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Rejection of Mobile AI-enabled Health Technologies: First Results from an Interview-based Study

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dc.contributor.authorLaumer, Sven
dc.contributor.authorHorneber, David
dc.date.accessioned2024-08-21T11:08:41Z
dc.date.available2024-08-21T11:08:41Z
dc.date.issued2024
dc.description.abstractOverweight and obesity are common health problems. To address these problems, mobile health technologies based on artificial intelligence (AI) have emerged. These technologies aim to encourage healthy behaviors by helping individuals monitor their exercise and dietary behavior and providing personalized recommendations to support their weight loss. Despite their potential, research has shown that users often abandon these apps prematurely. To address this, our research-in-progress investigates user resistance during the trial phase of mobile health AI-based technology adoption. We propose several factors that explain trial-period rejection, occurring when individuals either fully embrace or reject the technology. This rejection can manifest at the service, digital, or device level. By understanding these sources of resistance, we can enhance the effectiveness of mobile AI-enabled health technologies.en
dc.identifier.doi10.18420/muc2024-mci-ws05-220
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/44361
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofMensch und Computer 2024 - Workshopband
dc.relation.ispartofseriesMensch und Computer
dc.rightshttps://creativecommons.org/licenses/by-nc/4.0/
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectmobile health
dc.subjectrejection
dc.subjecttrial period
dc.subjectobesity
dc.titleRejection of Mobile AI-enabled Health Technologies: First Results from an Interview-based Studyen
dc.typeText/Workshop Paper
gi.conference.date1.-4. September 2024
gi.conference.locationKarlsruhe
gi.conference.sessiontitleMCI-WS05: AI and Health: Using Digital Twins to Foster Healthy Behavior

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