Phantom motor execution as a treatment for phantom limb pain: protocol of an international, double-blind, randomised controlled clinical trial

Eva Lendaro, Liselotte Hermansson, Helena Burger, Corry K Van der Sluis, Brian E McGuire, Monika Pilch, Lina Bunketorp-Käll, Katarzyna Kulbacka-Ortiz, Ingrid Rignér, Anita Stockselius, Lena Gudmundson, Cathrine Widehammar, Wendy Hill, Sybille Geers, Max Ortiz-Catalan, Eva Lendaro, Liselotte Hermansson, Helena Burger, Corry K Van der Sluis, Brian E McGuire, Monika Pilch, Lina Bunketorp-Käll, Katarzyna Kulbacka-Ortiz, Ingrid Rignér, Anita Stockselius, Lena Gudmundson, Cathrine Widehammar, Wendy Hill, Sybille Geers, Max Ortiz-Catalan

Abstract

Introduction: Phantom limb pain (PLP) is a chronic condition that can greatly diminish quality of life. Control over the phantom limb and exercise of such control have been hypothesised to reverse maladaptive brain changes correlated to PLP. Preliminary investigations have shown that decoding motor volition using myoelectric pattern recognition, while providing real-time feedback via virtual and augmented reality (VR-AR), facilitates phantom motor execution (PME) and reduces PLP. Here we present the study protocol for an international (seven countries), multicentre (nine clinics), double-blind, randomised controlled clinical trial to assess the effectiveness of PME in alleviating PLP.

Methods and analysis: Sixty-seven subjects suffering from PLP in upper or lower limbs are randomly assigned to PME or phantom motor imagery (PMI) interventions. Subjects allocated to either treatment receive 15 interventions and are exposed to the same VR-AR environments using the same device. The only difference between interventions is whether phantom movements are actually performed (PME) or just imagined (PMI). Complete evaluations are conducted at baseline and at intervention completion, as well as 1, 3 and 6 months later using an intention-to-treat (ITT) approach. Changes in PLP measured using the Pain Rating Index between the first and last session are the primary measure of efficacy. Secondary outcomes include: frequency, duration, quality of pain, intrusion of pain in activities of daily living and sleep, disability associated to pain, pain self-efficacy, frequency of depressed mood, presence of catastrophising thinking, health-related quality of life and clinically significant change as patient's own impression. Follow-up interviews are conducted up to 6 months after the treatment.

Ethics and dissemination: The study is performed in agreement with the Declaration of Helsinki and under approval by the governing ethical committees of each participating clinic. The results will be published according to the Consolidated Standards of Reporting Trials guidelines in a peer-reviewed journal.

Trial registration number: NCT03112928; Pre-results.

Keywords: clinical trials; neurological pain; rehabilitation medicine.

Conflict of interest statement

Competing interests: The sponsor of this study (Integrum AB) is a for-profit organisation that might commercialise the device used in this study (phantom motor execution and phantom motor imagery). MO-C was partially funded by Integrum AB. The core technology used in this study has been made freely available as open source by MO-C (machine learning, virtual reality and electronics).

© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Figures

Figure 1
Figure 1
Flow diagram for the randomised controlled clinical trial. At least 67 patients are recruited and randomly allocated to either phantom motor execution (PME) or phantom motor imagery (PMI) interventions in allocation ratio 2:1. Following the completion of the treatment protocol and wash-out period of 6 months, it is possible for the patient to cross over to the parallel interventional arm, according to their will.
Figure 2
Figure 2
Schematic illustration of the clinical investigation device with all its components. Myoelectric signals are acquired though surface electrodes (A) by a myoelectric amplifier (B), electrically isolated (C). The signals are then processed by the software installed on the computer (D). The camera (E) films the participant and the recorded image is displayed on the monitor (F) with a virtual limb superimposed where the marker (G) is detected. Figure courtesy of Jason Millenaar.

