Robotic Devices as Therapeutic and Diagnostic Tools for Stroke Recovery

Volpe, Bruce T.; Huerta, Patricio T.; Zipse, Johanna; Rykman, Avrielle; Edwards, Dylan; Dipietro, Laura; Hogan, Neville; Krebs, Hermano Igo

doi:10.1001/archneurol.2009.182

Cited by 107 publications

(76 citation statements)

References 33 publications

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“…Clinically, this suggests that the first 20 blocks of cumulative training (about 5-6 sessions) promotes a faster rate of gain, which is followed by slower improvements over the additional sessions. This result is similar to Volpe and colleagues who observed the biggest improvement in the first 9 sessions [18]. However, it is in contrast to those of Kahn and colleagues [19] who suggested improvements of speed from robotic training were gradual and continuous over the course of the intervention.…”

Section: Resultssupporting

confidence: 89%

A Clinically Relevant Method of Analyzing Continuous Change in Robotic Upper Extremity Chronic Stroke Rehabilitation

Massie

Conroy

et al. 2016

Neurorehabil Neural Repair

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Background. Robots designed for rehabilitation of the upper extremity after stroke facilitate high rates of repetition during practice of movements and record precise kinematic data, providing a method to investigate motor recovery profiles over time. Objective. To determine how motor recovery profiles during robotic interventions provide insight into improving clinical gains. Methods. A convenience sample (n = 22), from a larger randomized control trial, was taken of chronic stroke participants completing 12 sessions of arm therapy. One group received 60 minutes of robotic therapy (Robot only) and the other group received 45 minutes on the robot plus 15 minutes of translation-to-task practice (Robot + TTT). Movement time was assessed using the robot without powered assistance. Analyses (ANOVA, random coefficient modeling [RCM] with 2-term exponential function) were completed to investigate changes across the intervention, between sessions, and within a session. Results. Significant improvement (P < .05) in movement time across the intervention (pre vs post) was similar between the groups but there were group differences for changes between and within sessions (P < .05). The 2-term exponential function revealed a fast and slow component of learning that described performance across consecutive blocks. The RCM identified individuals who were above or below the marginal model. Conclusions. The expanded analyses indicated that changes across time can occur in different ways but achieve similar goals and may be influenced by individual factors such as initial movement time. These findings will guide decisions regarding treatment planning based on rates of motor relearning during upper extremity stroke robotic interventions.

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Section: Resultssupporting

confidence: 89%

A Clinically Relevant Method of Analyzing Continuous Change in Robotic Upper Extremity Chronic Stroke Rehabilitation

Massie

Conroy

et al. 2016

Neurorehabil Neural Repair

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“…Robotic devices are receiving increasing attention for their ability to quantify loss of body function/structure, 264 in some cases generating data that cannot be obtained by a human examiner. 265 Telemedicine may be used by examiners in remote locations to measure level of disability.…”

Section: Level Of Disabilitymentioning

confidence: 99%

Guidelines for Adult Stroke Rehabilitation and Recovery

Winstein¹,

Stein²,

Arena

et al. 2016

Stroke

2,028

840

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e98Purpose-The aim of this guideline is to provide a synopsis of best clinical practices in the rehabilitative care of adults recovering from stroke. Methods-Writing group members were nominated by the committee chair on the basis of their previous work in relevant topic areas and were approved by the American Heart Association (AHA) Stroke Council's Scientific Statement Oversight Committee and the AHA's Manuscript Oversight Committee. The panel reviewed relevant articles on adults using computerized searches of the medical literature through 2014. The evidence is organized within the context of the AHA framework and is classified according to the joint AHA/American College of Cardiology and supplementary AHA methods of classifying the level of certainty and the class and level of evidence. The document underwent extensive AHA internal and external peer review, Stroke Council Leadership review, and Scientific Statements Oversight Committee review before consideration and approval by the AHA Science Advisory and Coordinating Committee. Results-Stroke rehabilitation requires a sustained and coordinated effort from a large team, including the patient and his or her goals, family and friends, other caregivers (eg, personal care attendants), physicians, nurses, physical and occupational therapists, speech-language pathologists, recreation therapists, psychologists, nutritionists, social workers, and others. Communication and coordination among these team members are paramount in maximizing the effectiveness

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“…Total F-M score for right upper limb of the childhood stroke patient (closed circles) increased relatively more quickly with time since stroke in the first half of the robot-assisted therapy (i.e. over firstpy might be more beneficial as employed in this case report [6,10].…”

Section: Discussionmentioning

confidence: 72%

“…Robot-assisted therapy was introduced within the first 3 months as early treatment elicits greater reductions in motor impairment compared to its use in the chronic stage in adults (> 6 months post-stroke) [6,[8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Using Assistive Robotic Technology in Motor Neurorehabilitation After Childhood Stroke

Turner

2012

J Neurol Res

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An intracranial haemorrhage resulted in severe motor impairment in the right upper limb in a previously physically-active young adolescent. Robot-assisted therapy was implemented over the course of 9 months to reduce motor impairment in the affected right upper limb. Robot-assisted therapy consisted of twice weekly sessions containing repetitive and progressively intense practice of hand grasping and arm reaching. The amount of assistance offered by the robot during grasping and reaching was adaptive such that as impairment reduced, the robot offered less assistance. Motor impairment was evaluated with the Fugl-Meyer assessment score for the upper limb. The robot-assisted therapy was associated with an increase in a clinical outcome measure of motor impairment following childhood stroke. The increase was distributed across both upper and lower arm segments. The final, stable reduction in motor impairment in the patient matched that demonstrated in a group of adult unilateral stroke survivors receiving similar treatment with robotics early in their recovery process. We conclude that robotassisted therapy offers a promising treatment option in childhood stroke involving severe upper limb motor impairment. Robot-assisted therapy could be implemented safely in an acute in-patient hospital unit and be continued after discharge to the community setting.

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Robotic Devices as Therapeutic and Diagnostic Tools for Stroke Recovery

Cited by 107 publications

References 33 publications

A Clinically Relevant Method of Analyzing Continuous Change in Robotic Upper Extremity Chronic Stroke Rehabilitation

A Clinically Relevant Method of Analyzing Continuous Change in Robotic Upper Extremity Chronic Stroke Rehabilitation

Guidelines for Adult Stroke Rehabilitation and Recovery

Using Assistive Robotic Technology in Motor Neurorehabilitation After Childhood Stroke

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