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Cognitive motor training: use of dual tasks, virtual and augmented reality

(The present document “Cognitive motor training: use of dual tasks, virtual and augmented reality” is based on a free translation of the document entitled “Walking and balance training based on virtual and augmented reality” (1) with contributions by the author José López Sánchez, based on his clinical experience and other published scientific studies on the subject.)

1. Introduction

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2. Principles of treatment

The protocols used in the different studies and the outcome measures used are still very heterogeneous and do not allow comparison between groups. However, the training should follow a series of principles that, following the theories of motor learning, allow optimizing the interventions and improving the results. These principles should be applied in training:

1. Focus of attention.
2. Implicit learning.
3. Variation.
4. Intensity of training.
5. Task specificity.
6. Feedback.

Let’s explain one by one the principles of treatment:

1. Focus of attention

During rehabilitation, therapists have to explain the exercises to the patients, and the instructions they provide will influence the patient’s attention focus, the execution of the movement, and the outcome of the movement. Therapists often use instructions referring to body parts or movements (e.g., keep your knees behind your toes to promote greater knee extension). In motor learning this is known as “instructions that promote an internal focus of attention. This results in more conscious movements that interfere with automatic motor control (3).

In addition, in people with attention problems,  it consumes much or all of the attention capabilities that the person has, not leaving resources to be able to face other tasks at the same time (dual tasks). Recent studies indicate that instructions that promote an external focus, for example directing attention to the effect of movement in the environment (for example “touch your foot to the mark on the ground”), achieve an improvement in motor learning.

Studies conducted in sport (4-6) and balance training (7) consistently show better motor performance after a period of learning centered on an external focus, versus instructions centered on an internal focus. However, in daily practice it is sometimes difficult to find the right instructions that induce an external focus. One of the advantages of augmented reality is that it can facilitate gait adjustments, for example, through the external signals it provides, such as objectives on which the patient must take a step, projected onto the surface on which he or she walks, or auditory signals. In the following video we can see examples of this type of training:

In this case, augmented reality using external signals directs the patient’s attention focus to the virtual world, rather than to the patient’s body, which promotes the external attention focus and is likely to improve the outcome of the therapy, according to the principles of motor learning.

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2. Implicit learning

Traditionally, new motor skills are taught through explicit instructions, resulting in conscious control of movement. However, movement control is usually based on implicit knowledge. We know how to do the movement, but we are not usually aware of how we control our muscles and cannot explain it in words. Recent studies suggest that explicit learning can limit or interfere with such automatic processes, leading to worse execution, especially when people have to perform a task under pressure (8-12). Rehabilitation could therefore benefit from the use of implicit learning, e.g. learning without awareness of what is being learned.

For example, in patients after stroke, the execution of a dynamic balancing task was worse after a period of explicit versus implicit learning (13). A way of promoting implicit learning, through instructions or tasks that require an external focus of attention, has previously been described. Another alternative way is through the use of a dual task (9) or through variation in tasks so that learning through explicit rules is impossible. Virtual and augmented reality games often promote this implicit learning through one or more of these principles.

Definitely it is time to change old paradigms in neurorehabilitation where the patient goes to physiotherapy or occupational therapy when he wants to work motor aspects of the leg or arm and to the neuropsychologist when he wants to work cognitive aspects. The scientific evidence shows us the constant interaction of cognitive and motor aspects and the interaction between patient capabilities, task and environment are keys for relearning. This is why we have to think about what kind of learning is promoting the task we present to the patient and the environment in which it is going to be performed, adapting it to their abilities to progress as the patient practices and improves.

3. Variation

The importance of variation in exercises is another lesson we have learned from research in the field of motor learning.

Instead of training the exact same movement over and over again, small variations in movement will result in more robust motor learning (14). In addition, variations in the sequence of exercises (random versus block) will improve motor learning, especially retention and transfer (15). Although studies consistently favor variable practice, most have focused on laboratory tasks (15, 16) or sports applications (14, 17-19). When these principles are applied, for example, to balance training, standing postural balance is reduced after 15 minutes of varied balance exercises (weight transfer exercises and reduced support bases) while no difference is found after repetitive training or simply standing still (20).

