The rehabilitation of cognitive and behavioral abnormalities in individuals with stroke is essential for promoting patient’s recovery and autonomy. The aim of our study is to evaluate the effects of robotic neurorehabilitation using Lokomat with and without VR on cognitive functioning and psychological well-being in stroke patients, as compared to traditional therapy.
Ninety stroke patients were included in this randomized controlled clinical trial. The patients were assigned to one of the three treatment groups, i.e. the Robotic Rehabilitation group undergoing robotic rehab with VR (RRG+VR), the Robotic Rehabilitation Group (RRG-VR) using robotics without VR, and the Conventional Rehabilitation group (CRG) submitted to conventional physiotherapy and cognitive treatment.
The analysis showed that either the robotic training (with and without VR) or the conventional rehabilitation led to significant improvements in the global cognitive functioning, mood, and executive functions, as well as in activities of daily living. However, only in the RRG+VR we observed a significant improvement in cognitive flexibility and shifting skills, selective attention/visual research, and quality of life, with regard to the perception of the mental and physical state.
Our study shows that robotic treatment, especially if associated with VR, may positively affect cognitive recovery and psychological well-being in patients with chronic stroke, thanks to the complex interation between movement and cognition.
A stroke is usually considered chronic at the six-month mark. This article reviews research on chronic stroke recovery and promising therapy treatment approaches that target improving limb function, even for an “old” stroke.
By Natalie Miller, Clinical Manager / Occupational Therapist. More posts by Natalie Miller.
27 MAR 2020 • 5 MIN READ
What is a chronic stroke?
The term chronic stroke typically refers to a time frame of at least six months after the initial stroke incident occurred. As a person enters this stage and moves onward to years of stroke survival, he may start to encounter all new frustrations related to recovery, especially regarding motor recovery and use of the affected arm.
In the medical world, “most significant” recovery of movement is generally considered to happen within the first six months, with spontaneous recovery slowing after that time. There is a push for high-intensity and high-frequency of therapy while the stroke is still fairly fresh, in order to capitalize on the “critical window” of the highest responsiveness to treatment.
That doesn’t mean we should stop addressing motor recovery after six months. What if we still focus on intensive therapy early on in stroke rehab, but also find ways to promote motor recovery six or more months later? What if we don’t stop searching for new strategies to improve, or at the very least, not lose function of the weaker arm?
Can I still improve function if I am in the chronic stage of stroke?
Our understanding of the brain and its capabilities is constantly evolving. We used to think that adult brains couldn’t change at all after a certain age! Emerging research evidence suggests there are ways to challenge and improve the chronic stroke brain months and even years down the road. One large-scale study involving outcomes from 219 stroke survivors suggested the critical window for motor recovery may be as long as 18 months! Another recent case study highlighted motor recovery in a stroke survivor who was 23 years post-stroke!
What types of rehabilitation are effective for people with chronic stroke?
Stroke research suggests the following treatments are promising for individuals who are at least six months post-stroke:
Mental Practice with Motor Imagery
Constraint Induced Movement Therapy (CIMT)
Virtual Reality (VR)
Preventing Learned Non-Use
Mental Practice with Motor Imagery
This is a type of treatment where a specific movement is rehearsed mentally. Done best with a pre-recorded audio set, the person listens carefully as a task is described in detail. The details usually include every aspect of that task, including how the five senses may be experienced while performing it, as well as the exact movements that would be needed to complete the task. For example, if the task were “drinking a cup of water,” the recording would describe how to reach out with the arm, extend the fingers, feel the weight of the cup, experience the temperature and the liquid as it touches the mouth, and the exactness of the motion to set it back down gently.
Studies have shown that with this type of repetitive visualization and practice, actual movement and functional use of the arm can improve, such that an arm that was once fairly “useless” can now actually pick up a water cup and bring it to the mouth. The best part is, research also shows that this can be an effective treatment 12 months and beyond since when the stroke actually happened!
