Real-time Sensorimotor Feedback for Injury Prevention Assessed in Virtual Reality

Summary

Traumatic, debilitating anterior cruciate ligament (ACL) injuries occur at a 2 to 10-fold greater rate in female than male athletes. Consequently, there is a larger population of females that endure significant pain, functional limitations, and radiographic signs of knee osteoarthritis (OA) within 12 to 20 years following injury. To reduce the burden of OA, The National Public Health Agenda for Osteoarthritis recommends expanding and refining evidence-based prevention of ACL injury. Specialized training that targets modifiable risk factors shows statistical efficacy in high-risk athletes; however, clinically meaningful reduction of risk has not been achieved. A critical barrier that limits successful training outcomes is the requirement of qualified instructors to deliver personalized, intuitive, and accessible feedback to young athletes. Thus, a key gap in knowledge is how to efficiently deliver objective, effective feedback during training for injury prevention. The investigators long-term goal is to reduce ACL injuries and the subsequent sequela in young female athletes. The overall objective of this proposal is to implement and test innovative augmented neuromuscular training (aNMT) techniques to enhance sensorimotor learning and reduce biomechanical risk factors for ACL injury. The rationale that underlies this proposal is that, after completion, the investigators will be equipped to more effectively deliver biofeedback and decelerate the trend of increasing ACL injury rates in female athletes. This contribution will be significant for the reduction of the long-term sequel following ACL injury in young females.

Conditions

• Injury of Anterior Cruciate Ligament

Interventions

OTHER

aNMT Biofeedback

aNMT utilizes well-established visual feedback strategies to promote efficient, rapid and robust learning of complex movements. Athletes can discover how to move to create the desired feedback, even without explicit, conscious knowledge of how their movements relate to the visual pattern. aNMT biofeedback is created by calculating kinematic and kinetic data in real-time from the athlete's own movements. These values determine real-time transformations of the stimulus shape the athlete views via augmented-reality (AR) glasses during movement performance. The athlete's task is to move so as to create ("animate") a particular stimulus shape that corresponds to desired values of the biomechanical parameters targeted by the intervention.

OTHER

Sham Biofeedback

Sham biofeedback provides a similar phenomenological experience to aNMT biofeedback for athletes-both groups experience a shape that changes with their movements-but the sham biofeedback will not provide usable information to modify movement parameters during critical movement phases.

OTHER

Neuromuscular Training

Participants will complete a 12-session, pre-season training program, over 6 weeks.

Sponsors & Collaborators

• National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)

  collaborator NIH

• Emory University

  lead OTHER

Principal Investigators

• Gregory D Myer, PhD · Emory University

Study Design

Allocation

RANDOMIZED

Purpose

PREVENTION

Masking

DOUBLE

Model

PARALLEL

Eligibility

Min Age

12 Years

Max Age

18 Years

Sex

FEMALE

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2016-12-31

Primary Completion

2022-05-09

Completion

2022-05-30

Countries

• United States

Study Locations