[Review] Robotics in Upper Limb Rehabilitation Nursing – Full Text PDF

Purpose of this final thesis is to view what kind of robots there are in use in stroke rehabilitation nursing, focusing on upper limb rehabilitation. At the same time this work will view the attitudes of patients and therapist towards robotics in the health care field.

Robots are present and with robots engineers are trying to develop the health care services. Robots have come to different health care fields different technology is used in rehabilitation nursing. Robots help patients to recover more quickly.

I am implementing this work as a literature review. Search of articles were done in three different databases EBSCO host, PubMed and Science Direct. The work ends up to use articles choose from EBSCO host and Science Direct, total 6 articles.

I did found that there are several robots in use and I will introduce in this work some of them that are in commercial cell and one prototype. Attitudes were introduced to be good but still people trust more real humans.

Rehabilitation robots exist but they are not in that big use. Studies show that robotic use in rehabilitation increases the outcome. The problem with robots is mainly high price and attitudes of the therapist. …

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[ARTICLE] The Effect of rTMS with Rehabilitation on Hand Function and Corticomotor Excitability in Sub-Acute Stroke – Full Text PDF

Abstract

Objectives: Stroke is the leading cause of long-term disability. Hand motor impairment resulting from chronic stroke may have extensive physical, psychological, financial, and social implications despite available rehabilitative treatments. The best time to start treatment for stroke, is in sub-acute period. Repetitive transcranial magnetic stimulation (rTMS) is a method of stimulating and augmenting the neurophysiology of the motor cortex in order to promote the neuroplastic changes that are associated with motor recovery. The purpose of this study was to compare the effects of repetitive transcranial magnetic stimulation protocols plus routine rehabilitation on hand motor functions and hand corticomotor excitability in stroke patients with hemiplegia with pure routine rehabilitation programs.

Methods: This study was a randomized clinical trial which was performed on 24 patients with hemiplegia who were randomly divided in to three groups. One group (n=7), received high frequency repetitive transcranial magnetic stimulation (Hf rTMS) on lesioned M1 with routine rehabilitation program, and the other group (n=7), received rehabilitation program with low frequency repetitive transcranial magnetic stimulation stimulation (Lf rTMS) on nonlesined M1, and a control group (n=10), who were given only routine rehabilitation programs. The treatment was performed for 10 sessions, three times peri-test, Post and follow-up about neurophysiological contralesional hemisphere evaluations using record of MEP wave indices by single pulse TMS, and assessing functional wolf test and hand grip power of disabled hand by dynamometer.

Results: The results demonstrated that the rest MEP threshold reduction in experimental group which received high frequency magnetic stimulation was statistically significant (P<0.05). There was similar finding for active MEP threshold in the both high and low frequency but not in control group (P<0.05). Also there were more significant relation between obtained results from WOLF test and grip power with MEP mentioned parameters, in high frequency group, but not in low frequency and control group.

Discussion: According to the results, However it seems that Hf rTMS combined with routin physiotherapy can significantly improve hand functions and brain neurophysiology via specifically increase of contralesional corticomotor excitability in sever stroke patients that is representative of the role of neuroplasticity in nonlesioned hemisphere but the hypothesis of movement improvement related cognitive balance can’t be eliminated by exploring powerful approved effect of Hf rTMS on mood regulation.

 

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Source: The Effect of rTMS with Rehabilitation on Hand Function and Corticomotor Excitability in Sub-Acute Stroke – Iranian Rehabilitation Journal

[ARTICLE] Effectiveness of robotic-assisted gait training in stroke rehabilitation: A retrospective matched control study – Full Text HTML

Abstract

Objective

This study aimed to evaluate the effectiveness of robotic-assisted gait training (RAGT) in improving functional outcomes among stroke patients.

Design

This was a retrospective matched control study.

Setting

This study was conducted in an extended inpatient rehabilitation centre.

