The Psychometric Properties of the Voice Handicap Index in People With Parkinson's Disease

2017 ◽  
Vol 31 (2) ◽  
pp. 258.e13-258.e18 ◽  
Author(s):  
I. Guimaraes ◽  
R. Cardoso ◽  
S. Pinto ◽  
J.J. Ferreira
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Psychometric Properties ◽  
Voice Handicap Index ◽  
The Voice

scholarly journals The Effects of Intensive Speech Treatment on Conversational Intelligibility in Spanish Speakers With Parkinson's Disease

2018 ◽  
Vol 27 (1) ◽  
pp. 154-165 ◽  
Author(s):  
Gemma Moya-Galé ◽  
Alireza Goudarzi ◽  
Àngels Bayés ◽  
Megan McAuliffe ◽  
Bram Bulté ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Voice Handicap Index ◽  
Spanish Speakers ◽  
Perceived Disability ◽  
Perceptions Of Disability ◽  
Speech Treatment ◽  
Intelligibility Ratings ◽  
The Voice

Purpose The purpose of this study was to examine the effects of intensive speech treatment on the conversational intelligibility of Castilian Spanish speakers with Parkinson's disease (PD), as well as on the speakers' self-perceptions of disability. Method Fifteen speakers with a medical diagnosis of PD participated in this study. Speech recordings were completed twice before treatment, immediately posttreatment, and at a 1-month follow-up session. Conversational intelligibility was assessed in 2 ways—transcription accuracy scores and intelligibility ratings on a 9-point Likert scale. The Voice Handicap Index (Núñez-Batalla et al., 2007) was administered as a measure of self-perceived disability. Results Group data revealed that transcription accuracy and median ease-of-understanding ratings increased significantly immediately posttreatment, with gains maintained at the 1-month follow-up. The functional subscale of the Voice Handicap Index decreased significantly posttreatment, suggesting a decrease in perceived communication disability after speech treatment. Conclusion These findings support the implementation of intensive voice treatment to improve conversational intelligibility in Spanish speakers with PD with dysarthria as well as to improve the speakers' perception of their daily communicative capabilities. Clinical and theoretical considerations are discussed.

Classification of Parkinson's Disease from Smartphone Recording Data using Time-Frequency Analysis and Convolutional Neural Network (Preprint)

2021 ◽  
Author(s):  
Denchai Worasawate ◽  
Warisara Asawaponwiput ◽  
Natsue Yoshimura ◽  
Apichart Intarapanich ◽  
Decho Surangsrirat
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Fourier Transform ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Non Invasive ◽  
The Central Nervous System ◽  
Short Time ◽  
The Voice

BACKGROUND Parkinson’s disease (PD) is a long-term neurodegenerative disease of the central nervous system. The current diagnosis is dependent on clinical observation and the abilities and experience of a trained specialist. One of the symptoms that affect most patients over the course of their illness is voice impairment. OBJECTIVE Voice is one of the non-invasive data that can be collected remotely for diagnosis and disease progression monitoring. In this study, we analyzed voice recording data from a smartphone as a possible disease biomarker. The dataset is from one of the largest mobile PD studies, the mPower study. METHODS A total of 29,798 audio clips from 4,051 participants were used for the analysis. The voice recordings were from sustained phonation by the participant saying /aa/ for ten seconds into the iPhone microphone. The audio samples were converted to a spectrogram using a short-time Fourier transform. CNN models were then applied to classify the samples. RESULTS A total of 29,798 audio clips from 4,051 participants were used for the analysis. The voice recordings were from sustained phonation by the participant saying /aa/ for ten seconds into the iPhone microphone. The audio samples were converted to a spectrogram using a short-time Fourier transform. CNN models were then applied to classify the samples. CONCLUSIONS Classification accuracies of the proposed method with LeNet-5, ResNet-50, and VGGNet-16 are 97.7 ± 0.1%, 98.6 ± 0.2%, and 99.3 ± 0.1%, respectively. CLINICALTRIAL ClinicalTrials.gov NCT02696603; https://www.clinicaltrials.gov/ct2/show/NCT02696603

scholarly journals Parkinson’s Disease-Cognitive Rating Scale for Evaluating Cognitive Impairment in Parkinson’s Disease: A Systematic Review

2020 ◽  
Vol 10 (9) ◽  
pp. 588
Author(s):  
Elena Cecilia Rosca ◽  
Mihaela Simu
Keyword(s):  
Systematic Review ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Psychometric Properties ◽  
Rating Scale ◽  
Cross Sectional Study ◽  
Index Test ◽  
Cross Sectional ◽  
Level Ii

