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Learn more about our guest: Katie Riccio, MS, OTR/L
As occupational therapists, many of us have witnessed the wonder of motor learning in neuro rehab.
Yet despite years of research attempting to identify the most effective post-stroke interventions, the reality is that our understanding of how to improve recovery of function remains incomplete.
The article we are reviewing today serves as an update on the state of the science in how new theories of motor control and learning can be incorporated into practice.
The authors put particular emphasis on how new technologies may be needed to truly meet the many principles of neuroplasticity, and in particular they point to virtual, augment, and mixed reality technologies.
After we review the research, we are excited to welcome to the podcast an OT who works for one such virtual rehab solution as their Manager of Clinical Services, Katie Riccio, MS, OTR/L. Katie will share what she has learned about neurorehab in her role. And orient us to the stroke rehab tech tools on the market, how they can intersect with you in-person practice—and what to expect on the horizon.
Agenda
Intro and breakdown and analysis of journal article
- Intro to motor learning in neurorehab
- What was the intent of this research?
- Theories of motor control and learning
- General principles of exercise dependent neuroplasticity
- Applications of motor control and learning principles in neurorehab using VR
- Authors discussion/conclusions
Discussion on practical implications for OTs
- Can you tell me about your journey to your current job?
- Can you tell me about Neurophenix and how it fits into the larger ecosystem of virtual neurorehab?
- What were your impressions of this research?
- Did you find any of the theories presented helpful?
- How do you currently understand the interplay between in person rehab and virtual rehab?
- How are virtual rehab options currently paid for?
- What have you learned from your work about intensity levels in UL neuro rehab?
- What have you learned about the importance of progressive levels of challenge in neurorehab?
- What have you learned about feedback and its role in stroke rehab?
- In your view, what remains missing from virtual stroke rehab? (And, what changes do you see on the horizon?)
- How do we as OTs position ourselves as experts in stroke tech rehab?
Supplemental Resources
- Application of Home-Based Wearable Technologies in Physical Rehabilitation for Stroke: A Scoping Review.
- Neurorehabilitation From a Distance: Can Intelligent Technology Support Decentralized Access to Quality Therapy?
- Advances and Challenges in Stroke Rehabilitation.
Article Review
Read Full Text: Motor learning in neurological rehabilitation
Journal: Disability and Rehabilitation
Year Published: 2021
Ranked 54th on our 2019-2023 list of the 100 Most Influential OT Journal Articles
As occupational therapists, many of us have witnessed the wonders of neuroplasticity and motor learning in neurorehab.
Yet, despite numerous studies attempting to identify the most effective rehabilitation interventions, post-stroke upper limb recovery remain incomplete, with sensorimotor deficits persisting in a large portion of stroke survivors.
The article we are reviewing today gives the state of the science on new theories of motor control and learning—and explains how these theories can be incorporated into practice.
The authors specifically emphasize that new technologies may be needed to truly align with the many principles of neuroplasticity, pointing to virtual, augmented, and mixed reality technologies in particular.
Next week, we are excited to welcome to the podcast Katie Riccio, MS, OTR/L, the Manager of Clinical Services for one such virtual rehab solution. Katie will share what she has learned about neurorehab in her role and orient us to the stroke rehab tech tools on the market today, how they can intersect with your in-person practice, and what to expect in the future.
Intro to motor learning in neurorehab
Over the years, numerous studies have been conducted to identify the most effective rehab interventions for post-stroke upper limb recovery. But, our knowledge of this practice area remains incomplete.
Upper limb sensorimotor deficits continue to persist in a large portion (up to 62%) of stroke survivors for greater than 6 months. This means the burden of upper limb impairment remains high.
What was the intent of this article?
The authors of this article suggest that recovery potential may improve when upper limb training programs focus on:
“Remediating an individual’s specific motor impairment with the framework of a motor control theory.”
To support this conjecture, they review current theories of motor control and learning—and describe how these theories can be leveraged in training programs.
It is interesting to note that one author was trained as a PT and the other as an OT.
Okay, this is about to get nerdy. But, hang with me! (And please, dig into the full article for much more science and complexity! I feel like I’m doing quite a bit of simplifying for this one.)
Theories of motor control and learning
There are two major approaches to/theories of motor control and learning.
The first is the computation/physical approach, which assumes that central processes directly control movement characteristics. The image I found to illustrate computational motor control theory literally portrays the body as a machine:
In line with the computational approach to motor control, motor learning is then understood as a series of different systems that integrate information from the moving limb during task practice, and then use this information to build a movement schema that can be stored and recalled when needed.
