Exploring the effects of time pressure and distracting elements in an Augmented Reality game for emergency preparedness
Year: 2024
Type: article
Abstract: The development of emergency preparedness and response skills is crucial for ensuring individual safety and well-being during natural disasters. Realistic and engaging games can effectively promote these skills. This paper explores the impact of introducing additional game objects as distractors and time pressure on user experience and willingness to prepare for emergencies in an Augmented Reality (AR) game. Additional, distracting game elements and time pressure are included in the game scenario to simulate the cognitive demands and stress experienced in real emergency situations. To investigate the impact of distracting game elements and time pressure, a within-subjects study was conducted with 52 participants who played two different versions of an AR game designed to help locate recommended items for an emergency grab bag. In the first version, participants focused solely on finding the recommended items, while in the second version, distractors and a timer were introduced to emphasize the urgency of packing the bag. The results showed no significant difference in user experience between the two versions. However, the version with time pressure and distractors demonstrated a significant improvement in attitudes and perceived behavioral control regarding immediate actions in response to evacuation warnings. Qualitative feedback from participants supported these effects. These findings suggest new directions for further research and highlight the potential of AR games to enhance emergency response strategies. • AR game with distractions and time pressure enhanced participants’ attitudes and perceived control for immediate action on evacuation warnings. • User experience was similar in both game versions, with and without time pressure and distracting elements added.
Authors: Misha Mirza, Stephan Lukosch, Heide Lukosch
Institution: University of Canterbury
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,620
OpenAlex ID: W4403777198