The aim of the present systematic review was to examine the evidence on the accuracy and psychometric properties of the Parkinson’s Disease-Cognitive Rating Scale (PD-CRS) for evaluating the presence of cognitive impairment in patients with Parkinson’s disease (PD) as well as to highlight the quality and quantity of research available on the use of the PD-CRS in this population. We searched four databases from inception until July 2020. Eight studies, published between 2008 and 2020, met the inclusion criteria: One cross-sectional study in which participants were assessed with the index test (PD-CRS) and a reference standard diagnostic assessment, in accordance with the Level II criteria of the International Parkinson and Movement Disorder Society (MDS); one case-control study comparing the PD-CRS to an extensive battery of tests (i.e., MDS Level II diagnosis); and six studies comparing the PD-CRS to other short cognitive batteries. In patients with Parkinson’s disease, the PD-CRS test provides information about cortical and sub-cortical cognitive functions. Even if it demonstrated good psychometric properties, the results regarding the optimal threshold for detecting mild cognitive impairment and dementia in PD are somewhat inconsistent. Further cross-sectional studies are necessary to examine the optimum cut-off score for detecting cognitive dysfunction in PD patients.

scholarly journals Measures of balance and falls risk prediction in people with Parkinson’s disease: a systematic review of psychometric properties

2019 ◽  
Vol 33 (12) ◽  
pp. 1949-1962 ◽  
Author(s):  
Stanley J Winser ◽  
Priya Kannan ◽  
Umar Muhhamad Bello ◽  
Susan L Whitney
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Psychometric Properties ◽  
Risk Prediction ◽  
Outcome Measures ◽  
The Body ◽  
Activity Level ◽  
Psychometric Validation ◽  
Health Measurement ◽  
Falls Risk

Objective:To investigate the psychometric properties of measures of balance and falls risk prediction in people with Parkinson’s disease (PD).Data sources:PubMed, Embase, CINAHL, Ovid Medline, Scopus, and Web of Science were searched from inception to August 2019.Review method:Studies testing psychometric properties of measures of balance and falls risk prediction in PD were included. The four-point COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) assessed quality.Results:Eighty studies testing 68 outcome measures were reviewed; 43 measures assessed balance, 9 assessed falls risk prediction, and 16 assessed both. The measures with robust psychometric estimation with acceptable properties were the (1) Mini-Balance Evaluation Systems Test (Mini-BEST), (2) Berg Balance Scale, (3) Timed Up and Go test, (4) Falls Efficacy Scale International, and (5) Activities-Specific Balance Confidence scale. These measures assess balance and falls risk prediction at the body, structure and function level, falls risk and balance, and falls risk at the activity level. The motor examination of the Unified Parkinson’s Disease Rating Scale (UPDRS-ME) with robust psychometric analysis is a condition-specific measure with acceptable properties. Except the UPDRS-ME and Mini-BESTest, the responsiveness of the other four measures has yet to be established.Conclusion:Six of the 68 outcome measures have strong psychometric properties for the assessment of balance and falls risk prediction in PD. Measures assessing balance and falls risk prediction at the participatory level are limited in number with a lack of psychometric validation.

scholarly journals Generalized anxiety disorder and the Hamilton Anxiety Rating Scale in Parkinson's disease

2010 ◽  
Vol 68 (4) ◽  
pp. 495-501 ◽  
Author(s):  
Arthur Kummer ◽  
Francisco Cardoso ◽  
Antonio Lucio Teixeira
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Anxiety Disorder ◽  
Psychometric Properties ◽  
Rating Scales ◽  
Rating Scale ◽  
Cutoff Score ◽  
General Anxiety Disorder ◽  
Hamilton Anxiety Rating Scale ◽  
Hamilton Anxiety

Anxiety is common in Parkinson's disease (PD), but studies concerning specific anxiety disorders are scarce. Essential psychometric properties of anxiety rating scales are also lacking. OBJECTIVE: To investigate general anxiety disorder (GAD) in PD and psychometric properties of the Hamilton Anxiety Rating Scale (Ham-A). METHOD: Ninety-one PD patients underwent neurological and psychiatric examination, which included the MINI-Plus, the Ham-A and the Hamilton Depression Rating Scale (Ham-D). RESULTS: GAD was present in 30.8% of PD patients. Patients with GAD had longer disease duration (p=0.044) and were in use of higher doses of levodopa (p=0.034). They also tended to have more motor fluctuations and dyskinesias. The group with GAD scored higher in Ham-A (p<0.001), in the somatic (p=0.004) and psychic (p<0.001) subscales of Ham-A, and in Ham-D (p=0.004). The Ham-A showed good internal consistency (Cronbach's alpha=0.893) and a cutoff score of 10/11 is suggested to screen for GAD. CONCLUSION: GAD is frequent in PD and the Ham-A may be a useful instrument to screen for this disorder.

Adaptation and psychometric properties of the Italian version of the Non-Motor Symptoms Questionnaire for Parkinson’s disease

2017 ◽  
Vol 38 (4) ◽  
pp. 673-678 ◽  
Author(s):  
I. Cova ◽  
M. E. Di Battista ◽  
N. Vanacore ◽  
C. P. Papi ◽  
G. Alampi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Psychometric Properties ◽  
Motor Symptoms ◽  
Italian Version ◽  
Non Motor Symptoms
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