[Abstract] Satisfaction with Life after Mild Traumatic Brain Injury: A TRACK-TBI Study

Abstract

Identifying the principal determinants of life satisfaction following mild TBI (mTBI) may inform efforts to improve subjective well-being in this population. We examined life satisfaction among participants in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study who presented with mTBI (Glasgow Coma Scale [GCS] score = 13–15; n = 1152). An L1-regularization path algorithm was used to select optimal sets of baseline and concurrent symptom measures for prediction of scores on the Satisfaction with Life Scale (SWLS) at 2 weeks and 3, 6, and 12 months post-injury. Multi-variable linear regression models (all n = 744–894) were then fit to evaluate associations between the empirically selected predictors and SWLS scores at each follow-up visit. Results indicated that emotional post-TBI symptoms (all b = −1.27 to −0.77, all p < 0.05), anhedonia (all b = −1.59 to −1.08, all p < 0.01), and pain interference (all b = −1.38 to −0.89, all p < 0.001) contributed to the prediction of lower SWLS scores at all follow-ups. Insomnia predicted lower SWLS scores at 2 weeks, 3 months, and 6 months (all b = −1.11 to −0.83, all ps < 0.01); and negative affect predicted lower SWLS scores at 2 weeks, 3 months, and 12 months (all b = −1.38 to −0.80, all p < 0.005). Other post-TBI symptom domains and baseline socio-demographic, injury-related, and clinical characteristics did not emerge as robust predictors of SWLS scores during the year after mTBI. Efforts to improve satisfaction with life following mTBI may benefit from a focus on the detection and treatment of affective symptoms, pain, and insomnia. The results reinforce the need for tailoring of evidence-based treatments for these conditions to maximize efficacy in patients with mTBI.

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[WEB PAGE] AFO Puts Spring-cam Back into Stroke Patients’ Steps

A light weight and motor-less spring-cam attached to an AFO provides stroke patients with greater push-off power, stabilizing their walking, and reducing falls. Image courtesy of Tohoku University.

A research group from Tohoku University in Japan has developed a new, lightweight and motor-less device to aid stroke patients in their rehabilitation, improving their gait, and preventing falls. The new device can be easily attached to an ankle-foot orthosis (AFO).

Stroke patients often suffer from motor paralysis as a result of damage to the brain, significantly affecting their walking. Gait disorder results in restrictive disabilities and increased healthcare costs. Rehabilitation is key to stroke recovery. Yet around 40% of stroke patients struggle to function properly due to problems with their walking abilities. One part of the problem is due to insufficient knee flexion during walking. This leads to lower toe clearance and causes patients to fall. To overcome this, patients frequently hip hike on the affected side to move their foot, thus creating an awkward movement.

To help this population, the research group—comprised of Professor Shin-Ichi Izumi, MD, PhD, and Associate Professor Dai Owaki, PhD, from Tohoku University’s Graduate School of Medicine and Graduate School of Engineering, along with Takeo Nozaki and Dr. Ken-ichiro Fukushi from NEC Corporation, Tokyo, Japan—created a device that gives the ankle greater push-off power using a spring-cam mechanism. The elliptical shaped cam rotates in conjunction with the AFO, pushing against the spring. The resultant reactive force from the spring generates significant ankle push-off power.

The research group conducted clinical experiments on 11 stroke patients with paralysis on one side of the body, demonstrating that the device generated greater ankle power. This in turn aided knee flexion while the affected foot was in the swing phase of walking.

“Our device will pave the way for positive impacts on the rehabilitation of stroke patients,” said Owaki. “It will prevent falls and make patients feel more confident in their walking abilities.”

