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Gooch HJ, Jarvis KA, Stockley RC. Behavior Change Approaches in Digital Technology-Based Physical Rehabilitation Interventions Following Stroke: Scoping Review. J Med Internet Res 2024; 26:e48725. [PMID: 38656777 PMCID: PMC11079774 DOI: 10.2196/48725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 11/14/2023] [Accepted: 12/26/2023] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND Digital health technologies (DHTs) are increasingly used in physical stroke rehabilitation to support individuals in successfully engaging with the frequent, intensive, and lengthy activities required to optimize recovery. Despite this, little is known about behavior change within these interventions. OBJECTIVE This scoping review aimed to identify if and how behavior change approaches (ie, theories, models, frameworks, and techniques to influence behavior) are incorporated within physical stroke rehabilitation interventions that include a DHT. METHODS Databases (Embase, MEDLINE, PsycINFO, CINAHL, Cochrane Library, and AMED) were searched using keywords relating to behavior change, DHT, physical rehabilitation, and stroke. The results were independently screened by 2 reviewers. Sources were included if they reported a completed primary research study in which a behavior change approach could be identified within a physical stroke rehabilitation intervention that included a DHT. Data, including the study design, DHT used, and behavior change approaches, were charted. Specific behavior change techniques were coded to the behavior change technique taxonomy version 1 (BCTTv1). RESULTS From a total of 1973 identified sources, 103 (5%) studies were included for data charting. The most common reason for exclusion at full-text screening was the absence of an explicit approach to behavior change (165/245, 67%). Almost half (45/103, 44%) of the included studies were described as pilot or feasibility studies. Virtual reality was the most frequently identified DHT type (58/103, 56%), and almost two-thirds (65/103, 63%) of studies focused on upper limb rehabilitation. Only a limited number of studies (18/103, 17%) included a theory, model, or framework for behavior change. The most frequently used BCTTv1 clusters were feedback and monitoring (88/103, 85%), reward and threat (56/103, 54%), goals and planning (33/103, 32%), and shaping knowledge (33/103, 32%). Relationships between feedback and monitoring and reward and threat were identified using a relationship map, with prominent use of both of these clusters in interventions that included virtual reality. CONCLUSIONS Despite an assumption that DHTs can promote engagement in rehabilitation, this scoping review demonstrates that very few studies of physical stroke rehabilitation that include a DHT overtly used any form of behavior change approach. From those studies that did consider behavior change, most did not report a robust underpinning theory. Future development and research need to explicitly articulate how including DHTs within an intervention may support the behavior change required for optimal engagement in physical rehabilitation following stroke, as well as establish their effectiveness. This understanding is likely to support the realization of the transformative potential of DHTs in stroke rehabilitation.
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Affiliation(s)
- Helen J Gooch
- Stroke Research Team, School of Nursing and Midwifery, University of Central Lancashire, Preston, United Kingdom
| | - Kathryn A Jarvis
- Stroke Research Team, School of Nursing and Midwifery, University of Central Lancashire, Preston, United Kingdom
| | - Rachel C Stockley
- Stroke Research Team, School of Nursing and Midwifery, University of Central Lancashire, Preston, United Kingdom
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Wan X, Zhang Y, Liu T, Li D, Yu H, Wen D. Exercise therapy of mild cognitive impairment: EEG could enhance efficiency. Front Aging Neurosci 2024; 16:1373273. [PMID: 38659707 PMCID: PMC11039927 DOI: 10.3389/fnagi.2024.1373273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 03/04/2024] [Indexed: 04/26/2024] Open
Affiliation(s)
- Xianglong Wan
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
- Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems, Ministry of Education, Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
| | - Yifan Zhang
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
| | - Tiange Liu
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
- Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems, Ministry of Education, Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
| | - Danyang Li
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
- Department of Sports, University of Science and Technology Beijing, Beijing, China
| | - Hao Yu
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
- Department of Sports, University of Science and Technology Beijing, Beijing, China
| | - Dong Wen
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
- Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems, Ministry of Education, Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
