Real-time Sensorimotor Feedback for Injury Prevention Assessed in Virtual Reality
Condition: Injury of Anterior Cruciate Ligament
MeSH terms: Wounds and Injuries; Anterior Cruciate Ligament Injuries
Study type: Interventional

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.
    

Detailed description: 
      Augmented neuromuscular training (aNMT) integrates biomechanical screening with
      state-of-the-art augmented reality headsets to display real-time feedback that maps complex
      biomechanical variables onto simple visual feedback stimuli that athletes "control" via their
      own movements. The central hypothesis is that aNMT biofeedback will improve joint mechanics
      in evidence-based measures collected in realistic, sport-specific virtual reality scenarios.
      Specifically, the purpose of this investigation is to determine the efficacy of aNMT
      biofeedback to improve high-risk landing mechanics both in a laboratory task and during
      sport-specific scenarios. Based on the investigator's preliminary data, the investigators
      hypothesize that aNMT biofeedback will produce greater improvements in localized joint
      mechanics compared to neuromuscular training that incorporates sham feedback during the drop
      vertical jump (DVJ) task. In the secondary Aim, the investigators hypothesize aNMT will
      produce improved localized joint mechanics and global injury risk techniques during
      sport-specific maneuvers assessed in immersive virtual environments compared to the sham
      feedback. The expected outcomes will support increased efficiency and enhanced efficacy of
      feedback for personalized and targeted injury prevention training. The positive impact will
      be the improvement of injury risk mechanics and the potential to reduce injury on the field
      of play. A randomized, repeated-measures design will be used to test the two hypotheses for
      Aim 1: First, that aNMT will produce greater improvements in localized joint mechanics
      compared to the sham feedback group during the DVJ task; second, based on the preliminary
      data the investigators expect that innovative aNMT will lead to graduated joint improvements
      and reduced global injury risk mechanics that will exceed the overall task transferred
      reductions in high risk biomechanics following 12 real-time biofeedback training sessions.
      Previously described techniques will be used to measure biomechanical risk factors during a
      DVJ task performed at the beginning and end of the 6-week pre-competition training period.
      Athletes will be randomized into one of two groups: 1) aNMT biofeedback or (2) sham
      (augmented reality glasses with a stimulus that will provide exercise repetition count). Each
      athlete, as well as the statisticians, will be blinded to the intervention. All athletes will
      receive 12 training sessions over a 6-week period during their pre-competition season and
      each of the groups will have longitudinal assessment of biomechanical outcome measures
      captured at each biofeedback session. All participants will complete pre-training testing, 6
      weeks of intervention, post-training testing, and post-season testing.
    

Intervention name(s): aNMT Biofeedback; Sham Biofeedback; Neuromuscular Training
Intervention type(s): Other; Other; Other
Intervention description(s): 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.; 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.; Participants will complete a 12-week, pre-season training program, over 6 weeks.
Allocation: Randomized
Intervention model: Parallel Assignment
Masking: Double (Participant, Outcomes Assessor)
Primary purpose: Prevention
Keywords: ACL; biofeedback; injury prevention; virtual reality