An objective pronator drift test application (iPronator) using handheld device

Smartphone postural sway and pronator drift tests as measures of neurological disability

The COVID-19 pandemic and increased demands for neurologists have inspired the creation of remote, digitalized tests of neurological functions. This study investigates two tests from the Neurological Functional Tests Suite (NeuFun-TS) smartphone application, the "Postural Sway" and "Pronator Drift" tests. These tests capture different domains of postural control and motoric dysfunction in healthy volunteers (n = 13) and people with neurological disorders (n = 68 relapsing-remitting multiple sclerosis [MS]; n = 21 secondary progressive MS; n = 23 primary progressive MS; n = 13 other inflammatory neurological diseases; n = 21 non-inflammatory neurological diseases; n = 4 clinically isolated syndrome; n = 1 radiologically isolated syndrome). Smartphone accelerometer data was transformed into digital biomarkers, which were filtered in the training cohort (~ 80% of subjects) for test-retest reproducibility and correlations with subdomains of neurological examinations and validated imaging biomarkers. The independent validation cohort (~ 20%) determined whether biomarker models outperformed the best single digital biomarkers. Postural sway acceleration magnitude in the eyes closed and feet together stance demonstrated the highest reliability (ICC = 0.706), strongest correlations with age (Pearson r <= 0.82) and clinical and imaging outcomes (r <= 0.65, p < 0.001) and stronger predictive value for sway-relevant neurological disability outcomes than models that aggregated multiple biomarkers (coefficient of determination R2 = 0.46 vs 0.38). The pronator drift test only captured cerebellar dysfunction, had less reproducible biomarkers, but provided additive value when combined with postural sway biomarkers into models predicting global scales of neurological disability. In conclusion, a simple 1-min postural sway test accurately measures body oscillations that increase with natural aging and differentiates them from abnormally increased body oscillations in people with neurological disabilities.

Smartphone Postural Sway and Pronator Drift tests as Measures of Neurological Disability

The COVID-19 pandemic and increased demands for neurologists have inspired the creation of remote, digitalized tests of neurological functions. This study investigates two tests from the Neurological Functional Tests Suite (NeuFun-TS) smartphone application, the "Postural Sway" and "Pronator Drift" tests. These tests capture different domains of postural control and motoric dysfunction in healthy volunteers (n=13) and people with neurological disorders (n=68 relapsing-remitting multiple sclerosis [MS]; n=21 secondary progressive MS; n=23 primary progressive MS; n=13 other inflammatory neurological diseases; n=21 non-inflammatory neurological diseases; n=4 clinically isolated syndrome; n=1 radiologically isolated syndrome). Smartphone accelerometer data was transformed into digital biomarkers, which were filtered in the training cohort (~80% of subjects) for test-retest reproducibility and correlations with subdomains of neurological examinations and validated imaging biomarkers. The independent validation cohort (~20%) determined whether biomarker models outperformed the best single digital biomarkers. Postural sway acceleration magnitude in the eyes closed and feet together stance demonstrated the highest reliability (ICC=.706), strongest correlations with age (Pearson r<=.82) and clinical and imaging outcomes (r<=.65, p<0.001) and stronger predictive value for sway-relevant neurological disability outcomes than models that aggregated multiple biomarkers (coefficient of determination R2=.46 vs .38). The pronator drift test only captured cerebellar dysfunction, had less reproducible biomarkers, but provided additive value when combined with postural sway biomarkers into models predicting global scales of neurological disability. In conclusion, a simple 1-minute postural sway test accurately measures body oscillations that increase with natural aging and differentiates them from abnormally increased body oscillations in people with neurological disabilities.

Smartphone-based visual feedback trunk control training for gait ability in stroke patients: A single-blind randomized controlled trial

BACKGROUND

The gait ability of stroke patients is highly related with trunk control. Studies that addressed the effects of trunk control training using visual feedback on gait parameters of stroke patients are lacking.

OBJECTIVE

The purpose of this study was to investigate the role of smartphone-based visual feedback trunk control training for improvement of trunk control and spatio-temporal gait parameter in stroke patients.

METHODS

Twenty-four stroke patients participated in this study. Participants were randomly allocated to the experimental and control groups. Both groups completed conventional rehabilitation over 4 weeks. The experimental group additionally received smartphone-based visual feedback trunk control training for 4 weeks. The primary outcome of this study was evaluated by trunk impairment scale and spatio-temporal gait parameters.

