Sensor-Based Monitoring of Knee Osteoarthritis Symptoms in Free-Living Settings: A Scoping Review

Knee osteoarthritis (knee OA) is a heterogeneous condition characterized by chronic pain, stiffness, and fatigue that fluctuate rapidly over time. Traditional clinical assessments provide only static diagnoses of disease severity, failing to capture the dynamic, day-to-day symptom variability that impacts patient quality of life. While wearable technologies offer the potential for continuous, high-frequency monitoring, previous reviews have examined general technological interventions for knee OA management, yet they lack a specific synthesis of technologies for symptom monitoring.

This scoping review aims to synthesize current research on sensor technologies used for the continuous monitoring of knee OA symptoms in free-living or simulated daily environments. Specifically, the review seeks to: (1) map sensor modalities to specific symptom domains (biomechanical, physiological, and behavioral); (2) evaluate the alignment between objective sensor metrics and patient-reported outcome measures (PROMs); and (3) identify gaps in current monitoring paradigms.

A systematic literature search was conducted across PubMed, Embase, Web of Science, and IEEE Xplore. The review followed the PRISMA-ScR guidelines. Eligibility criteria included studies involving participants with knee OA, utilizing wearable or portable sensors capable of continuous monitoring (e.g., Inertial measurement units(IMUs), electrocardiography (ECG), and assessing clinical symptoms (e.g., pain, fatigue, stiffness). Studies relying solely on stationary laboratory equipment (e.g., force plates) without a portable component were excluded to ensure relevance to real-world applicability. Data were extracted regarding sensor types, sampling frequencies, monitored symptoms, and the statistical association between objective features and subjective symptom severity (key findings).

A total of 16 studies met the inclusion criteria. The summary constructed from the results revealed a distinct technological saturation: the majority of studies (n=6) utilized IMUs to quantify biomechanical deficits (e.g., gait asymmetry, range of motion), which showed robust correlations with functional limitations. In contrast, there was a notable scarcity of research utilizing physiological sensors (e.g., electrocardiography, bioimpedance) to monitor systemic symptoms. Crucially, findings highlighted a significant discrepancy between subjective and objective data, particularly in sleep monitoring, where poor self-reported sleep quality predicted pain exacerbations despite stable objective actigraphy metrics. Furthermore, most systems operated as passive data loggers, with a lack of integration into active feedback loops.

Unlike previous reviews focused solely on biomechanics, this study innovatively maps the use of sensors across a multidimensional symptom spectrum, revealing a critical gap in the monitoring of fatigue and physiological stress. The findings suggest that current sensor applications are limited by a lack of integration with subjective patient experiences. Real-world implementation requires a hybrid monitoring paradigm that combines the ecological validity of wearable sensors with the clinical relevance of patient-reported outcomes. This approach paves the way for Digital Phenotyping and active feedback systems, offering a personalized strategy for managing the complex symptom burden of knee OA.