It therefore appears that variable task practice may also improve rehabilitation outcomes. Through the use of virtual or augmented reality, variations can be easily created by modifying numerous exercise parameters, such as target placement, speed requirements, environmental elements, etc.

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4. Intensity of training

The intensity of the training (number of repetitions, frequency of training, difficulty of tasks, etc.) is a determining factor of the therapy result (21-23). High intensity training is recommended in order to maximize the effect of the treatment. Virtual and augmented reality may help to achieve high practice intensities, increasing the motivation of some patients and their adherence to the treatment, improving the efficiency of the training and providing an adequate challenge.

In addition, virtual and augmented reality (VR and RA) training facilitates two types of training: autonomous training by the patient, in the clinic and at home. In many rehabilitation centers the patient/therapist ratio is reduced and this is a challenge when it comes to increasing the intensity of the training. It is also the case that many patients only practice when they are with the therapist, but when they return home they stay sedentary most of the time. For these two situations, VR and RA can be a solution for some patients, as it provides the feedback they need to perform the exercises, they can be monitored remotely by a professional, adapted when necessary, and allow the collection of information on how much activity the patient is doing and how he is doing.

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Neurorehabilitation often requires relatively simple repetitive movement training. Certain exercises often become quickly boring, making it difficult for the patient to be motivated and concentrated. One of the benefits of virtual rehabilitation is the use of games, which for some patients can make therapy more fun and enjoyable (24-26). Some patients may become more engaged in the therapy session and make adherence to treatment increase (27-30).

Also the number of repetitions that can be achieved and the time of active treatment with virtual reality and augmented reality can be greater than with conventional therapy (31-33). For example, one study achieved twice as many steps during an AR task and treadmill training compared to conventional gait training (31). Increased motivation is certainly one of the factors explaining this but not the only one. Other practical aspects, such as the fact that there is no physical need to be setting up and modifying the different gait circuits, increase the time that can be devoted to active training by the patient within a session. It is also possible to control very precisely the level of challenge that is proposed to the patient according to their abilities. The difficulty of the games can be easily and gradually adapted, for example by changing speed requirements or distances from the objectives to be reached.

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5. Specificity of the task.

Another important recommendation for rehabilitation is to include task-specific training (22, 34). To improve the transfer of progress from motor function to activities outside of therapy, therapy should include the practice of daily living challenges. VR and AR can be used to simulate such challenges within a safe environment.

For example virtual reality and augmented reality could help train gait in difficult situations. This is essential, because walking in daily life is much more than putting one foot in front of the other, it also requires the ability to adjust the gait pattern to different situations. You may need to lift your leg more to avoid tripping over a loose cobblestone, or slow down to avoid hitting someone, or increase your speed to pass an amber traffic light, or avoid people in a crowded mall. The adaptability of the gait is defined as the agility to adjust the same according to the circumstances of the environment, and is therefore a crucial element when walking at home and especially in the community. The RA can be a useful tool to train the adaptability of the gait, projecting objectives for the feet or obstacles on the surface on which one walks (35,36). In addition, virtual reality can be used to create an optical flow when walking on a treadmill, to improve the natural sensation of walking (37,38).

Other examples of everyday challenges are activities that involve cognitive and motor tasks at the same time, such as crossing a street while attending to traffic, or walking while remembering what you had to buy at the supermarket, or while talking to a friend. When two tasks are performed simultaneously, the quality and performance of one or both tasks may be reduced. This is known as “dual task interference,” which occurs most often with age (39), and with some neurological pathologies such as stroke (40) or Parkinson’s disease (41).

Interference in dual tasks has been shown to predict falls (42). Dual task training is more effective at reducing “dual task interference” than single task training (43-46) and therefore fall prevention programs should always include dual tasks (47). Through virtual reality it is relatively simple to add cognitive elements to training, and with it training in dual tasks. One way to do this is to include a cognitive task that is not related to the motor task, for example counting backwards or a memory task.

