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dc.contributor.authorSulaiman, Muhammad
dc.contributor.authorHåkansson, Anne
dc.contributor.authorKarlsen, Randi
dc.date.accessioned2023-01-18T08:47:53Z
dc.date.available2023-01-18T08:47:53Z
dc.date.issued2022
dc.description.abstractHealth promotion is to enable people to take control over their health. Digital health with mHealth empowers users to establish proactive health, ubiquitously. The users shall have increased control over their health to improve their life by being proactive. To develop proactive health with the principles of prediction, prevention, and ubiquitous health, artificial intelligence with mHealth can play a pivotal role. There are various challenges for establishing proactive mHealth. For example, the system must be adaptive and provide timely interventions by considering the uniqueness of the user. The context of the user is also highly relevant for proactive mHealth. The context provides parameters as input along with information to formulate the current state of the user. Automated decision-making is significant with user-level decision-making as it enables decisions to promote well-being by technological means without human involvement. This paper presents a design framework of AI-enabled proactive mHealth that includes automated decision-making with predictive analytics, Just-in-time adaptive interventions and a P5 approach to mHealth. The significance of user-level decision-making for automated decision-making is presented. Furthermore, the paper provides a holistic view of the user's context with profile and characteristics. The paper also discusses the need for multiple parameters as inputs, and the identification of sources e.g., wearables, sensors, and other resources, with the challenges in the implementation of the framework. Finally, a proof-of-concept based on the framework provides design and implementation steps, architecture, goals, and feedback process. The framework shall provide the basis for the further development of AI-enabled proactive mHealth.en_US
dc.identifier.citationSulaiman M, Håkansson A, Karlsen R. A Framework for AI-enabled Proactive mHealth with Automated Decision-making for a User’s Context. Biostec. 2022:111-124en_US
dc.identifier.cristinIDFRIDAID 2102354
dc.identifier.doi10.5220/0010843200003123
dc.identifier.issn2184-349X
dc.identifier.issn2184-4305
dc.identifier.urihttps://hdl.handle.net/10037/28290
dc.language.isoengen_US
dc.publisherSCITEPRESSen_US
dc.relation.journalBiostec
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 by SCITEPRESS – Science and Technology Publicationsen_US
dc.rights.urihttps://creativecommons.org/licenses/by-nd/4.0en_US
dc.rightsAttribution-NoDerivatives 4.0 International (CC BY-ND 4.0)en_US
dc.titleA Framework for AI-enabled Proactive mHealth with Automated Decision-making for a User’s Contexten_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)
Except where otherwise noted, this item's license is described as Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)