RESUMO
Stress is associated with numerous chronic health conditions, both mental and physical. However, the heterogeneity of these associations at the individual level is poorly understood. While data generated from individuals in their day-to-day lives "in the wild" may best represent the heterogeneity of stress, gathering these data and separating signals from noise is challenging. In this work, we report findings from a major data collection effort using Digital Health Technologies (DHTs) and frontline healthcare workers. We provide insights into stress "in the wild", by using robust methods for its identification from multimodal data and quantifying its heterogeneity. Here we analyze data from the Stress and Recovery in Frontline COVID-19 Workers study following 365 frontline healthcare workers for 4-6 months using wearable devices and smartphone app-based measures. Causal discovery is used to learn how the causal structure governing an individual's self-reported symptoms and physiological features from DHTs differs between non-stress and potential stress states. Our methods uncover robust representations of potential stress states across a population of frontline healthcare workers. These representations reveal high levels of inter- and intra-individual heterogeneity in stress. We leverage multiple stress definitions that span different modalities (from subjective to physiological) to obtain a comprehensive view of stress, as these differing definitions rarely align in time. We show that these different stress definitions can be robustly represented as changes in the underlying causal structure on and off stress for individuals. This study is an important step toward better understanding potential underlying processes generating stress in individuals.
RESUMO
Aims and objectives: This study aimed to describe the application of low-cost inter-professional simulation over 4 phases in identifying structural and design issues, latent safety threats as well as test systems, processes, including facilitated team training during the design of a new pediatric intensive care unit (PICU). Materials and methods: The four-phase inter-professional simulation sessions involving clinical and non-clinical teams were conducted over a 3-month period in a corporate hospital during the designing of a new PICU. Low-cost resources, such as floor tapes, low-tech manikins, reused sterilized consumables, and actual patient beds and equipment, were used for the in situ simulation sessions. A plus-delta method of debriefing was done, and changes agreed on consensus were implemented after each simulated session. Results: There were 10 simulation sessions conducted over 4 phases during the 3-month period of designing the PICU. The participants included 10 doctors from PICU and adult critical care, 25 critical care nurses, 12 members from the project team, and 2 hospital administrators in various combinations. The first phase led to the re-design of workspace and clinical areas for better space utilization. The second phase required further revision to facilitate better mobility and facilities. In the third phase, the number of beds was reduced to 6 beds following the simulated drills involving the actual placement of patient cots and equipment. The fourth phase had thematic 5 simulated exercises involving the newly recruited clinical teams that enabled the identification of systems and process issues. Cognitive aids and video orientation of the setup, team training, and human factors training were addressed, and the unit was open for patient care in a week. Conclusion: A phased inter-professional simulation exercise with low-cost resources can enable the identification of structural challenges, design issues, latent safety threats, test systems, processes, patient flow, and facilitated team training during the design of a new PICU. Further studies are needed to understand the generalization of the study findings into designing PICU.