Prediction of upper limb recovery, general disability, and rehabilitation status by activity measurements assessed by accelerometers or the Fugl-Meyer score in acute stroke

Prediction of hospitalization time and independence level with functional outcomes for patients with acute stroke: a retrospective study

OBJECTIVES

The objective of this study was to develop predictive models for estimating the length of stay (LOS) with standardized clinical outcome measures (Functional Independence Measure, Trunk Impairment Scale, Postural Assessment Scale for Stroke Patients, Fugl Meyer Assessment Scale, and Functional Ambulation Category) during acute care setting.

METHODS

One hundred sixty-nine patients were included in the retrospective study. Predictors chosen for the LOS included scores of functional outcome measures at admission. We used Spearman's rank correlation coefficients to calculate correlations among clinical outcome measures and LOS, stepwise multiple regression analysis to develop a predictive model, and receiver operating characteristics curve to analyze the predictive value of explanatory factors obtained from the previous model for discharge Functional Independence Measure score.

RESULTS

The predictive equation explained 81% of the variance in LOS. The most important predictors were trunk impairment, motor function of the upper extremity, walking ability, and independence level at admission. The receiver operating characteristic curve was obtained with a cut-off score of 13 points for the Trunk Impairment Scale, 47 points for Fugl Meyer Assessment-Upper Extremity, and 2 points for Functional Ambulation Category, demonstrating the highest percentage of the accurately predicted ability of independence level at discharge.

DISCUSSION

The models presented in this study could help clinicians and researchers to predict the LOS and discharge independence level of clinical outcomes for patients with acute stroke enrolled in an acute care setting.

Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: a machine learning study

BACKGROUND

Machine Learning is increasingly used to predict rehabilitation outcomes in stroke in the context of precision rehabilitation and patient-centered care. However, predictors for patient-centered outcome measures for activities and participation in stroke rehabilitation requires further investigation.

METHODS

This study retrospectively analyzed data collected for our previous studies from 124 participants. Machine Learning models were built to predict postintervention improvement of patient-reported outcome measures of daily activities (i.e, the Motor Activity Log and the Nottingham Extended Activities of Daily Living) and participation (i.e, the Activities of Daily Living domain of the Stroke Impact Scale). Three groups of 18 potential predictors were included: patient demographics, stroke characteristics, and baseline assessment scores that encompass all three domains under the framework of International Classification of Functioning, Disability and Health. For each target variable, classification models were built with four algorithms, logistic regression, k-nearest neighbors, support vector machine, and random forest, and with all 18 potential predictors and the most important predictors identified by feature selection.

RESULTS

Predictors for the four target variables partially overlapped. For all target variables, their own baseline scores were among the most important predictors. Upper-limb motor function and selected demographic and stroke characteristics were also among the important predictors across the target variables. For the four target variables, prediction accuracies of the best-performing models with 18 features ranged between 0.72 and 0.96. Those of the best-performing models with fewer features ranged between 0.72 and 0.84.

CONCLUSIONS

Our findings support the feasibility of using Machine Learning for the prediction of stroke rehabilitation outcomes. The study was the first to use Machine Learning to identify important predictors for postintervention improvement on four patient-reported outcome measures of activities and participation in chronic stroke. The study contributes to precision rehabilitation and patient-centered care, and the findings may provide insights into the identification of patients that are likely to benefit from stroke rehabilitation.

Predicting later categories of upper limb activity from earlier clinical assessments following stroke: an exploratory analysis

BACKGROUND

Accelerometers allow for direct measurement of upper limb (UL) activity. Recently, multi-dimensional categories of UL performance have been formed to provide a more complete measure of UL use in daily life. Prediction of motor outcomes after stroke have tremendous clinical utility and a next step is to explore what factors might predict someone's subsequent UL performance category.

PURPOSE

To explore how different machine learning techniques can be used to understand how clinical measures and participant demographics captured early after stroke are associated with the subsequent UL performance categories.

METHODS

This study analyzed data from two time points from a previous cohort (n = 54). Data used was participant characteristics and clinical measures from early after stroke and a previously established category of UL performance at a later post stroke time point. Different machine learning techniques (a single decision tree, bagged trees, and random forests) were used to build predictive models with different input variables. Model performance was quantified with the explanatory power (in-sample accuracy), predictive power (out-of-bag estimate of error), and variable importance.

RESULTS

A total of seven models were built, including one single decision tree, three bagged trees, and three random forests. Measures of UL impairment and capacity were the most important predictors of the subsequent UL performance category, regardless of the machine learning algorithm used. Other non-motor clinical measures emerged as key predictors, while participant demographics predictors (with the exception of age) were generally less important across the models. Models built with the bagging algorithms outperformed the single decision tree for in-sample accuracy (26-30% better classification) but had only modest cross-validation accuracy (48-55% out of bag classification).

CONCLUSIONS

UL clinical measures were the most important predictors of the subsequent UL performance category in this exploratory analysis regardless of the machine learning algorithm used. Interestingly, cognitive and affective measures emerged as important predictors when the number of input variables was expanded. These results reinforce that UL performance, in vivo, is not a simple product of body functions nor the capacity for movement, instead being a complex phenomenon dependent on many physiological and psychological factors. Utilizing machine learning, this exploratory analysis is a productive step toward the prediction of UL performance. Trial registration NA.

Predictors for Upper-Limb Functional Recovery Trajectory in Individuals Receiving Stroke Rehabilitation: A Secondary Analysis of Data from Randomized Controlled Trials

BACKGROUND

The objective of the study was to determine predictors for upper-limb functional recovery trajectory after occupational therapy in a population with chronic stroke.

METHODS

In this retrospective secondary analysis, Fugl-Meyer Assessment-Upper Extremity (FMA-UE) scores before and after intervention and at the 3-month follow-up were used to divide 105 participants with chronic stroke into three groups of recovery trajectories: fast (participants who reached an improvement of 7 after intervention), extended (those who reached an improvement of 7 at follow-up), and limited (those who did not reach an improvement of 7) recovery. Comparisons among the three groups were made in demographics, stroke characteristics, and baseline assessment scores. Logistic regression analyses were performed to determine predictors for group membership.

