Muscle Synergies and Clinical Outcome Measures Describe Different Factors of Upper Limb Motor Function in Stroke Survivors Undergoing Rehabilitation in a Virtual Reality Environment
Abstract
:1. Introduction
2. Materials and Methods
2.1. Participants
2.2. Study Design
2.2.1. Clinical Assessment
2.2.2. sEMG Recording and Muscle Synergies
2.2.3. Rehabilitation Treatment
2.3. Statistical Analysis
2.3.1. Sample Characteristics
2.3.2. Exploratory Factor Analysis
2.3.3. Confirmatory Factor Analysis
2.3.4. General Linear Regression Model
3. Results
3.1. Sample characteristics
3.2. Exploratory Factor Analysis
3.2.1. Exploratory Factor Analysis with All Variables
3.2.2. Exploratory Factor Analysis with T0 Variables
3.2.3. Exploratory Factor Analysis with T1 Variables
3.3. Confirmatory Factor Analysis
3.3.1. Confirmatory Factor Analysis with All Variables
3.3.2. Confirmatory Factor Analysis T0 Variables
3.3.3. Confirmatory Factor Analysis T1 Variables
3.4. General Linear Regression Models
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
CNS | Central nervous system |
sEMG | Surface electromyography |
MCA | Middle cerebral artery |
MMSE | Mini Mental State Examination |
MAS | Modified Ashworth Scale |
UE-FMA | Upper Extremity Fugl-Meyer Assessment Scale |
RPS | Reaching Performance Scale |
VRRS | Virtual Reality Rehabilitation System |
SENIAM | Surface Electromyography for the Non-Invasive Assessment of Muscles |
NMF | Nonnegative matrix factorization |
N-aff | Number of affected synergies |
N-ctrl | Number of unaffected synergies |
N-sh | Number of shared synergies |
Nsh-naff | Percentage of synergies shared in the affected arm |
Nsh-nctrl | Percentage of synergies shared in the unaffected arm |
Median-sp | Median of the calar product between the affected and unaffected arm |
P1 | Merging parameter |
T0 | Pretherapy variable |
T1 | Posttherapy variable |
R2 | Correlation coefficient |
P | P-value |
MSA | Measure of sampling adequacy |
EFA | Exploratory factor analysis |
EFA0 | Exploratory factor analysis at T0 |
EFA1 | Exploratory factor analysis at T1 |
EFA-All | Exploratory factor analysis with all variables at T0 and T1 |
PCA | Principal component analysis |
PAF | Principal axis factoring |
PA | Principal axis |
FL | Factor loadings |
h2 | Communalities |
r | Factors correlation coefficient |
CFA | Confirmatory factor analysis |
CFA0 | Confirmatory factor analysis at T0 |
CFA1 | Confirmatory factor analysis at T1 |
CFA-All | Confirmatory factor analysis with all variables at T0 and T1 |
CFI | Comparative fit index |
χ2 | Chi-squared |
TLI | Tucker–Lewis index |
RMSEA | Root mean-squared error of approximation |
Df | Degrees of freedom |
SD | Standard deviation |
IQR | Interquartile range |
FA | Factor analysis |
PC | Principal component |
Estimate regression coefficient. |
References
- Cheung, V.C.K.; Seki, K. Approaches to Revealing the Neural Basis of Muscle Synergies: A Review and a Critique. J. Neurophysiol. 2021, 125, 1580–1597. [Google Scholar] [CrossRef]
