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.

BackgroundProprioceptive function can be affected after neurological injuries such as stroke. Severe and persistent proprioceptive impairments may be associated with a poor functional recovery after stroke. To better understand their role in the recovery process, and to improve diagnostics, prognostics, and the design of therapeutic interventions, it is essential to quantify proprioceptive deficits accurately and sensitively. However, current clinical assessments lack sensitivity due to ordinal scales and suffer from poor reliability and ceiling effects. Robotic technology offers new possibilities to address some of these limitations. Nevertheless, it is important to investigate the psychometric and clinimetric properties of technology-assisted assessments.MethodsWe present an automated robot-assisted assessment of proprioception at the level of the metacarpophalangeal joint, and evaluate its reliability, validity, and clinical feasibility in a study with 23 participants with stroke and an age-matched group of 29 neurologically intact controls. The assessment uses a two-alternative forced choice paradigm and an adaptive sampling procedure to identify objectively the difference threshold of angular joint position.ResultsResults revealed a good reliability (ICC(2,1) = 0.73) for assessing proprioception of the impaired hand of participants with stroke. Assessments showed similar task execution characteristics (e.g., number of trials and duration per trial) between participants with stroke and controls and a short administration time of approximately 12 min. A difference in proprioceptive function could be found between participants with a right hemisphere stroke and control subjects (p<0.001). Furthermore, we observed larger proprioceptive deficits in participants with a right hemisphere stroke compared to a left hemisphere stroke (p=0.028), despite the exclusion of participants with neglect. No meaningful correlation could be established with clinical scales for different modalities of somatosensation. We hypothesize that this is due to their low resolution and ceiling effects.ConclusionsThis study has demonstrated the assessment’s applicability in the impaired population and promising integration into clinical routine. In conclusion, the proposed assessment has the potential to become a powerful tool to investigate proprioceptive deficits in longitudinal studies as well as to inform and adjust sensorimotor rehabilitation to the patient’s deficits.

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Quantitative assessments of position sense are essential for the investigation of proprioception, as well as for diagnosis, prognosis and treatment planning for patients with somatosensory deficits. Despite the development and use of various paradigms and robotic tools, their clinimetric properties are often poorly evaluated and reported. A proper evaluation of the latter is essential to compare results between different studies and to identify the influence of possible confounds on outcome measures. The aim of the present study was to perform a comprehensive evaluation of a rapid robotic assessment of wrist proprioception using a passive gauge position matching task. Thirty-two healthy subjects undertook six test-retests of proprioception of the right wrist on two different days. The constant error (CE) was 0.87°, the absolute error (AE) was 5.87°, the variable error (VE) was 4.59° and the total variability (E) was 6.83° in average for the angles presented in the range from 10° to 30°. The intraclass correlation analysis provided an excellent reliability for CE (0.75), good reliability for AE (0.68) and E (0.68), and fair reliability for VE (0.54). Tripling the assessment length had negligible effects on the reliabilities. Additional analysis revealed significant trends of larger overestimation (constant errors), as well as larger absolute and variable errors with increased flexion angles. No proprioceptive learning occurred, despite increased familiarity with the task, which was reflected in significantly decreased assessment duration by 30%. In conclusion, the proposed automated assessment can provide sensitive and reliable information on proprioceptive function of the wrist with an administration time of around 2.5 min, demonstrating the potential for its application in research or clinical settings. Moreover, this study highlights the importance of reporting the complete set of errors (CE, AE, VE, and E) in a matching experiment for the identification of trends and subsequent interpretation of results.

Adaptation is a prominent feature of the human motor system and has been studied extensively in reaching movements. This study characterizes adaptation and generalization during isometric reaching in which the arm remains stationary and the participant controls a virtual cursor via force applied by the hand. We measured how learning of a visual cursor rotation generalizes across workspace 1) to determine the coordinate system that predominates visual rotation learning, and 2) to ascertain whether mapping type, namely position or velocity control, influences transfer. Participants performed virtual reaches to one of two orthogonal training targets with the applied rotation. In a new workspace, participants reached to a single target, similar to the training target in either hand or joint space. Furthermore, a control experiment measured within-workspace generalization to an orthogonal target. Across position and velocity mappings, learning transferred predominantly in intrinsic (joint) space, although the transfer was incomplete. The velocity mapping resulted in significantly larger aftereffects and broader within-workspace generalization than the position mapping, potentially due to slower peak speeds, longer trial times, greater target overshoot, or other factors. Although we cannot rule out a mixed reference frame in our task, the predominance of intrinsic coding of cursor kinematics in the isometric environment opposes the extrinsic coding of arm kinematics in real reaching but matches the intrinsic coding of dynamics found in prior work. These findings have implications for the design of isometric control systems in human-machine interaction or in rehabilitation when coordinated multi-degree-of-freedom movement is difficult to achieve.

