---
res:
  bibo_abstract:
  - "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.\r\n\r\nObjective:
    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.\r\n\r\nMethod: 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.\r\n\r\nResults: 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).\r\n\r\nConclusions:
    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.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Inga
      foaf_name: Jagemann, Inga
      foaf_surname: Jagemann
      foaf_workInfoHomepage: http://www.librecat.org/personId=252878
    orcid: 0000-0002-3468-3423
    orcid_put_code_url: https://api.orcid.org/v2.0/0000-0002-3468-3423/work/226873432
  - foaf_Person:
      foaf_givenName: Justin
      foaf_name: Baudisch, Justin
      foaf_surname: Baudisch
      foaf_workInfoHomepage: http://www.librecat.org/personId=247397
    orcid: 0000-0001-5565-0228
    orcid_put_code_url: https://api.orcid.org/v2.0/0000-0001-5565-0228/work/226873433
  - foaf_Person:
      foaf_givenName: Thorsten
      foaf_name: Jungeblut, Thorsten
      foaf_surname: Jungeblut
      foaf_workInfoHomepage: http://www.librecat.org/personId=242294
    orcid: 0000-0001-7425-8766
    orcid_put_code_url: https://api.orcid.org/v2.0/0000-0001-7425-8766/work/226873434
  - foaf_Person:
      foaf_givenName: Günter W
      foaf_name: Maier, Günter W
      foaf_surname: Maier
  - foaf_Person:
      foaf_givenName: Gerrit
      foaf_name: Hirschfeld, Gerrit
      foaf_surname: Hirschfeld
      foaf_workInfoHomepage: http://www.librecat.org/personId=234690
    orcid: 0000-0003-2143-4564
    orcid_put_code_url: https://api.orcid.org/v2.0/0000-0003-2143-4564/work/226873436
  bibo_doi: 10.2196/94589
  bibo_volume: 5
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2817-1705
  dct_language: eng
  dct_publisher: JMIR Publications Inc.@
  dct_title: 'Consumer Preferences for AI-Based Smart Home Medical Emergency Detection
    Among German Adults: Choice-Based Conjoint Analysis@'
...