Patients and intervention

There were 14 patients with subacute stroke (4–31 days after stroke) in the RAGT group. Apart from traditional physiotherapy, the RAGT group received RAGT. The number of sessions for RAGT ranged from five to 33, and the frequency was three to five sessions per week, with each session lasting for 15–30 minutes. In the control group, there were 27 subacute stroke patients who were matched with the RAGT group in terms of age, days since stroke, premorbid ambulatory level, functional outcomes at admission, length of training, and number of physiotherapy sessions received. The control group received traditional physiotherapy but not RAGT.

Outcome measures

Modified Functional Ambulation Category (MFAC), Modified Rivermead Mobility Index (MRMI), Berg’s Balance Scale (BBS), and Modified Barthel Index (MBI) to measure ambulation, mobility, balance, and activities of daily living, respectively.

Results

Both RAGT and control groups had significant within-group improvement in MFAC, MRMI, BBS, and MBI. However, the RAGT group had higher gain in MFAC, MRMI, BBS, and MBI than the control group. In addition, there were significant between-group differences in MFAC, MRMI, and BBS gains (p = 0.026, p = 0.010, and p = 0.042, respectively). There was no significant between-group difference (p = 0.597) in MBI gain (p = 0.597).

Conclusion

The results suggested that RAGT can provide stroke patients extra benefits in terms of ambulation, mobility, and balance. However, in the aspect of basic activities of daily living, the effect of RAGT on stroke patients is similar to that of traditional physiotherapy.

Introduction

Stroke, also known as cerebrovascular accident, is an acute disturbance of focal or global cerebral function, with signs and symptoms lasting more than 24 hours or leading to death, presumably of vascular origin [1]. In Hong Kong, around 25,000 stroke patients are admitted to public hospitals under the Hong Kong Hospital Authority annually [2]. Although mortality and morbidity among stroke patients have declined due to medical advances, impacts on stroke survivors and community remain significant. The most widely recognized impairment caused by stroke is motor impairment, which restricts muscle movement or mobility function [3]. Many stroke patients experience difficulties in walking, and improving walking is one of the main goals of rehabilitation [4]. Since it was shown that the process of spontaneous recovery is almost completed within 6–10 weeks [5], early rehabilitation is essential to maximize the function of patients after stroke. Recent evidence suggests that high-intensity repetitive task-specific practice might be the most effective principle when trying to promote motor recovery after stroke [3]. Robotic-assisted gait training (RAGT) is a new global physiotherapy technology that applies the high-intensity repetitive principle to improve mobility of patients with stroke or other neurological disorders. The advantage of RAGT may be the reduction of the effort required by therapists compared with treadmill training with partial bodyweight support, as they no longer need to set the paretic limbs or assist in trunk movements [6]. People who receive electromechanical-assisted gait training in combination with physiotherapy after stroke are more likely to achieve independent walking than people who receive gait training without these devices [7]. More specifically, people in the first 3 months after stroke and those who are not able to walk seem to benefit most from this type of intervention [7]. Evidence also shows that the use of RAGT in stroke patients has positive effects on their balance [8].

Randomized controlled trials and systemic reviews have demonstrated the effectiveness of RAGT for stroke patients in terms of functional outcomes such as walking ability [9], [10] and [11] and balance [8] and [11]. However, limited published evidence is available on the effectiveness of RAGT in improving other functioning activities such as basic activities of daily living (ADL) [12] and [13]. If RAGT can improve walking ability and balance of stroke patient, can RAGT also improve basic ADL of stroke patients? The hierarchical pattern of progression in basic ADL is in the following order: bathing, dressing, transferring, toileting, controlling continence, and feeding, with bathing being the most complex task and feeding the least [14]; however, walking ability and balance contribute to parts of basic ADL. Moreover, factors that make the greatest contribution to ADL after stroke were found to be balance, upper extremity function, and perceptual and cognitive functions [15]. If RAGT can improve ADL of stroke patients, which of the above factors is/are enhanced by RAGT? Can RAGT also enhance perceptual and cognitive functions of stroke patients? Hence, controlled studies are necessary to address these research questions. A retrospective study conducted by Dundar et al [13] investigated the effect of robotic training in functional independence measure and other functional outcomes of patients with subacute and chronic stroke. However, the study concluded that combining robotic training with conventional physiotherapy produced better improvement than conventional physiotherapy in terms of functional independence measure, but not walking status or balance. The result was opposite to the specificity of training principle [16] that gait training should produce more positive effect for walking and balance than ADL. Hence, this study intends to investigate the effectiveness of RAGT in improving functional mobility and basic ADL for stroke patients, and hopefully can lead to further randomized controlled studies to investigate the impact of RAGT on basic ADL.

