Background and Purpose— Assessing upper limb movements poststroke is crucial to monitor and understand sensorimotor recovery. Kinematic assessments are expected to enable a sensitive quantification of movement quality and distinguish between restitution and compensation. The nature and practice of these assessments are highly variable and used without knowledge of their clinimetric properties. This presents a challenge when interpreting and comparing results. The purpose of this review was to summarize the state of the art regarding kinematic upper limb assessments poststroke with respect to the assessment task, measurement system, and performance metrics with their clinimetric properties. Subsequently, we aimed to provide evidence-based recommendations for future applications of upper limb kinematics in stroke recovery research. Methods— A systematic search was conducted in PubMed, Embase, CINAHL, and IEEE Xplore. Studies investigating clinimetric properties of applied metrics were assessed for risk of bias using the Consensus-Based Standards for the Selection of Health Measurement Instruments checklist. The quality of evidence for metrics was determined according to the Grading of Recommendations Assessment, Development, and Evaluation approach. Results— A total of 225 studies (N=6197) using 151 different kinematic metrics were identified and allocated to 5 task and 3 measurement system groups. Thirty studies investigated clinimetrics of 62 metrics: reliability (n=8), measurement error (n=5), convergent validity (n=22), and responsiveness (n=2). The metrics task/movement time, number of movement onsets, number of movement ends, path length ratio, peak velocity, number of velocity peaks, trunk displacement, and shoulder flexion/extension received a sufficient evaluation for one clinimetric property. Conclusions— Studies on kinematic assessments of upper limb sensorimotor function are poorly standardized and rarely investigate clinimetrics in an unbiased manner. Based on the available evidence, recommendations on the assessment task, measurement system, and performance metrics were made with the goal to increase standardization. Further high-quality studies evaluating clinimetric properties are needed to validate kinematic assessments, with the long-term goal to elucidate upper limb sensorimotor recovery poststroke. Clinical Trial Registration— URL: https://www.crd.york.ac.uk/prospero/ . Unique identifier: CRD42017064279.

Digital health metrics promise to advance the understanding of impaired body functions, for example in neurological disorders. However, their clinical integration is challenged by an insufficient validation of the many existing and often abstract metrics. Here, we propose a data-driven framework to select and validate a clinically relevant core set of digital health metrics extracted from a technology-aided assessment. As an exemplary use-case, the framework is applied to the Virtual Peg Insertion Test (VPIT), a technology-aided assessment of upper limb sensorimotor impairments. The framework builds on a use-case-specific pathophysiological motivation of metrics, models demographic confounds, and evaluates the most important clinimetric properties (discriminant validity, structural validity, reliability, measurement error, learning effects). Applied to 77 metrics of the VPIT collected from 120 neurologically intact and 89 affected individuals, the framework allowed selecting 10 clinically relevant core metrics. These assessed the severity of multiple sensorimotor impairments in a valid, reliable, and informative manner. These metrics provided added clinical value by detecting impairments in neurological subjects that did not show any deficits according to conventional scales, and by covering sensorimotor impairments of the arm and hand with a single assessment. The proposed framework provides a transparent, step-by-step selection procedure based on clinically relevant evidence. This creates an interesting alternative to established selection algorithms that optimize mathematical loss functions and are not always intuitive to retrace. This could help addressing the insufficient clinical integration of digital health metrics. For the VPIT, it allowed establishing validated core metrics, paving the way for their integration into neurorehabilitation trials.

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Background Specific dietary proteins exert strong health-related effects compared with casein. Objective Herein, the hypothesis was tested using screening and conventional biochemical and molecular biological techniques that protein-rich insect meal compared with casein influences metabolic health in hyperlipidemic rats. Methods A 4-wk feeding trial with male, 8-wk-old homozygous obese Zucker rats (n = 36) and male, 8-wk-old heterozygous lean Zucker rats (n = 12) was performed. Obese rats were randomly divided into 3 obese groups (OC, OI50, and OI100) of 12 rats each and lean rats served as a lean control group (LC). LC and OC were fed a control diet with 20% casein as protein source, whereas in OI50 and OI100 50% and 100% of the casein, respectively, was replaced isonitrogenously by insect meal from Tenebrio molitor L. All data were analyzed by 1-factor ANOVA, except transcriptomic data which were analyzed by groupwise comparisons with the OC group. Results Transcript profiling revealed a coordinated inhibition by −17% to −521% and −37% to −859% of genes involved in fatty acid, triacylglycerol (TG), and cholesterol biosynthesis in the livers of OI100 and OI50, respectively, compared with OC (P < 0.05). Enzyme activities of fatty acid synthase, glucose-6 phosphate dehydrogenase, and 3-hydroxy-3-methylglutaryl-coenzyme-A reductase in the liver were 100–150% greater in OC compared with LC, but reduced by 50–60% in OI100 compared with OC (P < 0.05), to the same level as in LC. Liver and plasma concentrations of TG and cholesterol were 250–1000%, 30–800%, and 40–600% higher in OC, OI50, and OI100, respectively, than in LC (P < 0.05), but 40–60% and 20–60% lower in OI100 and OI50, respectively, than in group OC (P < 0.05). Plasma and liver concentrations of homocysteine were 20–30% lower in group OI100 than in group OC (P < 0.05). Conclusion Insect meal exerts pronounced lipid-lowering effects in hyperlipidemic rats and, thus, might be useful for hyperlipidemic individuals.

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.

Precise and objective assessments of upper limb movement quality after strokes in functional task conditions are an important prerequisite to improve understanding of the pathophysiology of movement deficits and to prove the effectiveness of interventions. Herein, a wearable inertial sensing system was used to capture movements from the fingers to the trunk in 10 chronic stroke subjects when performing reach-to-grasp activities with the affected and non-affected upper limb. It was investigated whether the factors, tested arm, object weight, and target height, affect the expressions of range of motion in trunk compensation and flexion-extension of the elbow, wrist, and finger during object displacement. The relationship between these metrics and clinically measured impairment was explored. Nine subjects were included in the analysis, as one had to be excluded due to defective data. The tested arm and target height showed strong effects on all metrics, while an increased object weight showed effects on trunk compensation. High inter- and intrasubject variability was found in all metrics without clear relationships to clinical measures. Relating all metrics to each other resulted in significant negative correlations between trunk compensation and elbow flexion-extension in the affected arm. The findings support the clinical usability of sensor-based motion analysis.

Though industrial exploitation of smart textile systems is still in its infancy, the technological implementation is increasing. This is the result of substantial research and development investments directed towards this emerging field. In order to stimulate the progress in smart textiles, emerging developments need to be identified and selectively strengthened. Hence, this issue reports on a three-dimensional roadmap on smart textiles. It aims at contributing to set future actions in research, education and technology development. Research activities and technological developments are mapped, barriers and drivers of technological, strategic and societal and economical origins are identified. Finally, recommendations are phrased on how to overcome barriers and to progress in the field of smart textiles

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