- Solnik, S.; Furmanek, M.P.; Piscitelli, D. Movement Quality: A Novel Biomarker Based on Principles of Neuroscience. Neurorehabil Neural Repair 2020, 34, 1067–1077. [Google Scholar] [CrossRef]
- Latash, M.L.; Scholz, J.P.; Schöner, G. Motor Control Strategies Revealed in the Structure of Motor Variability. Exerc. Sport Sci. Rev. 2002, 30, 26–31. [Google Scholar] [CrossRef]
- Loeb, G.E. Learning to Use Muscles. J. Hum. Kinet. 2021, 76, 9–33. [Google Scholar] [CrossRef]
- Bizzi, E.; Mussa-ivaldi, F.A.; Giszter, S. Computations Underlying the Execution of Movement: A Biological Perspective. Science 1991, 253, 287–291. [Google Scholar] [CrossRef] [Green Version]
- D’Avella, A.; Saltiel, P.; Bizzi, E. Combinations of Muscle Synergies in the Construction of a Natural Motor Behavior. Nat. Neurosci. 2003, 6, 300–308. [Google Scholar] [CrossRef]
- Saltiel, P.; Wyler-Duda, K.; D’Avella, A.; Tresch, M.C.; Bizzi, E. Muscle Synergies Encoded within the Spinal Cord: Evidence from Focal Intraspinal NMDA Iontophoresis in the Frog. J. Neurophysiol. 2001, 85, 605–619. [Google Scholar] [CrossRef] [PubMed]
- Levine, A.J.; Hinckley, C.A.; Hilde, K.L.; Driscoll, S.P.; Poon, T.H.; Montgomery, J.M.; Pfaff, S.L. Identification of a Cellular Node for Motor Control Pathways. Nat. Neurosci. 2014, 17, 586–593. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cheung, V.C.K.; Piron, L.; Agostini, M.; Silvoni, S.; Turolla, A.; Bizzi, E. Stability of Muscle Synergies for Voluntary Actions after Cortical Stroke in Humans. Proc. Natl. Acad. Sci. USA 2009, 106, 19563–19568. [Google Scholar] [CrossRef] [Green Version]
- Singh, R.E.; Iqbal, K.; White, G.; Hutchinson, T.E. A Systematic Review on Muscle Synergies: From Building Blocks of Motor Behavior to a Neurorehabilitation Tool. Appl. Bionics Biomech. 2018, 2018, 3615368. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Taborri, J.; Agostini, V.; Artemiadis, P.K.; Ghislieri, M.; Jacobs, D.A.; Roh, J.; Rossi, S. Feasibility of Muscle Synergy Outcomes in Clinics, Robotics, and Sports: A Systematic Review. Appl. Bionics Biomech. 2018, 2018, 3934698. [Google Scholar] [CrossRef] [PubMed]
- Clark, D.J.; Ting, L.H.; Zajac, F.E.; Neptune, R.R.; Kautz, S.A. Merging of Healthy Motor Modules Predicts Reduced Locomotor Performance and Muscle Coordination Complexity Post-Stroke. J. Neurophysiol. 2010, 103, 844–857. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pan, B.; Sun, Y.; Xie, B.; Huang, Z.; Wu, J.; Hou, J.; Liu, Y.; Huang, Z.; Zhang, Z. Alterations of Muscle Synergies during Voluntary Arm Reaching Movement in Subacute Stroke Survivors at Different Levels of Impairment. Front. Comput. Neurosci. 2018, 12, 1–11. [Google Scholar] [CrossRef] [PubMed]
- Cheung, V.C.K.; Turolla, A.; Agostini, M.; Silvoni, S.; Bennis, C.; Kasi, P.; Paganoni, S.; Bonato, P.; Bizzi, E. Muscle Synergy Patterns as Physiological Markers of Motor Cortical Damage. Proc. Natl. Acad. Sci. USA 2012, 109, 14652–14656. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hesam-Shariati, N.; Trinh, T.; Thompson-Butel, A.G.; Shiner, C.T.; McNulty, P.A. A Longitudinal Electromyography Study of Complex Movements in Poststroke Therapy. 2: Changes in Coordinated Muscle Activation. Front. Neurol. 2017, 8. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Irastorza-Landa, N.; García-Cossio, E.; Sarasola-Sanz, A.; Brötz, D.; Birbaumer, N.; Ramos-Murguialday, A. Functional Synergy Recruitment Index as a Reliable Biomarker of Motor Function and Recovery in Chronic Stroke Patients. J. Neural Eng. 2021, 18, 46061. [Google Scholar] [CrossRef]
