Background: While chronic workplace stress is known to be associated with health-related outcomes like mental and cardiovascular diseases, research about day-to-day occupational stress is limited. This systematic review includes studies assessing stress exposures as work environment risk factors and stress outcomes, measured via self-perceived questionnaires and physiological stress detection. These measures needed to be assessed repeatedly or continuously via Ecological Momentary Assessment (EMA) or similar methods carried out in real-world work environments, to be included in this review. The objective was to identify work environment risk factors causing day-to-day stress. Methods: The search strategies were applied in seven databases resulting in 11833 records after deduplication, of which 41 studies were included in a qualitative synthesis. Associations were evaluated by correlational analyses. Results: The most commonly measured work environment risk factor was work intensity, while stress was most often framed as an affective response. Measures from these two dimensions were also most frequently correlated with each other and most of their correlation coefficients were statistically significant, making work intensity a major risk factor for day-to-day workplace stress. Conclusions: This review reveals a diversity in methodological approaches in data collection and data analysis. More studies combining self-perceived stress exposures and outcomes with physiological measures are warranted.

Workplace stress remains a major interest of occupational health research, usually based on theoretical models and quantitative large-scale studies. Office workers are especially exposed to stressors such as high workload and time pressure. The aim of this qualitative research was to follow a phenomenological approach to identify work stressors as they are perceived by office workers. Six focus groups with office workers of different occupations were conducted in Belgium and Slovenia. A total of 39 participants were included in the study. We used the RQDA software for data processing and analysis and the seven job-quality indices of the European Working Conditions Survey (EWCS) to structure our findings. The results show that work intensity and social environment proved to be main stress categories, followed by skills and discretion, prospects, and working time quality. The physical environment and earnings were not covered in our results. We created organisational (structural/process-oriented and financial) stressors and office workers’ physical health as two additional categories since these topics did not fit into the EWCS. While our findings mainly confirm data from existing occupational stress literature and emphasise the multi-level complexity of work stress experiences, this paper suggests that there are relevant stressors experienced by office workers beyond existing quantitative frameworks.

Several studies have reported on increasing psychosocial stress in academia due to work environment risk factors like job insecurity, work-family conflict, research grant applications, and high workload. The STRAW project adds novel aspects to occupational stress research among academic staff by measuring day-to-day stress in their real-world work environments over 15 working days. Work environment risk factors, stress outcomes, health-related behaviors, and work activities were measured repeatedly via an ecological momentary assessment (EMA), specially developed for this project. These results were combined with continuously tracked physiological stress responses using wearable devices and smartphone sensor and usage data. These data provide information on workplace context using our self-developed Android smartphone app. The data were analyzed using two approaches: 1) multilevel statistical modelling for repeated data to analyze relations between work environment risk factors and stress outcomes on a within- and between-person level, based on EMA results and a baseline screening, and 2) machine-learning focusing on building prediction models to develop and evaluate acute stress detection models, based on physiological data and smartphone sensor and usage data. Linking these data collection and analysis approaches enabled us to disentangle and model sources, outcomes, and contexts of occupational stress in academia.