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Hao Z, Zhai X, Peng B, Cheng D, Zhang Y, Pan Y, Dou W. CAMBA framework: Unveiling the brain asymmetry alterations and longitudinal changes after stroke using resting-state EEG. Neuroimage 2023; 282:120405. [PMID: 37820859 DOI: 10.1016/j.neuroimage.2023.120405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 09/19/2023] [Accepted: 10/08/2023] [Indexed: 10/13/2023] Open
Abstract
Hemispheric asymmetry or lateralization is a fundamental principle of brain organization. However, it is poorly understood to what extent the brain asymmetries across different levels of functional organizations are evident in health or altered in brain diseases. Here, we propose a framework that integrates three degrees of brain interactions (isolated nodes, node-node, and edge-edge) into a unified analysis pipeline to capture the sliding window-based asymmetry dynamics at both the node and hemisphere levels. We apply this framework to resting-state EEG in healthy and stroke populations and investigate the stroke-induced abnormal alterations in brain asymmetries and longitudinal asymmetry changes during poststroke rehabilitation. We observe that the mean asymmetry in patients was abnormally enhanced across different frequency bands and levels of brain interactions, with these abnormal patterns strongly associated with the side of the stroke lesion. Compared to healthy controls, patients displayed significant alterations in asymmetry fluctuations, disrupting and reconfiguring the balance of inter-hemispheric integration and segregation. Additionally, analyses reveal that specific abnormal asymmetry metrics in patients tend to move towards those observed in healthy controls after short-term brain-computer interface rehabilitation. Furthermore, preliminary evidence suggests that baseline clinical and asymmetry features can predict poststroke improvements in the Fugl-Meyer assessment of the lower extremity (mean absolute error of about 2). Overall, these findings advance our understanding of hemispheric asymmetry. Our framework offers new insights into the mechanisms underlying brain alterations and recovery after a brain lesion, may help identify prognostic biomarkers, and can be easily extended to different functional modalities.
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Affiliation(s)
- Zexuan Hao
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Xiaoxue Zhai
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China
| | - Bo Peng
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China
| | - Dandan Cheng
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China
| | - Yanlin Zhang
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China
| | - Yu Pan
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China.
| | - Weibei Dou
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China.
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Ma ZZ, Wu JJ, Hua XY, Zheng MX, Xing XX, Ma J, Shan CL, Xu JG. Evidence of neuroplasticity with brain-computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis. Front Neurol 2023; 14:1135466. [PMID: 37346164 PMCID: PMC10281191 DOI: 10.3389/fneur.2023.1135466] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 05/03/2023] [Indexed: 06/23/2023] Open
Abstract
Background Brain-computer interface (BCI) has been widely used for functional recovery after stroke. Understanding the brain mechanisms following BCI intervention to optimize BCI strategies is crucial for the benefit of stroke patients. Methods Forty-six patients with upper limb motor dysfunction after stroke were recruited and randomly divided into the control group or the BCI group. The primary outcome was measured by the assessment of Fugl-Meyer Assessment of Upper Extremity (FMA-UE). Meanwhile, we performed resting-state functional magnetic resonance imaging (rs-fMRI) in all patients, followed by independent component analysis (ICA) to identify functionally connected brain networks. Finally, we assessed the topological efficiency of both groups using graph-theoretic analysis in these brain subnetworks. Results The FMA-UE score of the BCI group was significantly higher than that of the control group after treatment (p = 0.035). From the network topology analysis, we first identified seven subnetworks from the rs-fMRI data. In the following analysis of subnetwork properties, small-world properties including γ (p = 0.035) and σ (p = 0.031) within the visual network (VN) decreased in the BCI group. For the analysis of the dorsal attention network (DAN), significant differences were found in assortativity (p = 0.045) between the groups. Additionally, the improvement in FMA-UE was positively correlated with the assortativity of the dorsal attention network (R = 0.498, p = 0.011). Conclusion Brain-computer interface can promote the recovery of upper limbs after stroke by regulating VN and DAN. The correlation trend of weak intensity proves that functional recovery in stroke patients is likely to be related to the brain's visuospatial processing ability, which can be used to optimize BCI strategies. Clinical Trial Registration The trial is registered in the Chinese Clinical Trial Registry, number ChiCTR2000034848. Registered 21 July 2020.
