Assessment of the Voice Handicap Index as a Screening Tool in Dysphonic Patients

Niebudek-Bogusz, Ewa; Kuzańska, Anna; Woźnicka, Ewelina; Śliwińska-Kowalska, Mariola

doi:10.1159/000324214

Cited by 54 publications

(30 citation statements)

References 26 publications

(14 reference statements)

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“…Simberg et al [32] reported that combining perceptual voice quality and vocal symptoms can effectively screen potential voice disorders among teachers and students. A study demonstrated that self-completed questionnaires (i.e., VHI) can be applied to distinguish between subjects with or without voice disorders [33]. By using DT modeling, a recent study showed that other demographic factors (e.g., age, occupation, and smoking status) are significant risk factors for voice disorders in the general population in addition to VHI and voice symptoms [34].…”

Section: Discussionmentioning

confidence: 99%

Demographic and Symptomatic Features of Voice Disorders and Their Potential Application in Classification Using Machine Learning Algorithms

Tsui¹,

Tsao²,

Lin³

et al. 2018

Folia Phoniatr Logop

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Background: Studies have used questionnaires of dysphonic symptoms to screen voice disorders. This study investigated whether the differential presentation of demographic and symptomatic features can be applied to computerized classification. Methods: We recruited 100 patients with glottic neoplasm, 508 with phonotraumatic lesions, and 153 with unilateral vocal palsy. Statistical analyses revealed significantly different distributions of demographic and symptomatic variables. Machine learning algorithms, including decision tree, linear discriminant analysis, K-nearest neighbors, support vector machine, and artificial neural network, were applied to classify voice disorders. Results: The results showed that demographic features were more effective for detecting neoplastic and phonotraumatic lesions, whereas symptoms were useful for detecting vocal palsy. When combining demographic and symptomatic variables, the artificial neural network achieved the highest accuracy of 83 ± 1.58%, whereas the accuracy achieved by other algorithms ranged from 74 to 82.6%. Decision tree analyses revealed that sex, age, smoking status, sudden onset of dysphonia, and 10-item voice handicap index scores were significant characteristics for classification. Conclusion: This study demonstrated a significant difference in demographic and symptomatic features between glottic neoplasm, phonotraumatic lesions, and vocal palsy. These features may facilitate automatic classification of voice disorders through machine learning algorithms.

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Section: Discussionmentioning

confidence: 99%

Demographic and Symptomatic Features of Voice Disorders and Their Potential Application in Classification Using Machine Learning Algorithms

Tsui¹,

Tsao²,

Lin³

et al. 2018

Folia Phoniatr Logop

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“…The VHI measure was used in this study as a reference tool, as it is a validated tool which has proven reliable in a number of studies on Polish population [18,19] and has been commonly used in clinical practice.…”

Section: Methodsmentioning

confidence: 99%

“…The most commonly used self-report questionnaires are the Voice Handicap Index (VHI), Voice Symptom Scale (VoiSS) and Voice Related Quality of Life (V-RQOL). In Polish clinical practice, among phoniatricians, otolaryngologists and speech therapists the VHI has been so far the only such a tool used for monitoring occupational voice [18,19].…”

Section: Introductionmentioning

confidence: 99%

Screening value of V-RQOL in the evaluation of occupational voice disorders

Morawska

Niebudek-Bogusz

Wiktorowicz

et al. 2017

Med Pr

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“…43 However, one study reported poor predictive values for voice problems in teachers with VHI-10. 44 VHI has been shown to be a good screening tool, 45 and it is distinct from, and complements, acoustic analysis of voice. 46,47 Ideally, an annual screen could incorporate an automated acoustic analysis, although there are problems with deciding which parameters are important and relevant and which best correlate with the gold standards of perceptual voice analysis by GRBAS or CAPE-V.…”

Section: Discussionmentioning

confidence: 99%

Voice burden in teachers and non‐teachers in a UK population: A questionnaire‐based survey

Gadepalli

Fullwood

Ascott

et al. 2019

Clinical Otolaryngology

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Objective To characterise the burden of voice disorders in teachers in a UK population, compare it with non‐teachers and identify groups of teachers who may be particularly at risk of developing a voice problem. Design Questionnaire‐based survey of primary and secondary school teachers and non‐teachers. Questions consisted of general demographics, VHI‐10 and questions relating to voice problems. Methods Distribution of questionnaires to teachers and non‐teachers and statistical analysis of the responses. Setting University teaching hospital. Participants Teachers and non‐teachers in a region of North West England. Main outcome measures Identification of risk factors for voice problems in teachers, compared to non‐teachers. Results A total of 210 primary and 244 secondary school teachers and 304 non‐teachers participated in the questionnaire survey. Response rates were 67.9% from primary schools, 41.2% from secondary schools and 40.0% from the non‐teachers. 30.0% of teachers and 9.0% of non‐teachers had reported problems with their voice. 12.8% of teachers and 2.0% of non‐teachers had missed work due to voice problems. 14.1% of teachers and 5.3% non‐teachers had seen a general practitioner for voice‐related problems, whilst 7.1% of teachers and 6.3% of non‐teachers had been referred to an otolaryngologist or speech therapist for voice problems. Factors related to VHI‐10 (P < .05) were identified. Conclusions Voice disorders are an occupational health problem for teachers, with a significant burden of these disorders in this group of teachers in the UK. We have identified risk factors that could be exploited to identify groups of teachers who would benefit from early intervention.

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Assessment of the Voice Handicap Index as a Screening Tool in Dysphonic Patients

Cited by 54 publications

References 26 publications

Demographic and Symptomatic Features of Voice Disorders and Their Potential Application in Classification Using Machine Learning Algorithms

Demographic and Symptomatic Features of Voice Disorders and Their Potential Application in Classification Using Machine Learning Algorithms

Screening value of V-RQOL in the evaluation of occupational voice disorders

Voice burden in teachers and non‐teachers in a UK population: A questionnaire‐based survey

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