Predicting second injuries after primary ACL reconstruction using clinically accessible videography

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.