Continue —> Effectiveness of robotic-assisted gait training in stroke rehabilitation: A retrospective matched control study

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Figure 1. Flowchart of patient assignment. DAMA = discharged against medical advice; RAGT = robotic-assisted gait training.

[ARTICLE] Effects of Mirror Therapy on the Lower Limb Functionality Hemiparesis after Stroke – Full Text PDF

ABSTRACT

Introduction: Mental exercise using the mirror therapy (MT) improves the retention of newly acquired skills and the performance of sequential motor skills in subjects with post-stroke hemiparesis.

Objectives: The study aimed to analyze the mirror therapy effect on the range of motion and the lower limb functionality in post-stroke hemiparesis subjects.

Materials and Methods: Eleven participants with hemiparesis in the lower limb were subjected to the 10 sessions of a MT protocol. The interventions were three times per week per 30 minutes each day. Evaluation of active and passive ankle goniometry (dorsiflexion and eversion movements); Ascent and Descent Ladder Rate (ADLR); Time Up and Go test (TUG test); EFEI scale; and FAAM scale were performed. The data were collected before and after the intervention using MT, and then statistically compared. Results: The MT improved significantly (p < 0.01) the range of motion of the paretic lower limb both evaluated by active and passive ankle goniometry. An increase in the speed of gait and other functional tasks related to the paretic lower limbs were found through the TUG and ADLR tests. It also demonstrated a positive influence on the functionality of the paretic lower limb motor control through the analysis of the scores in the FAAM and EFEI scales.

Conclusion: It is concluded that the MT therapy can help the patients with post-stroke hemiparesis in the improvement of several functions. Probably, the mirror therapy would aid in the repair of the injuries in the cortical areas.

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Source: Effects of Mirror Therapy on the Lower Limb Functionality Hemiparesis after Stroke

[ARTICLE] A Personalized Self-Management Rehabilitation System for Stroke Survivors: A Quantitative Gait Analysis Using a Smart Insole – Full Text HTML

ABSTRACT

Background: In the United Kingdom, stroke is the single largest cause of adult disability and results in a cost to the economy of £8.9 billion per annum. Service needs are currently not being met; therefore, initiatives that focus on patient-centered care that promote long-term self-management for chronic conditions should be at the forefront of service redesign. The use of innovative technologies and the ability to apply these effectively to promote behavior change are paramount in meeting the current challenges.

Objective: Our objective was to gain a deeper insight into the impact of innovative technologies in support of home-based, self-managed rehabilitation for stroke survivors. An intervention of daily walks can assist with improving lower limb motor function, and this can be measured by using technology. This paper focuses on assessing the usage of self-management technologies on poststroke survivors while undergoing rehabilitation at home.

Methods: A realist evaluation of a personalized self-management rehabilitation system was undertaken in the homes of stroke survivors (N=5) over a period of approximately two months. Context, mechanisms, and outcomes were developed and explored using theories relating to motor recovery. Participants were encouraged to self-manage their daily walking activity; this was achieved through goal setting and motivational feedback. Gait data were collected and analyzed to produce metrics such as speed, heel strikes, and symmetry. This was achieved using a “smart insole” to facilitate measurement of walking activities in a free-living, nonrestrictive environment.

Results: Initial findings indicated that 4 out of 5 participants performed better during the second half of the evaluation. Performance increase was evident through improved heel strikes on participants’ affected limb. Additionally, increase in performance in relation to speed was also evident for all 5 participants. A common strategy emerged across all but one participant as symmetry performance was sacrificed in favor of improved heel strikes. This paper evaluates compliance and intensity of use.

Conclusion: Our findings suggested that 4 out of the 5 participants improved their ability to heel strike on their affected limb. All participants showed improvements in their speed of gait measured in steps per minute with an average increase of 9.8% during the rehabilitation program. Performance in relation to symmetry showed an 8.5% average decline across participants, although 1 participant improved by 4%. Context, mechanism, and outcomes indicated that dual motor learning and compensatory strategies were deployed by the participants.

