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Consumer Preferences for AI-Based Smart Home Medical Emergency Detection Among German Adults: Choice-Based Conjoint
Inga Jagemann1, Justin Baudisch1, Thorsten Jungeblut1
1Department of Engineering and Mathematics, Hochschule Bielefeld, Bielefeld, North Rhine-Westphalia, Germany, 49 521 106 ext 705.
Background:
Smart home technology powered by AI can detect anomalies and make emergency calls, enabling residents to live safely and independently. However, the adoption of such technologies for medical emergency detection remains limited.
Objective:
This study aimed to explore consumer preferences for AI-based smart home technology for medical emergency detection and identify predictors such as sociodemographic variables, AI literacy, and technology affinity.
Methods:
A sample of 300 participants (172/300, 57.33% female; 128/300, 42.67% male, aged 18-69 years) completed a choice-based conjoint analysis (CBCA). Participants evaluated 15 choice sets describing smart home variants based on cost, location, emergency detection rate, type of sensor, and data processing.
Results:
Cost was the most important attribute (relative importance [RI]=41%), followed by emergency detection rate (RI=19%), data processing (RI=14%), and location (RI=14%). The type of sensor was the least important attribute (RI=9%). The preferred configuration combined an annual subscription of €70 (US $82), a 95% detection rate, wearable sensors, personalized AI, and installation in both intimate and shared rooms. Notably, 68.3% (205/300) of participants showed a positive none utility, indicating that even the optimal configuration did not overcome general reluctance to adopt such systems. While most expected correlations between sociodemographic variables and attribute importances were not observed, a significant correlation between self-reported health status and emergency detection rate was found (r=.16, P=.007). Interestingly, 61% (167/270) of participants preferred AI over human involvement in data processing, but logistic regression revealed that a significant correlation between self-reported health status and emergency detection rate was found (OR=0.58, P=.04).
Conclusions:
These findings highlight the need to align smart home development with user preferences, emphasizing cost-effectiveness. Additionally, AI literacy plays an important role in technology adoption in the context of AI-based smart home technology. Further research is needed to understand and address the reluctance to adopt AI for medical emergency detection.