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[Abstract + References] Real-Time Evaluation of Hand Motor Function Recovery in Home Use Finger Rehabilitation Device Using Gaussian Process Regression – IEEE Conference Publication

Abstract:Continuous hand rehabilitation after discharge is important for hemiplegic patients to regain an independent finger movement. However, most patients cannot rehabilitate by themselves without therapists. For this problem, robotic rehabilitation has been investigated to support patients even at home. Most of the programs performed by these robots are focusing on the assistance for voluntary movement. However, the approach to the voluntary movement is not enough for regaining dexterous movement. Voluntary suppression of body parts that should not move is important. However, previous studies focusing on voluntary suppression are few. In this paper, we show a detailed program for voluntary suppression rehabilitation. The program is performed by our robotic finger rehabilitation device aiming at home use. In this program, a patient is requested to flex and extend an index finger independently. During moving, individual pressure sensors monitor the other fingers. If the device detects unnecessary movements such as compensatory movement at some fingers, the patient is notified that unnecessary movements are found there. The detection is based on 3σ range of healthy subject’s finger pressure data which was constructed by using Gaussian Process Regression. Through experiments with hemiplegic patients, we have shown that the frequency of deviation of patients’ data from 3σ range of healthy subjects decreases according to the degree of recovery.

References

1.C. L. Jones, F. Wang, R. Morrison, N. Sarkar and D. G. Kamper, “Design and Development of the Cable Actuated Finger Exoskeleton for Hand Rehabilitation Following Stroke”, IEEE/ASME Transactions on Mechatronics, vol. 19, no. 1, pp. 131-140, 2014.Show Context View Article Full Text: PDF (740KB) Google Scholar 2.D. Leonardis et al., “An EMG-Controlled Robotic Hand Exoskeleon for Bilateral Rehabilitation”, IEEE Transactions on Haptics, vol. 8, no. 2, pp. 140-151, 2015.Show Context View Article Full Text: PDF (1941KB) Google Scholar 3.S. Biggar and W. Yao, “Design and Evaluation of a Soft and Wearable Robotic Glove for Hand Rehabilitation”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 10, pp. 1071-1080, 2016.Show Context View Article Full Text: PDF (1597KB) Google Scholar 4.P. Polygerinos, K. C. Galloway, S. Sanan, M. Herman and C. J. Walsh, “EMG Controlled Soft Robotic Glove for Assistance during Activities of Daily Living”, 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 55-60, 2015.Show Context View Article Full Text: PDF (2640KB) Google Scholar 5.I. Ben Abdallah, Y. Bouteraa and C. Rekik, “Design and Development of 3D Printed Myoelectric Robotic Exoskeleton for Hand Rehabilitation”, International Journal on Smart Sensing and Intelligent Systems, vol. 10, pp. 341-366, 2017.Show Context CrossRef  Google Scholar 6.K. Yamamoto, Y. Furudate, K. Chiba, Y. Ishida and S. Mikami, “Home Robotic Device for Rehabilitation of Finger Movement of Hemiplegia Patients”, 2017 IEEE/SICE International Symposium on System Integration (SII), pp. 300-305, 2017.Show Context View Article Full Text: PDF (1305KB) Google Scholar 7.C. D. Takahashi, L. Der-Yeghiaian, V. Le, R. R. Motiwala and S. C. Cramer, “Robot-based Hand Motor Therapy after Stroke”, Brain, vol. 131, no. Pt 2, pp. 425-437, 2008.Show Context CrossRef  Google Scholar 8.L. Dovat et al., A Technique to Train Finger Coordination and Independence after Stroke, Disability and Rehabilitation:Assistive Technology, vol. 5, no. 4, pp. 279-287, 2010.Show Context Google Scholar 9.Y. Furudate, N. Onuki, K. Chiba, Y. Ishida and S. Mikami, “Automated Evaluation of Hand Motor Function Recovery by Using Finger Pressure Sensing Device for Home Rehabilitation”, 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 207-214, 2018.Show Context View Article Full Text: PDF (500KB) Google Scholar 10.Y. Furudate, N. Onuki, K. Chiba, Y. Ishida and S. Mikami, “Hand Motor Function Evaluation by Integrating Multi-Tasks Using Home Rehabilitation Device”, 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech), pp. 272-274, 2020.Show Context View Article Full Text: PDF (2090KB) Google Scholar 

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[ARTICLE] Factors predicting clinically significant functional gain and discharge to home in stroke in-patients after rehabilitation – A retrospective cohort study – Full Text

Abstract

Objective

This study explored factors which predict stroke survivors who could achieve “clinically significant functional gain” and return home when being discharged from a local hospital after in-patient stroke rehabilitation programme.

