Villareal Football Club
Villarreal, Spain, 12540
Location contact
Flavia Ms Costantino, PHD Student
CONTACT
Javier Mr Martínez Gramage, Professor
CONTACT
NCT Number: NCT07653633
Anterior cruciate ligament (ACL) injuries represent a highly relevant issue in both grassroots and professional sports, with a particularly high incidence in young and female populations. The objective of this project is to develop and validate a multiscale predictive algorithm for ACL injury risk in athletes from Villarreal CF (aged 10-45), integrating biomechanical, physiological, genetic, and gut microbiome biomarkers.
The study, with a prospective and longitudinal design (4 years), will include a cohort of 200-250 players from the academy and first team. The following assessments will be conducted: biomechanical analysis of jumps using force platforms (instrumented LESS), physiological monitoring through resting heart rate and nocturnal heart rate variability (HRV), genotyping from saliva samples, and characterization of the gut microbiome 16 rRNA sequencing. The systematic recording of training, exposures, and injuries will follow OSTRC criteria and will be supervised by the club's medical team.
The expected outcome is a multivariate predictive model, validated in a professional sports setting, capable of identifying individual risk profiles and generating a personalized score to guide preventive interventions (exercise, strength training, nutritional or probiotic strategies). This approach aims to reduce the incidence of ACL injuries, optimize performance, and translate biomedical knowledge into clinical and sports practice.
Trial opening soon.
Get Notified10 year–45 year
All sexes
Observational
Villarreal, Spain, 12540
Flavia Ms Costantino, PHD Student
CONTACT
Javier Mr Martínez Gramage, Professor
CONTACT
Quality Assurance Plan: The quality of the data and project processes is guaranteed through a centralized technological infrastructure and a strict biological sample traceability protocol. The pseudonymized database is hosted within the institutional cloud-based data infrastructure of the CEU Cardenal Herrera University (UCH-CEU). The system implements restricted access control through personal, non-transferable credentials, institutional authentication, and automated operation logging (activity logs). The laboratories responsible for biological processing (saliva and stool) operate under international quality certifications ISO 9001, ISO 15189, and ISO 27001. Additionally, the genetic bioinformatic analysis integrates an automated quality control system (QC-System) that filters and validates genetic variants based on international standards (call rate >98%, Hardy-Weinberg equilibrium, and minor allele frequency/MAF).
Data Checks:Automated and manual consistency checks are applied across the different analytical dimensions. In the bioinformatic layer, the AIG (Automated Intelligence Genetics) platform executes automatic duplication checks and filters artifacts during DNA sequencing. In the microbiome dimension, the bioinformatic workflow utilizes QIIME2® and DADA2 tools alongside positive controls (standard microbial communities) and negative controls (blank extractions) to identify and correct methodological biases or batch effects. For the biomechanical (LESS test) and clinical assessments (Lachmeter®), three valid trials are performed per participant, and the measurements are averaged, verifying internal consistency via the intraclass correlation coefficient (ICC), standard error of measurement (SEM), and smallest detectable change (SDC).
Source Data Verification (SDV):The study design requires cross-referencing records with external platforms to ensure the accuracy of longitudinal information. Daily physiological data (resting heart rate and nocturnal heart rate variability) are automatically synchronized from the WHOOP Strap 4.0 wearable devices to the internal monitoring platform of Villarreal CF. Furthermore, the primary variable-injury incidence-is verified through direct clinical confirmation by the Villarreal CF medical team following the club's standardized diagnostic criteria, which are prospectively contrasted against weekly exposure records filled out under technical staff supervision.
Data Dictionary:The protocol defines a set of multiscale variables coded under a unique alphanumeric code for each participant, omitting any directly identifiable information.