RESULTS

Time after onset of stroke and the baseline scores of FMA-UE, Stroke Impact Scale-Hand (SIS-Hand), Wolf Motor Function Test (WMFT)-Quality, WMFT-Time scores, Motor Activity Log-Amount of Use (MAL-AOU), and Motor Activity Log-Quality of Movement (MAL-QOM) scores were significantly different among the three groups. Univariate logistic regressions confirmed that SIS-Hand, WMFT-Quality, WMFT-Time, MAL-AOU, and MAL-QOM were significant predictors for both the fast versus limited recovery group membership and the extended versus limited group membership. Time after stroke onset and baseline FMA-UE were additional predictors for the fast versus limited recovery group membership.

CONCLUSION

These findings may assist healthcare professionals in making optimal therapeutic decisions and in informing clients and caregivers about the outcomes of stroke recovery.

Relation Between EEG Measures and Upper Limb Motor Recovery in Stroke Patients: A Scoping Review

Current clinical practice does not leverage electroencephalography (EEG) measurements in stroke patients, despite its potential to contribute to post-stroke recovery predictions. We review the literature on the effectiveness of various quantitative and qualitative EEG-based measures after stroke as a tool to predict upper limb motor outcome, in relation to stroke timeframe and applied experimental tasks. Moreover, we aim to provide guidance on the use of EEG in the assessment of upper limb motor recovery after stroke, suggesting a high potential for some metrics in the appropriate context. We identified relevant papers (N = 16) from databases ScienceDirect, Web of Science and MEDLINE, and assessed their methodological quality with the Joanna Briggs Institute (JBI) Critical Appraisal. We applied the Preferred Reporting Systems for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) Framework. Identified works used EEG to identify properties including event-related activation, spectral power in physiologically relevant bands, symmetry in brain dynamics, functional connectivity, cortico-muscular coherence and rhythmic coordination. EEG was acquired in resting state or in relation to behavioural conditions. Motor outcome was mainly evaluated with the Upper Limb Fugl-Meyer Assessment. Despite great variability in the literature, data suggests that the most promising EEG quantifiers for predicting post-stroke motor outcome are event-related measures. Measures of spectral power in physiologically relevant bands and measures of brain symmetry also show promise. We suggest that EEG measures may improve our understanding of stroke brain dynamics during recovery, and contribute to establishing a functional prognosis and choosing the rehabilitation approach.

A Random Tree Forest decision support system to personalize upper extremity robot-assisted rehabilitation in stroke: a pilot study

Robotic-based rehabilitation administered by means of serious games certainly represents the frontier of rehabilitation treatments, offering a high degree of customization of therapy, to meet individual patients' needs and to tailor a proper rehabilitation therapy. Despite the rush on developing complex rehabilitation systems, they often do not provide clinicians with long-term information about the outcome of rehabilitation, thus, not supporting them in the initial set-up phase of the therapy. In this paper, a Random-Forest based system was trained and tested to provide a prediction at discharge of several clinical scales outcomes (i.e. FMA, ARAT, and MI), having clinical scale scores and measures from the robotic system at the enrollment as inputs. The dataset includes 25 post-stroke patients from different clinics, that underwent a variable number of days of rehabilitation with a robotic treatment. Results have shown that the system is able to predict the final outcome with an accuracy ranging from 60% to 73% on the selected scales. Also results provide information on which variables are more relevant for the prediction of outcome of therapy, in particular clinical scales scores such as FMA, ARAT, MI, NRS, PCS, and MCS and robotic automatically extracted measurements related to patient's work expenditure and time. This supports the idea of using such a system in a clinical environment in a decision support tool for clinicians.

Suitability of accelerometry as an objective measure for upper extremity use in stroke patients

Background

Upper limb (UL) paresis is one of the most common stroke consequences and significantly restricts patients in everyday life. Instruments objectively measuring direct arm use in stroke patients are lacking, but might be helpful to understand patients’ impairment. Aiming to examine whether accelerometry is a suitable objective measure for everyday UL use in stroke patients, we conducted a systematic review on the association between accelerometer-derived measurements and clinical scales.

Methods

Articles were systematically searched in PubMed, Scopus, Cochrane Library, PeDro and LIVIVO through December 12^th, 2021, screened for inclusion by AH, and subsequently independently screened by CK and MK. Disagreements were discussed until consensus. We included English and German peer-reviewed articles dealing with the validity of accelerometers as a measurement of UL use in stroke patients and eligible systematic reviews. Studies exclusively using accelerometry as an outcome parameter, book contributions, conference abstracts and case studies were excluded. Data extraction was conducted by AH and confirmed by CK focussing on study type, objective, accelerometer device, sample size, stroke status, assessments conducted, measurement method, wearing time and key results. We analysed all eligible articles regarding the correlation between accelerometry and other clinical assessments and the validity in accordance with the type of accelerometer.

Results

Excluding duplicates, the initial search yielded 477 records. In the 34 eligible studies accelerometers was used with a predominance of tri-axial accelerometery (n = 12) and only few with two-axial application (n = 4). Regarding measures to examine association to accelerometer data different clinical scales were applied depending on the setting, the degree of impairment and/or the status of stroke. Cut-off values to determine correlations varied largely; most significant correlations are reported for the MAL [Range 0.31- 0.84] and the ARAT [Range 0.15–0.79].

Conclusions

Accelerometers can provide reliable data about daily arm use frequency but do not supply information about the movements´ quality and restrictions on everyday activities of stroke patients. Depending on the context, it is advisable to use both, accelerometry and other clinical measures. According to the literature there is currently no accelerometer device most suitable to measure UL activity. High correlations indicate that multi-dimensional accelerometers should be preferred.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-022-02743-w.

Validity study of a triaxial accelerometer for measuring energy expenditure in stroke inpatients of a physical medicine and rehabilitation center

PURPOSE

Establish the validity of a triaxial accelerometer (Dynaport®) for evaluating the energy expenditure of patients with stroke sequelae at a rehabilitation hospital.

METHODS

This is a cross-sectional study with 24 stroke inpatients of a rehabilitation hospital. The participants were assessed on energy expenditure by an ergospirometer system and the triaxial accelerometer simultaneously during a walk test. The data collected by both devices were compared by intraclass correlation coefficient (ICC) and Bland-Altman limits of agreement.

RESULTS

An almost perfect agreement (ICC = 0,94) in the energy expenditure measured by the accelerometer compared to the results of the ergospirometer system was found during the exercise test. The Bland-Altman analysis has shown suitable limits of agreement. Post hoc analyses with the maximum volume of oxygen and the total energy expenditure measured by the ergospirometer system evidenced significant correlation with the energy expenditure measurements by the accelerometer.

