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Enregistrement W3006513331

Speech treatment for people with hereditary ataxia – a feasibility study

2019· article· en· W3006513331 sur OpenAlexaboutno aff
Anja Lowit, A. Egan, Marios Hadjivassilliou

Notice bibliographique

RevueStrathprints: The University of Strathclyde institutional repository (University of Strathclyde) · 2019
Typearticle
Langueen
DomaineNeuroscience
ThématiqueGenetic Neurodegenerative Diseases
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDysarthriaAudiologyPsychologyAtaxiaProsodyIntelligibility (philosophy)Affect (linguistics)Quality of life (healthcare)Speech disorderMedicineSpeech recognitionCommunicationPsychiatryComputer sciencePsychotherapist
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Hereditary ataxias are frequently associated with ataxic dysarthria. The characteristics of ataxic dysarthria include imprecise articulation, distorted vowels, hypophonia, reduced speech rate, flat prosody and poor respiratory support. These changes lead to reduced speech intelligibility and a reduced effectiveness of communication which can affect quality of life. Communication breakdown has been ranked amongst the top three most upsetting symptoms of their disease by people with Friedreich’s Ataxia in a recent survey by Ataxia UK. Whilst we have significantly increased our understanding of the nature of the communication problems experienced by speakers with ataxic dysarthria, there are very few intervention studies and a recent Cochrane review concluded that “there is insufficient and low or very low quality evidence from either RCTs or observational studies to determine the effectiveness of any treatment for speech disorder in any of the hereditary ataxia syndromes.” (Vogel et al. 2014, p.1). Based on the speech symptoms prevalent across the various types of hereditary ataxia, one treatment approach that has potential to increase communication efficiency is Lee Silverman Voice Treatment (LSVT). This treatment approach focuses on increasing the level of loudness in a person’s speech. The method has been shown to positively affect the wider articulatory system, such as improving breath support for speech, slowing down rate, and improving voice quality and articulation, whilst at the same being simple enough for the patient to implement in everyday communication. There are many reports , including RCTs (Baumgartner et al. 2001, Ramig et al. 2018), showing the benefits of LSVT for people with Parkinson’s Disease (PD), as well as smaller studies on other disorders such as cerebral palsy (e.g. Boliek & Fox, 2014), traumatic brain injury and stroke (Wenke et al. 2008, Mahler & Ramig 2012). In addition, a single case study on a patient with ataxic dysarthria as a result of a thiamine deficiency demonstrated improvement in overall speech intelligibility following a course of LSVT (Sapir et al., 2003), highlighting its potential as a treatment for speakers with hereditary ataxias. However, further evidence about its effectiveness across a larger number of participants and a wider range of underlying neuropathologies is necessary before LSVT can be advocated as an appropriate treatment for people with ataxic dysarthria. We report on a feasibility study that aimed to investigate the effectiveness of LSVT to improve communication efficiency, and the acceptability of the approach to people with hereditary ataxia. We recruited 20 participants with hereditary ataxia and dysarthria (17 FRDA, one SPG7, one SCA6 and one unspecified cerebellar ataxia). 19 of these concluded treatment and all assessment points. Dysarthria severity levels ranged from mild to severe. Participants were offered extended LSVT on a 2 session a week basis over 8 weeks via Skype. Assessments included 2 baseline tests, as well as immediate and 6-8 week post treatment follow-ups, also conducted via Skype. Analysis included a range of speech measures, as well as qualitative assessments of voice handicap, communication participation, and fatigue, captured by rating scales as well as interviews. The interview data indicate that 16 of the 19 participants perceived considerable improvements to their communication following LSVT intervention, whilst three reported only minor improvements or no change. Prominent themes emerging from the interviews include the ability to produce longer utterances and speak for longer periods of time, decreased hypophonia, reduced anxiety and greater confidence. A further twelve participants also reported improvements in intelligibility, clearer speech and/or a reduced need to repeat themselves. The formal rating scales corroborate these qualitative results to some degree with the majority of participants showing improved scores after treatment, although this did not reach statistical significance due to variable group performance. Perceptual evaluations of intelligibility and naturalness of a reading sample showed similar results, with some participants showing improved scores after treatment, but not to the extent expressed in the qualitative evaluation. Acoustic voice analysis demonstrated statistically significant performance improvements following treatment, with increases in prolonged vowel length and reduced voice perturbation (jitter and shimmer) values. These effects were maintained 8 weeks post-treatment. Our study provides clear support for offering speech treatment such as LSVT for people with degenerative ataxia to improve their communication and psychosocial wellbeing. This finding is corroborated by a recent pilot study into intensive home-based treatment involving seven speakers with autosomal recessive spastic ataxia Charlevoix-Saguenay (ARSACS) by Vogel et al. (2019), which identified improvements in intelligibility and naturalness post-treatment.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,556
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,029
Tête enseignante GPT0,227
Écart entre enseignants0,198 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2019
Routes d'admission1
Résumé présentoui

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Même revueStrathprints: The University of Strathclyde institutional repository (University of Strathclyde)Même sujetGenetic Neurodegenerative DiseasesTravaux en français237 207