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[ARTICLE] Combining Upper Limb Robotic Rehabilitation with Other Therapeutic Approaches after Stroke: Current Status, Rationale, and Challenges – Full Text

Abstract

A better understanding of the neural substrates that underlie motor recovery after stroke has led to the development of innovative rehabilitation strategies and tools that incorporate key elements of motor skill relearning, that is, intensive motor training involving goal-oriented repeated movements. Robotic devices for the upper limb are increasingly used in rehabilitation. Studies have demonstrated the effectiveness of these devices in reducing motor impairments, but less so for the improvement of upper limb function. Other studies have begun to investigate the benefits of combined approaches that target muscle function (functional electrical stimulation and botulinum toxin injections), modulate neural activity (noninvasive brain stimulation), and enhance motivation (virtual reality) in an attempt to potentialize the benefits of robot-mediated training. The aim of this paper is to overview the current status of such combined treatments and to analyze the rationale behind them.

1. Introduction

Significant advances have been made in the management of stroke (including prevention, acute management, and rehabilitation); however cerebrovascular diseases remain the third most common cause of death and the first cause of disability worldwide [16]. Stroke causes brain damage, leading to loss of motor function. Upper limb (UL) function is particularly reduced, resulting in disability. Many rehabilitation techniques have been developed over the last decades to facilitate motor recovery of the UL in order to improve functional ability and quality of life [710]. They are commonly based on principles of motor skill learning to promote plasticity of motor neural networks. These principles include intensive, repetitive, task-oriented movement-based training [1119]. A better understanding of the neural substrates of motor relearning has led to the development of innovative strategies and tools to deliver exercise that meets these requirements. Treatments mostly target the neurological impairment (paresis, spasticity, etc.) through the activation of neural circuits or by acting on peripheral effectors. Robotic devices provide exercises that incorporate key elements of motor learning. Advanced robotic systems can offer highly repetitive, reproducible, interactive forms of training for the paretic limb, which are quantifiable. Robotic devices also enable easy and objective assessment of motor performance in standardized conditions by the recording of biomechanical data (i.e., speed, forces) [2022]. This data can be used to analyze and assess motor recovery in stroke patients [2326]. Since the 1990s, many other technology-based approaches and innovative pharmaceutical treatments have also been developed for rehabilitation, including virtual reality- (VR-) based systems, botulinum neurotoxin (BoNT) injections, and noninvasive brain stimulation (NIBS) (Direct Current Stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS)). There is currently no high-quality evidence to support any of these innovative interventions, despite the fact that some are used in routine practice [27]. By their respective mechanisms of action, each of these treatments could potentiate the effects of robotic therapy, leading to greater improvements in motor capacity. The aim of this paper is to review studies of combined treatments based on robotic rehabilitation and to analyze the rationale behind such approaches.[…]

 

Continue —> Combining Upper Limb Robotic Rehabilitation with Other Therapeutic Approaches after Stroke: Current Status, Rationale, and Challenges

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[Abstract+References] Greater Cortical Thickness Is Associated With Enhanced Sensory Function After Arm Rehabilitation in Chronic Stroke

Objective. Somatosensory function is critical to normal motor control. After stroke, dysfunction of the sensory systems prevents normal motor function and degrades quality of life. Structural neuroplasticity underpinnings of sensory recovery after stroke are not fully understood. The objective of this study was to identify changes in bilateral cortical thickness (CT) that may drive recovery of sensory acuity. Methods. Chronic stroke survivors (n = 20) were treated with 12 weeks of rehabilitation. Measures were sensory acuity (monofilament), Fugl-Meyer upper limb and CT change. Permutation-based general linear regression modeling identified cortical regions in which change in CT was associated with change in sensory acuity. Results. For the ipsilesional hemisphere in response to treatment, CT increase was significantly associated with sensory improvement in the area encompassing the occipital pole, lateral occipital cortex (inferior and superior divisions), intracalcarine cortex, cuneal cortex, precuneus cortex, inferior temporal gyrus, occipital fusiform gyrus, supracalcarine cortex, and temporal occipital fusiform cortex. For the contralesional hemisphere, increased CT was associated with improved sensory acuity within the posterior parietal cortex that included supramarginal and angular gyri. Following upper limb therapy, monofilament test score changed from 45.0 ± 13.3 to 42.6 ± 12.9 mm (P = .063) and Fugl-Meyer score changed from 22.1 ± 7.8 to 32.3 ± 10.1 (P < .001). Conclusions. Rehabilitation in the chronic stage after stroke produced structural brain changes that were strongly associated with enhanced sensory acuity. Improved sensory perception was associated with increased CT in bilateral high-order association sensory cortices reflecting the complex nature of sensory function and recovery in response to rehabilitation.

