Patients with Post-Stroke Hemiparetic Upper Limb
Abstract
The US and Europe are both experiencing the coming of age of an elderly population which is prone to strokes and will need health care focused on post-stroke therapies. The most promising therapies are robot-assisted motion exercises coupled with Non-invasive Brain Simulations (NBS) such as Transcranial Magnetic Simulation (TMS). At this time the most attractive combination for both effective and affordable therapy is the use of simply designed, easily manipulated robot systems coupled with TMS. Since 2003 the area of neurorehabilitation has been growing with many effective and exciting designs in robotics. It hasn’t been very long that the strategy of introducing ways to measure plasticity and to help encourage brain plasticity has been getting more attention. This literature review focuses on three key articles that demonstrate the progression in the state of the research. Other articles have been reviewed to use as comparisons for methodology and results. Also the implementation of simple robotics to computer modelled therapy has been addressed. The research is varied and offers hope for post-stroke victims to possibly one day recover their Activities of Daily Life (ADL). The job of the physical therapist is becoming more complicated both due to the hard work and the knowledge of technology which is now necessary.
(Robot-assisted therapy, stroke rehabilitation, impaired movement, brain plasticity, post stroke patients, stroke recovery)
Effects of Robotic-Assisted Movement Therapy in Patients with Hemiparetic Post-Stroke Upper Limb
Health care professionals are expecting larger numbers of stroke patients as a demographic shift continues to trend upwards with a growing elderly population. Post-stroke patients often suffer upper limb disabilities such as finger, hand, wrist, elbow, and shoulder paralysis. These disabilities can be successfully improved with physical therapy.
Helping the patients in rehabilitation is a one-on-one activity and time consuming for the therapist. In order to relieve workload problems as more patients enter the health care system robotic systems have been developed. The robot-assisted rehabilitations systems have become a regular part of post-stroke therapy.
Robotic systems are used in hospitals and health clinics; smaller systems can be used in the patients’ homes. Some systems have been designed especially to be portable and easy to set up for the patient’s convenient use in the home. Carrera et al. (2011) have designed a system that is light, easy to use and small enough to store after use in the patients home.
A heart attack (or stroke) occurs when blood and oxygen cannot reach the brain; so it is also a brain attack according to the National Stroke Association. Loureiro et al. (2003) confirms that the neurons are killed in the part of the brain that does not receive oxygenated blood. Therefore neural damage and muscle damage are connected; neurorehabilitation has become important and even expected by patients. A small stroke may only weaken a patient’s arm, but a larger stroke can paralyze one side of the body, inhibit speech, and/or paralyze one arm. (Natl. Factsheet). A chronic stroke is when movement problems are occurring up to 6 months and longer after the stroke (Edwards 2009). Strokes can cause permanent motor activity disabilities and neural brain damage making Activities of Daily Living (ADL) very difficult.
Huang and Krakauer (2009) report that up to one third of stroke survivors experience permanent movement impairment and one-fifth need nursing home care or some type of institutional care even three months after their stroke. Half of the stroke victims who are 65 years and older and have experienced an ischemic (artery blockage) stroke have hemiparesis. (Huang & Krakauer 2009)
Hemiparesis is the condition of ‘general weakness, motor control abnormalities, and spasticity’ that afflicts post-stroke patients (Edwards 2009). Edwards (2009) predicts that future health care strategies should plan for large numbers of patients suffering both from strokes (cerebrovascular disease) and from neurodegenerative diseases.
Robotic theory hypothesizes that robotic neurorehabilitation has the potential to speed recovering of movement and brain control more successfully than either conventional neurorehabilitation or spontaneous biology. Huang and Krakauer (2009) say the reasons are (a) robotics are easy to use, (b) robotic systems are easily adaptable, (c) it’s possible to make reliable measurements, and (d) it is possible to help the patient perform repetitive tasks for long time periods and/or with strong force.
Non-invasive Brain Stimulation (NBS) such as Transcranial Magnetic Stimulation (TMS) has been shown to facilitate the balancing of brain activity between the brain-damaged side and the non-affected side after a stroke (Colombo 2005). TMS coupled with robotic-assisted therapy has shown positive results (O’Malley and Lev, 2006).