Constraint Induced Movement Therapy (CIMT)
This is a type of treatment that involves blocking the stronger arm (usually with a cast or mitt) to promote engagement of the hemiplegic, or weaker arm. The more a person uses the weaker arm, the less they are at risk of “learned non-use.” By “forcing” the weaker arm to participate more, and even to be the primary or only source of function, it has a lot more chance to stay the same or get better, even years after the stroke happened. In fact, patients in Constraint Induced studies reported and showed increased use of their arms during normal activities, even if their strokes happened years before!
Virtual Reality (VR)
Virtual reality is another name for video games! This type of treatment may be immersive (using a headset) or non-immersive, with a participant engaging in a game on a screen. VR technology focusing on strengthening and improving limb function is becoming more prevalent in clinics and in homes. These programs are able to quantify arm or leg movement to control gameplay and provide immediate performance feedback.
Research supports the use of VR therapy to enhance motor recovery for adults with acute and chronic stroke. Virtual reality technology can also improve motivation in addition to movement outcomes, helping users stick with their self-training programs and continue using their affected side. Research shows that chronic stroke patients often find self-training programs that use video games to be user friendly and enjoyable.
Avoiding “learned non-use.”
We now know more about this phenomenon that affects many stroke survivors – especially those who are years out from a stroke. The stronger arm starts to take over to just get things accomplished, probably because there is a lot of positive feedback for using the stronger arm (It’s faster! It’s easier! I can just get it done!) and a lot of negative feedback for using the stroke-affected arm (It’s so frustrating! It takes me forever using it!). Research is showing that if people can still find motivation and dedication to actually trying to use the weaker arm, it is possible to still regain function – even years later.
The bottom line: don’t give up!
There IS hope. We can’t predict the exact amount of movement or strength that could come back, or what exactly you will be able to do with your affected arm or hand. But we are producing more research that is pointing us in the direction of believing recovery is still possible after that six month critical window. Don’t give up!
Ballester, BR, et al. (2019). A critical time window for recovery extends beyond one-year post-stroke. Journal of Neurophysiology, 122: 350-357. doi: 10.1152/jn.00762.2018 Soros, P, et al. (2017). Motor recovery beginning 23 years after ischemic stroke. Journal of Neurophysiology, 118(2): 778-781. doi: 10.1152/jn.00868.2016 Page, S, Levine, P, and Leonard, A. (2007). Mental practice in chronic stroke: results of a randomized, placebo-controlled trial. Stroke, 38(4): 1293-1297. doi: 10.1161/01.STR.0000260205.67348.2b Kunkel, A, et al. (1999). Constraint-induced movement therapy for motor recovery in chronic stroke patients. Archives of Physical Medicine and Rehabilitation, 80, 624-628. doi:10.1016/s0003-9993(99)90163-6 5. Taub, E, et al. (1993). Technique to improve chronic motor deficit after stroke. Archives of Physical Medicine and Rehabilitation, 74, 347-354. Subramanian, SK, et al. (2013). Arm motor recovery using a virtual reality intervention in chronic stroke: Randomized control trial. Neurorehabilitation and Neural Repair, 27(1), 13-23. doi: 10.1177/1545968312449695.
Human activity recognition (HAR) technology has been advanced with the development of wearable devices and the machine learning (ML) algorithm. Although previous researches have shown the feasibility of HAR technology for home rehabilitation, there has not been enough evidence based on clinical trial.
We intended to achieve two goals: (1) To develop a home-based rehabilitation (HBR) system, which can figure out the home rehabilitation exercise of patient based on ML algorithm and smartwatch; (2) To evaluate clinical outcomes for patients with chronic stroke using the HBR system.
We used off-the-shelf smartwatch and the convolution neural network (CNN) of ML algorithm for developing our HBR system. It was designed to be able to share the time data of home exercise of individual patient with physical therapist. To figure out the most accurate way for detecting exercise of chronic stroke patients, we compared accuracy results with dataset of personal/total data and accelerometer only/gyroscope/accelerometer combined with gyroscope data. Using the system, we conducted a preliminary study with two groups of stroke survivors (22 participants in HBR group and 10 participants in a control group). The exercise compliance was periodically checked by phone calls in both groups. To measure clinical outcomes, we assessed the Wolf motor function test (WMFT), Fugl-meyer assessment of upper extremity (FMA-UE), grip power test, Beck’s depression index and range of motion (ROM) of the shoulder joint at 0 (baseline), 6 (mid-term), 12 weeks (final) and 18 weeks(6 weeks after the final assessment without HBR system).
