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

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.

A Sensor-Based Feedback Device Stimulating Daily Life Upper Extremity Activity in Stroke Patients: A Feasibility Study

This study aims to evaluate the feasibility and explore the efficacy of the Arm Activity Tracker (AAT). The AAT is a device based on wrist-worn accelerometers that provides visual and tactile feedback to stimulate daily life upper extremity (UE) activity in stroke patients.

METHODS

A randomised, crossover within-subject study was conducted in sub-acute stroke patients admitted to a rehabilitation centre. Feasibility encompassed (1) adherence: the dropout rate and the number of participants with insufficient AAT data collection; (2) acceptance: the technology acceptance model (range: 7-112) and (3) usability: the system usability scale (range: 0-100). A two-way ANOVA was used to estimate the difference between the baseline, intervention and control conditions for (1) paretic UE activity and (2) UE activity ratio.

RESULTS

Seventeen stroke patients were included. A 29% dropout rate was observed, and two participants had insufficient data collection. Participants who adhered to the study reported good acceptance (median (IQR): 94 (77-111)) and usability (median (IQR): 77.5 (75-78.5)-). We found small to medium effect sizes favouring the intervention condition for paretic UE activity (η²G = 0.07, p = 0.04) and ratio (η²G = 0.11, p = 0.22).

CONCLUSION

Participants who adhered to the study showed good acceptance and usability of the AAT and increased paretic UE activity. Dropouts should be further evaluated, and a sufficiently powered trial should be performed to analyse efficacy.

Quantitative Assessment of Upper Limb Movement in Post-Stroke Adults for Identification of Sensitive Measures in Reaching and Lifting Activities

BACKGROUND

The assumption of this work is the achievement of objective results of the movement structure, which forms the basis for in-depth analysis and, consequently, for determining the upper limb movements that are most affected by stroke compared to healthy people.

METHODS

An analysis of relevant and systematically identified features of upper limb movement in post-stroke adults is presented based on scalable hypothesis tests. The basic features were calculated using movements defined by the x, y, and z coordinates (i.e., 3D trajectory time series) and compared to the results of post-stroke patients with healthy controls of similar age.

RESULTS

After automatic feature selection, out of the 1004 common features of upper limb movement, the most differentiated were the upper arm movements in reaching kinematics. In terms of movement type, movements in the frontal plane (shoulder abduction and adduction) were the most sensitive to changes. The largest number of discriminating features was determined on the basis of acceleration time series.

CONCLUSIONS

In the 3D assessment of functional activities of the upper limb, the upper arm turned out to be the most differentiated body segment, especially during abduction and adduction movements. The results indicate a special need to pay attention to abduction and adduction movements to improve the activities of daily living of the upper limbs after a stroke.

Responsiveness and trajectory of changes in the rating of everyday arm-use in the community and home (REACH) scale over the first-year post-stroke

OBJECTIVE

To examine the trajectory of the Rating of Everyday Arm-use in the Community and Home (REACH) scores over the first-year post-stroke, determine if REACH scores are modified by baseline impairment level and explore the responsiveness of the REACH scale through hypothesis testing.

DESIGN

Consecutive sample longitudinal study.

SETTING

Participants were recruited from an acute stroke unit and followed up at three, six, and 12 months post-stroke.

PARTICIPANTS

Seventy-three participants with upper limb weakness (Shoulder Abduction and Finger Extension score ≤ 8).

MAIN MEASURES

The REACH scale is a six-level self-report classification scale that captures how the affected upper limb is being used in one's own environment. The Fugl-Meyer Upper Limb Assessment (FMA-UL), Stroke Upper Limb Capacity Scale (SULCS), accelerometer-based activity count ratio and Global Rating of Change Scale (GRCS) were used to capture upper limb impairment, capacity, and use.

RESULTS

The following proportions of participants improved at least one REACH level: 64% from baseline to three months, 37% from three to six months and 13% from six to 12 months post-stroke. The trajectory of REACH scores over time was associated with baseline impairment. Change in REACH had a moderate correlation to change in SULCS and the GRCS but not FMA-UL or the activity count ratio.

CONCLUSIONS

Results of hypothesis testing provide preliminary evidence of the responsiveness of the REACH scale. On average, individuals with severe impairment continued to show improvement in use over the first year, while those with mild/moderate impairment plateaued and a small proportion decreased in the early chronic phase.

Effects of fine-scale habitat quality on activity, dormancy, habitat use, and survival after reproduction in Rana dybowskii (Chordata, Amphibia)

Amphibians are facing population declines and extinctions, and protecting and supplementing refuges can help species survive. However, the microhabitat requirements of most species are unknown, and artificial shelters or burrows have not been well tested for amphibians. Some amphibians exhibit complex behaviour during the transition from post-reproductive dormancy to activity. However, little is known about the ecology, post-reproductive dormancy, and terrestrial activity of amphibians. Here, habitat site selection in experimental enclosures and the effects of shelters (stones, soil) and shade (with and without shade netting) on the activity, exposed body percentage, burrow depth, body-soil contact percentage, and survival of Rana dybowskii were investigated during post-reproductive dormancy and post-dormant activity. The results showed that R. dybowskii live individually under leaves, soil, stones or tree roots. Furthermore, although the dormant sites of frogs are significantly different, the distribution of male and female frogs in these sites is similar. Shading and shelter significantly affected the exposed body percentage, burrow depth and body-soil contact percentage of frogs compared with soil. In the stone group, soil and stone form the frog's refuge/burrow, whereas in the soil group, the refuge/burrow is composed entirely of soil. Even though the soil group has a deeper burrow and a larger area of soil contact with the body, it still has a higher exposure rate than the stone group. Frog activity frequency was affected by shelter and shade; the interaction of shelter and time and the interaction of shading and time were significant. The soil group had a higher activity frequency than the stone group, and the no-shade group had a higher activity frequency than the shade group. Shelter and shading differences do not significantly affect frog survival; however, the death rate during post-reproductive dormancy is lower than that during the active period.