Methods

This study included 562 inpatients with stroke who were residing at community dwellings before onset of stroke, and transferred to a convalescent hospital for rehabilitation from four acute hospitals over one year. The main outcome variables of prediction were (a) achieving “clinically significant functional gain” as measured by (a1) achievement of “minimal clinically important difference” (MCID) of improvement in Functional Independence Measure Motor Measure (FIM-MM)”, (a2) one or more level(s) of improvement in function group according to the patients’ FIM-MM, and (b) discharge to home. Sixteen predictor variables were identified and studied firstly with univariate binary logistic regression and those significant variables were then put into multivariate binary logistic regression.

Results

Based on multivariate regression, the significant predictors for “clinically significant functional gain” were: younger age <75 years old, higher Glasgow Coma Scale score at admission, with haemorrhagic stroke, intermediate FIM-MM function group. Those significant predictors for “discharge to home” were: living with family/caregivers before stroke, higher FIM score at admission, and one or more level(s) of improvement in FIM-MM function group.

Conclusions

This study identified findings consistent with overseas studies in additional to some new interesting findings. Early prediction of stroke discharge outcomes helps rehabilitation professionals and occupational therapists to focus on the use of appropriate intervention strategies and pre-discharge preparation.

Introduction

Stroke is a major cause of disability with an indication for long term rehabilitation, which includes an in-patient phase as well as a community phase (Feigin et al., 2003). Despite the incidence rate of stroke in Hong Kong has decreased, the number of stroke survivors remains large due to reduction in mortality rate and population aging (Woo et al., 2014). That implies there is an increasing demand for stroke rehabilitation services. Early and accurate prediction of rehabilitation outcomes, such as discharge destinations, better functional improvement, etc. can facilitate the rehabilitation team to customize their plans of care (e.g. triage to different wards, intensive training versus reinforcing skills of care-givers, etc.) and allow more time for liaison and/or making referrals between transitions of care. Subsequently, it may improve patients’ outcomes, decrease length of stay, lower costs, and, improve utilization of resources (Summers et al., 2009).

Outcomes of stroke are associated with various factors including sociodemographic characteristics, clinical characteristics of the stroke incident, comorbid conditions, functional performance at the beginning of the treatment and rehabilitation process. Previous studies and systematic reviews reported that age, marital status, time from stroke onset to rehabilitation, aphasia, neglect, stroke severity presented in National Institutes of Health Stroke Scale, cognitive function, and motor function such as walking distance were associated with the gain score in Functional Independence Measure (FIM) after stroke rehabilitation (Brown et al., 2015; Fung, 2004; Leung et al., 2010; Meyer et al., 2015; Scrutinio et al., 2015). On the other hand among these variables it was found that functional independence was the most determinant factor of discharge destination in majority of studies (Mees et al., 2016). Furthermore, stroke survivors were more prone to institutional care if they had the characteristics of older age, living alone, having pre-existing comorbidities such as atrial fibrillation, severe stroke, dysphagia, cognitive, motor or functional impairment (Brown et al., 2015; Itaya et al., 2017; Mees et al., 2016; Nguyen et al., 2015). This study aimed to explore if these predictor variables of different aspects stated above showed similar positive association with achievement of clinically significant functional gain and discharge to home for stroke patients in a local rehabilitation hospital.[…]

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[WEB PAGE] Brain implant forecasts seizures days in advance

By Rich Haridy December 20, 2020

The researchers propose a system that delivers seizure forecasts, similar to weather forecasts, offering predictions of when a person is a greater risk of seizureMelanie Proix

An international study is showing, for the first time, that it may be possible to predict the onset of epileptic seizures several days in advance. By analyzing data from a clinically approved brain implant designed to monitor and prevent seizures, the new research hopes to develop a model offering patients with epilepsy a seizure forecasting tool to predict the likelihood of upcoming episodes.