CONCLUSION

Our results evidence that the triaxial accelerometer Dynaport® and its built-in software are valid for estimating the energy expenditure of stroke sequelae during a walk exercise.

Relationship Between Body-Specific Attention to a Paretic Limb and Real-World Arm Use in Stroke Patients: A Longitudinal Study

Learned nonuse is a major problem in upper limb (UL) rehabilitation after stroke. Among the various factors that contribute to learned nonuse, recent studies have focused on body representation of the paretic limb in the brain. We previously developed a method to measure body-specific attention, as a marker of body representation of the paretic limb and revealed a decline in body-specific attention to the paretic limb in chronic stroke patients by a cross-sectional study. However, longitudinal changes in body-specific attention and paretic arm use in daily life (real-world arm use) from the onset to the chronic phase, and their relationship, remain unknown. Here, in a longitudinal, prospective, observational study, we sought to elucidate the longitudinal changes in body-specific attention to the paretic limb and real-world arm use, and their relationship, by using accelerometers and psychophysical methods, respectively, in 25 patients with subacute stroke. Measurements were taken at baseline (TBL), 2 weeks (T2w), 1 month (T1M), 2 months (T2M), and 6 months (T6M) after enrollment. UL function was measured using the Fugl-Meyer Assessment (FMA) and Action Research Arm Test (ARAT). Real-world arm use was measured using accelerometers on both wrists. Body-specific attention was measured using a visual detection task. The UL function and real-world arm use improved up to T6M. Longitudinal changes in body-specific attention were most remarkable at T1M. Changes in body-specific attention up to T1M correlated positively with changes in real-world arm use up to T6M, and from T1M to T6M, and the latter more strongly correlated with changes in real-world arm use. Changes in real-world arm use up to T2M correlated positively with changes in FMA up to T2M and T6M. No correlation was found between body-specific attention and FMA scores. Thus, these results suggest that improved body-specific attention to the paretic limb during the early phase contributes to increasing long-term real-world arm use and that increased real-world use is associated with the recovery of UL function. Our results may contribute to the development of rehabilitation strategies to enhance adaptive changes in body representation in the brain and increase real-world arm use after stroke.

Quantification of Movement in Stroke Patients under Free Living Conditions Using Wearable Sensors: A Systematic Review

Stroke is a main cause of long-term disability worldwide, placing a large burden on individuals and health care systems. Wearable technology can potentially objectively assess and monitor patients outside clinical environments, enabling a more detailed evaluation of their impairment and allowing individualization of rehabilitation therapies. The aim of this review is to provide an overview of setups used in literature to measure movement of stroke patients under free living conditions using wearable sensors, and to evaluate the relation between such sensor-based outcomes and the level of functioning as assessed by existing clinical evaluation methods. After a systematic search we included 32 articles, totaling 1076 stroke patients from acute to chronic phases and 236 healthy controls. We summarized the results by type and location of sensors, and by sensor-based outcome measures and their relation with existing clinical evaluation tools. We conclude that sensor-based measures of movement provide additional information in relation to clinical evaluation tools assessing motor functioning and both are needed to gain better insight in patient behavior and recovery. However, there is a strong need for standardization and consensus, regarding clinical assessments, but also regarding the use of specific algorithms and metrics for unsupervised measurements during daily life.

Prediction of Upper Limb use Three Months after Stroke: A Prospective Longitudinal Study

BACKGROUND

A major goal of upper limb (UL) rehabilitation after stroke is to facilitate the use of the paretic arm in daily life activities.

PURPOSE

To examine if UL impairment two weeks after stroke can predict real-life UL use at three months. Furthermore, to identify additional factors which contribute to future UL use, and characteristics of patients who do not achieve normal UL use.

METHODS

This study included patients with stroke ≥ 18 years. UL impairment was assessed by Fugl-Meyer upper extremity motor assessment (FM). Use ratio between affected and unaffected UL was assessed with accelerometers at three months after stroke. The association between FM score and UL use ratio was investigated with linear regression models and adjusted for secondary variables. Non-normal use was examined by a logistic regression.

RESULTS

Eighty-seven patients were included. FM score two weeks after stroke predicted 38% of the variance in UL use ratio three months after stroke. A multivariate regression model predicted 55%, and the significant predictors were FM, motor-evoked potential (MEP) status, and neglect. Non-normal use could be predicted with a high accuracy based on MEP and/or neglect. In a logistic regression sensitivity for prediction of non-normal use was 0.93 and specificity was 0.75.

CONCLUSION

Better baseline capacity of the paretic UL predicted increased use of the arm and hand in daily life. Non-normal UL use could be predicted reliably based on the absence of MEPs and/or presence of neglect.

Exploring physiotherapists' and occupational therapists' perceptions of the upper limb prediction algorithm PREP2 after stroke in a rehabilitation setting: a qualitative study

OBJECTIVE

To explore how physiotherapists (PTs) and occupational therapists (OTs) perceive upper limb (UL) prediction algorithms in a stroke rehabilitation setting and identify potential barriers to and facilitators of their implementation.

DESIGN

This was a qualitative study.

SETTING

The study took place at a neurorehabilitation centre.

PARTICIPANTS

Three to six PTs and OTs.

METHODS

We conducted four focus group interviews in order to explore therapists' perceptions of UL prediction algorithms, in particular the Predict Recovery Potential algorithm (PREP2). The Consolidated Framework for advancing Implementation Research was used to develop the interview guide. Data were analysed using a thematic content analysis. Meaning units were identified and subthemes formed. Information gained from all interviews was synthesised, and four main themes emerged.

RESULTS

The four main themes were current practice, perceived benefits, barriers and preconditions for implementation. The participants knew of UL prediction algorithms. However, only a few had a profound knowledge and few were using the Shoulder Abduction Finger Extension test, a core component of the PREP2 algorithm, in their current practice. PREP2 was considered a potentially helpful tool when planning treatment and setting goals. A main barrier was concern about the accuracy of the algorithm. Furthermore, participants dreaded potential dilemmas arising from having to confront the patients with their prognosis. Preconditions for implementation included tailoring the implementation to a specific unit, sufficient time for acquiring new skills and an organisation supporting implementation.

CONCLUSION

In the present study, experienced neurological therapists were sceptical towards prediction algorithms due to the lack of precision of the algorithms and concerns about ethical dilemmas. However, the PREP2 algorithm was regarded as potentially useful.