The ML model created by personal data(99.9%) showed greater accuracy than total data(95.8%). The movement detection accuracy was the highest in accelerometer combined with gyroscope data (99.9%) compared to gyroscope(96.0%) or accelerometer alone(98.1%). With regards to clinical outcomes, drop-out rates of control and experimental group were 4/10 (40%) and 5/22 (22%) at 12 weeks and 10/10 (100%) and 10/22 (45%) at 18 weeks, respectively. The experimental group (N=17) showed a significant improvement in WMFT score (P=.02) and ROM (P<.01). The control group (N=6) showed a significant change only in shoulder internal rotation (P=.03).
This research found that the homecare system using the commercial smartwatch and ML model can facilitate the participation of home training and improve the functional score of WMFT and shoulder ROM of flexion and internal rotation for the treatment of patients with chronic stroke. We recommend our HBR system strategy as an innovative and cost-effective homecare treatment modality. Clinical Trial: Preliminary study (Phase I)
Neuroplasticity is the capacity of the nervous system to change its chemistry, structure, and function in response to intrinsic or extrinsic stimuli.1 Neuroplastic mechanisms are activated by environmental, behavioral, or neural processes, and by disease; they underpin the motor and cognitive learning associated with physical therapy or exercise. Neuroplasticity can lead to positive or negative changes in function, which are referred to as adaptive and maladaptive neuroplasticity, respectively. In their roles as clinicians and as scientists, physical therapists and other rehabilitation professionals harness neuroplasticity using evidence-based interventions to maintain or enhance functional performance in individuals with neurological disorders. There is still much to learn about the optimal interventions and parameters of dose and intensity necessary to achieve adaptive neuroplastic changes.
Beyond questions related to dose and intensity, more information is needed regarding the degree to which factors such as past experiences, age, sex, genetics, and the presence of a neurological disorder affects capacity for neuroplastic change. In addition, it is likely that these factors interact with each other, making it even harder to understand their influence on neuroplastic change. Improved measures for assessment of neuroplasticity in humans are needed, such as biomarkers (including movement-related biomarkers) for diagnosing disorders, and predicting and monitoring treatment effectiveness. Greater knowledge of effective rehabilitation and exercise interventions that drive adaptive neuroplasticity, and are tailored to each person’s unique characteristics, will improve patient outcomes. The idea for this special issue was born out of a desire to advance understanding of the mechanisms driving functional change.
Two studies in this special issue use a newer neuroimaging method called functional near-infrared spectroscopy to measure cortical activity during dual-task walking.2,3 Impaired dual-task walking is common in neurological populations and can interfere with the ability to perform daily life activities. Hoppes et al2 examine frontal lobe activation patterns in individuals with and without visual vertigo during dual-task walking. The differences in cortical activation patterns identified increase our understanding of possible mechanisms underlying decrements in dual-task performance in individuals with vestibular disorders, and may be useful for diagnosis, and for predicting or determining functional recovery in this population. Stuart and Mancini3 investigate how open and closed-loop tactile cueing influences prefrontal cortex activity during single- and dual-task walking and turning in individuals with Parkinson disease. Tactile cues delivered to the feet in an open-loop (continuous rhythmic stimuli) or closed-loop (intermittent stimuli based on an individual’s movement) mode are associated with improved gait and turning performance, and it is hypothesized that attention arising from the prefrontal cortex may underlie these cueing effects.4 Their findings of unchanged prefrontal cortex activity are unexpected, and raise additional questions regarding the role of the prefrontal cortex during gait.
Rehabilitation approaches such as task-oriented training that emphasize high repetition and challenge have been shown to facilitate recovery of mobility and function in neurological populations, but responses are varied and residual deficits often remain.5,6 There is still much to be learned about how to deliver the best interventions to optimize nervous system adaptive neuroplasticity and learning that ultimately lead to optimal functional recovery. In a proof-of-principle case series article in this special issue, Peters et al7 explore whether deficits in motor planning of stepping can be reduced by physical therapy focused on fast stepping retraining, or by conventional therapy focused on balance and mobility training, in individuals with subacute stroke. Both interventions altered electroencephalogic measures indicative of motor planning duration and amplitude of stepping; furthermore, duration changes for all participants were in the direction of those acquired from healthy adult values. These findings suggest that physical therapy may be able to drive neuroplasticity to improve initiation of stepping in individuals after stroke.
