{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T21:12:24Z","timestamp":1789506744834,"version":"build-2803163510"},"reference-count":74,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR AI"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Method<\/jats:title>\n A sample of 300 participants (172\/300, 57.33% female; 128\/300, 42.67% male, aged 18\u201069 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.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n 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 \u20ac70 (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,\n P<\/jats:italic>\n =.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 (\n OR=0.58, P<\/jats:italic>\n =.04).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/94589","type":"journal-article","created":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T16:10:07Z","timestamp":1784909407000},"page":"e94589-e94589","source":"Crossref","is-referenced-by-count":0,"title":["Consumer Preferences for AI-Based Smart Home Medical Emergency Detection Among German Adults: Choice-Based Conjoint Analysis"],"prefix":"10.2196","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3468-3423","authenticated-orcid":false,"given":"Inga","family":"Jagemann","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5565-0228","authenticated-orcid":false,"given":"Justin","family":"Baudisch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7425-8766","authenticated-orcid":false,"given":"Thorsten","family":"Jungeblut","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6818-5617","authenticated-orcid":false,"given":"G\u00fcnter W","family":"Maier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2143-4564","authenticated-orcid":false,"given":"Gerrit","family":"Hirschfeld","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.2196\/37347","article-title":"Health monitoring using smart home technologies: scoping review","volume":"11","author":"Morita","journal-title":"JMIR Mhealth Uhealth"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.a2227","article-title":"Inability to get up after falling, subsequent time on floor, and summoning help: prospective cohort study in people over 90","volume":"337","author":"Fleming","journal-title":"BMJ"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.1016\/j.arr.2025.102948","article-title":"Artificial intelligence for fall detection in older adults: a comprehensive survey of machine learning, deep learning approaches, and future directions","volume":"113","author":"Gattani","journal-title":"Ageing Res Rev"},{"issue":"3","key":"R4","doi-asserted-by":"publisher","DOI":"10.3390\/s23031105","article-title":"Transfer learning on small datasets for improved fall detection","volume":"23","author":"Maray","journal-title":"Sensors (Basel)"},{"key":"R5","doi-asserted-by":"publisher","DOI":"10.1016\/j.tele.2021.101689","article-title":"Users\u2019 preferences for smart home automation \u2013 investigating aspects of privacy and trust","volume":"64","author":"Schomakers","journal-title":"Telemat Inform"},{"key":"R6","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2023.1139667","article-title":"A study on community older people\u2019s willingness to use smart home-an extended technology acceptance model with intergenerational relationships","volume":"11","author":"Wei","journal-title":"Front Public Health"},{"issue":"4","key":"R7","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1177\/1539449218813906","article-title":"Profiles and predictors of smart home technology adoption by older adults","volume":"39","author":"Arthanat","journal-title":"OTJR (Thorofare N J)"},{"key":"R8","unstructured":"Awareness of a smart or connected home device in the United States in 2022, by age 2022. 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