Introduction

The global incidence of stroke is set to escalate from 15.3 million to 23 million by 2030 [1]. In the United Kingdom, stroke is the largest cause of disability [2] resulting in a cost to the economy of £8.9 billion a year [3]. It is estimated that following a stroke, only 15% of people will gain complete recovery for both the upper and lower extremities [4]. Walking and mobility are prominent challenges for many survivors who report the importance of mobility therapy [5]. Nevertheless, rehabilitative service needs cannot always be met and therefore initiatives that focus on patient-centered care promoting long-term self-management remain at the forefront of service redesign [6].

The adoption of technological solutions allows for patient and carer empowerment and a paradigm shift in control and decision-making to one of a shared responsibility. It also has the potential to reduce the burden for care professionals, and support the development of new interventions [7]. Incorporating technology into the daily lives of stroke survivors can be achieved by maintaining high levels of usability, acceptance, engagement, and removing any associated stigma involved with the use of assistive technology [8].

Technological aids for poststroke motor recovery hitherto have required the use of expensive, complex, and cumbersome apparatus that have typically necessitated the therapist to be present during use [9,10]. Recently, inexpensive, wearable, commercially-available sensors have become a more viable option for independent home-based poststroke rehabilitation [11,12]. A systematic review by Powell et al [13] identified a number of wearable lower-limb devices that have been trialed, such as robotics [14–16], virtual reality [16], functional electrical stimulation (FES) [17,18], electromyographic biofeedback (EMG-BFB) [19,20], and transcutaneous electrical nerve stimulation [21]. Of the identified trials exploring improvements in the International Classification of Functioning (ICF) domain of activities and participation, only 1 [21] found significant improvements. Studies that adopt a positivist randomized controlled trial paradigm often fail to give sufficient consideration as to how intervention components interact [22]. Indeed, creating and developing technological solutions for complex long-term conditions is challenging and requires multiple stakeholder input [23].

The Self-management supported by Assistive, Rehabilitation and Telecare Technologies consortium explored rehabilitation for stroke survivors focusing initially on the use of wearable sensors to support upper limb feedback on the achievement of functional goals [24–30]. User interface design, the practicalities surrounding deployment, and the ability of the participants to interact with the technology were explored [24].

The intervention model for the stroke system was based around a rehabilitation paradigm underpinned by theories of motor relearning and neuroplastic adaptation, motivational feedback, self-efficacy, and knowledge transfer [31–34]. In order to enhance and strengthen previous research, a realist evaluation [35] was adopted to evaluate the final personalized self-management rehabilitation system (PSMrS) prototype in order to gain an insight into the value, usability, and potential impact on an individual’s ability to self-manage their rehabilitation following a stroke [36].

The aim of this work was to understand the conditions under which technology-based rehabilitation would have an impact (outcome) on the motor behavior of the user—more specifically what would work for whom, in what context, and in what respect utilizing a realist evaluation framework [35]. This paper addresses this by focusing on the impact smart insole technology has on participants at home. The impacts are assessed by analyzing a participants’ gait over time, which are then presented and discussed.

Futhermore, the rehabilitation system, its architecture, and technical components are presented along with the evaluation of the prototype with regards to the performance and usability of the system in the homes of stroke survivors.

Continue —> JRAT-A Personalized Self-Management Rehabilitation System for Stroke Survivors: A Quantitative Gait Analysis Using a Smart Insole | Davies | JMIR Rehabilitation and Assistive Technologies

Figure 1. Technology infrastructure used to support the realist evaluation consisted of touch screen interactive components: (1) a smart insole produced by Tomorrow Options, (2) used to collect gait information, and (3) a server used to analyze data.

Figure 2. Walkinsense device. Top left: force sensitive resistors showing a typical layout configuration; bottom left: the size of a force sensitive resister in relation to a UK 5 pence piece; and right: attachment of devices to lower limb on a manikin.

