Exploring factors affecting the acceptance of fall detection technology among older adults and their families: a content analysis.

BACKGROUND: This study conducted in-depth interviews to explore the factors that influence the adoption of fall detection technology among older adults and their families, providing a valuable evaluation framework for healthcare providers in the field of fall detection, with the ultimate goal of assisting older adults immediately and effectively when falls occur. METHODS: The method employed a qualitative approach, utilizing semi-structured interviews with 30 older adults and 29 families, focusing on their perspectives and expectations of fall detection technology. Purposive sampling ensured representation from older adults with conditions such as Parkinson's, dementia, and stroke. RESULTS: The results reveal key considerations influencing the adoption of fall-detection devices, including health factors, reliance on human care, personal comfort, awareness of market alternatives, attitude towards technology, financial concerns, and expectations for fall detection technology. CONCLUSIONS: This study identifies seven key factors influencing the adoption of fall detection technology among older adults and their families. The conclusion highlights the need to address these factors to encourage adoption, advocating for user-centered, safe, and affordable technology. This research provides valuable insights for the development of fall detection technology, aiming to enhance the safety of older adults and reduce the caregiving burden.

Smoothing regression and impact measures for accidents of traffic flows.

Traffic pattern identification and accident evaluation are essential for improving traffic planning, road safety, and traffic management. In this paper, we establish classification and regression models to characterize the relationship between traffic flows and different time points and identify different patterns of traffic flows by a negative binomial model with smoothing splines. It provides mean response curves and Bayesian credible bands for traffic flows, a single index, and the log-likelihood difference, for traffic flow pattern recognition. We further propose an impact measure for evaluating the influence of accidents on traffic flows based on the fitted negative binomial model. The proposed method has been successfully applied to real-world traffic flows, and it can be used for improving traffic management.