The way I understand it, this theory pushes us to practice the same movements over and over until the schema is built.
But, there is a newer theory that better represents how we see motor learning occur in the real world—and it will feel very familiar to OTs:
The dynamical systems approach.
In this approach, motor learning is seen as a model of person, environment, and task-related constraints that lead to movement. Just look at this model. (Could there be anything more “OT?”)
Similarly, in a dynamical systems approach to motor control, the central nervous system is understood to govern—rather than simply produce—movement. There is a complex interaction between the neuromuscular system, the biomechanics of the body, and the object and environmental forces.
Here’s how the article describes skill learning under this approach:
“The mastery of degrees of freedom is achieved through a problem solving system that uses available constraints and possibilities to discover solutions to a movement problem.
The approach emphasizes dynamic exploratory activity of their perceptual/motor workspace to create optimal strategies for performing a task and give rise to adaptability based on demands and constraints.”
Okay, I’m sure you can already see how adopting one of these two theories is going to impact your rehab approach. From here, let’s look specifically at the general principles of exercise-dependent neuroplasticity—and how they intersect with the dynamic systems theories.
General principles of exercise-dependent neuroplasticity
When motor learning theory (like the dynamical systems theory) is combined with the 10 principles of neurorehabilitation, we start to get a really concrete guidance for rehabilitation.
In particular, the authors highlight the importance of intensity, repetition, and salience.
Definition of intensity in rehab
We know that rehab should:
- be delivered at high intensity (i.e., dose, frequency, and duration), and
- involve challenging practice.
However, the definitive number of repetitions actually needed for motor learning post-stroke is unknown.
One thing we do know is that compared to healthy individuals, it takes more repetitions for individuals with a neurological insult to achieve improvement in motor outcomes. In this study, healthy participants required around 20 repetitions to improve their performance for a reaching task. Individuals with neurological deficits required more than twice that number (55 repetitions).
Training specificity
While intensity is an important piece of the puzzle, it is not on its own enough to improve function—and neither is specificity.
As the authors point out, results of training programs for improving UL function have not demonstrated adequate carry-over to functional movement. This supports the idea that repetitive practice of a single movement pattern is unlikely to result in carry-over due to the dynamical nature of movement.
In the dynamical approach, less emphasis is placed on reproducing an optimal movement pattern. Instead, this approach focuses on learning a set of movement patterns that might be equally acceptable in light of constraints presented by the environment, the specific task, and the learner’s condition.
Training progression and motivation
Another principle of neuroplasticity is that practice should be progressive and optimally adapted to the individual’s capability and environment. Thus, to induce new learning, practice should:
- Challenge for the learner
- Progress over time
- Engage the learner in active problem solving
The challenge level is also related to motivation, another key factor for motor learning. In clinical and health populations, it has been shown that surrogate markers for motivation—such as self-confidence, hope, autonomy, support, and social relatedness—play important roles in promoting motor learning.
Type of task practice
Task practice that is organized according to person, environment, and task constraints plays another important role.
The two main practice paradigms are blocked practice and varied practice. More comprehensive studies are needed to determine the benefits of both practice types, but in terms of alignment with theory, varied practice better lends itself to active problem solving.
Type of feedback and delivery
While not mentioned in the 10 principles of neuroplasticity, we know that the frequency and type of feedback matter. Of note in this section, there is strong clinical evidence suggesting that individuals’ knowledge of performance post-stroke may lead to better motor learning outcomes and retention than knowledge of results.
Applications of motor control and learning principles in neurorehab using VR
Phew! There are clearly so many considerations for motor learning post-stroke that it is difficult to align theory with neurorehab practice in our traditional rehab settings.
Just looking at repetitions alone, the general consensus is that the number of repetitions performed in OT and PT sessions in both outpatient and inpatient rehab centers is inadequate. (For example, in this study, the average number of UL repetitions was only 32.)
The authors propose that rehab technology is poised to address not only the problem of practice intensity, but also practice structure and feedback.
(The authors spend quite a bit of time talking about what this could look like hypothetically. Thankfully, we have a guest coming on the podcast to discuss what it looks like in real-world scenarios—as well as the currently available tech options.)
Authors’ discussion/conclusions
In summary, VR applications have the potential to provide high-repetition, varied practice in changing environments to engage learners in active problem solving. VR systems can also incorporate monitoring and in vivo performance tracking to continually assess whether the challenge is optimal, while also providing real-time feedback.
This paper has given an overview of the theory of motor control and learning principles that will help therapists evaluate VR options—and ultimately, move the field forward to improve the lives of people with disabilities following neurological injury.
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