This literature review addresses robot-assisted upper body therapy use and robot-assisted neurological therapies particularly TMS. The theoretical question is, “Are NBS coupled with robot-assisted motion therapies showing consistently positive results?” The hypothesis is that NBS coupled with robotic-assisted therapy offers a more effective and a higher quality recovery for post-stroke patients.
Methodology
A variety of methodologies have been developed to gain high quality rehabilitation while at the same time increasing the rate of recovery of upper limb immobility post-stroke. The following paragraphs describe three key research studies that are representative of upper limb robotic-assisted neurorehabilitation research from 2003 to 2009.
Participants and timing of stroke
Fasoli et al. (2003) researched the use of constraint induced movement therapy (CIMT) which engages both motor and neural functions. Cesqui et al. (2008) compared results from the classic type robotic-assisted therapy to the Divergent Force (DF); a counterforce therapy which is designed to enhance error rather than measure error-free motion. Masiero et al. (2007) studied the advantages or disadvantages of the robotics system, NeReBot, which gives 3°-of-freedom during upper limb repetitive therapy and is coupled with visual stimuli.
There may be an optimum time for patients to start therapy after a stroke. Masiero et al. (2007) used participants with the least amount of time from stroke experience to therapy in this review; thirty-five participants who had experienced strokes one week or less before the start of a study. Fasoli et al. (2003) used twenty participants who each had experienced a single, unilateral stroke with continuing hemiparesis even after one to five years.
The fifteen participants in the DF study Cesqui et al. (2008) were not chosen on the basis of time from stroke occurrence, but because they were each afflicted with severe arm immobility problems.
Interventions
In a research study by Fasoli et al. (2003) the elbow and shoulder of the hemiparetic affected arm was exercised. After the participants reached predetermined targets during six weeks of sessions, therapy three times a week was randomly assigned to either sensorimotor or progressive-resistive robotic-assisted therapy Fasoli et al. (2003). Patients were asked to do reaching exercises in order to accomplish a goal along a one dimensional plane. (Fasoli et al. 2003)
Masiero et al. (2007) randomly chose two study groups from thirty-five participants. Members of both groups received the standard rehabilitation therapy but the experimental group was given an extra 4 hours/week of sensorimotor robotic assisted therapy. The control group used the therapy for 30 minutes, twice a week using the unaffected arm and hand. Masiero et al. (2007) research study used NeReBot which assists the patients in elbow and shoulder movement coupled with visual stimuli.
Fifteen patients took part in the Cesqui et al. (2008) study; eight male and seven female post-stroke patients with mild to severe arm immobility problems. Three two-week sessions were offered; the first for one hour therapy sessions for 10 days, the second two week period consisted of a break for resting and the last two-week period repeated the one hour therapy sessions for 10 days. Cesqui et al. (2008)
Critical Analysis
Results
Non-invasive Brain Stimulation (NBS) Fasoli et al. (2003) found that participants improved significantly from the start to the finish of the experiment using CIMT; the progress-resistive group had improved wrist and hand motion while the sensorimotor group showed no change. Park et al. (2004) used CIMT while gauging changes in TMS. Park et al. (2004) found that the oxy-haemoglobin concentration shifts towards a better balance between the affected and the unaffected side of the brain. After two weeks the research demonstrated increased TMS motor map area and other indications that TMS could be useful for encouraging recovery; their findings did not correspond with results of earlier research.
O’Malley and Levin (2006) reported in a literature review that the coupling of TMS and robotic-assisted motion therapy to encourage brain plasticity and movement recovery as well as assess movement recovery worked well. Also activities of daily life (ADL) demonstrated greater improvement with TMS than without. O’Malley and Levin (2006) determined that the main positive reasons for pairing robotics and TMS was that they both give positive recovery results, cost is low, and results are easy to assess. Fegni (Cromie 2005) using TMS had success in improving movement with short sessions of dose; he found the more sessions the greater improvement. Approximately a 50% improved reaction time was measured in ten participants after three sessions; the participants had experienced a stroke within 1 year of the experiment. Edwards (2009) suggested that the use of non-invasive brain stimulation (NBS) used with robotics can offer accurate data for assessment and also be incorporated into the therapy.
