Employing environmental data and machine learning to improve mobile health receptivity
Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-ti...
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| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
December 9, 2019
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| In: |
IEEE access
Year: 2019, Jahrgang: 7, Pages: 179823-179841 |
| ISSN: | 2169-3536 |
| Online-Zugang: |
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| Verfasserangaben: | Max-M. Theilig, Jakob J. Korbel, Gwendolyn Mayer, Christian Hoffmann, and Rüdiger Zarnekow |
| Zusammenfassung: | Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. |
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| Beschreibung: | Date of current version December 23, 2019 Gesehen am 01.04.2020 |
| Beschreibung: | Online Resource |
| ISSN: | 2169-3536 |