Interrater reliability of the Fugl-Meyer Motor assessment in stroke patients: a quality management project within the ESTREL study.

Introduction: The Fugl-Meyer Motor Assessment (FMMA) is recommended for evaluating stroke motor recovery in clinical practice and research. However, its widespread use requires refined reliability data, particularly across different health professions. We therefore investigated the interrater reliability of the FMMA scored by a physical therapist and a physician using video recordings of stroke patients. Methods: The FMMA videos of 50 individuals 3 months post stroke (28 females, mean age 71.64 years, median National Institutes of Health Stroke Scale score 3.00) participating in the ESTREL trial (Enhancement of Stroke Rehabilitation with Levodopa: a randomized placebo-controlled trial) were independently scored by two experienced assessors (i.e., a physical therapist and a physician) with specific training to ensure consistency. As primary endpoint, the interrater reliability was calculated for the total scores of the entire FMMA and the total scores of the FMMA for the upper and lower extremities using intraclass correlation coefficients (ICC). In addition, Spearman's rank order correlation coefficients (Spearman's rho) were calculated for the total score and subscale levels. Secondary endpoints included the FMMA item scores using percentage agreement, weighted Cohen's kappa coefficients, and Gwet's AC1/AC2 coefficients. Results: ICCs were 0.98 (95% confidence intervals (CI) 0.96-0.99) for the total scores of the entire FMMA, 0.98 (95% CI 0.96-0.99) for the total scores of the FMMA for the upper extremity, and 0.85 (95% CI 0.70-0.92) for the total scores of the FMMA for the lower extremity. Spearman's rho ranged from 0.61 to 0.94 for total and subscale scores. The interrater reliability at the item level of the FMMA showed (i) percentage agreement values with a median of 77% (range 44-100%), (ii) weighted Cohen's kappa coefficients with a median of 0.69 (range 0.00-0.98) and (iii) Gwet's AC1/AC2 coefficients with a median of 0.84 (range 0.42-0.98). Discussion and conclusion: The FMMA appears to be a highly reliable measuring instrument at the overall score level for assessors from different health professions. The FMMA total scores seem to be suitable for the quantitative measurement of stroke recovery in both clinical practice and research, although there is potential for improvement at the item level.

Classification of functional and non-functional arm use by inertial measurement units in individuals with upper limb impairment after stroke.

Background: Arm use metrics derived from wrist-mounted movement sensors are widely used to quantify the upper limb performance in real-life conditions of individuals with stroke throughout motor recovery. The calculation of real-world use metrics, such as arm use duration and laterality preferences, relies on accurately identifying functional movements. Hence, classifying upper limb activity into functional and non-functional classes is paramount. Acceleration thresholds are conventionally used to distinguish these classes. However, these methods are challenged by the high inter and intra-individual variability of movement patterns. In this study, we developed and validated a machine learning classifier for this task and compared it to methods using conventional and optimal thresholds. Methods: Individuals after stroke were video-recorded in their home environment performing semi-naturalistic daily tasks while wearing wrist-mounted inertial measurement units. Data were labeled frame-by-frame following the Taxonomy of Functional Upper Limb Motion definitions, excluding whole-body movements, and sequenced into 1-s epochs. Actigraph counts were computed, and an optimal threshold for functional movement was determined by receiver operating characteristic curve analyses on group and individual levels. A logistic regression classifier was trained on the same labels using time and frequency domain features. Performance measures were compared between all classification methods. Results: Video data (6.5 h) of 14 individuals with mild-to-severe upper limb impairment were labeled. Optimal activity count thresholds were ≥20.1 for the affected side and ≥38.6 for the unaffected side and showed high predictive power with an area under the curve (95% CI) of 0.88 (0.87,0.89) and 0.86 (0.85, 0.87), respectively. A classification accuracy of around 80% was equivalent to the optimal threshold and machine learning methods and outperformed the conventional threshold by ∼10%. Optimal thresholds and machine learning methods showed superior specificity (75-82%) to conventional thresholds (58-66%) across unilateral and bilateral activities. Conclusion: This work compares the validity of methods classifying stroke survivors' real-life arm activities measured by wrist-worn sensors excluding whole-body movements. The determined optimal thresholds and machine learning classifiers achieved an equivalent accuracy and higher specificity than conventional thresholds. Our open-sourced classifier or optimal thresholds should be used to specify the intensity and duration of arm use.

Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke.

Background: Stroke leads to motor impairment which reduces physical activity, negatively affects social participation, and increases the risk of secondary cardiovascular events. Continuous monitoring of physical activity with motion sensors is promising to allow the prescription of tailored treatments in a timely manner. Accurate classification of gait activities and body posture is necessary to extract actionable information for outcome measures from unstructured motion data. We here develop and validate a solution for various sensor configurations specifically for a stroke population. Methods: Video and movement sensor data (locations: wrists, ankles, and chest) were collected from fourteen stroke survivors with motor impairment who performed real-life activities in their home environment. Video data were labeled for five classes of gait and body postures and three classes of transitions that served as ground truth. We trained support vector machine (SVM), logistic regression (LR), and k-nearest neighbor (kNN) models to identify gait bouts only or gait and posture. Model performance was assessed by the nested leave-one-subject-out protocol and compared across five different sensor placement configurations. Results: Our method achieved very good performance when predicting real-life gait versus non-gait (Gait classification) with an accuracy between 85% and 93% across sensor configurations, using SVM and LR modeling. On the much more challenging task of discriminating between the body postures lying, sitting, and standing as well as walking, and stair ascent/descent (Gait and postures classification), our method achieves accuracies between 80% and 86% with at least one ankle and wrist sensor attached unilaterally. The Gait and postures classification performance between SVM and LR was equivalent but superior to kNN. Conclusion: This work presents a comparison of performance when classifying Gait and body postures in post-stroke individuals with different sensor configurations, which provide options for subsequent outcome evaluation. We achieved accurate classification of gait and postures performed in a real-life setting by individuals with a wide range of motor impairments due to stroke. This validated classifier will hopefully prove a useful resource to researchers and clinicians in the increasingly important field of digital health in the form of remote movement monitoring using motion sensors.

Economic Impact of Poststroke Delirium and Associated Risk Factors: Findings From a Prospective Cohort Study.

Background and Purpose: Delirium is a common severe complication of stroke. We aimed to determine the cost-of-illness and risk factors of poststroke delirium (PSD). Methods: This prospective single-center study included n=567 patients with acute stroke from a hospital-wide delirium cohort study and the Swiss Stroke Registry in 2014. Delirium was determined by Delirium Observation Screening Scale or Intensive Care Delirium Screening Checklist 3 times daily during the first 3 days of admission. Costs reflected the case-mix index and diagnosis-related groups from 2014 and were divided into nursing, physician, and total costs. Factors associated with PSD were assessed with multiple regression analysis. Partial correlations and quantile regression were performed to assess costs and other factors associated with PSD. Results: The incidence of PSD was 39.0% (221/567). Patients with delirium were older than non-PSD (median 76 versus 70 years; P<0.001), 52% male (115/221) versus 62% non-PSD (214/346) and hospitalized longer (mean 11.5 versus 9.3 days; P<0.001). Dementia was the most relevant predisposing factor for PSD (odds ratio, 16.02 [2.83­90.69], P=0.002). Moderate to severe stroke (National Institutes of Health Stroke Scale score 16­20) was the most relevant precipitating factor (odds ratio, 36.10 [8.15­159.79], P<0.001). PSD was a strong predictor for 3-month mortality (odds ratio, 15.11 [3.33­68.53], P<0.001). Nursing and total costs were nearly twice as high in PSD (P<0.001). There was a positive correlation between total costs and admission National Institutes of Health Stroke Scale (correlation coefficient, 0.491; P<0.001) and length of stay (correlation coefficient, 0.787; P<0.001) in all patients. Quantile regression revealed rising nursing and total costs associated with PSD, higher National Institutes of Health Stroke Scale, and longer hospital stay (all P<0.05). Conclusions: PSD was associated with greater stroke severity, prolonged hospitalization, and increased nursing and total costs. In patients with severe stroke, dementia, or seizures, PSD is anticipated, and additional costs are associated with hospitalization.