Keywords 

1. Wolf, SL, Winstein, CJ, Miller, JP; EXCITE Investigators. Effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial. JAMA. 2006;296:20952104. doi:10.1001/jama.296.17.2095. Google ScholarCrossrefMedlineISI
2. Lo, AC, Guarino, PD, Richards, LG. Robot-assisted therapy for long-term upper-limb impairment after stroke. N Engl J Med. 2010;362:17721783. doi:10.1056/NEJMoa0911341.Google ScholarCrossrefMedlineISI
3. McCabe, J, Monkiewicz, M, Holcomb, J, Pundik, S, Daly, JJ. Comparison of robotics, functional electrical stimulation, and motor learning methods for treatment of persistent upper extremity dysfunction after stroke: a randomized controlled trial. Arch Phys Med Rehabil. 2015;96:981990. doi:10.1016/j.apmr.2014.10.022. Google ScholarCrossrefMedlineISI
4. Johansen-Berg, H, Dawes, H, Guy, C, Smith, SM, Wade, DT, Matthews, PM. Correlation between motor improvements and altered fMRI activity after rehabilitative therapy. Brain. 2002;125(pt 12):27312742Google ScholarCrossrefMedline
5. Luft, AR, McCombe-Waller, S, Whitall, J. Repetitive bilateral arm training and motor cortex activation in chronic stroke: a randomized controlled trial. JAMA. 2004;292:18531861. doi:10.1001/jama.292.15.1853. Google ScholarCrossrefMedlineISI
6. Pundik, S, McCabe, JP, Hrovat, K. Recovery of post stroke proximal arm function, driven by complex neuroplastic bilateral brain activation patterns and predicted by baseline motor dysfunction severity. Front Hum Neurosci. 2015;9:394. doi:10.3389/fnhum.2015.00394. Google ScholarCrossrefMedline
7. Desrosiers, J, Noreau, L, Rochette, A, Bourbonnais, D, Bravo, G, Bourget, A. Predictors of long-term participation after stroke. Disabil Rehabil. 2006;28:221230. doi:10.1080/09638280500158372. Google ScholarCrossrefMedlineISI
8. Carey, L, Macdonell, R, Matyas, TA. SENSe: Study of the Effectiveness of Neurorehabilitation on Sensation: a randomized controlled trial. Neurorehabil Neural Repair. 2011;25:304313. doi:10.1177/1545968310397705. Google ScholarSAGE JournalsISI
9. Cramer, SC, Nelles, G, Benson, RR. A functional MRI study of subjects recovered from hemiparetic stroke. Stroke. 1997;28:25182527Google ScholarCrossrefMedlineISI
10. Carey, JR, Kimberley, TJ, Lewis, SM. Analysis of fMRI and finger tracking training in subjects with chronic stroke. Brain. 2002;125(pt 4):773788Google ScholarCrossrefMedline
11. Carey, LM, Abbott, DF, Lamp, G, Puce, A, Seitz, RJ, Donnan, GA. Same intervention-different reorganization: the impact of lesion location on training-facilitated somatosensory recovery after stroke. Neurorehabil Neural Repair. 2016;30:9881000. doi:10.1177/1545968316653836.Google ScholarSAGE JournalsISI
12. Carey, LM, Matyas, TA. Frequency of discriminative sensory loss in the hand after stroke in a rehabilitation setting. J Rehabil Med. 2011;43:257263. doi:10.2340/16501977-0662. Google ScholarCrossrefMedlineISI
13. Borstad, AL, Bird, T, Choi, S, Goodman, L, Schmalbrock, P, Nichols-Larsen, DS. Sensorimotor training and neural reorganization after stroke: a case series. J Neurol Phys Ther. 2013;37:2736. doi:10.1097/NPT.0b013e318283de0d. Google ScholarCrossrefMedlineISI
14. Gauthier, LV, Taub, E, Perkins, C, Ortmann, M, Mark, VW, Uswatte, G. Remodeling the brain: plastic structural brain changes produced by different motor therapies after stroke. Stroke. 2008;39:15201525. doi:10.1161/STROKEAHA.107.502229. Google ScholarCrossrefMedlineISI
15. Zheng, X, Schlaug, G. Structural white matter changes in descending motor tracts correlate with improvements in motor impairment after undergoing a treatment course of tDCS and physical therapy. Front Hum Neurosci. 2015;9:229. doi:10.3389/fnhum.2015.00229. Google ScholarCrossrefMedlineISI
16. Bailey, CH, Kandel, ER. Structural changes accompanying memory storage. Annu Rev Physiol. 1993;55:397426. doi:10.1146/annurev.ph.55.030193.002145. Google ScholarCrossrefMedlineISI
17. Jones, TA, Chu, CJ, Grande, LA, Gregory, AD. Motor skills training enhances lesion-induced structural plasticity in the motor cortex of adult rats. J Neurosci. 1999;19:1015310163Google ScholarCrossrefMedlineISI
18. Wang, L, Conner, JM, Rickert, J, Tuszynski, MH. Structural plasticity within highly specific neuronal populations identifies a unique parcellation of motor learning in the adult brain. Proc Natl Acad Sci U S A. 2011;108:25452550. doi:10.1073/pnas.1014335108. Google ScholarCrossrefMedline
19. Maguire, EA, Gadian, DG, Johnsrude, IS. Navigation-related structural change in the hippocampi of taxi drivers. Proc Natl Acad Sci U S A. 2000;97:43984403. doi:10.1073/pnas.070039597. Google ScholarCrossrefMedlineISI
20. Draganski, B, Gaser, C, Busch, V, Schuierer, G, Bogdahn, U, May, A. Neuroplasticity: changes in grey matter induced by training. Nature. 2004;427:311312. doi:10.1038/427311a. Google ScholarCrossrefMedlineISI
21. Sampaio-Baptista, C, Scholz, J, Jenkinson, M. Gray matter volume is associated with rate of subsequent skill learning after a long term training intervention. Neuroimage. 2014;96:158166. doi:10.1016/j.neuroimage.2014.03.056. Google ScholarCrossrefMedline
22. Nouri, S, Cramer, SC. Anatomy and physiology predict response to motor cortex stimulation after stroke. Neurology. 2011;77:10761083. doi:10.1212/WNL.0b013e31822e1482. Google ScholarCrossrefMedlineISI
23. Gauthier, LV, Taub, E, Mark, VW, Barghi, A, Uswatte, G. Atrophy of spared gray matter tissue predicts poorer motor recovery and rehabilitation response in chronic stroke. Stroke. 2012;43:453457. doi:10.1161/STROKEAHA.111.633255. Google ScholarCrossrefMedlineISI