References

    1. Dijkstra PU, Geertzen JH, Stewart R, et al. . Phantom pain and risk factors: a multivariate analysis. J Pain Symptom Manage 2002;24:578–85.
    1. Clark RL, Bowling FL, Jepson F, et al. . Phantom limb pain after amputation in diabetic patients does not differ from that after amputation in nondiabetic patients. Pain 2013;154:729–32. 10.1016/j.pain.2013.01.009
    1. Nikolajsen L, Jensen TS, pain Plimb. Br J Anaesth 2001;87:107–16.
    1. Batsford S, Ryan CG, Martin DJ. Non-pharmacological conservative therapy for phantom limb pain: A systematic review of randomized controlled trials. Physiother Theory Pract 2017;33:173–83. 10.1080/09593985.2017.1288283
    1. Ortiz-Catalan M, Sander N, Kristoffersen MB, et al. . Treatment of phantom limb pain (PLP) based on augmented reality and gaming controlled by myoelectric pattern recognition: a case study of a chronic PLP patient. Front Neurosci 2014;8 10.3389/fnins.2014.00024
    1. Ortiz-Catalan M, Guðmundsdóttir RA, Kristoffersen MB, et al. . Phantom motor execution facilitated by machine learning and augmented reality as treatment for Phantom Limb Pain. Lancet 2016;388:2885–94.
    1. Raffin E, Richard N, Giraux P, et al. . Primary motor cortex changes after amputation correlate with phantom limb pain and the ability to move the phantom limb. Neuroimage 2016;130:134–44. 10.1016/j.neuroimage.2016.01.063
    1. Lendaro E, Mastinu E, Håkansson B, et al. . Real-time Classification of Non-Weight Bearing Lower-Limb Movements Using EMG to Facilitate Phantom Motor Execution: Engineering and Case Study Application on Phantom Limb Pain. Front Neurol 2017;8 10.3389/fneur.2017.00470
    1. MacIver K, Lloyd DM, Kelly S, et al. . Phantom limb pain, cortical reorganization and the therapeutic effect of mental imagery. Brain 2008;131(Pt 8):2181–91. 10.1093/brain/awn124
    1. Chan BL, Witt R, Charrow AP, et al. . Mirror therapy for phantom limb pain. N Engl J Med 2007;357:2206–7. 10.1056/NEJMc071927
    1. Moseley GL. Graded motor imagery for pathologic pain: a randomized controlled trial. Neurology 2006;67:2129–34. 10.1212/01.wnl.0000249112.56935.32
    1. Bowering KJ, O’Connell NE, Tabor A, et al. . The effects of graded motor imagery and its components on chronic pain: a systematic review and meta-analysis. J Pain 2013;14:3–13. 10.1016/j.jpain.2012.09.007
    1. Ortiz-Catalan M, Brånemark R, Håkansson B. BioPatRec: A modular research platform for the control of artificial limbs based on pattern recognition algorithms. Source Code Biol Med 2013;8:11 10.1186/1751-0473-8-11
    1. Ortiz-Catalan M, Hkansson B, Brnemark R. Real-Time and Simultaneous Control of Artificial Limbs Based on Pattern Recognition Algorithms. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2014;22:756–64. 10.1109/TNSRE.2014.2305097
    1. Simon AM, Hargrove LJ, Lock BA, et al. . Target Achievement Control Test: evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses. J Rehabil Res Dev 2011;48:619–27. 10.1682/JRRD.2010.08.0149
    1. Melzack R. The short-form McGill Pain Questionnaire. Pain 1987;30:191–7. 10.1016/0304-3959(87)91074-8
    1. Pollard CA. Preliminary validity study of the pain disability index. Percept Mot Skills 1984;59:974 10.2466/pms.1984.59.3.974
    1. Melzack R. The McGill Pain Questionnaire: major properties and scoring methods. Pain 1975;1:277–99. 10.1016/0304-3959(75)90044-5
    1. Herdman M, Gudex C, Lloyd A, et al. . Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual Life Res 2011;20:1727–36. 10.1007/s11136-011-9903-x