A growing body of human and animal evidence supports thataerobic exercise promotes neuroplasticity and functional recovery in many neurological disorders.1 Chaves et al8 utilized transcranial magnetic stimulation to examine changes in brain excitability measured in the upper extremity following a 40-minute bout of aerobic exercise (ie, body weight-supported treadmill walking) in individuals with progressive multiple sclerosis requiring devices for walking. Improvements in brain excitability were found following the aerobic exercise, which suggest that the capacity for neuroplasticity exists in this population. Participants’ responses to the exercise were greater in those with higher cardiorespiratory fitness and less body fat. The authors discuss that maintaining an active lifestyle and participating in aerobic exercise may be beneficial for improving brain health and neuroplasticity in people with progressive multiple sclerosis.
Finally, for the first time Vive et al9 translate to the clinical setting the enriched environment model used in laboratory-based animal studies. Evidence from preclinical studies suggests that combinational therapies such as enriched environments, which take advantage of multiple mechanisms underlying neuroplasticity, may promote greater functional recovery than a single therapy.10 The researchers examine the effects of a high-dose enriched task-specific therapy, which combines physical therapy with social and cognitive stimulation on motor recovery in individuals with chronic stroke. Their findings demonstrate that the enriched task-specific therapy intervention is feasible, and suggest that it may be beneficial for repair and recovery long after a stroke.
The articles in this issue provide new insights to improve our understanding of adaptive neuroplastic changes in nervous system activity resulting from neurological disorders or following exercise interventions. Evidence regarding benefits of physical therapy and exercise interventions to promote motor and cognitive function across the lifespan and in the presence of neurological pathology may motivate individuals to adapt and adhere to healthier lifestyles.1 Physical therapists and rehabilitation professionals can use the evolving neuroplasticity research to assist with decision-making regarding individualized therapy goals, and the selection and monitoring of therapeutic interventions to best achieve compliance and goal attainment. Collaborations between rehabilitation clinicians and researchers will enhance and hasten the translation of neuroplasticity research into effective clinical therapies. In the end, these efforts will certainly lead us to improved interventions that help to restore function and health to our patients.
1. Cramer SC, Sur M, Dobkin BH, et al Harnessing neuroplasticity for clinical applications. Brain. 2011;134(pt 6):1591–1609. doi:10.1093/brain/awr039.
4. Maidan I, Bernad-Elazari H, Giladi N, Hausdorff JM, Mirelman A. When is higher level cognitive control needed for locomotor tasks among patients with Parkinson’s disease? Brain Topogr. 2017;30(4):531–538. doi:10.1007/s10548-017-0564-0.
5. Dobkin BH. Motor rehabilitation after stroke, traumatic brain, and spinal cord injury: common denominators within recent clinical trials. Curr Opin Neurol. 2009;22(6):563–569. doi:10.1097/WCO.0b013e3283314b11.
6. Hornby T, Reisman D, Ward I, et al Clinical practice guideline to improve locomotor functional following chronic stroke, incomplete spinal cord injury, and brain injury. J Neurol Phys Ther. 2020;40(1):49–100.
10. Malá H, Rasmussen CP. The effect of combined therapies on recovery after acquired brain injury: systematic review of preclinical studies combining enriched environment, exercise, or task-specific training with other therapies. Restor Neurol Neurosci. 2017;35(1):25–64. doi:10.3233/RNN-160682.
To investigate the validity and test–retest reliability of mHealth devices (Google Fit, Health, STEPZ, Pacer, and Fitbit Ultra) to estimate the number of steps in individuals after chronic stroke and to compare whether the measurement of the number of steps is affected by their location on the body (paretic and non-paretic side).
Observational study with repeated measures.
Fifty-five community-dwelling individuals with chronic stroke.
