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

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...

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Autores principales: Fernández-Carnero, Josué, Beltrán-Alacreu, Hector, Arribas-Romano, Alberto, Cerezo-Téllez, Ester, Cuenca-Zaldivar, Juan Nicolás, Sánchez-Romero, Eleuterio A., Lerma Lara, Sergio, Villafañe, Jorge Hugo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860852/
https://www.ncbi.nlm.nih.gov/pubmed/36675997
http://dx.doi.org/10.3390/life13010048
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author Fernández-Carnero, Josué
Beltrán-Alacreu, Hector
Arribas-Romano, Alberto
Cerezo-Téllez, Ester
Cuenca-Zaldivar, Juan Nicolás
Sánchez-Romero, Eleuterio A.
Lerma Lara, Sergio
Villafañe, Jorge Hugo
author_facet Fernández-Carnero, Josué
Beltrán-Alacreu, Hector
Arribas-Romano, Alberto
Cerezo-Téllez, Ester
Cuenca-Zaldivar, Juan Nicolás
Sánchez-Romero, Eleuterio A.
Lerma Lara, Sergio
Villafañe, Jorge Hugo
author_sort Fernández-Carnero, Josué
collection PubMed
description 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, Classsification 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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spelling pubmed-98608522023-01-22 Prediction of Patient Satisfaction after Treatment of Chronic Neck Pain with Mulligan’s Mobilization Fernández-Carnero, Josué Beltrán-Alacreu, Hector Arribas-Romano, Alberto Cerezo-Téllez, Ester Cuenca-Zaldivar, Juan Nicolás Sánchez-Romero, Eleuterio A. Lerma Lara, Sergio Villafañe, Jorge Hugo Life (Basel) Article 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, Classsification 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. MDPI 2022-12-23 /pmc/articles/PMC9860852/ /pubmed/36675997 http://dx.doi.org/10.3390/life13010048 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Fernández-Carnero, Josué
Beltrán-Alacreu, Hector
Arribas-Romano, Alberto
Cerezo-Téllez, Ester
Cuenca-Zaldivar, Juan Nicolás
Sánchez-Romero, Eleuterio A.
Lerma Lara, Sergio
Villafañe, Jorge Hugo
Prediction of Patient Satisfaction after Treatment of Chronic Neck Pain with Mulligan’s Mobilization
title Prediction of Patient Satisfaction after Treatment of Chronic Neck Pain with Mulligan’s Mobilization
title_full Prediction of Patient Satisfaction after Treatment of Chronic Neck Pain with Mulligan’s Mobilization
title_fullStr Prediction of Patient Satisfaction after Treatment of Chronic Neck Pain with Mulligan’s Mobilization
title_full_unstemmed Prediction of Patient Satisfaction after Treatment of Chronic Neck Pain with Mulligan’s Mobilization
title_short Prediction of Patient Satisfaction after Treatment of Chronic Neck Pain with Mulligan’s Mobilization
title_sort prediction of patient satisfaction after treatment of chronic neck pain with mulligan’s mobilization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860852/
https://www.ncbi.nlm.nih.gov/pubmed/36675997
http://dx.doi.org/10.3390/life13010048
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