[BOOK] Rehabilitation Robotics – OPEN ACCESS

Rehabilitation Robotics Edited by Sashi S Kommu, ISBN 978-3-902613-04-2, 648 pages, Publisher: I-Tech Education and Publishing, Chapters published August 01, 2007 under CC BY-NC-SA 3.0 license Edited Volume

The coupling of several areas of the medical field with recent advances in robotic systems has seen a paradigm shift in our approach to selected sectors of medical care, especially over the last decade. Rehabilitation medicine is one such area. The development of advanced robotic systems has ushered with it an exponential number of trials and experiments aimed at optimising restoration of quality of life to those who are physically debilitated. Despite these developments, there remains a paucity in the presentation of these advances in the form of a comprehensive tool. This book was written to present the most recent advances in rehabilitation robotics known to date from the perspective of some of the leading experts in the field and presents an interesting array of developments put into 33 comprehensive chapters. The chapters are presented in a way that the reader will get a seamless impression of the current concepts of optimal modes of both experimental and ap- plicable roles of robotic devices.

• BOOK CONTENTS
• MOST DOWNLOADED CHAPTERS
• HOW TO LINK

• Chapter 1  Robotic Solutions in Pediatric Rehabilitationby Michael Bailey-Van Kuren
• Chapter 2  Biomechanical Constraints in the Design of Robotic Systems for Tremor Suppressionby Juan-Manuel Belda-Lois, Alvaro Page, Jose-Maria Baydal-Bertomeu Rakel Poveda and Ricard Barbera
• Chapter 3  Robotics and Virtual Reality Applications in Mobility Rehabilitationby Rares F. Boian and Grigore C. Burdea & Judith E. Deutsch
• Chapter 4  Designing Safety-Critical Rehabilitation Robotsby Stephen Roderick and Craig Carignan
• Chapter 5  Work Assistive Mobile Robot for the Disabled in a Real Work Environmentby Hyun Seok Hong, Jung Won Kang and Myung Jin Chung
• Chapter 6  The Evolution and Ergonomics of Robotic-Assisted Surgical Systemsby Oussama Elhage, Ben Challacombe, Declan Murphy Mohammed S. Khan and Prokar Dasgupta
• Chapter 7  Design and Implementation of a Control Architecture for Rehabilitation Robotic Systemsby Duygun Erol and Nilanjan Sarkar
• Chapter 8  A 3-D Rehabilitation System for Upper Limbs “EMUL”, and a 6-DOF Rehabilitation System “Robotherapist” and Other Rehabilitation Systems with High Safetyby Junji Furusho and Takehito Kikuchi
• Chapter 9  The Rehabilitation Robots FRIEND-I & II: Daily Life Independency through Semi-Autonomous Task-Executionby Christian Martens, Oliver Prenzel and Axel Graeser
• Chapter 10  Functional Rehabilitation: Coordination of Artificial and Natural Controllersby Rodolphe Heliot, Christine Azevedo and Bernard Espiau
• Chapter 11  Passive-type Intelligent Walker Controlled Based on Caster-like Dynamicsby Yasuhisa Hirata, Asami Muraki and and Kazuhiro Kosuge
• Chapter 12  Powered Human Gait Assistanceby Kevin W. Hollander and Thomas G. Sugar
• Chapter 13  Task-oriented and Purposeful Robot-Assisted Therapyby Michelle J. Johnson, Kimberly J. Wisneski, John Anderson, Dominic Nathan, Elaine Strachota, Judith Kosasih, Jayne Johnston and and Roger O. Smith
• Chapter 14  Applications of Robotics to Assessment and Physical Therapy of Upper Limbs of Stroke Patientsby M.-S. Ju, C.-C. K. Lin, S.-M. Chen, I.-S. Hwang, P.-C. Kung and Z.-W. Wu
