Effects of long-term vestibular rehabilitation therapy with vibrotactile sensory augmentation for people with unilateral vestibular disorders – A randomized preliminary study

Bao, Tian; Klatt, Brooke N; Carender, Wendy J.; Kinnaird, Catherine R.; Alsubaie, Saud F; Whitney, Susan L.; Sienko, Kathleen H.

doi:10.3233/ves-190683

Cited by 26 publications

(61 citation statements)

References 62 publications

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“…Although a carry-over effect was seen up to 2 months post training, the effect was not as strong as the feedback effect (see Tables 3 and 4). Our carry-over effect is less pronounced than that observed by Bao et al [42] in their pilot study on vestibular loss patients, and more pronounced than that observed in other pilot studies on the elderly and patients with uncompensated unilateral vestibular loss [16]. In the study of Bao et al [42] patients received a more intense and longer training protocol -3 sessions a week for 8 weekswhich may have influenced the carry-over effect.…”

Section: Discussioncontrasting

confidence: 52%

“…Our carry-over effect is less pronounced than that observed by Bao et al [42] in their pilot study on vestibular loss patients, and more pronounced than that observed in other pilot studies on the elderly and patients with uncompensated unilateral vestibular loss [16]. In the study of Bao et al [42] patients received a more intense and longer training protocol -3 sessions a week for 8 weekswhich may have influenced the carry-over effect. In another study of carry-over effects, Brugnera et al [48] showed a more significant improvement in the medium term carry-over effect for condition 6 (eyes closed on a sway-referenced platform) of the Equitest system which is an easier but comparable to the task standing eyes closed on foam task used in the current study [49].…”

Section: Discussioncontrasting

confidence: 52%

“…It would seem important to include this subjective rating of perceived balance capabilities when considering the length of a sensory augmentation training period. Most studies using VTfb have chosen to use training durations of 6 weeks similar to those of vestibular rehabilitation therapy [25,41,42] without consideration of possible criteria for selection of the optimal training duration. A rapid early improvement followed by a slower learning phase as we observed is typical of motor skill learning [43].…”

Section: Discussionmentioning

confidence: 99%

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Characteristics of improvements in balance control using vibro-tactile biofeedback of trunk sway for multiple sclerosis patients

Allum

Rust

Lutz

et al. 2021

Journal of the Neurological Sciences

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Background and aims: Previously, we determined that training with vibrotactile feedback (VTfb) of trunk sway improves MS patients' balance impairment. Here, we posed 5 questions: 1) How many weeks of VTfb training are required to obtain the best short-term carry over effect (CoE) with VTfb? 2) How long does the CoE last once VTfb training terminates? 3) Is the benefit similar for stance and gait? 4) Is position or velocity based VTfb more effective in reducing trunk sway? 5) Do patients' subjective assessments of balance control improve? Methods: Balance control of 16 MS patients was measured with gyroscopes at the lower trunk. The gyroscopes drove directionally active VTfb in a head-band. Patients trained twice per week with VTfb for 4 weeks to determine when balance control with and without VTfb stopped improving. Thereafter, weekly assessments without VTfb over 4 weeks and at 6 months determined when CoEs ended. Results: A 20% improvement in balance to normal levels occurred with VTfb. Short term CoEs improved from 15 to 20% (p ≤ 0.001). Medium term (1-4 weeks) CoEs were constant at 19% (p ≤ 0.001). At 6 months improvement was not significant, 9%. Most improvement was for lateral sway. Equal improvement occurred when angle position or velocity drove VTfb. Subjectively, balance improvements peaked after 3 weeks of training (32%, p ≤ 0.05). Conclusions: 3-4 weeks VTfb training yields clinically relevant sway reductions and subjective improvements for MS patients during stance and gait. The CoEs lasted at least 1 month. Velocity-based VTfb was equally effective as position-based VTfb.

