Evidence map›Paper›PMID 30702430›Full record

ArticleJMIR research protocols2019

Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Protocol of a Microrandomized Trial.

Jan-Niklas Kramer, Florian Künzler, Varun Mishra, Bastien Presset, David Kotz, Shawna Smith, Urte Scholz, Tobias Kowatsch

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03384550 (Investigating Different Intervention Components of a Smartphone App to Promote Physical Activity), which is not on this map. Cited by 38 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 2 pooled it
7.4field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03384550 nacompletednot on this map

Investigating Different Intervention Components of a Smartphone App to Promote Physical Activity: The ALLY Micro-Randomized Trial

TypeinterventionalSponsorUniversity of St.GallenRan2017 to 2018Enrolled274ConditionsPhysical ActivityArmsSelf-regulation coaching, Planning, Financial Incentives, Charity Incentives
3 · Its place in the literature

Who cites it

38 citing papers in PubMed, 2 syntheses or guidelines pooled it, 76 citations in OpenAlex.

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  4. Which Components of a Smartphone Walking App Help Users to Reach Personalized Step Goals? Results From an Optimization Trial.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 5 institutions in 2 countries.

Jan-Niklas KramerCenter for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland.ORCID http://orcid.org/0000-0002-8912-1209
Florian KünzlerCenter for Digital Health Interventions, Department of Management, Technology and Economics, Swiss Federal Institute of Technology, Zurich, Switzerland.ORCID http://orcid.org/0000-0001-9389-6931
Varun MishraDepartment of Computer Science, Dartmouth College, Hanover, NH, United States.ORCID http://orcid.org/0000-0003-3891-5460
Bastien PressetInstitute of Sports Studies, University of Lausanne, Lausanne, Switzerland.ORCID http://orcid.org/0000-0002-3263-7087
David KotzDepartment of Computer Science, Dartmouth College, Hanover, NH, United States.ORCID http://orcid.org/0000-0001-7411-2783
Shawna SmithInstitute for Social Research, University of Michigan - Ann Arbor, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0003-3526-3129
Urte ScholzDepartment of Psychology, University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0003-0184-5921
Tobias KowatschCenter for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland.ORCID http://orcid.org/0000-0001-5939-4145
Dartmouth College · USUniversity of St.Gallen · CHUniversity of Lausanne · CHUniversity of Michigan · USUniversity of Zurich · CH

Funding

Treatment Development & Evaluation CoreP30DA029926 · NIDA · DARTMOUTH COLLEGE · PI Lisa A. Marsch · 2011 to 2026
$21.5M
6 · The paper itself

Abstract

backgroundSmartphones 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.

objectiveThe 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.

methodsIn 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.

resultsData 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.

conclusionsThis 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.

trial registrationClinicalTrials.gov NCT03384550; https://clinicaltrials.gov/ct2/show/NCT03384550 (Archived by WebCite at http://www.webcitation.org/74IgCiK3d). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/11540.

Indexed as

incentivesmHealthphysical activityself-regulationsmartphonewalking

Identifiers

PMID30702430
PMCPMC6374735
OpenAlexW2898978558

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.