The Effect of Yoga Asana "Vrksasana (Tree Pose)" on Balance in Patients With Postmenopausal Osteoporosis: A Randomized Controlled Trial

The effectiveness of virtual reality exercise games on balance functions and fear of falling in women with osteoporosis

To investigate and compare the effectiveness of Nintendo Wii games and home exercises on balance functions in patients with osteoporosis, an important disease adversely affecting balance functions. The patients included in the study were randomized into two groups the Wii exercise group (n = 30) and the home exercise group (n = 30). Wii exercise group performed balance exercises with a Nintendo Wii device and balance board three times a week for 12 weeks under the supervision of a physiotherapist in the hospital, and home exercise group was prescribed home exercises three days a week for 12 weeks. Balance functions were evaluated with the timed up-and-go-test and Berg Balance Scale, and the fall risk was evaluated with the Falls Efficacy Scale at the beginning and end of 12 weeks of treatment. Comparison of pre- and post-treatment timed up-and-go-test, Berg Balance Scale, and Falls Efficacy Scale results in both groups revealed statistically significant improvements (p = 0.001; p < 0.05). Furthermore, post-treatment test scores between the two groups demonstrated a significant enhancement in Wii exercise group regarding the Berg Balance Scale score (Mean ± SD 52.9 ± 3.63) (p = 0.001; p < 0.05). Within the osteoporotic population, balance functions serve as robust predictors of fall risk. Improvement in balance functions is crucial for the prevention of falls and subsequent osteoporotic fractures. In our study, we found that balance exercises performed with Wii games are effective in improving balance functions in patients with osteoporosis.

Effects of Mind-Body Exercises for Osteoporosis in Older Adults: A Systematic Review and Meta-analysis of Randomized Controlled Trials

Introduction

Osteoporosis is a major cause of fractures and even life-threatening fractures in the elderly. Mind-body exercise is a beneficial intervention to improve flexibility, control body balance and reduce pain. We aimed to evaluate the effects of physical and mental exercise on osteoporosis in the elderly.

Methods

Randomized controlled trials (RCTs) focusing on mind-body exercises for osteoporosis were included. Web of Science, PubMed, Science Direct, Medline, Cochrane Library, China National Knowledge Infrastructure (CNKI), and Wanfang were searched from inception to January 2023. Outcomes included bone mineral density (BMD), bone mineral content (BMC), body balance (BB), pain, indicators of bone metabolism (BMI), lower extremity function, fearing level, and quality of life (QOL). The quality of study reporting was rated by 2 reviewers independently, and Review Manager software (version 5.3) was used for meta-analysis.

Results

Thirty-nine trials with 2325 participants were included. The pooled results showed that mind-body exercises have encouraging effect on elderly people with osteoporosis, especially in aspects of BMD, BMC, QOL, improving the function of lower extremity, reducing pain and fearing level. While, dance and eight-section brocade could not improve the quality of life,or dance and eight-section brocade have no effect on BMD.

Conclusions

Mind-body exercises may have potential efficacy for osteoporosis in the elderly. However, due to the poor methodological quality of the included trials, more clinical trials with precise methodological design and rigorous reporting are needed.

Superior Gait Symmetry and Postural Stability among Yoga Instructors-Inertial Measurement Unit-Based Evaluation

This study investigates gait symmetry and single-leg stance balance of professional yoga instructors versus age-matched typically developed controls using inertial measurement unit (IMU)-based evaluation. We recruited twenty-five yoga instructors and twenty-five healthy control subjects to conduct the walking experiments and single-leg stance tests. Kinematic data were measured by attaching IMUs to the lower limbs and trunk. We assessed the asymmetry of swing phases during the normal-walk and tandem-walk tests with eyes open and closed, respectively. The subjects subsequently conducted four single-leg stance tests, including a single-leg stance on both legs with eyes open and closed. Two balance indexes regarding the angular velocities of the waist and chest were defined to assess postural stability. The gait asymmetry indexes of yoga instructors were significantly lower than those of the typically developed controls. Similarly, the yoga instructors had better body balance in all four single-leg stance tests. This study's findings suggest that yoga improves gait asymmetry and balance ability in healthy adults. In the future, further intervention studies could be conducted to confirm the effect of yoga training.

Neural Network-Oriented Big Data Model for Yoga Movement Recognition

The use of computer vision for target detection and recognition has been an interesting and challenging area of research for the past three decades. Professional athletes and sports enthusiasts in general can be trained with appropriate systems for corrective training and assistive training. Such a need has motivated researchers to combine artificial intelligence with the field of sports to conduct research. In this paper, we propose a Mask Region-Convolutional Neural Network (MR-CNN)- based method for yoga movement recognition based on the image task of yoga movement recognition. The improved MR-CNN model is based on the framework and structure of the region-convolutional network, which proposes a certain number of candidate regions for the image by feature extraction and classifies them, then outputs these regions as detected bounding boxes, and does mask prediction for the candidate regions using segmentation branches. The improved MR-CNN model uses an improved deep residual network as the backbone network for feature extraction, bilinear interpolation of the extracted candidate regions using Region of Interest (RoI) Align, followed by target classification and detection, and segmentation of the image using the segmentation branch. The model improves the convolution part in the segmentation branch by replacing the original standard convolution with a depth-separable convolution to improve the network efficiency. Experimentally constructed polygon-labeled datasets are simulated using the algorithm. The deepening of the network and the use of depth-separable network improve the accuracy of detection while maintaining the reliability of the network and validate the effectiveness of the improved MR-CNN.