RESULTS

The trunk impairment scale in the experimental group was significantly improved compared to the control group (P< 0.05). The spatio-temporal gait parameters are significantly more different in the experimental group than in the control group (P< 0.05).

CONCLUSIONS

Additional smartphone-based visual feedback trunk control training may improve trunk control and spatio-temporal gait parameter in chronic stroke patients.

Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients

Background

The pronator drift test (PDT), a neurological examination, is widely used in clinics to measure motor weakness of stroke patients.

Objective

The aim of this study was to develop a PDT tool with machine learning classifiers to detect stroke symptoms based on quantification of proximal arm weakness using inertial sensors and signal processing.

Methods

We extracted features of drift and pronation from accelerometer signals of wearable devices on the inner wrists of 16 stroke patients and 10 healthy controls. Signal processing and feature selection approach were applied to discriminate PDT features used to classify stroke patients. A series of machine learning techniques, namely support vector machine (SVM), radial basis function network (RBFN), and random forest (RF), were implemented to discriminate stroke patients from controls with leave-one-out cross-validation.

Results

Signal processing by the PDT tool extracted a total of 12 PDT features from sensors. Feature selection abstracted the major attributes from the 12 PDT features to elucidate the dominant characteristics of proximal weakness of stroke patients using machine learning classification. Our proposed PDT classifiers had an area under the receiver operating characteristic curve (AUC) of .806 (SVM), .769 (RBFN), and .900 (RF) without feature selection, and feature selection improves the AUCs to .913 (SVM), .956 (RBFN), and .975 (RF), representing an average performance enhancement of 15.3%.

Conclusions

Sensors and machine learning methods can reliably detect stroke signs and quantify proximal arm weakness. Our proposed solution will facilitate pervasive monitoring of stroke patients.

An Automated Test of Rat Forelimb Supination Quantifies Motor Function Loss and Recovery After Corticospinal Injury

BACKGROUND

Rodents are the primary animal model of corticospinal injury and repair, yet current behavioral tests do not show the large deficits after injury observed in humans. Forearm supination is critical for hand function and is highly impaired by corticospinal injury in both humans and rats. Current tests of rodent forelimb function do not measure this movement.

OBJECTIVE

To determine if quantification of forelimb supination in rats reveals large-scale functional loss and partial recovery after corticospinal injury.

METHODS

We developed a knob supination device that quantifies supination using automated and objective methods. Rats in a reaching box have to grasp and turn a knob in supination in order to receive a food reward. Performance on this task and the single pellet reaching task were measured before and after 2 manipulations of the pyramidal tract: a cut lesion of 1 pyramid and inactivation of motor cortex using 2 different drug doses.

RESULTS

A cut lesion of the corticospinal tract produced a large deficit in supination. In contrast, there was no change in pellet retrieval success. Supination function recovered partially over 6 weeks after injury, and a large deficit remained. Motor cortex inactivation produced a dose-dependent loss of knob supination; the effect on pellet reaching was more subtle.

CONCLUSIONS

The knob supination task reveals in rodents 3 signature hand function changes observed in humans with corticospinal injury: (1) large-scale loss with injury, (2) partial recovery in the weeks after injury, and (3) loss proportional to degree of dysfunction.

The Computerized Table Setting Test for Detecting Unilateral Neglect

Background

Patients with unilateral neglect fail to respond normally to stimuli on the left side. To facilitate the evaluation of unilateral spatial neglect, we developed a new application that runs on a tablet device and investigated its feasibility in stroke patients.

Methods

We made the computerized table setting test (CTST) to run on the tablet computer. Forty acute ischemic stroke patients (20 patients with right hemispheric infarction with neglect, 10 patients with right hemispheric infarction without neglect, and 10 patients with left hemispheric infarction) and 10 healthy controls were prospectively enrolled to validate the CTST. The test requires subjects to set a table by dragging 12 dishes located below the table on the tablet screen. The horizontal deviation of the 12 dishes from the midline of the table, the selection tendency measured by the sequence of the dish selection, and the elapsed time for table setting were calculated automatically.

Results

Parameters measured by the CTST were correlated with the results of conventional neglect tests. The horizontal deviation was significantly higher in patients with right hemispheric infarction with neglect compared with the other groups. The selection tendency and elapsed time also were significantly different in patients with right hemispheric infarction with neglect compared with the left hemispheric infarction and control groups, but were similar to those with right hemispheric infarction without neglect.

