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Using athlete information to predict the risk of injury using machine learning algorithms.

Predicting injury risk using machine learning in university level football players - NIL

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2026/01/102497
Enrollment
81
Registered
2026-01-30
Start date
Unknown
Completion date
Unknown
Last updated
2026-02-02

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

None listed

Interventions

Intervention1: Nil: Nil

Sponsors

nil
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: 1) University level football players 2) Players do not have any chronic health conditions

Exclusion criteria

Exclusion criteria: 1) History of any injury and surgery of spine and lower limbs in last 1 year. 2) Diagnosed Disc herniation and Radiculopathy. 3) Neurological and neuromuscular disorders. 4) Any Musculoskeletal problem in within last 1 month. 5) Patients with any systemic diseases.

Design outcomes

Primary

MeasureTime frame
PHYSICAL FITNESS, NEUROMUSCULAR CAPABILITY AND BIOMECHANICAL MEASURES Timepoint: 4 weeks—

Secondary

MeasureTime frame
PSYHOLOGICAL CONSTRUCTS Timepoint: 4 weeks;PARTICIPANTS PERSONAL DATA AND INDIVIDUAL CHARACTERSTICSTimepoint: 4 weeks—

Countries

India

Contacts

Public ContactTanu Agarwal

SGT University

piyush_sphy@sgtuniversity.org9971009811

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Feb 7, 2026