Psychophysical procedures are applied in various fields to assess sensory thresholds. During experiments, sampled psychometric functions are usually assumed to be stationary. However, perception can be altered, for example by loss of attention to the presentation of stimuli, leading to biased data, which results in poor threshold estimates. The few existing approaches attempting to identify non-stationarities either detect only whether there was a change in perception, or are not suitable for experiments with a relatively small number of trials (e.g., [Formula: see text] 300). We present a method to detect inattention periods on a trial-by-trial basis with the aim of improving threshold estimates in psychophysical experiments using the adaptive sampling procedure Parameter Estimation by Sequential Testing (PEST). The performance of the algorithm was evaluated in computer simulations modeling inattention, and tested in a behavioral experiment on proprioceptive difference threshold assessment in 20 stroke patients, a population where attention deficits are likely to be present. Simulations showed that estimation errors could be reduced by up to 77% for inattentive subjects, even in sequences with less than 100 trials. In the behavioral data, inattention was detected in 14% of assessments, and applying the proposed algorithm resulted in reduced test-retest variability in 73% of these corrected assessments pairs. The novel algorithm complements existing approaches and, besides being applicable post hoc, could also be used online to prevent collection of biased data. This could have important implications in assessment practice by shortening experiments and improving estimates, especially for clinical settings.

Neurological injuries such as stroke can lead to proprioceptive impairment. For an informed diagnosis, prognosis, and treatment planning, it is essential to be able to distinguish between healthy performance and deficits following the neurological injury. Since there is some evidence that proprioception declines with age and stroke occurs predominantly in the elderly population, it is important to create a healthy reference model in this specific age group. However, most studies investigate age effects by comparing young and elderly subjects and do not provide a model within a target age range. Moreover, despite the functional relevance of the hand in activities of daily living, age-based models of distal proprioception are scarce. Here, we present a proprioception model based on the assessment of the metacarpophalangeal joint angle difference threshold in 30 healthy elderly subjects, aged 55–80 years (median: 63, interquartile range: 58–66), using a robotic tool to apply passive flexion–extension movements to the index finger. A two-alternative forced-choice paradigm combined with an adaptive algorithm to define stimulus magnitude was used. The mixed-effects model analysis revealed that aging has a significant, increasing effect on the difference threshold at the metacarpophalangeal joint, whereas other predictors (eg, tested hand or sex) did not show a significant effect. The adaptive algorithm allowed reaching an average assessment duration <15 minutes, making its clinical applicability realistic. This study provides further evidence for an age-related decline in proprioception at the level of the hand. The established age-based model of proprioception in elderly may serve as a reference model for the proprioceptive performance of stroke patients, or of any other patient group with central or peripheral proprioceptive impairments. Furthermore, it demonstrates the potential of such automated robotic tools as a rapid and quantitative assessment to be used in research and clinical settings.

Hand function is often impaired after neurological injuries such as stroke. In order to design patient-specific rehabilitation, it is essential to quantitatively assess those deficits. Current clinical scores cannot provide the required level of detail, and most assessment devices have been developed for the proximal joints of the upper limb. This paper presents a new robotic platform for the assessment of proprioceptive, motor, and sensorimotor hand impairments. A detailed technical evaluation demonstrated the capabilities to render different haptic environments required for a comprehensive assessment battery, and showed that the device is suitable for human interaction due to its ergonomic design. A preliminary study on proprioceptive assessment using a gauge position matching task with one healthy, one stroke, and one multiple sclerosis subject showed that the robotic system is able to rapidly and sensitively quantify proprioceptive deficits, and has the potential to be integrated into the clinical settings.