Posts Tagged Protocols

[Abstract + References] Complex network changes during a virtual reality rehabilitation protocol following stroke: a case study

Abstract

Stroke is one of the main causes of disabilities caused by injuries to the human central nervous system, yielding a wide range of mild to severe impairments that can compromise sensorimotor and cognitive functions. Although rehabilitation protocols may improve function of stroke survivors, patients often reach plateaus while undergoing therapy. Recently, virtual reality (VR) technologies have been paired with traditional rehabilitation aiming to improve function recovery after stroke. Aiming to better understand structural brain changes due to VR rehabilitation protocols, we modeled the brain as a graph and extracted three measures representing the network’s topology: degree, clustering coefficient and betweenness centrality (BC). In this single case study, our results indicate that all metrics increased on the ipsilesional hemisphere, while remaining about the same at the contrale-sional site. Particularly, the number of functional connections increased in the lesion area overtime. In addition, the BC displayed the highest variations, and in brain regions related to the patient’s cognitive and motor impairments; hence, we argue that this measure could be regarded as an indicative for brain plasticity mechanisms.
1. J-H. Shin , H. Ryu & S. H. Jang . A task-specific interactive game-based virtual reality rehabilitation system for patients with stroke: a usability test and two clinical experiments. Journal of NeuroEngineering and Rehabilitation. 2014: 11-32

2. M. S. Cameirão , S. B. i Badia , E. D. Oller & P. F. M. J. Verschure . Neurorehabilitation using the virtual reality based Rehabilitation Gaming System: methodology, design, psychometrics, usability and validation. Journal of NeuroEngineering and Rehabilitation. 2010: 7-48

3. R. M. Yerkes & J. D. Dodson . The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology. 1908. 18: 459-482

4. E. J. Calabrese . Converging concepts: Adaptive response, preconditioning, and the YerkesDodson Law are manifestations of hormesis. Ageing Research Reviews. 2008: 7(1), 820.

5. Page S. J. , Fulk G. D. , Boyne P. Clinically Important Differences for the Upper-Extremity Fugl-Meyer Scale in People With Minimal to Moderate Impairment Due to Chronic Stroke. Physical Therapy 92(6): 791798, 2012. doi: 10.2522/ptj.20110009

6. Ogawa S , Lee TM , Kay AR , Tank DW . Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A. 1990; 87(24):9868-72. doi: 10.1073/pnas.87.24.9868

7. NK. Logothetis , J. Pauls , M. Augath , T. Trinath , A. Oeltermann . Neurophysiological investigation of the basis of the fMRI signal. Nature. 2001. 412(6843):150-7

8. M.D. Fox , M. E. Raichle . Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci. 2007. 8(9):700-11.

9. de Campos, B. M. , Coan, A. C. , Lin Yasuda, C. , Casseb, R. F. and Cendes, F. (2016), Large-scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy. Hum. Brain Mapp. doi: 10.1002/hbm.23231

10. J. D. Power , A. L. Cohen , S. M. Nelson , G. S. Wig , K. A. Barnes , J. A. Church , A. C. Vogel , T. O. Laumann , F. M. Miezin , B. L. Schlagger , S. E. Petersen . Functional network organization of the human brain. Neuron. 2011: 72(4): 665 – 678.

11. Rubinov M. and Sporns O. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 2010, 52(3): 1059-1069. doi: 10.1016/j.neuroimage.2009.10.003

12. M. E. J. Newman . A measure of betweenness centrality based on random walks. Soc. Netw. 2005. 27: 39 – 57.

 

via Complex network changes during a virtual reality rehabilitation protocol following stroke: a case study – IEEE Conference Publication

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[Abstract] The SonicHand Protocol for Rehabilitation of Hand Motor Function: a validation and feasibility study