Corrigendum: Consensus-Based Core Set of Outcome Measures for Clinical Motor Rehabilitation After Stroke-A Delphi Study.

[This corrects the article DOI: 10.3389/fneur.2020.00875.].

Consensus-Based Core Set of Outcome Measures for Clinical Motor Rehabilitation After Stroke-A Delphi Study.

Introduction: Outcome measures are key to tailor rehabilitation goals to the stroke patient's individual needs and to monitor poststroke recovery. The large number of available outcome measures leads to high variability in clinical use. Currently, an internationally agreed core set of motor outcome measures for clinical application is lacking. Therefore, the goal was to develop such a set to serve as a quality standard in clinical motor rehabilitation poststroke. Methods: Outcome measures for the upper and lower extremities, and activities of daily living (ADL)/stroke-specific outcomes were identified and presented to stroke rehabilitation experts in an electronic Delphi study. In round 1, clinical feasibility and relevance of the outcome measures were rated on a 7-point Likert scale. In round 2, those rated at least as "relevant" and "feasible" were ranked within the body functions, activities, and participation domains of the International Classification of Functioning, Disability, and Health (ICF). Furthermore, measurement time points poststroke were indicated. In round 3, answers were reviewed in reference to overall results to reach final consensus. Results: In total, 119 outcome measures were presented to 33 experts from 18 countries. The recommended core set includes the Fugl-Meyer Motor Assessment and Action Research Arm Test for the upper extremity section; the Fugl-Meyer Motor Assessment, 10-m Walk Test, Timed-Up-and-Go, and Berg Balance Scale for the lower extremity section; and the National Institutes of Health Stroke Scale, and Barthel Index or Functional Independence Measure for the ADL/stroke-specific section. The Stroke Impact Scale was recommended spanning all ICF domains. Recommended measurement time points are days 2 ± 1 and 7; weeks 2, 4, and 12; 6 months poststroke and every following 6th month. Discussion and Conclusion: Agreement was found upon a set of nine outcome measures for application in clinical motor rehabilitation poststroke, with seven measurement time points following the stages of poststroke recovery. This core set was specifically developed for clinical practice and distinguishes itself from initiatives for stroke rehabilitation research. The next challenge is to implement this clinical core set across the full stroke care continuum with the aim to improve the transparency, comparability, and quality of stroke rehabilitation at a regional, national, and international level.

Augmented Reality-Based Rehabilitation of Gait Impairments: Case Report.

BACKGROUND: Gait and balance impairments are common in neurological diseases, including stroke, and negatively affect patients' quality of life. Improving balance and gait are among the main goals of rehabilitation. Rehabilitation is mainly performed in clinics, which lack context specificity; therefore, training in the patient's home environment is preferable. In the last decade, developed rehabilitation technologies such as virtual reality and augmented reality (AR) have enabled gait and balance training outside clinics. Here, we propose a new method for gait rehabilitation in persons who have had a stroke in which mobile AR technology and a sensor-based motion capture system are combined to provide fine-grained feedback on gait performance in real time. OBJECTIVE: The aims of this study were (1) to investigate manipulation of the gait pattern of persons who have had a stroke based on virtual augmentation during overground walking compared to walking without AR performance feedback and (2) to investigate the usability of the AR system. METHODS: We developed the ARISE (Augmented Reality for gait Impairments after StrokE) system, in which we combined a development version of HoloLens 2 smart glasses (Microsoft Corporation) with a sensor-based motion capture system. One patient with chronic minor gait impairment poststroke completed clinical gait assessments and an AR parkour course with patient-centered performance gait feedback. The movement kinematics during gait as well as the usability and safety of the system were evaluated. RESULTS: The patient changed his gait pattern during AR parkour compared to the pattern observed during the clinical gait assessments. He recognized the virtual objects and ranked the usability of the ARISE system as excellent. In addition, the patient stated that the system would complement his standard gait therapy. Except for the symptom of exhilaration, no adverse events occurred. CONCLUSIONS: This project provided the first evidence of gait adaptation during overground walking based on real-time feedback through visual and auditory augmentation. The system has potential to provide gait and balance rehabilitation outside the clinic. This initial investigation of AR rehabilitation may aid the development and investigation of new gait and balance therapies.