• Chapter 15  Applications of a Fluidic Artificial Hand in the Field of Rehabilitationby Artem Kargov, Oleg Ivlev, Christian Pylatiuk, Tamim Asfour, Stefan Schulz, Axel Graeser, Ruediger Dillmann and Georg Bretthauer
• Chapter 16  Upper-Limb Exoskeletons for Physically Weak Personsby Kazuo Kiguchi and Toshio Fukuda
• Chapter 17  Cyberthosis: Rehabilitation Robotics With Controlled Electrical Muscle Stimulationby Patrick Metrailler, Roland Brodard, Yves Stauffer, Reymond Clavel and Rolf Frischknecht
• Chapter 18  Haptic Device System For Upper Limb Motor Impairment Patients: Developing And Handling In Healthy Subjectsby Tasuku Miyoshi, Yoshiyuki Takahashi, Hokyoo Lee, Takafumi Terada, Yuko Ito, Kaoru Inoue and Takashi Komeda
• Chapter 19  Rehabilitation of the Paralyzed Lower Limbs Using Functional Electrical Stimulation: Robust Closed Loop Controlby Samer Mohammed, Philippe Poignet, Philippe Fraisse and David Guiraud
• Chapter 20  Risk Evaluation of Human-Care Robotsby Makoto Nokata and Koji Ikuta
• Chapter 21  Robotic Exoskeletons for Upper Extremity Rehabilitationby Abhishek Gupta and Marcia K. O’Malley
• Chapter 22  Upper Limb Rehabilitation System for Self-Supervised Therapy: Computer-Aided Daily Performance Evaluation for the Trauma and Disorder in the Spinal Cord and Peripheral Nervesby Kengo Ohnishi, Keiji Imado, Yukio Saito and Hiroomi Miyagawa
• Chapter 23  PLEIA: A Reconfigurable Platform for Evaluation of HCI Actingby Peralta H., Monacelli E., Riman C., Baklouti M., Ben Ouezdou F., Mougharbel I., Laffont I. and Bouteille J.
• Chapter 24  Facial Automaton for Conveying Emotions as a Social Rehabilitation Tool for People with Autismby Giovanni Pioggia, Maria Luisa Sica, Marcello Ferro, Silvia Casalini, Roberta Igliozzi, Filippo Muratori, Arti Ahluwalia and Danilo De Rossi
• Chapter 25  Upper-Limb Robotic Rehabilitation Exoskeleton: Tremor Suppressionby J.L. Pons, E. Rocon, A.F. Ruiz and J.C. Moreno
• Chapter 26  Lower-Limb Wearable Exoskeletonby J.L. Pons, J.C. Moreno, F.J. Brunetti and E. Rocon
• Chapter 27  Exoskeleton-Based Exercisers for the Disabilities of the Upper Arm and Handby Ioannis Sarakoglou, Sophia Kousidou, Nikolaos G. Tsagarakis and Darwin G. Caldwell
• Chapter 28  Stair Gait Classification from Kinematic Sensorsby Wolfgang Svensson and Ulf Holmberg
• Chapter 29  The ALLADIN Diagnostic Device: An Innovative Platform for Assessing Post-Stroke Functional Recoveryby Stefano Mazzoleni, Jo Van Vaerenbergh, Andras Toth, Marko Munih, Eugenio Guglielmelli and Paolo Dario
• Chapter 30  Synthesis of Prosthesis Architectures and Design of Prosthetic Devices for Upper Limb Amputeesby Marco Troncossi and Vincenzo Parenti-Castelli
• Chapter 31  An Embedded Control Platform of a Continuous Passive Motion Machine for Injured Fingersby Zhang Fuxiang
• Chapter 32  A Portable Robot for Tele-Rehabilitation: Remote Therapy and Outcome Evaluationby Park, Hyung-Soon, and Zhang, Li-Qun
• Chapter 33  Bio-Inspired Interaction Control of Robotic Machines for Motor Therapyby Loredana Zollo, Domenico Formica and Eugenio Guglielmelli

Source: Rehabilitation Robotics | InTechOpen

[VIDEO] Lower Limb Neurological Examination – OSCE guide (New Version) – YouTube

Δημοσιεύτηκε στις 16 Φεβ 2015

See the written guide alongside the video on our website http://geekymedics.com/2010/10/02/low…

This video aims to give you an idea of what’s required in the Lower Limb Neurological Examination OSCE.

