Traditional epidemiological studies have identified a number of risk factors for various diseases using regression-based methods that examine the association between an exposure and an outcome (i.e., one-to-one correspondences). One of the major limitations of this approach is the “black-box” aspect of the analysis, in the sense that this approach cannot fully explain complex relationships such as biological pathways. With high-throughput data in current epidemiology, comprehensive analyses are needed. The network approach can help to integrate multi-omics data, visualize their interactions or relationships, and make inferences in the context of biological mechanisms. This review aims to introduce network analysis for systems epidemiology, its procedures, and how to interpret its findings.

Traditional epidemiological studies have identified a number of risk factors for various diseases using regression-based methods that examine the association between an exposure and an outcome (i.e., one-to-one correspondences). One of the major limitations of this approach is the “black-box” aspect of the analysis, in the sense that this approach cannot fully explain complex relationships such as biological pathways. With high-throughput data in current epidemiology, comprehensive analyses are needed. The network approach can help to integrate multi-omics data, visualize their interactions or relationships, and make inferences in the context of biological mechanisms. This review aims to introduce network analysis for systems epidemiology, its procedures, and how to interpret its findings.

Objective: This study evaluated the effectiveness of a virtual reality (VR) based stress reduction program tailored for healthcare and information technology (IT) professionals during the coronavirus disease-2019 pandemic. Methods: The 2-week program, based on forest healing principles, was designed to alleviate occupational stress and improve sleep quality. Participants (n=54; 46 healthcare, 8 IT professionals) underwent pre- and post-intervention assessments using validated psychological scales and physiological measurements. Results: Results showed significant reductions in stress (Perceived Stress Scale [PSS], p=0.001) and anxiety (Hospital Anxiety and Depression Scale [HADS] anxiety, p=0.002) across all participants. Healthcare professionals demonstrated significant decreases in depression (Patient Health Questionnaire-9, p=0.015), anxiety (HADS anxiety, p<0.001), and stress (PSS, p=0.001). Unexpectedly, weekday sleep quality (Pittsburgh Sleep Quality Index) worsened in the healthcare group (p=0.013). The IT group showed no significant changes, possibly due to the small sample size. Physiological measurements revealed significant differences between groups post-intervention, including melatonin levels (p=0.001) and electrocardiogram values (p=0.031), suggesting occupation-specific responses to VR interventions. Conclusion Despite limitations such as unequal sample sizes, this study provides valuable insights into the potential of VR-based stress management programs. The findings underscore the need for occupation-specific approaches and further research with larger, balanced samples to validate these results and explore long-term effects.

After the coronavirus disease 2019 pandemic, the widespread adoption of working from home, or teleworking, has prompted extensive research regarding its effects on work productivity and the physical and mental health of employees. In this context, our study aimed to investigate the association between working from home and health-related productivity loss (HRPL).

Objective: This study evaluated the effectiveness of a virtual reality (VR) based stress reduction program tailored for healthcare and information technology (IT) professionals during the coronavirus disease-2019 pandemic. Methods: The 2-week program, based on forest healing principles, was designed to alleviate occupational stress and improve sleep quality. Participants (n=54; 46 healthcare, 8 IT professionals) underwent pre- and post-intervention assessments using validated psychological scales and physiological measurements. Results: Results showed significant reductions in stress (Perceived Stress Scale [PSS], p=0.001) and anxiety (Hospital Anxiety and Depression Scale [HADS] anxiety, p=0.002) across all participants. Healthcare professionals demonstrated significant decreases in depression (Patient Health Questionnaire-9, p=0.015), anxiety (HADS anxiety, p<0.001), and stress (PSS, p=0.001). Unexpectedly, weekday sleep quality (Pittsburgh Sleep Quality Index) worsened in the healthcare group (p=0.013). The IT group showed no significant changes, possibly due to the small sample size. Physiological measurements revealed significant differences between groups post-intervention, including melatonin levels (p=0.001) and electrocardiogram values (p=0.031), suggesting occupation-specific responses to VR interventions. Conclusion Despite limitations such as unequal sample sizes, this study provides valuable insights into the potential of VR-based stress management programs. The findings underscore the need for occupation-specific approaches and further research with larger, balanced samples to validate these results and explore long-term effects.

