TY  - JOUR
DO  - 10.22454/PRiMER.2024.576211
VL  - 8
DA  - 2024/08/14
N2  - Introduction: Posterior canal-type benign paroxysmal positional vertigo (BPPV) is commonly treated using the Epley maneuver; however, the maneuver’s use in primary care is limited by insufficient expertise. Therefore, this study aimed to evaluate the efficacy of a three-dimensional (3D) semicircular canal model as a self-learning tool for primary care physicians to improve their Epley maneuver technique.
Methods: Thirty-one participants (18 family physicians, seven residents, and six medical students) performed the Dix–Hallpike maneuver on a nursing manikin, followed by the Epley maneuver on the covered 3D models before and after a 5-minute self-study period with the uncovered 3D model. We measured the number of moved beads from posterior canal into the utricle of the 3D model, time spent on the Epley maneuver, and head suspension angle of the Dix–Hallpike maneuver.
Results: Preintervention performance was divided into a skilled group (n=7) that could move almost all the beads and an unskilled group (n=24) that could move a few beads. Postintervention, the unskilled group members significantly improved their skill: The average moved beads increased from 0.35 to 8.00, maneuver time from 26.1 to 35.8 seconds, and head suspension angle from 10.3° to 16.4°. Most participants recognized the importance of correct positioning and spent adequate time.
Conclusions: The 3D model was effective as a self-learning tool for improving Epley maneuver performance, particularly for less experienced practitioners. This approach could bridge the gap between evidence and practice in primary care for BPPV treatment, enhancing patient outcomes and reducing the need for specialist referrals.
PB  - Society of Teachers of Family Medicine
AU  - Kita, Keiichiro
AU  - Watanabe, Kazuhiro
AU  - Saito, Mayuko
AU  - Kuroiwa, Maiko
L2  - http://journals.stfm.org/primer/2024/kita-2024-0041
L1  - http://journals.stfm.org/media/itadsnnz/primer-8-45.pdf
TI  - Epley Maneuver Skills in Primary Care: 3D Semicircular Canal Models for Self-Learning
ER  -