- Safavynia, S.; Torres-Oviedo, G.; Ting, L. Muscle Synergies: Implications for Clinical Evaluation and Rehabilitation of Movement. Top. Spinal Cord Inj. Rehabil. 2011, 17, 16–24. [Google Scholar] [CrossRef] [Green Version]
- Bowden, M.G.; Clark, D.J.; Kautz, S.A. Evaluation of Abnormal Synergy Patterns Poststroke: Relationship of the Fugl-Meyer Assessment to Hemiparetic Locomotion. Neurorehabilit. Neural Repair 2010, 24, 328–337. [Google Scholar] [CrossRef]
- Scano, A.; Chiavenna, A.; Malosio, M.; Tosatti, L.M.; Molteni, F. Muscle Synergies-Based Characterization and Clustering of Poststroke Patients in Reaching Movements. Front. Bioeng. Biotechnol. 2017, 5, 62. [Google Scholar] [CrossRef] [Green Version]
- Abdullahi, A.; Truijen, S.; Umar, N.A.; Useh, U.; Egwuonwu, V.A.; Van Criekinge, T.; Saeys, W. Effects of Lower Limb Constraint Induced Movement Therapy in People With Stroke: A Systematic Review and Meta-Analysis. Front. Neurol. 2021, 12, 638904. [Google Scholar] [CrossRef]
- Van Criekinge, T.; Vermeulen, J.; Wagemans, K.; Schröder, J.; Embrechts, E.; Truijen, S.; Hallemans, A.; Saeys, W. Lower Limb Muscle Synergies during Walking after Stroke: A Systematic Review. Disabil. Rehabil. 2020, 42, 2836–2845. [Google Scholar] [CrossRef] [PubMed]
- Pellegrino, L.; Coscia, M.; Muller, M.; Solaro, C.; Casadio, M. Evaluating Upper Limb Impairments in Multiple Sclerosis by Exposure to Different Mechanical Environments. Sci. Rep. 2018, 8, 2110. [Google Scholar] [CrossRef] [Green Version]
- Lencioni, T.; Fornia, L.; Bowman, T.; Marzegan, A.; Caronni, A.; Turolla, A.; Jonsdottir, J.; Carpinella, I.; Ferrarin, M. A Randomized Controlled Trial on the Effects Induced by Robot-Assisted and Usual-Care Rehabilitation on Upper Limb Muscle Synergies in Post-Stroke Subjects. Sci. Rep. 2021, 11, 5323. [Google Scholar] [CrossRef] [PubMed]
- Laver, K.E.; George, S.; Thomas, S.; Deutsch, J.E.; Crotty, M. Virtual Reality for Stroke Rehabilitation. Cochrane Database Syst. Rev. 2011, 9, CD008349. [Google Scholar] [CrossRef] [Green Version]
- You, S.H.; Jang, S.H.; Kim, Y.-H.; Hallett, M.; Ahn, S.H.; Kwon, Y.-H.; Kim, J.H.; Lee, M.Y. Virtual Reality–Induced Cortical Reorganization and Associated Locomotor Recovery in Chronic Stroke: An Experimenter-Blind Randomized Study. Stroke 2005, 36, 1166–1171. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pezzella, F.R.; Picconi, O.; De Luca, A.; Lyden, P.D.; Fiorelli, M. Development of the Italian Version of the National Institutes of Health Stroke Scale It-NIHSS. Stroke 2009, 40, 2557–2559. [Google Scholar] [CrossRef] [Green Version]
