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.
Reinkensmeyer DJ, Burdet E, Casadio M, Krakauer JW, Kwakkel G, Lang CE, Swinnen SP, Ward NS, Schweighofer N. Computational neurorehabilitation: modeling plasticity and learning to predict recovery. J Neuroeng Rehabil. 2016;13:1. CrossRef
Park H, Kim S, Winstein CJ, Gordon J, Schweighofer N. Short-Duration and Intensive Training Improves Long-Term Reaching Performance in Individuals With Chronic Stroke. Neurorehabilitation and Neural Repair. 2016;30(6):551-61.
Dipietro L, Krebs H, Volpe B, Stein J, Bever C, Mernoff S, Fasoli S, Hogan N. Learning, not adaptation, characterizes stroke motor recovery: evidence from kinematic changes induced by robot-assisted therapy in trained and untrained task in the same workspace. IEEE Trans Neural Syst Rehabil Eng. 2012;20:48–57. CrossRefPubMed
Rohrer B, Fasoli S, Krebs HI, Hughes R, Volpe B, Frontera WR, Stein J, Hogan N. Movement smoothness changes during stroke recovery. J Neurosci. 2002;22:8297–304. PubMed
Bosecker C, Dipietro L, Volpe B, Krebs HI. Kinematic robot-based evaluation scales and clinical counterparts to measure upper limb motor performance in patients with chronic stroke. Neurorehabilitation and neural repair. 2010;24(1):62-9.
Schmidt RA, Lee TD. Motor control and learning: A behavioral emphasis. 4th ed. Champaign, IL: Human Kinetics; 2005.
West BT, Welch KB, Galecki AT. Linear mixed models: a practical guide using statistical software. Boca Raton, FL: Chapman & Hall/CRC; 2014.
Davidian M, Giltinan DM. Nonlinear models for repeated measurement data: an overview and update. J Agric Biol Environ Stat. 2003;8:387–419. CrossRef
Fullerton K, McSherry D, Stout R. Albert’s test: a neglected test of perceptual neglect. Lancet. 1986;327:430–2. CrossRef
Wulf G, Chiviacowsky S, Lewthwaite R, Hooyman A. Altering mindset can enhance motor learning in older adults. Journal of Sport and Exercise Psychology. 2011;33:S122-S122.
van Dokkum L, Hauret I, Mottet D, Froger J, Métrot J, Laffont I: The contribution of kinematics in the assessment of upper limb motor recovery early after stroke. Neurorehabilitation and neural repair 2013:1545968313498514
- Nonlinear mixed-effects model reveals a distinction between learning and performance in intensive reach training post-stroke
- BioMed Central