School of Medicine and Health Sciences Poster Presentations
Pain Catastrophizing: Can it predict functioning?
Document Type
Poster
Keywords
Pain; Catastrophizing; Function; Chronic Pain
Publication Date
Spring 2017
Abstract
Introduction:
Pain catastrophizing is a maladaptive cognitive process in response to painful stimuli1. This enhanced neural mechanism is thought to worsen the experience of pain2 and adversely impact functioning. Recent studies suggest that catastrophizing may be a predictor of pain severity and quality of life3.
Catastrophizing is quantified using the Pain Catastrophizing Scale (PCS) score, consisting of three subscales, rumination, magnification, and helplessness4. The individual components can be evaluated as distinct predictors of functioning and thus modify treatment to target a specific aspect of catastrophizing.
Methods:
With IRB approval, the PCS was randomly administered to 98 patients. An additional questionnaire assessed interference with indices of functioning: activity, mood, walking, relations, sleep, enjoyment of life, and work. Pearson correlations were used to examine inter-item correlations and Cronbach’s alpha for overall associations. Indicators of functioning and PCS components were evaluated using general linear regression models, adjusting for age, gender, duration, opioid use, and knowledge of the cause of pain.
Results:
Among the 98 observations, 37.76% were male, 57.14% used opioid, 73.47% knew the cause of pain. The Cronbach’s alpha is 0.86 for overall functioning. There were no significant associations among baseline variables. Helplessness was a significant predictor of worsening activity, mood, enjoyment, relationships, sleep, and walking, with p<0.05, while rumination predicted ability to work.
Conclusions:
Of the three components of the PCS, helplessness was found to be the only significant predictor of the vast majority of functioning outcomes. Treatment targeting a patient’s sense of helplessness may directly improve most aspects of functioning.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Open Access
1
Pain Catastrophizing: Can it predict functioning?
Introduction:
Pain catastrophizing is a maladaptive cognitive process in response to painful stimuli1. This enhanced neural mechanism is thought to worsen the experience of pain2 and adversely impact functioning. Recent studies suggest that catastrophizing may be a predictor of pain severity and quality of life3.
Catastrophizing is quantified using the Pain Catastrophizing Scale (PCS) score, consisting of three subscales, rumination, magnification, and helplessness4. The individual components can be evaluated as distinct predictors of functioning and thus modify treatment to target a specific aspect of catastrophizing.
Methods:
With IRB approval, the PCS was randomly administered to 98 patients. An additional questionnaire assessed interference with indices of functioning: activity, mood, walking, relations, sleep, enjoyment of life, and work. Pearson correlations were used to examine inter-item correlations and Cronbach’s alpha for overall associations. Indicators of functioning and PCS components were evaluated using general linear regression models, adjusting for age, gender, duration, opioid use, and knowledge of the cause of pain.
Results:
Among the 98 observations, 37.76% were male, 57.14% used opioid, 73.47% knew the cause of pain. The Cronbach’s alpha is 0.86 for overall functioning. There were no significant associations among baseline variables. Helplessness was a significant predictor of worsening activity, mood, enjoyment, relationships, sleep, and walking, with p<0.05, while rumination predicted ability to work.
Conclusions:
Of the three components of the PCS, helplessness was found to be the only significant predictor of the vast majority of functioning outcomes. Treatment targeting a patient’s sense of helplessness may directly improve most aspects of functioning.
Comments
Poster to be presented at GW Annual Research Days 2017.