES) and survival in acute myeloid leukemia (AML) and multiple myeloma (MM) has not been established in developed countries. We assessed the impact of SES on survival in two large population-based cohorts of AML and MM patients diagnosed in Sweden 1973 to 2005. Patients and Methods The relative risk of death (all cause and cause specific) in relation to SES was estimated using Cox's proportional hazards regression. We also conducted analyses stratified by calendar periods (1973 to 1979, 1980 to 1989, 1990 to 1999, and 2000 to 2005). Results We identified a total of 9,165 and 14,744 patients with AML and MM, respectively. Overall, higher white-collar workers had a lower mortality than other SES groups for both AML (P = .005) and MM (P < .005). In AML patients, a consistently higher overall mortality was observed in blue-collar workers compared with higher white-collar workers in the last three periods (hazard ratio [HR], 1.26; 95% CI, 1.05 to 1.51; HR, 1.23; 95% CI, 1.05 to 1.45; HR, 1.28; 95% CI, 1.04 to 1.57, respectively). In MM, no difference was observed in the first two calendar periods. However, in 1990 to 1999, self-employed (HR, 1.18; 95% CI, 1.02 to 1.37), blue-collar workers (HR, 1.18; 95% CI, 1.04 to 1.32), and retired (HR, 1.45; 95% CI, 1.16 to 1.80) had a higher mortality compared to higher white-collar workers. In 2000 to 2005, blue-collar workers had a higher mortality (HR, 1.31; 95% CI, 1.07 to 1.60) compared with higher white-collar workers. Conclusion SES was significantly associated with survival in both AML and MM. Most conspicuously, a lower mortality was observed among the highest SES group during more recent calendar periods. Differences in management, comorbidity, and lifestyle, are likely factors to explain these findings.

scholarly journals Objectively-measured physical activity and stress levels in workers: a systematic review physical activity and stress in workers

2021 ◽  
Vol 26 ◽  
pp. 1-8
Author(s):  
Marcelo Frio Marins ◽  
Barbara Sutil da Silva ◽  
Natan Feter ◽  
Marcelo Cozzensa da Silva
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Vigorous Physical Activity ◽  
White Collar ◽  
Blue Collar ◽  
Control Group ◽  
Blue Collar Workers ◽  
White Collar Workers ◽  
Objectively Measured ◽  
Objectively Measured Physical Activity

To investigate the relationship between objectively measured physical activity and occupational stress in different work environments. This systematic review, registered in the PROSPERO database (CRD42020214884), followed the PRISMA methodology. The search took place in October/2020 in the following databases: Web of Science, SPORTDiscus, MedLine/PubMed, PsycINFO, EMBASE, OVID MEDLINE, Scielo and CINAHL. Keywords related to eligible participants (adults and workers), interventions (physical activity objectively measured), comparison (control group or baseline), outcome (stress), and study design (observational studies) were combined using Boolean terms. From 1,524 identified records, 12 articles were included, totaling 2,082 workers. 66.7% of the studies were carried out in Europe and 50.0% among health professionals. Blue collar workers (20.7% [n = 430]) and white collar workers (18.3% [n = 382]), medical resident (6.5% [n = 135]) and protection services (9.7% [n = 202]) were the predominant occupations. Physical activity was higher in blue-collar workers than in white-collar workers, and shift-working nurses were more active compared to non-shift workers and office workers. Increased mental workload was not associated with time spent on physical activities in most studies (10 [83.3%)]). Some studies showed that light physical activity was associated with higher levels of stress and moderate to vigorous physical activity was beneficial for reducing stress dimensions. In conclusion, most studies did not find an association between objectively measured physical activity and the level of stress in workers. Studies with robust methodologies and covering different groups of workers remain necessary.

scholarly journals Lifestyle-associated health risk indicators across a wide range of occupational groups: a cross-sectional analysis in 72,855 workers

