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Uncovering emotional and network dynamics in the speech of patients with chronic low back pain.
Reis, Felipe J J; Bonfim, Igor da Silva; Corrêa, Leticia Amaral; Nogueira, Leandro Calazans; Meziat-Filho, Ney; Almeida, Renato Santos de.
Afiliación
  • Reis FJJ; Physical Therapy Department, Instituto Federal do Rio de Janeiro (IFRJ), Rio de Janeiro, RJ, Brazil; Pain in Motion Research Group, Department of Physiotherapy, Human Physiology and Anatomy, Faculty of Physical Education & Physiotherapy, Vrije Universiteit Brussel, Brussels, Belgium. Electronic
  • Bonfim IDS; Postgraduate Program in Rehabilitation Sciences, Centro Universitário Augusto Motta (UNISUAM), Rio de Janeiro, RJ, Brazil.
  • Corrêa LA; Department of Chiropractic, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.
  • Nogueira LC; Physical Therapy Department, Instituto Federal do Rio de Janeiro (IFRJ), Rio de Janeiro, RJ, Brazil; Postgraduate Program in Rehabilitation Sciences, Centro Universitário Augusto Motta (UNISUAM), Rio de Janeiro, RJ, Brazil.
  • Meziat-Filho N; Postgraduate Program in Rehabilitation Sciences, Centro Universitário Augusto Motta (UNISUAM), Rio de Janeiro, RJ, Brazil.
  • Almeida RS; Physical Therapy Department, Instituto Federal do Rio de Janeiro (IFRJ), Rio de Janeiro, RJ, Brazil; Postgraduate Program in Rehabilitation Sciences, Centro Universitário Augusto Motta (UNISUAM), Rio de Janeiro, RJ, Brazil.
Musculoskelet Sci Pract ; 70: 102925, 2024 04.
Article en En | MEDLINE | ID: mdl-38430821
ABSTRACT

BACKGROUND:

Computational linguistics allows an understanding of language structure and different forms of expression of patients' perceptions.

AIMS:

The aims of this study were (i) to carry out a descriptive analysis of the discourse of people with chronic low back pain using sentiment analysis (SA) and network analysis; (ii) to verify the correlation between patients' profiles, pain intensity and disability levels with SA and network analysis; and (iii) to identify clusters in our sample according to language and SA using an unsupervised machine learning technique.

METHODS:

We performed a secondary analysis of a qualitative study including participants with chronic non-specific low back pain. We used the data related to participants' feelings when they received the diagnosis. The SA and network analysis were performed using the Valence Aware Dictionary and sEntiment Reasoner, and the Speech Graph, respectively. Clustering was performed using the K-means algorithm.

RESULTS:

In the SA, the mean composite score was -0.31 (Sd. = 0.58). Most participants presented a negative discourse (n = 41; 72%). Word Count (WC) and Largest Strongly connected Component (LSC) positively correlated with education. No statistically significant correlations were observed between pain intensity, disability levels, SA, and network analysis. Two clusters were identified in our sample.

CONCLUSION:

The SA showed that participants reported their feeling when describing the moment of the diagnosis using sentences with negative discourse. We did not find a statistically significant correlation between pain intensity, disability levels, SA, and network analysis. Education level presented positive correlation with WC and LSC.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Dolor de la Región Lumbar / Personas con Discapacidad Límite: Humans Idioma: En Revista: Musculoskelet Sci Pract Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Dolor de la Región Lumbar / Personas con Discapacidad Límite: Humans Idioma: En Revista: Musculoskelet Sci Pract Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos