Posts Tagged mixed effect models

[Abstract + References] The Efficiency, Efficacy, and Retention of Task Practice in Chronic Stroke

Abstract

In motor skill learning, larger doses of practice lead to greater efficacy of practice, lower efficiency of practice, and better long-term retention. Whether such learning principles apply to motor practice after stroke is unclear. Here, we developed novel mixed-effects models of the change in the perceived quality of arm movements during and following task practice. The models were fitted to data from a recent randomized controlled trial of the effect of dose of task practice in chronic stroke. Analysis of the models’ learning and retention rates demonstrated an increase in efficacy of practice with greater doses, a decrease in efficiency of practice with both additional dosages and additional bouts of training, and fast initial decay following practice. Two additional effects modulated retention: a positive “self-practice” effect, and a negative effect of dose. Our results further suggest that for patients with sufficient arm use post-practice, self-practice will further improve use.

References

1.Winstein, CJ, Merians, AS, Sullivan, KJ. Motor learning after unilateral brain damage. Neuropsychologia. 1999;37:975-987.
Google Scholar | Crossref | Medline | ISI
2.Kitago, T, Goldsmith, J, Harran, M, et al. Robotic therapy for chronic stroke: general recovery of impairment or improved task-specific skill? J Neurophysiol. 2015;114:1885-1894.
Google Scholar | Crossref | Medline | ISI
3.Lohse, KR, Lang, CE, Boyd, LA. Is more better? Using metadata to explore dose-response relationships in stroke rehabilitation. Stroke. 2014;45:2053-2058.
Google Scholar | Crossref | Medline | ISI
4.Krakauer, JW, Carmichael, ST, Corbett, D, Wittenberg, GF. Getting neurorehabilitation right: what can be learned from animal models? Neurorehabil Neural Repair. 2012;26:923-931.
Google Scholar | SAGE Journals | ISI
5.Krakauer, JW, Carmichael, ST. Broken Movement: The Neurobiology of Motor Recovery After Stroke. MIT Press; 2017.
Google Scholar | Crossref
6.Schmidt, RA, Lee, TD, Winstein, CJ, Wulf, G, Zelaznik, HN. Motor Control and Learning: A Behavioral Emphasis. 6th ed. Human Kinetics; 2019.
Google Scholar
7.Newell, KM, Liu, YT, Mayer-Kress, G. Time scales in motor learning and development. Psychol Rev. 2001;108:57-82.
Google Scholar | Crossref | Medline | ISI
8.Ammons, RB, Farr, RG, Bloch, E, et al. Long-term retention of perceptual motor skills. J Exp Psychol. 1958;55:318-328.
Google Scholar | Crossref | Medline
9.Fleishman, EA, Parker, JF Factors in the retention and relearning of perceptual-motor skill. J Exp Psychol. 1962;64:215-226.
Google Scholar | Crossref | Medline
10.Winstein, C, Kim, B, Kim, S, Martinez, C, Schweighofer, N. Dosage matters: a phase IIb randomized controlled trial of motor therapy in the chronic phase after stroke Stroke. 2019;50:1831-1837.
Google Scholar | Crossref | Medline
11.Ward, NS, Brander, F, Kelly, K. Intensive upper limb neurorehabilitation in chronic stroke: outcomes from the Queen Square programme. J Neurol Neurosurg Psychiatry. 2019;90:498-506.
Google Scholar | Crossref | Medline
12.Daly, JJ, McCabe, JP, Holcomb, J, Monkiewicz, M, Gansen, J, Pundik, S. Long-dose intensive therapy is necessary for strong, clinically significant, upper limb functional gains and retained gains in severe/moderate chronic stroke. Neurorehabil Neural Repair. 2019;33:523-537.
Google Scholar | SAGE Journals | ISI
13.Kwakkel, G. Impact of intensity of practice after stroke: issues for consideration. Disabil Rehabil. 2006;28:823-830.
Google Scholar | Crossref | Medline | ISI
14.Lang, CE, Strube, MJ, Bland, MD, et al. Dose response of task-specific upper limb training in people at least 6 months poststroke: a phase II, single-blind, randomized, controlled trial. Ann Neurol. 2016;80:342-354.
Google Scholar | Crossref | Medline | ISI
15.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. Neurorehabil Neural Repair. 2016;30:551-561.