In the vast majority of cases, cognitive training is done sitting at a table, rarely moving. It would be very interesting to include the use of stimulation systems and cognitive rehabilitation while walking, practicing balance exercises, or simply standing.

Another way to incorporate the cognitive task into the virtual reality game, for example, is through games that require planning or strategy development.

Finally, cognitive elements can be added by simulating dual task challenges that arise in everyday life, such as walking in a virtual supermarket while placing a series of objects in the shopping basket (48) or crossing the street while avoiding obstacles (49).

6. Feedback

In order to improve our motor execution we need at least some information about how we are performing a task. This feedback often comes from intrinsic sources such as vision or proprioception. Intrinsic feedback can be increased by providing information that would normally be inaccessible to the patient, such as exact angles of joints or movements (biofeedback).

Through virtual reality, biofeedback can be shown to the patient or even incorporated into the exercise. Providing biofeedback can be very useful for gait training or balance training.

Balance training with feedback usually consists of weight transfer exercises in which the patient receives information about the position of their pressure center. In a systematic review, the effectiveness of balance training based on feedback in older adults was evaluated and it was concluded that such training results in reduced postural balance, improved ability to transfer weight, reduced attention demands while standing still and improved scores on the Berg scale (50). There is also some evidence to suggest that adding biofeedback to balance training in people with post-stroke sequelae may be beneficial (51,52).

There is a lot of literature showing the effectiveness of biofeedback for gait retraining in different patient populations. For example, feedback training may reduce knee adduction movement or increase toe angle for the prevention of knee osteoarthritis (53-55). It may also improve propulsion during take-off in healthy older people, making their gait pattern more similar to that of young adults (56).

Feedback can help people with Parkinson’s disease, or incomplete spinal cord injury, take longer steps (57,58) and improve walking after transfemoral amputation (59). It has been shown to help modulate gait parameters in children with cerebral palsy (60). There are other applications to prevent injuries in runners, patterns of hyperextension of knees (61,62), etc..

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All these examples show how biofeedback is an effective and versatile tool that allows patients to adapt specific aspects of their gait. In conclusion, the ability to provide biofeedback is one of the greatest assets of VR training. Through the incorporation of increased feedback into a game, patient motivation and involvement can be increased.

