Digital health technology implementation has grown rapidly in recent years. To standardize the quality of digital health implementation research and increase the transparency and integrity of reporting, Perrin published iCHECK-DH: guidelines and checklist for the reporting on digital health implementations in 2023. This article interprets the contents of the list with a view to improving the reporting quality of digital implementation studies to develop more effective digital health interventions and achieve better health outcomes.
The surgical case report（SCARE）statement is the report checklist made by European researchers in 2016, which is specialized for surgical case report. As a reference for enhancing the research quality and transparency, the SCARE statement provides a fundamental framework for surgical case reports. The last SCARE statement was revised in 2020, and this paper interprets it to provide a practical tool for domestic researchers in surgical case report.
The increasing prevalence of the aging population, and inadequate and uneven distribution of medical resources, have led to a growing demand for telemedicine services. Gait disturbance is a primary symptom of neurological disorders such as Parkinson’s disease (PD). This study proposed a novel approach for the quantitative assessment and analysis of gait disturbance from two-dimensional (2D) videos captured using smartphones. The approach used a convolutional pose machine to extract human body joints and a gait phase segmentation algorithm based on node motion characteristics to identify the gait phase. Moreover, it extracted features of the upper and lower limbs. A height ratio-based spatial feature extraction method was proposed that effectively captures spatial information. The proposed method underwent validation via error analysis, correction compensation, and accuracy verification using the motion capture system. Specifically, the proposed method achieved an extracted step length error of less than 3 cm. The proposed method underwent clinical validation, recruiting 64 patients with Parkinson’s disease and 46 healthy controls of the same age group. Various gait indicators were statistically analyzed using three classic classification methods, with the random forest method achieving a classification accuracy of 91%. This method provides an objective, convenient, and intelligent solution for telemedicine focused on movement disorders in neurological diseases.