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Objectives: In this paper, we aim to estimate the effect cancer diagnosis has on labour-force participation among middle-aged and older populations in Japan. We investigate the impact of cancer diagnosis on job cessation and the gap between gender or job types. Methods: We sourced data from a nationwide, annual survey targeted population aged 51–70 featuring the same cohort throughout, and examined respondents' cancer diagnoses and whether they continued to work, while also considering differences between gender (observations: 53 373 for men and 44 027 for women) and occupation type (observations: 64 501 for cognitive worker and 20 921 for manual worker) in this regard. We also examined one-year lag effects, using propensity score matching to control for confounding characteristics. We also implement Logistic regression and derive the odds ratio to evaluate the relative risk of cancer diagnosis, which supplements the main result by propensity score matching. Results: Overall, the diagnosis of cancer has a huge effect on labour-force participation among the population, but this effect varies across subpopulations. Male workers are more likely to quit their job in the year they are diagnosed with cancer (10.1 percentage points), and also in the following year (5.0 percentage points). Contrastingly, female workers are more likely to quit their job immediately after being diagnosed with cancer (18.6 percentage points); however, this effect totally disappears when considering likelihoods for the following year. Cognitive workers are more prone to quit their job in the year of diagnosis by 11.6 percentage points, and this effect remains significant, 3.8 percentage points, in the following year. On the other hand, for manual workers the effect during the year of diagnosis is huge. It amounts to 18.7 percentage points; however, the effect almost disappears in the following year. Conclusion: Our results indicate the huge effect of cancer on job cessation, and that there might be a degree of discrimination in workplaces between gender and job types. [ABSTRACT FROM AUTHOR]

We conducted a field experiment in a small electronics manufacturing firm in the US with the specific aim to improve minutes worked, punctuality, tardiness and safety checks. Our intervention was to put posters on the production floor on a random day, which made salient to the blue-collar employees the meaning and importance of their job, which comprised of routine repetitive tasks, in a before and after design. Overall, the intervention was a success with positive and significant effects consistently found for the outcomes both immediately after the experiment finished (+3 days) and also more than two weeks after (+15 days). Our study highlights it is possible to motivate blue collar manual workers intrinsically by drawing attention to the meaning of their work. [ABSTRACT FROM AUTHOR]

Background: Social epidemiologic research in relation to the health impacts of precarious employment has grown markedly during the past decade. While the multidimensional nature of precarious employment has long been acknowledged theoretically, empirical studies have mostly focused on one-dimensional approach only (based either on employment temporariness or perceived job insecurity). This study compares the use of a multidimensional employment precariousness scale (EPRES) with traditional one-dimensional approaches in relation to distinct health outcomes and across various socio-demographic characteristics. Methods: We used a subsample of formal salaried workers (n = 3521) from the first Chilean employment and working conditions survey (2009–2010). Multilevel modified Poisson regressions with fixed effects (individuals nested within regions) and survey weights were conducted to estimate the association between general health, mental health and occupational injuries and distinct precarious employment exposures (temporary employment, perceived job insecurity, and the multidimensional EPRES scale). We assessed the presence of effect measure modification according to sex, age, educational level, and occupational class (manual/non-manual). Results: Compared to one-dimensional approaches to precarious employment, the multidimensional EPRES scale captured a larger picture of potential health effects and differences across subgroups of workers. Patterns of effect measure that modification were consistent with the expectations that groups in greater disadvantage (women, older individuals, less educated and manual workers) were more vulnerable to poor employment conditions. Conclusions: Multidimensional measures of precarious employment better capture its association with a breath of health outcomes, being necessary tools for research in order to strengthen the evidence base for policy making in the protection of workers' health. [ABSTRACT FROM AUTHOR]

The new technologies, the digitalisation of processes and automation of work will change the manner of doing business, working and living. The effects of digitalisation on the economy, society and quality of life imply significant challenges of the labour market. All the participants will be concerned: authorities, companies and ordinary people. The objective of this research is to analyse the perceptions of the EU citizens about digitalisation and to highlight the differences among specific socio-demographic groups. The analysis is grounded on a composite methodology, comprising several statistical and econometric methods that provide scientific support to achieved conclusions: statistical analysis (with the primary goal to shed light on the EU citizens' perceptions about their digital technology skills), TwoStep Cluster Analysis (TSCA) (with the purpose to identify the 'digital vulnerable groups' and then the 'digital vulnerable countries' in terms of the exposure to digital divide) and logistic regression (with the main aim to quantify the impact of the relevant factors on citizens' perceptions about digitalisation). We identified a group of respondents evaluating themselves as having meagre digital skills, very afraid that robots could steal their jobs and with low usage of the internet. They are elderly, with a low level of education, manual workers or not working, with a relatively low level of income and little Internet use. The originality of our approach is given by the fact that we focused on investigating if digital divide leads to the creation of vulnerable groups (citizens and/or countries) and if there are specific patterns in terms of the perception on being skilled in the use of digital technologies in daily life or at work and of the understanding that robots replace human on the labour market. We aim to find relevant factors for the labour market to assume targeted measures that should be taken for a better match of supply and demand on the labour market and for creating a smart labour market. It is highly needed to increase the people's confidence in their skills level and to make the most of digitalisation of the societies. The results show consistent patterns in term of socio-demographic characteristics and perception towards digitalisation. The latter will have a meaningful impact on the economy and the society in the European Union in the next period. That is why a positive attitude towards digitalisation is essential for transforming this relatively new challenge into an excellent opportunity for the future. [ABSTRACT FROM AUTHOR]

