title: Nonlinear mixed-effects model reveals a distinction between learning and performance in intensive reach training post-stroke.
abstract: We recently showed that individuals with chronic stroke who completed two sessions of intensive unassisted arm reach training exhibited improvements in movement times up to one month post-training. Here, we study whether changes in movement times during training can predict long-term changes.
  
  
  
  Sixteen participants with chronic stroke and ten non-disabled age-matched participants performed two sessions of reach training with 600 movements per session. Movement time data during training were fitted to a nonlinear mixed-effects model consisting of a decreasing exponential term to model improvements of performance due to learning and an increasing linear term to model worsening of performance due to activity-dependent fatigability and/or other factors unrelated to learning.
  
  
  
  For non-disabled age-matched participants, movement times gradually decreased overall during training and overall changes in movement times during training predicted long-term changes. In contrast, for participants post-stroke, movement times often worsened near the end of training. As a result, overall changes in movement times during training did not predict long-term changes in movement times in the stroke group. However, improvements in movement times due to training, as estimated by the exponential term of the model, predicted long-term changes in movement times.
  
  
  
  Participants post-stroke showed a distinction between learning and performance in unassisted intensive arm reach training. Despite worsening of performance in later trials, extended training was beneficial for long-term gains.
authors.affiliation: Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, USA. hyeshinp@usc.edu.
authors.forename: Hyeshin
authors.initials: H
authors.lastname: Park
authors.affiliation: Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, USA.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Schweighofer
mesh_terms: D002908:Chronic Disease; D005260:Female; D006801:Humans; D007858:Learning; D008297:Male; D008875:Middle Aged; D009068:Movement; D017711:Nonlinear Dynamics; D020521:Stroke; D000071939:Stroke Rehabilitation
keywords: Activity-dependent fatigability; Learning; Nonlinear statistical mixed-effects model; Performance; Prediction of long-term gains; Reach training; Stroke rehabilitation