Always adhere to your medical schools / local hospital trusts guidelines when performing examinations or clinical procedures. Do NOT perform any examination or procedure on patients based purely upon the content of these videos. Geeky Medics accepts no liability for loss of any kind incurred as a result of reliance upon information provided in this video.

[VIDEO] Upper Limb Neurological Examination – OSCE Guide (New Version) – YouTube

Δημοσιεύτηκε στις 16 Φεβ 2015

To see the written guide alongside the video head over to our website http://geekymedics.com/2010/10/02/upp…

This video aims to give you an idea of what’s required in the Upper Limb Neurological Examination OSCE.

Always adhere to your medical schools / local hospital trusts guidelines when performing examinations or clinical procedures. Do NOT perform any examination or procedure on patients based purely upon the content of these videos. Geeky Medics accepts no liability for loss of any kind incurred as a result of reliance upon information provided in this video.

[WEB SITE] Hand Function After Stroke Is Improved By Electrical Stiimulation

A new electrical stimulation remedy helped stroke survivors with hand weakness improve hand dexterity greater than an existing stimulation approach, in line with new studies in the American Heart Association’s journal Stroke.

Approximately 800,000 people within the United States of America have strokes every year, according to the American Heart Association. Stroke generally results in some paralysis or partial paralysis on one side of the body that can result in survivors having difficulties in executing function. A common remedy in stroke rehabilitation uses low tiers of electrical current to stimulate the paralyzed muscle groups to open the hand, enhance muscle power and likely repair hand function. Stimulation intensity, cycle timing, and repetitions are set by a therapist.

Electrical Stimulation

In the new experimental therapy discovered by researchers at the MetroHealth System, Case Western Reserve University and the Cleveland Functional Electrical Stimulation Center, sufferers manage the stimulation to their vulnerable hand by wearing a glove with sensors on the opposite, unaffected hand. When the affected person opens their unaffected hand, they receive a corresponding amount of stimulation that opens their susceptible stroke-affected hand. This places the affected person in control of their hand and permits them to participate in therapy with the help of electrical stimulation.

According to Jayme S. Knutson, Ph.D., senior author of the study and an assistant professor of Physical Medicine and Rehabilitation at Case Western Reserve University School of Medicine in Cleveland, Ohio, Based on positive findings from our previous studies, we sought to determine if the new glove-controlled hand stimulation therapy could be more effective than the common therapy in improving hand dexterity in patients who are more than six months past their stroke.

Researchers enrolled 80 stroke survivors. For 12 weeks, half of the survivors received remedy using the new glove, and the remainder received the common remedy. Both groups used an electrical stimulator on their own at home for 10 hours every week, plus three hours per week training hand tasks with an occupational therapist in the lab. Hand feature was measured earlier and after remedy with a standard dexterity test that measured the number of blocks members can pick out up, elevate over a barrier and launch in some other place on a desk within a 60 second duration. They determined that sufferers who acquired the new therapy had extra improvement at the dexterity test (4.6 blocks) than the common institution (1.8 blocks). Patients who had greater improvements in hand dexterity following the new therapy have been much less than two years post-stroke and had at least a few finger movements when they started the study. These sufferers saw a development of 9.6 blocks on the dexterity test, compared to 4.1 blocks in the common group.

Sufferers without a finger movement additionally noticed upgrades in arm movement after the new remedy. At the end of treatment, 97 percent of the subjects who obtained the new therapy agreed that they might use their hand greatly than on the start of the study.

Due to the fact that the therapy is new and this was a single-site study, researchers do not know if similar outcomes may also be seen in other rehab centers. They plan to perform a multi-site study to verify their consequences, as well as measure quality of life enhancements for sufferers. And whilst the researchers speculate that the new remedy can be converting neural connections within the brain that manage hand dexterity, extra research is yet to prove what consequences it is able to have on the central nervous system.

The study additionally demonstrates that stroke sufferers can correctly use technology for self-administered therapy at home. According to Knutson, Home-based therapy is becoming increasingly important to offset increasing healthcare costs and to meet the need for high doses of therapy that are critical for attaining the best outcomes. The more therapy a patient can get the better potential outcome they will get.

