[Editorial] Repetitions and dose in stroke rehabilitation – Journal of Physiotherapy

This editorial introduces another of Journal of Physiotherapy’s article collections.1, 2, 3 The studies in this article collection relate to stroke rehabilitation. Specifically, they are concerned with the dose-response relationship between practice and activity outcomes, and with strategies to increase the amount of practice undertaken by people with stroke.

Stroke is the second most common cause of death globally.⁴ Although stroke incidence has declined over time, the overall stroke burden (ie, absolute number of people affected or disabled by stroke) has increased globally.⁵ In the community as well as during inpatient rehabilitation, people with stroke typically achieve very low levels of any type of physical activity.

In an observational study by Alzahrani et al,⁶ people with stroke living in the community were monitored to establish the amount of free-living physical activity that they undertook during the day. Compared with age-matched healthy peers, people with stroke spent similar amounts of time in physical activity but achieved about half as many activity counts.

Kaur et al undertook an observational study of people in inpatient rehabilitation after stroke.⁷ Physiotherapists estimated the patients‘ total time in therapy and time spent in active practice during therapy. These times were also measured objectively from video recordings. Physiotherapists systematically overestimated both measures, which were very low: an average of 56 minutes total therapy time, of which only 31 minutes was active practice. These low amounts of time in practice are typical8, 9, 10, 11, 12 and persist even though there have been strong indications of a dose-response relationship between practice and outcomes for many years.13, 14, 15, 16, 17, 18

Since the late 1990s, many systematic reviews have examined the outcomes of increased dose of practice in stroke rehabilitation.14, 15, 16^,18,19 The review by Schneider was the first to look at the dosage question without confounding by type of practice, because they included only studies in which the increased amount of therapy was of the same type of therapy.²⁰ This review confirmed that increasing the amount of therapy improves outcomes, but only when the dosage is increased by at least 240%. For example if the usual dose of practice is 30 minutes, this needs to increase to 100 minutes to reach the threshold amount.

The systematic review by de Sousa et al²¹ was designed to find out if repetitive practice that is known to improve activity outcomes also improves strength after stroke. Most people with stroke have an extensive loss of strength²² and in many people that loss of strength precludes the use of progessive resistance training as they do not have sufficient strength to move against gravity, let alone against resistance. However, they are generally able to do some form of repetitive practice of (modified) tasks. The pooled analysis in the review by de Sousa et al²¹ showed that repetitive practice substantially improves strength after stroke. The authors hypothesise that one of the mechanisms for improved outcomes with repetitive practice is its ability to improve strength in addition to improvements in co-ordination of muscle activity.

The same group of investigators then examined the effect of two extra sessions of sit-to-stand training per day for people with stroke who were unable to stand up without assistance.²³ The extra sessions led to improved sit-to-stand ability and lower limb strength. This study and the systematic review by de Sousa et al²¹ support the concept that repetitive practice can increase strength as well as activity after stroke. This is important information because loss of strength is the main impairment contributing to activity limitations.^24,25

Providing extra therapy can also shorten the stay in hospital. In a meta-analysis of individual participant data from two clinical trials, English et al examined the difference in length of stay and outcomes between patients who did and did not receive weekend therapy.²⁶ Randomisation to the weekend therapy group reduced length of stay by 8 days (95% CI 2 to 13).

In the majority of trials above and other trials of increased practice in stroke rehabilitation,²⁷ extra therapy is delivered as one-to-one practice. Maintaining this would require additional staff, which may be unsustainable. The studies discussed below examine strategies to increase the amounts of time during which people with stroke have the opportunity to practise and strategies to increase the intensity of practice during their therapy time.

Providing therapy using groups or classes can increase opportunities to practise and is associated with improved activity outcomes.²⁸ An observational study by English et al²⁹ looked at both time in therapy and time in active practice when people with stroke were allocated to either circuit class therapy or one-to-one therapy in inpatient rehabilitation. There was no important difference in the proportion of each therapy session that was active practice. However the people in the circuit class were able to be in the gym area for much longer than the people in one-to-one therapy: an average of 73 minutes versus 47 minutes. Hence, overall the people in the circuit class spent longer doing active practice than the people in one-to-one therapy: an average of 35 minutes versus 23 minutes.