The number of steps was measured using mHealth devices (Google Fit, Health, STEPZ, Pacer, and Fitbit Ultra), and compared against criterion-standard measure during the Two-Minute Walk Test using habitual speed.
Our sample was 54.5% men, mean age of 62.5 years (SD 14.9) with a chronicity after stroke of 66.8 months (SD 55.9). There was a statistically significant association between the actual number of steps and those estimated by the Google Fit, STEPZ Iphone and Android applications, Pacer iphone and Android, and Fitbit Ultra (0.30 ⩽ r ⩾ 0.80). The Pacer iphone application demonstrated the highest reliability coefficient (ICC(2,1) = 0.80; P < 0.001). There were no statistically significant differences in device measurements that depended on body location.
mHealth devices (Pacer–iphone, Fitbit Ultra, Google Fit, and Pacer–Android) are valid and reliable for step counting in chronic stroke survivors. Body location (paretic or non-paretic side) does not affect validity or reliability of the step count metric.
INTRODUCTION: Long-term functional cognitive impairments are common sequelae of stroke, often resulting in decreased participation in daily life activities. Earlier research showed the benefits of training paradigms targeted at memory, attention, and some executive functions.
METHODS: The current study examined the feasibility of a functionally relevant training program called Strategic Memory Advanced Reasoning Training (SMART). The SMART program teaches strategies to improve abstract reasoning skills and has been shown to enhance aspects of functional cognition, strengthen brain networks, and improve participation in daily life activities across clinical populations. The current study describes the benefits of the SMART program in adults (N = 12) between 54 and 77 years (64.46 ± 8.14 years) with chronic stroke. Participants had 10 sessions of the SMART program over a period of 6 weeks.
RESULTS: The findings showed significant gains in abstract reasoning (p < .05) and participation in daily activities after the SMART program. These gains were relatively stable 6 months later.
CONCLUSION: These findings offer the promise of cognitive gains, even years after stroke. Limitations of the study include a small sample size, potential confounding as a result of additional ongoing therapy, and a relatively short period of follow-up. Further research is needed to examine the benefits of the SMART program. [Annals of International Occupational Therapy. 2020;X(X):xx–xx.]
Purpose: For stroke survivors, abnormal gait patterns lead to a significant risk of falls. We have recently developed an IoT-based Upper and Lower Extremity Rehabilitation Medical Device (RoBoGat) that enables continuous passive motion (CPM) training, squat training (ST), and gait training (GT). The purpose of this study was to test the effectiveness of RoBoGat on gait in a chronic stroke survivor.
Methods: In this study, an individual with right-side chronic hemiparesis post-stroke participated. The participant underwent 14 days of RoBoGat training that involved continuous passive motion training, squat training, and gait training. During the training, knee and hip joint angles were adjusted within the range where the subject felt no pain. We assessed gait, timed up and go test, and visual analog scale at baseline and after first and final interventions.
Results: After the intervention, positive changes were observed such as stride, gait velocity, and loading phase. Improvements were also observed in timed up and go tests. However, there was no significant change in VAS, which assessed pain in training and daily life.
Conclusion: The main finding of this case-control study is that robot-based upper and lower extremity training may be a feasible approach in the neurorehabilitation field. It can be concluded that repetitive and continuous robot rehabilitation exercises have a positive effect on improving the physical function of chronic stroke survivors.
This study investigated the effectiveness of Ai Chi compared to conventional water-based exercise on balance performance in individuals with chronic stroke. A total of 20 individuals with chronic stroke were randomly allocated to receive either Ai Chi or conventional water-based exercise for 60 min/time, 3 times/week, and a total of 6 weeks. Balance performance assessed by limit of stability (LOS) test and Berg balance scale (BBS). Fugl-Meyer assessment (FMA) and gait performance were documented for lower extremity movement control and walking ability, respectively. Excursion and movement velocity in LOS test was significantly increased in anteroposterior axis after receiving Ai Chi (p = 0.005 for excursion, p = 0.013 for velocity) but not conventional water-based exercise. In particular, the improvement of endpoint excursion in the Ai Chi group has significant inter-group difference (p = 0.001). Both groups showed significant improvement in BBS and FMA yet the Ai Chi group demonstrated significantly better results than control group (p = 0.025). Ai Chi is feasible for balance training in stroke, and is able to improve weight shifting in anteroposterior axis, functional balance, and lower extremity control as compared to conventional water-based exercise.