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Daniel Väisänen ◽  
Lena V. Kallings ◽  
Gunnar Andersson ◽  
Peter Wallin ◽  
Erik Hemmingsson ◽  
...  
Keyword(s):  
Health Risk ◽  
White Collar ◽  
Blue Collar ◽  
Risk Indicators ◽  
Physical Work ◽  
Future Health ◽  
Occupational Groups ◽  
Blue Collar Workers ◽  
Wide Range ◽  
White Collar Workers

Abstract Background Identify and compare health risk indicators for common chronic diseases between different occupational groups. Methods A total of 72,855 participants (41% women) participating in an occupational health service screening in 2014–2019 were included. Occupation was defined by the Swedish Standard Classification of Occupation, and divided into nine major and additionally eight sub-major groups. These were analysed separately, as white- and blue-collar occupations and as low- and high-skilled occupations. Seven health risk indicators were self-reported: exercise, physical work situation, sitting at work and leisure, smoking, diet, and perceived health, whereas cardiorespiratory fitness, BMI and blood pressure were measured. These were further dichotomized (yes/no) and as clustering of risk indicators (≥3 vs. <3). Results The greatest variation in OR across sub-major and major occupational groups were seen for daily smoking (OR = 0.68 to OR = 5.12), physically demanding work (OR = 0.55 to OR = 45.74) and high sitting at work (OR = 0.04 to OR = 1.86). For clustering of health risk indicators, blue-collar workers had significantly higher clustering of health risks (OR: 1.80; 95% CI 1.71–1.90) compared to white-collar workers (reference). Compared to high-skilled white-collar workers, low-skilled white-collar workers had similar OR (2.00; 1.88–2.13) as high-skilled blue-collar workers (1.98; 1.86–2.12), with low-skilled blue-collar workers having the highest clustered risk (2.32; 2.17–2.48). Conclusion There were large differences in health risk indicators across occupational groups, mainly between high-skilled white-collar occupations and the other occupations, with important variations also between major and sub-major occupational groups. Future health interventions should target the occupational groups identified with the highest risk for effective disease prevention.

scholarly journals Physical Activity Levels by Occupational Category in Non-Metropolitan Australian Adults

2010 ◽  
Vol 7 (6) ◽  
pp. 718-723 ◽  
Author(s):  
Mitch J. Duncan ◽  
Hannah M. Badland ◽  
William Kerry Mummery
Keyword(s):  
Physical Activity ◽  
Leisure Time Physical Activity ◽  
White Collar ◽  
Blue Collar ◽  
Activity Levels ◽  
Cross Sectional ◽  
Occupational Category ◽  
Occupational Sitting ◽  
Blue Collar Workers ◽  
Physical Activity Levels

Background:The aim of this study was to examine the relationship between occupational category and 3 health-related behaviors: participation in leisure-time physical activity, active transport (AT) and occupational sitting in a sample of employed Australian adults.Methods:A random, cross-sectional sample of 592 adults aged 18 to 71 years completed a telephone survey in October/November 2006. Reported occupations were categorized as professional (n = 332, 56.1%), white-collar (n = 181, 30.6%), and blue-collar (n = 79, 13.3%). Relationships between occupational category and AT, sufficient physical activity and occupational sitting were examined using logistic regression.Results:White-collar employees (OR = 0.36, 95% CI 0.14−0.95) were less likely to engage in AT and more likely to engage in occupational sitting (OR = 3.10, 95% CI 1.63−5.92) when compared with blue-collar workers. Professionals (OR = 3.04, 95% CI 1.94−4.76) were also more likely to engage in occupational sitting compared with blue-collar workers. No relationship was observed between occupational category and engagement in sufficient physical activity.Conclusions:No association between occupational category and sufficient physical activity levels was observed, although white-collar and professionals were likely to engage in high levels of occupational sitting. Innovative and sustainable strategies are required to reduce occupational sitting to improve health.

scholarly journals Identification of occupations susceptible to high exposure and risk associated with multiple toxicants in an observational study: National Health and Nutrition Examination Survey 1999-2014

