Predicting second injuries after primary ACL reconstruction using clinically accessible videography
Project Number5R01AR083709-02
Contact PI/Project LeaderHART, JOSEPH M
Awardee OrganizationUNIV OF NORTH CAROLINA CHAPEL HILL
Description
Abstract Text
The current study proposal is a mechanistic ancillary grant application that will leverage the infrastructure of an
actively enrolling, NIH-funded, multi-site R01 research project (1 R01 AR078396-01A1). This proposal is
time-sensitive because the parent R01 is currently recruiting and enrolling patients at UNC-Chapel Hill and
Virginia Tech and if delayed beyond the proposed start date, the resulting sample size loss will negatively
impact the power of our expanded and more comprehensive prognostic models. The parent R01 is actively
recruiting patients with first-time (primary) ACL reconstructions (ACLR) to participate in a single visit to collect
clinical data, patient reported outcomes, muscle strength and kinetic loading data using in-shoe wearable
sensors. This session is scheduled at the time when patients are released from medical care by their
physician to return to unrestricted physical activity. After the data collection session, patients are followed for
18 months via monthly electronic surveys to determine engagement in physical activity, perceived function,
and occurrence of a second ACL injury. The parent R01 grant submission did originally not include motion
capture due to high cost, time-burden to research participants and lack of access of the equipment required to
collect kinematic data in a clinical setting. Since the parent R01 was awarded, an opensource markerless
motion capture technology became available, presenting a unique opportunity to capture lower body
kinematics using clinically accessible methods. The current ancillary study proposal will benefit the parent
R01 tremendously through the addition of kinematic data in a clinical setting, which was not possible when
the parent grant was submitted, and at a much lower cost and shorter time-line than submitting a separate
grant application. In this ancillary proposal, we will utilize markerless videography while participants enrolled
in the parent R01 perform jump-landing and hopping procedures. We will record and calculate joint kinematics
from the ankle, knee, and hip in the sagittal and frontal planes, using two iPads in positioned within the testing
area and processed using an NIH-supported open-source data capture technology (OpenCap.ai). The resulting
movement data will be analyzed using advanced multi-joint approaches to derive kinematic features that will
enable our research team to develop predictive models for second ACL injuries using lower body kinematics
and joint coordination. The kinematic data will be combined with the existing kinetic-loading data collected from
wearable in-shoe sensors (Parent R01) to develop a comprehensive mechanistic prognostic model for second
ACL injury risk after primary ACLR. This highly innovative proposal will advance the understanding of
mechanisms of risk for second ACL injuries through inclusion of multi-joint movement coordination during
unilateral and bilateral landing tasks. The ability to detect subtle movement coordination features in a natural
and unrestricted environment will empower clinicians and scientists to track progress, identify risk and optimize
outcomes over the course of rehabilitation using clinically accessible and open-source technologies.
Public Health Relevance Statement
Narrative
An unacceptably high risk for second ACL injuries exists for young and physically active
individuals who suffer a first-time anterior cruciate ligament (ACL) injury and opt for surgical
reconstruction to restore stability in preparation to return to pre-injury levels of activity. The
current ancillary study aims to develop a predictive model for underlying kinematic mechanisms
for this increased risk of re-injury by using easily accessible, open-source, markerless video
capture technology combined with innovative data processing techniques to derive movement
coordination features during single-leg jump maneuvers. The findings of this study will provide
an evidence-based approach for personalized & precision care for patients recovering from ACL
reconstruction surgeries that leverages current data analytics and technology to better inform
healthcare decisions and to promote health and well-being through safe engagement in physical
activity despite prior injury and surgery.
National Institute of Arthritis and Musculoskeletal and Skin Diseases
CFDA Code
846
DUNS Number
608195277
UEI
D3LHU66KBLD5
Project Start Date
21-February-2024
Project End Date
31-January-2028
Budget Start Date
01-February-2025
Budget End Date
31-January-2026
Project Funding Information for 2025
Total Funding
$353,507
Direct Costs
$286,242
Indirect Costs
$67,265
Year
Funding IC
FY Total Cost by IC
2025
National Institute of Arthritis and Musculoskeletal and Skin Diseases
$353,507
Year
Funding IC
FY Total Cost by IC
Sub Projects
No Sub Projects information available for 5R01AR083709-02
Publications
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Outcomes
The Project Outcomes shown here are displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed are those of the PI and do not necessarily reflect the views of the National Institutes of Health. NIH has not endorsed the content below.
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History
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