Actigraphic Measurement of the Upper Limbs for the Prediction of Ischemic Stroke Prognosis: An Observational Study

Background: It is often challenging to formulate a reliable prognosis for patients with acute ischemic stroke. The most accepted prognostic factors may not be sufficient to predict the recovery process. In this view, describing the evolution of motor deficits over time via sensors might be useful for strengthening the prognostic model. Our aim was to assess whether an actigraphic-based parameter (Asymmetry Rate Index for the 24 h period (AR2_24 h)) obtained in the acute stroke phase could be a predictor of a 90 d prognosis. Methods: In this observational study, we recorded and analyzed the 24 h upper limb movement asymmetry of 20 consecutive patients with acute ischemic stroke during their stay in a stroke unit. We recorded the motor activity of both arms using two programmable actigraphic systems positioned on patients’ wrists. We clinically evaluated the stroke patients by NIHSS in the acute phase and then assessed them across 90 days using the modified Rankin Scale (mRS). Results: We found that the AR2_24 h parameter positively correlates with the 90 d mRS (r = 0.69, p < 0.001). Moreover, we found that an AR2_24 h > 32% predicts a poorer outcome (90 d mRS > 2), with sensitivity = 100% and specificity = 89%. Conclusions: Sensor-based parameters might provide useful information for predicting ischemic stroke prognosis in the acute phase.

Arm Motor Recovery After Ischemic Stroke: A Focus on Clinically Distinct Trajectory Groups

BACKGROUND AND PURPOSE

Recovery of arm function poststroke is highly variable with some people experiencing rapid recovery but many experiencing slower or limited functional improvement. Current stroke prediction models provide some guidance for clinicians regarding expected motor outcomes poststroke but do not address recovery rates, complicating discharge planning. This study developed a novel approach to defining recovery groups based on arm motor recovery trajectories poststroke. In addition, between-group differences in baseline characteristics and therapy hours were explored.

METHODS

A retrospective cohort analysis was conducted where 40 participants with arm weakness were assessed 1 week, 6 weeks, 3 months, and 6 months after an ischemic stroke. Arm recovery trajectory groups were defined on the basis of timing of changes in the Fugl-Meyer Assessment Upper Extremity (FMA-UE), at least the minimal clinically important difference (MCID), 1 week to 6 weeks or 6 weeks to 6 months. Three recovery trajectory groups were defined: Fast (n = 19), Extended (n = 12), and Limited (n = 9). Between-group differences in baseline characteristics and therapy hours were assessed. Associations between baseline characteristics and group membership were also determined.

RESULTS

Three baseline characteristics were associated with trajectory group membership: FMA-UE, NIH Stroke Scale, and Barthel Index. The Fast Recovery group received the least therapy hours 6 weeks to 6 months. No differences in therapy hours were observed between Extended and Limited Recovery groups at any time points.

DISCUSSION AND CONCLUSIONS

Three clinically relevant recovery trajectory groups were defined using the FMA-UE MCID. Baseline impairment, overall stroke severity, and dependence in activities of daily living were associated with group membership and therapy hours differed between groups. Stratifying individuals by recovery trajectory early poststroke could offer additional guidance to clinicians in discharge planning.

(See Supplemental Digital Content 1 for Video Abstract, available at: http://links.lww.com/JNPT/A337.).

Robot-assisted rehabilitation of hand function after stroke: Development of prediction models for reference to therapy

BACKGROUND

Recovery of hand function after stroke represents the hardest target for clinicians. Robot-assisted therapy has been proved to be effective for hand recovery. Nevertheless, studies aimed to refer patients to the best therapy are missing.

METHODS

With the aim to identify which clinical features are predictive for referring to robot-assisted hand therapy, 174 stroke patients were assessed with: Fugl-Meyer Assessment (FMA), Functional Independence Measure (FIM), Reaching Performance Scale (RPS), Box and Block Test (BBT), Modified Ashworth Scale (MAS), Nine Hole Pegboard Test (NHPT). Moreover, patients ability to control the robot with residual force and surface EMG (sEMG) independently, was checked. ROC curves were calculated to determine which of the measures were the predictors of the event.

RESULTS

sEMG control (AUC = 0.925) was significantly determined by FMA upper extremity (FMUE) (>24/66) and sensation (>23/24) sections, MAS at Flexor Carpi (<3/4) and total MAS (>4/20). Force control (AUC = 0.928) was correlated only with FMUE (>24/66).

CONCLUSIONS

FMUE and MAS were the best predictors of preserved ability to control the device by two different modalities. This finding opens the possibility to plan specific therapies aimed at maximizing the highest functional outcome achievable after stroke.

Accuracy of the Upper Limb Prediction Algorithm PREP2 Applied 2 Weeks Poststroke: A Prospective Longitudinal Study

BACKGROUND

The Predict Recovery Potential algorithm (PREP2) was developed to predict upper limb (UL) function early after stroke. However, assessment in the acute phase is not always possible.

OBJECTIVE

To assess the prognostic accuracy of the PREP2 when applied in a subacute neurorehabilitation setting.

METHODS

This prospective longitudinal study included patients ≥18 years old with UL impairment following stroke. Patients were assessed in accordance with the PREP2 approach. However, 2 main components, the shoulder abduction finger extension (SAFE) score and motor-evoked potentials (MEPs) were obtained 2 weeks poststroke. UL function at 3 months was predicted in 1 of 4 categories and compared with the actual outcome at 3 months as assessed by the Action Research Arm Test. The prediction accuracy of the PREP2 was quantified using the correct classification rate (CCR).

RESULTS

Ninety-one patients were included. Overall CCR of the PREP2 was 60% (95% CI 50%-71%). Within the 4 categories, CCR ranged from the lowest value at 33% (95% CI 4%-85%) for the category Limited to the highest value at 78% (95% CI 43%-95%) for the category Poor. In the present study, the overall CCR was significantly lower (P < .001) than the 75% reported by the PREP2 developers.

CONCLUSIONS

The low overall CCR makes PREP2 obtained 2 weeks poststroke unsuited for clinical implementation. However, PREP2 may be used to predict either excellent UL function in already well-recovered patients or poor UL function in patients with persistent severe UL paresis.

Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches

BACKGROUND

Accurate prediction of motor recovery after stroke is critical for treatment decisions and planning. Machine learning has been proposed to be a promising technique for outcome prediction because of its high accuracy and ability to process large volumes of data. It has been used to predict acute stroke recovery; however, whether machine learning would be effective for predicting rehabilitation outcomes in chronic stroke patients for common contemporary task-oriented interventions remains largely unexplored. This study aimed to determine the accuracy and performance of machine learning to predict clinically significant motor function improvements after contemporary task-oriented intervention in chronic stroke patients and identify important predictors for building machine learning prediction models.

METHODS

This study was a secondary analysis of data using two common machine learning approaches, which were the k-nearest neighbor (KNN) and artificial neural network (ANN). Chronic stroke patients (N = 239) that received 30 h of task-oriented training including the constraint-induced movement therapy, bilateral arm training, robot-assisted therapy and mirror therapy were included. The Fugl-Meyer assessment scale (FMA) was the main outcome. Potential predictors include age, gender, side of lesion, time since stroke, baseline functional status, motor function and quality of life. We divided the data set into a training set and a test set and used the cross-validation procedure to construct machine learning models based on the training set. After the models were built, we used the test data set to evaluate the accuracy and prediction performance of the models.

RESULTS

Three important predictors were identified, which were time since stroke, baseline functional independence measure (FIM) and baseline FMA scores. Models for predicting motor function improvements were accurate. The prediction accuracy of the KNN model was 85.42% and area under the receiver operating characteristic curve (AUC-ROC) was 0.89. The prediction accuracy of the ANN model was 81.25% and the AUC-ROC was 0.77.

CONCLUSIONS

Incorporating machine learning into clinical outcome prediction using three key predictors including time since stroke, baseline functional and motor ability may help clinicians/therapists to identify patients that are most likely to benefit from contemporary task-oriented interventions. The KNN and ANN models may be potentially useful for predicting clinically significant motor recovery in chronic stroke.

Characterizing upper extremity motor behavior in the first week after stroke

Background

Animal models of brain recovery identify the first days after lesioning as a time of great flux in sensorimotor function and physiology. After rodent motor system lesioning, daily skill training in the less affected forelimb reduces skill acquisition in the more affected forelimb. We asked whether spontaneous human motor behaviors of the less affected upper extremity (UE) early after stroke resemble the animal training model, with the potential to suppress clinical recovery.

Methods

This prospective observational study used a convenience sample of patients (n = 25, mean 4.5 ±1.8) days after stroke with a wide severity range; Controls were hospitalized for non-neurological conditions (n = 12). Outcome measures were Accelerometry, Upper-Extremity Fugl-Meyer (UEFM), Action Research Arm Test (ARAT), Shoulder Abduction/ Finger Extension Test (SAFE), NIH Stroke Scale (NIHSS).

Results

Accelerometry indicated total paretic UE movement was reduced compared to controls, primarily due to a 44% reduction of bilateral UE use. Unilateral paretic movement was unchanged. Thus, movement shifted early after stroke; bilateral use was reduced and unilateral use of the non-paretic UE was increased by 77%. Low correlations between movement time and motor performance prompted an exploratory factor analysis (EFA) revealing a 2-component solution; motor performance tests load on one component (motor performance) whereas accelerometry-derived variables load on a second orthogonal component (quantity of movement).

Conclusions

Early after stroke, spontaneous overall UE movement is reduced, and movement shifts to unilateral use of the non-paretic UE. Two mechanisms that could influence motor recovery may already be in place 4.5 ± 1.8 days post stroke: (1) the overuse of the less affected UE, which could set the stage for learned non-use and (2) skill acquisition in the non-paretic limb that could impede recovery. Accurate UE motor assessment requires two independent constructs: motor performance and quantity of movement. These findings provide opportunities and measurement methods for studies to develop new behaviorally-based stroke recovery treatments that begin early after onset.

Does the addition of concurrent visual feedback increase adherence to a home exercise program in people with stroke: a single-case series?

OBJECTIVE

Evidence is accumulating for the potential benefits of technology use in stroke rehabilitation. However, few studies have examined ways in which technology can be used to increase adherence to programs after discharge from rehabilitation. The aim of this study was to determine if the addition of concurrent visual feedback, via a tablet computer, increased adherence to an exercise program following stroke. Ten participants were provided with a self-administered exercise program and were asked to perform 60 min of the exercises daily. After a baseline phase (1 week), participants were given a tablet computer (2 weeks) and were asked to video record each exercise session. The tablet computer was removed during the fourth week of the program.

RESULTS

Exercise duration, measured via wrist-worn accelerometry, was investigated over the 4 weeks using the two-standard deviation (2 SD) band method. A statistically significant effect was observed in four out of ten cases, demonstrated by two successive data points occurring outside the 2 SD band during the intervention phase, suggesting that adherence was increased in response to the tablet computer use. This preliminary study indicates that the use of visual feedback, via a tablet computer, may increase adherence to an exercise program in people with stroke. Trial registration ACTRN: ACTRN12620000252910 (26 February 2020, Retrospectively registered).

Easily Conducted Tests During the First Week Post-stroke Can Aid the Prediction of Arm Functioning at 6 Months

Background: Prognostic models can estimate the recovery of arm functioning after stroke, guide the selection of individual training strategies, and inform patient selection in clinical trials. Several models for early prediction of arm recovery have been proposed, but their implementation has been hindered by insufficient external validation, limited evidence of their impact on patient outcomes, and reliance on predictors that are not feasible in regular clinical practice.

Objectives: To determine the predictive value of new and previously reported tests that can be easily conducted in regular clinical settings for early prognosis of two levels of favorable arm recovery at 6 months post-stroke.

Methods: We performed a secondary analysis of merged data (n = 223) from two Scandinavian prospective longitudinal cohorts. The candidate predictors were seven individual tests of motor function and the sensory function measured by the Fugl-Meyer Assessment of Upper Extremity within 7 days post-stroke, and the whole motor section of this assessment. For each candidate predictor, we calculated the adjusted odds ratio (OR) of two levels of residual motor impairment in the affected arm at 6 months post-stroke: moderate-to-mild (≥32 points on the motor section of the Fugl-Meyer Assessment of Upper Extremity, FMA-UE) and mild (FMA-UE ≥ 58 points).

Results: Patients with partial shoulder abduction (OR 14.6), elbow extension (OR 15.9), and finger extension (OR 9.5) were more likely to reach FMA-UE ≥ 32. Patients with full function on all individual motor tests (OR 5.5–35.3) or partial elbow extension, pronation/supination, wrist dorsiflexion and grasping ability (OR 2.1–18.3) were more likely to achieve FMA-UE ≥ 58 compared with those with absent function. Intact sensory function (OR 2.0–2.2) and moderate motor impairment on the FMA-UE (OR 7.5) were also associated with favorable outcome.

