Skip Navigation
Skip to contents

Res Vestib Sci : Research in Vestibular Science

OPEN ACCESS
SEARCH
Search

Author index

Page Path
HOME > Issue > Author index
Search
Sungmin Park 1 Article
The Assumption of Diagnosis and Site of Lesion in Benign Paroxysmal Positional Vertigo Using a Questionnaire
Sungmin Park, Chang Deok Han, Kyu Sung Kim, Wookey Lee
Res Vestib Sci. 2013;12(3):106-109.
  • 1,862 View
  • 27 Download
AbstractAbstract
Background and Objectives: Diagnosis of patients with dizziness requires detailed history taking. Using a questionnaire may be helpful for accurate and rapid diagnosis. However, no reliable questionnaire was developed yet. The purpose of this study is to know if authors’ questionnaire is reliable for diagnosing of benign paroxysmal positional vertigo (BPPV) and deciding affected canal. Materials and Methods: We evaluated 45 patients presenting with positional vertigo from January 2012 to September 2012. We developed a questionnaire by extracting specific questions on positional vertigo from the Dizziness Handicap Inventory and the activities-specific balance confidence scale. All the patients answered the questionnaire, followed by Dix-Hallpike test and head rolling test. Affected canal suspected by the questionnaire was analyzed and compared with affected canal confirmed by positioning test. Results: Among 45 patients, 24 (53%) was diagnosed with BPPV by positioning test. Patients with posterior canal BPPV (p-BPPV) answered positive of dizziness in pitch axis movement more frequently than roll and yaw, but it was not statistically significant (p>0.05). In the patients with lateral canal BPPV (l-BPPV), no significant difference was observed among three axes. Concordance rate of suspected canal by the questionnaire and positioning test was 36% in p-BPPV and 39% in l-BPPV. Conclusion: Diagnosis of BPPV and affected canal by the questionnaire based on movement axis is limited in this study. Development of more reliable questionnaire is necessary.

Res Vestib Sci : Research in Vestibular Science