The association between dexterity and upper limb impairment during stroke recovery.

Introduction: Stroke-induced upper limb disabilities can be characterized by both motor impairments and activity limitations, commonly assessed using Fugl-Meyer Motor Assessment for Upper Extremity (FMMA-UE) and Action Research Arm Test (ARAT), respectively. The relationship between the two assessments during recovery is largely unstudied. Expectedly they diverge over time when recovery of impairment (restitution) plateaus, but compensation-driven improvements still occur. The objective of this study is to evaluate the alignment between FMMA-UE and ARAT in defining upper limb functional recovery categories by ARAT scores. We aimed to establish cut-off scores for both measures from the acute/early subacute, subacute and chronic stages of stroke recovery. Methods: Secondary analysis of four prospective cohort studies (acute/early subacute: n = 133, subacute: n = 113, chronic: n = 92) stages post-stroke. Receiver operating characteristic curves calculated the area under the curve (AUC) to establish optimal FMMA-UE cut-offs based on predefined ARAT thresholds distinguishing five activity levels from no activity to full activity. Weighted kappa was used to determine agreement between the two assessments. We used minimally clinically important difference (MCID) and minimal detectable change (MDC95) for comparison. Results: FMMA-UE and ARAT scores showed no relevant divergence across all recovery stages. Results indicated similar cut-off scores in all recovery stages with variability below MCID and MDC95 levels. Cut-off scores demonstrated robust AUC values from 0.77 to 0.86 at every recovery stage. Only in highly functional patients at the chronic stage, we found a reduced specificity of 0.55. At all other times sensitivity ranged between 0.68 and 0.99 and specificity between 0.71 and 0.99. Weighted kappa at the acute/early subacute, subacute and chronic stages was 0.76, 0.83, and 0.81, respectively. Discussion: Our research shows a strong alignment between FMMA-UE and ARAT cut-off scores throughout stroke recovery, except among the subgroup of highly recovered patients at the chronic stage. Discrepancies in specificity potentially stem from fine motor deficits affecting dexterity outcomes that are not captured by FMMA-UE. Additionally, the high congruence of both measures suggests they are not suited to distinguish between restitution and compensation. Calling for more comprehensive assessment methods to better understand upper limb functionality in rehabilitation.

The Flow State Scale for Rehabilitation Tasks: A New Flow Experience Questionnaire for Stroke Patients.

IMPORTANCE: Flow can be described as a subjective state that people report when they fully engage in an activity and experience pleasure, satisfaction, and enjoyment. Flow experiences are measured to determine the extent to which patients engage in therapy activities. Several flow questionnaires are used in neurorehabilitation. However, none have been validated for patients with (sub)acute stroke. OBJECTIVE: To develop and validate a new flow questionnaire for patients with (sub)acute stroke. DESIGN: Single-center prospective cohort study. SETTING: Neurorehabilitation unit of the Neurocenter of the Luzerner Kantonsspital in Lucerne, Switzerland. PARTICIPANTS: Fifty patients with (sub)acute stroke. OUTCOMES AND MEASURES: Development of the Flow State Scale for Rehabilitation Tasks (FSSRT) and determination of the psychometric properties of the FSSRT (internal consistency, test-retest reliability, structural and construct validity) in (sub)acute stroke patients. RESULTS: The FSSRT showed good internal consistency and excellent test-retest reliability. Composed of four components-concentration, pleasure, movement control, and absorption-the FSSRT correlated significantly negatively with the Hospital Anxiety and Depression Scale, indicating good divergent validity. CONCLUSIONS AND RELEVANCE: The FSSRT is a reliable and valid questionnaire measuring flow experience in patients with (sub)acute stroke. This questionnaire can be easily used in occupational therapy as well as in physical therapy and gives therapists important information about the flow experience of patients during therapy to adjust the therapy accordingly. Plain-Language Summary: Measuring flow experience, or the extent to which patients engage in therapy activities, in the context of occupational therapy and physical therapy is a new approach. This study confirmed that the Flow State Scale for Rehabilitation Tasks questionnaire is reliable and valid for measuring the flow experience of patients after (sub)acute stroke. Occupational therapists and physical therapists can use the FSSRT to optimally adjust the therapy program and increase patient engagement during therapy.

Rasch validation of a new scale to measure dependency in arm use in daily life: the Upper Limb Lucerne ICF-based Multidisciplinary Observation Scale.