Google Scholar | SAGE Journals | ISI
16.Schweighofer, N, Wang, C, Mottet, D, et al. Dissociating motor learning from recovery in exoskeleton training post-stroke. J Neuroeng Rehabil. 2018;15:89.
Google Scholar | Crossref | Medline
17.Raghavan, P. Upper limb motor impairment after stroke. Phys Med Rehabil Clin N Am. 2015;26:599-610.
Google Scholar | Crossref | Medline
18.Hidaka, Y, Han, CE, Wolf, SL, Winstein, CJ, Schweighofer, N. Use it and improve it or lose it: interactions between arm function and use in humans post-stroke. PLoS Comput Biol. 2012;8:e1002343.
Google Scholar | Crossref | Medline | ISI
19.Winstein, CJ, Wolf, SL, Dromerick, AW, et al. Effect of a task-oriented rehabilitation program on upper extremity recovery following motor stroke: the ICARE randomized clinical trial. JAMA. 2016;315:571-581.
Google Scholar | Crossref | Medline | ISI
20.Page, SJ, Murray, C, Hermann, V. Affected upper-extremity movement ability is retained 3 months after modified constraint-induced therapy. Am J Occup Ther. 2011;65:589-593.
Google Scholar | Crossref | Medline | ISI
21.Han, CE, Arbib, MA, Schweighofer, N. Stroke rehabilitation reaches a threshold. PLoS Comput Biol. 2008;4:e1000133.
Google Scholar | Crossref | Medline | ISI
22.Schweighofer, N, Han, CE, Wolf, SL, Arbib, MA, Winstein, CJ. A functional threshold for long-term use of hand and arm function can be determined: predictions from a computational model and supporting data from the Extremity Constraint-Induced Therapy Evaluation (EXCITE) Trial. Phys Ther. 2009;89:1327-1336.
Google Scholar | Crossref | Medline | ISI
23.Cramer, SC . Repairing the human brain after stroke: I. Mechanisms of spontaneous recovery. Ann Neurol. 2008;63:272-287.
Google Scholar | Crossref | Medline | ISI
24.Kollen, B, van de Port, I, Lindeman, E, Twisk, J, Kwakkel, G. Predicting improvement in gait after stroke: a longitudinal prospective study. Stroke. 2005;36:2676-2680.
Google Scholar | Crossref | Medline | ISI
25.Park, H, Schweighofer, N. Nonlinear mixed-effects model reveals a distinction between learning and performance in intensive reach training post-stroke. J Neuroeng Rehabil. 2017;14:21.
Google Scholar | Crossref | Medline
26.Sheiner, LB, Beal, SL. Evaluation of methods for estimating population pharmacokinetic parameters. II. Biexponential model and experimental pharmacokinetic data. J Pharmacokinet Biopharm. 1981;9:635-651.
Google Scholar | Crossref | Medline
27.Casadio, M, Sanguineti, V. Learning, retention, and slacking: a model of the dynamics of recovery in robot therapy. IEEE Trans Neural Syst Rehabil Eng. 2012;20:286-296.
Google Scholar | Crossref | Medline
28.Kording, KP, Tenenbaum, JB, Shadmehr, R. The dynamics of memory as a consequence of optimal adaptation to a changing body. Nat Neurosci. 2007;10:779-786.
Google Scholar | Crossref | Medline | ISI
29.Lee, JY, Schweighofer, N. Dual adaptation supports a parallel architecture of motor memory. J Neurosci. 2009;29:10396-10404.
Google Scholar | Crossref | Medline | ISI
30.Scheidt, RA, Stoeckmann, T. Reach adaptation and final position control amid environmental uncertainty after stroke. J Neurophysiol. 2007;97:2824-2836.
Google Scholar | Crossref | Medline | ISI
31.Schweighofer, N, Lee, JY, Goh, HT, et al. Mechanisms of the contextual interference effect in individuals poststroke. J Neurophysiol. 2011;106:2632-2641.
Google Scholar | Crossref | Medline | ISI
32.Smith, MA, Ghazizadeh, A, Shadmehr, R. Interacting adaptive processes with different timescales underlie short-term motor learning. PLoS Biol. 2006;4:e179.
Google Scholar | Crossref | Medline | ISI
33.Kim, S, Oh, Y, Schweighofer, N. Between-trial forgetting due to interference and time in motor adaptation. PLoS One. 2015;10:e0142963.
Google Scholar | Medline
34.Kim, S, Ogawa, K, Lv, J, Schweighofer, N, Imamizu, H. Neural substrates related to motor memory with multiple timescales in sensorimotor adaptation. PLoS Biol. 2015;13:e1002312.
