The impact of Yoga on patients with knee osteoarthritis: A systematic review and meta-analysis of randomized controlled trials.

OBJECTIVE: The objective of this review is to conduct a comprehensive and systematic assessment of the efficacy of Yoga as an intervention for knee osteoarthritis (KOA). METHODS: We searched PubMed, Cochrane Library, Embase, Web of Science, and PEDro as of January 3, 2024. Retrieved a total of 200 articles. Standardised mean differences (SMDs) and 95% confidence intervals (CI) were calculated. RESULTS: The study included a total of 8 trials and involved 756 KOA patients. The results indicated that compared to the control group, Yoga exercise showed significant improvements in alleviating pain (SMD = -0.92; 95% CI = -1.64 ~ - 0.20; P = 0.01, I2 = 94%), stiffness (SMD = -0.51; 95% CI = -0.91 ~ -0.12; P = 0.01; I2 = 66%) and physical function (SMD = -0.53; 95% CI = -0.89 ~ -0.17; P = 0.004; I2 = 59%) among KOA patients. However, there was no significant improvement observed in terms of activities of activity of daily living (ADL) (SMD = 1.03; 95% CI = -0.01 ~ 2.07; P = 0.05; I2 = 84%), and quality of life (QOL) (SMD = 0.21; 95% CI = -0.33 ~ 0.74; P = 0.44; I2 = 83%) with the practice of Yoga. CONCLUSIONS: In general, Yoga has been found to be effective in reducing pain and stiffness in KOA patients, it can also improve the physical function of patients. However, there is limited evidence to suggest significant improvements in terms of ADL and QOL.

Upper limb motor assessment for stroke with force, muscle activation and interhemispheric balance indices based on sEMG and fNIRS.

Introduction: Upper limb rehabilitation assessment plays a pivotal role in the recovery process of stroke patients. The current clinical assessment tools often rely on subjective judgments of healthcare professionals. Some existing research studies have utilized physiological signals for quantitative assessments. However, most studies used single index to assess the motor functions of upper limb. The fusion of surface electromyography (sEMG) and functional near-infrared spectroscopy (fNIRS) presents an innovative approach, offering simultaneous insights into the central and peripheral nervous systems. Methods: We concurrently collected sEMG signals and brain hemodynamic signals during bilateral elbow flexion in 15 stroke patients with subacute and chronic stages and 15 healthy control subjects. The sEMG signals were analyzed to obtain muscle synergy based indexes including synergy stability index (SSI), closeness of individual vector (CV) and closeness of time profile (CT). The fNIRS signals were calculated to extract laterality index (LI). Results: The primary findings were that CV, SSI and LI in posterior motor cortex (PMC) and primary motor cortex (M1) on the affected hemisphere of stroke patients were significantly lower than those in the control group (p < 0.05). Moreover, CV, SSI and LI in PMC were also significantly different between affected and unaffected upper limb movements (p < 0.05). Furthermore, a linear regression model was used to predict the value of the Fugl-Meyer score of upper limb (FMul) (R2 = 0.860, p < 0.001). Discussion: This study established a linear regression model using force, CV, and LI features to predict FMul scale values, which suggests that the combination of force, sEMG and fNIRS hold promise as a novel method for assessing stroke rehabilitation.