Item Type | Journal paper |
Abstract | Just-In-Time Adaptive Intervention (JITAI) is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user’s receptivity. To this end, we conducted a study in which we deployed machine-learning models to detect receptivity in the natural environment, i.e., in free-living conditions. |
Authors | Mishra, Varun; Künzler, Florian; Kramer, Jan-Niklas; Fleisch, Elgar; Kowatsch, Tobias & Kotz, David |
Journal or Publication Title | arXiv.org |
Language | English |
Subjects | computer science information management social sciences health sciences |
HSG Classification | contribution to scientific community |
HSG Profile Area | SoM - Business Innovation |
Refereed | No |
Date | 16 November 2020 |
Official URL | https://arxiv.org/abs/2011.08302 |
Depositing User | Prof. Dr. Tobias Kowatsch |
Date Deposited | 23 Nov 2020 15:00 |
Last Modified | 23 Nov 2020 15:01 |
URI: | https://www.alexandria.unisg.ch/publications/261516 |
Download
CitationMishra, Varun; Künzler, Florian; Kramer, Jan-Niklas; Fleisch, Elgar; Kowatsch, Tobias & Kotz, David (2020) Detecting Receptivity for mHealth Interventions in the Natural Environment. arXiv.org, Statisticshttps://www.alexandria.unisg.ch/id/eprint/261516
|