Upper Limb Performance in Daily Life Improves Over the First 12 Weeks Poststroke

Factors Influencing Real-World Use of the More-Affected Upper Limb After Stroke: A Scoping Review

IMPORTANCE

Current interventions are limited in improving use of the more-affected upper limb in real-world daily occupations and functional independence poststroke. A comprehensive understanding of the factors influencing real-world upper limb use is required to develop interventions to improve functional independence poststroke.

OBJECTIVE

To systematically review the factors that influence real-world use of the more-affected upper limb poststroke.

DATA SOURCES

We searched MEDLINE, Embase, PsycINFO, and the Physiotherapy Evidence Database for English-language articles from 2012 to 2023.

STUDY SELECTION AND DATA COLLECTION

Of 774 studies, we included 33 studies that had participants at least age 18 yr who exhibited upper limb impairments poststroke, objectively measured real-world upper limb use using a movement sensor, and measured factors affecting upper limb use. Two reviewers independently screened the abstracts.

FINDINGS

The results were categorized by International Classification of Functioning, Disability and Health domains. Prominent factors were upper limb impairment; motor ability; functional independence; task type; hand dominance; stroke-related factors, including time since stroke; and perception of use of the more-affected upper limb.

CONCLUSIONS AND RELEVANCE

Existing interventions primarily focus on upper limb impairments and motor ability. Our findings suggest that interventions should also incorporate other factors: task type (unilateral vs. bilateral), hand dominance, self-efficacy, and perception of more-affected limb use as active ingredients in improving real-world use of the more-affected upper limb poststroke. We also provide recommendations to use behavioral activation theory in designing an occupation-focused intervention to augment self-efficacy and confidence in use of the more-affected upper limb in daily occupations. Plain-Language Summary: In order to develop interventions to improve functional independence poststroke, occupational therapy practitioners must have a comprehensive understanding of the factors that influence real-world more-affected upper limb use. The study findings provide a set of distinct factors that practitioners can target separately or in combination to improve real-world use of the more-affected upper limb poststroke.

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.

[Modern view on upper limb physical rehabilitation after stroke. Literature review]

Stroke is the world's second leading cause of death and the first cause of disability among all diseases. The most common complication of a stroke is a violation of the motor function of the limbs, which significantly worsens the quality of life and the level of self-care and independence of patients. Restoring the function of the upper limb is one of the priority tasks of rehabilitation after a stroke. A large number of factors, such as the location and size of the primary brain lesion, the presence of complications in the form of spasticity, impaired skin and proprioceptive sensitivity, and comorbidities, determine the patient's rehabilitation potential and the prognosis of ongoing rehabilitation measures. Of particular note are the timing of the start of rehabilitation measures, the duration and regularity of the treatment methods. A number of authors propose scales for assessing the rehabilitation prognosis, as well as algorithms for compiling rehabilitation programs for restoring the function of the upper limb. A fairly large number of rehabilitation methods and their combinations have been proposed, including special methods of kinesitherapy, robotic mechanotherapy with biofeedback, the use of physiotherapeutic factors, manual and reflex effects, as well as ready-made programs that include sequential and combined use of various methods. Dozens of studies have been devoted to comparative analysis and evaluation of the effectiveness of these methods. The purpose of this work is to review current research on a given topic and draw up our own conclusion on the appropriateness of using and combining these methods at various stages of rehabilitation in stroke patients.

Prognosis of Individual-Level Mobility and Daily Activities Recovery From Acute Care to Community, Part 2: A Proof-of-Concept Single Group Prospective Cohort Study

OBJECTIVE

To demonstrate a proof-of-concept for prognostic models of post-stroke recovery on activity level outcomes.

DESIGN

Longitudinal cohort with repeated measures from acute care, inpatient rehabilitation, and post-discharge follow-up to 6 months post-stroke.

SETTING

Enrollment from a single Midwest USA inpatient rehabilitation facility with community follow-up.

PARTICIPANTS

One-hundred fifteen persons recovering from stroke admitted to an acute rehabilitation facility (N=115).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE(S)

Activity Measure for Post-Acute Care Basic Mobility and Daily Activities domains administered as 6 Clicks and patient-reported short forms.

