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

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Injury of Anterior Cruciate Ligament

Intervention / Treatment
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* Other: aNMT Biofeedback
* Other: Sham Biofeedback
* Other: Neuromuscular Training


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: intend to participate on an organized competitive sports team (volleyball, soccer, or basketball) be physically able to participate in their sport and complete the testing procedures at the time of study enrollment Exclusion Criteria: none

Ages Eligible for Study
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Minimum Age: 12 Years
Maximum Age: 18 Years

Sexes Eligible for Study
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Female

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Prevention
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Double


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: aNMT Biofeedback<br>Participants randomized to receive a neuromuscular training intervention that incorporates biofeedback training. | Other: aNMT Biofeedback<br>* 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.<br>Other: Neuromuscular Training<br>* Participants will complete a 12-week, pre-season training program, over 6 weeks.<br>|
| Sham Comparator: Sham Biofeedback<br>Participants randomized to receive a neuromuscular training intervention with sham feedback training. | Other: Sham Biofeedback<br>* 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.<br>Other: Neuromuscular Training<br>* Participants will complete a 12-week, pre-season training program, over 6 weeks.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Change in Lateral Trunk Flexion | Lateral trunk flexion during the vertical drop task will be compared between study arms. Optimal lateral trunk flexion is 0°. | Baseline (pre-training testing), Week 6 (post-training testing) | 
| Change in Knee to Hip Sagittal Plane Moment Ratio | Knee to hip sagittal plane moment ratio during the vertical drop task will be compared between study arms. Optimal knee to hip sagittal plane ratio is < 1. | Baseline (pre-training testing), Week 6 (post-training testing) | 
| Change in Knee Abduction Moment | Knee abduction moment during the vertical drop task will be compared between study arms. Optimal knee abduction moment is ≤ 0 newton meter (Nm). | Baseline (pre-training testing), Week 6 (post-training testing) | 
| Change in Foot Placement | Foot placement during the vertical drop task will be compared between study arms. Optimal foot placement is 1:1 ratio to hip width. | Baseline (pre-training testing), Week 6 (post-training testing) | 
| Change in Vertical Ground Reaction Force (VGRF) Ratio | VGRF during the vertical drop task will be compared between study arms. Optimal VGRF ratio is 1:1 ratio between limbs. | Baseline (pre-training testing), Week 6 (post-training testing) | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Change in Post-training Lateral Trunk Flexion | Retention of effects of the intervention is assessed with lateral trunk flexion during the vertical drop task will be compared between study arms. Optimal lateral trunk flexion is 0°.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. | Week 6 (post-training testing), up to Month 11 (post-season testing) | 
| Change in Post-training Knee to Hip Sagittal Plane Moment Ratio | Retention of effects of the intervention is assessed with knee to hip sagittal plane moment ratio during the vertical drop task will be compared between study arms. Optimal knee to hip sagittal plane ratio is < 1. | Week 6 (post-training testing), up to Month 11 (post-season testing) | 
| Change in Post-training Knee Abduction Moment | Retention of effects of the intervention is assessed with knee abduction moment during the vertical drop task will be compared between study arms. Optimal knee abduction moment is ≤ 0 newton meter (Nm). | Week 6 (post-training testing), up to Month 11 (post-season testing) | 
| Change in Post-training Foot Placement | Retention of effects of the intervention is assessed with foot placement during the vertical drop task will be compared between study arms. Optimal foot placement is 1:1 ratio to hip width. | Week 6 (post-training testing), up to Month 11 (post-season testing) | 
| Change in Post-training Vertical Ground Reaction Force (VGRF) Ratio | Retention of effects of the intervention is assessed with VGRF during the vertical drop task will be compared between study arms. Optimal VGRF ratio is 1:1 ratio between limbs. | Week 6 (post-training testing), up to Month 11 (post-season testing) | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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ACL, biofeedback, injury prevention, virtual reality