24. Sterr, A, Dean, PJ, Vieira, G, Conforto, AB, Shen, S, Sato, JR. Cortical thickness changes in the non-lesioned hemisphere associated with non-paretic arm immobilization in modified CI therapy. Neuroimage Clin. 2013;2:797803. doi:10.1016/j.nicl.2013.05.005. Google ScholarCrossrefMedline
25. Schaechter, JD, Moore, CI, Connell, BD, Rosen, BR, Dijkhuizen, RM. Structural and functional plasticity in the somatosensory cortex of chronic stroke patients. Brain. 2006;129(pt 10):27222733. doi:10.1093/brain/awl214. Google ScholarCrossrefMedline
26. Kopp, B, Kunkel, A, Flor, H. The Arm Motor Ability Test: reliability, validity, and sensitivity to change of an instrument for assessing disabilities in activities of daily living. Arch Phys Med Rehabil. 1997;78:615620Google ScholarCrossrefMedlineISI
27. Duncan, PW, Lai, SM, Keighley, J. Defining post-stroke recovery: implications for design and interpretation of drug trials. Neuropharmacology. 2000;39:835841Google ScholarCrossrefMedlineISI
28. Fischl, B. FreeSurfer. Neuroimage. 2012;62:774781. doi:10.1016/j.neuroimage.2012.01.021.Google ScholarCrossrefMedlineISI
29. Reuter, M, Rosas, HD, Fischl, B. Highly accurate inverse consistent registration: a robust approach. Neuroimage. 2010;53:11811196. doi:10.1016/j.neuroimage.2010.07.020. Google ScholarCrossrefMedlineISI
30. Greve, DN, Van der Haegen, L, Cai, Q. A surface-based analysis of language lateralization and cortical asymmetry. J Cogn Neurosci. 2013;25:14771492. doi:10.1162/jocn_a_00405. Google ScholarCrossrefMedlineISI
31. Winkler, AM, Ridgway, GR, Webster, MA, Smith, SM, Nichols, TE. Permutation inference for the general linear model. Neuro-image. 2014;92:381397. doi:10.1016/j.neuroimage.2014.01.060. Google ScholarCrossrefMedlineISI
32. Smith, SM, Nichols, TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage. 2009;44:8398. doi:10.1016/j.neuroimage.2008.03.061. Google ScholarCrossrefMedlineISI
33. Nichols, T, Holmes, A. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp. 2002;15:125. doi:10.1016/B978-012264841-0/50048-2.Google ScholarCrossrefMedlineISI
34. Winkler, AM, Ridgway, GR, Douaud, G, Nichols, TE, Smith, SM. Faster permutation inference in brain imaging. Neuroimage. 2016;141:502516. doi:10.1016/j.neuroimage.2016.05.068.Google ScholarCrossrefMedline
35. Desikan, RS, Ségonne, F, Fischl, B. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968980. doi:10.1016/j.neuroimage.2006.01.021. Google ScholarCrossrefMedlineISI
36. Thut, G, Théoret, H, Pfennig, A. Differential effects of low-frequency rTMS at the occipital pole on visual-induced alpha desynchronization and visual-evoked potentials. Neuroimage. 2003;18:334347Google ScholarCrossrefMedline
37. Amedi, A, Floel, A, Knecht, S, Zohary, E, Cohen, LG. Transcranial magnetic stimulation of the occipital pole interferes with verbal processing in blind subjects. Nat Neurosci. 2004;7:12661270. doi:10.1038/nn1328. Google ScholarCrossrefMedlineISI
38. Merabet, LB, Swisher, JD, McMains, SA. Combined activation and deactivation of visual cortex during tactile sensory processing. J Neurophysiol. 2006;97:16331641. doi:10.1152/jn.00806.2006. Google ScholarCrossrefMedline
39. Amedi, A, Malach, R, Hendler, T, Peled, S, Zohary, E. Visuo-haptic object-related activation in the ventral visual pathway. Nat Neurosci. 2001;4:324330. doi:10.1038/85201. Google ScholarCrossrefMedlineISI
40. Stoesz, MR, Zhang, M, Weisser, VD, Prather, SC, Mao, H, Sathian, K. Neural networks active during tactile form perception: common and differential activity during macrospatial and microspatial tasks. Int J Psychophysiol. 2003;50:4149Google ScholarCrossrefMedline
41. Lacey, S, Tal, N, Amedi, A, Sathian, K. A putative model of multisensory object representation. Brain Topogr. 2009;21:269274. doi:10.1007/s10548-009-0087-4. Google ScholarCrossrefMedline
42. Kim, JK, Zatorre, RJ. Tactile-auditory shape learning engages the lateral occipital complex. J Neurosci. 2011;31:78487856. doi:10.1523/JNEUROSCI.3399-10.2011. Google ScholarCrossrefMedline
43. Botvinick, M, Cohen, J. Rubber hands “feel” touch that eyes see. Nature. 1998;391:756. doi:10.1038/35784. Google ScholarCrossrefMedlineISI
44. Zangaladze, A, Epstein, CM, Grafton, ST, Sathian, K. Involvement of visual cortex in tactile discrimination of orientation. Nature. 1999;401:587590. doi:10.1038/44139. Google ScholarCrossrefMedlineISI
45. Sathian, K. Analysis of haptic information in the cerebral cortex. J Neurophysiol. 2016;116:17951806. doi:10.1152/jn.00546.2015. Google ScholarCrossrefMedline
46. Hsiao, S, Gomez-Ramirez, M. Touch. In: Gottfried, JA ed. Neurobiology of Sensation and Reward. Frontiers in Neuroscience. Boca Raton, FLCRC Press/Taylor & Francis2011Google ScholarCrossref
47. Vincent, JL, Snyder, AZ, Fox, MD. Coherent spontaneous activity identifies a hippocampal-parietal memory network. J Neurophysiol. 2006;96:35173531. doi:10.1152/jn.00048.2006.Google ScholarCrossrefMedlineISI
48. Roland, PE, O’Sullivan, B, Kawashima, R. Shape and roughness activate different somatosensory areas in the human brain. Proc Natl Acad Sci U S A. 1998;95:32953300Google ScholarCrossrefMedlineISI
49. Bodegård, A, Geyer, S, Grefkes, C, Zilles, K, Roland, PE. Hierarchical processing of tactile shape in the human brain. Neuron. 2001;31:317328Google ScholarCrossrefMedline
50. Karhu, J, Tesche, CD. Simultaneous early processing of sensory input in human primary (SI) and secondary (SII) somatosensory cortices. J Neurophysiol. 1999;81:20172025Google ScholarCrossrefMedline
51. Fabri, M, Polonara, G, Pesce, MD, Quattrini, A, Salvolini, U, Manzoni, T. Posterior corpus callosum and interhemispheric transfer of somatosensory information: an fMRI and neuropsychological study of a partially callosotomized patient. J Cogn Neurosci. 2001;13:10711079. doi:10.1162/089892901753294365. Google ScholarCrossrefMedlineISI
