Background: Prostate and colorectum cancers rank among the most common cancers, and incontinence is a significant postsurgical issue affecting the physical and psychological well-being of cancer survivors. Social media, particularly YouTube, has emerged as a vital source of health information. While YouTube offers valuable content, users must exercise caution due to potential misinformation. Objective: This study aims to assess the quality of publicly available YouTube videos related to incontinence after pelvic cancer surgery. Methods: A search on YouTube related to “Incontinence after cancer surgery” was performed, and 108 videos were analyzed. Multiple quality assessment tools (DISCERN, GQS, JAMA, PEMAT, and MQ-VET) and statistical analyses (descriptive statistics and intercorrelation tests) were used to evaluate the characteristics and popularity, educational value, quality, and reliability of these videos, relying on novel graphical representation techniques such as Sankey and Chord diagrams. Results: Strong positive correlations were found among quality rating scales, emphasizing agreement. The performed graphical analysis reinforced the reliability and validity of quality assessments. Conclusions: This study found strong correlations among five quality scales, suggesting their effectiveness in assessing health information quality. The evaluation of YouTube videos consistently revealed “high” quality content. Considering the source is mandatory when assessing quality, healthcare and academic institutions are reliable sources. Caution is advised with ad-containing videos. Future research should focus on policy improvements and tools to aid patients in finding high-quality health content.

Several pelvic area cancers exhibit high incidence rates, and their surgical treatment can result in adverse effects such as urinary and fecal incontinence, significantly impacting patients' quality of life. Post-surgery incontinence is a significant concern, with prevalence rates ranging from 25 to 45% for urinary incontinence and 9–68% for fecal incontinence. Cancer survivors are increasingly turning to YouTube as a platform to connect with others, yet caution is warranted as misinformation is prevalent. This study aims to evaluate the information quality in YouTube videos about post-surgical incontinence after pelvic area cancer surgery. A YouTube search for "Incontinence after cancer surgery" yielded 108 videos, which were subsequently analyzed. To evaluate these videos, several quality assessment tools were utilized, including DISCERN, GQS, JAMA, PEMAT, and MQ-VET. Statistical analyses, such as descriptive statistics and intercorrelation tests, were employed to assess various video attributes, including characteristics, popularity, educational value, quality, and reliability. Also, artificial intelligence techniques like PCA, t-SNE, and UMAP were used for data analysis. HeatMap and Hierarchical Clustering Dendrogram techniques validated the Machine Learning results. The quality scales presented a high level of correlation one with each other (p < 0.01) and the Artificial Intelligence-based techniques presented clear clustering representations of the dataset samples, which were reinforced by the Heat Map and Hierarchical Clustering Dendrogram. YouTube videos on "Incontinence after Cancer Surgery" present a "High" quality across multiple scales. The use of AI tools, like PCA, t-SNE, and UMAP, is highlighted for clustering large health datasets, improving data visualization, pattern recognition, and complex healthcare analysis.

Background: The Medical Quality Video Evaluation Tool (MQ-VET) is a standardized instrument for assessing health-related video quality, yet it is only available in English. This study addresses the growing demand for a Spanish version to better support the increasing Spanish-speaking population seeking reliable digital health content. Objective: To adapt and validate the MQ-VET into Spanish, ensuring robust psychometric reliability and validity through rigorous cross-cultural adaptation methods, augmented by the integration of artificial intelligence (AI) tools. Materials and methods: Following international guidelines, the MQ-VET was translated, back-translated, and reviewed by experts. AI-based tools were employed to refine linguistic and cultural accuracy. Psychometric properties were evaluated by 60 participants (30 healthcare and 30 nonhealthcare professionals), focusing on reliability, agreement, and concurrent validity with the DISCERN instrument. Results: The Spanish MQ-VET showed excellent reliability (Cronbach's alpha>0.90, ICC=0.81) and strong concurrent validity (Pearson r = 0.9435, Spearman r = 0.9482, p < 0.0001), alongside with a robust linear regression result (R²=0.8902). Bland-Altman analysis confirmed a robust agreement, and AI-driven tools performed the factorial analysis that revealed a clear three-factor structure explaining 81.1% of the variance. Conclusions: The Spanish MQ-VET is a reliable and valid instrument for assessing the quality of health-related videos, applicable to both healthcare professionals and individuals outside the healthcare field. Leveraging AI-driven methodologies, it serves as a robust resource for enhancing digital health literacy and promoting critical appraisal of video content among Spanish-speaking populations.

The dataset presented in this article was used in a pilot randomized and controlled trial that evaluated the effectiveness of manual physical therapy (MPT) compared to conventional pharmacologic treatment (CPT) for treating chronic pediatric functional constipation (CPC). The pilot trial was carried out at the Central University Hospital of Asturias in Oviedo, Spain, with 47 children between 2 and 12 years old being evaluated by a Pediatric Gastroenterologist. Participants received 9 sessions of MPT which were spaced out weekly for the first two months and bi-weekly for the third month. The Pediatric Quality of Life questionnaire (PedsQL^TM) scores were assessed at the start of the trial (Time 1), its end (Time 2), and 5 years later (Time 3). The dataset contains the collected raw data, including participant demographics data and PedsQL^TM scores categorized by question group, as well as total scores for each participant at each time point. This dataset can be used to further analyze the results and the study can be potentially replicated.