Conclusions

The CTST is feasible to administer and comparable with conventional neglect tests. This new application may be useful for the initial diagnosis and follow-up of neglect patients.

Feasibility of using a mobile application for the monitoring and management of stroke-associated risk factors

Background and Purpose

Recent advances in information technology have created opportunities for advances in the management of stroke. The objective of this study was to test the feasibility of using a smartphone software application (app) for the management of vascular risk factors in patients with stroke.

Methods

This prospective clinical trial developed a smartphone app, the 'Korea University Health Monitoring System for Stroke: KUHMS₂,' for use by patients with stroke. During a 6-month follow-up period, its feasibility was assessed by measuring the changes in their vascular risk-factor profiles and the number of days per patient with data registration into the app. The effect of the app on the achievement rate of risk-factor targets was assessed by classifying subjects into compliant and noncompliant groups.

Results

At the end of the trial, data on 48 patients were analyzed. The number of days on which data were registered into the app was 60.42±50.17 (mean±standard deviation). Among predefined vascular risk factors, the target achievement rate for blood pressure and glycated hemoglobin (Hb_A1c) improved significantly from baseline to the final measurement. The serial changes in achievement rates for risk-factor targets did not differ between the compliant and noncompliant groups.

Conclusions

Many challenges must be overcome before mobile apps can be used for patients with stroke. Nevertheless, the app tested in this study induced a shift in the risk profiles in a favorable direction among the included stroke patients.

An acute stroke evaluation app: a practice improvement project

A point-of-care workflow checklist in the form of an iOS (iPhone Operating System) app for use by stroke providers was introduced with the objective of standardizing acute stroke evaluation and documentation at 2 affiliated academic medical centers. Providers used the app in unselected, consecutive patients undergoing acute stroke evaluation in an emergency department or hospital setting between August 2012 and January 2013 and August 2013 and February 2014. Satisfaction surveys were prospectively collected pre- and postintervention from residents, staff neurologists, and clinical data specialists. Residents (20 preintervention and 16 postintervention), staff neurologists (6 pre and 5 post), and clinical data specialists (4 pre and 4 post) participated in this study. All 16 (100%) residents had increased satisfaction with their ability to perform an acute stroke evaluation postintervention but only 9 (56%) of 16 felt the app was more help than hindrance. Historical controls aligned with preintervention results. Staff neurologists conveyed increased satisfaction with resident presentations and decision making when compared to preintervention surveys. Stroke clinical data specialists estimated a 50% decrease in data abstraction when the app data were used in the clinical note. Concomitant effect on door-to-needle (DTN) time at 1 site, although not a primary study measure, was also evaluated. At that 1 center, the mean DTN time decreased by 16 minutes when compared to the corresponding months from the year prior. The point-of-care acute stroke workflow checklist app may assist trainees in presenting findings in a standardized manner and reduce data abstraction time. The app may help reduce DTN time, but this requires further study.

Facilitating Stroke Management using Modern Information Technology

BACKGROUND AND PURPOSE

Information technology and mobile devices may be beneficial and useful in many aspects of stroke management, including recognition of stroke, transport and triage of patients, emergent stroke evaluation at the hospital, and rehabilitation. In this review, we address the contributions of information technology and mobile health to stroke management.

SUMMARY OF ISSUES

Rapid detection and triage are essential for effective thrombolytic treatment. Awareness of stroke warning signs and responses to stroke could be enhanced by using mobile applications. Furthermore, prehospital assessment and notification could be streamlined for use in telemedicine and teleradiology. A mobile telemedicine system for assessing the National Institutes of Health Stroke Scale scores has shown higher correlation and fast assessment comparing with face-to-face method. Because the benefits of thrombolytic treatment are time-dependent, treatment should be initiated as quickly as possible. In-hospital communication between multidisciplinary team members can be enhanced using information technology. A computerized in-hospital alert system using computerized physician-order entry was shown to be effective in reducing the time intervals from hospital arrival to medical evaluations and thrombolytic treatment. Mobile devices can also be used as supplementary tools for neurologic examination and clinical decision-making. In post-stroke rehabilitation, virtual reality and telerehabilitation are helpful. Mobile applications might be useful for public awareness, lifestyle modification, and education/training of healthcare professionals.

CONCLUSIONS

Information technology and mobile health are useful tools for management of stroke patients from the acute period to rehabilitation. Further improvement of technology will change and enhance stroke prevention and treatment.