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Affiliation(s)
- Zhen-Zhen Ma
- Department of Rehabilitation Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
| | - Jia-Jia Wu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xu-Yun Hua
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Trauma and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Mou-Xiong Zheng
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Trauma and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiang-Xin Xing
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jie Ma
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chun-Lei Shan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Li Y, Li C, Zhang T, Wu L, Lin X, Li Y, Wang L, Yang H, Lu D, Miao D, Fang P. Questionnaires based on natural language processing elicit immersive ruminative thinking in ruminators: Evidence from behavioral responses and EEG data. Front Neurosci 2023; 17:1118650. [PMID: 36950128 PMCID: PMC10025410 DOI: 10.3389/fnins.2023.1118650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/06/2023] [Indexed: 03/08/2023] Open
Abstract
Rumination is closely related to mental disorders and can thus be used as a marker of their presence or a predictor of their development. The presence of masking and fabrication in psychological selection can lead to inaccurate detection of psychological disorders. Human language is considered crucial in eliciting specific conscious activities, and the use of natural language processing (NLP) in the development of questionnaires for psychological tests has the potential to elicit immersive ruminative thinking, leading to changes in neural activity. Electroencephalography (EEG) is commonly used to detect and record neural activity in the human brain and is sensitive to changes in brain activity. In this study, we used NLP to develop a questionnaire to induce ruminative thinking and then recorded the EEG signals in response to the questionnaire. The behavioral results revealed that ruminators exhibited higher arousal rates and longer reaction times, specifically in response to the ruminative items of the questionnaire. The EEG results showed no significant difference between the ruminators and the control group during the resting state; however, a significant alteration in the coherence of the entire brain of the ruminators existed while they were answering the ruminative items. No differences were found in the control participants while answering the two items. These behavioral and EEG results indicate that the questionnaire elicited immersive ruminative thinking, specifically in the ruminators. Therefore, the questionnaire designed using NLP is capable of eliciting ruminative thinking in ruminators, offering a promising approach for the early detection of mental disorders in psychological selection.
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Affiliation(s)
- Yulong Li
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Chenxi Li
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Tian Zhang
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Lin Wu
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Xinxin Lin
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Yijun Li
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Lingling Wang
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Huilin Yang
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Diyan Lu
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Danmin Miao
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
- Key Laboratory of Military Medical Psychology and Stress Support of PLA, Xi'an, China
- *Correspondence: Danmin Miao
| | - Peng Fang
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
- Key Laboratory of Military Medical Psychology and Stress Support of PLA, Xi'an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
- Peng Fang
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Cao L, Wang W, Huang C, Xu Z, Wang H, Jia J, Chen S, Dong Y, Fan C, de Albuquerque VHC. An Effective Fusing Approach by Combining Connectivity Network Pattern and Temporal-Spatial Analysis for EEG-Based BCI Rehabilitation. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2264-2274. [PMID: 35969547 DOI: 10.1109/tnsre.2022.3198434] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Motor-modality-based brain computer interface (BCI) could promote the neural rehabilitation for stroke patients. Temporal-spatial analysis was commonly used for pattern recognition in this task. This paper introduced a novel connectivity network analysis for EEG-based feature selection. The network features of connectivity pattern not only captured the spatial activities responding to motor task, but also mined the interactive pattern among these cerebral regions. Furthermore, the effective combination between temporal-spatial analysis and network analysis was evaluated for improving the performance of BCI classification (81.7%). And the results demonstrated that it could raise the classification accuracies for most of patients (6 of 7 patients). This proposed method was meaningful for developing the effective BCI training program for stroke rehabilitation.
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