2021 ◽  
Author(s):  
Vy Kim Nguyen ◽  
Justin Colacino ◽  
Chirag J Patel ◽  
Maureen Sartor ◽  
Olivier Jolliet
Keyword(s):  
White Collar ◽  
Chemical Exposure ◽  
Occupational Group ◽  
Blue Collar ◽  
Nutrition Examination Survey ◽  
Occupational Groups ◽  
Chemical Exposures ◽  
Blue Collar Workers ◽  
Health And Nutrition ◽  
Similar Chemical

Background: According to the World Health Organization, occupational exposures to hazardous chemicals are estimated to cause over 370,000 premature annual deaths. The risks due to multiple workplace chemical exposures, and those occupations most susceptible to the resulting health effects, remain poorly characterized. Objectives: The aim of this study is to identify occupations with elevated toxicant biomarker concentrations and increased health risk associated with toxicant exposures in a working US population from diverse categories of occupation. More specifically, we aim to 1) define differences in chemical exposures based on occupation description, 2) identify occupational groups with similar chemical exposure profiles, and 3) identify occupational groups with chemical biomarker levels exceeding acceptable health-based biomarker levels. Methods: For this observational study of 51,008 participants, we used data from the 1999-2014 National Health and Nutrition Examination Survey. We characterized differences in chemical exposures by occupational group for 129 chemicals by applying a series of generalized linear models with the outcome as biomarker concentrations and the main predictor as the occupational groups, adjusting for age, sex, race/ethnicity, poverty income ratio, study period, and biomarker of tobacco use. We identified groups of occupations with similar chemical exposure profiles via hierarchical clustering. For each occupational group, we calculated percentages of participants with chemical biomarker levels exceeding acceptable health-based guidelines. Results: Blue collar workers from "Construction", "Professional, Scientific, Technical Services", "Real Estate, Rental, Leasing", "Manufacturing", and "Wholesale Trade" have higher biomarker levels of toxic chemicals such as several heavy metals, acrylamide, glycideamide, and several volatile organic compounds compared to their white-collar counterparts. For these toxicants, 1-58% of blue-collar workers from these industries have toxicant concentrations exceeding acceptable levels. Discussion: Blue collar workers have toxicant levels higher relative to their white-collar counterparts, often exceeding acceptable levels associated with noncancer effects. Our findings identify multiple occupations to prioritize for targeted interventions and health policies to monitor and reduce high toxicant exposures.

scholarly journals Occupational Differences in Metabolic Syndrome Incidence Among Older Workers

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 52-52
Author(s):  
Katharina Runge ◽  
Sander K R van Zon ◽  
Ute Bültmann ◽  
Kène Henkens
Keyword(s):  
Metabolic Syndrome ◽  
Health Behaviors ◽  
Older Workers ◽  
White Collar ◽  
Occupational Group ◽  
Blue Collar ◽  
Occupational Groups ◽  
Blue Collar Workers ◽  
Physical Measurements ◽  
Occupational Differences

Abstract This study investigates whether the incidence of metabolic syndrome (MetS), and its components, differs by occupational group among older workers (45-65 years) and whether health behaviors (smoking, leisure-time physical activity, diet quality) can explain these differences. We analyzed data from older workers (N=23 051) from two comprehensive measurement waves of the Lifelines Cohort Study and Biobank. MetS components were determined by physical measurements, blood markers, medication use, and self-reports. Occupational group and health behaviors were assessed by questionnaires. The association between occupational groups and MetS incidence was examined using Cox regression analysis. Health behaviors were subsequently added to the model to examine whether they can explain differences in MetS incidence between occupational groups. Low skilled white-collar (HR: 1.25, 95% CI: 1.13, 1.39) and low skilled blue-collar (HR: 1.45, 95% CI: 1.25, 1.69) workers had a significantly higher MetS incidence risk during 3.65 years follow-up than high skilled white-collar workers. Health behaviors reduced the strength of the association between occupational group and MetS incidence most among low skilled blue-collar workers (i.e. 10.3% reduction) as unhealthy behaviors were more prevalent in this occupational group. Similar occupational differences were observed on MetS component level. To conclude, MetS incidence in older workers differs between occupational groups and health behaviors only explain a small part of these differences. Health promotion tailored to occupational groups may be beneficial specifically among older low skilled blue-collar workers. Research into other factors that contribute to occupational differences is needed, as well as studies spanning the entire working life course.