The research looked at data from a responsive brain stimulation implant called NeuroPace. The device was approved for clinical uses back in 2013 and it works to prevent seizures by delivering imperceptible pulses of electrical stimulation to certain parts of the brain upon detecting abnormal brain activity.

Scientists have been working on a variety of seizure prediction tools for decades. But despite some incredible advances, such as the NeuroPace device, no innovation to date has successfully shown it possible to predict seizures more than a few minutes in advance, at best.

The NeuroPace innovation offers researchers the first chance to study the relationship between seizures and brain activity using years of EEG data. The new study initially analyzed long-term data from 18 patients with the brain implant who were closely tracked for several years. From this data the researchers developed predictive algorithms to forecast seizures. These predictive algorithms were then tested on long-term data gathered from the more than 150 people who participated in the decade-long clinical trials testing the brain implant system.

Vikram Rao, co-senior author on the new study, says the data shows seizure risk could be effectively forecasted three days ahead in nearly 40 percent of subjects and one day ahead in 66 percent of subjects.

“For forty years, efforts to predict seizures have focused on developing early warning systems, which at best could give patients warnings just a few seconds or minutes in advance of a seizure,” says Rao. “This is the first time anyone has been able to forecast seizures reliably several days in advance, which could really allow people to start planning their lives around when they’re at high or low risk.”

The researchers propose a system that delivers seizure forecasts, similar to weather forecasts, offering predictions of when a person is a greater risk of seizure

Rao does stress the current algorithm can only predict when one is at higher risk of seizure, and not specifically when a seizure will take place. A number of other unaccounted environmental triggers, from stress to erratic sleep, can play a role in the onset of a seizure. So the system currently developed is more like a weather forecast, offering probabilities designed to help guide a person’s future activities.

“I don’t think I’m ever going to be able to tell a patient that she is going to have a seizure at precisely 3:17 pm tomorrow—that’s like predicting when lightning will strike,” explains Rao. “But our findings in this study give me hope that I may someday be able to tell her that, based on her brain activity, she has a 90 percent chance of a seizure tomorrow, so she should consider avoiding triggers like alcohol and refrain from high-risk activities like driving.”

Much more work is needed before the system is ready for clinical use. This preliminary study uncovered a significant amount of variability from person to person. It is unclear why reliable forecasting could not be generated from some patient’s brain activity data. Future investigations to optimize the algorithm and perhaps incorporate multimodal physiological data may enhance the algorithm’s predictive capacity.

Plus, currently the system requires data gathered from a device requiring surgical implantation. This would limit the use of the device to only those with the most severe forms of epilepsy. More superficial subscalp EEG devices could offer a less invasive way of capturing this brain activity data over long periods of time.

“It is worth remembering that, currently, patients have absolutely no information about the future—which is like having no idea what the weather tomorrow might be—and we think our results could help significantly reduce that uncertainty for many people,” adds Rao. “Truly determining the utility of these forecasts, and which patients will benefit most, will require a prospective trial, which is the next step.”

The new study was published in the journal The Lancet Neurology.

Source: UCSF

[ARTICLE] Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation – Full Text

Abstract

Socioeconomic reasons post-COVID-19 demand unsupervised home-based rehabilitation and, specifically, artificial ambient intelligence with individualisation to support engagement and motivation. Artificial intelligence must also comply with accountability, responsibility, and transparency (ART) requirements for wider acceptability. This paper presents such a patient-centric individualised home-based rehabilitation support system. To this end, the Timed Up and Go (TUG) and Five Time Sit To Stand (FTSTS) tests evaluate daily living activity performance in the presence or development of comorbidities. We present a method for generating synthetic datasets complementing experimental observations and mitigating bias. We present an incremental hybrid machine learning algorithm combining ensemble learning and hybrid stacking using extreme gradient boosted decision trees and k-nearest neighbours to meet individualisation, interpretability, and ART design requirements while maintaining low computation footprint. The model reaches up to 100% accuracy for both FTSTS and TUG in predicting associated patient medical condition, and 100% or 83.13%, respectively, in predicting area of difficulty in the segments of the test. Our results show an improvement of 5% and 15% for FTSTS and TUG tests, respectively, over previous approaches that use intrusive means of monitoring such as cameras.

