- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05872945
Model-based Systems for Professional Football Teams, Aimed at Optimizing Health and Performance (AIPROFB)
Development and Implementation of Model-based Systems for Professional Football Teams, Aimed at Optimizing Health and Performance
LIST OF PLANNED ORIGINAL PUBLICATIONS
- T wave inversion detection with machine learning to prevent sudden death in professional football players.
- Machine learning applied to biological parameters for control and advisory in professional football players (Machine learning applied to biological parameters for control and advisory in professional football players.)
- Machine learning applied to sport geolocation systems for injury prevention in professional football players.
Study Overview
Status
Conditions
Intervention / Treatment
Detailed Description
1. Introduction The approach of this project arises from the concern to use intelligence systems artificial intelligence and machine learning in professional sports as assistance for the optimization of health and performance in professional soccer players. In professional sport, increasing physical, biological and physiological efforts are required and we need help tools.
In this regard, the proposal of several publications within the project has been raised:
Detection of T-wave inversion with machine learning to prevent sudden death in professional soccer players.
Players undergo various pre-competitive screening tests to assess their state of health, specifically one of them is a resting 12-lead electrocardiogram. Based on the waveform findings in this complementary test, the risk of a professional athlete and the need for more complementary tests can be classified (Drezner et al., 2017). Our proposal is to reanalyze these tests and subject them to a machine learning mathematical model that is capable of detecting T wave inversions in said leads and presenting the results and recommendations in accordance with international criteria for electrocardiographic study in athletes.
Machine learning applied to biological parameters for control and advice in professional soccer players.
During the season, routine analyzes are carried out to control biochemical parameters related to health and performance that fluctuate or change throughout the season: vitamin D, vitamin B12, vitamin B9, ferritin, etc. (Galan et al. ., 2012). Said data will be subjected to a machine learning procedure that can notify us of alterations in the habitual pattern of the players and that can cause alterations in performance, even generating pathologies.
- Machine learning applied to sports geolocation systems for the prevention of injuries in professional soccer players.
The data obtained during training sessions and matches regarding physical data such as duration, distance, distance at different speeds, training density, etc. Which are provided by sports geolocation systems, are of great importance when studying the effort and performance profile of each player. Obtaining the player's performance profile standardized according to the training day, we can detect adverse situations such as: over-training or lack of physical condition. Warning and alarm systems aimed at injury prevention can be designed. (Rossi, Pappalardo, Marcello, Javier, & May, 2017).
2. Description The studies will be implemented by implementing artificial intelligence and machine learning systems on the physical, biological and physiological data collected during the routine sports and health activity of the professional football players in the 2019-20 and 2020-21, 2021-22, 2022-23 y 2023-24 seasons.
2.1 General Objectives
- Evaluate the installation of artificial intelligence systems such as automatic learning to obtain models and results in the interpretation of physical, biomedical and physiological parameters of the players.
Develop advisory/advertising systems in the area of health and performance based on profiles.
3. Practical application The project has great potential for practical applicability and could generate a paradigm shift, since it is based on the generation of mathematical and/or programming models that will help in health controls and sports load controls that are applied to professional soccer players. A notable aspect is the possible improvement in the calculation of the probabilistic weights of the risk factors on health and performance.
Study Type
Enrollment (Actual)
Contacts and Locations
Study Locations
-
-
-
Palma De Mallorca, Spain, 07011
- RCD Mallorca SAD
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria
• Healthy young and professional players of legal age who play their role in professional football teams.
Exclusion criteria:
- Players who are not a regular part of these professional teams.
- Players with known pathology.
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Waves Detection
Time Frame: 2023-2024
|
Detection waves changes in the electrocardiogram from pro football players
|
2023-2024
|
Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: Adolfo Munoz Macho, Dr., RCD Mallorca SAD
Publications and helpful links
Helpful Links
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Estimated)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 1573N19
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
IPD Sharing Time Frame
IPD Sharing Access Criteria
IPD Sharing Supporting Information Type
- STUDY_PROTOCOL
- SAP
- ICF
- ANALYTIC_CODE
- CSR
Study Data/Documents
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
Clinical Trials on Arrhythmias, Cardiac
-
Ostfold University CollegeNot yet recruitingCardiac Arrest | Cardiac Arrhythmia | Cardiac Disease | Cardiac Death
-
Medtronic BRCCompletedAtrial Fibrillation | Risk of Cardiac ArrhythmiasNetherlands, Germany, Austria, Belgium, Canada, Czech Republic, Russian Federation, Slovakia
-
Medical University of LodzRecruiting
-
Centro Cardiologico MonzinoMinistry of Health, ItalyRecruitingCardiac ArrhythmiaItaly
-
Ratika ParkashCardiac Arrhythmia Network of CanadaRecruiting
-
Boston Scientific CorporationRecruitingCardiac ArrythmiasUnited States, Monaco, Italy
-
EPD Solutions, A Philips CompanyPhilips HealthcareTerminatedCardiac ArrhythmiaUnited States
-
EPD Solutions, A Philips CompanyWithdrawn
-
Zoll Medical CorporationCompletedCardiac ArrhythmiaUnited States
-
Emory UniversityCompleted
Clinical Trials on Electrocardiogram
-
University of CambridgeUniversity of Leicester; City, University of LondonRecruitingAtrial Fibrillation | Paroxysmal Atrial FibrillationUnited Kingdom
-
Ain Shams UniversityRecruitingWolf Parkinson White Syndrome | ArrythmiasEgypt
-
Khon Kaen UniversityRecruitingCardiovascular Diseases | Arrhythmias, Cardiac | COVID-19 Acute Respiratory Distress SyndromeThailand
-
Garmin InternationalCompleted
-
University Hospital, MontpellierTerminated
-
Assiut UniversityUnknownDiabetes Mellitus, Type 1
-
Population Health Research InstituteHamilton Health Sciences CorporationCompletedAtrial Fibrillation New OnsetCanada
-
Cardiovascular Academy Society, TurkeyCompletedLeft Ventricular HypertrophyTurkey
-
University of Toledo Health Science CampusCompletedElectrocardiographyUnited States