Background: Fibromyalgia (FM) is a chronic condition characterized by widespread muscular or musculoskeletal pain of at least 3 months' duration, occurring above and below the waist, on both sides of the body.
Objective: The aim of this study was to evaluate the effectiveness of a rehabilitation program based on motor imagery versus a conventional exercise program in FM in terms of pain, functional and psychological outcomes.
Methods: Twenty-nine female subjects were randomly assigned to a group receiving motor imagery-based rehabilitation (MIG) or to a control group (CG) performing conventional rehabilitation. Outcome assessments were performed before (T0) and after 10 sessions of treatment (T1) and at a 12-week follow-up (T2). Pain, function and psychological measurements were conducted by means of different questionnaires.
Results: Both treatments improved all outcomes at post-treatment (T1) and follow-up (T2). The MIG showed a significant improvement in anxiety disorder associated with FM with respect to the CG, as well as improvements in coping strategies.
Conclusions: Rehabilitation treatment based on motor imagery showed a stronger effect on anxiety and coping behavior than traditional physiotherapy in patients with FM. Integrated psychological support would be desirable in this setting. Further research is needed to explore the aspects investigated in more depth.
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http://dx.doi.org/10.3233/BMR-200328 | DOI Listing |
Sci Rep
November 2024
Department of Rehabilitation Medicine, Tong Ren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
The utilization of motor imagery-based brain-computer interfaces (MI-BCI) has been shown to assist stroke patients activate motor regions in the brain. In particular, the brain regions activated by unilateral upper limb multi-task are more extensive, which is more beneficial for rehabilitation, but it also increases the difficulty of decoding. In this paper, self-attention convolutional neural network based partial prior transfer learning (SACNN-PPTL) is proposed to improve the classification performance of patients' MI multi-task.
View Article and Find Full Text PDFCogn Neurodyn
June 2024
Department of Electronic Information Engineering, School of Information Engineering, Nanchang University, Nanchang, 330031 Jiangxi China.
The number of electrode channels in a brain-computer interface (BCI) affects not only its classification performance, but also its convenience in practical applications. Despite many studies on channel selection in motor imagery (MI)-based BCI systems, they consist in matrix analysis of EEG signals, which inevitably loses the interactive information among multiple domains such as space, time and frequency. In this paper, a tensor decomposition-based channel selection (TCS) method is employed for MI BCIs.
View Article and Find Full Text PDFComput Methods Biomech Biomed Engin
October 2024
School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, China.
IEEE J Biomed Health Inform
October 2024
Eur J Phys Rehabil Med
December 2024
Department of Rehabilitation Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China -
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