The variable dictionary explicitly includes:
Standard Operating Procedures (SOPs):The study operations are governed by sequential, standardized processes:
Sample Size Calculation:The calculation is based on the documented incidence of ACL injuries in Spanish First Division professional football (approximately 0.0364 injuries per 1,000 total exposure hours) and the significantly higher relative risk (2- to 8-fold higher) observed in female players. Under the assumptions of a 95% confidence level and 80% statistical power, it was estimated that at least 16 to 20 incident ACL cases are required to achieve adequate power for sex-based comparisons. Given that a professional team accumulates roughly 30,000 exposure hours per season, it was determined that this number of events can be achieved through the longitudinal follow-up of a cohort of approximately 200 to 250 athletes monitored continuously over a minimum of two to three consecutive seasons.
Statistical Analysis Plan:The integration of data into a learning matrix is structured across six successive phases combining classical statistics and machine learning:
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Time frame: From baseline up to 36 months, across 3 consecutive seasons.
Number of new acute ACL injuries confirmed clinically and radiologically by the medical staff, recorded alongside total training and match exposure hours to calculate injury rates. Unit of measure: absolute number.
Time frame: From enrollment to the beginning and end of each season, across 36 months.
Assessment of landing biomechanics and neuromuscular control using the instrumented Landing Error Scoring System (LESS). It is a total score (a numerical score ranging from 0 to 17). Unit of measure: absolute value.
Time frame: From enrollment to the beginning and end of each season, across 36 months
Peak vertical force, The maximum impact force recorded along the vertical axis during landing. (unit of measure: Newton)
Time frame: From enrollment to the beginning and end of each season, across 36 months
Time to peak force: The time interval that elapses from the exact moment of initial ground contact until the peak vertical force (described above) is reached. Unit of measurement: Milliseconds (ms) or Seconds (s).
Time frame: From enrollment to the beginning and end of each season, across 36 months
Loading Rate, ndicates how rapidly force is applied to the body during impact. Mathematically, it is the slope of the force-time curve. Unit of measurement: Newtons per second (N/s).
Time frame: From enrollment to the beginning and end of each season, across 36 months
Interlimb Asymmetries, Compares the difference in performance or loading between the dominant (or healthy) limb and the non-dominant (or injured) limb using data from the dual force plates. Unit of measurement: Percentage (%).
Time frame: From enrollment to the beginning and end of each season, across 36 months.
Measures an athlete's ability to quickly transition from an eccentric to a concentric contraction (the stretch-shortening cycle) during a plyometric jump (such as a Drop Jump). It is calculated by dividing jump height by contact time.Unit of measurement: it is derived from dividing meters by seconds (m/s).
Time frame: Baseline
Identification of genetic polymorphisms related to collagen structure, inflammatory response, oxidative stress, and mechanotransduction, analyzed from saliva samples using high-density genotyping technologies.
Time frame: From the beginning and end of each season, across 36 months.
Characterization of the gut microbiome using 16S rRNA sequencing (V3-V4 region), including alpha diversity, beta diversity, taxonomic composition, and identification of pro-inflammatory or tissue-recovery-modulating microbial profiles.
Time frame: From the beginning and end of each season, across 36 months.
Normalized muscle activation (%) and median frequency (Hz) of the rectus femoris during treadmill running
Time frame: From the beginning and end of each season, across 36 months.
Normalized muscle activation (%) and median frequency (Hz) of gluteus maximus during treadmill running.
Time frame: From the beginning and end of each season, across 36 months.
Normalized muscle activation (%) and median frequency (Hz) of biceps femoris during treadmill running
Time frame: From the beginning and end of each season, across 36 months.
Normalized muscle activation (%) and median frequency (Hz) of semitendinosus during treadmill running
Time frame: Daily continuous monitoring from baseline up to 36 months
Resting heart rate measures the number of times the heart beats per minute while the subject is completely at rest (in this case, during sleep). Unit of measurement: Beats per minute (bpm).
Time frame: Daily continuous monitoring from baseline up to 36 months
HRV measures the variation in time intervals between consecutive heartbeats (R-R intervals).
Contact information is provided by the study sponsor or research team.
Flavia Ms Costantino, Student
CONTACT
Javier Martínez Gramage, Professor
CONTACT
Cardenal Herrera University
Other
A Single-season Prospective Observational Cohort Study of the Villarreal CF Male Youth Academy"
Acronym: VILLAREALFC
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