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Objective To summarize the current shooting trends of this type of video, discuss the effect of non-medical factors on the spread of videos, and develop prediction models using machine learning (ML) algorithms. Methods We searched and filtered medical science popularization videos on TikTok, then labeled non-medical features as variables and record the number of “Thumb-Up”, “Comment”, “Share” and “Collection” as outcome indicators. A total of 286 samples and 34 variables were included in the construction of the ML model, and 13 algorithms were employed with the area under the curve (AUC) for performance assessment and a ten-fold cross-validation for accuracy testing. Results In the quantitative analysis of the 4 outcome indicators, we identified significant disparities among different videos. Subsequently, five best-performing models were ultimately confirmed to predict the reasons for differences: “Thumb-Up” RF Model (AUC = 0.7331), “Collection” RF Model (AUC = 0.7439), “Share” RF Model (AUC = 0.7077), “Comment” RF Model (AUC = 0.7960), “Comment” BNB Model (AUC = 0.7844). By ML models, the video duration, title and description length, shooting location emerged and body language as the most five crucial parameters across all five models. Conclusion ML models demonstrated superior performance in predicting the influence of non-medical factors on the spread of medical science popularization videos. The weight of these variables will provide valuable guidance for video preparation. This study contributes to the dissemination and acceptance of medical science popularization videos by the public, thereby promoting health education and enhancing public awareness and competence in healthcare.
Objective To summarize the current shooting trends of this type of video, discuss the effect of non-medical factors on the spread of videos, and develop prediction models using machine learning (ML) algorithms. Methods We searched and filtered medical science popularization videos on TikTok, then labeled non-medical features as variables and record the number of “Thumb-Up”, “Comment”, “Share” and “Collection” as outcome indicators. A total of 286 samples and 34 variables were included in the construction of the ML model, and 13 algorithms were employed with the area under the curve (AUC) for performance assessment and a ten-fold cross-validation for accuracy testing. Results In the quantitative analysis of the 4 outcome indicators, we identified significant disparities among different videos. Subsequently, five best-performing models were ultimately confirmed to predict the reasons for differences: “Thumb-Up” RF Model (AUC = 0.7331), “Collection” RF Model (AUC = 0.7439), “Share” RF Model (AUC = 0.7077), “Comment” RF Model (AUC = 0.7960), “Comment” BNB Model (AUC = 0.7844). By ML models, the video duration, title and description length, shooting location emerged and body language as the most five crucial parameters across all five models. Conclusion ML models demonstrated superior performance in predicting the influence of non-medical factors on the spread of medical science popularization videos. The weight of these variables will provide valuable guidance for video preparation. This study contributes to the dissemination and acceptance of medical science popularization videos by the public, thereby promoting health education and enhancing public awareness and competence in healthcare.
BACKGROUND Acute pancreatitis (AP) is one of the most prevalent gastrointestinal diseases in clinical practice. In addition to essential medication therapy, a nutritional diet also plays a vital part in the treatment. People are increasingly using online short video platforms to look up health-related information with the widespread use of smartphones. However, the quality and reliability of health content on these platforms remain unknown. OBJECTIVE This study aimed to assess the quality and reliability of the information in AP diet–related videos on Chinese short-video-sharing platforms. METHODS A total of 147 videos were included to analyze from three of the most widely used short-video sharing platforms in China, TikTok, BiliBili, and WeChat channels. Each video was assessed by two physicians separately for content (by content score), quality (by Global Quality Score), and reliability (by an adjusted DISCERN tool). Poisson regression and correlation analysis were used to explore the variables that might affect the quality of the video. RESULTS videos from TikTok had the most likes and comments than videos from TikTok and WeChat channels, and videos from BiliBili were longer in duration and in days since published than other videos (all p<.001). However, there was no significant difference in the GQS, content score and the DISCERN score among videos from TikTok, BiliBili, and WeChat channels (p>.05). The overall quality of the videos was poor. videos from medical professionals had a relatively greater advice value than those from non-medical professionals in the field of content trustworthiness, quality, and comprehensiveness. The subsequent variables were correlated positively: likes and shares (r=0.326, p<.001), likes and comments (r=0.439, p<.001), comments and shares (r=0.337, p<0.001). DISCERN scores and days since published were found to be negatively correlated (r=-0.259, p<.001). CONCLUSIONS The findings showed that these videos’ quality was inadequate and varied greatly based on the kind of source. In general, videos uploaded by medical professionals were proved to be more reliable, comprehensive, and high-quality than non-medical professionals' videos in content quality. these platforms were not a suitable source of information for patient education. But given the rise in popularity of video-sharing platforms, necessary regulations and restrictions should be taken.
Background/Objectives: Cerebral palsy (CP) causes movement and posture challenges due to central nervous system damage, requiring lifelong management. During the COVID-19 pandemic, there was limited access to facility-based treatments, which increased the demand for home-based therapies and digital resources. We analyzed the qualitative and quantitative aspects of YouTube videos focusing on CP therapy for children. Methods: A total of 95 videos were evaluated for content quality using the modified DISCERN (mDISCERN) tool and Global Quality Scale (GQS). The therapeutic program efficacy was assessed via the International Consensus on Therapeutic Exercise and Training (i-CONTENT) tool, Consensus on Therapeutic Exercise Training (CONTENT) scale, and Consensus on Exercise Reporting Template (CERT), and popularity was measured by the video power index (VPI). Results: YouTube-based therapeutic videos for children with CP generally exhibit reliability in video content and effectiveness in therapeutic programming, and no correlations were found between video popularity and quality. However, the qualitative analysis reveals insufficient mention of uncertainty in the treatment principles within the video content as well as a lack of detailed treatment descriptions encompassing aspects such as intensity, frequency, timing, setting, outcome measurement during and post-treatment, and safety considerations within therapeutic programs. In particular, this tendency was consistent regardless of the uploader’s expertise level and the classification of the neuromotor therapy type in contrast to that of the exercise type. Conclusions: YouTube-based content for CP children still has significant limitations in how substantive viewers, such as caregivers, can acquire tailored information and apply practical information to their exercise and treatment programs.
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