Abstract

Musical sonification therapy is a new technique that can reinforce conventional rehabilitation treatments by increasing therapy intensity and engagement through challenging and motivating exercises. Aim of this study is to evaluate the feasibility and validity of the SonicHand protocol, a new training and assessment method for the rehabilitation of hand function. The study was conducted in 15 healthy individuals and 15 stroke patients. The feasibility of implementation of the training protocol was tested in stroke patients only, who practiced a series of exercises concurrently to music sequences produced by specific movements. The assessment protocol evaluated hand motor performance during pronation/supination, wrist horizontal flexion/extension and hand grasp without sonification. From hand position data, 15 quantitative parameters were computed evaluating mean velocity, movement smoothness and angular excursions of hand/fingers. We validated this assessment in terms of its ability to discriminate between patients and healthy subjects, test-retest reliability and concurrent validity with the upper limb section of the Fugl-Meyer scale (FM), the Functional Independence Measure (FIM) and the Box & Block Test (BBT). All patients showed good understanding of the assigned tasks and were able to correctly execute the proposed training protocol, confirming its feasibility. A moderate-to-excellent intraclass correlation coefficient was found in 8/15 computed parameters. Moderate-to-strong correlation was found between the measured parameters and the clinical scales. The SonicHand training protocol is feasible and the assessment protocol showed good to excellent between-group discrimination ability, reliability and concurrent validity, thus enabling the implementation of new personalized and motivating training programs employing sonification for the rehabilitation of hand function.

via The SonicHand Protocol for Rehabilitation of Hand Motor Function: a validation and feasibility study – IEEE Journals & Magazine

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[Abstract + References] A Bilateral Training System for Upper-limb Rehabilitation: A Follow-up Study

Abstract

Previously, we reported a novel bilateral upper-limb rehabilitation system, an adaptive admittance controller and a related bilateral recovery strategy. In this study, we want to get a stronger evidence to verify the robustness of the proposed system, controller and recovery strategy as well as to further investigate the possibility of bilateral trainings for clinical applications. To this end, ten healthy subjects took part in a 60-minute experiment. Trajectories of robots and interaction force were recorded under the proposed bilateral recovery strategy which contained four exercise modes. For mode-l and mode-2, results showed that the trajectories of master and slave robots can catch the reference trajectory very well, and be changed with active interaction force applied by participants. For mode-3 and mode-4, participants finished tasks very well by drawing the ‘square-shaped’ trajectories through their own force. In conclusion, the experimental results were good enough to provide a strong and positive evidence for the proposed system and controller. Moreover, according to the feedbacks from participants, the bilateral recovery strategy can be treated as a new and interesting training as compared to the traditional unilateral training, and could be tested in clinical applications further.

I. Introduction

Compared to the traditional manual therapy, the robot involved therapy can alleviate labor-intensive aspects of conventional rehabilitation trainings, and provide precise passive/active repetitive trainings in a sufficiently long timeframe [1], [2]. In terms of upper-limb rehabilitation trainings, some robotic systems have been developed for bilateral exercises, and figured out a problem that performing most activities of daily living tasks with one-hand is awkward, difficult and time-consuming [2].

 

1. M. Cortese, M. Cempini, P. R. de Almeida Ribeiro, S. R. Soekadar, M. C. Carrozza, N. Vitiello, “A mechatronic system for robot-mediated hand telerehabilitation”, IEEE/ASME Transactions on Mechatronics, vol. 20, pp. 1753-1764, September 2015.

2. P. S. Lum, C. G. Burgar, P. C. Shor, “Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke”, Archives of physical medicine and rehabilitation, vol. 83, pp. 952-959, July 2002.

3. B. Sheng, Y. Zhang, W. Meng, C. Deng, S. Xie, “Bilateral robots for upper-limb stroke rehabilitation: State of the art and future prospects”, Medical engineering & physics, vol. 38, pp. 587-606, July 2016.

4. P. R. Culmer, A. E. Jackson, S. Makower, R. Richardson, J. A. Cozens, M. C. Levesley et al., “A control strategy for upper limb robotic rehabilitation with a dual robot system”, IEEE/ASME Transactions on Mechatronics, vol. 15, pp. 575-585, September 2010.

