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dc.contributor.author
Kramer, Jan-Niklas
dc.contributor.author
Künzler, Florian
dc.contributor.author
Mishra, Varun
dc.contributor.author
Presset, Bastien
dc.contributor.author
Kotz, David
dc.contributor.author
Smith, Shawna
dc.contributor.author
Scholz, Urte
dc.contributor.author
Kowatsch, Tobias
dc.date.accessioned
2019-02-14T16:17:48Z
dc.date.available
2018-11-04T21:53:48Z
dc.date.available
2018-11-07T09:40:56Z
dc.date.available
2019-02-14T16:17:01Z
dc.date.available
2019-02-14T16:17:48Z
dc.date.issued
2019-01-31
dc.identifier.issn
1929-0748
dc.identifier.other
10.2196/11540
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/300895
dc.identifier.doi
10.3929/ethz-b-000300895
dc.description.abstract
Background: Smartphones enable the implementation of just-in-time adaptive interventions (JITAIs) that tailor the delivery of health interventions over time to user- and time-varying context characteristics. Ideally, JITAIs include effective intervention components, and delivery tailoring is based on effective moderators of intervention effects. Using machine learning techniques to infer each user’s context from smartphone sensor data is a promising approach to further enhance tailoring. Objective: The primary objective of this study is to quantify main effects, interactions, and moderators of 3 intervention components of a smartphone-based intervention for physical activity. The secondary objective is the exploration of participants’ states of receptivity, that is, situations in which participants are more likely to react to intervention notifications through collection of smartphone sensor data. Methods: In 2017, we developed the Assistant to Lift your Level of activitY (Ally), a chatbot-based mobile health intervention for increasing physical activity that utilizes incentives, planning, and self-monitoring prompts to help participants meet personalized step goals. We used a microrandomized trial design to meet the study objectives. Insurees of a large Swiss insurance company were invited to use the Ally app over a 12-day baseline and a 6-week intervention period. Upon enrollment, participants were randomly allocated to either a financial incentive, a charity incentive, or a no incentive condition. Over the course of the intervention period, participants were repeatedly randomized on a daily basis to either receive prompts that support self-monitoring or not and on a weekly basis to receive 1 of 2 planning interventions or no planning. Participants completed a Web-based questionnaire at baseline and postintervention follow-up. Results: Data collection was completed in January 2018. In total, 274 insurees (mean age 41.73 years; 57.7% [158/274] female) enrolled in the study and installed the Ally app on their smartphones. Main reasons for declining participation were having an incompatible smartphone (37/191, 19.4%) and collection of sensor data (35/191, 18.3%). Step data are available for 227 (82.8%, 227/274) participants, and smartphone sensor data are available for 247 (90.1%, 247/274) participants. Conclusions: This study describes the evidence-based development of a JITAI for increasing physical activity. If components prove to be efficacious, they will be included in a revised version of the app that offers scalable promotion of physical activity at low cost.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
JMIR Publications
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Physical activity
en_US
dc.subject
mHealth
en_US
dc.subject
JITAI
en_US
dc.subject
Incentives
en_US
dc.subject
Self-regulation
en_US
dc.subject
Planning
en_US
dc.subject
State of receptivity
en_US
dc.title
Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Study Protocol of the ALLY Micro-Randomized Trial
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
JMIR Research Protocols
ethz.journal.volume
8
en_US
ethz.journal.issue
1
en_US
ethz.pages.start
e11540
en_US
ethz.size
17 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.publication.place
Toronto
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.
en_US
ethz.date.deposited
2018-11-04T21:53:50Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-02-14T16:17:03Z
ethz.rosetta.lastUpdated
2019-02-14T16:17:54Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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