Assessments are absolutely fucking useless. Survivors want EXACT stroke protocols leading to 100% recovery. When the fuck will you do research that directly helps survivors? When you are the 1 in 4 per WHO that has a stroke?
Don't you think that might be a little late to solve stroke?
Technology-aided assessment of functionally relevant sensorimotor impairments in arm and hand of post-stroke individuals
Journal of NeuroEngineering and Rehabilitation volume 17, Article number: 128 (2020)
Abstract
Background
Assessing arm and hand sensorimotor impairments that are functionally relevant is essential to optimize the impact of neurorehabilitation interventions. Technology-aided assessments should provide a sensitive and objective characterization of upper limb impairments, but often provide arm weight support and neglect the importance of the hand, thereby questioning their functional relevance. The Virtual Peg Insertion Test (VPIT) addresses these limitations by quantifying arm and hand movements as well as grip forces during a goal-directed manipulation task requiring active lifting of the upper limb against gravity. The aim of this work was to evaluate the ability of the VPIT metrics to characterize arm and hand sensorimotor impairments that are relevant for performing functional tasks.
Methods
Arm and hand sensorimotor impairments were systematically characterized in 30 chronic stroke patients using conventional clinical scales and the VPIT. For the latter, ten previously established kinematic and kinetic core metrics were extracted. The validity and robustness of these metrics was investigated by analyzing their clinimetric properties (test-retest reliability, measurement error, learning effects, concurrent validity).
Results
Twenty-three of the participants, the ones with mild to moderate sensorimotor impairments and without strong cognitive deficits, were able to successfully complete the VPIT protocol (duration 16.6 min). The VPIT metrics detected impairments in arm and hand in 90.0% of the participants, and were sensitive to increased muscle tone and pathological joint coupling. Most importantly, significant moderate to high correlations between conventional scales of activity limitations and the VPIT metrics were found, thereby indicating their functional relevance when grasping and transporting objects, and when performing dexterous finger manipulations. Lastly, the robustness of three out of the ten VPIT core metrics in post-stroke individuals was confirmed.
Conclusions
This work provides evidence that technology-aided assessments requiring goal-directed manipulations without arm weight support can provide an objective, robust, and clinically feasible way to assess functionally relevant sensorimotor impairments in arm and hand in chronic post-stroke individuals with mild to moderate deficits. This allows for a better identification of impairments with high functional relevance and can contribute to optimizing the functional benefits of neurorehabilitation interventions.
Introduction
Stroke is a leading cause of acquired adult disability [1]. The incident commonly causes chronic sensorimotor deficits in arm and hand (impairments) [2, 3]. Impairments that are functionally relevant are especially critical for affected individuals, as these impairments reduce the spectrum of activities that an individual can perform (activity limitations) and determine the level of dependence on caregivers. Neurorehabilitation attempts to decrease the level of disability through inter-disciplinary interventions, including physical therapy [4, 5]. Achieving successful rehabilitation, with clear benefits for the independence of individuals typically requires the identification and therapy of functionally relevant impairments [6–8].
Conventional clinical scales are the current standard to evaluate upper limb sensorimotor impairments in research studies and the described impairments mostly show strong links to activity limitations (i.e., functional relevance) [9–13]. However, conventional assessments commonly rely on subjectively rated ordinal scales with ceiling effects that are not sensitive enough to detect fine changes in impairments and even introduce bias when attempting to model sensorimotor recovery [14–16]. Hence, providing a more objective assessment of functionally relevant sensorimotor impairments with sensitive scales should be of primary interest to neurorehabilitation researchers.
Digital health metrics extracted from technology-aided assessments can provide objective and traceable descriptions of upper limb behavior on sensitive, continuous scales without ceiling effects [17–19]. However, the majority of technology-aided assessments focus on characterizing impairments during planar arm movements while providing gravity support [20–23]. This neglects the importance of hand impairments and shadows the effects of certain deficits, such as weakness [19], which are both fundamental when performing daily activities. This questions the functional relevance of these assessments.
More recently, technology-aided approaches started emphasizing the importance of assessing impairments during tasks involving arm movements and hand manipulations without providing arm weight support [24–27]. Such tasks are expected to provide crucial information on fine upper limb impairments in individuals with mild to moderate disability levels and are promising to better identify functionally relevant impairments. However, existing approaches typically rely on time-consuming and complex measurement setups, which reduces their clinical applicability. Further, they mostly focus on kinematic metrics and do not quantify grip force control and its essential role in daily life activities [28, 29]. Also, the clinimetric properties of such digital health metrics are often insufficiently evaluated, thereby challenging their interpretability and acceptability as clinical endpoints [17, 30].
The Virtual Peg Insertion Test (VPIT) addresses many of the limitations of existing technology-aided assessments by recording movement and grip force patterns during a virtual goal-directed manipulation task requiring coordinated arm and hand movements [31, 32]. Previous research indicated the feasibility of the approach in neurologic individuals with mild to moderate sensorimotor impairments [32–35]. In addition, ten digital health metrics capturing sensorimotor impairments have been established for the VPIT and allowed for an accurate discrimination between neurologically intact and affected individuals [32]. However, whether the VPIT metrics provide a multi-dimensional evaluation of impairments in arm and hand that are functionally relevant has not been evaluated yet. Further, the clinimetric properties (test-retest reliability, measurement error, learning effects, concurrent validity) of the VPIT metrics have mainly been evaluated in unaffected subjects, thereby leaving their applicability and robustness in post-stroke individuals unexplored.
The objective of this work was to evaluate the ability of the digital health metrics from the VPIT to characterize arm and hand sensorimotor impairments that are relevant for performing functional tasks, by evaluating their clinimetric properties in 30 chronic post-stroke subjects.
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