ABSTRACT
Background and objectives: The Movement Disorder Society's Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III) is mostly common used for assessing the motor symptoms of Parkinson's disease (PD). In remote circumstances, vision-based techniques have many strengths over wearable sensors. However, rigidity (item 3.3) and postural stability (item 3.12) in the MDS-UPDRS III cannot be assessed remotely since participants need to be touched by a trained examiner during testing. We developed the four scoring models of rigidity of the neck, rigidity of the lower extremities, rigidity of the upper extremities, and postural stability based on features extracted from other available and touchless motions. Methods: The red, green, and blue (RGB) computer vision algorithm and machine learning were combined with other available motions from the MDS-UPDRS III evaluation. A total of 104 patients with PD were split into a train set (89 individuals) and a test set (15 individuals). The light gradient boosting machine (LightGBM) multiclassification model was trained. Weighted kappa (k), absolute accuracy (ACC ± 0), and Spearman's correlation coefficient (rho) were used to evaluate the performance of model. Results: For model of rigidity of the upper extremities, k = 0.58 (moderate), ACC ± 0 = 0.73, and rho = 0.64 (moderate). For model of rigidity of the lower extremities, k = 0.66 (substantial), ACC ± 0 = 0.70, and rho = 0.76 (strong). For model of rigidity of the neck, k = 0.60 (moderate), ACC ± 0 = 0.73, and rho = 0.60 (moderate). For model of postural stability, k = 0.66 (substantial), ACC ± 0 = 0.73, and rho = 0.68 (moderate). Conclusion: Our study can be meaningful for remote assessments, especially when people have to maintain social distance, e.g., in situations such as the coronavirus disease-2019 (COVID-19) pandemic.
ABSTRACT
BACKGROUND: This study examined how different health organizations (i.e., the Chinese CDC, the Korean CDC, the United States CDC, and WHO) communicated about the COVID-19 pandemic on social media, thus providing implications for organizations touse social media effectively in global health crises in the future. METHODS: Three bilingual researchers conducted a content analysis ofsocial media posts (N = 1,343) of these health organizations on Twitter and Sina Weibo to explore the frames of the COVID-19 pandemic, the purposes, and the strategies to communicate about it. RESULTS: Prevention was the dominant frame of the social media content of these four health organizations. Information update was the major communication purpose for WHO, the United States CDC, and the Korean CDC; however, guidance was the primary communication purpose for the Chinese CDC. The United States CDC, the Chinese CDC, and the Korean CDC heavily relied on multiple social media strategies (i.e., visual, hyperlink, and authority quotation) in their communication to the public about the COVID-19 pandemic, whereas WHO primarily employed quoting authorities. Significantdifferences were revealed across these health organizations in frames, communication purposes, and strategies. Theoretical and practical implications and limitations were discussed. CONCLUSIONS: This study examined how different global health organizations communicate about the COVID-19 pandemic on social media. We discussed how and why these global health organizations communicate the COVID-19 pandemic, which would help health-related organizations design messages strategically on global public health issues in the future.