Ghasemi Mahshid, Behnaz Faranak, Hassanzad Nima, Taheri Farinaz
Anesthesiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Shohada Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Anesth Pain Med. 2022 Mar 4;12(1):e119354. doi: 10.5812/aapm.119354. eCollection 2022 Feb.
This study aimed to investigate the relationship between depression and pain anxiety with pain catastrophizing in patients with coronavirus disease 2019 (COVID-19).
In this descriptive, correlational study, 180 patients with COVID-19 in Akhtar and Imam Hossein hospitals in Tehran, Iran, were included from March 2019 to April 2020. All participants completed three questionnaires, including the Pain Catastrophizing Scale (PCS), Pain Anxiety Symptoms Scale (PASS), and Beck's Depression Inventory (BDI). The data were analyzed using Pearson correlation coefficient and multivariate regression.
There was a positive and significant relationship between the dimensions of rumination, magnification, and helplessness with total score of pain catastrophizing, as well as moderate to severe dimensions with total pain anxiety and depression in patients with COVID-19.
According to the results of regression analysis, pain anxiety based on pain catastrophizing dimensions was statistically significant, so that rumination, magnification, and helplessness could predict pain anxiety and explain a total of 15.1% of changes in pain anxiety. Also, depression was statistically significant based on dimensions of pain catastrophizing, so rumination, magnification, and helplessness could predict the patients' depression and explain 13.6% of depression changes.
本研究旨在调查2019冠状病毒病(COVID-19)患者中抑郁、疼痛焦虑与疼痛灾难化之间的关系。
在这项描述性相关性研究中,纳入了2019年3月至2020年4月期间伊朗德黑兰阿赫塔尔医院和伊玛目侯赛因医院的180例COVID-19患者。所有参与者均完成了三份问卷,包括疼痛灾难化量表(PCS)、疼痛焦虑症状量表(PASS)和贝克抑郁量表(BDI)。使用Pearson相关系数和多元回归分析数据。
COVID-19患者的沉思、放大和无助维度与疼痛灾难化总分之间存在显著正相关,并且中重度维度与总疼痛焦虑和抑郁之间也存在显著正相关。
根据回归分析结果,基于疼痛灾难化维度的疼痛焦虑具有统计学意义,即沉思、放大和无助可预测疼痛焦虑,并解释疼痛焦虑变化的15.1%。此外,基于疼痛灾难化维度的抑郁具有统计学意义,因此沉思、放大和无助可预测患者的抑郁,并解释抑郁变化的13.6%。