RESULTS

The final Basic Mobility model defined a group-averaged trajectory rising from a baseline (pseudo-intercept) T score of 35.5 (P<.001) to a plateau (asymptote) T score of 56.4 points (P<.001) at a negative exponential rate of -1.49 (P<.001). Individual baseline scores varied by age, acute care tissue plasminogen activator, and acute care length of stay. Individual plateau scores varied by walking speed, acute care tissue plasminogen activator, and lower extremity Motricity Index scores. The final Daily Activities model defined a group-averaged trajectory rising from a baseline T score of 24.5 (P<.001) to a plateau T score of 41.3 points (P<.001) at a negative exponential rate of -1.75 (P<.001). Individual baseline scores varied by acute care length of stay, and plateau scores varied by self-care, upper extremity Motricity Index, and Berg Balance Scale scores.

CONCLUSIONS

As a proof-of-concept, individual activity-level recovery can be predicted as patient-level trajectories generated from electronic medical record data, but models require attention to completeness and accuracy of data elements collected on a fully representative patient sample.

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.

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.

Upper Limb Performance in Daily Life Approaches Plateau Around Three to Six Weeks Post-stroke

Background. Wearable sensors allow for direct measurement of upper limb (UL) performance in daily life. Objective. To map the trajectory of UL performance and its relationships to other factors post-stroke. Methods. Participants (n = 67) with first stroke and UL paresis were assessed at 2, 4, 6, 8, 12, 16, 20, and 24 weeks after stroke. Assessments captured UL impairment (Fugl-Meyer), capacity for activity (Action Research Arm Test), and performance of activity in daily life (accelerometer variables of use ratio and hours of paretic limb activity), along with other potential modifying factors. We modeled individual trajectories of change for each measurement level and the moderating effects on UL performance trajectories. Results. Individual trajectories were best fit with a 3-parameter logistic model, capturing the rapid growth early after stroke within the longer data collection period. Plateaus (90% of asymptote) in impairment (bootstrap mean ± SE: 32 ± 4 days post-stroke) preceded those in capacity (41 ± 4 days). Plateau in performance, as measured by the use ratio (24 ± 5 days), tended to precede plateaus in impairment and capacity. Plateau in performance, as measured by hours of paretic activity (41 ± 6 days), occurred at a similar time to that of capacity and slightly lagged impairment. Modifiers of performance trajectories were capacity, concordance, UL rehabilitation, depressive symptomatology, and cognition. Conclusions. Upper limb performance in daily life approached plateau 3 to 6 weeks post-stroke. Individuals with stroke started to achieve a stable pattern of UL use in daily life early, often before neurological impairments and functional capacity started to stabilize.

Whole-Body Movements Increase Arm Use Outcomes of Wrist-Worn Accelerometers in Stroke Patients

Wrist-worn accelerometers are often applied to measure arm use after stroke. They measure arm movements during all activities, including whole-body movements, such as walking. Whole-body movements may influence clinimetric properties of arm use measurements—however, this has not yet been examined. This study investigates to what extent arm use measurements with wrist-worn accelerometers are affected by whole-body movements. Assuming that arm movements during whole-body movements are non-functional, we quantify the effect of whole-body movements by comparing two methods: Arm use measured with wrist-worn accelerometers during all whole-body postures and movements (P&M method), and during sitting/standing only (sit/stand method). We have performed a longitudinal observational cohort study with measurements in 33 stroke patients during weeks 3, 12, and 26 poststroke. The P&M method shows higher daily paretic arm use outcomes than the sit/stand method (p < 0.001), the mean difference increased from 31% at week three to 41% at week 26 (p < 0.001). Differences in daily paretic arm use between methods are strongly related to daily walking time (r = 0.83–0.92). Changes in the difference between methods are strongly related to changes in daily walking time (r = 0.89). We show that not correcting arm use measurements for whole-body movements substantially increases arm use outcomes, thereby threatening the validity of arm use outcomes and measured arm use changes.

Recovery and Prediction of Bimanual Hand Use After Stroke

OBJECTIVE

To determine similarities and differences in key predictors of recovery of bimanual hand use and unimanual motor impairment after stroke.

METHOD

In this prospective longitudinal study, 89 patients with first-ever stroke with arm paresis were assessed at 3 weeks and 3 and 6 months after stroke onset. Bimanual activity performance was assessed with the Adult Assisting Hand Assessment Stroke (Ad-AHA), and unimanual motor impairment was assessed with the Fugl-Meyer Assessment (FMA). Candidate predictors included shoulder abduction and finger extension measured by the corresponding FMA items (FMA-SAFE; range 0-4) and sensory and cognitive impairment. MRI was used to measure weighted corticospinal tract lesion load (wCST-LL) and resting-state interhemispheric functional connectivity (FC).