- Kim, K.S.; Lee, S.J.; Suh, J.C. Numerical Simulation of the Vortical Flow around an Oscillating Circular Cylinder. In Proceedings of the International Offshore and Polar Engineering Conference, Seoul, Korea, 19–24 June 2005; pp. 162–167. [Google Scholar] [CrossRef]
- Huber, W.; Poeck, K.; Willmes, K. The Aachen Aphasia Test. Adv. Neurol. 1984, 42, 291–303. [Google Scholar]
- Bohannon, R.W.; Smith, M.B. Interrater Reliability of a Modified Ashworth Scale of Muscle Spasticity. Phys. Ther. 1987, 67, 206–207. [Google Scholar] [CrossRef]
- Fugl Meyer, A.R.; Jaasko, L.; Leyman, I. The Post Stroke Hemiplegic Patient. I. A Method for Evaluation of Physical Performance. Scand. J. Rehabil. Med. 1975, 7, 13–31. [Google Scholar]
- Lèvin, M.F.; Desrosiers, J.; Beauchemin, D.; Bergeron, N.; Rochette, A. Development and Validation of a Scale for Rating Motor Compensations Used for Reaching in Patients with Hemiparesis: The Reaching Performance Scale. Phys. Ther. 2004, 84, 8–22. [Google Scholar] [CrossRef] [PubMed]
- Piron, L.; Turolla, A.; Agostini, M.; Zucconi, C.S.; Ventura, L.; Tonin, P.; Dam, M. Motor Learning Principles for Rehabilitation: A Pilot Randomized Controlled Study in Poststroke Patients. Neurorehabilit. Neural Repair 2010, 24, 501–508. [Google Scholar] [CrossRef] [PubMed]
- Todorov, E.; Shadmehr, R.; Bizzi, E. Augmented Feedback Presented in a Virtual Environment Accelerates Learning of a Difficult Motor Task. J. Mot. Behav. 1997, 29, 147–158. [Google Scholar] [CrossRef]
- Merletti, R.; Hermens, H. Introduction to the Special Issue on the SENIAM European Concerted Action. J. Electromyogr. Kinesiol. 2000, 10, 283–286. [Google Scholar] [CrossRef]
- Katirji, B. Anatomical Guide for the Electromyographer: The Limbs and Trunk, 3rd Ed. Neurology 1994, 44, 2221. [Google Scholar] [CrossRef]
- Lee, D.D.; Seung, H.S. Learning the Parts of Objects by Non-Negative Matrix Factorization. Nature 1999, 401, 788–791. [Google Scholar] [CrossRef]
- Saito, A.; Tomita, A.; Ando, R.; Watanabe, K.; Akima, H. Muscle Synergies Are Consistent across Level and Uphill Treadmill Running. Sci. Rep. 2018, 8, 5979. [Google Scholar] [CrossRef]
- Bartlett, M.S. A Further Note on Tests of Significance in Factor Analysis. Br. J. Stat. Psychol. 1951, 4, 1–2. [Google Scholar] [CrossRef]
- Kaiser, H.F.; Rice, J. Little Jiffy, Mark Iv. Educ. Psychol. Meas. 1974, 34, 111–117. [Google Scholar] [CrossRef]
- Thompson, B. Exploratory and Confirmatory Factor Analysis; American Psychological Association: Washington, DC, USA, 2004; pp. 245–248. [Google Scholar] [CrossRef]
- Carroll, J.B. How Shall We Study Individual Differences in Cognitive Abilities?—Methodological and Theoretical Perspectives. Intelligence 1978, 2, 87–115. [Google Scholar] [CrossRef]
- Fabrigar, L.R.; Wegener, D.T. Exploratory Factor Analysis; Oxford University Press: Oxford, UK; New York, NY, USA, 2012; ISBN 0199813515. [Google Scholar]
- Wood, P. Confirmatory Factor Analysis for Applied Research; The Guilford Press: New York, NY, USA, 2015; ISBN 978-1462515363. [Google Scholar]
- Gorsuch, R.L. Exploratory Factor Analysis. In Handbook of Multivariate Experimental Psychology; Springer: Boston, MA, USA, 1988; pp. 231–258. [Google Scholar]
