Classification of illness attributions in patients with coronary artery disease
Oliver Friedrich1, Evelyn Kunschitz1,2, Lisa Pongratz2
1Karl Landsteiner Institute for Scientific Research in Clinical Cardiology, Hanusch Hospital, Vienna, Austria.
Insights
Patients with coronary artery disease attribute their condition to stress and behavior, not modifiable risks. A new classification scheme helps understand these attributions for better self-management.
Area of Science:
- Psychology and Medicine
- Health Psychology
- Behavioral Medicine
Background:
- Understanding patient-reported causal attributions is crucial for managing chronic conditions like coronary artery disease (CAD).
- Attribution theory provides a framework for analyzing how individuals explain the causes of events, including illness.
- Previous research indicates a need to explore patient perspectives on CAD etiology.
Purpose of the Study:
- To investigate patient-reported causal attributions in individuals diagnosed with coronary artery disease (CAD).
- To classify these attributions using established principles of attribution theory.
- To identify potential communication strategies for enhancing disease self-management.
Main Methods:
- A cohort of 459 patients with angiographically verified coronary artery disease (CAD) participated.
- Patients reported causal attributions via an open-ended question from the Brief Illness Perception Questionnaire (BIPQ).
- Attributions were descriptively categorized and a novel dimensional classification scheme was developed, resulting in four groups: Behaviour/Emotional State, Past Behaviour/Emotional State, Physical/Psychological Trait, and External.
Main Results:
- Stress was identified as the most prominent attribution, followed by behavioral factors and genetic predisposition.
- A significant discrepancy was observed between patients' reported attributions and the presence of modifiable risk factors (e.g., smoking, obesity).
- The developed classification scheme categorized 15 common attributions into four distinct groups.
Conclusions:
- The identified pattern of illness attributions in CAD patients aligns with previous findings.
- The dimensional classification offers a structured approach to understanding patient attributions.
- This classification highlights opportunities for physicians to improve patient-physician communication and support effective disease self-management.
Objective:
To examine patient-reported causal attributions in patients with coronary artery disease and classify them according to attribution theory.
Design:
Patients with angiographically verified coronary artery disease (n = 459) were asked to report causal attributions by answering the respective open-ended item of the Brief Illness Perception Questionnaire.
Main Outcome Measures:
Groups resulting from classifications were characterised with regard to sociodemographic and clinical variables, Quality of Life (SF-12), depression (PHQ-9), anxiety (GAD-7), and illness perception (BIPQ).
Results:
Stress emerged as the single most important attribution followed by various behavioural factors and genetic predisposition. There was a remarkable mismatch between the presence of modifiable risk factors (smoking, obesity) and patient-reported illness attributions. Based on the results of the descriptive categorisation of illness attributions we developed a transparent, easily reproducible scheme for dimensional classification of the fifteen most common responses according to attribution theory. The classification resulted in four groups: Behaviour/Emotional State, Past Behaviour/Emotional State, Physical/Psychological Trait and External.
Conclusion:
We found a pattern of illness attributions largely in line with previous trials. The dimensional classification resulted in four groups and highlighted potential entry points for physician-patient communication aimed at establishing beneficial disease self-management.
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