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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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Endurance and Avoidance Response Patterns in Pain Patients: Application of Action Control Theory in Pain Research

Research datakeyboard_double_arrow_right Dataset 28 Dec 2020 English Publisher:Zenodo
Authors: Buchmann, Jana; Baumann, Nicola; Meng, Katrin; Semrau, Jana; Kuhl, Julius; Pfeifer, Klaus; Kazén, Miguel; +2 Authors

doi: 10.5281/zenodo.4396288 , 10.5281/zenodo.4396287

Endurance and Avoidance Response Patterns in Pain Patients: Application of Action Control Theory in Pain Research

Abstract

Identifying pain-related response patterns and understanding underlying functional mechanisms of symptom formation and recovery are essential steps for tailoring interventions to increase the effectiveness of treatment. The focus of this study thus was to replicate pain-related avoidance and endurance response patterns associated with the Fear-Avoidance Model (FAM) and Avoidance-Endurance Model (AEM) by means of latent profile analysis. Apart from the analysis of primary and other secondary characteristics of the pain response profiles, we especially analyzed the influence of stress and action control as covariates in accordance with Action Control Theory (ACT).

The data acquisition for this project was funded by the German Pension Insurance (Deutsche Rentenversicherung Bund) and the Federal Ministry of Education and Research (BMBF) within the research priority "Chronic diseases and patient orientation" - Grant number: 0421-FSCP-0529.

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low back pain

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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