© Copyright 2015 Doctor Tipster

Source: Hand Function After Stroke Is Improved By Electrical Stiimulation – Doctor Tipster

[ARTICLE] Prediction of Walking and Arm Recovery after Stroke: A Critical Review – Full Text HTML

Abstract

Clinicians often base their predictions of walking and arm recovery on multiple predictors. Multivariate prediction models may assist clinicians to make accurate predictions. Several reviews have been published on the prediction of motor recovery after stroke, but none have critically appraised development and validation studies of models for predicting walking and arm recovery.

In this review, we highlight some common methodological limitations of models that have been developed and validated. Notable models include the proportional recovery model and the PREP algorithm. We also identify five other models based on clinical predictors that might be ready for further validation. It has been suggested that neurophysiological and neuroimaging data may be used to predict arm recovery. Current evidence suggests, but does not show conclusively, that the addition of neurophysiological and neuroimaging data to models containing clinical predictors yields clinically important increases in predictive accuracy.

1. Introduction

It would be useful to be able to predict recovery of walking and arm after stroke. Accurate predictions are needed so that clinicians can provide patients with prognoses, set goals, select therapies and plan discharge [1,2,3,4]. For example, if it was possible to predict with some certainty that a particular patient would be unable to walk independently at six months, the clinicians providing that patient with acute and subacute care might work toward a discharge goal of safe transfers. Therapy might involve carer training and equipment prescription rather than intensive gait training. The ability to make accurate predictions could reduce the length of stay in hospitals and enable efficient utilization of stroke care resources [4,5].

Several systematic reviews have identified strong predictors of walking and arm recovery after stroke [2,3,6]. In one systematic review of prognostic studies on walking, clinical variables such as age, severity of paresis and leg power were found to be strong predictors of walking after stroke (based on five studies, each of between 197 and 804 patients) [2]. In another systematic review of prognostic studies on arm recovery, clinical, neurophysiological and neuroimaging data were found to be strong predictors of arm recovery after stroke (based on 58 studies of 9–1197 patients) [3]. These clinical, neurophysiological and neuroimaging data included measures of upper limb impairment, upper limb function, lower limb impairment, motor and somatosensory evoked potentials, and measures obtained with diffusion tensor imaging [3].

In practice, clinicians base their predictions about clinical outcomes on multiple variables [7,8,9]. If multiple predictors are to be used to make prognoses, there needs to be a proper accounting of the independent (incremental) predictive value of each predictor variable. Therefore the most useful information about prognosis is likely to come from multivariate prediction models [7,8,9].

The research which underpins establishment of clinically useful multivariate prediction models involves several steps. First ‘development studies’ are conducted to build the multivariate prediction models [7]. Subsequently the predictive accuracy of the models is tested on new cohorts [7,10]. These studies are known as ‘validation studies’ [7]. It is recommended that prediction models should not be used in clinical practice until both development and validation studies have been conducted [7,10]. Once development and validation studies have been conducted, impact studies may be conducted, although the reality is that few reports of impact studies are published. Impact studies resemble clinical trials; they test the efficacy of use of prediction models on patient outcomes, clinician behaviour and cost-effectiveness of care [7,11]. Recent narrative reviews have provided updates on the prediction of motor recovery after stroke [5,12] but these reviews have not focused on development and validation studies of models for predicting walking and arm recovery.

This review provides a critical review of prediction models of walking and arm recovery after stroke. Studies were identified using the search strategy and inclusion criteria in the Appendix. The review begins in the second section with the definitions and measurements of walking and arm recovery. The third section provides a detailed description of the recommended process for developing and validating a prediction model because this process provides a benchmark against which prediction modelling studies of walking and arm recovery can be evaluated. The fourth section critically appraises development and validation studies of walking and arm recovery with the aim of identifying multivariate models that could potentially be implemented in clinical practice. Much has been written about the role of neurophysiological and neuroimaging data in predicting arm recovery. The fifth section considers whether neurophysiological and neuroimaging data provide additional predictive value over clinical data alone in predicting arm recovery. We conclude with a summary and recommendations for future prediction modelling studies.

Continue —> Brain Sciences | Free Full-Text | Prediction of Walking and Arm Recovery after Stroke: A Critical Review | HTML