Increased time to practise in the gym area without direct therapist supervision can be achieved without compromising time spent in active practice or patient safety. The observational study by Dorsch et al³⁰ categorised the conditions of practice in an inpatient rehabilitation gym as: practice with a therapist, practice with a family member or practice with no direct supervision. Of the inpatients using the gym area at the time, 43% were people with stroke. Of the active practice taking place, 59% was being done with a therapist, 15% with a family member and 24% with no direct supervision. Evidently, semi-supervised practice (ie, practice that takes place in the gym area but without the direct supervision of a therapist) is a strategy that could greatly increase the opportunities for practice in inpatient rehabilitation by allowing patients to be in the gym area for longer times. The inclusion of semi-supervised practice as a strategy did not have a deleterious effect on the time spent in active practice with 78% of the total observations being of active practice, compared with 67% of time observed in English et al²⁹ and 63% of time observed in Kaur et al.⁷ The inclusion of semi-supervised practice as a strategy did not have a deleterious effect on patient safety with no adverse incidents such as falls ocurring during this time.

In addition to increasing opportunities for people with stroke to practise, there are strategies to increase the intensity of practice that is done during the available therapy time. The instructions a therapist uses can make an important difference to the intensity of practice that a person with stroke achieves. In a repeated-measures, experimental study by Hillig et al,³¹ three types of instructions were evaluated for their effect on intensity of practice. This was measured as the number of repetitions of an exercise completed in one minute. The most effective instruction (‘do (exercise) 25 times, as fast as you can, aiming for a personal best’) resulted in a rate of repetitions that was more than double that done with a non-goal-oriented instruction. The use of goal-oriented instructions is a simple, no-cost strategy that can be used to increase the intensity of practice in stroke rehabilitation.

Quantifying the dosage of practice as the number of exercise repetitions is a more accurate and valid method of quantifying the dose of practice than time in therapy or time in active practice. The earlier study by Kaur et al⁷ indicates that recording time in therapy is not an accurate measure of dosage of practice. As well as being an inaccurate measure, it does not appear to be a valid measure of practice. An observational study by Scrivener et al³² designed to assess the accuracy of patients’ ability to count their repetitions of practice showed great variability in the amount of practice done. In this study, inpatients in a mixed rehabilitation unit completed somewhere between 4 and 369 repetitions in 30 minutes. With this variability in the rate of practice being done, recording the number of repetitions would be a more valid measure of dosage of practice than recording time practising or time in the gym. This study, in which 43% of the participants had stroke, showed that inpatients in rehabilitation can be accurate in counting repetitions of practice. Hence, there is a strong argument to use the counting of repetitions to quantify the amount of practice people have done.

If future research in this field used repetitions of practice to quantify dosage, we could generate more precise knowledge to share with people with stroke about the amounts of practice needed to achieve specific outcomes. An additional advantage of counting and recording exercise repetitions is that numbers of repetitions can be used within instructions and goals, and baseline numbers of repetitions can be used in discussions about increasing amounts of practice. However, to date there does not appear to be research that has evaluated the effect of counting and recording repetitions of practice on amounts of practice over time. This research question could form part of future research into strategies with the potential to increase engagement in practice and motivation to practise. Such research would improve our ability to help people with stroke to achieve effective amounts of practice.

References

Source

[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

[Abstract] Realistic modeling of transcranial current stimulation: The electric field in the brain

Highlights

• Computational models are required to optimize the electric field in the brain.
• tCS pipelines allow for fast and semi-automatic production of realistic head models.
• In-vivo validation studies corroborate electric field predictions from tCS models.
• tCS modeling could help identify the causes for intra and inter-subject variability.
• Successful application of multi-electrode montages strongly depends on tCS models

Abstract

Computational models of transcranial current stimulation (tCS) derived from MRI predict the electric field distribution in individual brains with reasonable accuracy and should be used to guide the selection of optimal stimulation parameters. Some recent advances that support this claim are: free toolboxes to generate individual head models for electric field calculations, the validation of model predictions in comparison to in-vivomeasurements, and new algorithms to optimize the electric field at the target with multi-electrode stimulation. Electrical impedance tomography may provide subject-specific estimates of the electric conductivity of the scalp and skull, thereby improving the accuracy of the electric field calculations. In the future, electric field models should be coupled with electrophysiological models to predict experimental outcomes.

Graphical abstract

 

via Realistic modeling of transcranial current stimulation: The electric field in the brain – ScienceDirect