Substance P signalling in primary motor cortex facilitates motor learning in rats.

Among the genes that are up-regulated in response to a reaching training in rats, Tachykinin 1 (Tac1)-a gene that encodes the neuropeptide Substance P (Sub P)-shows an especially strong expression. Using Real-Time RT-PCR, a detailed time-course of Tac1 expression could be defined: a significant peak occurs 7 hours after training ended at the first and second training session, whereas no up-regulation could be detected at a later time-point (sixth training session). To assess the physiological role of Sub P during movement acquisition, microinjections into the primary motor cortex (M1) contralateral to the trained paw were performed. When Sub P was injected before the first three sessions of a reaching training, effectiveness of motor learning became significantly increased. Injections at a time-point when rats already knew the task (i.e. training session ten and eleven) had no effect on reaching performance. Sub P injections did not influence the improvement of performance within a single training session, but retention of performance between sessions became strengthened at a very early stage (i.e. between baseline-training and first training session). Thus, Sub P facilitates motor learning in the very early phase of skill acquisition by supporting memory consolidation. In line with these findings, learning related expression of the precursor Tac1 occurs at early but not at later time-points during reaching training.

Protein Synthesis Inhibition in the Peri-Infarct Cortex Slows Motor Recovery in Rats.

Neuroplasticity and reorganization of brain motor networks are thought to enable recovery of motor function after ischemic stroke. Especially in the cortex surrounding the ischemic scar (i.e., peri-infarct cortex), evidence for lasting reorganization has been found at the level of neurons and networks. This reorganization depends on expression of specific genes and subsequent protein synthesis. To test the functional relevance of the peri-infarct cortex for recovery we assessed the effect of protein synthesis inhibition within this region after experimental stroke. Long-Evans rats were trained to perform a skilled-reaching task (SRT) until they reached plateau performance. A photothrombotic stroke was induced in the forelimb representation of the primary motor cortex (M1) contralateral to the trained paw. The SRT was re-trained after stroke while the protein synthesis inhibitor anisomycin (ANI) or saline were injected into the peri-infarct cortex through implanted cannulas. ANI injections reduced protein synthesis within the peri-infarct cortex by 69% and significantly impaired recovery of reaching performance through re-training. Improvement of motor performance within a single training session remained intact, while improvement between training sessions was impaired. ANI injections did not affect infarct size. Thus, protein synthesis inhibition within the peri-infarct cortex impairs recovery of motor deficits after ischemic stroke by interfering with consolidation of motor memory between training sessions but not short-term improvements within one session.

Dopamine Promotes Motor Cortex Plasticity and Motor Skill Learning via PLC Activation.

Dopaminergic neurons in the ventral tegmental area, the major midbrain nucleus projecting to the motor cortex, play a key role in motor skill learning and motor cortex synaptic plasticity. Dopamine D1 and D2 receptor antagonists exert parallel effects in the motor system: they impair motor skill learning and reduce long-term potentiation. Traditionally, D1 and D2 receptor modulate adenylyl cyclase activity and cyclic adenosine monophosphate accumulation in opposite directions via different G-proteins and bidirectionally modulate protein kinase A (PKA), leading to distinct physiological and behavioral effects. Here we show that D1 and D2 receptor activity influences motor skill acquisition and long term synaptic potentiation via phospholipase C (PLC) activation in rat primary motor cortex. Learning a new forelimb reaching task is severely impaired in the presence of PLC, but not PKA-inhibitor. Similarly, long term potentiation in motor cortex, a mechanism involved in motor skill learning, is reduced when PLC is inhibited but remains unaffected by the PKA inhibitor. Skill learning deficits and reduced synaptic plasticity caused by dopamine antagonists are prevented by co-administration of a PLC agonist. These results provide evidence for a role of intracellular PLC signaling in motor skill learning and associated cortical synaptic plasticity, challenging the traditional view of bidirectional modulation of PKA by D1 and D2 receptors. These findings reveal a novel and important action of dopamine in motor cortex that might be a future target for selective therapeutic interventions to support learning and recovery of movement resulting from injury and disease.