3. Conclusions


  1. Virtual and Augmented reality based balance and gait training. Selma Papegaaij, Floris Morang, Frans Steenbrink.
  2. Motor-Cognitive Dual-Task Training in Persons With Neurologic Disorders: A Systematic Review. Fritz NE, Cheek FM, Nichols-Larsen DS.
  3. The automaticity of complex motor skill learning as a function of attentional focus. Wulf G, McNevin N, Shea CH.
  4. Increased movement accuracy and reduced EMG activity as the result of adopting an external focus of attention. Zachry T, Wulf G, Mercer J, Bezodis N.
  5. Enhancing the Learning of Sport Skills Through External-Focus Feedback. Wulf G, Mcconnel N, Gärtner M, Schwarz A.
  6. The effects of attentional focusing strategies on novice dart throwing performance and Their task experiences. Marchant DC, Clough PJ, Crawshaw M.
  7. Effects of external focus of attention on balance: a short review. Park SH, Yi CW, Shin JY, Ryu YU.
  8. Reinvestment and movement disruption following stroke. Orrell AJ, Masters RSW, Eves FF.
  9. Knowledge, knerves and know-how: The role of explicit versus implicit knowledge in the breakdown of a complex motor skill under pressure. Masters R.
  10. The role of reinvestment in walking and falling in community- dwelling older adults. Wong W-L, Masters RSW, Maxwell JP, Abernethy B.
  11. Duration of Parkinson disease is associated with an increased propensity for “reinvestment”. Masters RSW, Pall HS, MacMahon KMA, Eves FF.
  12. Analogy learning: A means to implicit motor learning. Liao C-M, Masters RSW.
  13. Motor learning of a dynamic balancing task after stroke: implicit implications for stroke rehabilitation. Orrell AJ, Eves FF, Masters RSW.
  14. A quantitative dynamical systems approach to differential learning: self-organization principle and order parameter equations. Frank TD, Michelbrink M, Beckmann H, Schöllhorn WI.
  15. Contextual interference effects on the acquisition, retention, and transfer of a motor skill. Shea J, Morgan R.
  16. Programming and reprogramming sequence timing following high and low contextual interference practice. Wright DL, Magnuson CE, Black CB.
  17. Does noise provide a basis for the unification of motor learning theories?. Schollhorn W, Beckmann H.
  18. Differencial learning in shot put. Beckmann H, Schöllhorn WI.
  19. Contextual interference effects with skilled baseball players. Hall KG, Domingues DA, Cavazos R.
  20. Short-term differential training decreases postural sway. James EG.
  21. Potential Contributions of Training Intensity on Locomotor Performance in Individuals With Chronic Stroke. Holleran CL, Rodriguez KS, Echauz A, Leech KA, Hornby TG.
  22. Stroke rehabilitation. Langhorne P, Bernhardt J, Kwakkel G.
  23. Effects of Balance Training on Balance Performance in Healthy Older Adults: A Systematic Review and Meta-analysis. Lesinski M, Hortobagyi T, Muehlbauer T, Gollhofer A, Granacher U.
  24. A controlled pilot trial of two commercial video games for rehabilitation of arm function after stroke. Chen M-H, Huang L-L, Lee C-F, et al.
  25. Effectiveness of conventional versus virtual reality-based balance exercises in vestibular rehabilitation for unilateral peripheral vestibular loss: results of a randomized controlled trial. Meldrum D, Herdman S, Vance R, et al.
  26. Efficacy of virtual reality-based balance training versus the Biodex balance system training on the body balance of adults. Ibrahim MS, Mattar AG, Elhafez SM.
  27. Exergaming With Additional Postural Demands Improves Balance and Gait in Patients With Multiple Sclerosis as Much as Conventional Balance Training and Leads to High Adherence to Home-Based Balance Training. Kramer A, Dettmers C, Gruber M.
  28. Patient perspectives on virtual reality-based rehabilitation after knee surgery: Importance of level of difficulty. Lee M, Suh D, Son J, Kim J, Eun S-D, Yoon B.
  29. Effects of virtual reality-enhanced exercise equipment on adherence and exercise-induced feeling states. Annesi JJ, Mazas J.
  30. Usability and Effects of an Exergame-Based Balance Training Program. Wüest S, Borghese NA, Pirovano M, Mainetti R, van de Langenberg R, de Bruin ED.
  31. Feasibility of C-mill gait-adaptability training in older adults after fall-related hip fracture: user’s perspective and training content. van Ooijen MW, Roerdink M, Timmermans C, et al.’s_perspective_and_training_content
  32. Eliciting Upper Extremity Purposeful Movements Using Video Games. Rand D, Givon N, Weingarden H, Nota A, Zeilig G.
  33. Is upper limb virtual reality training more intensive than conventional training for patients in the subacute phase after stroke? An analysis of treatment intensity and content. Brunner I, Skouen JS, Hofstad H, et al.
