Quality and reliability of sarcopenia-related short videos on Douyin

Sarcopenia is a geriatric syndrome marked by the progressive decline in skeletal muscle mass, strength, and physical function. It typically develops insidiously and advances gradually, significantly increasing the risk of adverse outcomes such as falls, frailty, and mortality among older adults. With the rise of social media platforms like Douyin, the Chinese domestic counterpart of TikTok, for public health messaging, it is crucial to systematically evaluate the quality of content on this platform related to sarcopenia. In this cross-sectional content analysis, we collected Douyin videos related to sarcopenia using the platform’s default ranking algorithm and, after applying inclusion and exclusion criteria, obtained 156 videos. For each video, we recorded user interaction metrics (likes, comments, shares, collections) and extracted associated metadata. We evaluated content quality with the Global Quality Scale (GQS), the modified DISCERN (mDISCERN), and the JAMA benchmark criteria. We conducted Spearman’s correlation analyses to examine correlations among video variables, GQS, mDISCERN, and the JAMA score. The majority of the content originated from medical professionals (43.6%), with a primary focus on disease overviews (34.6%). Video quality assessments indicated low overall standards: GQS scores frequently reached 3 (44.9%), mDISCERN scores predominantly reached 2 (42.9%), and JAMA scores were concentrated at 2 (55.1%). Videos created by general users scored significantly lower than those from other sources (all P  < .05). Furthermore, video duration was positively correlated with GQS ( r  = 0.470, P  < .001) and mDISCERN scores ( r  = 0.363, P  < .001), but not with JAMA scores ( r  = 0.166, P  = .691). In contrast, no significant correlations emerged between quality and interaction metrics. While Douyin has the potential to raise awareness about sarcopenia, the prevalence of subpar content highlights the need for stronger oversight, credential verification, and better algorithmic prioritization to improve the quality of health information on the platform.