Been there, done that, so what’s next for arm and hand rehabilitation in stroke?

Winstein, Carolee J.; Varghese, Rini

doi:10.3233/nre-172412

Cited by 46 publications

(40 citation statements)

References 104 publications

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“…Numerous studies have shown that rehabilitation interventions are beneficial across a number of neurological conditions as they result in a decrease in the severity of disability 8 , 9 . However, choosing the most effective intervention among the myriad of available rehabilitation approaches is challenging 10 – 12 . High variability in response to interventions aimed to restore upper-limb function is observed across patients 13 – 15 , hence pointing to the need for designing “precision rehabilitation” interventions that account for the unique characteristics of each individual.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…To support their use, researchers and developers often emphasize the potential advantages of VR/AVG systems over conventional interventions, including that these technologies may enhance a patient’s affective experience in therapy for the purpose of facilitating recovery [8–11]. Examining the role of affective factors for motor learning is an emerging area of emphasis in rehabilitation [2, 12–15].…”

Section: Introductionmentioning

confidence: 99%

What is the impact of user affect on motor learning in virtual environments after stroke? A scoping review

Rohrbach

Chicklis

Levac

2019

J NeuroEngineering Rehabil

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Purpose The purported affective impact of virtual reality (VR) and active video gaming (AVG) systems is a key marketing strategy underlying their use in stroke rehabilitation, yet little is known as to how affective constructs are measured or linked to intervention outcomes. The purpose of this scoping review is to 1) explore how motivation, enjoyment, engagement, immersion and presence are measured or described in VR/AVG interventions for patients with stroke; 2) identify directional relationships between these constructs; and 3) evaluate their impact on motor learning outcomes. Methods A literature search was undertaken of VR/AVG interventional studies for adults post-stroke published in Medline, PEDro and CINAHL databases between 2007 and 2017. Following screening, reviewers used an iterative charting framework to extract data about construct measurement and description. A numerical and thematic analytical approach adhered to established scoping review guidelines. Results One hundred fifty-five studies were included in the review. Although the majority (89%; N = 138) of studies described at least one of the five constructs within their text, construct measurement took place in only 32% ( N = 50) of studies. The most frequently described construct was motivation (79%, N = 123) while the most frequently measured construct was enjoyment (27%, N = 42). A summative content analysis of the 50 studies in which a construct was measured revealed that constructs were described either as a rationale for the use of VR/AVGs in rehabilitation (76%, N = 38) or as an explanation for intervention results (56%, N = 29). 38 (76%) of the studies proposed relational links between two or more constructs and/or between any construct and motor learning. No study used statistical analyses to examine these links. Conclusions Results indicate a clear discrepancy between the theoretical importance of affective constructs within VR/AVG interventions and actual construct measurement. Standardized terminology and outcome measures are required to better understand how enjoyment, engagement, motivation, immersion and presence contribute individually or in interaction to VR/AVG intervention effectiveness. Electronic supplementary material The online version of this article (10.1186/s12984-019-0546-4) contains supplementary material, which is available to authorized users.

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“…That a measure of attention/arousal predicted non-use behavior above and beyond sensorymotor functioning highlights the importance of motivated engagement to drive use of the paretic limb. As such, it is increasingly clear that rehabilitation efforts are more likely to be successful when they engage the participant by providing meaningful task practice and motivational enhancements (see 55 for discussion). Larger-scale studies of the nonuse phenomenon in individuals ranging in severity, and with incorporation of additional measures (e.g., mental health, lesion volume and white matter connectivity, pain, traitand state-level motivation) will be required to extend these findings.…”

Section: Resultsmentioning

confidence: 99%

Predictors of arm non-use in chronic stroke: a preliminary investigation

Buxbaum

Varghese

Stoll

et al. 2019

Preprint

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Background:The phenomenon of non-use after stroke is characterized by failure to use the contralesional arm despite adequate capacity. It has been suggested that nonuse is a consequence of the greater effort and/or attention required to use the affected limb, but such accounts have not been tested, and we have poor understanding of the characteristics of individuals who exhibit non-use.Objective: We aimed to provide preliminary evidence regarding the demographic, neuropsychological, and psychological factors that may influence non-use in chronic stroke.Methods: Twenty chronic stroke survivors (10 left and 10 right hemisphere stroke) with mild-to-moderate sensory-motor impairment on the upper extremity Fugl-Meyer (UEFM)were assessed with the Actual Amount of Use Test (AAUT), which measures the disparity between amount of use and quality of movement in "spontaneous" versus "forced" conditions. Participants were als assessed with measures of limb apraxia, spatial neglect, attention/arousal, and self-efficacy (confidence in arm movement).Using stepwise multiple regression, we determined whether demographic information and/or performance on these measures predicted AAUT non-use scores.Results: Scores on the UEFM as well as attention/arousal and self-efficacy predicted the degree of non-use. Attention/arousal predicted non-use above and beyond UEFM. Conclusions:Given the complexity of the non-use phenomenon, it follows that a combination of impairment, attention/arousal, and perceived confidence predicted nonuse behavior. That a measure of attention/arousal predicted non-use behavior above and beyond sensory-motor functioning highlights the importance of motivated Predictors of non-use in stroke 3 engagement to drive use of the paretic limb. Larger-scale studies incorporating additional measures (e.g., mental health, lesion volume and white matter connectivity, pain, motivation) will be important for future investigations.

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Been there, done that, so what’s next for arm and hand rehabilitation in stroke?

Abstract: The good news is that there is evidence that we learned from the past and have invented a future that appears to be much more exciting and promising than the past.

Cited by 46 publications

References 104 publications

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

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

What is the impact of user affect on motor learning in virtual environments after stroke? A scoping review

Predictors of arm non-use in chronic stroke: a preliminary investigation

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