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Section: Discussioncontrasting

confidence: 52%

Section: Discussioncontrasting

confidence: 52%

Section: Discussionmentioning

confidence: 99%

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Characteristics of improvements in balance control using vibro-tactile biofeedback of trunk sway for multiple sclerosis patients

Allum

Rust

Lutz

et al. 2021

Journal of the Neurological Sciences

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“…The proposed methods were compared with previously developed methods with respect to the results listed in Table 5 . SVMs, random forest models, and cohorts have been applied to detect motor [ 48 , 49 ], balance, or gait function [ 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ]. The highest accuracy in classifying motor function was 97%, achieved by an SVM.…”

Section: Discussionmentioning

confidence: 99%

Detection of Postural Control in Young and Elderly Adults Using Deep and Machine Learning Methods with Joint–Node Plots

Lee

Chen

Wang

et al. 2021

Sensors

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Postural control decreases with aging. Thus, an efficient and accurate method of detecting postural control is needed. We enrolled 35 elderly adults (aged 82.06 ± 8.74 years) and 20 healthy young adults (aged 21.60 ± 0.60 years) who performed standing tasks for 40 s, performed six times. The coordinates of 15 joint nodes were captured using a Kinect device (30 Hz). We plotted joint positions into a single 2D figure (named a joint–node plot, JNP) once per second for up to 40 s. A total of 15 methods combining deep and machine learning for postural control classification were investigated. The accuracy, sensitivity, specificity, positive predicted value (PPV), negative predicted value (NPV), and kappa values of the selected methods were assessed. The highest PPV, NPV, accuracy, sensitivity, specificity, and kappa values were higher than 0.9 in validation testing. The presented method using JNPs demonstrated strong performance in detecting the postural control ability of young and elderly adults.

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“…Participants with balance deficits including a diagnosed vestibular disorder or peripheral neuropathy were recruited by PTs and via flyers at the university medical center. Participants were excluded if they had confounding neurologic or neuromuscular disorders; known pregnancy; recent lower extremity fractures/severe sprains (within the last six months); previous lower extremity joint replacement; incapacitating back or lower extremity pain; the inability to stand for three minutes without rest; a body habitus that exceeded the dimensions of the measuring equipment (waist circumference >50 inches); or a Montreal Cognitive Assessment score of less than 26 points [36]. All participants gave written informed consent, and the study was conducted in accordance with the Declaration of Helsinki.…”

Section: A Datamentioning

confidence: 99%

Automatically Evaluating Balance: A Machine Learning Approach

Bao

Klatt

Whitney

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

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Compared to in-clinic balance training, in-home training is not as effective. This is, in part, due to the lack of feedback from physical therapists (PTs). Here, we analyze the feasibility of using trunk sway data and machine learning (ML) techniques to automatically evaluate balance, providing accurate assessments outside of the clinic. We recruited sixteen participants to perform standing balance exercises. For each exercise, we recorded trunk sway data and had a PT rate balance performance on a scale of 1 to 5. The rating scale was adapted from the Functional Independence Measure. From the trunk sway data, we extracted a 61-dimensional feature vector representing performance of each exercise. Given these labeled data, we trained a multi-class support vector machine (SVM) to map trunk sway features to PT ratings. Evaluated in a leave-one-participant-out scheme, the model achieved a classification accuracy of 82%. Compared to participant self-assessment ratings, the SVM outputs were significantly closer to PT ratings. The results of this pilot study suggest that in the absence of PTs, ML techniques can provide accurate assessments during standing balance exercises. Such automated assessments could reduce PT consultation time and increase user compliance outside of the clinic.

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Effects of long-term vestibular rehabilitation therapy with vibrotactile sensory augmentation for people with unilateral vestibular disorders – A randomized preliminary study

Cited by 26 publications

References 62 publications

Characteristics of improvements in balance control using vibro-tactile biofeedback of trunk sway for multiple sclerosis patients

Characteristics of improvements in balance control using vibro-tactile biofeedback of trunk sway for multiple sclerosis patients

Detection of Postural Control in Young and Elderly Adults Using Deep and Machine Learning Methods with Joint–Node Plots

Automatically Evaluating Balance: A Machine Learning Approach

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