  34. Understanding the pattern of functional recovery after stroke: facts and theories. Kwakkel G, Kollen B, Lindeman E.
  35. Feasibility and Preliminary Efficacy of Visual Cue Training to Improve Adaptability of Walking after Stroke: Multi-Centre, Single- Blind Randomised Control Pilot Trial. Hollands KL, Pelton TA, Wimperis A, et al.
  36. Step by step: a proof of concept study of C-Mill gait adaptability training in the chronic phase after stroke. Heeren A, van Ooijen M, Geurts ACH, et al.
  37. Effects of adding a virtual reality environment to different modes of treadmill walking. Sloot LH, van der Krogt MM, Harlaar J.
  38. Self-paced versus fixed speed walking and the effect of virtual reality in children with cerebral palsy. Sloot LH, Harlaar J, van der Krogt MM.
  39. Changes in Standing and Walking Performance Under Dual-Task Conditions Across the Lifespan. Ruffieux J, Keller M, Lauber B, Taube W.
  40. Dual-task-related gait changes in individuals with stroke. Yang Y-R, Chen Y-C, Lee C-S, Cheng S-J, Wang R-Y.
  41. Characterization of cognitive and motor performance during dual-tasking in healthy older adults and patients with Parkinson’s disease. Wild LB, de Lima DB, Balardin JB, et al.
  42. Stops walking when talking: a predictor of falls in older adults?. Beauchet O, Annweiler C, Dubost V, et al.
  43. Training effects on motor–cognitive dual-task performance in older adults. Wollesen B, Voelcker-Rehage C.
  44. The effect of single-task and dual-task balance exercise programs on balance performance in adults with osteoporosis: a randomized controlled preliminary trial. Konak HE, Kibar S, Ergin ES.
  45. Virtual Reality Training with Cognitive Load Improves Walking Function in Chronic Stroke Patients. Cho KH, Kim MK, Lee H-J, Lee WH.
  46. Multicomponent physical exercise with simultaneous cognitive training to enhance dual-task walking of older adults: A secondary analysis of a 6-month randomized controlled trial with I-year follow-up. Eggenberger P, Theill N, Holenstein S, Schumacher V, de Bruin ED.
  47. Comparison of traditional and recent aproaches in the promotion of balance and strength in older adults. Granacher U, Muehlbauer T, Zahner L, Gollhofer A, Kressig RW.
  48. Cognitive load and dual-task performance during locomotion poststroke: a feasibility study using a functional virtual environment. Kizony R, Levin MF, Hughey L, Perez C, Fung J.
  49. A treadmill and motion coupled virtual reality system for gait training post-stroke. Fung J, Richards CL, Malouin F, McFadyen BJ, Lamontagne A.
  50. Biofeedback for training balance and mobility tasks in older populations: a systematic review. Zijlstra A, Mancini M, Chiari L, Zijlstra W.
  51. Influence of posturographic platform biofeedback training on the dynamic balance of adult stroke patients. Maciaszek J, Borawska S, Wojcikiewicz J.
  52. Symmetrical body-weight distribution training in stroke patients and its effect on fall prevention. Cheng PT, Wu SH, Liaw MY, Wong AM, Tang FT.
  53. Comparison of mirror, raw video, and real-time visual biofeedback for training toe-out gait in individuals with knee osteoarthritis. Hunt MA, Takacs J, Hart K, Massong E, Fuchko K, Biegler J.
  54. Gait Retraining with real-time Biofeedback to reduce Knee adduction moment: systematic review of effects and methods used. Richards R, van den Noort JC, Dekker J, Harlaar J.
  55. Real-time visual feedback for gait retraining: toward application in knee osteoarthritis. van den Noort JC, Steenbrink F, Roeles S, Harlaar J.
  56. Real-time feedback enhances forward propulsion during walking in old adults. Franz JR, Maletis M, Kram R.
  57. A System for Real-Time Feedback to Improve Gait and Posture in Parkinson’s Disease. Jellish J, Abbas JJ, Ingalls TM, et al.
  58. Augmented multisensory feedback enhances locomotor adaptation in humans with incomplete spinal cord injury. Yen S-C, Landry JM, Wu M.
  59. Gait training with virtual reality- based real-time feedback: improving gait performance following transfemoral amputation. Darter BJ, Wilken JM.
  60. Real-time feedback to improve gait in children with cerebral palsy. van Gelder L, Booth ATC, van de Port I, Buizer AI, Harlaar J, van der Krogt MM.
  61. Gait modifications to change lower extremity gait biomechanics in runners: a systematic review. Napier C, Cochrane CK, Taunton JE, Hunt MA.
  62. Short and long-term effects of gait retraining using real-time biofeedback to reduce knee hyperextension pattern in young women. Teran-Yengle P, Cole KJ, Yack HJ.

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