Osteoarthritis (OA) poses a growing challenge for the aging population, especially in the hip and knee joints, contributing significantly to disability and societal costs. Exploring the integration of wearable technology, this study addresses the limitations of traditional rehabilitation assessments in capturing real-world experiences and dynamic variations. Specifically, it focuses on continuously monitoring physical activity in hip and knee OA patients using automated unsupervised evaluations within the rehabilitation process. We analyzed data from 1144 patients who used a mobile health application after surgery; the activity data were collected using the Garmin Vivofit 4. Several parameters, such as the total number of steps per day, the peak 6-minute consecutive cadence (P6MC) and peak 1-minute cadence (P1M), were computed and analyzed on a daily basis. The results indicated that cadence-based measurements can effectively, and earlier, differ among patients with hip and knee conditions, as well as in the recovery process. Comparisons based on recovery status and type of surgery reveal distinctive trajectories, emphasizing the effectiveness of P6MC and P1M in detecting variations earlier than total steps per day. Furthermore, cadence-based measurements showed a lower inter-day variability (40%) compared to the total number of steps per day (80%). Automated assessments, including P1M and P6MC, offer nuanced insights into the patients’ dynamic activity profiles.

Background/Objective: Knee osteoarthritis (OA) is a common and debilitating condition affecting older adults, often progressing to advanced stages and requiring total joint replacement. Exercise therapy is widely recognized as the first-line approach for the prevention and initial management of OA. This systematic review assessed the effectiveness of home-based exercises (HBEs) compared to supervised exercises in alleviating pain and reducing disability among patients with knee OA. Methods: A systematic search of PubMed, Cochrane Library, and ScienceDirect identified randomized controlled trials (RCTs) published between January 2001 and October 2024. Methodological quality was evaluated using the Physiotherapy Evidence Database (PEDro) scale, and a meta-analysis was conducted to quantify the efficacy of these interventions. Results: Ten RCTs involving 917 patients were included, ranging in moderate to high methodological quality (PEDro score: 6.3 ± 1.2). Intervention durations ranged from 4 to 12 weeks. Both supervised and HBEs were found to be effective, but supervised exercises demonstrated statistically significant improvements in pain (SMD = −0.45 [95% CI −0.79; −0.11], p = 0.015) and disability (SMD = −0.28 [95% CI −0.42; −0.14], p < 0.001) compared to HBEs. Conclusions: Despite the superiority of supervised exercises over HBEs, considering the cost-effectiveness and ease of implementation of HBEs, we developed recommendations to create a hybrid rehabilitation program that combines both approaches to maximize clinical outcomes.

Osteoarthritis (OA) is a highly prevalent musculoskeletal disorder and a major cause of disability, posing growing challenges for healthcare systems worldwide. Conventional supervised clinical assessments provide valuable insights but are largely limited to cross-sectional snapshots and often fail to reflect the variability of real-world functioning, physical activity patterns, and symptom fluctuations experienced by individuals with OA, especially those with knee OA. This perspective introduces a multisensor digital phenotyping framework for smart knee OA assessment, integrating supervised laboratory evaluations with unsupervised continuous monitoring in daily living environments using wearable sensors, smart insoles, activity trackers, and mobile devices. Feasibility was tested in 40 participants (20 knee OA patients, 20 controls). Raw data from questionnaires, electronic goniometry, dynamometry, force plate, connected insoles, and seven-day home monitoring were harmonized via a standardized pipeline aligned with the ICF framework. The pipeline employed anomaly detection, missing data imputation, z-score normalization, and cloud-based storage. This framework is envisioned to facilitate advanced data integration and machine-learning-ready analytics, enabling longitudinal monitoring, pattern recognition, and individualized health profiling. By conceptually bridging cross-sectional and continuous sensing modalities, this approach has the potential to enhance ecological validity, support earlier identification of functional decline, and inform data-driven clinical decision-making. Key methodological, technological, and ethical challenges—including data quality, interpretability, privacy, digital literacy, and clinical adoption—are also highlighted. Overall, this paper underscores the promise of AI-enabled multisensor digital phenotyping to advance smart, personalized, and precision healthcare for individuals with knee OA.

Osteoarthritis (OA) poses a growing challenge for the aging population, especially in hip and knee joints, contributing significantly to disability and societal costs. Conventional clinical evaluations, typically performed in controlled settings, face difficulties in capturing the dynamic characteristics of rehabilitation and functioning in real-life scenarios. The study assessed the use of automated unsupervised evaluations in the rehabilitation process for patients after hip and knee replacements. We analyzed data from 1144 individuals who used a digital application after surgery, activity data were collected using the Garmin Vivofit 4. Several parameters such as the Peak 6-Minute Consecutive Cadence (P6MC) and Peak 1-Minute Cadence (P1M) were computed and analyzed on a daily basis. The results indicated that cadence measurements, specifically, can effectively differ among individuals with hip and knee conditions as well as the recovery process. Moreover, these metrics demonstrated a high level of responsiveness in monitoring the progress of recovery, surpassing the effectiveness of simply measuring the total number of steps taken. The study highlighted the capacity of technology-driven, patient-centered rehabilitation to improve accuracy, tailor therapy to individual patients, and increased patient involvement. This research also highlighted the significance of automated, unsupervised assessments in enhancing rehabilitation results for OA patients following hip and knee replacement, as tele-rehabilitation becomes increasingly important.

Background Hip and knee osteoarthritis are among the most prevalent and disabling conditions affecting mainly older adults, with a risk of undergoing a total hip or knee replacement for the end stage. Increasing recommendations of conservative treatments have been reported as the first-line strategy for the initial management of this condition. Objective This review sought to investigate the effects of supervised exercises versus non-pharmacological conservative therapies to reduce pain and disability levels on patients with hip or knee osteoarthritis. Methods Three databases (PubMed, Cochrane Library, and ScienceDirect) were systematically searched for randomized-controlled trials published between 01-01-2001 and 31-10-2022. PEDro scale was used to assess the methodological quality of the included studies. The PRISMA guidelines were applied for this review. Results Twelve randomized-controlled trials of moderate-to-high quality were included in this review. The studies involved 1,049 participants with a mean age (SD) of 64 (6) years old. The duration of the intervention and follow-up varied from 2 weeks to 16 months. Supervised exercises for hip or knee osteoarthritis were significantly less effective in terms of pain reduction (SMD=-0.40 [95%CI 0.16, 0.65], p=0.001) compared to home-based exercises (active musculoskeletal therapies), but not in terms of disability reduction (SMD=-0.04[95%CI −0.43, 0.36], p=0.86). There was a non-significant difference of supervised exercises compared to passive musculoskeletal therapies in terms of disability (SMD=0.21[95%CI −0.09, 0.50], p=0.17), or pain reduction (SMD=-0.19; [95%CI −0.57, 0.19], p=0.33). Conclusion Supervised exercises were found to be less effective in reducing pain, but not in disability reduction when compared to home-based exercises. Systematic review registration Prospero CRD42021271912