- Cudeck, R. Exploratory Factor Analysis. In Handbook of Applied Multivariate Statistics and Mathematical Modeling; Academic Press: San Diego, CA, USA, 2000; pp. 265–296. ISBN 0-12-691360-9. (Hardcover). [Google Scholar]
- Briggs, N.E.; MacCallum, R.C. Recovery of Weak Common Factors by Maximum Likelihood and Ordinary Least Squares Estimation. Multivar. Behav. Res. 2003, 38, 25–56. [Google Scholar] [CrossRef]
- Hendrickson, A.E.; White, P.O. Promax: A Quick Method for Rotation to Oblique Simple Structure. Br. J. Stat. Psychol. 1964, 17, 65–70. [Google Scholar] [CrossRef]
- Emerson, R.W. Exploratory Factor Analysis. J. Vis. Impair. Blindness 2017, 111, 301–302. [Google Scholar] [CrossRef] [Green Version]
- Hair, J.; Black, W.; Babin, B.; Anderson, R. Multivariate Data Analysis: A Global Perspective; Pearson Education: Upper Saddle River, NJ, USA; London, UK, 2010; Volume 7, ISBN 0135153093. [Google Scholar]
- Beran, T.N.; Violato, C. Structural Equation Modeling in Medical Research: A Primer. BMC Res. Notes 2010, 3, 267. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bentler, P.M. Fit Indexes, Lagrange Multipliers, Constraint Changes and Incomplete Data in Structural Models. Multivar. Behav. Res. 1990, 25, 163–172. [Google Scholar] [CrossRef] [PubMed]
- Hooper, D.; Coughlan, J.; Mullen, M. Structural Equation Modeling: Guidelines for Determining Model Fit. Electron. J. Bus. Res. Methods 2007, 6, 53–60. [Google Scholar] [CrossRef]
- Bentler, P.M.; Bonett, D.G. Significance Tests and Goodness of Fit in the Analysis of Covariance Structures. Psychol. Bull. 1980, 88, 588–606. [Google Scholar] [CrossRef]
- Tucker, L.R.; Lewis, C. A Reliability Coefficient for Maximum Likelihood Factor Analysis. Psychometrika 1973, 38, 1–10. [Google Scholar] [CrossRef]
- Steiger, J.H. Structural Model Evaluation and Modification: An Interval Estimation Approach. Multivar. Behav. Res. 1990, 25, 173–180. [Google Scholar] [CrossRef] [Green Version]
- Steiger, J.H.; Lind, J.C. Statistically Based Tests for the Number of Common Factors. In Proceedings of the Annual Meeting of the Psychometric Society, Iowa City, IA, USA, 30 May 1980. [Google Scholar]
- Kline, R.B. Principles and Practice of Structural Equation Modeling; The Guilford Press: New York, NY, USA, 2015; ISBN 1462523358. [Google Scholar]
- Hu, L.T.; Bentler, P.M. Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Struct. Equ. Model. 1999, 6, 1–55. [Google Scholar] [CrossRef]
- Byrne, B.M. Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming, 2nd ed.; Routledge: New York, NY, USA, 2013. [Google Scholar] [CrossRef]
- R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2017; Available online: http://www.R-project.org. (accessed on 1 June 2019).
- Thompson-Butel, A.G.; Lin, G.G.; Shiner, C.T.; McNulty, P.A. Two Common Tests of Dexterity Can Stratify Upper Limb Motor Function after Stroke. Neurorehabilit. Neural Repair 2014, 28, 788–796. [Google Scholar] [CrossRef]