5. Z. Song, S. Guo, M. Pang, S. Zhang, N. Xiao, B. Gao et al., “Implementation of resistance training using an upper-limb exoskeleton rehabilitation device for elbow joint”, J. Med. Biol. Eng, vol. 34, pp. 188-196, 2014.

6. R. C. Loureiro, W. S. Harwin, K. Nagai, M. Johnson, “Advances in upper limb stroke rehabilitation: a technology push”, Medical & biological engineering & computing, vol. 49, pp. 1103, July 2011.

7. S. Hesse, C. Werner, M. Pohl, S. Rueckriem, J. Mehrholz, M. Lingnau, “Computerized arm training improves the motor control of the severely affected arm after stroke”, Stroke, vol. 36, pp. 1960-1966, August 2005.

8. C.-L. Yang, K.-C. Lin, H.-C. Chen, C.-Y. Wu, C.-L. Chen, “Pilot comparative study of unilateral and bilateral robot-assisted training on upper-extremity performance in patients with stroke”, American Journal of Occupational Therapy, vol. 66, pp. 198-206, March 2012.

9. E. Taub, G. Uswatte, R. Pidikiti, “Constraint-Induced Movement Therapy: a new family of techniques with broad application to physical rehabilitation-a clinical review”, Journal of rehabilitation research and development, vol. 36, pp. 237, July 1999.

10. S. B. Brotzman, R. C. Manske, “Clinical Orthopaedic Rehabilitation E-Book: An Evidence-Based Approach-Expert Consult” in Elsevier Health Sciences, 2011.

11. K. C. Lin, Y. F. Chang, C. Y. Wu, Y. A. Chen, “Effects of constraint-induced therapy versus bilateral arm training on motor performance daily functions and quality of life in stroke survivors”, Neurorehabilitation and Neural Repair, vol. 23, pp. 441-448, December 2009.

12. J. Chen, N. Y. Yu, D. G. Huang, B. T. Ann, G. C. Chang, “Applying fuzzy logic to control cycling movement induced by functional electrical stimulation”, IEEE transactions on rehabilitation engineering, vol. 5, pp. 158-169, Jun 1997.

13. D. A. Winter, “Biomechanics and motor control of human movement” in John Wiley & Sons, 2009.

 

via A Bilateral Training System for Upper-limb Rehabilitation: A Follow-up Study – IEEE Conference Publication

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[ARTICLE] Adherence to Guidelines in Adult Patients with Traumatic Brain Injury: A Living Systematic Review – Full Text HTML/PDF

ABSTRACT

Guidelines aim to improve the quality of medical care and reduce treatment variation. The extent to which guidelines are adhered to in the field of traumatic brain injury (TBI) is unknown. The objectives of this systematic review were to (1) quantify adherence to guidelines in adult patients with TBI, (2) examine factors influencing adherence, and (3) study associations of adherence to clinical guidelines and outcome. We searched EMBASE, MEDLINE, Cochrane Central, PubMed, Web of Science, PsycINFO, SCOPUS, CINAHL, and grey literature in October 2014. We included studies of evidence-based (inter)national guidelines that examined the acute treatment of adult patients with TBI. Methodological quality was assessed using the Research Triangle Institute item bank and Quality in Prognostic Studies Risk of Bias Assessment Instrument. Twenty-two retrospective and prospective observational cohort studies, reported in 25 publications, were included, describing adherence to 13 guideline recommendations. Guideline adherence varied considerably between studies (range 18–100%) and was higher in guideline recommendations based on strong evidence compared with those based on lower evidence, and lower in recommendations of relatively more invasive procedures such as craniotomy. A number of patient-related factors, including age, Glasgow Coma Scale, and intracranial pathology, were associated with greater guideline adherence. Guideline adherence to Brain Trauma Foundation guidelines seemed to be associated with lower mortality. Guideline adherence in TBI is suboptimal, and wide variation exists between studies. Guideline adherence may be improved through the development of strong evidence for guidelines. Further research specifying hospital and management characteristics that explain variation in guideline adherence is warranted.