scholarly journals Lifestyle-associated health risk indicators across a wide range of occupational groups: a cross-sectional analysis in 72 855 workers

2020 ◽  
Author(s):  
Daniel Väisänen ◽  
Lena V Kallings ◽  
Gunnar Andersson ◽  
Peter Wallin ◽  
Erik Hemmingsson ◽  
...  
Keyword(s):  
Health Risk ◽  
White Collar ◽  
Blue Collar ◽  
Risk Indicators ◽  
Physical Work ◽  
Future Health ◽  
Occupational Groups ◽  
Blue Collar Workers ◽  
Wide Range ◽  
White Collar Workers

Abstract ObjectivesIdentify and compare health risk indicators for common chronic diseases between different occupational groups. Methods A total of 72,855 participants (41% women) participating in an occupational health servicescreening in 2014–2019 were included. Occupation was defined by the Swedish Standard Classification of Occupation, and divided into nine major and additionally eight sub-major groups. These were analysed separately, as white- and blue-collar occupations and as low- and high-skilled occupations. Seven health risk indicators were self-reported: exercise, physical work situation, sitting at work and leisure, smoking, diet, and perceived health, whereas cardiorespiratory fitness, BMI and blood pressure were measured. These were further dichotomized (yes/no) and as clustering of risk indicators (≥3 vs. <3).ResultsThe greatest variation in OR across sub-major and major occupational groups were seen for daily smoking (OR=0.68 to OR=5.12), physically demanding work (OR=0.55 to OR=45.74) and high sitting at work (OR=0.04 to OR=1.86). For clustering of health risk indicators, blue-collar workers had significantly higher clustering of health risks (OR: 1.80; 95% CI 1.71-1.90) compared to white-collar workers (reference). Compared to high-skilled white-collar workers, low-skilled white-collar workers had similar OR (2.00; 1.88-2.13) as high-skilled blue-collar workers (1.98; 1.86-2.12), with low-skilled blue-collar workers having the highest clustered risk (2.32; 2.17-2.48).ConclusionThere were large differences in health risk indicators across occupational groups, mainly between high-skilled white-collar occupations and the other occupations, with important variations also between major and sub-major occupational groups. Future health interventions should target the occupational groups identified with the highest risk for effective disease prevention.

High Socioeconomic Status (SES) Is Associated with Superior Survival in Patients with Acute Myeloid Leukemia (AML) and Multiple Myeloma (MM). A Population-Based Study.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 1485-1485
Author(s):  
Sigurdur Y. Kristinsson ◽  
Asa R. Derolf ◽  
Paul W. Dickman ◽  
Gustaf Edgren ◽  
Magnus Bjorkholm
Keyword(s):  
White Collar ◽  
Population Based ◽  
Blue Collar ◽  
Lower Mortality ◽  
Calendar Period ◽  
White Collar Worker ◽  
Blue Collar Workers ◽  
The Third ◽  
White Collar Workers ◽  
The Impact