- Scano, A.; Chiavenna, A.; Caimmi, M.; Malosio, M.; Tosatti, L.M.; Molteni, F. Effect of Human-Robot Interaction on Muscular Synergies on Healthy People and Post-Stroke Chronic Patients. In Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR), London, UK, 17–20 July 2017; pp. 527–532. [Google Scholar] [CrossRef]
- Carmichael, S.T. Cellular and Molecular Mechanisms of Neural Repair after Stroke: Making Waves. Ann. Neurol. 2006, 59, 735–742. [Google Scholar] [CrossRef]
- Cheung, V.C.K.; Cheung, B.M.F.; Zhang, J.H.; Chan, Z.Y.S.; Ha, S.C.W.; Chen, C.-Y.; Cheung, R.T.H. Plasticity of Muscle Synergies through Fractionation and Merging during Development and Training of Human Runners. Nat. Commun. 2020, 11, 4356. [Google Scholar] [CrossRef] [PubMed]
- Levin, M.F.; Hiengkaew, V.; Nilanont, Y.; Cheung, D.; Dai, D.; Shaw, J.; Bayley, M.; Saposnik, G. Relationship Between Clinical Measures of Upper Limb Movement Quality and Activity Poststroke. Neurorehabilit. Neural Repair 2019, 33, 432–441. [Google Scholar] [CrossRef] [PubMed]
- Pan, B.; Huang, Z.; Jin, T.; Wu, J.; Zhang, Z.; Shen, Y. Motor Function Assessment of Upper Limb in Stroke Patients. J. Healthc. Eng. 2021, 2021, 6621950. [Google Scholar] [CrossRef] [PubMed]
- Cheung, V.C.K.; Niu, C.M.; Li, S.; Xie, Q.; Lan, N. A Novel FES Strategy for Poststroke Rehabilitation Based on the Natural Organization of Neuromuscular Control. IEEE Rev. Biomed. Eng. 2019, 12, 154–167. [Google Scholar] [CrossRef]
- Mundfrom, D.J.; Shaw, D.G.; Ke, T.L. Minimum Sample Size Recommendations for Conducting Factor Analyses. Int. J. Test. 2005, 5, 159–168. [Google Scholar] [CrossRef]
- Levin, M.F.; Liebermann, D.G.; Parmet, Y.; Berman, S. Compensatory Versus Noncompensatory Shoulder Movements Used for Reaching in Stroke. Neurorehabilit. Neural Repair 2016, 30, 635–646. [Google Scholar] [CrossRef] [Green Version]
- Bernhardt, J.; Hayward, K.S.; Kwakkel, G.; Ward, N.S.; Wolf, S.L.; Borschmann, K.; Krakauer, J.W.; Boyd, L.A.; Carmichael, S.T.; Corbett, D.; et al. Agreed Definitions and a Shared Vision for New Standards in Stroke Recovery Research: The Stroke Recovery and Rehabilitation Roundtable Taskforce. Int. J. Stroke 2017, 12, 444–450. [Google Scholar] [CrossRef]
- Winters, C.; Kwakkel, G.; van Wegen, E.E.H.; Nijland, R.H.M.; Veerbeek, J.M.; Meskers, C.G.M. Moving Stroke Rehabilitation Forward: The Need to Change Research. NeuroRehabilitation 2018, 43, 19–30. [Google Scholar] [CrossRef]
- Douiri, A.; Grace, J.; Sarker, S.-J.; Tilling, K.; McKevitt, C.; Wolfe, C.D.A.; Rudd, A.G. Patient-Specific Prediction of Functional Recovery after Stroke. Int. J. Stroke 2017, 12, 539–548. [Google Scholar] [CrossRef] [Green Version]
Patients (N = 50) | |
---|---|
Sex, males/females, n (%) | 33 (66%)/17 (34%) |
Age, years, mean ± SD | 63.62 ± 12.29 |
Diagnosis, ischemic/hemorrhagic, n (%) | 45 (90%)/5 (10%) |
Hemisphere, left/right, n (%) | 25 (50%)/25 (50%) |
Time-stroke, months, mean ± SD | 6.99 ± 13.07 |
0–3 months, n, mean ± SD | 15, 2.32 ± 0.42 |
3–6 months, n, mean ± SD | 17, 4.25 ± 0.87 |
>6 months, n, mean ± SD | 18, 20.61 ± 19.83 |
Clinical Parameters | T0 | T1 | p Value | ||
