This study aims to confirm a structural model of serious leisure traits, constraints, involvement, and resultant participation with a sample of triathletes in Taiwan. Questionnaires were collected from 371 triathlon participants in the 2015 Ironman 70.3 Taiwan. The findings confirmed the antecedent role of serious leisure traits in the constraint–involvement relationship. In particular, serious leisure traits exerted a positive influence on sport participation through constraints and involvement, and sport constraints and involvement fully mediated between serious leisure traits and sport participation. The results show that triathletes with stronger serious leisure traits participated more frequently in daily training, finished longer distances in the event, and had spent more years as triathletes due to their low perceived constraints and high sport involvement. Based on these findings, several managerial implications for triathlon event organisers and governing bodies are discussed.

Purpose: Online medical repositories provide a platform for users to share information and dynamically access abundant electronic health data. It is important to determine whether case report information can assist the general public in appropriately managing their diseases. Therefore, this paper aims to introduce a novel deep learning-based method that allows non-professionals to make inquiries using ordinary vocabulary, retrieving the most relevant case reports for accurate and effective health information.Design/methodology/approach: The dataset of case reports was collected from both the patient-generated research network and the digital medical journal repository. To enhance the accuracy of obtaining relevant case reports, the authors propose a retrieval approach that combines BERT and BiLSTM methods. The authors identified representative health-related case reports and analyzed the retrieval performance, as well as user judgments.Findings: This study aims to provide the necessary functionalities to deliver relevant health case reports based on input from ordinary terms. The proposed framework includes features for health management, user feedback acquisition and ranking by weights to obtain the most pertinent case reports.Originality/value: This study contributes to health information systems by analyzing patients' experiences and treatments with the case report retrieval model. The results of this study can provide immense benefit to the general public who intend to find treatment decisions and experiences from relevant case reports.

This study introduces a deep learning (DL) framework designed to classify YouTube videos into five exercise categories: TABATA, cardiorespiratory endurance, free weight muscular endurance, training aid muscular endurance, and flexibility. These categories were identified by two domain experts in sports science based on video content, tags, and user interaction patterns. Utilizing data from 1284 Traditional Chinese-language YouTube videos and 39,141 comments posted between March 2023 and June 2024, we applied text preparation techniques and sentiment analysis to clean and analyze the dataset. The study incorporated five key features—titles, hashtags, descriptions, comments, and sentiment, and employed Word2Vec with the Continuous Bag of Words (CBOW) model for word embedding, effectively handling short YouTube comments. We evaluated several predictive models, including Support Vector Machine (SVM), Extreme Gradient Boosting (XGBOOST), Text Convolutional Neural Network (TextCNN), Bidirectional Long Short-Term Memory (BiLSTM), CNN-BiLSTM, and BERT, using 10-fold cross-validation and comparing their performance based on accuracy, precision, recall, and F-measure. The results indicate that BERT outperformed the other models, achieving an accuracy of 92.25% (based on 10-fold cross-validation) when all five features were used. Incorporating sentiment analysis enhanced prediction performance across all models, with an average accuracy improvement of 3.85%. Ablation analysis confirmed the significant role of sentiment in improving prediction outcomes. Furthermore, a paired samples t-test revealed statistically significant differences in performance metrics when sentiment was included as a feature. This study demonstrates the effectiveness of combining deep learning and sentiment analysis to predict home-based exercise video categories on YouTube, offering valuable insights into content trends and user engagement in the evolving digital fitness landscape.

Whether automatically generated summaries of health social media can assist users in appropriately managing their diseases and ensuring better communication with health professionals becomes an important issue. This paper aims to develop a novel deep learning-based summarization approach for obtaining the most informative summaries from online patient reviews accurately and effectively. This paper proposes a framework to generate summaries that integrates a domain-specific pre-trained embedding model and a deep neural extractive summary approach by considering content features, text sentiment, review influence and readability features. Representative health-related summaries were identified, and user judgements were analysed. Experimental results on the three real-world health forum data sets indicate that awarding sentences without incorporating all the adopted features leads to declining summarization performance. The proposed summarizer significantly outperformed the comparison baseline. User judgement through the questionnaire provides realistic and concrete evidence of crucial features that remarkably influence patient forum review summaries. This study contributes to health analytics and management literature by exploring users’ expressions and opinions through the health deep learning summarization model. The research also developed an innovative mindset to design summarization weighting methods from user-created content on health topics.