    1. Nicholas MK. The pain self-efficacy questionnaire: Taking pain into account. Eur J Pain 2007;11:153–63. 10.1016/j.ejpain.2005.12.008
    1. Nicholas MK, McGuire BE, Asghari A, et al. . A 2-item short form of the Pain Self-efficacy Questionnaire: development and psychometric evaluation of PSEQ-2. J Pain 2015;16:153–63. 10.1016/j.jpain.2014.11.002
    1. McWilliams LA, Kowal J, Wilson KG. Development and evaluation of short forms of the Pain Catastrophizing Scale and the Pain Self-efficacy Questionnaire. Eur J Pain 2015;19:1342–9. 10.1002/ejp.665
    1. Sullivan MJL, Bishop SR, Pivik J. The Pain Catastrophizing Scale: Development and validation. Psychol Assess 1995;7:524–32. 10.1037/1040-3590.7.4.524
    1. Sullivan MJ, Thorn B, Haythornthwaite JA, et al. . Theoretical perspectives on the relation between catastrophizing and pain. Clin J Pain 2001;17:52–64. 10.1097/00002508-200103000-00008
    1. Spitzer RL, Kroenke K, Williams JBW. Validation and Utility of a Self-report Version of PRIME-MD. J Am Med Assoc 1999;282:1737–44.
    1. Kroenke K, Spitzer RL, Williams JB. The Patient Health Questionnaire-2: validity of a two-item depression screener. Med Care 2003;41:1284–92. 10.1097/01.MLR.0000093487.78664.3C
    1. Hurst H, Bolton J. Assessing the clinical significance of change scores recorded on subjective outcome measures. J Manipulative Physiol Ther 2004;27:26–35. 10.1016/j.jmpt.2003.11.003
    1. Jones SMW, Lange J, Turner J, et al. . Development and Validation of the EXPECT Questionnaire: Assessing Patient Expectations of Outcomes of Complementary and Alternative Medicine Treatments for Chronic Pain. The Journal of Alternative and Complementary Medicine 2016;22 936–46. 10.1089/acm.2016.0242
    1. Mooney TK, Beth M, Gibbons C, et al. . Credibility and the Relation of Credibility to Therapy Outcome. 2015;24:565–77.
    1. Williams GC, Freedman ZR, Deci EL. Supporting autonomy to motivate patients with diabetes for glucose control. Diabetes Care 1998;21:1644–51. 10.2337/diacare.21.10.1644
    1. Saghaei M, Saghaei S. Implementation of an open-source customizable minimization program for allocation of patients to parallel groups in clinical trials. J Biomed Sci Eng 2011;04:734–9. 10.4236/jbise.2011.411090
    1. Ersland L, Rosén G, Lundervold A. Smievoll a I, Tillung T, Sundberg H, et al. Phantom limb imaginary fingertapping causes primary motor cortex activation: an fMRI study. Neuroreport 1996;8:207–10.
    1. Hugdahl K, Rosén G, Ersland L, et al. . Common pathways in mental imagery and pain perception: an fMRI study of a subject with an amputated arm. Scand J Psychol 2001;42:269–75. 10.1111/1467-9450.00236
    1. Lotze M, Flor H, Grodd W, et al. . Phantom movements and pain. An fMRI study in upper limb amputees. Brain 2001;124(Pt 11):2268–77. 10.1093/brain/124.11.2268
    1. Rosén G, Hugdahl K, Ersland L, et al. . Different brain areas activated during imagery of painful and non-painful ’finger movements' in a subject with an amputated arm. Neurocase 2001;7:255–60. 10.1093/neucas/7.3.255
    1. Roux FE, Ibarrola D, Lazorthes Y, et al. . Virtual movements activate primary sensorimotor areas in amputees: report of three cases. Neurosurgery 2001;49:736–41.
    1. Roux FE, Lotterie JA, Cassol E, et al. . Cortical areas involved in virtual movement of phantom limbs: comparison with normal subjects. Neurosurgery 2003;53:1342–53. 10.1227/01.NEU.0000093424.71086.8F

Source: PubMed

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