Evidence map›Paper›PMID 34199880›Full record

ArticleInternational journal of environmental research and public health2021

Reinforcement Learning to Send Reminders at Right Moments in Smartphone Exercise Application: A Feasibility Study.

Shihan Wang, Karlijn Sporrel, Herke van Hoof, Monique Simons, Rémi D D de Boer, Dick Ettema, Nicky Nibbeling, Marije Deutekom, Ben Kröse

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Observational
  5. Article
  6. Article
  7. Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care.Proceedings of the ... Innovative Applications of Artificial Intelligence Conference. Innovative Applications of Artificial Intelligence Conference · 2023
    Article
  8. Article
  9. Article
  10. Article
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

9 authors.

Shihan WangInformatics Institute, University of Amsterdam, 1090 GH Amsterdam, The Netherlands.
Karlijn SporrelDepartment of Human Geography and Spatial Planning, Utrecht University, 3584 CS Utrecht, The Netherlands.ORCID 0000-0002-2757-8553
Herke van HoofInformatics Institute, University of Amsterdam, 1090 GH Amsterdam, The Netherlands.
Monique SimonsConsumption & Healthy Lifestyles Group, Wageningen University & Research, 6700 HB Wageningen, The Netherlands.
Rémi D D de BoerDigital Life Centre, Amsterdam University of Applied Science, 1091 GC Amsterdam, The Netherlands.
Dick EttemaDepartment of Human Geography and Spatial Planning, Utrecht University, 3584 CS Utrecht, The Netherlands.
Nicky NibbelingCentre of Expertise Urban Vitality, Amsterdam University of Applied Science, 1097 DZ Amsterdam, The Netherlands.ORCID 0000-0002-3982-2349
Marije DeutekomFaculty of Health, Sports and Welfare, Inholland University of Applied Sciences, 2015 CE Haarlem, The Netherlands.
Ben KröseInformatics Institute, University of Amsterdam, 1090 GH Amsterdam, The Netherlands.ORCID 0000-0003-1237-0618

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Just-in-time adaptive intervention (JITAI) has gained attention recently and previous studies have indicated that it is an effective strategy in the field of mobile healthcare intervention. Identifying the right moment for the intervention is a crucial component. In this paper the reinforcement learning (RL) technique has been used in a smartphone exercise application to promote physical activity. This RL model determines the 'right' time to deliver a restricted number of notifications adaptively, with respect to users' temporary context information (i.e., time and calendar). A four-week trial study was conducted to examine the feasibility of our model with real target users. JITAI reminders were sent by the RL model in the fourth week of the intervention, while the participants could only access the app's other functionalities during the first 3 weeks. Eleven target users registered for this study, and the data from 7 participants using the application for 4 weeks and receiving the intervening reminders were analyzed. Not only were the reaction behaviors of users after receiving the reminders analyzed from the application data, but the user experience with the reminders was also explored in a questionnaire and exit interviews. The results show that 83.3% reminders sent at adaptive moments were able to elicit user reaction within 50 min, and 66.7% of physical activities in the intervention week were performed within 5 h of the delivery of a reminder. Our findings indicated the usability of the RL model, while the timing of the moments to deliver reminders can be further improved based on lessons learned.

Indexed as

ExerciseSmartphoneFeasibility StudiesHumansSurveys and Questionnairesjust-in-time adaptive interventionmobile applicationphysical activityreinforcement learningreminder

Identifiers

PMID34199880
PMCPMC8200090

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Registered trials

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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.