Mobile Technology to Measure Knee Health in Osteoarthritis

Overview

Veterans face a high prevalence of knee osteoarthritis (OA), but current diagnostic methods often miss early stages when interventions are more effective. This project will evaluate smartphone-based motion capture via OpenCap to measure joint mechanics in knee OA patients during functional activities, comparing its performance to a conventional motion capture system, patient-reported symptoms, and knee joint structure. The findings will have the potential to enable clinicians to trial OpenCap in its current form, provide insights into tracking joint health, and guide refinements to advance toward earlier diagnosis of knee OA by complementing symptom assessments with measures of joint mechanics.

Significance to VA: Veterans, particularly the younger age group, have a higher prevalence of osteoarthritis (OA) than the general population. Among Veterans, OA most commonly affects the knee, a joint with a high injury rate in the US Military. Current diagnostic criteria for knee OA, which often rely on radiographic evidence, do not consistently identify younger patients or those in the early stages of OA, when interventions may be most effective. At the onset of OA symptoms, there is a critical window to quantify mechanical markers that could predict disease progression and provide insights beyond pain. While mechanical markers are predictive and capable of tracking OA progression, their clinical utility has been limited by conventional marker-based motion capture (Mocap), which requires specialized equipment, trained experts, and dedicated resources, making it inaccessible in many clinical settings. Innovation and Impact: A novel mobile technology, OpenCap, uses smartphone video-based motion capture to estimate movement mechanics, offering a low-cost and highly accessible alternative to traditional Mocap. OpenCap requires at a minimum of two smartphones and applies machine learning and musculoskeletal modeling to quantify mechanical markers. This technology has the potential to overcome significant barriers to implementing mechanical markers in clinical care. However, OpenCap has not yet been evaluated in knee OA patients, and its validity for quantifying mechanical markers during activities relevant to knee OA management remains underexplored. Therefore, this mentored career development award application has an objective to evaluate the utility of mobile technology OpenCap in quantifying mechanical markers that may provide insights into joint health in patients with early knee OA and to extract these markers from functional activities commonly used in knee OA management. Specific Aims: Aim 1 will evaluate the current potential use of the mobile technology OpenCap in patients with knee OA by testing the hypotheses that (1a) mechanical markers estimated by the mobile technology significantly differ but are associated with those measured using conventional Mocap and (1b) the mobile technology detects within-person, within-visit mechanical differences introduced by functional activity variations. Aim 2 will explore the broader use of the mobile technology OpenCap in patients with knee OA by (2a) associating mechanical markers estimated by the mobile technology with patient-reported outcomes (PROs), performance-based measures, and structural metrics and (2b) determining the test-retest reliability of the mechanical markers. Anticipated Research Outcomes: The project findings will have the potential to enable clinicians to trial the technology in its current form, leveraging its potential to quantify and document movement mechanics in patients at risk of or with knee OA. At the same time, the project's results will explore more advanced applications, such as tracking functional changes over time during OA treatment and contributing critical data to refine and further develop the technology. On the other hand, recalling an existing research cohort offers an invaluable opportunity for longitudinal follow-up. Anticipated Training Outcomes: This award will provide the applicant with training in musculoskeletal modeling, data science, and clinical and translational science, enabling the applicant to validate and refine mobile motion capture technologies. This training will prepare the applicant to integrate mobile technologies into clinical practice and support applicant's advancement to independence through next-level CDA award. Path to Translation/Implementation: This study will provide clinicians with practical insights on using OpenCap in its current form to quantify and document joint health. Findings will inform future refinements and support subsequent efforts to evaluate the feasibility of video-based motion capture via OpenCap in OA care. This project aligns with VA priorities by improving early diagnosis and management of knee OA to enhance care for Veterans.