The continuous and rapid development of AI-based systems comes along with an increase in automation of tasks and, therewith, a qualitative shift in opportunities and challenges for occupational safety and health. A fundamental aspect of humane working conditions is the ability to exert influence over different aspects of one's own work. Consequently, stakeholders contribute to the prospect of maintaining the workers' autonomy albeit increasing automation and summarize this aspiration with the human in control principle. Job control has been part of multiple theories and models within the field of occupational psychology. However, most of the models do not include specific technical considerations nor focus on task but rather on job level. That is, they are possibly not able to fully explain specific changes regarding the digitalization of tasks. According to the results of a large-scale study on German workers (DiWaBe), this seems to be the case to some extend: the influence of varying degrees of automation, moderated by perceived autonomy, on workers' wellbeing was not consistent. However, automation is a double-edged sword: on a high level, it can be reversely related to the workers' job control while highly autonomous and reliable systems can also create opportunities for more flexible, impactful and diverse working tasks. Consequently, automation can foster and decrease the factor of job control. Models about the optimal level of automation aim to give guidelines on how the former can be achieved. The results of the DiWaBe study indicate that automation in occupational practice does not always happen in line with these models. Instead, a substantial part of automation happens at the decision-making level, while executive actions remain with the human. From an occupational safety and health perspective, it is therefore crucial to closely monitor and anticipate the implementation of AI in working systems. Constellations where employees are too controlled by technology and are left with a high degree of demands and very limited resources should be avoided. Instead, it would be favorable to use AI as an assistance tool for the employees, helping them to gather and process information and assisting them in decision-making.

For the ergonomic design of workplaces and products, a representative anthropometric dataset of the working-age population is needed. As body proportions are constantly changing and the latest publicly available dataset for Germany was published in 2004 (data collection period 1999 – 2002), the aim of this study was to create and publish an updated anthropometric dataset of the German working-age population. Within a regional epidemiological health study, 3D body scan data from 2313 subjects were collected and used to create an anthropometric dataset with a total of 39 ISO 7250-1 measures. In order to approximate the goal of generating representative values for Germany, the collected regional dataset was weighted with an algorithm, using values from a known nationally representative survey. Based on the weighted dataset, a gender stratified percentile table with values for the 5^th, 50^th, and 95^th percentile was calculated. Practitioner Summary: Body proportions are constantly changing and the latest publicly available anthropometric dataset for Germany was published in 2004. A new dataset was created, using 3D body scans from an epidemiological health study and a weighting algorithm. Ultimately, percentile tables with values for the 5^th, 50^th, and 95^th percentile are published.

With the increasing digital transformation, work tasks are changing—in some cases, significantly. Our study addresses the question of whether the established criteria for work design are still sufficient or if they should get updated and additional criteria become necessary in the context of digitalization. In a multistage consensus process involving interdisciplinary groups of experts, we have identified specific criteria for the humane design of work in a world increasingly permeated by digitalized work tools. Starting with an expert workshop using a combined nominal group/focus group technique, followed by a real-time Delphi study, a content analysis and a five-stage peer comment process, we detected 13 criteria and 38 design guidelines for human-centered work in digital transformation. Mapping these with established criteria, it became apparent that some established criteria have experienced a new dynamic because of the digital transformation. For other criteria, a need for digitization-sensitive design is discernible. In addition, criteria have emerged whose necessity is rooted in the digital transformation. A diffusion and stronger interconnection of the various levels of the work system in connection with the digital transformation of work is apparent.

The aim of our study was to evaluate the long-term effects of back-support exoskeletons (BSEs) in occupational settings. Valuable insights were gained through repeated in-depth interviews with occupational safety professionals over a one-year period regarding the use and impact of BSEs over a one-year period. The results highlight a significant lack of user acceptance, primarily due to movement restrictions and discomfort. Passive BSEs (PassEXOs) were reported to be sometimes uncomfortable, restricting mobility and causing pressure point discomfort, while quasipassive soft BSEs (SoftEXO) received more positive feedback. In addition, factors such as proper fit, adjustability, hygiene maintenance, and the ease of donning and doffing were identified as key determinants of employee acceptance of BSEs. By gaining a deeper understanding of these factors, organisations can facilitate a successful and sustainable integration of BSE technology into the workplace.

This letter presents the validation of a usability questionnaire tailored specifically to robotic systems, based on the ISO 9241–110 standard. The use of robotic systems in the workplace is becoming increasingly common. Hence, humans and robots interact more frequent. The quality of these interactions is significantly influenced by the robot's usability. The ISO 9241–110:2020 presents seven interaction principles as ergonomic design guidelines in human-machine interaction. An evaluation tool capable of reflecting the norm benefits both system designers and researchers. A pool of 100 robot specific items based on the ISO 9241–110:2020 and the IsoMetrics questionnaire was created and evaluated in a video-based online study. Confirmatory factor analysis (CFA) assessed which items best reflect the standards’ structure. While the structure of the standard could not be fully reflected by the CFA, allowing one additional factor results in a sufficient model fit, while maintaining high content validity. The final questionnaire has 8 factors and 41 items. Its high content validity and usage efficiency makes it a valuable addition to any usability assessment toolkit, however further validation steps are needed.