1. Introduction

Modern healthcare and socioeconomic reasons in the post-COVID-19 world require home-based rehabilitation without specialist assistance. As discussed in detail in [1], the need for home-based rehabilitation is multifaceted, from patient psychology to reduced hospital funding per patient. Approximately 50,000 patients are discharged for home-based rehabilitation every year in England alone [2].According the review in  [1], patient motivation and engagement have a critical impact on the success [3] of home-based rehabilitation, as there are no frequent checks from a supervising person. Many current approaches are abandoned within the first 90 days of use because the lack motivating and engaging user interfaces [4]. The motivation is linked to goal setting and it depends on individual achievements [5]. Thus, the technology must be co-created with the end user to both adapt to the individual and provide relevant and achievable goals through a dialogic approach to simple motivational user feedback [1].A detailed technological review of home-based rehabilitation technologies [6] concludes that current existing technologies provide generic, “one size fits all” solutions—instead of being tailored to specific user needs, leading often to poor motivation and the user’s quick loss of interest, but also sometimes unfair and biased outcomes. Thus, there is an increased need for new approaches that combine artificial ambient intelligence (AmI) and individualisation to support engagement and motivation in home-based rehabilitation [6] that also comply with ethical principles and that incorporate accountability, responsibility, and transparency. The latter is particularly important to improve trust through being able to explain to technology providers and end users the decisions made by the AmI. This explanation is necessary for the acceptance of motivational feedback and to provide user awareness and an understanding of the individual goals set by the technology against their progress.Existing AmI solutions often introduce assumptions and bias of the developer in the decisions taken by the algorithm [7]. This issue is reflected in recent efforts for ethical Artificial Intelligence (AI) requirements and design practices, which highlight a deeper underlying consideration regarding the used values and norms, and their reinforcement through the evolution of the machine’s autonomy [8]. Thus, there is an urgent global need for accountable [9], responsible [10], and transparent [11] artificial intelligence (ART AI) to enable wider use of AI [12] based on generally acceptable value systems [13].Responsible AI models consider user practices, value systems, ethics, and implications for various communities. They are based on an approach which is ethically sound, regulation compliant, governed, robust, unbiased, fair (just), interpretable, explainable, and secure (non-maleficent) [14].In this paper, we discuss a design of a patient-centric individualised, home-based rehabilitation support system focusing on the following aspects of ART AI:

• Unbiased AI: A significant body of work has contributed to methods for bias-free machine learning (ML) models [15,16,17]. We aim to follow state-of-the-art bias reduction approaches [18] in our proposed rehabilitation support approach based on designing an appropriate and balanced training dataset that would remove bias due to class under-representation.
• Explainable AI: There has been strong research interest in the field of explainable AI (XAI) in recent years [19]. However, these approaches are mostly focused on the social aspect (user response to XAI) [11], or the implications of manipulating inputs to generate false negative or false positive responses [20].
• Interpretable AI: Interpretable and justifiable outcomes of AI require transparency of the ML model as well as the model’s decisions and behaviours [21]. Current EU regulation allows for individuals to enquire about AI decisions [22]. However, regulation is not well defined for the design and development of such models, particularly in the medical applications domain [23]. Moreover, accountability and transparency are strongly interlinked with interpretability [17].

