The number of individuals suffering from type 2 diabetes is dramatically increasing worldwide, resulting in an increasing burden on society and rising healthcare costs. With increasing evidence supporting lifestyle intervention programs to reduce type 2 diabetes, and the use of scenario simulations for policy support, there is an opportunity to improve population interventions based upon cost–benefit analysis of especially complex lifestyle intervention programs through dynamic simulations. In this article, we used the System Dynamics (SD) modeling methodology aiming to develop a simulation model for policy makers and health professionals to gain a clear understanding of the patient journey of type 2 diabetes mellitus and to assess the impact of lifestyle intervention programs on total cost for society associated with prevention and lifestyle treatment of pre-diabetes and type 2 diabetes in The Netherlands. System dynamics describes underlying structure in the form of causal relationships, stocks, flows, and delays to explore behavior and simulate scenarios, in order to prescribe intervention programs. The methodology has the opportunity to estimate and simulate the consequences of unforeseen interactions in order to prescribe intervention programs based on scenarios tested through “what-if” experiments. First, the extensive knowledge of diabetes, current available data on the type 2 diabetes population, lifestyle intervention programs, and associated cost in The Netherlands were captured in one simulation model. Next, the relationships between leverage points on the growth of type 2 diabetes population were based upon available data. Subsequently, the cost and benefits of future lifestyle intervention programs on reducing diabetes were simulated, identifying the need for an integrated adaptive design of lifestyle programs while collecting the appropriate data over time. The strengths and limitations of scenario simulations of complex lifestyle intervention programs to improve the (cost)effectiveness of these programs to reduce diabetes in a more sustainable way compared to usual care are discussed.

The phenomenon of burnout is a complex issue, which despite major efforts from researchers and organizations remains hard to prevent. The current literature highlights an increasing global prevalence of employees that are dealing with burnout. What has been largely missing is a more systemic, dynamic, and personal perspective on the interactions of the key determinants of burnout. Burnout can be seen as the outcome of a complex system involving feedback loops between individual mental models, individual behavior, and external social influences. Understanding the feedback loops involved may enable employees and organizations to intervene in burnout trajectories early and effectively. System dynamics (SD) modeling is a methodology that can describe the structure and behavior of a complex system. The current paper describes the development of an SD model of burnout. First, an expert- and literature-informed causal loop diagram (CLD) of burnout is developed. Then, a novel approach is developed to collect personal retrospective scenario data. Finally, the CLD and data are translated into a quantitative SD model. The potential of the SD model is illustrated by simulating the behavior of three realistic personas during the onset of and recovery from burnout. The process of development of an SD model of burnout is presented and the strengths and limitations of the approach are discussed.

This study demonstrates an innovative approach to capture the complexity of individual workplace well-being, improving our understanding of multicausal relationships and feedback loops involved. The literature shows that a high number of interacting factors are related to individual workplace well-being. However, many studies focus on subsets of factors, and causal loops are seldomly studied. The aim of the current study was, therefore, to capture individual workplace well-being in a comprehensive conceptual causal loop diagram (CLD). We followed an iterative, qualitative, and transdisciplinary systems-thinking approach including literature search, group model building sessions, retrospective in-depth interviews with employees, and group sessions with human resource professionals, managers, job coaches, and management consultants. The results were discussed with HR and well-being officers of twelve organizations for their critical reflection on the recognizability and potential of the developed CLD. The final result, a conceptual individual workplace well-being CLD, provides a comprehensive overview of multiple, measurable key factors relating to individual workplace well-being and of the way these factors may causally interact over time, either improving or deteriorating workplace well-being. In future studies, the CLD can be translated to a quantitative system dynamics model for simulating workplace well-being scenarios. Ultimately, these simulations could be used to design effective workplace well-being interventions.

Cooperative robots in the workspace have an effect on safety that is not yet fully understood. This work collates pre-existing knowledge on human, technological and organizational factors for human-robot interaction and develops a system dynamics model that captures the complex interactions. Expert consultation in the form of a Delphi study is used to derive a tractable model from pre-existing puzzle pieces. A final model is presented, which contains 10 nodes and 20 relationships containing the three key outcome factors of human-robot interaction, viz. Safety, Efficiency and Sustainability. By combining these factors into a single tractable framework, this model bridges the gap between individual efforts from previous works in the field of robotics.