52. Chung, Y, Han, S, Kim, HS. Intra- and inter-hemispheric effective connectivity in the human somatosensory cortex during pressure stimulation. BMC Neurosci. 2014;15:43. doi:10.1186/1471-2202-15-43. Google ScholarCrossrefMedline
53. Mohajerani, MH, Aminoltejari, K, Murphy, TH. Targeted mini-strokes produce changes in interhemispheric sensory signal processing that are indicative of disinhibition within minutes. Proc Natl Acad Sci U S A. 2011;108:E183E191. doi:10.1073/pnas.1101914108. Google ScholarCrossrefMedlineISI
54. Blankenburg, F, Ruff, CC, Bestmann, S. Interhemispheric effect of parietal TMS on somatosensory response confirmed directly with concurrent TMS-fMRI. J Neurosci. 2008;28:1320213208. doi:10.1523/JNEUROSCI.3043-08.2008. Google ScholarCrossrefMedline
55. Inoue, K, Kawashima, R, Sugiura, M. Activation in the ipsilateral posterior parietal cortex during tool use: a PET study. Neuroimage. 2001;14:14691475. doi:10.1006/nimg.2001.0942. Google ScholarCrossrefMedlineISI
56. Takatsuru, Y, Fukumoto, D, Yoshitomo, M, Nemoto, T, Tsukada, H, Nabekura, J. Neuronal circuit remodeling in the contralateral cortical hemisphere during functional recovery from cerebral infarction. J Neurosci. 2009;29:1008110086. doi:10.1523/JNEUROSCI.1638-09.2009.Google ScholarCrossrefMedlineISI
57. Nelles, G, Spiekermann, G, Jueptner, M. Reorganization of sensory and motor systems in hemiplegic stroke patients. A positron emission tomography study. Stroke. 1999;30:15101516Google ScholarCrossrefMedlineISI
58. Jang, SH, Lee, MY. Correlation between somatosensory function and cortical activation induced by touch stimulation in patients with intracerebral hemorrhage. Int J Neurosci. 2013;123:248252. doi:10.3109/00207454.2012.755968. Google ScholarCrossrefMedlineISI
59. Bannister, LC, Crewther, SG, Gavrilescu, M, Carey, LM. Improvement in touch sensation after stroke is associated with resting functional connectivity changes. Front Neurol. 2015;6:165. doi:10.3389/fneur.2015.00165. Google ScholarCrossrefMedline
60. Kang, X, Herron, TJ, Cate, AD, Yund, EW, Woods, DL. Hemispherically-unified surface maps of human cerebral cortex: reliability and hemispheric asymmetries. PloS One. 2012;7:e45582. doi:10.1371/journal.pone.0045582. Google ScholarCrossrefMedline
61. Maingault, S, Tzourio-Mazoyer, N, Mazoyer, B, Crivello, F. Regional correlations between cortical thickness and surface area asymmetries: a surface-based morphometry study of 250 adults. Neuropsychologia. 2016;93(pt B):350364. doi:10.1016/j.neuropsychologia.2016.03.025. Google ScholarCrossrefMedline
62. Van de Winckel, A, Wenderoth, N, De Weerdt, W. Frontoparietal involvement in passively guided shape and length discrimination: a comparison between subcortical stroke patients and healthy controls. Exp Brain Res. 2012;220:179189. doi:10.1007/s00221-012-3128-2. Google ScholarCrossrefMedline
63. Borstad, A, Schmalbrock, P, Choi, S, Nichols-Larsen, DS. Neural correlates supporting sensory discrimination after left hemisphere stroke. Brain Res. 2012;1460:7887. doi:10.1016/j.brainres.2012.03.060. Google ScholarCrossrefMedlineISI
64. Lindberg, PG, Schmitz, C, Engardt, M, Forssberg, H, Borg, J. Use-dependent up- and down-regulation of sensorimotor brain circuits in stroke patients. Neurorehabil Neural Repair. 2007;21:315326. doi:10.1177/1545968306296965. Google ScholarSAGE JournalsISI
65. Dechaumont-Palacin, S, Marque, P, De Boissezon, X. Neural correlates of proprioceptive integration in the contralesional hemisphere of very impaired patients shortly after a subcortical stroke: an FMRI study. Neurorehabil Neural Repair. 2008;22:154165. doi:10.1177/1545968307307118. Google ScholarSAGE JournalsISI
66. Zatorre, RJ, Fields, RD, Johansen-Berg, H. Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat Neurosci. 2012;15:528536. doi:10.1038/nn.3045.Google ScholarCrossrefMedlineISI
67. Fields, RD. Changes in brain structure during learning: fact or artifact? Reply to Thomas and Baker. Neuroimage. 2013;73:260267. doi:10.1016/j.neuroimage.2012.08.085. Google ScholarCrossrefMedline
68. Xu, T, Yu, X, Perlik, AJ. Rapid formation and selective stabilization of synapses for enduring motor memories. Nature. 2009;462:915919. doi:10.1038/nature08389. Google ScholarCrossrefMedlineISI
69. Kleim, JA, Hogg, TM, VandenBerg, PM, Cooper, NR, Bruneau, R, Remple, M. Cortical synaptogenesis and motor map reorganization occur during late, but not early, phase of motor skill learning. J Neurosci. 2004;24:628633. doi:10.1523/JNEUROSCI.3440-03.2004. Google ScholarCrossrefMedlineISI
70. Sampaio-Baptista, C, Khrapitchev, AA, Foxley, S. Motor skill learning induces changes in white matter microstructure and myelination. J Neurosci. 2013;33:1949919503. doi:10.1523/JNEUROSCI.3048-13.2013. Google ScholarCrossrefMedlineISI
71. Erickson, KI. Evidence for structural plasticity in humans: comment on Thomas and Baker (2012). Neuroimage. 2013;73:237238. doi:10.1016/j.neuroimage.2012.07.003. Google ScholarCrossrefMedline
72. Feydy, A, Carlier, R, Roby-Brami, A. Longitudinal study of motor recovery after stroke: recruitment and focusing of brain activation. Stroke. 2002;33:16101617Google ScholarCrossrefMedlineISI
73. Hamzei, F, Liepert, J, Dettmers, C, Weiller, C, Rijntjes, M. Two different reorganization patterns after rehabilitative therapy: an exploratory study with fMRI and TMS. Neuroimage. 2006;31:710720. doi:10.1016/j.neuroimage.2005.12.035. Google ScholarCrossrefMedlineISI

