Position-Specific Lower Limb Performance and AI-Based Injury Risk Analysis in Young Male Basketball Players

NCT07677371 | Completed

• 1 other identifier: IGU-SINTECH-BASKET-AI-2026-01
• Study Type: observational
• Target: 81
• Locations: 1 country
• Sites: 1

Brief Summary

This observational study aims to evaluate lower extremity performance characteristics in young male basketball players according to their playing positions and to explore injury risk patterns using artificial intelligence (AI)-based analysis. Basketball requires frequent jumping, sprinting, rapid changes of direction, acceleration, deceleration, and single-leg movements, all of which place significant demands on lower extremity function. Participants will undergo a single assessment session including anthropometric measurements and performance tests such as countermovement jump, reactive strength testing, single-leg performance tests, and agility assessments. Information regarding previous lower extremity injuries, training history, and playing position will also be collected. The study will compare performance characteristics among different basketball positions, including guards, forwards, and centers. In addition, AI and machine learning techniques will be used to analyze the collected performance data and identify patterns associated with injury risk. The purpose of the AI analysis is not to diagnose injuries but to investigate whether combinations of performance variables can help identify athletes who may demonstrate higher-risk movement or performance profiles. The findings may contribute to the development of position-specific training strategies, individualized performance monitoring, and evidence-based injury prevention approaches in youth basketball players.

Conditions

Basketball Performance; Sports Injury Risk; Lower Extremity Performance; Athletic Performance; Injury Prevention; Youth Basketball Players

Outcome Measures

Primary Outcomes (3)

• Countermovement Jump Height

  Vertical jump height measured during the countermovement jump (CMJ) test using an artificial intelligence-based performance assessment system.

  Baseline assessment (single session)

• Reactive Strength Index (RSI)

  Reactive strength performance calculated from jump height and ground contact time during reactive jump testing.

  Baseline assessment (single session)

• AI-Based Injury Risk Classification Score

  Injury risk profile generated using artificial intelligence and machine learning algorithms based on lower extremity performance variables, asymmetry measures, agility performance, and injury history.

  Baseline assessment (single session)

Secondary Outcomes (6)

• Single-Leg Hop Performance

  Baseline assessment (single session)

• Time to Complete the T-Test Agility Assessment (seconds)

  Baseline assessment (single session)

• Limb Symmetry Index Calculated from Single-Leg Countermovement Jump Height (%)

  Baseline assessment (single session)

• Previous Lower Extremity Injury History

  At study enrollment

• Countermovement Jump Height Measured by the D-Wall Athlete Performance System (cm)

  Baseline

Study Arms (4)

Point Guard Players

Young male basketball players whose primary playing position is point guard. Participants will undergo lower extremity performance assessments and AI-based injury risk analysis.

Diagnostic Test: Lower Extremity Performance Assessment

Shooting Guard / Small Forward Players

Young male basketball players whose primary playing position is shooting guard or small forward. Participants will undergo lower extremity performance assessments and AI-based injury risk analysi

Diagnostic Test: Lower Extremity Performance Assessment

Power Forward Players

Young male basketball players whose primary playing position is power forward. Participants will undergo lower extremity performance assessments and AI-based injury risk analysis.

Diagnostic Test: Lower Extremity Performance Assessment

Center Players

Young male basketball players whose primary playing position is center. Participants will undergo lower extremity performance assessments and AI-based injury risk analysis.

Diagnostic Test: Lower Extremity Performance Assessment

Interventions

Lower Extremity Performance Assessment DIAGNOSTIC_TEST

Participants will complete lower extremity performance tests including countermovement jump, reactive strength index testing, single-leg performance tests, and agility/change-of-direction assessments. Performance data will be analyzed using artificial intelligence-based methods to explore injury risk patterns.

Center Players; Point Guard Players; Power Forward Players; Shooting Guard / Small Forward Players

Eligibility Criteria

• Age: 12 Years - 18 Years
• Sex: male
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

The study population consists of youth male basketball players competing at the developmental level. Participants are recruited from basketball clubs and youth training programs and are classified according to their primary playing position, including point guard, shooting guard/small forward, power forward, and center.

You may qualify if:

• Male basketball players aged 12 to 18 years.
• Registered youth basketball athletes.
• Participation in regular basketball training for at least 1 year.
• Participation in basketball training at least 3 times per week.
• No acute lower extremity injury affecting performance testing within the previous 6 months.
• Ability to understand and follow testing instructions.
• Written informed consent provided by a parent or legal guardian and assent provided by the participant.

You may not qualify if:

• Lower extremity surgery within the previous 6 months.
• Acute musculoskeletal injury affecting test performance.
• Neurological, vestibular, or severe orthopedic conditions affecting balance, coordination, or motor performance.
• Pain, illness, fatigue, or any health condition that could affect performance on the assessment day.
• Cardiovascular or systemic conditions preventing completion of the testing protocol.
• Incomplete performance testing data or technically invalid measurements.

Sponsors & Collaborators

• Istanbul Gelisim University: lead

Study Sites (1)

Istanbul Gelisim University

Istanbul, 34290, Turkey (Türkiye)

Location

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 18, 2026
• First Posted: June 30, 2026
• Study Start: May 15, 2026
• Primary Completion: May 22, 2026
• Study Completion: May 22, 2026
• Last Updated: June 30, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)