Adaptation in motor strategies for postural control associated with sensory reweighting.

The purpose of this study was to identify and differentiate the motor strategies associated with sensory reweighting adapted during specific sensory integration tasks by healthy young adults. Thirty-six subjects (age range: 21-33 years) performed standing computerized dynamic posturography balance tasks across progressively increasing amplitudes of visual (VIS), somatosensory (SOM) and both (VIS+SOM) systems perturbation conditions. Adaptation in the motor strategy was measured as changes in electromyographic (EMG) activities and joint angles. The contribution of the perturbed sensory input in maintaining postural stability was calculated to determine the sensory reweighting. A multivariate design was used to model a linear combination of motor adaptation variables that discriminates specific sensory integration tasks. Results showed a significant progressive decrease in postural sway per unit amplitude of sensory perturbation in each condition, indicating dynamic sensory reweighting. Linear discriminant function analysis indicated that the adaptation in motor strategy during the VIS condition was associated with increased activity of EMG and joint angles in the upper body compared to the lower body. Conversely, during the SOM and VIS+SOM conditions, the adaptation in motor strategy was associated with decreased activity of EMG and joint angles in the lower body compared to the upper body. Therefore, the adaptation in motor strategies associated with sensory reweighting were different for different sensory integration tasks.

Effect of Auditory Task Type on Physiological and Subjective Measures of Listening Effort in Individuals With Normal Hearing.

Purpose Listening effort has traditionally been measured using subjective rating scales and behavioral measures. Recent physiological measures of listening effort have utilized pupil dilation. Using a combination of physiological and subjective measures of listening effort, this study aimed to identify differences in listening effort during 2 auditory tasks: sentence recognition and word recognition. Method Pupil dilation and subjective ratings of listening effort were obtained for auditory tasks utilizing AzBio sentences recognition and Northwestern University Auditory Test No. 6 words recognition, across 3 listening situations: in quiet, at +6 dB signal-to-noise ratio, and at 0 dB signal-to-noise ratio. Task accuracy was recorded for each of the 6 conditions, as well as peak pupil dilation and a subjective rating of listening effort. Results A significant impact of listening situation (quiet vs. noise) and task type (sentence recognition vs. word recognition) on both physiological and subjective measures was found. There was a significant interaction between listening situation and task type, suggesting that contextual cues may only be beneficial when audibility is uncompromised. The current study found no correlation between the physiological and subjective measures, possibly suggesting that these measures analyze different aspects of cognitive effort in a listening task.

Differentiation of Patients with Balance Insufficiency (Vestibular Hypofunction) versus Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor.

Balance disorders present a significant healthcare burden due to the potential for hospitalization or complications for the patient, especially among the elderly population when considering intangible losses such as quality of life, morbidities, and mortalities. This work is a continuation of our earlier works where we now examine feature extraction methodology on Dynamic Gait Index (DGI) tests and machine learning classifiers to differentiate patients with balance problems versus normal subjects on an expanded cohort of 60 patients. All data was obtained using our custom designed low-cost wireless gait analysis sensor (WGAS) containing a basic inertial measurement unit (IMU) worn by each subject during the DGI tests. The raw gait data is wirelessly transmitted from the WGAS for real-time gait data collection and analysis. Here we demonstrate predictive classifiers that achieve high accuracy, sensitivity, and specificity in distinguishing abnormal from normal gaits. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real-time using various classifiers. Our ultimate goal is to be able to use a remote sensor such as the WGAS to accurately stratify an individual's risk for falls.

Importance of an Interprofessional Team Approach in Achieving Improved Management of the Dizzy Patient.

BACKGROUND: Because of its multifaceted nature, dizziness is difficult for clinicians to diagnose and manage independently. Current treatment trends suggest that patients are often referred to the otolaryngologist for intervention despite having a nonotologic disorder. Additionally, many individuals with atypical presentations are often misdiagnosed and spend a significant amount of time waiting for consultation by the otolaryngologist. Few studies have alluded that implementation of an interprofessional team approach in the diagnosis and management of the dizzy patient can improve clinical decision-making. However, to the authors' knowledge, there is no information specifically quantifying the outcomes and potential benefits of using an interprofessional balance care team approach. PURPOSE: To compare dizziness diagnoses trends and referral practices with and without the use of an interprofessional management approach within a university healthcare system. RESEARCH DESIGN: Over the course of a 3-yr period, a retrospective review of the diagnosis and management of dizziness was performed with and without implementation of an interprofessional team. To observe potential differences, year 3 incorporated the interprofessional management approach while years 1-2 did not. The two periods were then compared to each other. STUDY SAMPLE: A total of 134 patients referred to a university hearing clinic for a vestibular and balance function evaluation. DATA COLLECTION AND ANALYSIS: Diagnoses and management trends were examined with descriptive statistics (percentages and frequencies). Fisher's exact tests, analysis of contingency tables, were conducted to evaluate the influence of interprofessional management on dizziness diagnoses and treatment patterns. RESULTS: Results demonstrated that before implementation of an interprofessional team approach, (1) referring clinicians used unspecific dizziness diagnosis codes (e.g., dizziness and giddiness), (2) a low number of patients with dizziness were referred for balance function testing, (3) diagnoses remained unspecific following the balance function assessment, and (4) the most frequently occurring vestibular diagnoses were unilateral vestibular hypofunction and benign paroxysmal positional vertigo. Following the use of an interprofessional management approach, it was determined that (1) disease-specific diagnoses increased, (2) patients with dizziness were referred for balance function testing mainly by otolaryngologists, (3) dizziness was considered to be multifaceted for a greater number of patients, (4) a larger percentage of patients were referred to a specialist other than the otolaryngologist as a result of their diagnosis, and (5) patients reported reduction or resolution of their symptoms. CONCLUSIONS: An interprofessional management approach for the dizzy patient can lead to more specific diagnoses and provide alternative referral pathways to other health-care professionals (e.g., audiologists, physical therapists, and pharmacists) in an effort to reduce over-referral to one specialist. Future studies should address the utility of an interprofessional team approach in the overall management of patients with dizziness.

Real-Time Classification of Patients with Balance Disorders vs. Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor.

Gait analysis using wearable wireless sensors can be an economical, convenient and effective way to provide diagnostic and clinical information for various health-related issues. In this work, our custom designed low-cost wireless gait analysis sensor that contains a basic inertial measurement unit (IMU) was used to collect the gait data for four patients diagnosed with balance disorders and additionally three normal subjects, each performing the Dynamic Gait Index (DGI) tests while wearing the custom wireless gait analysis sensor (WGAS). The small WGAS includes a tri-axial accelerometer integrated circuit (IC), two gyroscopes ICs and a Texas Instruments (TI) MSP430 microcontroller and is worn by each subject at the T4 position during the DGI tests. The raw gait data are wirelessly transmitted from the WGAS to a near-by PC for real-time gait data collection and analysis. In order to perform successful classification of patients vs. normal subjects, we used several different classification algorithms, such as the back propagation artificial neural network (BP-ANN), support vector machine (SVM), k-nearest neighbors (KNN) and binary decision trees (BDT), based on features extracted from the raw gait data of the gyroscopes and accelerometers. When the range was used as the input feature, the overall classification accuracy obtained is 100% with BP-ANN, 98% with SVM, 96% with KNN and 94% using BDT. Similar high classification accuracy results were also achieved when the standard deviation or other values were used as input features to these classifiers. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real time using various classifiers, the success of which may eventually lead to accurate and objective diagnosis of abnormal human gaits and their underlying etiologies in the future, as more patient data are being collected.