Median [IQR] | Mean ± SD | Median [IQR] | Mean ± SD | ||
MAS | 1 [2.75] | 1.92 ± 2.69 | 0.5 [2] | 1.60 ± 2.44 | 0.098 |
UE-FMA | 125.5 [34.75] | 117.20 ± 24.57 | 131.5 [33.25] | 124.26 ± 25.41 | <0.001 * |
RPS | 30 [6] | 24.4 ± 11.19 | 17 [6] | 26.46 ± 12.25 | <0.001 * |
Synergies Parameters | T0 | T1 | p Value | ||
Median [IQR] | Mean ± SD | Median [IQR] | Mean ± SD | ||
N-aff | 8 [1] | 8.42 ± 1.40 | 8 [2] | 8.20 ± 1.47 | 0.289 |
N-ctrl | 8 [2] | 7.86 ± 1.31 | 8 [1.75] | 7.84 ± 1.22 | 0.855 |
N-sh | 6 [2] | 6.24 ± 1.39 | 6 [2] | 6.12 ± 1.36 | 0.456 |
Nsh-naff | 0.75 [0.13] | 0.74 ± 0.12 | 0.78 [0.22] | 0.75 ± 0.13 | 0.616 |
Nsh-nctrl | 0.79 [0.16] | 0.79 ± 0.12 | 0.78 [0.14] | 0.78 ± 0.12 | 0.432 |
Median-sp | 0.93 [0.04] | 0.92 ± 0.04 | 0.94 [0.05] | 0.93 ± 0.03 | 0.056 |
P1 | 1.19 [0.58] | 1.25 ± 0.39 | 1.24 [0.44] | 1.24 ± 0.34 | 0.913 |
Outcome | Factor 1 | Factor 2 | Factor 3 | h2 |
---|---|---|---|---|
MAS-T0 | −0.579 | 0.528 | ||
UE-FMA-T0 | 0.914 | 0.839 | ||
RPS-T0 | 0.948 | 0.885 | ||
MAS-T1 | −0.554 | 0.467 | ||
UE-FMA-T1 | 0.988 | 0.920 | ||
RPS-T1 | 0.918 | 0.882 | ||
Median-sp-T0 | 0.616 | 0.441 | ||
N-aff-T0 | 0.913 | 0.848 | ||
N-ctrl-T0 | 0.922 | 0.669 | ||
N-sh-T0 | 0.972 | 0.849 | ||
Nsh-ctrl-T0 | 0.301 | 0.218 | ||
N-ctrl-T1 | 0.537 | 0.415 | ||
N-sh-T1 | 0.780 | 0.847 | ||
Nsh-aff-T1 | 0.881 | 0.769 | ||
Nsh-ctrl-T1 | 0.921 | 0.687 | ||
Median-sp-T1 | 0.589 | 0.503 | ||
% variance of the factor | 33.7% | 16.5% | 15.9% |
Outcome | Factor 1 | Factor 2 | h2 |
---|---|---|---|
MAS | −0.618 | 0.420 | |
UE-FMA | 0.847 | 0.705 | |
RPS | 0.886 | 0.775 | |
N-aff | 0.631 | 0.759 | |
Nsh-aff | 0.811 | 0.751 | |
N-ctrl | 0.674 | 0.538 | |
Nsh-ctrl | 0.811 | 0.751 | |
N-sh | 1.067 | 1.157 | |
% variance of the factor | 39.3% | 30.9% |
Outcome | Factor 1 | Factor 2 | h2 |
---|---|---|---|
MAS | −0.562 | 0.627 | |
UE-FMA | 0.889 | 0.786 | |
RPS | 0.948 | 0.948 | |
N-ctrl | 0.550 | 0.310 | |
Nsh-ctrl | 0.505 | 0.255 | |
N-sh | 1.390 | 1.933 | |
% variance of the factor | 42.8% | 33.4% |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Maistrello, L.; Rimini, D.; Cheung, V.C.K.; Pregnolato, G.; Turolla, A. Muscle Synergies and Clinical Outcome Measures Describe Different Factors of Upper Limb Motor Function in Stroke Survivors Undergoing Rehabilitation in a Virtual Reality Environment. Sensors 2021, 21, 8002. https://doi.org/10.3390/s21238002
Maistrello L, Rimini D, Cheung VCK, Pregnolato G, Turolla A. Muscle Synergies and Clinical Outcome Measures Describe Different Factors of Upper Limb Motor Function in Stroke Survivors Undergoing Rehabilitation in a Virtual Reality Environment. Sensors. 2021; 21(23):8002. https://doi.org/10.3390/s21238002
Chicago/Turabian StyleMaistrello, Lorenza, Daniele Rimini, Vincent C. K. Cheung, Giorgia Pregnolato, and Andrea Turolla. 2021. "Muscle Synergies and Clinical Outcome Measures Describe Different Factors of Upper Limb Motor Function in Stroke Survivors Undergoing Rehabilitation in a Virtual Reality Environment" Sensors 21, no. 23: 8002. https://doi.org/10.3390/s21238002