Conclusions: Easily conducted motor tests can be useful for early prediction of arm recovery. The added value of this study is the prediction of two levels of a favorable functional outcome from simple motor tests. This knowledge can be used in the development of prognostic models feasible in regular clinical settings, inform patient selection and stratification in future trials, and guide clinicians in the selection of individualized training strategies for improving arm functioning after stroke.

Clinical Trial Registration:ClinicalTrials.gov: NCT02250365, NCT01115348.

Actigraphic measurement of the upper limbs movements in acute stroke patients

Background

Stroke units provide patients with a multiparametric monitoring of vital functions, while no instruments are actually available for a continuous monitoring of patients motor performance. Our aim was to develop an actigraphic index able both to identify the paretic limb and continuously monitor the motor performance of stroke patients in the stroke unit environment.

Methods

Twenty consecutive acute stroke patients (mean age 69.2 years SD 10.1, 8 males and 12 females) and 17 bed-restrained patients (mean age 70.5 years SD 7.3, 7 males and 10 females) hospitalized for orthopedic diseases of the lower limbs, but not experiencing neurological symptoms, were enrolled. This last group represented our control group. The motor activity of arms was recorded for 24 h using two programmable actigraphic systems showing off as wrist-worn watches. The firmware segmented the acquisition in epochs of 1 minute and for each epoch calculates two motor activity indices: MA_e1 (Epoch-related Motor Activity index) and MA_e2 (Epoch-related Motor Activity index 2). MA_e1 is defined as the standard deviation of the acceleration module and MA_e2 as the module of the standard deviation of acceleration components. To describe the 24 h motor performance of each limb, we calculated the mean value of MA_e1 and MA_e2 (respectively MA_{1_24h} and MA_{2_24h}). Then we obtained two Asymmetry Rate Indices: AR_{1_24h} and AR_{2_24h} to show the motor activity prevalence. AR_{1_24h} refers to the asymmetry index between the values of MA_e1 of both arms and AR_{2_24h} to MA_e2 values.

The stroke patients were clinically evaluated by NIHSS at the beginning (NIHSS_T0) and at the end (NIHSS_T1) of the 24 h actigraphic recordings.

Results

Both MA_{1_24h} and MA_{2_24h} indices were smaller in the paretic than in the unaffected arm (respectively p = 0.004 and p = 0.004). AR_{2_24h} showed a better capability (95% of paretic arms correctly identified, Phi Coefficient: 0.903) to discriminate the laterality of the clinical deficit than AR_{1_24h} (85% of paretic arms correctly identified, Phi Coefficient: 0,698). We also found that AR_{1_24h} did not differ between the two groups of patients while AR_{2_24h} was greater in stroke patients than in controls and positively correlated with NIHSS total scores (r: 0.714, p < 0.001 for NIHSS, IC95%: 0.42–0.90) and with the sub-score relative to the paretic upper limb (r: 0.812, p < 0.001, IC95%: 0.62–0.96).

Conclusions

Our data show that actigraphic monitoring of upper limbs can detect the laterality of the motor deficit and measure the clinical severity. These findings suggest that the above described actigraphic system could implement the existing multiparametric monitoring in stroke units.

Whole Brain White Matter Microstructure and Upper Limb Function: Longitudinal Changes in Fractional Anisotropy and Axial Diffusivity in Post-Stroke Patients

Background

Diffusion tensor imaging (DTI) magnetic resonance imaging (MRI) measuring fractional anisotropy (FA) and axial diffusivity (AD) may be a useful biomarker for monitoring changes in white matter after stroke, but its associations with upper-limb motor recovery have not been well studied. We aim to describe changes in the whole-brain FA and AD in five post-stroke patients in relation to kinematic measures of elbow flexion to better understand the relationship between FA and AD changes and clinico-kinematic measures of upper limb motor recovery.

Methods

We performed DTI MRI at two timepoints during the acute phase of stroke, measuring FA and AD across 48 different white matter tract regions in the brains of five hemiparetic patients with infarcts in the cortex, pons, basal ganglia, thalamus, and corona radiata. We tracked the progress of these patients using clinical Fugl-Meyer Assessments and kinematic measures of elbow flexion at the acute phase within 14 (mean: 9.4 ± 2.49) days of stroke symptom onset and at a follow-up appointment 2 weeks later (mean: 16 ± 1.54) days.

Results

Changes in FA and AD in 48 brain regions occurring during stroke rehabilitation are described in relation to motor recovery. In this case series, one patient with a hemipontine infarct showed an increase in FA of the ipsilateral and contralateral corticospinal tract, whereas other patients with lesions involving the corona radiata and middle cerebral artery showed widespread decreases in perilesional FA. On the whole, FA and AD seemed to behave inversely to each other.

Conclusions

This case series describes longitudinal changes in perilesional and remote FA and AD in relation to kinematic parameters of elbow flexion at the subacute post-stroke period. Although studies with larger sample sizes are needed, our findings indicate that longitudinally measured changes in DTI-based measurements of white matter microstructural integrity may aid in the prognostication of patients affected by motor stroke.

Relative Efficacy of Sensor Modalities for Estimating Post-Stroke Motor Impairment

Wearable inertial sensing has been beneficial in the development of measures of motor impairment after stroke. While most early work focused on the use of accelerometry, recent work has increasingly shown that rate gyroscopes may provide complementary information. Differences in performance of accelerometers and gyroscopes in activity recognition may be due to the nature of the impairment. The current approach seeks to investigate the relative sensitivity of these sensor modalities to impairment by evaluating their classification accuracy for tasks adapted from the Fugl-Meyer Assessment. Our findings indicated that, for upper-extremity motion, classifiers trained using a combination of accelerometer and rate gyroscope data performed the best (accuracy of 73.1%). Classifiers trained using accelerometer data alone and rate gyroscope data alone performed slightly worse than the combined data classifier (70.2% and 65.7%, respectively).

