Injury Risk Prediction in Recreational Cyclists (INJU-23)

Basic Info

Department(s)

Sports Science, Data Science

Applicant Role

—

Primary Banner

Joint

Project Types

Master Student Project

Legal Signature Required

Yes

Line Manager Approval Required

Yes

Supervisor Approval Required

Yes

Classification

Funding Type

Internal

Strategic Alignments

—

Research Pillars

—

Expected Contributions

—

Summary

Abstract: This project investigates Injury Risk Prediction in Recreational Cyclists, using a structured data-collection protocol to characterize patterns relevant to injury prevention and performance optimization over a defined study period.

Objectives: Characterize Injury Risk Prediction patterns in Recreational Cyclists.; Identify actionable indicators for practitioners.

Methodology: Observational study collecting Injury Risk Prediction-related data across multiple sessions, analyzed with standard statistical methods.

Expected Outputs: Publications

Keywords: injury risk prediction; recreational cyclists

Impact, Risk & Ethics

Scientific impact: Contributes new data on Injury Risk Prediction not previously characterized in Recreational Cyclists.

Societal impact: Supports safer and more effective practice for Recreational Cyclists.

Student involvement: 1 × MSc

Ethics approval required: Yes

Lab & Equipment Bookings

• IMUs — qty 1
• Space: Biomechanics Lab — 2 sessions/week
• Support: Clinician — General support

Compliance & Governance

Data Management Plan Required

Yes

GDPR Considerations

Yes

Open Science Plan S Compliance

No

IP Considerations

No