Deans' stroke musings

Changing stroke rehab and research worldwide now.Time is Brain!Just think of all the trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 493 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It's quite disgusting that this information is not available from every stroke association and doctors group.
My back ground story is here:http://oc1dean.blogspot.com/2010/11/my-background-story_8.html

Tuesday, August 9, 2016

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

But is measuring changes better by using these?

Upper limb post stroke rehabilitation performance monitoring tools using optical mouse

Inexpensive Wearables and a Smartphone Aid Stroke Rehabilitation. Credit: New York University 

Special jacket for stroke rehabilitation. Credit: New York University 

 The latest:

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

  1. Crystal L. Massie, PhD, OTR1,2
  2. Yue Du, MA3
  3. Susan S. Conroy, DSc.PT4
  4. H. Igo Krebs, PhD5
  5. George F. Wittenberg, MD, PhD1,4
  6. Christopher T. Bever, MD, MBA1,4
  7. Jill Whitall, PhD1,6
  1. 1University of Maryland School of Medicine, Baltimore, MD, USA
  2. 2Indiana University, Indianapolis, IN, USA
  3. 3University of Maryland College Park, College Park, MD, USA
  4. 4VA Maryland Health Care System, Baltimore, MD, USA
  5. 5Massachusetts Institute of Technology, Cambridge, MA, USA
  6. 6University of Southampton, Southampton, UK
  1. Crystal L. Massie, PhD, OTR, Indiana University, 1140 W Michigan St CF 306, Indianapolis, IN 46202, USA. Email: massiec@iu.edu

Abstract

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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