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Objective: To investigate the feasibility of peripheral sensory nerve stimulation combined with task-oriented training in patients with stroke during inpatient rehabilitation.
Design: A pilot randomized crossover trial.
Setting: Two rehabilitation hospitals.
Subjects: Twenty-two patients with subacute stroke.
Interventions: Participants were randomly assigned to two groups and underwent two weeks of training in addition to conventional inpatient rehabilitation. The immediate group underwent peripheral sensory nerve stimulation combined with task-oriented training in the first week, followed by another week with task-oriented training alone. The delayed group underwent the same training in reverse order.
Main Measures: Outcome measures were the level of fatigue and Wolf Motor Function Test. Patients were assessed at baseline, one and two weeks.
Results: All participants completed the study with no adverse events. There was no significant difference in level of fatigue between each treatment. From baseline to one week, the immediate group showed larger improvements than the delayed groups in the Wolf Motor Function Test (decrease in mean time (± SD) from 41.9 ± 16.2 seconds to 30.6 ± 11.4 seconds versus from 46.8 ± 19.4 seconds to 42.9 ± 14.7 seconds, respectively) but the difference did not reach significance after Bonferroni correction (P = 0.041). Within-group comparison showed significant improvements in the Wolf Motor Function Test mean time after the peripheral sensory nerve stimulation combined with task-oriented training periods in each group (P < 0.01).
Conclusion: Peripheral sensory nerve stimulation is feasible in clinical settings and may enhance the effects of task-oriented training in patients with subacute stroke.
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http://dx.doi.org/10.1177/0269215512441476 | DOI Listing |
Front Neurol
August 2025
Department of Rehabilitation Medicine, Shaoguan First People's Hospital, Shaoguan, China.
Backgrounds: In clinical practice, many patients cannot undergo inpatient rehabilitation in hospitals for extended periods due to personal financial constraints, as well as China's health insurance policy. They are often forced to terminate their rehabilitation training during the prime recovery phase. This makes tele-rehabilitation-based, home-based rehabilitation particularly important.
View Article and Find Full Text PDFBMC Health Serv Res
August 2025
Department of Public Health Science, Graduate School of Public Health, Seoul National University, Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea.
Background: Patient-centered care (PCC) has become a global standard for improving communication and health outcomes. However, in time-pressured clinical settings, especially in high-volume outpatient systems such as South Korea’s, the implementation of PCC remains challenging. While consultation time is often cited as a key barrier, few studies have examined how actual communication patterns relate to consultation duration using observational methods.
View Article and Find Full Text PDFCan Geriatr J
September 2025
Sunnybrook Health Sciences Centre, Department of Geriatric Medicine, Toronto, ON.
Background: Competency-based medical education (CBME) aims to enhance the quality of medical training by providing timely, actionable feedback through entrustable professional activities (EPAs). However, variability in feedback quality remains a concern across residency programs.
Methods: We conducted a retrospective analysis of EPA feedback forms from a geriatric medicine program, comparing two distinct time periods: 2019-2020 and 2021-2022.
Entropy (Basel)
July 2025
Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA.
This paper investigates point-to-point multimodal digital semantic communications in a task-oriented setup, where messages are classified at the receiver. We employ a pre-trained transformer model to extract semantic information and propose three methods for generating semantic codewords. First, we propose semantic quantization that uses quantized embeddings of source realizations as a codebook.
View Article and Find Full Text PDFEntropy (Basel)
July 2025
Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin 541004, China.
This paper studies deep reinforcement learning (DRL)-based joint resource allocation and three-dimensional (3D) trajectory optimization for unmanned aerial vehicle (UAV)-ground access point (GAP) cooperative non-orthogonal multiple access (NOMA) communication in Industrial Internet of Things (IIoT) systems. Cooperative and non-cooperative users adopt different signal transmission strategies to meet diverse, task-oriented, quality-of-service requirements. Specifically, the DRL framework based on the Soft Actor-Critic algorithm is proposed to jointly optimize user scheduling, power allocation, and UAV trajectory in continuous action spaces.
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