ArticleBMC public health2018
Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol.
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.
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.
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.
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
Who cites it
17 citing papers in PubMed.
- Tailored Parenting Plans of Young Adults With Sickle Cell Disease or Sickle Cell Trait.Computers, informatics, nursing : CIN · 2022Trial
- A Mobile Health Solution Complementing Psychopharmacology-Supported Smoking Cessation: Randomized Controlled Trial.JMIR mHealth and uHealth · 2020Trial
- Incorporating Behavioral Trigger Messages Into a Mobile Health App for Chronic Disease Management: Randomized Clinical Feasibility Trial in Diabetes.JMIR mHealth and uHealth · 2020Trial
- Developing Message Strategies to Engage Racial and Ethnic Minority Groups in Digital Oral Self-Care Interventions: Participatory Co-Design Approach.JMIR formative research · 2023Article
- Development and Promotion of an mHealth App for Adolescents Based on the European Code Against Cancer: Retrospective Cohort Study.JMIR cancer · 2023Article
- Feasibility of generating structured motivational messages for tailored physical activity coaching.Frontiers in digital health · 2023Article
- Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review.International journal of environmental research and public health · 2022Article
- A Digital Video and Text Messaging Intervention to Support People With Chronic Pain During Opioid Tapering: Content Development Using Co-design.JMIR formative research · 2022Article
- Article
- Development of a computer-aided text message platform for user engagement with a digital Diabetes Prevention Program: a case study.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Article
- Need-based care of multi-morbid patients - supporting general practitioners with algorithm-generated recommendations of healthcare services (telemedicine-project ATMoSPHÄRE).BMC family practice · 2021Article
- Evaluating the use of a recommender system for selecting optimal messages for smoking cessation: patterns and effects of user-system engagement.BMC public health · 2021Article
- Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering.PloS one · 2021Article
- A minimum data set of user profile or electronic health record for chemical warfare victims' recommender system.Journal of family medicine and primary care · 2020Article
- Digital Health Behavior Change Technology: Bibliometric and Scoping Review of Two Decades of Research.JMIR mHealth and uHealth · 2019Article
- Using the Social-Local-Mobile App for Smoking Cessation in the SmokeFreeBrain Project: Protocol for a Randomized Controlled Trial.JMIR research protocols · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
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
Identifiers
What OpenQuestion holds
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.