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dc.contributor.author
Mishra, Varun
dc.contributor.author
Künzler, Florian
dc.contributor.author
Kramer, Jan-Niklas
dc.contributor.author
Fleisch, Elgar
dc.contributor.author
Kowatsch, Tobias
dc.contributor.author
Kotz, David
dc.date.accessioned
2021-07-29T11:40:13Z
dc.date.available
2021-04-19T03:19:31Z
dc.date.available
2021-04-22T13:10:06Z
dc.date.available
2021-06-06T08:44:19Z
dc.date.available
2021-06-07T05:00:38Z
dc.date.available
2021-06-07T05:04:34Z
dc.date.available
2021-07-29T11:40:13Z
dc.date.issued
2021-06
dc.identifier.issn
2474-9567
dc.identifier.other
10.1145/3463492
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/479191
dc.identifier.doi
10.3929/ethz-b-000479191
dc.description.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. We leveraged prior work regarding receptivity to JITAIs and deployed a chatbot-based digital coach – Ally – that provided physical-activity interventions and motivated participants to achieve their step goals. We extended the original Ally app to include two types of machine-learning model that used contextual information about a person to predict when a person is receptive: a static model that was built before the study started and remained constant for all participants and an adaptive model that continuously learned the receptivity of individual participants and updated itself as the study progressed. For comparison, we included a control model that sent intervention messages at random times. The app randomly selected a delivery model for each intervention message. We observed that the machine-learning models led up to a 40% improvement in receptivity as compared to the control model. Further, we evaluated the temporal dynamics of the different models and observed that receptivity to messages from the adaptive model increased over the course of the study.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Association for Computing Machinery
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
digital health
en_US
dc.subject
digital health intervention
en_US
dc.subject
just-in-time adaptive intervention
en_US
dc.subject
physical activity
en_US
dc.subject
State of receptivity
en_US
dc.subject
machine learning
en_US
dc.title
Detecting Receptivity for mHealth Interventions in the Natural Environment
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2021-06-24
ethz.journal.title
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
ethz.journal.volume
5
en_US
ethz.journal.issue
2
en_US
ethz.journal.abbreviated
IMWUT
ethz.pages.start
74
en_US
ethz.size
24 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.scopus
ethz.publication.place
New York , NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03681 - Fleisch, Elgar / Fleisch, Elgar
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03681 - Fleisch, Elgar / Fleisch, Elgar
en_US
ethz.relation.isNewVersionOf
20.500.11850/452556
ethz.date.deposited
2021-04-19T03:19:47Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-07-29T11:40:19Z
ethz.rosetta.lastUpdated
2022-03-29T10:48:35Z
ethz.rosetta.versionExported
true
ethz.COinS
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