Background: In today’s performance-oriented society, burnout symptoms, defined as consequences of chronic work stress, are an increasing problem. To counteract this development, the important aims are (1) to find protective and modifiable factors that reduce the risk of developing and harboring burnout symptoms and (2) to understand the underlying mechanisms. A phenomenon potentially furthering both aims is flow experience. Based on the earlier literature, we developed a psycho-physiological “Flow-Burnout-Model”, which postulates positive or negative associations between flow and burnout symptoms, depending on the prevailing situational and personal conditions. Methods: To test our Flow-Burnout-Model, we conducted a systematic literature search encompassing flow and burnout symptoms. Eighteen empirical studies met the inclusion criteria and were analyzed. Results: The findings of the systematic review as a whole suggest a negative association between flow and burnout symptoms, both cross-sectional and longitudinal. According to the findings from longitudinal studies, flow can be interpreted as a protective factor against burnout symptoms, and burnout symptoms can be interpreted as a factor inhibiting flow. In our conclusion, we maintain the assumption of a bidirectional association between flow and burnout symptoms in the Flow-Burnout-Model but modify the initially suggested positive and negative associations between flow and burnout symptoms towards a predominantly negative relationship. Discussion: Mindful of the heterogeneous findings of earlier studies, the resulting comprehensive Flow-Burnout-Model will lay the foundations for future hypothesis-based research. This includes physiological mechanisms explaining the relationship between flow and burnout symptoms, and likewise, the conditions of their longitudinal association.

The purpose of this study is to examine the relationship between contextual work-related factors in terms of job demands (workload—WL) and job resources (work flexibility—WF), work–life conflict (WLC) and the burnout dimension emotional exhaustion (EE) in a large population-based sample. Building on the job demands resources model (JDRM), we have developed the hypothesis that WL has an indirect effect on EE that is mediated by WLC. We conducted a secondary analysis using data from the Dresden Burnout Study (DBS, N = 4246, mean age (SD) = 42.7 years (10.5); 36.4% male). Results from structural equation modelling revealed that EE is positively associated with WL (β = 0.15, p = 0.001) and negatively associated with WF (β = −0.13, p = 0.001), also after accounting for potential confounding variables (demography, depressive symptoms, and lifetime diagnosis of burnout). Both effects are mediated by WLC (β = 0.18; p = 0.001 and β = 0.08; p = 0.001, respectively) highlighting the important role of WLC in employee health. In summary, WF may help to reduce burnout symptoms in employees, whereas WL may increase them. Study results suggest that both associations depend on WLC levels.

Objective: Previous research indicates a link between burnout symptoms and reduced vagally mediated heart rate variability (HRV); however, the directionality of this relationship is still largely unknown. The objective of the present study was to examine the longitudinal relationship between HRV and burnout symptoms for 1 year, with a special focus on the emotional exhaustion (EE) burnout subdimension, which remains inadequately distinguished from overlapping with depressive symptoms. Methods: Here we present HRV and behavioral data from 167 individuals (mean [SD] age = 43.43 [11.78] years; 30.5% male) who attended two biomarker samplings (T1 and T2) of the Dresden Burnout Study approximately 12 months apart. Results: In hierarchical linear regression analyses, T1 HRV significantly inversely predicted T2 overall burnout symptoms (β = −.16; p = .03) and EE (β = −.23; p = .02), adjusting for age, sex, body mass index, adverse health behaviors, and depressive symptoms. Importantly, only high EE at T1 (β = −.22; p = .04), and not the T1 Maslach Burnout Inventor total score, predicted reductions in HRV from T1 to T2. Conclusions: We report for the first time longitudinal evidence that HRV is associated with changes in burnout symptoms, independently of depressive symptoms. Results suggest vagal dysfunction being predictive and specific for burnout symptoms, making HRV a promising starting point for the explanation of biophysiological mechanisms underlying burnout symptoms and cardiovascular diseases. The finding of only EE at T1 being predictive for changes in HRV underscores the importance of exhaustion for modulations in autonomic regulation.

Safe and agile experimentation spaces are essential for developing AI-enabled medical devices and digital health innovations. “Living Labs” offer a collaborative environment for testing technologies in near-real-world settings, capturing user perspectives and outcomes, often missed in traditional testing. However, the flexibility of Living Labs often clashes with the European Union’s rigid regulatory frameworks for medical devices that were not designed for digital technologies. We examine this intersection, showing how flexibility in evaluation and patient interaction can coexist with safety guardrails. Our approach integrates technology readiness, preemptive planning, and iterative modifications to bridge innovation and regulation, fostering adaptive health care technology development.

Digital health tools, platforms, and artificial intelligence– or machine learning–based clinical decision support systems are increasingly part of health delivery approaches, with an ever-greater degree of system interaction. Critical to the successful deployment of these tools is their functional integration into existing clinical routines and workflows. This depends on system interoperability and on intuitive and safe user interface design. The importance of minimizing emergent workflow stress through human factors research and purposeful design for integration cannot be overstated. Usability of tools in practice is as important as algorithm quality. Regulatory and health technology assessment frameworks recognize the importance of these factors to a certain extent, but their focus remains mainly on the individual product rather than on emergent system and workflow effects. The measurement of performance and user experience has so far been performed in ad hoc, nonstandardized ways by individual actors using their own evaluation approaches. We propose that a standard framework for system-level and holistic evaluation could be built into interacting digital systems to enable systematic and standardized system-wide, multiproduct, postmarket surveillance and technology assessment. Such a system could be made available to developers through regulatory or assessment bodies as an application programming interface and could be a requirement for digital tool certification, just as interoperability is. This would enable health systems and tool developers to collect system-level data directly from real device use cases, enabling the controlled and safe delivery of systematic quality assessment or improvement studies suitable for the complexity and interconnectedness of clinical workflows using developing digital health technologies.