 

Introduction

Traumatic brain injury (TBI) is a major public health concern affecting approximately 150–300 per 100,000 persons annually in Europe.1 The World Health Organization has predicted that TBI will be one of the leading causes of death and disability worldwide by the year 2020.2

The care for patients with TBI is often complex and multidisciplinary. Guidelines, protocols, and care pathways have been developed to improve quality of care, to reduce variation in practice, and to ensure that evidence-based care is optimally implemented.3

A 2013 systematic review4 found that the use of protocols in the management of severe TBI in the intensive care unit (ICU) led to improved patient outcomes. The findings, however, were based on observational studies that did not report on adherence rates. Without an understanding of adherence rates, the improved outcomes stated in the review cannot be directly attributed to the use of protocols.

Guideline adherence can be defined as the proportion of patients treated according to a guideline recommendation, which often represents evidence-based or best practice care. Previous studies have found that guideline adherence in medicine is generally low5–7 and varies widely across centers,7,8 medical condition,9 types of guideline,10,11 and time period.8,10 As a result, many patients do not receive evidence-based care, while others receive unnecessary care that may even be harmful.5 To date, no systematic review of the literature about guideline adherence in TBI has been conducted.

The aim of this systematic review was to provide a comprehensive overview of professionals’ adherence to guidelines in adult patients with TBI. The objectives were threefold:

  • 1. To quantify adherence to guidelines in adult patients with TBI.

  • 2. To explore factors influencing adherence to TBI guidelines in those studies reporting on adherence.

  • 3. To examine the association between adherence to guidelines and outcome in patients with TBI in those studies reporting on adherence.

[…]

Continue —>  Adherence to Guidelines in Adult Patients with Traumatic Brain Injury: A Living Systematic Review

FIG. 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart of the selection process. Reasons for exclusion full text: Study design: the study was no prospective or retrospective cohort study, randomized controlled trial, clinical trial, cross-sectional study, or time series; Guideline: the study did not describe a guideline, the guideline was local or not evidence-based, the guideline was not implemented or disseminated before the study period; Adherence: the study did not measure adherence per patient, adherence was self-reported; traumatic brain injury (TBI): the study was not about patients with TBI; Setting: the study was not conducted during the hospital and pre-hospital setting; Language: the study was not published in English; Solely about children: the study did not include adults. Adapted from: Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6: e1000097.

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[Abstract] Technical validation of an integrated robotic hand rehabilitation device: Finger independent movement, EMG control, and EEG-based biofeedback

Abstract:

The objective of this work was to design and experiment a robotic hand rehabilitation device integrated with a wireless EEG system, going towards patient active participation maximization during the exercise. This has been done through i) hand movement actively triggered by patients muscular activity as revealed by electromyographic signals (i.e., a target hand movement for the rehabilitation session is defined, the patient is required to start the movement and only when the muscular activity overcomes a predefined threshold, the patient-initiated movement is supported); ii) an EEG-based biofeedback implemented to make the user aware of his/her level of engagement (i.e., brain rhythms power ratio Beta/Alpha). The designed system is composed by the Gloreha hand rehabilitation glove, a device for electromyographic signals recording, and a wireless EEG headset. A strong multidisciplinary approach was the base to reach this goal, which is the fruitful background of the Think and Go project. Within this project, research institutes (Politecnico di Milano), clinical centers (INRCA-IRCCS), and companies (ab medica s.p.a., Idrogent, SXT) have worked together throughout the development of the integrated robotic hand rehabilitation device. The integrated device has been tested on a small pilot group of healthy volunteers. All the users were able to calibrate and correctly use the system, and they reported that the system was more challenging to be used with respect to the standard passive hand mobilization session, and required more attention and involvement. The results obtained during the preliminary tests are encouraging, and demonstrate the feasibility of the proposed approach.

Source: Technical validation of an integrated robotic hand rehabilitation device: Finger independent movement, EMG control, and EEG-based biofeedback – IEEE Xplore Document

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