Prediction of Patient Satisfaction after Treatment of Chronic Neck Pain with Mulligan’s Mobilization

  1. Fernández-Carnero, Josué 122334
  2. Beltrán-Alacreu, Hector 56
  3. Arribas-Romano, Alberto 11
  4. Cerezo-Téllez, Ester 7
  5. Cuenca-Zaldivar, Juan Nicolás 789
  6. Sánchez-Romero, Eleuterio A. 2233
  7. Lerma Lara, Sergio 44
  8. Villafañe, Jorge Hugo 10
  1. 1 Universidad Rey Juan Carlos
    info

    Universidad Rey Juan Carlos

    Madrid, España

    ROR https://ror.org/01v5cv687

  2. 2 Universidad Europea de Madrid
    info

    Universidad Europea de Madrid

    Madrid, España

    ROR https://ror.org/04dp46240

  3. 3 Universidad Europea de Canarias
    info

    Universidad Europea de Canarias

    Orotava, España

    ROR https://ror.org/051xcrt66

  4. 4 Universidad Autónoma de Madrid
    info

    Universidad Autónoma de Madrid

    Madrid, España

    ROR https://ror.org/01cby8j38

  5. 5 Universidad de Castilla-La Mancha
    info

    Universidad de Castilla-La Mancha

    Ciudad Real, España

    ROR https://ror.org/05r78ng12

  6. 6 CranioSPain Research Group, Centro Superior de Estudios Universitarios La Salle
  7. 7 Universidad de Alcalá
    info

    Universidad de Alcalá

    Alcalá de Henares, España

    ROR https://ror.org/04pmn0e78

  8. 8 Instituto de Investigación Sanitaria Puerta de Hierro
    info

    Instituto de Investigación Sanitaria Puerta de Hierro

    Madrid, España

  9. 9 Primary Health Center “El Abajón”
  10. 10 IRCCS Fondazione Don Carlo Gnocchi, Piazzale Morandi 6, 20148 Milan, Italy
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Revista:
Life

ISSN: 2075-1729

Año de publicación: 2022

Volumen: 13

Número: 1

Páginas: 48

Tipo: Artículo

DOI: 10.3390/LIFE13010048 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Life

Resumen

Chronic neck pain is among the most common types of musculoskeletal pain. Manual therapy has been shown to have positive effects on this type of pain, but there are not yet many predictive models for determining how best to apply manual therapy to the different subtypes of neck pain. The aim of this study is to develop a predictive learning approach to determine which basal outcome could give a prognostic value (Global Rating of Change, GRoC scale) for Mulligan’s mobilization technique and to identify the most important predictive factors for recovery in chronic neck pain subjects in four key areas: the number of treatments, time of treatment, reduction of pain, and range of motion (ROM) increase. A prospective cohort dataset of 80 participants with chronic neck pain diagnosed by their family doctor was analyzed. Logistic regression and machine learning modeling techniques (Generalized Boosted Models, Support Vector Machine, kernel, classification and decision trees, random forest and neural networks) were each used to form a prognostic model for each of the nine outcomes obtained before and after intervention: disability—neck disability index (NDI), patient satisfaction (GRoC), quality of life (12-Item Short Form Survey, SF-12), State-Trait Anxiety Inventory (STAI), Beck Depression Inventory (BDI II), pain catastrophizing scale (ECD), kinesiophobia-Tampa scale of kinesiophobia (TSK-11), Pain Intensity Visual Analogue Scale (VAS), and cervical ROM. Pain descriptions from the subjects and pain body diagrams guided the physical examination. The most important predictive factors for recovery in chronic neck pain patients indicated that the more anxiety and the lower the ROM of lateroflexion, the higher the probability of success with the Mulligan concept treatment.

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Información de financiación

This research was funded by the Spanish Ministry of Science and Innovation, the OASIS project (Grant PID2020-113222RB-C21) and the OASIS-T project (Grant PID2020-113222RB-C22).

Financiadores

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