via Greater Cortical Thickness Is Associated With Enhanced Sensory Function After Arm Rehabilitation in Chronic Stroke – Svetlana Pundik, Aleka Scoco, Margaret Skelly, Jessica P. McCabe, Janis J. Daly, 2018

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[ARTICLE] Measurements by A LEAP-Based Virtual Glove for the Hand Rehabilitation – Full Text

Abstract

Hand rehabilitation is fundamental after stroke or surgery. Traditional rehabilitation requires a therapist and implies high costs, stress for the patient, and subjective evaluation of the therapy effectiveness. Alternative approaches, based on mechanical and tracking-based gloves, can be really effective when used in virtual reality (VR) environments. Mechanical devices are often expensive, cumbersome, patient specific and hand specific, while tracking-based devices are not affected by these limitations but, especially if based on a single tracking sensor, could suffer from occlusions. In this paper, the implementation of a multi-sensors approach, the Virtual Glove (VG), based on the simultaneous use of two orthogonal LEAP motion controllers, is described. The VG is calibrated and static positioning measurements are compared with those collected with an accurate spatial positioning system. The positioning error is lower than 6 mm in a cylindrical region of interest of radius 10 cm and height 21 cm. Real-time hand tracking measurements are also performed, analysed and reported. Hand tracking measurements show that VG operated in real-time (60 fps), reduced occlusions, and managed two LEAP sensors correctly, without any temporal and spatial discontinuity when skipping from one sensor to the other. A video demonstrating the good performance of VG is also collected and presented in the Supplementary Materials. Results are promising but further work must be done to allow the calculation of the forces exerted by each finger when constrained by mechanical tools (e.g., peg-boards) and for reducing occlusions when grasping these tools. Although the VG is proposed for rehabilitation purposes, it could also be used for tele-operation of tools and robots, and for other VR applications.

1. Introduction

Hand rehabilitation is extremely important for recovering from post-stroke or post-surgery residual impairments and its effectiveness depends on frequency, duration and quality of the rehabilitation sessions [1]. Traditional rehabilitation requires a therapist for driving and controlling patients during sessions. Procedure effectiveness is evaluated subjectively by the therapist, basing on experience. In the last years, several automated (tele)rehabilitation gloves, based on mechanical devices or tracking sensors, have been presented [2,3,4,5,6,7,8,9,10]. These gloves allow the execution of therapy at home and rehabilitation effectiveness can be analytically calculated and summarized in numerical parameters, controlled by therapists through Internet. Moreover, these equipment can be easily interfaced with virtual reality (VR) environments [11], which have been proven to increase rehabilitation efficacy [12]. Mechanical devices are equipped with pressure sensors and pneumatic actuators for assisting and monitoring the hand movements and for applying forces to which the patient has to oppose [13,14]. However, they are expensive, cumbersome, patient specific (different patients cannot reuse the same system) and hand specific (the patient cannot use the same system indifferently with both hands). Tracking-based gloves consist of computer vision algorithms for the analysis and interpretation of videos from depth sensing sensors to calculate hand kinematics in real time [10,15,16,17,18,19]. Besides depth sensors, LEAP [20] is a small and low-cost hand 3D tracking device characterized by high-resolution and high-reactivity [21,22,23], used in VR [24], and has been recently presented and tested with success in the hand rehabilitation, with exercises designed in VR environments [25]. Despite the advantages of using LEAP with VR, a single sensor does not allow accurate quantitative evaluation of hand and fingers tracking in case of occlusions. The system proposed in [10] consisted on two orthogonal LEAPs designed to reduce occlusions and to improve objective hand-tracking evaluation. The two sensors were fixed to a wood support that maintained them orthogonal each other. The previous prototype was useful to test the robustness of each sensor, in presence of the other, to the potential infra-red interferences, to evaluate the maintenance of the maximum operative range of each sensor and, finally, to demonstrate the hand tracking idea. However, it was imprecise, due to the usage of raw VG support and positioning system, the non-optimal reciprocal positioning of the sensors, and the impossibility of performing a reciprocal calibration independent of the sensors measurements. This fact did not allow the evaluation of the intrinsic precision of the VG and to perform accurate, real-time quantitative hand tracking measurements. In this paper, we present a method for constructing an engineered version of the LEAP based VG, a technique for its accurate calibration and for collecting accurate positioning measurements and high-quality evaluation of positioning errors, specific of VG. Moreover, real-time experimental hand tracking measurements were collected (a video demonstrating its real-time performance and precision was also provided in the Supplementary Materials), presented and discussed.[…]

 

Continue —>  Measurements by A LEAP-Based Virtual Glove for the Hand Rehabilitation

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Figure 1
VG mounted on its aluminium support.

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[ARTICLE] Automatic Control of Wrist Rehabilitation Therapy (WRist-T) device for Post-Ischemic Stroke Patient – Full Text PDF

Abstract

Since a decade, the wrist rehabilitation services in Malaysia has been operated by the physiotherapist (PT). Throughout the rehabilitative procedure, PT commonly used a conventional method which later triggered some problems related to the effectiveness of the rehab services. Timeconsuming, long-waiting time, lack of human power and all those leading to exhaustion, both for the patient and the provider. Patients could not commit to the therapy session due to logistic and domestic problems. This problem can be greatly solved with rehabilitation robot, but the current product in the market is expensive and not affordable especially for lowincome earners family. In this paper, an automatic control of wrist rehabilitation therapy; called WRist-T device has been developed. There are based on three different modes of exercises that can be carried out by the device which is the flexion/extension, radial/ulnar deviation and pronation/supination. By using this device, the patient can easily receive physiotherapy session with minor supervision from the physiotherapist at the hospital or rehabilitation centre and also can be conducted at patient home.

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References

N. Bayona,“The role of task-specific training in rehabilitation therapies,”Topics in Stroke Rehabilitation, vol. 12, 2005,pp. 58–65.

R. Bonita, R. Beaglehole, “Recovery of motor function after stroke,”Stroke, 1988,pp. 19.

S. Cramer, J. Riley, “Neuroplasticity and brain repair after stroke,”Current Opinion in Neurology,vol. 21, 2008,pp. 76–82.

D.J. Reinkensmeyer, J. Emken, S. Cramer, “Robotics, motor learning, and neurologic recovery,”Annual Review of Biomedical Engineering, vol. 6, 2004, pp. 497-525.

M. Takaiwa, “Wrist rehabilitation training simulator for P.T. using pneumatic parallel manipulator,”IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2016, pp. 276-281.

H. Al-Fahaam, S. Davis, S. Nefti-Meziani, “Wrist Rehabilitation exoskeleton robot based on pneumatic soft actuators,”International Conference for Students of Applied Engineering (ICSAE), 2016, pp. 491-496.

D. Dauria, F. Persia, B. Siciliano,“Human-Computer Interaction in Healthcare: How to Support Patients during their Wrist Rehabilitation,”IEEE Tenth International Conference on Semantic Computing (ICSC), 2016, pp. 325-328.

W.M. Hsieh, Y.S. Hwang, S.C. Chen, S.Y. Tan,C.C. Chen, and Y.L. Chen, “Application of the Blobo Bluetooth ball in wrist rehabilitation training,”Journal of Physical Therapy Science, vol. 28, 2016, pp. 27- 32.

A. Hacıoğlu, O.F. Özdemir, A,K, Şahin, Y.S. Akgül, “Augmented reality based wrist rehabilitation system,”Signal Processing and Communication Application Conference (SIU), 2016. pp. 1869-1872.

Z.J. Lu, L.C.B. Wang, L.H. Duan, Q.Q. Lui, H.Q. Sun, Z.I. Chen, “Development of a robot MKW-II for hand and Wrist Rehabilitation Training,”The Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, 2016, pp. 302-307.

 

via Automatic Control of Wrist Rehabilitation Therapy (WRist-T) device for Post-Ischemic Stroke Patient | Mohd Adib | Journal of Telecommunication, Electronic and Computer Engineering (JTEC)

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[BOOK] New Vibratory Device for Wrist Rehabilitation – Innovation, Engineering and Entrepreneurship – Google Books

New Vibratory Device for Wrist Rehabilitation

H Puga – Innovation, Engineering and Entrepreneurship, 2018
Wrist injuries are very common in most of the population, specially bone fractures,
but also other pathologies such as tendinitis and neurological diseases. When the
wrist is injured, their flexion-extension and radial-ulnar deviation and pronation …

 

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[ARTICLE] Assessing Hand Muscle Structural Modifications in Chronic Stroke – Full Text

The purpose of the study is to assess poststroke muscle structural alterations by examining muscular electrical conductivity and inherent electrophysiological properties. In particular, muscle impedance and compound muscle action potentials (CMAP) were measured from the hypothenar muscle bilaterally using the electrical impedance myography and the electrophysiological techniques, respectively. Significant changes of muscle impedance were observed in the paretic muscle compared with the contralateral side (resistance: paretic: 27.54 ± 0.97 Ω, contralateral: 25.46 ± 0.91 Ω, p < 0.05; phase angle: paretic: 8.81 ± 0.61°, contralateral: 10.79 ± 0.69°, p< 0.05). In addition, impedance changes correlated moderately with the CMAP amplitude in the paretic hand (phase angle: r = 0.66, p < 0.05; reactance: r = 0.58, p < 0.05). The study discloses significant muscle rearrangements as a result of fiber loss or atrophy, fat infiltration or impaired membrane integrity in chronic stroke.

Introduction

Muscle weakness is a remarkable symptom in stroke and contributes significantly to impaired motor functions. To understand mechanisms underlying weakness, studies can focus on assessing changes in neural control and muscular properties. In particular, intramuscular electromyography (EMG) and morphological techniques have been applied to examine muscle structural rearrangements poststroke. Increased motor unit fiber density, larger and complex motor unit action potentials (13), small angular fibers, as well as fiber type grouping (45) have been observed in the acute and chronic stages of stroke suggesting the process of muscle denervation and reinnervation. While these studies characterize structural alterations in the paretic muscles, most approaches involve invasive recording and are limited by sampling only small selective areas of the muscle.

Electrical impedance myography (EIM) is an emerging technique for noninvasive evaluation of muscle electrical conductive properties. It applies weak, high-frequency alternating current to the muscles and produces raw bio-impedance data without causing neuronal and muscular depolarization (67). EIM measures three impedance parameters in terms of resistance (R), reactance (X), and phase angle [θ = arctan (X/R)] (78), which represent the inherent resistivity of skeletal muscle relative to extracellular and intracellular fluid, the integrity of cell membranes, tissue interfaces and non-ionic substances, and membrane oscillation properties of the muscle respectively (912).

Electrical impedance myography has been used to examine muscle structural alterations in a number of neuromuscular diseases including amyotrophic lateral sclerosis (ALS), muscular dystrophy, and spinal muscular atrophy (671319). It is sensitive to muscle structural modifications in terms of atrophy, increased fat infiltration or connective tissue growth (2022). In addition, the technique demonstrates strong correlations with standard measures of ALS including ALS functional rating scale-revised, handheld dynamometry, and motor unit number estimation in tracking the progression of the disease (131723).

Applications of EIM to assess poststroke muscle conditions are relatively limited in the literature. In a previous study, we examined muscle impedance properties in the biceps brachii and found significant changes of muscle structural properties in the paretic side (24). Since proximal muscles demonstrate different extents of impairment from distal muscles (25), it remains unknown whether findings from biceps brachii are applicable to hand muscles. In this study, we applied EIM technique to examine impedance changes in the hypothenar muscle poststroke. In addition, we measured the compound muscle action potentials (CMAP) of the muscle, to assess inherent electrical properties. CMAP is evoked by electrical activation of all functioning motor units and represents summation of all action potentials in spatial distribution. Application of the two different techniques to the same muscle may disclose different features of the muscle and improve current knowledge on structural changes in the paretic hand muscle.[…]

 

Continue —> Frontiers | Assessing Hand Muscle Structural Modifications in Chronic Stroke | Neurology

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[Abstract] The effect of robot therapy assisted by surface EMG on hand recovery in post-stroke patients. A pilot study

