Spasticity over time during acute rehabilitation: a study of patient and clinician scores

Remote Monitoring for the Management of Spasticity: Challenges, Opportunities and Proposed Technological Solution

Spasticity is disabling feature of long-term neurological conditions that has substantial impact on people' quality of life. Assessing spasticity and determining the efficacy of current treatments is limited by the measurement tools available in clinical practice. We convened an expert panel of clinicians and engineers to identify a solution to this urgent clinical need. We established that a reliable ambulatory spasticity monitoring system that collates clinically meaningful data remotely would be useful in the management of this complex condition. This paper provides an overview of current practices in managing and monitoring spasticity. Then, the paper describes how a remote monitoring system can help in managing spasticity and identifies challenges in development of such a system. Finally the paper proposes a monitoring system solution that exploits recent advancements in low-energy wearable systems comprising of printable electronics, a personal area network (PAN) to low power wide area networks (LPWAN) alongside back-end cloud infrastructure. The proposed technology will make an important contribution to patient care by allowing, for the first time, longitudinal monitoring of spasticity between clinical follow-up, and thus has life altering and cost-saving implications. This work in spasticity monitoring and management serves as an exemplar for other areas of rehabilitation.

Quantitative assessment of spasticity: a narrative review of novel approaches and technologies

Spasticity is a complex neurological disorder, causing significant physical disabilities and affecting patients' independence and quality of daily lives. Current spasticity assessment methods are questioned for their non-standardized measurement protocols, limited reliabilities, and capabilities in distinguishing neuron or non-neuron factors in upper motor neuron lesion. A series of new approaches are developed for improving the effectiveness of current clinical used spasticity assessment methods with the developing technology in biosensors, robotics, medical imaging, biomechanics, telemedicine, and artificial intelligence. We investigated the reliabilities and effectiveness of current spasticity measures employed in clinical environments and the newly developed approaches, published from 2016 to date, which have the potential to be used in clinical environments. The new spasticity scales, taking advantage of quantified information such as torque, or echo intensity, the velocity-dependent feature and patients' self-reported information, grade spasticity semi-quantitatively, have competitive or better reliability than previous spasticity scales. Medical imaging technologies, including near-infrared spectroscopy, magnetic resonance imaging, ultrasound and thermography, can measure muscle hemodynamics and metabolism, muscle tissue properties, or temperature of tissue. Medical imaging-based methods are feasible to provide quantitative information in assessing and monitoring muscle spasticity. Portable devices, robotic based equipment or myotonometry, using information from angular, inertial, torque or surface EMG sensors, can quantify spasticity with the help of machine learning algorithms. However, spasticity measures using those devices are normally not physiological sound. Repetitive peripheral magnetic stimulation can assess patients with severe spasticity, which lost voluntary contractions. Neuromusculoskeletal modeling evaluates the neural and non-neural properties and may gain insights into the underlying pathology of spasticity muscles. Telemedicine technology enables outpatient spasticity assessment. The newly developed spasticity methods aim to standardize experimental protocols and outcome measures and enable quantified, accurate, and intelligent assessment. However, more work is needed to investigate and improve the effectiveness and accuracy of spasticity assessment.

Revisiting Spasticity After Stroke: Clustering Clinical Characteristics for Identifying At-Risk Individuals

Purpose

To collectively identify the clinical characteristics determining the risk of developing spasticity after stroke.

Patients and Methods

A cross-sectional study was conducted at a single rehabilitation outpatient clinic from June to December 2019. Inclusion criteria were stroke duration of over four weeks, aged 18 years and above. Exclusion criteria were presence of concurrent conditions other than stroke that could also lead to spasticity. Recruited patients were divided into “Spasticity” and “No spasticity” groups. Univariate analysis was deployed to identify significant predictive spasticity factors between the two groups followed by a two-step clustering approach for determining group of characteristics that collectively contributes to the risk of developing spasticity in the “Spasticity” group.

Results

A total of 216 post-stroke participants were recruited. The duration after stroke (p < 0.001) and the absence of hemisensory loss (p = 0.042) were two significant factors in the “Spasticity” group revealed by the univariate analysis. From a total of 98 participants with spasticity, the largest cluster of individuals (40 patients, 40.8%) was those within less than 20 months after stroke with moderate stroke and absence of hemisensory loss, while the smallest cluster was those within less than 20 months after severe stroke and absence of hemisensory loss (21 patients, 21.4%).