Unbiased AI is particularly significant for home-based rehabilitation systems, as they are used by a variety of users including elderly and young, male and female, and of varied height and weight, and all need to receive the same and fair level of support. Explainability and interpretability are also key to enable user engagement as we have identified in [6] and strongly link to complexity and motivation. In this work, we will focus on the information the model can generate to provide further insight to the decision-making process, establish a dataset to address the bias issue, and aim for interpetability to address some of the challenges in home-based rehabilitation AI.In summary, the contributions of this paper are:

• Methodological steps to produce a new synthetic dataset based on statistical clinical results reported in the literature for training ML algorithms to avoid bias and ensure fairness in autonomous system outcomes (Section 3.1.2);
• A novel hybrid ML algorithm to meet the individualisation, interpretability, and ART design considerations while maintaining a low computational footprint (Section 4);
• Interpretability of the designed solution, including feature importance for a patient-centric individualised, responsible home-based rehabilitation support (Section 4.1);
• A detailed simulation performance comparison and analysis demonstrating that the proposed approach outperforms existing work, used as benchmark, by 5% for FTSTS and 15% percent for TUG test (Section 5 and Section 6).

A detailed review of the methodology, performance, and limitations of current home-based rehabilitation digital technologies is provided in [6]. Next, we focus on ART artificial ambient intelligence approaches in Section 2 and identify the state-of-the-art as well as further improvements required. We investigate and present a system to address them in Section 3 and Section 4. The evaluation of the proposed system along with the results is presented in Section 5, followed by the discussion in Section 6. Finally, conclusions and the identified future work are presented in Section 7. […]

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[Dissertation] Hand Rehabilitation after Stroke: Understanding and Optimizing the Usage of Wearable Robotic Technologies – Full Text

Abstract

The hand is a highly complex machine as evidenced by its mechanical structure and the large amount of cortical resources it requires for both sensation and motor control. Stroke is a pervasive, global problem that causes disability by damaging hand neural control systems. Movement practice can help drive the changes in neural connectivity needed to restore these systems, however, stroke patients typically undertake limited amounts of movement practice. The premise of this dissertation is that mechanical engineering techniques, and, specifically, the appropriate design of robotic therapy technologies based on an engineering-informed understanding of human hand mechanics and function, can improve the biomedical situation for individuals after a stroke.

Specifically, this dissertation addresses the question “How do we optimize the usage of wearable robotic technologies for hand rehabilitation after stroke?” Here we demonstrate progress in answering this question by considering three key areas: usership patterns of wearable hand sensing technology in real-world settings, sensory and motor control of the hand after stroke, and the mechanical design and intuitive control of wearable soft robotic technologies for the hand.

Regarding usership patterns, we studied a simple wearable sensor – the MusicGlove – in the home setting with individuals in the sub-acute phase of stroke. We found that only 14% of stroke patients have enough residual function in the hand for sensor-only rehabilitation, motivating us to work toward a device that can offer robotic assistance. Further, we demonstrated a connection between machine failure theory and usership via the functional form of the statistical distribution of the amount of use. Finally, we observed that — when left to self-adjust the parameters of their worn device — people make logical decisions relating to challenge, suggesting the strategy of building rehabilitation devices that allow individuals freedom by which to adapt their own control strategies.

In the area of sensory and motor control we address two specific questions: How does isometric grip force control compare to other aspects of hand function after stroke, and how do sensory deficits measured robotically correlate to motor function after stroke? Through a series of experiments conducted with chronic stroke survivors we showed that isometric grip force control is not only a well preserved control signal after stroke, but is also more preserved than strength or manual dexterity. This provided the conceptual basis for a novel exoskeleton control strategy — residual force control – in which isometric grip control by some fingers drivers full movement control of other fingers. Additionally, we showed sensory deficits, and, specifically, finger position sensing versus tactile deficits, are correlated with hand function after a stroke, suggesting the importance of developing devices that can retrain, promote, and challenge finger position sensing.

