Exploring Biomechanical Correlates in Voice Analysis of Multiple Sclerosis Patients

  1. Romero-Arias, Tatiana 1
  2. Hernández-Velasco, Rocío
  3. Betancort, Moisés 1
  4. Mena-Chamorro, Patricio
  5. Sabater Gálvez, Lucía
  6. Pérez del Olmo, Adrián 2
  1. 1 Universidad de La Laguna
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

  2. 2 Universidad Pontificia de Salamanca (España)
Journal:
Folia Phoniatrica et Logopaedica

ISSN: 1021-7762 1421-9972

Year of publication: 2024

Pages: 1-14

Type: Article

DOI: 10.1159/000540457 WoS: WOS:001366213900001 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Folia Phoniatrica et Logopaedica

Abstract

Introduction: The predominant alterations in voice of patients with multiple sclerosis (MS) are phonatory instability, vocal asthenia and roughness, shortness of breath, hypophonia, and hypernasality. However, research on alterations of acoustic parameters has few studies and disparate results. The objective of this study was to investigate voice disturbances in patients with MS, both with objective measures (analysis of biomechanical) and subjective measures (scales and questionnaires). Methods: This is an experimental study with a total of 20 participants with MS. Voice samples were collected, and biomechanical correlates were analyzed through the Clinical Voice Systems program, Online Lab App. The VHI-30 (Voice Handicap Index) questionnaire, the GRBAS (grade, roughness, breathiness, asthenia, strain) scale, and the Hospital Anxiety and Depression Scale were used as subjective measures. Results: Ninety-five percentages of participants feel and describe dysphonic difficulties. Self-perception of vocal disability correlated with auditory vocal perceptual analysis in the sample of women. Conclusion: The biomechanical parameters showed alterations in the strength of the glottic closure, the efficiency index, and the structural imbalance index.

Bibliographic References

  • 10.1111/ene.13819
  • 10.1016/j.bbe.2020.12.009
  • 10.1101/cshperspect.a028928
  • 10.1016/j.msard.2020.102206
  • 10.1159/000021531
  • 10.1016/j.clineuro.2013.09.015
  • 10.1590/s0004-282x2013000300003
  • 10.1186/s40814-017-0222-z
  • 10.1212/wnl.41.8.1251
  • 10.1155/2012/143813
  • 10.3233/JAD-180037
  • 10.3233/JPD-181431
  • 10.1055/s-0034-1397332
  • 10.1016/j.jvoice.2003.05.004
  • 10.1007/s10072-009-0170-3
  • 10.1016/j.jvoice.2012.10.016
  • 10.1016/j.jvoice.2006.05.006
  • 10.1016/j.jvoice.2006.01.008
  • 10.1016/j.jvoice.2022.06.033
  • 10.1159/000533289
  • 10.1017/S0022215119002652
  • 10.1044/1058-0360.0603.66
  • 10.1016/s2173-5735(07)70376-9
  • 10.1111/j.1600-0447.1983.tb09716.x
  • 10.1016/s0163-8343(03)00043-4
  • 10.1016/s0025-7753(07)72585-x
  • 10.1213/ANE.0000000000002864
  • 10.5812/semj.64857
  • 10.3390/computers6040030