Abstract Introduction The association between SES and survival in MM and AML has not been studied in detail and the limited results are inconclusive. In the present study the impact of SES on survival was analyzed in a large population-based cohort of MM and AML patients. Patients and Methods From the Swedish Cancer Register we identified all individuals diagnosed with MM and AML between 1973 and 2003. We used type of occupation, combined into seven groups (blue-collar worker, farmer, lower white-collar worker, higher white-collar worker, self-employed, retired, and unknown), from the Swedish National Census Databases as a proxy for SES. The relative risk of death (any cause) in relation to type of occupation and calendar period was estimated using Cox’s proportional hazards regression adjusted for age, sex, calendar period and area of residence. We also conducted analyses stratified by calendar period (1973–1979, 1980–1989, 1990–1999, and 2000–2003). Results A total of 14,200 and 8,831 patients were diagnosed with MM and AML, respectively. The median age at diagnosis was 71.8 years in patients with MM and 69.1 years in AML. The SES distribution was similar between the two diseases. The majority of patients were blue-collar (38.0; 39.5%) and white-collar workers (36.4; 37%), with lower white-collar workers dominating the latter group. Women had a significantly lower mortality than men both among MM (p&lt;0.001) and AML (p&lt;0.05) patients. The mortality among patients diagnosed in more recent calendar periods was lower than among patients diagnosed earlier (p&lt;0.001) Overall, higher white-collar workers had a significantly lower mortality compared to blue-collar workers for both MM (p&lt;0.001) and AML (p&lt;0.001). No significant differences were found between the other SES groups. In MM, analyses stratified by calendar period revealed that the mortality did not differ between the SES groups in the first two calendar periods, but in the third calendar period, 1990–1999, both higher and lower white-collar workers had a significantly lower mortality compared to blue collar workers, hazard ratios (HR) 0.85 (95% CI, 0.75–0.96) and 0.91 (95% CI 0.85–0.98), respectively. In the fourth period the mortality followed the same pattern as in the third period with lower mortality among both higher [HR 0.66 (95% CI, 0.50–0.88)] and lower [HR 0.82 (95% CI, 0.69–0.96)] white-collar workers. In AML patients no difference in mortality in relation to SES was found during the first calendar period. During the last three periods, however, a lower mortality was observed in higher white-collar workers compared to blue-collar workers, HR: 0.79 (0.66–0.95), 0.79 (0.67–0.93) and 0.74 (0.57–0.96) in the periods 1980–1989, 1990–1999 and 2000–2003, respectively. Conclusion SES, here defined as occupational profession, was significantly associated with prognosis in both MM and AML. Most conspicuously, a lower mortality was recorded in white-collar workers during more recent calendar periods. Differences in time to diagnosis (lead-time bias) and treatment strategies may be important factors contributing to this finding. Future studies may identify the relative impact of these and potentially other factors.

scholarly journals The Impact of Lifetime Work and Non-work Physical Activity on Physical Fitness Among White – and Blue – Collar Retirees: A Cross-Sectional Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Tomasz Trzmiel ◽  
Anna Pieczyńska ◽  
Ewa Zasadzka ◽  
Mariola Pawlaczyk
Keyword(s):  
Physical Activity ◽  
Physical Fitness ◽  
Physical Performance ◽  
Large Scale ◽  
White Collar ◽  
Blue Collar ◽  
Occupational Activity ◽  
Work Related ◽  
White Collar Workers ◽  
The Impact

Objective: The literature offers significant amount of data on the effects of occupational activity on health, with a distinct link between retirement and health among the most frequently tackled topics. Studies on the relationship between past occupational activity and physical fitness among older retirees remain scarce. The aim of the study was to assess the effects of physical activity on physical fitness in white- and blue-collar retirees.Methods: A total of 200 participants (aged ≥60) were included in the study. Lifetime physical activity was assessed using the Lifetime Physical Activity Questionnaire. Mean MET/week/year values of total Physical Activity and for each domain separately (occupational, sports, household) were calculated. Participants were stratified to blue- or white- collar group. Physical performance, hand-grip strength (HGS) and pulmonary function were assessed.Results: Mean total MET/week/year values for the blue- and the white-collar workers were 140.48 ± 55.13 and 100.75 ± 35.98, respectively. No statistically significant differences in physical performance scores were found between the white- and blue- collar groups. Adjustment for age, sex weight and height revealed a statistically significant association between work-related PA FEV*1 in the blue-collar group. White – collar workers presented higher odds ratio for membership in highest quartile in regard to short physical performance battery test score.Conclusion: Only minimal association of type of occupation on physical fitness were found despite statistically significant differences between mean intensity and duration of sports- and work-related lifetime physical activity. These findings may indicate that the type of past work is not an independent factor influencing the state of a person in old age. Large-scale investigations with physically fit and unfit participants, are necessary.

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