Stroke is a cerebral vascular disease caused by the interruption of the blood supply to the brain, cutting off the supply of oxygen and nutrients1. Damage to the brain tissue leads to sensory, motor, cognitive, and emotional deficits. With impaired motor and sensory functions, stroke patients suffer from deficits in balance control which plays crucial role in ambulatory function and thus as an important clinical indicator2,3,4,5. Balance is defined as the ability to maintain center of mass (COM) within the stability limits, the boundaries of the base of support (BOS)6. Balance control can be quantified by limit of stability (LOS) test, expressed by movement velocity, displacement excursion, and directional control7,8. Individuals with stroke usually show decline in the abovementioned balance performance9,10,11,12. Bohannon13 noted the correlation between static standing ability and independent mobility in stroke patients (r = 0.62). Lee et al.14 found that walking velocity is associated with maximal displacement excursion in LOS test (r = 0.68, p < 0.01) and Berg balance scale (r = 0.66, p < 0.01) in patients with stroke. In addition, the balance-related fall risks should also be addressed in people with chronic stroke15,16. Therefore, it is crucial to improve balance control in order to improve the balance-related activities for individuals with stroke.
Several elements, such as strengthening, postural control, weight shifting, and agility exercise, are necessary to be incorporated during balance training17. It has also been noted that increased somatosensory inputs and visual deprivation might exert positive effects on top of balance training, as well as enriched environment4,5,18,19. Water-based exercise, by utilizing the properties of water, including buoyancy, viscosity, turbulence, and hydrostatic pressure, has been suggested to improve balance control20,21. Two reviews summarized that the water-based exercise for neurological disorder covers a wide variety, including resistance training, movement facilitation, motor control training, balance training, coordination training and other specific techniques21,22. They indicated that stroke patients improved significantly more in weight shifting ability, dynamic balance, and functional mobility as compared with the land-based intervention21,22.
Ai Chi, first developed by Jun Konno in 1990s23, is one kind of water-based exercise emphasizing characteristics of balance training24. It resembles Tai Chi on land, complemented by Zen shiatzu and Watsu concepts25. Ai Chi is composed of 16 katas (movements), including breathing, upper extremity movements, lower extremity movements, trunk control, and coordinated movements23. With the properties and advantages of water, less weight bearing is required and larger displacement can be achieved. Currently, some studies have mentioned the benefits of Ai Chi for neurological involved patients21,22. Bayraktar et al. showed positive effects of 8 weeks of Ai Chi training on muscle strength, muscle endurance, functional mobility, and fatigue severity in patients with multiple sclerosis26. Noh et al. found that the balance performance and knee flexors strength improved more in the Ai Chi combining Halliwick therapy group than the conventional physiotherapy group in patients with stroke27. Pérez-de la Cruz et al. also showed the feasibility of Ai Chi on balance and functional capacity for people with Parkinson’s disease28.
Taking together, water-based exercise is beneficial for balance performance in patients with stroke. Ai Chi is a specific water-based exercise which emphasizes the characteristics of balance control. However, whether Ai Chi can exert better effect on balance performance than conventional water-based exercise in people with stroke is not known. The aim of this study was to compare the effects of Ai Chi training with conventional water-based exercise on balance performance in people with stroke. We hypothesized that Ai Chi can result in superior effects on balance control than conventional water-based exercise people with stroke. […]
Outpatient stroke rehabilitation is often lengthy and expensive due to patients’ lack of functional use of the impaired arm outside of the clinic caused by “learned non-use.” Learned non-use is detrimental to stroke recovery, often resulting in chronic disability. To overcome learned non-use, a wearable “personal assistant” solution is proposed that employs ubiquitous cueing to stimulate patient use of the paretic arm while outside of therapy sessions. A pilot user study is presented that evaluated stroke survivors’ tolerance and acceptance of cueing, and the usability of the proposed implementation.
