METHODS: Baseline and 1-year follow-up data from 5800 participants in the PREDIMED-Plus study were used. Each participant's food intake was estimated using validated semi-quantitative food frequency questionnaires, and the adherence to MD using the Dietary Score. The influence of diet on environmental impact was assessed through the EAT-Lancet Commission tables. The influence of diet on environmental impact was assessed through the EAT-Lancet Commission tables. The association between MD adherence and its environmental impact was calculated using adjusted multivariate linear regression models.
RESULTS: After one year of intervention, the kcal/day consumed was significantly reduced (-125,1 kcal/day), adherence to a MD pattern was improved (+0,9) and the environmental impact due to the diet was significantly reduced (GHG: -361 g/CO2-eq; Acidification:-11,5 g SO2-eq; Eutrophication:-4,7 g PO4-eq; Energy use:-842,7 kJ; and Land use:-2,2 m2). Higher adherence to MD (high vs. low) was significantly associated with lower environmental impact both at baseline and one year follow-up. Meat products had the greatest environmental impact in all the factors analysed, both at baseline and at one-year follow-up, in spite of the reduction observed in their consumption.
CONCLUSIONS: A program promoting a MD, after one year of intervention, significantly reduced the environmental impact in all the factors analysed. Meat products had the greatest environmental impact in all the dimensions analysed.
OBJECTIVES: In this paper, the Advanced Human-Robot Collaboration Model (AHRCM) approach is to enhance the risk assessment and to make the workplace involving security robots. The robots use perception cameras and generate scene diagrams for semantic depictions of their environment. Furthermore, Artificial Intelligence (AI) and Information and Communication Technology (ICT) have utilized to develop a highly protected security robot based risk management system in the workplace.
RESULTS: The experimental results show that the proposed AHRCM method achieves high performance in human-robot mutual adaption and reduce the risk.
CONCLUSION: Through an experiment in the field of human subjects, demonstrated that policies based on the proposed model improved the efficiency of the human-robot team significantly compared with policies assuming complete human-robot adaptation.
DESIGN/METHODOLOGY/APPROACH: A literature review was performed on issues, sources, management and approaches to HISs-induced errors. A critical review of selected models was performed in order to identify medical error dimensions and elements based on human, process, technology and organisation factors.
FINDINGS: Various error classifications have resulted in the difficulty to understand the overall error incidents. Most classifications are based on clinical processes and settings. Medical errors are attributed to human, process, technology and organisation factors that influenced and need to be aligned with each other. Although most medical errors are caused by humans, they also originate from other latent factors such as poor system design and training. Existing evaluation models emphasise different aspects of medical errors and could be combined into a comprehensive evaluation model.
RESEARCH LIMITATIONS/IMPLICATIONS: Overview of the issues and discourses in HIS-induced errors could divulge its complexity and enable its causal analysis.
PRACTICAL IMPLICATIONS: This paper helps in understanding various types of HIS-induced errors and promising prevention and management approaches that call for further studies and improvement leading to good practices that help prevent medical errors.
ORIGINALITY/VALUE: Classification of HIS-induced errors and its management, which incorporates a socio-technical and multi-disciplinary approach, could guide researchers and practitioners to conduct a holistic and systematic evaluation.