Robotic systems Cesqui et al. (2008) found that the more movement a participant had upon entering an experiment the larger the improvement; hand path movement in particular improved. The Masiero et al. (2007) using the NeReBot positive results for the shoulder/elbow but not for the wrist flexors. MacClellan et al. (2005) used a short term robotic assisted therapy with positive results for chronic patients experiencing low to high severity. Mahoney et al. (2003) learned that simple robotic system designs can be more effective in mimicking natural movement and gaining more improvement.
Huang and Jipling (2004) successfully designed a pneumatic muscle powered robotic arm with results that can be expressed quantitatively. Quantitative measurements of patients’ improvement were also achieved by Colombo et al. (2005) in research using robotic-assisted movement therapy; both a wrist manipulator and an elbow/shoulder manipulator. Finley et al. (2005) found that short-duration robotic assisted movement improved severe post-stroke impairment. Their robotic-derived measurements could detect smaller changes than the clinical evaluations; this corresponds to similar research results in the literature.
Carrera et al. (2011) point out that using the devices in the patients’ homes needs to be taken into account when designing robot-assisted systems. A small apparatus that doesn’t take up too much space and that is easy such as the ROAD: domestic assistant and rehabilitation robot was satisfactory for this purpose Carrera et al. (2011).
Molecular level Volpe et al. 2008) concluded that intensive protocols by the therapists must match that the robot produces in order to avoid a plateau in change. They call for the recognition that changes at the cellular and even the molecular level can be beneficial to patients by intense, reproducible protocols which can change when a plateau is reached. They found no proof that patients reach a point where no more improvement is possible Volpe et al. (2008). The transitions to another level of therapy are very important in post-stroke patients’ ability to regain ADL. The authors point out that the brain may recover ahead of the motion abilities with for example, constrained-induced movement therapy (CIMT) Volpe et al. (2008). When a patient habitually uses a compensation strategy for dressing or bathing that habit can be difficult to break although the brain is ready to help the body progress; CIMT can help by inhibiting the use of the healthy arm. (Huang and Krakauer 2009)
Learning behaviours Huang and Krakauer (2009) reported that neurorehabilitation does not affect spontaneous biological recovery. They regard recovery as a challenge for patients to learn behaviours which will help them gain improvement. They conclude that neurorehabilitation coupled with intensive, repeated robotic assisted motion must include in the design readiness for transition to a different regime. (Huang and Krakauer 2009)
Limitations
The use of randomized selection of participants within an experiment, the inclusion of a control group, and future follow up with the participants should be in the design of each research study but these features were not consistently found in the literature reviewed research. Colombo et al. (2005) did not use a control group so comparison of robotic-assisted therapy to traditional therapy cannot be assessed.
Cesqui et al. (2008) tested a new theory so developed equations to quantify their data so comparisons could be made with other systems. Comparison of data is easier when the classical rehabilitation scales are used and quantitative measures are applied such as in the studies by Masiero et al. (2007) and Fasoli et al. (2003). Colombo et al. (2005) notes the need for techniques that offer quantifiable measurements.
There is a problem of subjectivity entering into the assessment of the inputs such as jerk and patient’s error in following a straight path. The more accurate the measurements the more reproducible results will be and therefore the more useful.
Colombo et al. (2005) notes that lack of flexibility of therapy protocols as the patient progresses through different stages of recovery could be hindering optimal recovery Huang and Krakauer (2009) point to the problem of not knowing whether continuing the same exercises will lead to more or less recovery success.
Fasoli et al. (2003) used a single blinded therapist to analyze the results which was very important in getting objective results. Unfortunately the sample size was small and the post-stroke time increment was long (anytime within 1 to 5 years). Masiero et al. (2007) only had a sample size of 35 patients which is understandable because their participants had experienced a stroke within or up to 7days from the start of the experiment. Masiero et al. (2007) also made use of a single blinded therapist to analyse their results. The importance of using a single blinded therapist to analyze the results especially with such a small sample cannot be overstated. The use of this strategy for analysing results gives validity to the studies.