Effect of Mirror Therapy on Recovery of Stroke Survivors: A Systematic Review and Network Meta-analysis

Mirror therapy (MT) as a relatively new rehabilitation technique has been widely applied in stroke patients. A number of randomized controlled trials (RCTs) have investigated the effects of MT for stroke survivors. The main purpose of this network meta-analysis was to investigate the effects of MT on motor function, activities of daily living (ADL), and pain perception in stroke survivors. Several databases were searched to identify RCTs evaluating the effects of MT in stroke patients to perform this network meta-analysis. Thirty-seven RCTs (42 analyses, 1685 subjects) were eligible for inclusion in the meta-analysis. Standard meta-analysis showed that MT significantly improved of motor function according to the increased Fugl-Meyer Assessment (FMA), Functional Independence Measure (FIM), and decreased Modified Ashworth Scale (MAS) score. In addition, ADL was promoted by MT as the elevated Modified Barthel Index (MBI) and Motor Activity Log (MAL) score. Moreover, MT effectively relieved the pain of stroke patients as the Visual Analog Scale (VAS) score was reduced. Subgroup analyses and meta-regressions identified that the sources of heterogeneity might be different intervention arms and duration of interventions. Network meta-analysis showed that MT combined with electrical stimulation (ES) for less than 4 weeks along with conventional rehabilitation therapy (CT), and MT accompanied with CT for less than 4 weeks might be the most suitable interventions for improvement of motor function and ADL, respectively. Overall, MT could effectively improve motor function and ADL, as well as relieve pain for stroke survivors. The study was registered at PROSPERO (CRD42017081742).

Hemispheric asymmetry in myelin after stroke is related to motor impairment and function

The relationships between impairment, function, arm use and underlying brain structure following stroke remain unclear. Although diffusion weighted imaging is useful in broadly assessing white matter structure, it has limited utility in identifying specific underlying neurobiological components, such as myelin. The purpose of the present study was to explore relationships between myelination and impairment, function and activity in individuals with chronic stroke. Assessments of paretic upper-extremity impairment and function were administered, and 72-hour accelerometer based activity monitoring was conducted on 19 individuals with chronic stroke. Participants completed a magnetic resonance imaging protocol that included a high resolution T₁ anatomical scan and a multi-component T₂ relaxation imaging scan to quantify myelin water fraction (MWF). MWF was automatically parcellated from pre- and post-central subcortical regions of interest and quantified as an asymmetry ratio (contralesional/ipsilesional). Cluster analysis was used to group more and less impaired individuals based on Fugl-Meyer upper extremity scores. A significantly higher precentral MWF asymmetry ratio was found in the more impaired group compared to the less impaired group (p < 0.001). There were no relationships between MWF asymmetry ratio and upper-limb use. Stepwise multiple linear regression identified precentral MWF asymmetry as the only variable to significantly predict impairment and motor function in the upper extremity (UE). These results suggest that asymmetric myelination in a motor specific brain area is a significant predictor of upper-extremity impairment and function in individuals with chronic stroke. As such, myelination may be utilized as a more specific marker of the neurobiological changes that predict long term impairment and recovery from stroke.

Systematic review of mirror therapy compared with conventional rehabilitation in upper extremity function in stroke survivors

BACKGROUND/AIM

Stroke is a leading cause of disability in developed countries. One of the most widespread techniques in clinical practice is mirror therapy (MT). To determine the effectiveness of MT over other methods of intervention in the recovery of upper limb function in people who have had a stroke.

METHODS

A systematic review was conducted. The search string was established based on the last systematic review about MT that dated from 2009: "upper extremity" OR "upper limb "AND "mirror therapy" AND stroke. For this search Pubmed, Scopus and SciELO databases were used.

RESULTS

Fifteen studies were included in the systematic review. Recovery of the upper limb, upper limb function and gross manual dexterity were frequently measured in these studies.

CONCLUSIONS

In the primary variables in promoting recovery, MT alone showed better results in acute and chronic stroke patients in upper limb functioning than either conventional rehabilitation (CR) or CR plus MT. PROSPERO registration number: CRD42015026869.

Music Upper Limb Therapy-Integrated: An Enriched Collaborative Approach for Stroke Rehabilitation

Stroke is a leading cause of disability worldwide. It leads to a sudden and overwhelming disruption in one’s physical body, and alters the stroke survivors’ sense of self. Long-term recovery requires that bodily perception, social participation and sense of self are restored; this is challenging to achieve, particularly with a single intervention. However, rhythmic synchronization of movement to external stimuli facilitates sensorimotor coupling for movement recovery, enhances emotional engagement and has positive effects on interpersonal relationships. In this proof-of-concept study, we designed a group music-making intervention, Music Upper Limb Therapy-Integrated (MULT-I), to address the physical, psychological and social domains of rehabilitation simultaneously, and investigated its effects on long-term post-stroke upper limb recovery. The study used a mixed-method pre-post design with 1-year follow up. Thirteen subjects completed the 45-min intervention twice a week for 6 weeks. The primary outcome was reduced upper limb motor impairment on the Fugl-Meyer Scale (FMS). Secondary outcomes included sensory impairment (two-point discrimination test), activity limitation (Modified Rankin Scale, MRS), well-being (WHO well-being index), and participation (Stroke Impact Scale, SIS). Repeated measures analysis of variance (ANOVA) was used to test for differences between pre- and post-intervention, and 1-year follow up scores. Significant improvement was found in upper limb motor impairment, sensory impairment, activity limitation and well-being immediately post-intervention that persisted at 1 year. Activities of daily living and social participation improved only from post-intervention to 1-year follow up. The improvement in upper limb motor impairment was more pronounced in a subset of lower functioning individuals as determined by their pre-intervention wrist range of motion. Qualitatively, subjects reported new feelings of ownership of their impaired limb, more spontaneous movement, and enhanced emotional engagement. The results suggest that the MULT-I intervention may help stroke survivors re-create their sense of self by integrating sensorimotor, emotional and interoceptive information and facilitate long-term recovery across multiple domains of disability, even in the chronic stage post-stroke. Randomized controlled trials are warranted to confirm the efficacy of this approach. Clinical Trial Registration: National Institutes of Health, clinicaltrials.gov, NCT01586221.

Combining levodopa and virtual reality-based therapy for rehabilitation of the upper limb after acute stroke: pilot study Part II

INTRODUCTION

This study aimed to evaluate the safety and efficacy of a combination of levodopa and virtual reality (VR)-based therapy for the enhancement of upper limb recovery following acute stroke.

