Novel coronavirus pneumonia is a new type of respiratory infectious disease that has rapidly spread in many countries or regions around the world. The World Health Organization (WHO) named it “coronavirus disease 2019 (COVID-19)”. Glucocorticoids (GC) have certain application value in patients with COVID-19, but they need to be used with caution and strict indications and dosage. Application of large doses of GC can also cause osteonecrosis of femoral head (ONFH). On the basis of the latest literature and evidence-based medical evidence on the fight against COVID-19 epidemic and steroid ONFH diagnosis and treatment, the Bone Circulation and Osteonecrosis Professional Committee, Shockwave Medical Specialty Committee of Chinese Research Hospital Association organized Chinese bone necrosis related experts to jointly write this consensus, focusing on the prevention strategy and the protective management measures in the ONFH diagnosis and treatment process during the prevention and control of COVID-19, which can provide reference for hospitals at all levels to carry out early prevention and treatment of ONFH.
Exercise intervention is an important non-pharmacological intervention for various diseases, and establishing precise exercise load assessment techniques can improve the quality of exercise intervention and the efficiency of disease prevention and control. Based on data collection from wearable devices, this study conducts nonlinear optimization and empirical verification of the original "Fitness-Fatigue Model". By constructing a time-varying attenuation function and specific coefficients, this study develops an optimized mathematical model that reflects the nonlinear characteristics of training responses. Thirteen participants underwent 12 weeks of moderate-intensity continuous cycling, three times per week. For each training session, external load (actual work done) and internal load (heart rate variability index) data were collected for each individual to conduct a performance comparison between the optimized model and the original model. The results show that the optimized model demonstrates a significantly improved overall goodness of fit and superior predictive ability. In summary, the findings of this study can support dynamic adjustments to participants' training programs and aid in the prevention and control of chronic diseases.