This study contributes with new acknowledgment to the literature focussed on the role of sports on subjective well-being. Distinct from the existent literature, this body of work suggests a model aiming at the following: 1) exhibit the relationship between sports participation – weekly frequency and minutes of practice – and individual’s subjective well-being; and 2) determine the extent to which perceived value of elite sports impacts individual’s subjective well-being. Data was collected from 511 respondents. SEM was used to analyze the variables that were measured in accordance to previous studies. Findings showed that: 1) sports participation positively affected individual’s subjective well-being; and 2) perceived value of elite sports positively affected individual’s subjective well-being; These results have key managerial implications for sports policymakers, as well as for managers responsible for physical activity and sports development programmes.

Due to the COVID-19 pandemic, higher education institutions with a face-to-face model have found themselves in the contingency of migrating to online learning. This study explores the perspective of all the lecturers at a Portuguese private higher education institution who were invited to participate, regardless of their research area, in this questionnaire. It aims to propose and test a conceptual model that combines attitudes, preferred activities, and technological experience with the sentiment about the impact of this experience on students’ learning process, on their teaching activity, and on the strategy of higher education institutions. An online questionnaire was conducted to 65 lecturers engaging in emergency online lecturing. The obtained results showed that lecturers reveal a positive attitude towards online lecturing, tend to prefer activities in which they feel most comfortable in face-to-face lecturing, and consider having technological experience useful for online activities. Lecturers have a positive sentiment about the impact of online learning on students’ learning, their faculty career, and the strategy of higher education institutions. The proposed conceptual model test shows that the model has well-fitting conditions. The results confirm the hypotheses formulated: namely, the predictive effect of attitude, preferred activities, and technological experience on sentiment. Faculty engagement in emergency online lecturing shows that the members are available to participate in the changing process, and the proposed conceptual model can be used to assess this readiness.

Taekwondo is a martial art and combat sport that originated in medieval or similar cultures, but today it is primarily a combat activity conditioned by safety rules. The kick technique is called “chagi” (Korean language). “Eyeballing” is a problem that is being addressed with traditional training methods used in Taekwondo. (1) Background: To solve this problem, the main aim of this study was to develop an Observation System for Technical Performance Indicators-Chagi (OSTPI-C). The validation and reliability processes were carried out by 19 expert judges, who were required to meet at least four out of the five selected inclusion criteria. (2) Methods: The content validity was calculated using Aiken’s V coefficient value on qualitative (relevance, univocity, and importance) and quantitative levels (rating scale from one to five), through the agreement and consensus of the panel experts. The reliability of the instrument was analyzed using Cronbach’s alpha coefficient. (3) Results: The results demonstrated the high content validity indexes (0.90) and high reliability (alpha > 0.70) of the observational instrument. (4) Conclusions: The OSTIP-C observational instrument could be used in the Olympic and Paralympic Taekwondo sport environment as a valid and reliable tool to evaluate the technical execution of kick technical processes.

The article presents the reference framework for multidisciplinary research at the Life Quality Research Centre (LQRC). The research paradigm about the citizens’ quality of life in society imposes a multifaceted and complex analysis. At the LQRC we address this as thematically divided into six scientific areas: education and training; physical activity and healthy lifestyles; food production and technology on food behaviors; organizational dynamics; motor behavior; and individual and community health. These areas are clearly related to the concept of quality of life. They fit the goals and dynamics of the research center. In each section, the authors develop the themes of research projects and the challenges that characterize research in the area of quality of life. It is clear that there is an agreement and understanding that ensures that quality of life research is on the agenda of the world organizations related to scientific research and human development. This is a real challenge for scientific research centers, since multidisciplinary paradigms are the foundation of our collective organization, and the evolution of research on quality of life could fulfil current needs and lead to the improvement of citizen lives.

Background: Artificial intelligence (AI) is transforming medical practice, enhancing diagnostic accuracy, personalisation, and clinical efficiency. However, this transition raises complex ethical challenges related to transparency, accountability, fairness, and human oversight. This study examines how the literature conceptualises and distributes ethical responsibility in AI-assisted healthcare. Methods: This semi-systematic, theory-informed thematic review was conducted in accordance with the PRISMA 2020 guidelines. Publications from 2020 to 2025 were retrieved from PubMed, ScienceDirect, IEEE Xplore databases, and MDPI journals. A semi-quantitative keyword-based scoring model was applied to titles and abstracts to determine their relevance. High-relevance studies (n = 187) were analysed using an eight-category ethical framework: transparency and explainability, regulatory challenges, accountability, justice and equity, patient autonomy, beneficence–non-maleficence, data privacy, and the impact on the medical profession. Results: The analysis revealed a fragmented ethical landscape in which technological innovation frequently outperforms regulatory harmonisation and shared accountability structures. Transparency and explainability were the dominant concerns (34.8%). Significant gaps in organisational responsibility, equitable data practices, patient autonomy, and professional redefinition were reported. A multilevel ethical responsibility model was developed, integrating micro (clinical), meso (institutional), and macro (regulatory) dimensions, articulated through both ex ante and ex post perspectives. Conclusions: AI requires governance frameworks that integrate ethical principles, regulatory alignment, and epistemic justice in medicine. This review proposes a multidimensional model that bridges normative ethics and operational governance. Future research should explore empirical, longitudinal, and interdisciplinary approaches to assess the real impact of AI on clinical practice, equity, and trust.

The phenomenon of dropout is often found among customers of sports services. In this study we intend to evaluate the performance of machine learning algorithms in predicting dropout using available data about their historic use of facilities. The data relating to a sample of 5209 members was taken from a Portuguese fitness centre and included the variables registration data, payments and frequency, age, sex, non-attendance days, amount billed, average weekly visits, total number of visits, visits hired per week, number of registration renewals, number of members referrals, total monthly registrations, and total member enrolment time, which may be indicative of members’ commitment. Whilst the Gradient Boosting Classifier had the best performance in predicting dropout (sensitivity = 0.986), the Random Forest Classifier was the best at predicting non-dropout (specificity = 0.790); the overall performance of the Gradient Boosting Classifier was superior to the Random Forest Classifier (accuracy 0.955 against 0.920). The most relevant variables predicting dropout were “non-attendance days”, “total length of stay”, and “total amount billed”. The use of decision trees provides information that can be readily acted upon to identify member profiles of those at risk of dropout, giving also guidelines for measures and policies to reduce it.