METHODS

This was a pilot single-blinded case series of acute stroke patients with upper extremity hemiparesis. Patients were randomised to standard care with concomitant administration of either levodopa alone (control group) or combination therapy consisting of VR-based motivational visuomotor feedback training with levodopa neuromodulation (VR group). Main clinical outcome measures were the Fugl-Meyer Upper Extremity (FM-UE) assessment and Action Research Arm Test (ARAT). Kinematic measurements of affected upper limb movement were evaluated as a secondary measure of improvement.

RESULTS

Of 42 patients screened, four patients were enrolled in each of the two groups. Two patients dropped out from the control group during the trial. Patients receiving combination therapy had clinically significant improvements in FM-UE assessment scores of 16.5 points compared to a 3.0-point improvement among control patients. Similarly, ARAT scores of VR group patients improved by 15.3 points compared to a 10.0-point improvement in the control group. Corresponding improvements were noted in kinematic measures, including hand-path ratio, demonstrating that the quality of upper limb movement improved in the VR group.

CONCLUSION

Our results suggest that VR-based therapy and pharmacotherapy may be combined for acute stroke rehabilitation. Bedside acquisition of kinematic measurements allows accurate assessment of the quality of limb movement, offering a sensitive clinical tool for quantifying motor recovery during the rehabilitation process after acute stroke.

Remote Physical Activity Monitoring in Neurological Disease: A Systematic Review

OBJECTIVE

To perform a systematic review of studies using remote physical activity monitoring in neurological diseases, highlighting advances and determining gaps.

METHODS

Studies were systematically identified in PubMed/MEDLINE, CINAHL and SCOPUS from January 2004 to December 2014 that monitored physical activity for ≥24 hours in adults with neurological diseases. Studies that measured only involuntary motor activity (tremor, seizures), energy expenditure or sleep were excluded. Feasibility, findings, and protocols were examined.

RESULTS

137 studies met inclusion criteria in multiple sclerosis (MS) (61 studies); stroke (41); Parkinson's Disease (PD) (20); dementia (11); traumatic brain injury (2) and ataxia (1). Physical activity levels measured by remote monitoring are consistently low in people with MS, stroke and dementia, and patterns of physical activity are altered in PD. In MS, decreased ambulatory activity assessed via remote monitoring is associated with greater disability and lower quality of life. In stroke, remote measures of upper limb function and ambulation are associated with functional recovery following rehabilitation and goal-directed interventions. In PD, remote monitoring may help to predict falls. In dementia, remote physical activity measures correlate with disease severity and can detect wandering.

CONCLUSIONS

These studies show that remote physical activity monitoring is feasible in neurological diseases, including in people with moderate to severe neurological disability. Remote monitoring can be a psychometrically sound and responsive way to assess physical activity in neurological disease. Further research is needed to ensure these tools provide meaningful information in the context of specific neurological disorders and patterns of neurological disability.

Effects of adjustment of transcranial direct current stimulation on motor function of the upper extremity in stroke patients

[Purpose] The purpose of this study was to examine the effects of transcranial direct current stimulation (tDCS) applied to the cerebral cortex motor area on the upper extremity functions of hemiplegic patients. [Subjects and Methods] Twenty four Patients with hemiplegia resulting from a stroke were divided into two groups: a tDCS group that received tDCS and physical therapy and a control group that received only physical therapy. A functional evaluation of the two groups was performed, and an electrophysiological evaluation was conducted before and after the experiment. Statistical analyses were performed to verify differences before and after the experiment. All statistical significance levels were set at 0.05. [Results] The results showed that functional evaluation scores for the elbow joint and hand increased after the treatment in both the experimental group and the control group, and the increases were statistically significantly different. [Conclusion] tDCS was effective in improving the upper extremity motor function of stroke patients. Additional research is warranted on the usefulness of tDCS in the rehabilitation of stroke patients in the clinical field.

Exploring the Role of Accelerometers in the Measurement of Real World Upper-Limb Use After Stroke

The ultimate goal of upper-limb rehabilitation after stroke is to promote real-world use, that is, use of the paretic upper-limb in everyday activities outside the clinic or laboratory. Although real-world use can be collected through self-report questionnaires, an objective indicator is preferred. Accelerometers are a promising tool. The current paper aims to explore the feasibility of accelerometers to measure upper-limb use after stroke and discuss the translation of this measurement tool into clinical practice. Accelerometers are non-invasive, wearable sensors that measure movement in arbitrary units called activity counts. Research to date indicates that activity counts are a reliable and valid index of upper-limb use. While most accelerometers are unable to distinguish between the type and quality of movements performed, recent advancements have used accelerometry data to produce clinically meaningful information for clinicians, patients, family and care givers. Despite this, widespread uptake in research and clinical environments remains limited. If uptake was enhanced, we could build a deeper understanding of how people with stroke use their arm in real-world environments. In order to facilitate greater uptake, however, there is a need for greater consistency in protocol development, accelerometer application and data interpretation.

Predicting daily use of the affected upper extremity 1 year after stroke

BACKGROUND

The ultimate goal of upper extremity (UE) stroke rehabilitation is for the individual with stroke to return using their arms and hands during daily activities in their own environment. No studies have monitored arm use as individuals with stroke transition from rehabilitation to the home setting. This longitudinal study compared the functional ability and daily use of the affected UE of individuals with stroke between discharge to home and 12 months after stroke and predicted the UE daily use 12 months after stroke.

METHODS

Participants were assessed on discharge to home from rehabilitation and at 12 months after stroke. UE daily use was measured by wrist accelerometers and self-report by the Motor Activity Log (MAL). Multivariate logistic regression models were used to predict UE daily use 12 months after stroke.

RESULTS

The UE functional ability improved significantly from discharge to 12 months after stroke. The amount of self-report UE daily use significantly improved (z = -2.9, P = .004), but accelerometer activity counts did not (z = -0.15, P = .88), and the daily use of the nonaffected UE was 3 times more than the affected UE. After controlling for age and accelerometer daily use on discharge, UE variables of movement, function, dexterity, and strength accounted for an additional 10.9%-13.6% of the variance for accelerometer readings. After controlling for gender and MAL daily use on discharge, UE variables accounted for an additional 7%-12% of the variance for the MAL.

CONCLUSIONS

UE daily use 12 months after stroke is very limited despite the motor and functional improvement. Enhanced motor and functional ability at discharge predicts more UE daily use at 12 months after stroke. Interventions that monitor and encourage these individuals to use their UE are required to ensure that functional gains translate to daily use.