RESULTS

Initial Ad-AHA performance was poor but improved over time in all (mild-severe) impairment subgroups. Ad-AHA correlated with FMA at each time point (r > 0.88, p < 0.001), and recovery trajectories were similar. In patients with moderate to severe initial FMA, FMA-SAFE score was the strongest predictor of Ad-AHA outcome (R ² = 0.81) and degree of recovery (R ² = 0.64). Two-point discrimination explained additional variance in Ad-AHA outcome (R ² = 0.05). Repeated analyses without FMA-SAFE score identified wCST-LL and cognitive impairment as additional predictors. A wCST-LL >5.5 cm³ strongly predicted low to minimal FMA/Ad-AHA recovery (≤10 and 20 points respectively, specificity = 0.91). FC explained some additional variance to FMA-SAFE score only in unimanual recovery.

CONCLUSION

Although recovery of bimanual activity depends on the extent of corticospinal tract injury and initial sensory and cognitive impairments, FMA-SAFE score captures most of the variance explained by these mechanisms. FMA-SAFE score, a straightforward clinical measure, strongly predicts bimanual recovery.

CLINICALTRIALSGOV IDENTIFIER

NCT02878304.

CLASSIFICATION OF EVIDENCE

This study provides Class I evidence that the FMA-SAFE score predicts bimanual recovery after stroke.

Role of Self-efficacy in the Predictive Relationship of Motor Ability to Functional Performance After Task-Related Training in Stroke: A Secondary Analysis of Longitudinal Data

OBJECTIVE

To determine whether self-efficacy (SE) mediates or moderates the relationship between motor ability at pretest and functional use of the affected arm at posttest in task-related training for stroke.

DESIGN

Retrospective, observational cohort study.

SETTING

Outpatient rehabilitation settings.

PARTICIPANTS

Eighty patients with chronic stroke (N=80).

INTERVENTIONS

The training was delivered to the participants for 60-90 min/session, 3-5 sessions/wk for 4-6 weeks. The training involved specific robot-assisted, mirror, or combined therapy, followed by functional task practice for approximately 30 minutes in each session.

MAIN OUTCOME MEASURES

The outcome measure was the perceived amount of functional arm use and quality of movement evaluated by the Motor Activity Log (MAL) at posttest. The predictor was scores on the Fugl-Meyer Assessment (FMA)-Upper Extremity subscale at pretest. The tested mediator and moderator were scores on the Stroke Self-Efficacy Questionnaire (SSEQ) at pretest and posttest.

RESULTS

The SSEQ scores at pretest and posttest moderated the predictive relationship of pretest FMA to posttest MAL. The interaction between pretest FMA and SSEQ accounted for an additional 3.14%-5.37% of the variance in the posttest MAL. The predictive relationship between FMA and MAL was its greatest when the SSEQ was high, with a less amplified positive relationship at low levels of SSEQ.

CONCLUSIONS

The results suggest the evaluation of SE at pretest for a better prediction of an individual patient's functional arm use after an intervention and recommend aiming at SE during training to make the most of motor ability transferred to functional use. Future research may compare the effectiveness of task-related training with and without SE building to verify the findings of this study.

Objectively measured arm use in daily life improves during the first 6 months poststroke: a longitudinal observational cohort study

Background

It is unclear how arm use in daily life changes after stroke since studies investigating the change in arm use poststroke are scarce. The aim of this study was to investigate the change in arm use during the first six months poststroke. Secondary aim was to compare arm use changes between arm recovery clusters.

Methods

Arm use was measured during week 3, 12, and 26 poststroke with accelerometers on the wrists and the nonaffected leg. Outcomes were the amount of affected and nonaffected arm use during sitting and standing per day and per sit/stand hour, and the daily ratio between arms. Arm function was measured with the Fugl-Meyer Upper Extremity Scale to identify recovery clusters (poor/moderate/excellent). Generalized estimating equations compared arm use outcomes between time points and between recovery clusters.

Results

Thirty-three stroke patients participated. Affected arm use per day increased between week 3 and 12 (30 %; p = 0.04) and it increased per sit/stand hour between week 3–12 (31 %; p < 0.001) and between week 3 and 26 (48 %; p = 0.02). Nonaffected arm use per day decreased between week 3 and 12 (13 %; p < 0.001) and between week 3 and 26 (22 %; p < 0.001) and it decreased per sit/stand hour between week 3 and 26 (18 %; p = 0.003). The daily ratio increased between week 3 and 12 (43 %; p < 0.001) and between week 3 and 26 (95 %; p < 0.001). Changes in arm use did not differ significantly between recovery clusters (p = 0.11–0.62). Affected arm use was higher in the excellent recovery cluster (p < 0.001).

