Factors affecting fear of attacks in patients with episodic migraine – a cross-sectional study
Abstract
Background Interictal burden impacts the quality of life of migraine patients especially the fear of the next attack. However, it is unclear which variables influence the fear of the development of a future migraine attack. Therefore, the aim of this study was to identify factors contributing to the fear of attacks in patients with episodic migraine and to investigate the differences between individuals with low and high attack anxiety. Methods Potential factors affecting fear of attacks were assessed by using the Headache Impact Test, Central Sensitization Inventory, Allodynia Symptom Checklist and the Neck Disability Index. Fear of attacks was quantified using Fear of Attacks in Migraine questionnaire. A multiple linear regression was used to evaluate which of these factors would explain the dependent variable fear of attacks. Results The included sample of patients with episodic migraine (n = 46) had a mean of 79.4 (21.2) points on the Fear of Attacks in Migraine Inventory questionnaire and according to its subscales; attention and anticipation had the highest score of 37.8 (9.3) points. Headache impact and allodynia symptoms explained 35.2% of the results of the fear of attacks questionnaire in episodic migraine (p < 0.001). High attack anxiety individuals had higher scores regarding headache impact, central sensitization, allodynia symptoms and neck disability, compared with low attack anxiety. Conclusion Allodynia and headache impact are major factors affecting the…
How strong is the evidence?
Evidence Hub reads this as a cross-sectional, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause. Reported size: 46 participants or records.
Read the paper
Related studies
- Calcium Ions in the Physiology and Pathology of the Central Nervous System
- AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare
- In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene
- In Silico drug repurposing pipeline using deep learning and structure based approaches in epilepsy
- Targeting serotonin receptors with phytochemicals – an in-silico study
- In silico exploration of anti-prostate cancer compounds from differential expressed genes
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.