Objective: This study evaluated the effectiveness of a virtual reality (VR) based stress reduction program tailored for healthcare and information technology (IT) professionals during the coronavirus disease-2019 pandemic. Methods: The 2-week program, based on forest healing principles, was designed to alleviate occupational stress and improve sleep quality. Participants (n=54; 46 healthcare, 8 IT professionals) underwent pre- and post-intervention assessments using validated psychological scales and physiological measurements. Results: Results showed significant reductions in stress (Perceived Stress Scale [PSS], p=0.001) and anxiety (Hospital Anxiety and Depression Scale [HADS] anxiety, p=0.002) across all participants. Healthcare professionals demonstrated significant decreases in depression (Patient Health Questionnaire-9, p=0.015), anxiety (HADS anxiety, p<0.001), and stress (PSS, p=0.001). Unexpectedly, weekday sleep quality (Pittsburgh Sleep Quality Index) worsened in the healthcare group (p=0.013). The IT group showed no significant changes, possibly due to the small sample size. Physiological measurements revealed significant differences between groups post-intervention, including melatonin levels (p=0.001) and electrocardiogram values (p=0.031), suggesting occupation-specific responses to VR interventions. Conclusion Despite limitations such as unequal sample sizes, this study provides valuable insights into the potential of VR-based stress management programs. The findings underscore the need for occupation-specific approaches and further research with larger, balanced samples to validate these results and explore long-term effects.

After the coronavirus disease 2019 pandemic, the widespread adoption of working from home, or teleworking, has prompted extensive research regarding its effects on work productivity and the physical and mental health of employees. In this context, our study aimed to investigate the association between working from home and health-related productivity loss (HRPL).

Objective: This study evaluated the effectiveness of a virtual reality (VR) based stress reduction program tailored for healthcare and information technology (IT) professionals during the coronavirus disease-2019 pandemic. Methods: The 2-week program, based on forest healing principles, was designed to alleviate occupational stress and improve sleep quality. Participants (n=54; 46 healthcare, 8 IT professionals) underwent pre- and post-intervention assessments using validated psychological scales and physiological measurements. Results: Results showed significant reductions in stress (Perceived Stress Scale [PSS], p=0.001) and anxiety (Hospital Anxiety and Depression Scale [HADS] anxiety, p=0.002) across all participants. Healthcare professionals demonstrated significant decreases in depression (Patient Health Questionnaire-9, p=0.015), anxiety (HADS anxiety, p<0.001), and stress (PSS, p=0.001). Unexpectedly, weekday sleep quality (Pittsburgh Sleep Quality Index) worsened in the healthcare group (p=0.013). The IT group showed no significant changes, possibly due to the small sample size. Physiological measurements revealed significant differences between groups post-intervention, including melatonin levels (p=0.001) and electrocardiogram values (p=0.031), suggesting occupation-specific responses to VR interventions. Conclusion Despite limitations such as unequal sample sizes, this study provides valuable insights into the potential of VR-based stress management programs. The findings underscore the need for occupation-specific approaches and further research with larger, balanced samples to validate these results and explore long-term effects.

After the coronavirus disease 2019 pandemic, the widespread adoption of working from home, or teleworking, has prompted extensive research regarding its effects on work productivity and the physical and mental health of employees. In this context, our study aimed to investigate the association between working from home and health-related productivity loss (HRPL).

Objective: This study evaluated the effectiveness of a virtual reality (VR) based stress reduction program tailored for healthcare and information technology (IT) professionals during the coronavirus disease-2019 pandemic. Methods: The 2-week program, based on forest healing principles, was designed to alleviate occupational stress and improve sleep quality. Participants (n=54; 46 healthcare, 8 IT professionals) underwent pre- and post-intervention assessments using validated psychological scales and physiological measurements. Results: Results showed significant reductions in stress (Perceived Stress Scale [PSS], p=0.001) and anxiety (Hospital Anxiety and Depression Scale [HADS] anxiety, p=0.002) across all participants. Healthcare professionals demonstrated significant decreases in depression (Patient Health Questionnaire-9, p=0.015), anxiety (HADS anxiety, p<0.001), and stress (PSS, p=0.001). Unexpectedly, weekday sleep quality (Pittsburgh Sleep Quality Index) worsened in the healthcare group (p=0.013). The IT group showed no significant changes, possibly due to the small sample size. Physiological measurements revealed significant differences between groups post-intervention, including melatonin levels (p=0.001) and electrocardiogram values (p=0.031), suggesting occupation-specific responses to VR interventions. Conclusion Despite limitations such as unequal sample sizes, this study provides valuable insights into the potential of VR-based stress management programs. The findings underscore the need for occupation-specific approaches and further research with larger, balanced samples to validate these results and explore long-term effects.