{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:07:18Z","timestamp":1782846438421,"version":"3.54.5"},"reference-count":61,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR XR Spatial Comput"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n All analyses used a significance of \u03b1=.05. MRT scores did not predict baseline performance time (Pearson\n r<\/jats:italic>\n =0.15, 95% CI \u22120.32 to 0.55;\n P<\/jats:italic>\n =.54) or error rates (\n r<\/jats:italic>\n =0.18, 95% CI \u22120.27 to 0.57;\n P<\/jats:italic>\n =.43). Participants with extensive video game experience (>5 hours\/week) made fewer slips (unpaired\n t<\/jats:italic>\n test; mean difference \u22122.62 slips, 95% CI \u22125.19 to \u22120.04;\n P<\/jats:italic>\n =.047), without faster completion times (Mann-Whitney test; median difference \u221222 seconds, 95% CI \u22127.00 to 57.00;\n P<\/jats:italic>\n =.24). Video game experience did not predict baseline performance time (Pearson\n r<\/jats:italic>\n =\u22120.35, 95% CI \u22120.69 to 0.13;\n P<\/jats:italic>\n =.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;\n P<\/jats:italic>\n <.001; 28.00 seconds, 95% CI 10.75-45.25;\n P<\/jats:italic>\n =.002, respectively) with fewer slips (Friedman test:\n \u03c7<\/jats:italic>\n 2<\/jats:sup>\n 2<\/jats:sub>\n =17.8;\n P<\/jats:italic>\n <.001; Dunn post hoc:\n P<\/jats:italic>\n =.008 and\n P<\/jats:italic>\n <.001, respectively). Orbit tracing (Wilcoxon test: median difference \u22125 seconds;\n P<\/jats:italic>\n =.004) and virtual landmark placement times improved (Friedman test:\n \u03c7<\/jats:italic>\n 2<\/jats:sup>\n 3<\/jats:sub>\n =14.6;\n P<\/jats:italic>\n =.002; Dunn post hoc;\n P<\/jats:italic>\n =.009 and\n P<\/jats:italic>\n =.02), but physical landmark placement did not. Covariate-adjusted models revealed no significant trial-by-covariate interactions.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Visuospatial ability does not predict clinically relevant AR performance, while extensive video game experience was associated with fewer errors. Despite previous studies emphasizing inherent learner characteristics in laparoscopy and endoscopy, covariate-adjusted models showed that AR learning curves were not significantly modified by MRT or video game experience. These findings suggest that early AR performance improvements among novice users are primarily driven by learning rather than visuospatial ability, supporting training approaches that emphasize structured practice, although the modest sample size limits detection of smaller effects.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/81236","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T06:50:06Z","timestamp":1773298206000},"page":"e81236-e81236","source":"Crossref","is-referenced-by-count":0,"title":["Predictive Factors of Augmented Reality\u2013Based Clinical Task Performance Among Novice Users: Cross-Sectional Quantitative Study"],"prefix":"10.2196","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6771-2460","authenticated-orcid":false,"given":"Amogh 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