ArticleBMC public health2021
Evaluating the use of a recommender system for selecting optimal messages for smoking cessation: patterns and effects of user-system engagement.
Article in BMC public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03224520 (Smoker-to-Smoker), which is not on this map. Cited by 7 papers.
What it found
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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.
Smoker-to-Smoker (S2S) Peer Marketing and Messaging to Disseminate Tobacco
Who cites it
7 citing papers in PubMed.
- Artificial intelligence in clinical trial participant recruitment and retention: A scoping review and meta-analysis.Journal of clinical and translational science · 2026Review
- Artificial Intelligence in Health Promotion and Disease Reduction: Rapid Review.Journal of medical Internet research · 2025Review
- Testing a Machine Learning-Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial.JMIR research protocols · 2025Article
- Article
- Predictors of smoking cessation outcomes identified by machine learning: A systematic review.Addiction neuroscience · 2023Article
- Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence Mapping.Journal of medical Internet research · 2023Article
- Health recommender systems to facilitate collaborative decision-making in chronic disease management: A scoping review.Digital healthArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundMotivational messaging is a frequently used digital intervention to promote positive health behavior changes, including smoking cessation. Typically, motivational messaging systems have not actively sought feedback on each message, preventing a closer examination of the user-system engagement. This study assessed the granular user-system engagement around a recommender system (a new system that actively sought user feedback on each message to improve message selection) for promoting smoking cessation and the impact of engagement on cessation outcome.
methodsWe prospectively followed a cohort of current smokers enrolled to use the recommender system for 6 months. The system sent participants motivational messages to support smoking cessation every 3 days and used machine learning to incorporate user feedback (i.e., user's rating on the perceived influence of each message, collected on a 5-point Likert scale with 1 indicating strong disagreement and 5 indicating strong agreement on perceiving the influence on quitting smoking) to improve the selection of the following message. We assessed user-system engagement by various metrics, including user response rate (i.e., the percent of times a user rated the messages) and the perceived influence of messages. We compared retention rates across different levels of user-system engagement and assessed the association between engagement and the 7-day point prevalence abstinence (missing outcome = smoking) by using multiple logistic regression.
resultsWe analyzed data from 731 participants (13% Black; 73% women). The user response rate was 0.24 (SD = 0.34) and user-perceived influence was 3.76 (SD = 0.84). The retention rate positively increased with the user response rate (trend test P < 0.001). Compared with non-response, six-month cessation increased with the levels of response rates: low response rate (odds ratio [OR] = 1.86, 95% confidence interval [CI]: 1.07-3.23), moderate response rate (OR = 2.30, 95% CI: 1.36-3.88), high response rate (OR = 2.69, 95% CI: 1.58-4.58). The association between perceived message influence and the outcome showed a similar pattern.
conclusionsHigh user-system engagement was positively associated with both high retention rate and smoking cessation, suggesting that investigation of methods to increase engagement may be crucial to increase the impact of the recommender system for smoking cessation.
trial registrationRegistration Identifier: NCT03224520 . Registration date: July 21, 2017.
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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.