Background: The concept of virtual reality (VR)-based rehabilitation therapy for treating people with low back pain is of growing research interest. However, the effectiveness of such therapy for pain reduction in clinical settings remains controversial. Methods: The present study was conducted according to the reporting guidelines presented in the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. We searched the PubMed, Embase, CENTRAL, and ProQuest databases for both published and unpublished papers. The Cochrane risk of bias tool (version 2) was used to evaluate the quality of the selected studies. GRADEprofiler software (version 3.6.4) was used to evaluate the level of evidence. We analyzed the included research results using RevMan software (version 5.4.1). Results: We included a total of 11 articles in the systematic review and meta-analysis, with a total of 1761 subjects. Having assessed the quality of these studies, the risk of bias was generally low with high heterogeneity. The results revealed a small to medium effect (standardized mean difference = ±0.37, 95% confidence interval: 0.75 to 0) based on evidence of moderate overall quality. Conclusion: There is evidence that treatment using VR improves patients’ pain. The effect size was small to medium, with the studies presenting evidence of moderate overall quality. VR-based treatment can reduce pain; therefore, it may help in rehabilitation therapy.

Digital therapeutics (DTx) are emerging as a transformative innovation in healthcare offering evidence-based digital interventions for the treatment, management, and prevention of various diseases and disorders. In Korea, DTx have gained significant attention as potential solutions to the increasing burden of chronic diseases and mental health conditions. However, the Korean DTx market faces several challenges that hinder its widespread adoption and integration into the national healthcare system. This study provides a comprehensive analysis of the current state of the DTx market in Korea, identifies the key challenges impeding its growth, and proposes strategies for overcoming these obstacles. This study utilized a literature review and market analysis approach to examine the latest research, industry reports, and regulatory documents related to DTx. The analysis focused on three primary areas: (1) the current regulatory landscape, (2) technological advancements and challenges, and (3) economic and commercial factors influencing DTx adoption in Korea. A comparative analysis of global regulatory practices was also conducted to identify best practices. The findings revealed that while Korea has made significant strides in supporting DTx development, the market remains in its early stages. The key challenges include underdeveloped regulatory frameworks, issues with data quality and security, and a lack of established reimbursement pathways. We recommend developing tailored regulatory frameworks for DTx, enhancing policy support for small and medium-sized enterprises involved in DTx development, and increasing investments in technological infrastructure. By addressing these challenges, Korea could position itself as a leader in the global DTx market, delivering innovative and effective treatments to enhance patient care and outcomes.

The purpose of this study was to classify ULTT videos through transfer learning with pre-trained deep learning models and compare the performance of the models. We conducted transfer learning by combining a pre-trained convolution neural network (CNN) model into a Python-produced deep learning process. Videos were processed on YouTube and 103,116 frames converted from video clips were analyzed. In the modeling implementation, the process of importing the required modules, performing the necessary data preprocessing for training, defining the model, compiling, model creation, and model fit were applied in sequence. Comparative models were Xception, InceptionV3, DenseNet201, NASNetMobile, DenseNet121, VGG16, VGG19, and ResNet101, and fine tuning was performed. They were trained in a high-performance computing environment, and validation and loss were measured as comparative indicators of performance. Relatively low validation loss and high validation accuracy were obtained from Xception, InceptionV3, and DenseNet201 models, which is evaluated as an excellent model compared with other models. On the other hand, from VGG16, VGG19, and ResNet101, relatively high validation loss and low validation accuracy were obtained compared with other models. There was a narrow range of difference between the validation accuracy and the validation loss of the Xception, InceptionV3, and DensNet201 models. This study suggests that training applied with transfer learning can classify ULTT videos, and that there is a difference in performance between models.

Background: Physical inactivity remains a major public health challenge, particularly for underserved populations lacking exercise facility access. AI-powered smartphone applications with real-time human pose estimation offer scalable solutions, but they lack rigorous clinical validation. Objective: This study validates the clinical efficacy of a 16-week on-device AI-driven resistance training program using MediaPipe pose estimation technology in young adults with limited facility access. Primary outcomes included muscular strength (1RM squat), body composition, functional movement (FMS), and cardiorespiratory fitness (VO₂max). Methods: A single-group pre–post study enrolled 216 participants (mean age 23.77 ± 4.02 years; 69.2% male), with 146 (67.6%) completing the protocol. Participants performed three 30 min weekly sessions of seven compound exercises delivered via a smartphone app providing real-time pose analysis (97.2% key point accuracy, 28.6 ms inference), multimodal feedback, and personalized progression using self-selected equipment. Results: Significant improvements across all domains: muscular strength (+4.39 kg 1RM squat, p < 0.001, d = 1.148), body fat (−2.92%, p < 0.001, d = −1.373), skeletal muscle mass (+2.19 kg, p < 0.001, d = 1.433), FMS (+0.29 points, p = 0.001, d = 0.285), and VO₂max (+1.82 mL/kg/min, p < 0.001, d = 0.917). Pose classification accuracy reached 95.8% vs. physiotherapist assessment (ICC = 0.94). Conclusions: This study provides the first clinical evidence that on-device AI pose estimation enables facility-independent resistance training with outcomes comparable to traditional programs. Unlike cloud-based systems, our lightweight model (28.6 ms inference) supports real-time mobile deployment, advancing accessible precision exercise medicine. Limitations include a single-arm design and gender imbalance, warranting future RCTs with diverse cohorts.

While the physical conditions of stroke patients are diverse, the joystick-type steering controller of the electric-powered wheelchair (EPW) is almost the same, making the user uncomfortable and not fully utilizing the function of the wheelchair. The purpose of this study was to investigate the effects of the EPW steering controller, specifically the so-called joystick type (3DSC; 3D-printed steering controller, conventional steering controllers; CSC), on surface electromyography (sEMG), Wheelchair Skills Test 4.2 (WST), and QUEST 2.0. The participants were 23 hemiplegic stroke patients (14 males and 9 females) (range 40–65 years) recruited from multi-center process. The 3DSC manufacturing process used a scanner (Precision Laser Probe SLP-500) and a modelling program (SOLIDWORKS 2015). The CSC users’ muscle activities were generally higher than those of the 3DSC users in both males and females (p < 0.05). WST total performance score of CSC is statistically significantly lower than those of 3DSC for both males (3DSC = 49.28 ± 2.19; CSC = 42.85 ± 4.31) (z = −3.935; p < 0.05) and females (3DSC = 48.17 ± 0.44; CSC = 41.11 ± 0.78) (z = −1.910; p < 0.05). QUEST 2.0 scores in CSC (male = 2.40 ± 0.70; female = 2.11 ± 0.78) were significantly lower than those of 3DSC (male = 3.50 ± 0.85; female = 2.90 ± 0.51) in effectiveness categories (p < 0.05). We suggest that 3DSC contributes to reducing the user’s muscle activities and raising the scores of WST performance and QUEST.

There was an error in the funding statement of the original publication