In the last area — mechanical design and control – we integrated the above findings as follows. First, we developed a novel, compact, soft actuator capable of providing the biologically-scaled force and impedance that the large fraction of stroke survivors we identified needed to assist their finger movement practice. Second, we integrated this actuator into a form-fitting, minimalistic exoskeleton — the IGRIP exoskeleton – that facilitates active sensory-based control of pinch grip using the residual force control strategy. Third, we tested the IGRIP exoskeleton with ten unimpaired individuals by substituting it for their index finger in a prosthesis-like mode. We found that these individuals were able to learn to incorporate finger sensory input in order to take advantage of the residual force control strategy, thereby improving their performance at a manual lifting task beyond levels achievable without active, sensory-based control. These advances define a potential path forward toward user-accepted, worn, therapeutic, assistive robotics for the hand after stroke.

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[Abstract] International consensus recommendations for outcome measurement in poststroke arm rehabilitation trials

BACKGROUND: Existing randomised controlled trials (RCTs) of arm rehabilitation interventions after stroke use a wide range of outcome measures, limiting ability to pool data to determine efficacy. Published recommendations also lack stroke survivor, carer and clinician involvement specifically about perceived relevance and importance of outcomes and measures.
AIM: To generate international consensus recommendations for selection of outcome measures for use in future stroke RCTs in arm rehabilitation considering outcomes important to stroke survivors, carers and clinicians. The recommendations are the Standardising Measurement in Arm Rehabilitation Trials [SMART] toolbox.
DESIGN: Two-round international e-Delphi survey and consensus meeting.
SETTING: Online and University.
POPULATION: Fifty-five researchers and clinicians with expertise in stroke upper limb rehabilitation from 18 countries (e-Delphi); n=13 researchers and clinicians, n=2 stroke survivors, n=1 carer (consensus meeting).
METHODS: Using systematically identified outcome measures from published RCTs, we conducted a two-round international e-Delphi survey with researchers and clinicians to identify the most important measures for inclusion in the toolbox. Measures that achieved ≥60% consensus were categorised using the International Classification of Functioning, Disability and Health framework (ICF); psychometric properties were ascertained from literature and research resources. At a final consensus meeting, expert stakeholders selected measures for inclusion in the toolbox.
RESULTS: E-Delphi participants recommended 28/170 measures for discussion at the final consensus meeting. Expert stakeholders (n=16) selected the Visual Analogue Scale for pain/ 0-10 Numeric Pain Rating Scale, Dynamometry, Action Research Arm Test, Wolf Motor Function Test, Barthel Index, Motricity Index and Fugl-Meyer Assessment (upper limb section of each), Box and Block Test, Motor Activity Log 14, Nine Hole Peg Test, Functional Independence Measure, EQ-5D, and Canadian Occupational Performance Measure for inclusion in the toolbox.
CONCLUSIONS: The SMART Toolbox provides a refined selection of measures that capture outcomes considered important by stakeholders for each ICF domain.
CLINICAL REHABILITATION IMPACT: The toolbox will facilitate data aggregation for efficacy analyses thereby strengthening evidence to inform clinical practice. Clinicians can also use the toolbox to guide selection of measures ensuring a patient-centred focus.

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[Abstract] Effect of Rhythm of Music Therapy on Gait in Patients with Stroke

Abstract

Aim: This study aims to analyze the effects of rhythm of music therapy on gait in patients with ischemic stroke, and explore the value of music therapy in walking training in stroke.

Methods: The present study is a prospective clinical study. Sixty patients with ischemic stroke, who were admitted to our hospital from October 2017 to December 2018, were enrolled. These patients were divided into two groups, according to the method of the random number table, with thirty patients in each group: control group and study group. Patients in the control group received conventional drug therapy, rehabilitation training and walking training, while the patients in the study group were given music therapy on the basis of the above mentioned therapies for four weeks, during which Sunday was regarded as a rest day, and the music therapy was suspended. The main outcome measures included indexes in evaluating the walking ability of patients in these two groups. At each time point, the Fugl-Meyer Assessment (FMA), Berg Balance Scale (BBS) and stroke rehabilitation treatment satisfaction questionnaire were used.

