Purpose: Telerehabilitation, as an effective means of treatment, is not inferior to traditional rehabilitation, and solves the problem of many patients who do not have access to hospital-based training due to costs and distance. So far, the knowledge structure of the global use of telerehabilitation has not been formed. This study aimed to demonstrate the state of emerging trends and frontiers concerning the studies of telerehabilitation through bibliometric software.Methods: Literature about telerehabilitation from 2000 to 2021 was retrieved from the Web of Science Core Collection. We used CiteSpace 5.8.R3 to analyze the publication years, journals/cited journals, countries, institutions, authors/cited authors, references, and keywords. Based on the analysis results, we plotted the co-citation map to more intuitively observe the research hotspots and knowledge structure.Results: A total of 1,986 records were obtained. The number of annual publications gradually increased over the investigated period. The largest increase occurred between 2019 and 2020. J TELEMED TELECARE was the most prolific and the most cited journal. The United States was the most influential country, with the highest number of publications and centrality. The University of Queensland was the most productive institution. The author Tousignant M ranked the highest in the number of publications and Russell TG ranked the first in the cited authors. Respectively, the articles published by Cottrell MA and Russell TG ranked the first in the frequency and centrality of cited references. The four hot topics in telerehabilitation were “care”,“stroke”, “telemedicine” and “exercise”. The keyword “stroke” showed the strongest citation burst. The two frontier keywords were “physical therapy” and “participation”. The keywords were clustered to form 21 labels.Conclusion: This study uses visualization software CiteSpace to provide the current status and trends in clinical research of telerehabilitation over the past 20 years, which may help researchers identify new perspectives concerning potential collaborators and cooperative institutions, hot topics, and research frontiers in the research field. Bibliometric analysis of telerehabilitation supplements and improves the knowledge field of telemedicine from the concept of rehabilitation medicine and provides new insights into therapists during the COVID-19 pandemic.

The aim of this study is to evaluate if Kinect is a valid and reliable clinical gait analysis tool for children with cerebral palsy (CP), and whether linear regression and long short-term memory (LSTM) recurrent neural network methods can improve its performance. A gait analysis was conducted on ten children with CP, on two occasions. Lower limb joint kinematics computed from the Kinect and a traditional marker-based Motion Analysis system were investigated by calculating the root mean square errors (RMSE), the coefficients of multiple correlation (CMC), and the intra-class correlation coefficients (ICC_2,k). Results showed that the Kinect-based kinematics had an overall modest to poor correlation (CMC—less than 0.001 to 0.70) and an angle pattern similarity with Motion Analysis. After the calibration, RMSE on every degree of freedom decreased. The two calibration methods indicated similar levels of improvement in hip sagittal (CMC—0.81 ± 0.10 vs. 0.75 ± 0.22)/frontal (CMC—0.41 ± 0.35 vs. 0.42 ± 0.37) and knee sagittal kinematics (CMC—0.85±0.07 vs. 0.87 ± 0.12). The hip sagittal (CMC—0.97±0.05) and knee sagittal (CMC—0.88 ± 0.12) angle patterns showed a very good agreement over two days. Modest to excellent reliability (ICC_2,k—0.45 to 0.93) for most parameters renders it feasible for observing ongoing changes in gait kinematics.

Objective: Stand-to-sit task is an important daily function, but there is a lack of research evidence on whether knee osteoarthritis (knee OA) affects the postural balance during the task. This study aimed to compare individuals with knee OA and asymptomatic controls in postural balance and identify kinematic and lower extremity muscle activity characteristics in individuals with knee OA during the stand-to-sit task.Methods: In total, 30 individuals with knee OA and 30 age-matched asymptomatic controls performed the 30-s Chair Stand Test (30sCST) at self-selected speeds. Motion analysis data and surface electromyography (sEMG) were collected while participants performed the 30sCST. To quantify postural balance, the displacement of the center of mass (CoM) and the peak instantaneous velocity of the CoM were calculated. The kinematic data included forward lean angles of the trunk and pelvic, range of motion (RoM) of the hip, knee, and ankle joints in the sagittal plane. The averaged activation levels of gluteus maximus, vastus lateralis, vastus medialis, rectus femoris, biceps femoris (BF), tibialis anterior (TA), and medial head of gastrocnemius muscles were indicated by the normalized root mean square amplitudes.Results: Compared with the asymptomatic control group, the knee OA group prolonged the duration of the stand-to-sit task, demonstrated significantly larger CoM displacement and peak instantaneous CoM velocity in the anterior-posterior direction, reduced ankle dorsiflexion RoM, greater anterior pelvic tilt RoM, and lower quadriceps femoris and muscles activation level coupled with higher BF muscle activation level during the stand-to-sit task.Conclusion: This study indicates that individuals with knee OA adopt greater pelvic forward lean RoM and higher BF muscle activation level during the stand-to-sit task. However, these individuals exist greater CoM excursion in the anterior-posterior direction and take more time to complete the task. This daily functional activity should be added to the rehabilitation goals for individuals with knee OA. The knee OA group performs reduced ankle dorsiflexion RoM, quadriceps femoris, and TA activation deficit. In the future, the rehabilitation programs targeting these impairments could be beneficial for restoring the functional transfer in individuals with knee OA.