Colombo et al. (2005) researched two groups having 7 and 9 participants for a 3 week rehabilitation program but no follow up was available. On the other hand Volpe et al. (2008)
first screened the patients, all of who had experienced UL impairments for at least six months; follow up measurements were taken monthly for three months. Volpe et al. (2008) compared two groups using robotic training for 11 participants and using movement protocol for 10 participants which is also a use of a small sample group.
Future Research
Recommendations for Further Research
The recommendations for future research are many because there are so many variables involved with neurorehabilitation.
NBS Further studies on Non-invasive Brain Stimulation NBS are essential; both the neurological and the physiological connections need to be better understood. O’Malley et al. (2006) suggests that TMS and robotics need to be researched further in tandem. Volpe et al. (2008) recommends researching whether there is on biological recovery and reaching plateaus during recovery plus more research needs to done at the molecular and cellular level.
Individual characteristics Edwards (2009) calls for serious improvements in neurorehabilitation strategies by incorporating accurate learning by observing the physiological and behavioural nature of post-stroke patients’ recovery of motion. Better defined information is needed even to the point of gaining more detailed characteristics of individual participants (Edwards 2009). Finley et al. (2005) notes that robotic systems can adapt to the needs of the patient, from low to high severity so comparative research needs to be done with other therapeutic interventions using randomized control trials and discernment of best regimes for Upper Limb post-stroke therapy. Postero and Micera (2009) and Edwards (2009) suggest more development of individually designed therapies for patients. Huang and Krakauer (2009) recommend respecting the functional needs of patients by flexibility in methods to meet different needs as therapy progresses.
Physiological Mechanisms Masiero et al. (2007) suggested determining exactly what physiological mechanisms are involved in robot assisted therapy. Fasoli et al. (2003) suggested studying the effect on motor deficits of robotic-assisted exercise. Cesqui et al. (2008) noted the need to understand how the correct order of therapies allows for the most effective recovery, such as whether using the active assisted method before or after the DF therapy would be better. Researching in more detail can be done by deconstructing the upper limb and studying each part; that is elbow, shoulder and hand/fingers. This could give interesting results for understanding which part of the upper limb brings the most change to brain plasticity. Postero and Micera (2009) recommend continued research of the Divergent Force therapy by researching the effect on patients with different degrees of severity coupled with clinical trials to validate results.
Data Cesqui et al. (2008) introduced a method to incorporate the physical health information available on the patients and the careful measurements taken in the study make their results useful and informative for developing new ways to access useful information from the data available. Participants within the same timeframe of stroke occurrence and the same degree of impairment could be studied in a series of projects to give important background information. This type of data would be useful as a foundational study to compare with ongoing research.
Conclusion
Fasoil et al. (2003) research demonstrated the feasibility of robotic therapy on chronic stroke patients. Cesqui et al. (2008) offered a new way to think about how to use the available health data on participants to improve the understanding of the results.
Masiero et al. (2007) research study using NeReBot to assist the patients in elbow and shoulder movement coupled with visual stimuli was the best designed experimental study. The participants in the group had experienced a stroke one week or less before the start of the research which is a novel idea to start therapy so early. They used a randomized method when choosing the participants for the three study groups. Two of the groups received the standard rehabilitation therapy while the experimental group received more sessions of robot-assisted therapy; a control used the unaffected arm during therapy. The experiment was a single-blind randomized trial and included a follow up.
Post-stroke patients need improved neurorehabilitation techniques right after their stroke and even after they have returned home.
Positive results in movement and brain function rehabilitation have been found using robotics and plasticity as part of the therapy. NBS is considered by many researchers as important for inducing plasticity which allows improvement in patients’ neurological abilities. The research since 2003 has indicated that post-stroke patients improve with a protocol of robotic-assisted therapy and Non-invasive brain theory NBS such as Transcranial Magnetic Stimulation (TMS).