Conclusion

Analyzing collectively the significant factors of developing spasticity may have the potential to be more clinically relevant in a heterogeneous post-stroke population that may assist in the spasticity management and treatment.

Spasticity Measurement Based on the HHT Marginal Spectrum Entropy of sEMG Using a Portable System: A Preliminary Study

To facilitate stretch reflex onset (SRO) detection and improve accuracy and reliability of spasticity assessment in clinical settings, a new method to measure dynamic stretch reflex threshold (DSRT) based on Hilbert-Huang transform marginal spectrum entropy (HMSEN) of surface electromyography (sEMG) signals and a portable system to quantify modified Ashworth scale (MAS) for spasticity assessment were developed. The sEMG signals were divided into frames using a fixed-length sliding window, and the HMSEN of each frame was calculated. An adaptive threshold was set to measure the DSRT. The HMSEN based method can quantify muscle activity through time-frequency and nonlinear dynamics analysis, therefore providing deeper insight about the spastic muscle mechanisms during stretching and a reliable SRO detection method. Experimental results revealed that the HMSEN based method could reliably detect the SRO and measure the DSRT (recognition rate: 95.45%), and could achieve improved performance over the time-domain based method. There was a strong correlation ( to -0.900) between the MAS scores and the DSRT index, and the test-retest reliability was high. Additionally, limitations of the MAS were analyzed. This paper indicates that the presented framework can provide a promising tool to measure DSRT and a clinical quantitative approach for spasticity assessment.

Understanding Symptoms of Muscle Tightness, Weakness, and Rigidity From a Nursing Perspective

PURPOSE

This study examined the nature of muscle tightness from nurses' perspectives and explored how the symptoms of muscle tightness are communicated, managed, and differentiated from other conditions, such as muscle rigidity and muscle weakness.

DESIGN

An exploratory, descriptive qualitative design was used.

METHODS

Eight rehabilitation nurses described lexicons, care strategies, and communication for muscle tightness, weakness, and rigidity.

FINDINGS

Nurses used conflicting terms to describe muscle tightness, weakness, and rigidity. They identified medications and range of motion as the best strategies to manage muscle conditions. Nurses approach care holistically and do not differentiate care strategies that are based only on a symptoms lens.

CONCLUSIONS

Nurses were unable to clearly differentiate between muscle tightness and rigidity.

CLINICAL RELEVANCE

Nurses influence patients' choice of vocabulary; therefore, they must use simple but precise terminologies to educate their patients. Miscommunication between nurses and patients can lead to errors, which can have negative consequences.

CE: Managing Movement Disorders: A Clinical Review

: Neuromuscular disorders are complex, difficult both to differentiate and to manage. Yet nurses, who encounter a symptomatically diverse neuromuscular patient population in various practice settings, are expected to be well versed in managing the variable associated symptoms of these disorders. Here the authors discuss how to assess such neuromuscular conditions as muscle tightness, spasticity, and clonus; the pathophysiology underlying each; and the available recommended treatments, an understanding of which is necessary for successful symptom management and clear provider-patient communication.

Spasticity assessment based on the Hilbert-Huang transform marginal spectrum entropy and the root mean square of surface electromyography signals: a preliminary study

BACKGROUND

Most of the objective and quantitative methods proposed for spasticity measurement are not suitable for clinical application, and methods for surface electromyography (sEMG) signal processing are mainly limited to the time-domain. This study aims to quantify muscle activity in the time-frequency domain, and develop a practical clinical method for the objective and reliable evaluation of the spasticity based on the Hilbert-Huang transform marginal spectrum entropy (HMSEN) and the root mean square (RMS) of sEMG signals.

METHODS

Twenty-six stroke patients with elbow flexor spasticity participated in the study. The subjects were tested at sitting position with the upper limb stretched towards the ground. The HMSEN of the sEMG signals obtained from the biceps brachii was employed to facilitate the stretch reflex onset (SRO) detection. Then, the difference between the RMS of a fixed-length sEMG signal obtained after the SRO and the RMS of a baseline sEMG signal, denoted as the RMS difference (RMSD), was employed to evaluate the spasticity level. The relations between Modified Ashworth Scale (MAS) scores and RMSD were investigated by Ordinal Logistic Regression (OLR). Goodness-of-fit of the OLR was obtained with Hosmer-Lemeshow test.