Results: The results revealed that the stride length, cadence and maximum velocity were higher in patients in the study group, when compared to patients in the control group, at the second week and end of the therapy, and the difference in step length between the affected side and healthy side was significantly lower in the study group than in the control group. These differences were statistically significant (P < 0.05). In the second week of therapy and at the end of therapy, the FMA and BBS scores were higher in the study group than in the control group, and the difference was statistically significant (P < 0.05). The total satisfaction rate was higher in the study group than in the control group, and the difference was statistically significant (P < 0.05).

Conclusion: Under the stimulation of music rhythm, applying music therapy to patients with ischemic stroke can improve their gait, walking ability, lower limb motor function, balance ability and treatment satisfaction.

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[Editorial] Breakthrough BCI Applications in Medicine – Neuroscience

Scope

A brain-computer interface (BCI) provides a direct connection between cortical activity and external devices. BCIs may use non-invasive methods such as the Electroencephalogram (EEG) or invasive methods such as the Electrocorticogram (ECoG) or neural spike recordings (Homer et al., 2013; Guger et al., 2015, 2018). In the last decades, many BCI approaches have been developed, based on slow waves, evoked potentials (EPs), steady-state evoked potentials (SSEPs), code-based EPs or motor imagery (MI) paradigms, with the aim of bringing medical applications that help people to the market. The first BCI systems were used to spell, control prosthetic devices, or move cursors on a computer screen (Guger et al., 2015; Allison et al., 2020). Early BCI work focused on locked-in or completely locked-in patients. Nowadays, many more clinical applications of BCIs technology are being developed.

Research Highlights

Several neurological disorders impair voluntary movements and communication, despite intact cognitive functioning. The spectrum of BCI usage for control is extremely wide and includes neural prostheses, wheelchairs (Fernández-Rodríguez et al.), home environments, humanoid robots, and much more (Fukuma et al.). Another exciting clinical application of BCIs focuses on facilitating the recovery of motor function after a stroke or spinal cord injury (Thompson et al.). BCIs for rehabilitation integrate BCIs with conventional methods and devices for rehabilitation like functional electrical stimulation (FES)-based neuroprostheses (Colachis et al.; Remsik et al.), transcranial direct current stimulation (tDCS) (Rodriguez-Ugarte et al.) etc. to enhance the brain’s reorganization of corticospinal and cortico-muscular connections after acute, sub-acute, or chronic lesions.

Beside motor deficits, BCI-induced brain plasticity might contribute to the treatment of high-order cortical dysfunctions, such as improving social and emotional behaviors in autism spectrum disorder (Amaral et al.), training inhibitory control and working memory in ADHD, as well as contributing to the rehabilitation of cognitive deficits related to dementia. Moreover, BCI-based brain training can help preserve cognitive performance in healthy older adults, promoting successful aging and reducing the social burden of the population’s increasing aging. BCIs are also used to establish closed-loop control of brain sensing and stimulation technology to improve, for example, tremor, or to provide sensation. Another new challenge described in this Research Topic refers to the inner speech detection, defined as the ability to generate internal speech representations, in the absence of any external speech stimulation or self-generated overt speech (Martin et al.).

Finally, BCIs may increase the diagnostic accuracy of brain disorders. For instance, BCIs could be used to detect neural signatures of cognitive processes in persons diagnosed with disorders of consciousness (DOC) (Annen et al.; Guger et al.; Heilinger et al.), provide real-time functional brain mapping for neurosurgery (Jiang et al.), improve visual function assessment in glaucoma, detect the intraoperative awareness during general anesthesia (Rimbert et al.), screening for cognitive function in complete immobility (Lulé et al.), etc.

Summary

The articles here present different BCI approaches that could enter mainstream clinical practice, improving the assessment, rehabilitation, and management of several neurological diseases. All presented papers use elaborate, task-specific experiment setups with both invasive and non-invasive BCIs. Future research can build on these pioneering works and bring new standardized BCI applications in medicine.

References

Allison, B. Z., Kübler, A., and Jin, J. (2020). 30+ years of P300 brain-computer interfaces. Psychophysiology 57:e13569. doi: 10.1111/psyp.13569

PubMed Abstract | CrossRef Full Text | Google Scholar

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