V. L. Roger et al., “Heart disease and stroke statistics—2012 update: A report from the American Heart Association,” Circulation, vol. 125, no. 1, pp. e2–e220, Jan. 2012.
E. Taub, J. E. Crago, L. D. Burgio, T. E. Groomes, E. W. Cook, S. C. DeLuca, and N. E. Miller, “An operant approach to rehabilitation medicine: Overcoming learned nonuse by shaping,” J Exp Anal Behav, vol. 61, no. 2, pp. 281–293, Mar. 1994.
C. E. Lang et al., “Upper extremity use in people with hemiparesis in the first few weeks after stroke,” Journal of Neurologic Physical Therapy, vol. 31, no. 2, pp. 55–63, Jun. 2007.
W. S. Verplanck, “The operant conditioning of human motor behavior,” Psychological Bulletin, vol. 53, no. 1, pp. 70–83, 1956.
M. S. Cameirão, S. B. i Badia, E. Duarte, A. Frisoli, and P. F. M. J. Verschure, “The combined impact of virtual reality neurorehabilitation and its interfaces on upper extremity functional recovery in patients with chronic stroke,” Stroke, vol. 43, no. 10, pp. 2720–2728, Oct. 2012.
J. Lieberman and C. Breazeal, “TIKL: Development of a wearable vibrotactile feedback suit for improved human motor learning,” IEEE Transactions on Robotics, vol. 23, no. 5, pp. 919–926, Oct. 2007.
P. Kapur, M. Jensen, L. J. Buxbaum, S. A. Jax, and K. J. Kuchenbecker, “Spatially distributed tactile feedback for kinesthetic motion guidance,” in IEEE Haptics Symposium, pp. 519–526, 2010.
T. Markow et al., “Mobile Music Touch: Vibration stimulus in hand rehabilitation,” in International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 1–8, 2010.
P. Markopoulos, A. A. A. Timmermans, L. Beursgens, R. van Donselaar, and H. A. M. Seelen, “Us’em: The user-centered design of a device for motivating stroke patients to use their impaired arm-hand in daily life activities,” in Annual Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5182–5187, 2011.
G. Uswatte, C. Giuliani, C. Winstein, A. Zeringue, L. Hobbs, and S. L. Wolf, “Validity of accelerometry for monitoring real-world arm activity in patients with subacute stroke: Evidence from the extremity constraint-induced therapy evaluation trial,” Archives of Physical Medicine and Rehabilitation, vol. 87, no. 10, pp. 1340–1345, Oct. 2006.
HOUSTON, Texas — An implanted device that stimulates the vagus nerve has shown promising improvement of arm function in stroke patients in a second small clinical study.
While the primary endpoint — change in functional score after 6 weeks of therapy — was not significantly different between treatment groups, the improvement did appear to become significant after a further 60 days of treatment, as did responder rates.
Lead investigator, Jesse Dawson, MD, University of Glasgow, United Kingdom, reported that the group receiving active stimulation with the device showed a 9-point improvement in upper-limb Fugl-Meyer (UEFM) score at this time point.
Dr Jesse Dawson
“All in all, we feel this is quite promising,” Dr Dawson said. “A 9-point change in this scale is highly likely to be clinically significant.”
This magnitude of change would mean different things for different patients, depending on where they start, he said. “If they start at 20 — which is not much function at all — they might regain some grasp ability so they might be able to carry a plate, for example. If they were in the 30s to start with, they would probably already have the grasp function but they would be able to get back to do more specific tasks.”
The results were presented here at the International Stroke Conference (ISC) 2017.
Commenting on the study, American Heart Association/American Stroke Association spokesperson, Philip Gorelick, MD, MPH, medical director, Hauenstein Neuroscience Center, Grand Rapids, Michigan, described the results as “pretty spectacular.”
Dr Philip Gorelick
“It is always difficult to know what you are getting with these scales, but when you see jumps like this I think it’s safe to conclude that there is clinical significance. There is probably something real going on,” Dr Gorelick said.
“You must remember that these are chronic patients with moderate to severe arm weakness at 18 months down the line from their stroke,” he added. “We think these patients are finished — they are not going to be doing much with that arm. Obviously this study is exploratory, but this raises a lot of hope.”