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

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

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

Background: Stroke leads to motor impairment which reduces physical activity, negatively affects social participation, and increases the risk of secondary cardiovascular events. Continuous monitoring of physical activity with motion sensors is promising to allow the prescription of tailored treatments in a timely manner. Accurate classification of gait activities and body posture is necessary to extract actionable information for outcome measures from unstructured motion data. We here develop and validate a solution for various sensor configurations specifically for a stroke population.

Methods: Video and movement sensor data (locations: wrists, ankles, and chest) were collected from fourteen stroke survivors with motor impairment who performed real-life activities in their home environment. Video data were labeled for five classes of gait and body postures and three classes of transitions that served as ground truth. We trained support vector machine (SVM), logistic regression (LR), and k-nearest neighbor (kNN) models to identify gait bouts only or gait and posture. Model performance was assessed by the nested leave-one-subject-out protocol and compared across five different sensor placement configurations.

Results: Our method achieved very good performance when predicting real-life gait versus non-gait (Gait classification) with an accuracy between 85% and 93% across sensor configurations, using SVM and LR modeling. On the much more challenging task of discriminating between the body postures lying, sitting, and standing as well as walking, and stair ascent/descent (Gait and postures classification), our method achieves accuracies between 80% and 86% with at least one ankle and wrist sensor attached unilaterally. The Gait and postures classification performance between SVM and LR was equivalent but superior to kNN.

Conclusion: This work presents a comparison of performance when classifying Gait and body postures in post-stroke individuals with different sensor configurations, which provide options for subsequent outcome evaluation. We achieved accurate classification of gait and postures performed in a real-life setting by individuals with a wide range of motor impairments due to stroke. This validated classifier will hopefully prove a useful resource to researchers and clinicians in the increasingly important field of digital health in the form of remote movement monitoring using motion sensors.

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.

Evolution and prediction of mismatch between observed and perceived upper limb function after stroke: a prospective, longitudinal, observational cohort study

BACKGROUND

A previously shown 'mismatch' group of patients with good observed upper limb (UL) motor function but low perceived UL activity at six months post stroke tends to use the affected UL less in daily life than would be expected based on clinical tests, and this mismatch may also be present at 12 months. We aimed to confirm this group in another cohort, to investigate the evolution of this group from six to 12 months, and to determine factors on admission to inpatient rehabilitation and at 6 months that can discriminate between mismatch and good match groups at 12 months.

METHODS

Persons after stroke were recruited on rehabilitation admission and re-assessed at six and 12 months. Observed UL function was measured with the upper extremity subscale of the Fugl-Meyer Assessment (FMA-UE) and perceived UL activity by the hand subscale of the Stroke Impact Scale 3.0 (SIS-Hand). We defined mismatch as good observed UL function (FMA-UE > 50/66) but low perceived activity (SIS-Hand≤75/100). Potential discriminators at admission and 6 months (demographic characteristics, stroke characteristics, UL somatosensory function, cognitive deficits, mental function and activity) were statistically compared for match and mismatch groups at 12 months.

RESULTS

We included 60 participants (female: 42%) with mean (SD) age of 65 (12) years. We confirmed a mismatch group of 11 (18%) patients at 6 months, which increased to 14 (23%) patients at 12 months. In the mismatch group compared to the good match group at 12 months, patients had a higher stroke severity and more somatosensory impairments on admission and at 6 months.

CONCLUSIONS

We confirmed a group of patients with good observed UL function but low perceived activity both at six and at 12 months post stroke. Assessment of stroke severity and somatosensory impairments on admission into rehabilitation could determine mismatch at 12 months and might warrant intervention. However, large differences in clinical outcomes between patients in the mismatch group indicate the importance of tailoring training to the individual needs.

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.

Daily Life Upper Limb Activity for Patients with Match and Mismatch between Observed Function and Perceived Activity in the Chronic Phase Post Stroke

We investigated actual daily life upper limb (UL) activity in relation to observed UL motor function and perceived UL activity in chronic stroke in order to better understand and improve UL activity in daily life. In 60 patients, we collected (1) observed UL motor function (Fugl-Meyer Assessment (FMA-UE)), (2) perceived UL activity (hand subscale of the Stroke Impact Scale (SIS-Hand)), and (3) daily life UL activity (bilateral wrist-worn accelerometers for 72 h) data. Data were compared between two groups of interest, namely (1) good observed (FMA-UE >50) function and good perceived (SIS-Hand >75) activity (good match, n = 16) and (2) good observed function but low perceived (SIS-Hand ≤75) activity (mismatch, n = 15) with Mann-Whitney U analysis. The mismatch group only differed from the good match group in perceived UL activity (median (Q1-Q3) = 50 (30-70) versus 93 (85-100); p < 0.001). Despite similar observed UL motor function and other clinical characteristics, the affected UL in the mismatch group was less active in daily life compared to the good match group (p = 0.013), and the contribution of the affected UL compared to the unaffected UL for each second of activity (magnitude ratio) was lower (p = 0.022). We conclude that people with chronic stroke with low perceived UL activity indeed tend to use their affected UL less in daily life despite good observed UL motor function.

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.