Predictive Factors of Augmented Reality–Based Clinical Task Performance Among Novice Users: Cross-Sectional Quantitative Study

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Authors: Amogh J. Vellore, Shovan Bhatia, Michael Ryan Kann, Nicolás M. Kass, Regan Mary Shanahan, Jacquelyn Jardini, Jayne Miner, Sohail Rajesh Daulat, Griffin J. Hurt, Rishi Basdeo, Nicole Don, Jacob T. Biehl
+1 more Edward G. Andrews
Cited by: 0
References: 58

Abstract

Abstract Background Augmented reality (AR) can provide risk-free training for medical trainees, yet little is known about which learner characteristics facilitate adoption or inform training design. Objective We aimed to identify which learner characteristics predict AR performance in novices. We hypothesized that higher visuospatial ability and greater video game experience would be associated with faster completion times and fewer errors. Methods In this cross-sectional study, 21 undergraduate, graduate, and medical students (median age 22, IQR 21-24 years) without previous AR experience were recruited between June and December 2024. Participants completed a technology experience survey, the mental rotation task (MRT) for visuospatial ability, a standardized 7-task AR protocol mimicking clinical use on the Microsoft HoloLens 2 (hologram manipulation, orbit tracing, anatomical plane visualization, and hologram-to-object registration), and the National Aeronautics and Space Administration Task Load Index for cognitive load assessment. Outcome measures included completion time, slips (unintentional errors), and tracing quality. Results All analyses used a significance of α=.05. MRT scores did not predict baseline performance time (Pearson r =0.15, 95% CI −0.32 to 0.55; P =.54) or error rates ( r =0.18, 95% CI −0.27 to 0.57; P =.43). Participants with extensive video game experience (>5 hours/week) made fewer slips (unpaired t test; mean difference −2.62 slips, 95% CI −5.19 to −0.04; P =.047), without faster completion times (Mann-Whitney test; median difference −22 seconds, 95% CI −7.00 to 57.00; P =.24). Video game experience did not predict baseline performance time (Pearson r =−0.35, 95% CI −0.69 to 0.13; P =.14). Significant learning effects emerged in unadjusted analyses: completion times decreased on attempts 2 and 3 compared with attempt 1 (mixed-effects analysis: mean difference 28.75 seconds, 95% CI 12.98-44.52; P

Fields of Study & Subjects

Augmented Reality Applications Virtual Reality Applications and Impacts Surgical Simulation and Training

Publication details

Journal / source
JMIR XR and spatial computing.
Volume
3
Pages
e81236–e81236
Publication date
12 March 2026
Language
EN