Clinical Trial Details
— Status: Not yet recruiting
Administrative data
| NCT number |
NCT04068701 |
| Other study ID # |
2019-0769 |
| Secondary ID |
|
| Status |
Not yet recruiting |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
September 2021 |
| Est. completion date |
December 2026 |
Study information
| Verified date |
January 2021 |
| Source |
Children's Hospital Medical Center, Cincinnati |
| Contact |
Kim D Barber Foss, MS |
| Phone |
5136365971 |
| Email |
kim.foss[@]cchmc.org |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial 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.
Description:
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.