Introduction: The aim of the study was to investigate if (a) substituting total sedentary time or long sedentary bouts with standing or various types of physical activity and (b) substituting long sedentary bouts with brief sedentary bouts; is associated with obesity indicators using a cross sectional isotemporal substitution approach among blue-collar workers. Methods: A total of 692 workers from transportation, manufacturing and cleaning sectors wore an Actigraph GT3X+ accelerometer on the thigh for 1–4 working days. The sedentary (sit and lie), standing, walking, and moderate to vigorous physical activity (MVPA) time on working days was computed using validated Acti4 software. The total sedentary time and uninterrupted sedentary time spent in brief (≤5 mins), moderate (>5 and ≤30 mins), and long (>30mins) bouts, were determined for the whole day and during work and non-work time separately. The obesity indicators, BMI (kg/m2), waist circumference (cm) and fat percentage were objectively measured. Isotemporal substitution modelling was utilized to determine the linear association with obesity indicators of replacing 30 min of total sedentary time or long sedentary bouts with standing, walking or MVPA and separately replacing 30 min of long sedentary bouts with brief sedentary bouts. Results: Workers [mean (standard deviation, SD); age = 45.1 (9.9) years, BMI = 27.5 (4.9) kg/m2, %BF = 29.6 (9.5), waist circumference = 94.4 (13.0) cm] sat for 2.4 hours (~32% of the measured time, SD = 1.8 hours) across the day during work period and 5.5 hours (~62% of the measured time, SD = 1.5 hours) during non-work period. Most of the sedentary time was accrued in moderate bouts [work = 1.40 (SD = 1.09) hours] during work and in long bouts during non-work [2.7 (SD = 1.4) hours], while least in long sedentary bouts during work [work = 0.5 (SD = 0.9)] and in brief sedentary bouts [0.5 hours (SD = 0.3)] during non-work. Significant associations with all obesity indicators were found when 30 min of total sedentary time or long sedentary bouts were replaced with standing time (~1–2% lower) or MVPA (~4–9% lower) during whole day, work, and non-work periods. The exception was that a statistically significant association was not observed with any obesity indicator when replacing total sedentary time or long sedentary bouts with standing time during the work period. Significant beneficial associations were found when replacing the long sedentary bouts with brief sedentary bouts (~3–5% lower) during all domains. Conclusion: Replacing total sedentary time and long sedentary bouts, respectively, not only with MVPA but also standing time appears to be beneficially associated with obesity indicators among blue-collar workers. Additionally, replacing long sedentary bouts with brief sedentary bouts was also beneficially associated with obesity indicators. Studies using prospective design are needed to confirm the findings. [ABSTRACT FROM AUTHOR]

Background: Studies on the association between sitting time and low back pain (LBP) have found contrasting results. This may be due to the lack of objectively measured sitting time or because socioeconomic confounders were not considered in the analysis. Objectives: To investigate the association between objectively measured sitting time (daily total, and occupational and leisure-time periods) and LBP among blue-collar workers. Methods: Two-hundred-and-one blue-collar workers wore two accelerometers (GT3X+ Actigraph) for up to four consecutive working days to obtain objective measures of sitting time, estimated via Acti4 software. Workers reported their LBP intensity the past month on a scale from 0 (no pain) to 9 (worst imaginable pain) and were categorized into either low (≤5) or high (>5) LBP intensity groups. In the multivariate-adjusted binary logistic regression analysis, total sitting time, and occupational and leisure-time sitting were both modeled as continuous (hours/day) and categorical variables (i.e. low, moderate and high sitting time). Results: The multivariate logistic regression analysis showed a significant positive association between total sitting time (per hour) and high LBP intensity (odds ratio; OR=1.43, 95%CI=1.15-1.77, P=0.01). Similar results were obtained for leisure-time sitting (OR=1.45, 95%CI=1.10-1.91, P=0.01), and a similar but non-significant trend was obtained for occupational sitting time (OR=1.34, 95%CI 0.99-1.82, P=0.06). In the analysis on categorized sitting time, high sitting time was positively associated with high LBP for total (OR=3.31, 95%CI=1.18-9.28, P=0.03), leisure (OR=5.31, 95%CI=1.57-17.90, P=0.01), and occupational (OR=3.26, 95%CI=0.89-11.98, P=0.08) periods, referencing those with low sitting time. Conclusion: Sitting time is positively associated with LBP intensity among blue-collar workers. Future studies using a prospective design with objective measures of sitting time are recommended. [ABSTRACT FROM AUTHOR]

Objective: To examine the associations between occupation, sleep duration and sleep quality. Methods: The data for this study was extracted from data collected from the 2008 Chinese Sub-optimal Health Study. Our study sample consisted of 18,316 Chinese subjects aged 18-65. Occupation and other relevant characteristics to sleep were collected. We used the Pittsburgh Sleep Quality Index (PSQI) to measure sleep quality and multiple logistic regression models to examine the association of occupation with shortened sleep duration and poor sleep quality. Results: Farmers had the longest sleep duration (mean=8.22 hours) while the civil servants had the shortest sleep duration (mean=7.85 hours). Farmers also had the best sleep quality (mean score=3.74) while professional workers had the worst sleep quality (mean score=4.87). Compared to civil servants, the OR of shortened sleep duration and poor sleep quality for blue collar workers is 1.39 (95%CI: 1.11-1.73) and 1.28 (95%-CI: 1.15-1.42), respectively, after adjusting for age, sex, marital status, education, area, smoking, drinking, pain, and health status. Conclusion: sleep duration and quality varied among different Chinese occupation populations. The blue collar workers are more likely to have shortened sleep duration and poor sleep quality. [ABSTRACT FROM AUTHOR]