RESULTS

The HMSEN based method can precisely detect the SRO, and the RMSD scores and the MAS scores were fairly well related (test: χ2 = 8.8060, p = 0.2669; retest: χ2 = 1.9094, p = 0.9647). The prediction accuracies were 85% (test) and 77% (retest) when using RMSD for predicting MAS scores. In addition, the test-retest reliability was high, with an interclass correlation coefficient of 0.914 and a standard error of measurement of 1.137. Bland-Altman plots also indicated a small bias.

CONCLUSIONS

The proposed method is manually operated and easy to use, and the HMSEN based method is robust in detecting SRO in clinical settings. Hence, the method is applicable to clinical practice. The RMSD can assess spasticity in a quantitative way and provide greater resolution of spasticity levels compared to the MAS in clinical settings. These results demonstrate that the proposed method could be clinically more useful for the accurate and reliable assessment of spasticity and may be an alternative clinical measure to the MAS.

Lived Experiences of Muscle Tightness Symptoms From Patients' Perspectives

The aims of this study were to understand symptoms of chronic muscle tightness from the patient's perspective and explore symptom management strategies used by them. Muscle tightness, a common symptom, is a challenge to manage in clinical practice because it is commingled with other orthopedic conditions. Nurses may not be aware of the negative impact of tight muscles because this symptom is presumed to be self-limiting; however, if not treated appropriately, muscle tightness can become chronic. The focus of this study is the lived experience of patients with chronic muscle tightness. The researchers used a qualitative descriptive design in which patients provided insights into the experiences and self-management of chronic muscle tightness. Sixteen adult subjects experiencing physical impairments who were managed by physical therapists in a specialty clinic participated in the study. The subjects participated in 45- to 60-minute semistructured interviews to provide understanding of chronic muscle tightness. The interviews were recorded and transcribed for content analysis. Results indicate that patients identify day-to-day experiences of chronic muscle tightness as unresolving; these patients experience myriads of sensations and live with life restrictions that negatively affect their quality of life. Uses of complementary therapies are commonplace in managing this symptom. The symptom of chronic muscle tightness may linger, and patients use workarounds to manage their lives. Nurses must understand patients' perspectives to assist them in achieving an acceptable quality of life.

Understanding symptom experiences of muscle tightness from patients' and clinicians' perspectives

AIMS AND OBJECTIVES

The purpose of this study was to determine how patients' symptom experiences of muscle tightness correlate with examiner assessments. To address this question, we (1) obtained the vocabularies used by patients and examiners to describe muscle tightness, (2) correlated patient- reported Visual Analog Scale ratings for locations of muscle tightness on a body diagram and (3) explored the similarities and differences between patient and examiner evaluation of muscle tightness analytically and graphically.

BACKGROUND

Symptoms of muscle tightness are common complaints that occur due to musculoskeletal and neuromusculoskeletal injuries. Terms such as muscle tightness are often intermingled with other conditions including muscle tension, muscle spasticity and muscle rigidity. Discrepancies between patients and clinicians understanding of similar symptoms have been reported in the literature.

DESIGN

A concurrent exploratory mixed methods design was used.

METHOD

Fifty-seven participants (six physical therapists, 51 patients) participated. Participants provided semi-structured interviews, ratings through Visual Analog Scale and concurrently provided the words used to describe muscle tightness. Patients also provided the location of muscle tightness on a body diagram. Content analysis and hierarchical linear modelling were used for data analysis.

RESULTS

The patients' vocabularies contained more sensory and pain experiences when compared to the clinicians' vocabularies. Examiners and patients ratings were variable (standard deviation >20) and contained discrepancies. Stress played a role in the symptom experience of muscle tightness. Examiners tended to focus on patients' chief complaints, while patients reported their symptoms from a whole-body perspective.

CONCLUSIONS

Symptom experiences of muscle tightness can occur with or without pain. Use of complementary therapy and development of an objective tool that accounts for patients' sensory experiences is warranted.

RELEVANCE TO CLINICAL PRACTICE

Findings from this study indicate that in addition to all other available treatments options, nurses must also educate patients about correct posture alignment, breathing exercises and stress management.