Evidence map›Paper›PMID 29871595›Full record

ArticleBMC public health2018

Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol.

Santiago Hors-Fraile, Francine Schneider, Luis Fernandez-Luque, Francisco Luna-Perejon, Anton Civit, Dimitris Spachos, Panagiotis Bamidis, Hein de Vries

Registry-linked trialAbstract read
In one paragraph

Article in BMC public health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03206619 (Study on Patient Acceptance and Engagement of Timely Tailored Motivational Messages Sent by a Health Recommender System and Delivered Via a Mobile App for Smoking Cessation), which is not on this map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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.

NCT03206619 completednot on this map

Study on Patient Acceptance and Engagement of Timely Tailored Motivational Messages Sent by a Health Recommender System and Delivered Via a Mobile App for Smoking Cessation

TypeobservationalSponsorUniversity of SevilleRan2016 to 2018Enrolled120ConditionsSmoking Cessation, Health Behavior, Smoking, Tobacco, SmokingArmsSocial, Local and Mobile
3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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  7. Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review.International journal of environmental research and public health · 2022
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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.

Santiago Hors-FraileDepartment of Architecture and Computer Technology, Universidad de Sevilla, ETSII, Avenida Reina Mercedes S/N, 41012, Seville, Spain. sanhorfra@gmail.com.ORCID 0000-0003-3979-2044
Francine SchneiderDepartment of Health Promotion, School for Public Health and Primary Care (Caphri), Maastricht University, P. Debyeplein 1, 6229, HA, Maastricht, The Netherlands.
Luis Fernandez-LuqueQatar Computing Research Institute, Hamad bin Khalifa University, Education City, Doha, Qatar.
Francisco Luna-PerejonDepartment of Architecture and Computer Technology, Universidad de Sevilla, ETSII, Avenida Reina Mercedes S/N, 41012, Seville, Spain.
Anton CivitDepartment of Architecture and Computer Technology, Universidad de Sevilla, ETSII, Avenida Reina Mercedes S/N, 41012, Seville, Spain.
Dimitris SpachosMedical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Panagiotis BamidisMedical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Hein de VriesDepartment of Health Promotion, School for Public Health and Primary Care (Caphri), Maastricht University, P. Debyeplein 1, 6229, HA, Maastricht, The Netherlands.

Funding

Horizon 2020 681120
6 · The paper itself

Abstract

backgroundSmoking is one of the most avoidable health risk factors, and yet the quitting success rates are low. The usage of tailored health messages to support quitting has been proved to increase quitting success rates. Technology can provide convenient means to deliver tailored health messages. Health recommender systems are information-filtering algorithms that can choose the most relevant health-related items-for instance, motivational messages aimed at smoking cessation-for each user based on his or her profile. The goals of this study are to analyze the perceived quality of an mHealth recommender system aimed at smoking cessation, and to assess the level of engagement with the messages delivered to users via this medium.

methodsPatients participating in a smoking cessation program will be provided with a mobile app to receive tailored motivational health messages selected by a health recommender system, based on their profile retrieved from an electronic health record as the initial knowledge source. Patients' feedback on the messages and their interactions with the app will be analyzed and evaluated following an observational prospective methodology to a) assess the perceived quality of the mobile-based health recommender system and the messages, using the precision and time-to-read metrics and an 18-item questionnaire delivered to all patients who complete the program, and b) measure patient engagement with the mobile-based health recommender system using aggregated data analytic metrics like session frequency and, to determine the individual-level engagement, the rate of read messages for each user. This paper details the implementation and evaluation protocol that will be followed. DISCUSSION: This study will explore whether a health recommender system algorithm integrated with an electronic health record can predict which tailored motivational health messages patients would prefer and consider to be of a good quality, encouraging them to engage with the system. The outcomes of this study will help future researchers design better tailored motivational message-sending recommender systems for smoking cessation to increase patient engagement, reduce attrition, and, as a result, increase the rates of smoking cessation.

trial registrationThe trial was registered at clinicaltrials.org under the ClinicalTrials.gov identifier NCT03206619 on July 2nd 2017. Retrospectively registered.

Indexed as

MotivationTelemedicineAlgorithmsElectronic Health RecordsHealth CommunicationHumansMobile ApplicationsResearch DesignSmoking CessationmHealthMobile appPatientRecommender systemSmoking cessationTailored messages

Identifiers

PMID29871595
PMCPMC5989385

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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.