Abstract

Background: Hemiparesis caused by a stroke negatively limits a patient’s motor function. Nowadays, innovative technologies such as robots are commonly used in upper limb rehabilitation. The main goal of robot-aided therapy is to provide a maximum number of stimuli in order to stimulate brain neuroplasticity. Treatment applied in this study via the AMADEO robot aimed to improve finger flexion and extension.
Aim: To assess the effect of rehabilitation assisted by a robot and enhanced by surface EMG.
Research project: Before-after study design.
Materials and methods: The study group consisted of 10 post-stroke patients enrolled for therapy with the AMADEO robot for at least 15 sessions. At the beginning and at the end of treatment, the following tests were used for clinical assessment: Fugl-Meyer scale, Box and Block test and Nine Hole Peg test. In the present study, we used surface electromyography (sEMG) to maintain optimal kinematics of hand motion. Whereas sensorial feedback, provided by the robot, was vital in obtaining closed-loop control. Thus, muscle contraction was transmitted to the amplifier through sEMG, activating the mechanism of the robot. Consequentially, sensorial feedback was provided to the patient.
Results: Statistically significant improvement of upper limb function was observed in: Fugl-Meyer (p = 0.38) and Box and Block (p = 0.27). The Nine Hole Peg Test did not show statistically significant changes in motor skills of the hand. However, the functional improvement was observed at the level of 6% in the Fugl-Meyer, 15% in the Box and Block, and 2% in the Nine Hole Peg test.
Conclusions: Results showed improvement in hand grasp and overall function of the upper limb. Due to sEMG, it was possible to implement robot therapy in the treatment of patients with severe hand impairment.

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[Abstract + References] A New Approach to Design Glove-Like Wearable Hand Exoskeletons for Rehabilitation – Conference paper

Abstract

The synthesis of hand exoskeletons for rehabilitation is a challenging theoretical and technical task. A huge number of solutions have been proposed in the literature. Most of them are based on the concept to consider the phalanges of the finger as fixed to some links of the exoskeleton mechanism. This approach makes the exoskeleton synthesis a difficult problem that compels the designer to devise approximate technical solutions which, frequently, reduce the efficiency of the rehabilitation system and are rather bulky.

This paper proposes a different approach. Namely, the phalanges are not fixed to some links of the exoskeleton, but they can have a relative motion, with one or two degrees of freedom when planar systems are considered. An example is presented to show the potentiality of this approach, which makes it possible: (i) to design glove-like exoskeletons that only approximate the human finger motion; (ii) to leave the fingers have their natural motion; (iii) to adapt a wider range of patient hand sizes to a given hand exoskeleton.

References

  1. 1.
    Agarwal, P., Hechanova, A., Deshpande, A.D.: Kinematics and Dynamics of a biologically inspired index finger exoskeleton. In: Proceedings of the ASME 2013 Dynamic Systems and Control Conference DSCC 2013, Palo Alto, CA, USA, pp. 1–10 (2013)Google Scholar
  2. 2.
    Heo, P., Min, GuG, Lee, S.J., Rhee, K., Kim, J.: Current hand exoskeleton technologies for rehabilitation and assistive engineering. Int. J. Precis. Eng. Manuf. 3(5), 807–824 (2012)CrossRefGoogle Scholar
  3. 3.
    Balasubramanian, S., Klein, J., Burdet, E.: Robot-assisted rehabilitation and hand function. Curr. Opin. Neurol. 23, 661–670 (2010)CrossRefGoogle Scholar
  4. 4.
    Troncossi, M., Mozaffari-Foumashi, M., Parenti-Castelli, V.: An original classification of rehabilitation hand exoskeletons. J. Robot. Mech. Eng. Res. 1(4), 17–29 (2016)CrossRefGoogle Scholar
  5. 5.
    Abdallah, I.B., Bouteraa, Y., Rekik, C.: Design and development of 3D printed myoelectric robotic exoskeleton for hand rehabilitation. Int. J. Smart Sens. Intell. Syst. 10(2), 341–366 (2017)Google Scholar
  6. 6.
    Foumashi, M., Troncossi, M., Parenti-Castelli, V.: Design of a new hand exo-skeleton for rehabilitation of post-stroke patients. In: Romansy 19-Robot Design, Dynamics and Control, pp. 159–169 (2013)CrossRefGoogle Scholar
  7. 7.
    Yap, H.K., Hoon, J., Nashrallah, F., Goh, J.C.H., Yeow, R.C.H.: A soft exoskeleton for hand assistive and rehabilitation application using pneumatic actuators with variable stiffness. In: 2015 IEEE International Conference on Robotics and Automation, ICRA, Seattle, Washington, USA, pp. 4967–4972 (2015)Google Scholar
  8. 8.
    Arata, J., Ohmoto, K., Gassert, R., Lambercy, O., Fujimoto, H., Wada, I.: A new hand exoskeleton device for rehabilitation using a three-layered sliding spring mechanism. In: 2013 IEEE International Conference on Robotics and Automation, ICRA, Karlsruhe, Germany, pp. 3902–3907 (2013)Google Scholar
  9. 9.
    Leonardis, D., Barsotti, M., Loconsole, C., Solazzi, M., Troncossi, M., Mazzotti, M., Parenti, C.V., Procopio, C., Lamola, G., Chisari, C., Bergamasco, M., Frisoli, A.: An EMG-controlled robotic hand exoskeleton for bilateral rehabilitation. J. Haptics 8(2), 140–151 (2015)CrossRefGoogle Scholar
  10. 10.
    Gulke, J., Watcher, N.J., Geyer, T., Scholl, H., Apic, G., Mentzler, M., et al.: Motion coordination pattern during cylinder grip analyzed with a sensor glove. J. Hand Surg. 35(5), 797 (2010)CrossRefGoogle Scholar
  11. 11.
    Li, J., Wang, S., Zheng, R., Zhang, Y., Chen, Z.: Development of a hand exoskeleton system for index finger rehabilitation. Chin. J. Mech. Eng. 25(2), 223–233 (2012)CrossRefGoogle Scholar

via A New Approach to Design Glove-Like Wearable Hand Exoskeletons for Rehabilitation | SpringerLink

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[Abstract+References] The Use of Image Processing Methods to Improve the Detection of User’s Hand in Vision Based Games Used in Neurological Rehabilitation

Abstract

Vision based games is a type of software that can become a promising, modern neurorehabilitation tool. This paper presents the possibilities offered for the implementation of this kind of software by the open source vision library. The methods and functions related to the aspect of image processing and analysis are presented in terms of their usefulness in creating programs based on the analysis of the images acquired from the camera. On the basis of the issues contained in the paper, the functionality of the library is presented in terms of the possibilities related primarily to the processing of video sequences, detection, tracking and analysis of the movement of objects.

