real-time sensorimotor feedback for injury prevention in males assessed in virtual reality

Conditions: ACL Injury

Summary: even though females are 2- to 10-times more likely to suffer an anterior cruciate ligament (acl) injury, males represent the largest population of total acl injuries. consequently, there is a larger population of males that endure significant pain, functional limitations, and radiographic signs of knee osteoarthritis (oa) within 12 to 20 years of 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 investiagator's long-term goal is to reduce acl injuries and the subsequent sequela in young male athletes.

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.

Eligibility criteria:
inclusion criteria: actively participate on the soccer, voleyball, or basketball team exclusion criteria: do not participate on the soccer, voleyball, or basketball team

Keywords: acl biofeedback injury prevention virtual reality