Google Scholar | Crossref | Medline
35.Oh, Y, Schweighofer, N. Minimizing precision-weighted sensory prediction errors via memory formation and switching in motor adaptation. J Neurosci. 2019;39:9237-9250.
Google Scholar | Crossref | Medline
36.Winstein, C, Lewthwaite, R, Blanton, SR, Wolf, LB, Wishart, L. Infusing motor learning research into neurorehabilitation practice: a historical perspective with case exemplar from the accelerated skill acquisition program. J Neurol Phys Ther. 2014;38:190-200.
Google Scholar | Crossref | Medline
37.Han, CE, Kim, S, Chen, S, et al. Quantifying arm nonuse in individuals poststroke. Neurorehabil Neural Repair. 2013;27:439-447.
Google Scholar | SAGE Journals | ISI
38.Fuhrer, MJ, Keith, RA. Facilitating patient learning during medical rehabilitation: a research agenda. Am J Phys Med Rehabil. 1998;77:557-561.
Google Scholar | Crossref | Medline
39.Uswatte, G, Taub, E, Morris, D, Light, K, Thompson, PA. The Motor Activity Log-28: assessing daily use of the hemiparetic arm after stroke. Neurology. 2006;67:1189-1194.
Google Scholar | Crossref | Medline | ISI
40.Winstein, CJ, Pohl, PS, Lewthwaite, R. Effects of physical guidance and knowledge of results on motor learning: support for the guidance hypothesis. Res Q Exerc Sport. 1994;65:316-323.
Google Scholar | Crossref | Medline | ISI
41.MacLellan, CL, Keough, MB, Granter-Button, S, Chernenko, GA, Butt, S, Corbett, D. A critical threshold of rehabilitation involving brain-derived neurotrophic factor is required for poststroke recovery. Neurorehabil Neural Repair. 2011;25:740-748.
Google Scholar | SAGE Journals | ISI
42.Fritz, SL, George, SZ, Wolf, SL, Light, KE. Participant perception of recovery as criterion to establish importance of improvement for constraint-induced movement therapy outcome measures: a preliminary study. Phys Ther. 2007;87:170-178.
Google Scholar | Crossref | Medline | ISI
43.Waddell, KJ, Lang, CE. Comparison of self-report versus sensor-based methods for measuring the amount of upper limb activity outside the clinic. Arch Phys Med Rehabil. 2018;99:1913-1916.
Google Scholar | Crossref | Medline
44.Mohabbati-Kalejahi, N, Yazdi, MAA, Megahed, FM, et al. Streamlining science with structured data archives: insights from stroke rehabilitation. Scientometrics. 2017;113:969-983.
Google Scholar | Crossref
45.Kim, S, Park, H, Han, CE, Winstein, CJ, Schweighofer, N. Measuring habitual arm use post-stroke with a bilateral time-constrained reaching task. Front Neurol. 2018;9:883.
Google Scholar | Crossref | Medline
46.Kwakkel, G, van Wegen, EEH, Burridge, JH, et al. Standardized measurement of quality of upper limb movement after stroke: consensus-based core recommendations from the second stroke recovery and rehabilitation roundtable. Neurorehabil Neural Repair. 2019;33:951-958.
Google Scholar | SAGE Journals | ISI
47.van Dokkum, L, Hauret, I, Mottet, D, Froger, J, Metrot, J, Laffont, I. The contribution of kinematics in the assessment of upper limb motor recovery early after stroke. Neurorehabil Neural Repair. 2014;28:4-12.
Google Scholar | SAGE Journals | ISI
48.Zeiler, SR, Hubbard, R, Gibson, EM, et al. Paradoxical motor recovery from a first stroke after induction of a second stroke: reopening a postischemic sensitive period. Neurorehabil Neural Repair. 2016;30:794-800.
Google Scholar | SAGE Journals | ISI
49.Murphy, TH, Corbett, D. Plasticity during stroke recovery: from synapse to behaviour. Nat Rev Neurosci. 2009;10:861-872.
Google Scholar | Crossref | Medline | ISI
50.Bahrick, HP. Retention of Spanish vocabulary over 8 years. J Exp Psychol. 1987;13:344-349.
Google Scholar
51.Druckman, D, Bjork, RA. Optimizing long-term retention and transfer. In: Druckman, D, Bjork, RA, eds. In the Mind’s Eyes: Enhancing Human Performance. National Academy Press; 1991.
Google Scholar
52.Ebbinghaus, H. Memory. Teacher’s College; 1913.
Google Scholar

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