title: A Clinically Relevant Method of Analyzing Continuous Change in Robotic Upper Extremity Chronic Stroke Rehabilitation.
abstract: 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.
  
  
  
  To determine how motor recovery profiles during robotic interventions provide insight into improving clinical gains.
  
  
  
  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.
  
  
  
  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.
  
  
  
  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.
authors.affiliation: University of Maryland School of Medicine, Baltimore, MD, USA Indiana University, Indianapolis, IN, USA massiec@iu.edu.
authors.forename: Crystal L
authors.initials: CL
authors.lastname: Massie
authors.affiliation: University of Maryland College Park, College Park, MD, USA.
authors.forename: Yue
authors.initials: Y
authors.lastname: Du
authors.affiliation: VA Maryland Health Care System, Baltimore, MD, USA.
authors.forename: Susan S
authors.initials: SS
authors.lastname: Conroy
authors.affiliation: Massachusetts Institute of Technology, Cambridge, MA, USA.
authors.forename: H Igo
authors.initials: HI
authors.lastname: Krebs
authors.affiliation: University of Maryland School of Medicine, Baltimore, MD, USA VA Maryland Health Care System, Baltimore, MD, USA.
authors.forename: George F
authors.initials: GF
authors.lastname: Wittenberg
authors.affiliation: University of Maryland School of Medicine, Baltimore, MD, USA VA Maryland Health Care System, Baltimore, MD, USA.
authors.forename: Christopher T
authors.initials: CT
authors.lastname: Bever
authors.affiliation: University of Maryland School of Medicine, Baltimore, MD, USA University of Southampton, Southampton, UK.
authors.forename: Jill
authors.initials: J
authors.lastname: Whitall
mesh_terms: D000328:Adult; D000368:Aged; D000704:Analysis of Variance; D001696:Biomechanical Phenomena; D002908:Chronic Disease; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011597:Psychomotor Performance; D012371:Robotics / Q000379:methods*; D020521:Stroke / Q000503:physiopathology*; D000071939:Stroke Rehabilitation; D016896:Treatment Outcome; D034941:Upper Extremity / Q000503:physiopathology*
keywords: robotics; stroke rehabilitation; upper extremity