Evaluating User Perceptions and Usability of CommHEAT: A Community-Based Heat Alert Application
Date
2025-07-27
Authors
Yao, Tian
Thomas, Ruby
Diaz, Angelica Brito
Maduwuba, Chukwuma
Schwab, Nicholas
Losch, Mary
Krejci, Caroline C.
Caron, Jeremy
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Sage Publications
Abstract
This research aims to design a community-focused indoor heat emergency alert system for vulnerable populations during extreme heat events. The impacts of extreme heat events are becoming more severe, especially for vulnerable populations. Current warning systems lack information about indoor conditions, and many people do not realize the heat risk, appropriate actions, or available cooling resources. The CommHEAT application is designed based on the Fogg Behavior Model to promote people’s heat-protective behaviors during extreme heat events. It has three key features: customized indoor heat risk prediction, community monitoring and support, and location-based resources. A multi-mode concept testing (exploratory interaction, task-based scenarios, and semi-structured interviews) was conducted with nine heat-vulnerable residents and three stakeholders to evaluate the CommHEAT application prototype. The result shows that this smartphone-based app prototype is promising as an intervention for protecting people from extreme heat. However, the results also identified challenges like trust issues and privacy concerns. Future work will focus on improving the application and assessing its impact on residents’ behavior change during extreme heat events.
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Comments
This is a manuscript of a proceeding published as Yao, Tian, Michael C. Dorneich, Ruby Thomas, Angelica Brito Diaz, Chukwuma Maduwuba, Nicholas Schwab, Mary Losch, Caroline C. Krejci, Jeremy Caron, and Ulrike Passe. "Evaluating User Perceptions and Usability of CommHEAT: A Community-Based Heat Alert Application." In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, p. 10711813251359996. Sage CA: Los Angeles, CA: SAGE Publications, 2025. doi: https://doi.org/10.1177/10711813251359996.
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Funding
This project was funded by the National Science Foundation (NSF) Award # 2226880.