Conclusions

Affected arm use and the ratio between arms increase during the first 26 weeks poststroke especially in patients with excellent arm recovery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-021-00847-x.

Sensor-based categorization of upper limb performance in daily life of persons with and without neurological upper limb deficits

Background: The use of wearable sensor technology (e. g., accelerometers) for tracking human physical activity have allowed for measurement of actual activity performance of the upper limb (UL) in daily life. Data extracted from accelerometers can be used to quantify multiple variables measuring different aspects of UL performance in one or both limbs. A limitation is that several variables are needed to understand the complexity of UL performance in daily life.

Purpose: To identify categories of UL performance in daily life in adults with and without neurological UL deficits.

Methods: This study analyzed data extracted from bimanual, wrist-worn triaxial accelerometers from adults from three previous cohorts (N = 211), two samples of persons with stroke and one sample from neurologically intact adult controls. Data used in these analyses were UL performance variables calculated from accelerometer data, associated clinical measures, and participant characteristics. A total of twelve cluster solutions (3-, 4-, or 5-clusters based with 12, 9, 7, or 5 input variables) were calculated to systematically evaluate the most parsimonious solution. Quality metrics and principal component analysis of each solution were calculated to arrive at a locally-optimal solution with respect to number of input variables and number of clusters.

Results: Across different numbers of input variables, two principal components consistently explained the most variance. Across the models with differing numbers of UL input performance variables, a 5-cluster solution explained the most overall total variance (79%) and had the best model-fit.

Conclusion: The present study identified 5 categories of UL performance formed from 5 UL performance variables in cohorts with and without neurological UL deficits. Further validation of both the number of UL performance variables and categories will be required on a larger, more heterogeneous sample. Following validation, these categories may be used as outcomes in UL stroke research and implemented into rehabilitation clinical practice.

Relationships between accelerometry and general compensatory movements of the upper limb after stroke

Background

Standardized assessments are used in rehabilitation clinics after stroke to measure restoration versus compensatory movements of the upper limb. Accelerometry is an emerging tool that can bridge the gap between in- and out-of-clinic assessments of the upper limb, but is limited in that it currently does not capture the quality of a person’s movement, an important concept to assess compensation versus restoration. The purpose of this analysis was to characterize how accelerometer variables may reflect upper limb compensatory movement patterns after stroke.

Methods

This study was a secondary analysis of an existing data set from a Phase II, single-blind, randomized, parallel dose–response trial (NCT0114369). Sources of data utilized were: (1) a compensatory movement score derived from video analysis of the Action Research Arm Test (ARAT), and (2) calculated accelerometer variables quantifying time, magnitude and variability of upper limb movement from the same time point during study participation for both in-clinic and out-of-clinic recording periods.

Results

Participants had chronic upper limb paresis of mild to moderate severity. Compensatory movement scores varied across the sample, with a mean of 73.7 ± 33.6 and range from 11.5 to 188. Moderate correlations were observed between the compensatory movement score and each accelerometer variable. Accelerometer variables measured out-of-clinic had stronger relationships with compensatory movements, compared with accelerometer variables in-clinic. Variables quantifying time, magnitude, and variability of upper limb movement out-of-clinic had relationships to the compensatory movement score.

Conclusions

Accelerometry is a tool that, while measuring movement quantity, can also reflect the use of general compensatory movement patterns of the upper limb in persons with chronic stroke. Individuals who move their limbs more in daily life with respect to time and variability tend to move with less movement compensations and more typical movement patterns. Likewise, individuals who move their paretic limbs less and their non-paretic limb more in daily life tend to move with more movement compensations at all joints in the paretic limb and less typical movement patterns.

Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery

The need to develop patient-specific interventions is apparent when one considers that clinical studies often report satisfactory motor gains only in a portion of participants. This observation provides the foundation for "precision rehabilitation". Tracking and predicting outcomes defining the recovery trajectory is key in this context. Data collected using wearable sensors provide clinicians with the opportunity to do so with little burden on clinicians and patients. The approach proposed in this paper relies on machine learning-based algorithms to derive clinical score estimates from wearable sensor data collected during functional motor tasks. Sensor-based score estimates showed strong agreement with those generated by clinicians. Score estimates of upper-limb impairment severity and movement quality were marked by a coefficient of determination of 0.86 and 0.79, respectively. The application of the proposed approach to monitoring patients' responsiveness to rehabilitation is expected to contribute to the development of patient-specific interventions, aiming to maximize motor gains.