Augmented Reality Animations for Smoking Cessation Counseling Training

This proposed study aims to develop augmented reality (AR) based intervention during the smoking cessation counselling(SCC) training for master nursing students in the University of Hong Kong. Hypotheses to be tested: (1) AR animations can improve nursing students\' knowledge, skills in SCC compared to conventional teaching methods. (2)Student satisfaction and engagement with AR-based learning can be improved. (3) The training will increase master nursing students\' self-efficacy to application of 5As SCC skill. The study is a pragmatic randomized controlled trial (RCT) with a 1:1 allocation ratio, using AR animations for intervention and control groups. The intervention group receives messages related to SCC, while the control group receives generic mental health information. The subjects are MN students enrolled in the \"Tobacco Dependency Nursing Intervention and Management\" course. The study uses various tools for measurement, including Providers Smoking Cessation Training Evaluation(ProSCiTE), and The Instructional Materials Motivation Survey (IMMS). The main outcome measures include SCC practice frequency,SCC knowledge score, SCC attitude score, and SCC practice score. Data will be entered into SPSS for Windows (version 20) for analysis. Descriptive statistics including frequency, percentage, and mean will be used to summarize the outcomes and other variables. By intention-to-treat analysis, participants who are lost or refuse the follow-up will be treated as no change in the training outcomes. Chi-square tests and t-tests will be used to compare outcomes between intervention and control groups. The effect size, Cohen\'s d, will be computed for the standardized mean of the pre-post differences, both with and without adjustment for baseline characteristics. Additionally, Cohen\'s f will be calculated to assess the magnitude of the intervention effect in the linear mixed models. These models, which account for multiple observations per subject and clustering effects, will be used to analyze the intervention\'s impact on self-efficacy, motivation, and KAP. Both the main effect (group allocation) and interaction effect (group × time) will be examined to determine the intervention\'s effectiveness.