Children with cerebral palsy (CP) have high risks of falling. It is necessary to evaluate gait stability for children with CP. In comparison to traditional motion capture techniques, the Kinect has the potential to be utilised as a cost-effective gait stability assessment tool, ensuring frequent and uninterrupted gait monitoring. To evaluate the validity and reliability of this measurement, in this study, ten children with CP performed two testing sessions, of which gait data were recorded by a Kinect V2 sensor and a referential Motion Analysis system. The margin of stability (MOS) and gait spatiotemporal metrics were examined. For the spatiotemporal parameters, intraclass correlation coefficient (ICC_2,k) values were from 0.83 to 0.99 between two devices and from 0.78 to 0.88 between two testing sessions. For the MOS outcomes, ICC_2,k values ranged from 0.42 to 0.99 between two devices and 0.28 to 0.69 between two test sessions. The Kinect V2 was able to provide valid and reliable spatiotemporal gait parameters, and it could also offer accurate outcome measures for the minimum MOS. The reliability of the Kinect V2 when assessing time-specific MOS variables was limited. The Kinect V2 shows the potential to be used as a cost-effective tool for CP gait stability assessment.

Objectives: This meta-analysis aims to synthesize the available data on the effectiveness of hamstring stretching exercises in relieving pain intensity and improving function for patients with low back pain. Methods: Google Scholars, PubMed, Embase, Cochrane, MEDLINE, CNKI, Wanfang, and VIP were searched from inception to August 2023. We included randomized controlled trials that investigated the effectiveness of hamstring stretching exercises in patients with low back pain. The primary outcomes assessed were pain intensity, hamstring muscle flexibility, and function. Study selection, data extraction, and assessment of risk of bias were performed independently by two reviewers. Results: Our searches retrieved 344 trials, of which 14 met the inclusion criteria for this review ( n = 735 participants). The combined meta-analysis showed hamstring stretching resulted in lower pain scores (standardized mean difference = −0.72, 95% confidence interval: −1.35 to −0.09; I² = 89%, p = 0.03) in different categories of low back pain. Subgroup analysis showed that hamstring stretching led to a larger range of motion for cases of back pain with radiating pain (standardized mean difference = 2.39, 95% confidence interval: 1.76 to 3.02; I² = 0%, p < 0.001). The combined meta-analysis revealed that hamstring stretching resulted in lower Oswestry Disability Index scores in comparison to regular treatment, particularly in individuals suffering from low back pain across all subtypes (mean difference = −6.97, 95% confidence interval: −13.34 to −0.60; I² = 95%, p = 0.03). Conclusions: This meta-analysis demonstrates the effectiveness of hamstring stretching exercises in reducing pain intensity in various categories of low back pain and improving the straight leg raise in patients experiencing back pain with radiating pain. Additionally, it highlights the improvement in function for patients with back pain across all subtypes.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Early identification of knee osteoarthritis (OA) can improve treatment outcomes and reduce medical costs. However, there are major limitations amongst existing classification or prediction models including abstract data processing and complicated dataset attributes, which hinder their applications in clinical practice. Objective: Develop a Bayesian network (BN)-based classification model to classify people with knee OA. The proposed model can be treated as a prescreening tool which can provide decision support for health professionals. Methods: The proposed model’s structure is based on a three-level BN structure, and then retrained by the Bayesian Search (BS) learning algorithm. The model’s parameters are determined by the Expectation-Maximization (EM) algorithm. The utilized dataset includes backgrounds (the basic characteristics of subjects), the target disease (namely the knee OA), and predictors (the scores of physical fitness tests). The performance of the model is evaluated based on classification accuracy, area under a curve (AUC), specificity, sensitivity, positive predictive value (PPV) and negative predictive value (NPV), and is also compared with other well-known classification models. A test is also performed to explore whether physical fitness tests could improve the performance of the proposed model. Results: 249 elderly people between 60 and 80 years of age, living in the Kongjiang community (shanghai) were recruited from April to September 2007. A total of 157 instances are adopted as the dataset after data preprocessing. The experimental results show that the proposed model’s results are higher than, or equal to, the mean scores of the other classification models: 0.754 for accuracy, 0.78 for AUC, 0.78 for specificity and 0.73 for sensitivity. The proposed model can provide 0.45 for PPV and 0.92 for NPV at the prevalence of 20%. The proposed model also shows a significant improvement when compared to the traditional BN model: 6.35% increase in accuracy (from 0.709 to 0.754), 4.00% increase in AUC (from 0.75 to 0.78), 6.85% increase in specificity (from 0.73 to 0.78), 5.80% increase in sensitivity (from 0.69 to 0.73), 15.38% increase in PPV (from 0.39 to 0.45) and 2.22% increase in NPV (from 0.90 to 0.92). Furthermore, the test results show that the performance of the proposed model could be largely enhanced through physical fitness tests in three evaluation indexes: 10.56% increase in accuracy (from 0.682 to 0.754), 16.42% increase in AUC (from 0.67 to 0.78) and 30.00% increase in specificity (from 0.60 to 0.78). Conclusions: The proposed model presents a promising method to classify people with knee OA when compared to other classification models and the traditional BN model. The proposed model could be implemented in clinical practice as a prescreening tool for knee OA, which could, not only improve the quality of healthcare for elderly people, but also reduce overall medical costs.