Introduction: About 77% of adults with stroke have upper limb impairments. Many scales are available to measure the impairment and activity level of the affected limb. However, an observational scale to assess dependency on others in upper limb performance during daily life activities instead of laboratory settings is lacking. Therefore, we developed a new 5-item Upper Limb Lucerne ICF-based Multidisciplinary Observation Scale (UL-LIMOS). As next step in the psychometric analysis, we evaluated the unidimensionality and structural validity of the UL-LIMOS with Rasch Measurement Theory and we calculated a cut-off score for independent arm use in daily life activities at discharge. Methods: This is a single-center cross-sectional study in adults with (sub) acute stroke. We applied Rasch Measurement Theory (RMT) to analyze the structural validation and unidimensionality of the UL-LIMOS. The outputs provide evidence of unidimensionality, item and person fit, overall fit, differential item functioning (DIF), principal component analysis of residuals (PCAR), person separation reliability (PSR), and residual item correlations (to identify local item dependence). Person mean location, floor and ceiling effects identify proper targeting. Results: We recruited 407 adults with (sub) acute stroke (median age 63 years, 157 women). All items and persons fit the Rasch model. The PSR of 0.90 indicates that clinicians and researchers can reliably use the scale for individual decision-making. There were small floor (2.70%) and ceiling (13.00%) effects. The average person mean location was 1.32 ± 2.99 logits. There was no DIF. PCAR eigenvalue was 2.46 with 49.23% explained variance. Paired t-tests revealed that 0.89% of person locations were significantly different, confirming unidimensionality. One pair of items (arm and hand use and fine hand use) showed residual item correlations. The ROC's AUC was 0.90, CI95% = [0.85-0.96] with cut-off score of ≥14/20, and high sensitivity (87%, CI95% = [81%-91%]), specificity (83%, CI95% = [77%-87%]) for independent arm use in daily living at discharge. Discussion: The new Rasch-based UL-LIMOS is a valid ICF-based observation performance scale at the ICF-activity level, to evaluate dependency during upper limb use in daily life in adults with stroke. Additional psychometric analyses are warranted. The UL-LIMOS would be a valuable addition to the core assessments of adults with (sub) acute stroke.

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.

Speed up discharge planning at the acute stroke unit: A development and external validation study for the early prediction of discharge home.

Background: To reduce healthcare costs, it has become increasingly important to shorten the length of stay in acute stroke units. The goal of this study was to develop and externally validate a decision tree model applicable < 48 h poststroke for discharge home from an acute stroke unit with a short length of stay, and to assess the inappropriate home discharge rate. Methods: A prospective study including two samples of stroke patients admitted to an acute stroke unit. The outcome was discharge home (yes/no). A classification and regression tree analysis was performed in Sample 1. The model's performance was tested in Sample 2. Results: In total, 953 patients were included. The final decision tree included the patients' activities of daily living (ADL) performance <48 h poststroke, including motor function, cognition, and communication, and had an area under the curve (AUC) of 0.84 (95% confidence interval 0.76, 0.91). External validation resulted in an AUC of 0.74 (95% confidence interval 0.72, 0.77). None of the patients discharged home were re-admitted < 2 months after discharge to a hospital or admitted to a rehabilitation center for symptoms that had needed inpatient neurorehabilitation. Conclusions: The developed decision tree shows acceptable external validity in predicting discharge home in a heterogeneous sample of stroke patients, only based on the patient's actual ADL performance <48 h poststroke. Importantly, discharge was safe, i.e., no re-hospitalization was registered. The tree's application to speed up discharge planning should now be further evaluated.

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.

What is the evidence for physical therapy poststroke? A systematic review and meta-analysis.

BACKGROUND: Physical therapy (PT) is one of the key disciplines in interdisciplinary stroke rehabilitation. The aim of this systematic review was to provide an update of the evidence for stroke rehabilitation interventions in the domain of PT. METHODS AND FINDINGS: Randomized controlled trials (RCTs) regarding PT in stroke rehabilitation were retrieved through a systematic search. Outcomes were classified according to the ICF. RCTs with a low risk of bias were quantitatively analyzed. Differences between phases poststroke were explored in subgroup analyses. A best evidence synthesis was performed for neurological treatment approaches. The search yielded 467 RCTs (N = 25373; median PEDro score 6 [IQR 5-7]), identifying 53 interventions. No adverse events were reported. Strong evidence was found for significant positive effects of 13 interventions related to gait, 11 interventions related to arm-hand activities, 1 intervention for ADL, and 3 interventions for physical fitness. Summary Effect Sizes (SESs) ranged from 0.17 (95%CI 0.03-0.70; I(2) = 0%) for therapeutic positioning of the paretic arm to 2.47 (95%CI 0.84-4.11; I(2) = 77%) for training of sitting balance. There is strong evidence that a higher dose of practice is better, with SESs ranging from 0.21 (95%CI 0.02-0.39; I(2) = 6%) for motor function of the paretic arm to 0.61 (95%CI 0.41-0.82; I(2) = 41%) for muscle strength of the paretic leg. Subgroup analyses yielded significant differences with respect to timing poststroke for 10 interventions. Neurological treatment approaches to training of body functions and activities showed equal or unfavorable effects when compared to other training interventions. Main limitations of the present review are not using individual patient data for meta-analyses and absence of correction for multiple testing. CONCLUSIONS: There is strong evidence for PT interventions favoring intensive high repetitive task-oriented and task-specific training in all phases poststroke. Effects are mostly restricted to the actually trained functions and activities. Suggestions for prioritizing PT stroke research are given.