It was found in answer to the thesis that according to this literature review NBS coupled with robot-assisted motion therapies show consistently positive results. Also the hypothesis is that NBS coupled with robotic-assisted therapy offers a more effective and a higher quality recovery for post-stroke patients was found to be correct.
Although the robot-assisted therapy can do the work of a therapist; even more therapists will be needed to meet the hard work good protocols require and the high number of patients expected. This field of research is exciting and offers a many topics for future research studies.
References
Bowling A. (2005) Measuring Health: A Review of Quality of Life Measurement Scales. Maidenhead, England: Open University Press, pp. 10-42.
Bowman D. (2002) Principles for the design of performance-oriented interaction techniques. In Stanney KM. (ed.) Handbook of Virtual Environments: Design, Implementation, and Applications: Erlbaum Associates, pp.277-301.
Carrera I, Moreno HA, Saltarén R, Pérez C, Puglisi L, Garcia C. (2011) ROAD: Domestic assistant and rehabilitation robot. Med. Biol. Eng. Comput. Vol. 49, pp. 1201-1211.
Cesqui B, Aliboni S, Mazzoleni S, Carrozza MC, Posteraro F, Micera S. (2008) On the use of Divergent Force Fields in robot-mediated neurorehabilitation. Proceedings of the 2nd Biennial IEEE/RAS-EMBS Int. Conf on Biomedical Robotics and Biomechatronics. Oct 19-22. Scottsdale, AZ, USA. pp. 854-861.
Colombo R, Pisano F, Micera S, Mazzone A, Delconte C, Carrozza MC, Dario P, Minuco G. (2005) Robotic techniques for upper limb evaluation and rehabilitation of stroke patients. IEEE Transactions on neural systems and rehabilitation engineering. Vol. 13(3), pp. 311-24.
Cromie WJ. (2005) Magnetic stimulation helps stroke victims. Electric Wand used to expand their motor skills. Harvard News Office. Available at http://www.news.harvard.edu (Accessed 20 Dec 2011).
Edwards DJ. (2009) On the understanding and development of modern physical neurorehabilitation methods: Robotics and non-invasive brain stimulation. Commentary J. of NeuroEngineering and Rehabilitation. Vol. 6(3), pp. 1-4.
Fasoli SE, Drebs HI, Stein J, Frontera WR, Hogan N. (2003) Effects of robotic therapy on motor impairment and recovery in chronic stroke. Arch Phys Med Rehabil. Vol. 84, pp. 477-482.
Finley MA, Fasoli SE, Dipietro L, Ohlhoff J, MacClellan L, Meister C, Whitall J, Macko R, Bever CT, Krebs HI, Hogan N. (2005) Short-duration robotic therapy in stroke patients with severe upper-limb motor impairment. VA J. of Rehabilitation Research & Dev. Vol. 42(5), pp. 683-692.
Frisoli A, Sotgiu E, Procopio C, Bergamasco M, Rosssi B, Chisari C. (2011) Design and implementation of a training strategy in chronic stroke with an arm robotic exoskeleton. IEEE International Conference on Rehabilitation Robotics. June 29 – July . ETH Zurich, Switzerland.
Grubb NR, Fox KA, Smith K, Best J, Blane A, Ebmeier KP, Glabus MF, O’Carroll RE. (2011) Memory impairment in out-of-hospital cardiac arrest survivors is associated with global reduction in brain volume, not focal hippocampus injury. Stroke. Vol. 31(7), pp. 1509-14.
Hesse S, Schmidt H, Werner C, Bardeleben A. (2003) Upper and lower extremity robotic devices for rehabilitation and for studying motor control. Curr Opin Neurol. Vol 16(6), pp. 705-10.
Huang H, Jiping H. (2004) Utilization of biomechanical modelling in design of robotic arm for rehabilitation of stroke patients. Conf. Proc. IEEE Eng Med Biol Soc. Vol. 4, pp. 2718-2721.
Huang VS, Krakauer JW. (2009) Robotive neurorehabilitation: A computational motor learning perspective. J. of NeuroEngineering & Rehabilitation. Vol. 6(5), pp. 1-13.
