Data-Driven Evaluation of a Gamified Breath-Holding Training Application to Improve CT Scan Quality and Reduce Patient Anxiety

Abstract

This study presents the development and evaluation of Breathe Well, an innovative three-tiered Graphical User Interface (GUI) application designed to address motion-induced step artifacts and patient anxiety during Computed Tomography (CT) scans. The core idea of the application is to combine relaxation techniques, guided breathing exercises, and gamified training modules within a single interactive platform that allows patients to practice breath-holding and anxiety control prior to scanning. The objective is to enhance patient cooperation, reduce involuntary movement, and improve overall image quality while minimizing the time healthcare staff spend on manual breath-hold instruction. The study involved a comparative analysis between a control group and an intervention group trained using the Breathe Well system. Quantitative results demonstrated a significant improvement in imaging outcomes, with the mean artifact score decreasing from 3.1 ± 0.8 in the control group to 2.1 ± 0.7 in the intervention group (p < 0.01). Psychological assessment using the State-Trait Anxiety Inventory (STAI) revealed a marked reduction in patient anxiety, with mean scores declining from 48.6 ± 6.4 before training to 38.2 ± 5.8 after using the application (p < 0.01). Qualitative feedback further confirmed increased patient confidence, comfort, and comprehension of CT procedures. The findings indicate that integrating gamified digital interventions into pre-scan preparation significantly improves both patient experience and diagnostic precision. The novelty of this research lies in the creation of a self-guided, multi-level digital platform that bridges behavioral training and imaging technology, offering a scalable, patient-centered solution for modern radiology workflows.