ABSTRACT
This paper aims to provide a comprehensive and innovative 12-lead electrocardiogram (ECG) dataset tailored to understand the unique needs of professional football players. Other ECG datasets are available but collected from common people, normally with diseases confirmed, while it is well known that ECG characteristics change in athletes and elite players as a result of their intense long-term physical training. This initiative is part of a broader research project employing machine learning (ML) to analyse ECG data in this athlete population and explore them according to the International criteria for ECG interpretation in athletes. The dataset is generated through the establishment of a prospective observational cohort consisting of 54 male football players from La Liga, representing a UEFA Pro-level team. Named the Pro-Football 12-lead Resting Electrocardiogram Database (PF12RED), it comprises 163 10-s ECG recordings, offering a detailed examination of the at-rest heart activity of professional football athletes. Data collection spans five phases over multiple seasons, including the 2018-2019 postseason, the 2019-20 preseason, the 2020-21 preseason, and the 2021-22 preseason. Athletes undergo medical evaluations that include a 10-s resting 12-lead ECG performed with General Electric's USB-CAM 14 module (https://co.services.gehealthcare.com/gehcstorefront/p/900995-002), with data saved using General Electric's CardioSoft V6.73 12SL V21 ECG Software. (https://www.gehealthcare.es/products/cardiosoft-v7) The data collection adheres to ethical principles, with clearance granted by the Autonomous Community of Andalusia Ethics Committee (Spain) under protocol number 1573-N-19 in December 2019. Participants provide informed consent, and data sharing is permitted following anonymization. The study aligns with the Declaration of Helsinki and adheres to the recommendations of the International Committee of Medical Journal Editors (ICMJE). The generated dataset serves as a valuable resource for research in sports cardiology and cardiac health. Its potential for reuse encompasses:1.International Comparison: Enabling cross-regional comparisons of cardiac characteristics among elite football players, enriching international studies.2.ML Model Development: Facilitating the development and refinement of machine learning models for arrhythmia detection, serving as a benchmark dataset.3.Validation of Diagnostic Methods: Allowing the validation of automatic diagnostic methods, contributing to enhanced accuracy in detecting cardiac conditions.4.Research in Sports Cardiology: Supporting future investigations into specific cardiac adaptations in elite athletes and their relation to cardiovascular health.5.Reference for Athlete Protection Policies: Influencing athlete protection policies by providing data on cardiac health and suggesting guidelines for medical assessments.6.Health Professionals Training: Serving as a training resource for health professionals interested in interpreting ECGs in sports contexts.7.Tool and Application Development: Facilitating the development of tools and applications related to the visualization, simulation and analysis of ECG signals in athletes.
ABSTRACT
BACKGROUND: Fatigue is the most common symptom in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and long COVID, impacting patients' quality of life; however, there is currently a lack of evidence-based context-aware tools for fatigue self-management in these populations. OBJECTIVE: This study aimed to (1) address fatigue in ME/CFS and long COVID through the development of digital mobile health solutions for self-management, (2) predict perceived fatigue severity using real-time data, and (3) assess the feasibility and potential benefits of personalized digital mobile health solutions. METHODS: The MyFatigue project adopts a patient-centered approach within the participatory health informatics domain. Patient representatives will be actively involved in decision-making processes. This study combines inductive and deductive research approaches, using qualitative studies to generate new knowledge and quantitative methods to test hypotheses regarding the relationship between factors like physical activity, sleep behaviors, and perceived fatigue in ME/CFS and long COVID. Co-design methods will be used to develop a personalized digital solution for fatigue self-management based on the generated knowledge. Finally, a pilot study will evaluate the feasibility, acceptance, and potential benefits of the digital health solution. RESULTS: The MyFatigue project opened to enrollment in November 2023. Initial results are expected to be published by the end of 2024. CONCLUSIONS: This study protocol holds the potential to expand understanding, create personalized self-management approaches, engage stakeholders, and ultimately improve the well-being of individuals with ME/CFS and long COVID. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/50157.
ABSTRACT
BACKGROUND: The Internet, and its popularity, continues to grow at an unprecedented pace. Watching videos online is very popular; it is estimated that 500 h of video are uploaded onto YouTube, a video-sharing service, every minute and that, by 2019, video formats will comprise more than 80% of Internet traffic. Health-related videos are very popular on YouTube, but their quality is always a matter of concern. One approach to enhancing the quality of online videos is to provide additional educational health content, such as websites, to support health consumers. This study investigates the feasibility of building a content-based recommender system that links health consumers to reputable health educational websites from MedlinePlus for a given health video from YouTube. METHODS: The dataset for this study includes a collection of health-related videos and their available metadata. Semantic technologies (such as SNOMED-CT and Bio-ontology) were used to recommend health websites from MedlinePlus. A total of 26 healths professionals participated in evaluating 253 recommended links for a total of 53 videos about general health, hypertension, or diabetes. The relevance of the recommended health websites from MedlinePlus to the videos was measured using information retrieval metrics such as the normalized discounted cumulative gain and precision at K. RESULTS: The majority of websites recommended by our system for health videos were relevant, based on ratings by health professionals. The normalized discounted cumulative gain was between 46% and 90% for the different topics. CONCLUSIONS: Our study demonstrates the feasibility of using a semantic content-based recommender system to enrich YouTube health videos. Evaluation with end-users, in addition to healthcare professionals, will be required to identify the acceptance of these recommendations in a nonsimulated information-seeking context.