As part of the work, the software that meets the requirements for modern neurorehablitation games has been implemented. Its main part is responsible for the identification of the current position of the user’s hand and is based on the image captured from the webcam. Whereas the tasks set for the user used among others supporting visual-motor coordination.

The main subject of the research was the analysis of the impact of the applied methods of initial image processing on the correctness of the chosen tracking algorithm. It was proposed and experimentally examined the impact of operations such as morphological transformations or apply an additional mask on a functioning of the CamShift algorithm.  And hence on the functioning of the whole game which analyzing the user’s hand movement.

References

Allen G. J., Richard Xu Y. D., Jin J. S. (2004). Object Tracking Using CAMShift Algorithm and Multiple Quantized Feature Spaces, Proceedings of the Pan-Sydney area workshop on Visual information processing , Sydney, 3-7.

Bradski G., Kaehler A. (2008). Learning OpenCV. Computer Vision with the OpenCV Library, Sebastopol, CA: O’Reilly Media.

Buczyński P. (2005). Optymalna reprezentacja kolorów w analizie i przetwarzaniu obrazów komputerowych, Praca doktorska. Warszawa: Politechnika Warszawska.

Burke J. W., Morrow P.J., et al. (2008). Vision Based Games for Upper-Limb Stroke Rehabilitation, Machine Vision and Image Processing Conference, 159 – 164.

Burke J. W. McNeill M. D. J., et al. (2010). Designing engaging, playable games for rehabilitation”, International Conference Series On Disability, Virtual Reality and Associated Technologies (ICDVRAT), 195-202.

Cameirão M.S. , et al. (2010). Neurorehabilitation using the virtual reality based Rehabilitation Gaming System: methodology, design, psychometrics, usability and validation, Journal of NeuroEngineering and Rehabilitation, 7, 48.

Comaniciu D., Ramesh V., Meer P. (2003). Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions 2003, p. 564-577.

Derpanis K. G. (2005). Mean Shift Clustering, http://www.cse.yorku.ca/~kosta/ Comp-Vis_Notes/mean_shift.pdf

Di Loreto I., Gouaich A., Hocine N., (2011). Mixed reality serious games for post-stroke rehabilitation, Pervasive Computing Technologies for Healthcare , 5th International Conference on, 530-537.

Garcia-Marin J., Felix-Navarro K., Law-rence E. (2011). Serious games to Improve the Physical Health of the Elderly: A Categorization Scheme, Fourth International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services (CENTRIC 2011), 64-71.

Jog A., Halbe S. (2013). Multiple Objects Tracking Using CAMShift Algorithm and Implementation of Trip Wire, International Journal of Image, Graphics and Signal Processing, 43-48.

Joshi S., Gujarathi S., Mirgemoving A. (2014). Moving object tracking method using improved camshift with surf algorithm. International Journal of Advances in Science Engineering and Technology, 2(2), 14-19.

Laganière R. (2011). “OpenCV 2 Computer Vision Application Programming Cookbook”, Packt Publishing, 2011.

Lange B., Flynn S.M., Rizzo A. A., (2009). Game-based telerehabilitation, European Journal of Physical and Rehabilitation Medicine, 45(1), 143-151.

Rafajłowicz E, Rafajłowicz W. (2010). Wstęp do przetwarzania obrazów przemysłowych, Wrocław: Oficyna Wydawnicza Politechniki Wrocławskiej.

Rayavel P., Appasami G., Nakeeran R. (2011). Noise removal for object tracking based on HSV color space parameter using CAMSHIFT. International Journal of Computational Intelligence & Telecommunication Systems, 2(1), 39–45.

Yilmaz A., Javed O., Shah M. (2006). Object tracking: A survey, ACM Computing Surveys, 38(4), Article 13, 1-45.

 

via The Use of Image Processing Methods to Improve the Detection of User’s Hand in Vision Based Games Used in Neurological Rehabilitation | Gospodarek | IMAGE PROCESSING & COMMUNICATIONS

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[Abstract] Pain-related psychological issues in hand therapy

Highlights

  • Pain is a subjective experience that results from the complex modulation of nociception conveyed to the brain via the nervous system.
  • Psychological factors such as cognitions (eg, pain catastrophizing), emotions (eg, depression), and pain-related behaviors (eg, avoidance) can influence perceived pain intensity, physical function, and treatment outcomes.
  • Several evidence-based interventions to address pain-related psychological risk factors are available and can be integrated into hand therapy.

Abstract

Study Design

Literature review.

Introduction

Pain is a subjective experience that results from the modulation of nociception conveyed to the brain via the nervous system. Perception of pain takes place when potential or actual noxious stimuli are appraised as threats of injury. This appraisal is influenced by one’s cognitions and emotions based on her/his pain-related experiences, which are processed in the forebrain and limbic areas of the brain. Unarguably, patients’ psychological factors such as cognitions (eg, pain catastrophizing), emotions (eg, depression), and pain-related behaviors (eg, avoidance) can influence perceived pain intensity, disability, and treatment outcomes. Therefore, hand therapists should address the patient pain experience using a biopsychosocial approach. However, in hand therapy, a biomedical perspective predominates in pain management by focusing solely on tissue healing.

Purpose of the Study

This review aims to raise awareness among hand therapists of the impact of pain-related psychological factors.

Methods and Results

This literature review allowed to describe (1) how the neurophysiological mechanisms of pain can be influenced by various psychological factors, (2) several evidence-based interventions that can be integrated into hand therapy to address these psychological issues, and (3) some approaches of psychotherapy for patients with maladaptive pain experiences.

Discussion and Conclusion

Restoration of sensory and motor functions as well as alleviating pain is at the core of hand therapy. Numerous psychological factors including patients’ beliefs, cognitions, and emotions alter their pain experience and may impact on their outcomes. Decoding the biopsychosocial components of the patients’ pain is thus essential for hand therapists.

via Pain-related psychological issues in hand therapy – Journal of Hand Therapy

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