Using Mobile Technology to Extract Mechanical Markers of Joint Health and Function in Early Knee Osteoarthritis
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 47
- 试验地点
- 1
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 30 Years 至 —(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Veteran and non-Veterans
- •males and females
- •diagnosed with early knee osteoarthritis
- •qualified for and participated in the Precision Assessment of Platelet Rich Plasma for Joint Preservation study (ClinicalTrials.gov ID: NCT03460236)
- •able and willing to provide informed consent for follow-up study
排除标准
- •symptomatic OA in joints other than the knee in the lower body
- •joint replacement
- •rheumatic disease
- •BMI > 35 kg/m^2
- •severe systematic disease defined as American Society of Anesthesiologists (ASA) 3 or above
- •pregnant or intending to become pregnant during the study