Kahn LE, Lum PS., Rymer, WZ, Reinkensmeyer DJ. (2006) Robot-assisted movement training for the stroke-impaired arm: Does it matter what the robot does? J Rehabil Res Dev. Vol. 43(5), pp. 619-30.
King M, Hale L, Pekkari A, Persson M, Gregorsson M, Nilsson M. (2010) An affordable computerized table-based exercise system for stroke survivors. Disabil Rehabil Assist Technol. Vol. 5(4), pp. 288-93.
Loureiro R, Amirabdollahian F, Topping M, Driessen B, Harwin W. (2003) Upper limb robot mediated stroke therapy – GENTLE/s approach. Autonomous Robots. Vol. 15, pp. 35-51.
Lucca LF. (2009) Virtual reality and motor rehabilitation of the upper limb after stroke: A generation of progress? J. Rehabl Med. Vol. 41(12), pp. 1003-1006.
MacClellan L, Bradham DD, Whitall J, Volpe B, Wilson PD, Ohlhoff J, Meister C, Hogan N, Krebs HI, Bever CT. (2005) Robotic upper-limb neurorehabilitation in chronic stroke patients. J. of Rehabilitation Research & Dev. Vol. 42(6), pp. 717-22.
Mahoney RM, Machiel Van der Loos HF, Lum PS, Burgar C. (2003) Robotic stroke therapy assistant. Robotica. Vol. 21, pp. 33-44.
Masiero S, Celia A, Rosati G, Armani M. (2007) Robotic-assisted rehabilitation of the upper limb after acute stroke. Arch. Phys. Med. Rehabil. Vol. 88, pp. 142-9.
National Stroke Association. (2010) Stroke 101 Factsheet. Available at www.stroke.org (Accessed 12 December 2011)
O’Malley MK, Ro T, Levin HS. (2006) Assessing and inducing neuroplasticity with transcranial magnetic-stimulation and robotics for motor function. Supplemental. Arch Phys Med Rehabil. Vol. 87(2), pp. S59-S66.
Paradiso S, Anderson BM, Boles-Ponto LL, Tranel D, Robinson RG. (2011) Altered neural activity and emotions following right middle cerebral artery stroke. J Stroke Cerebrovasc. Dis. Vol. 20(2), pp. 94-104.
Park S, Butler AJ, Cavalheiro V, Alberts JL, Wolf SL. (2004) Changes in serial topography and TMS during task performance after constraint-induced movement therapy in stroke: A case study. Neurorehabil Neural Repair. Vol. 18(95), pp. 94-105.
Posteraro F, Micera S. (2009) On the use of divergent (unstable) force fields in robot-mediated neurorehabilitation. CORNER Workshop, IIT, Genoa, IT. Available at: http://www.ospedalebambinogesu.it/Portale2008/ItemFiles/05_CORNER_14122009_Micera_Posteraro.pdf (Accessed 2 February 2012).
Rossi S, Hallett M, Rossinis P M, Pascual-Leone A, The Safety TMS Consensus Group. (2009) Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research. NIH Public Access Author Manuscript. Clin. Neurophysiol. Vol. 120(12), pp. 2008-2039.
Summer JJ. (1992) Approaches to the Study of Motor Control and Learning. New York, NY: North-Holland.
Takeuchi H, Sugiura M, Sassa Y, Sekiguchi A, Yomogida Y, Taki Y, Kawashima R. (2012) Neural correlates of the difference between working memory speed and simple sensorimotor speed: An fMRI Study. PLoS One. Vol. 7(1):e30579.
Van Kemenade BM, Muggleton N, Walsh V, Saygin AP. (2012) Effects of TMS over Premotor and Superior Temporal Cortices on Biological Motion Perception. J. Cogn. Neurosci. Available at: http://www.mitpressjournals.org/loi/jocn/. (Accessed 2 February 2012).
Volpe BT, Lynch D, Rykman-Berland A, Ferraro M, Galgano M, Hogan N, Krebs HI. (2008) Intensive sensorimotor arm training mediated by therapist or robot improves hemiparesis in patients in patients with chronic stroke. Neurorehabilitation and Neural Repair. Vol. 22, pp. 305-10.
(3028 words)
