ArticleJMIR mHealth and uHealth2020
Comparison of a Collective Intelligence Tailored Messaging System on Smoking Cessation Between African American and White People Who Smoke: Quasi-Experimental Design.
Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02200432 (PERSPECT), which is not on this map. Cited by 10 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.
PERSPECT: Patient Experience Recommender System for Persuasive Communication Tailoring
Who cites it
10 citing papers in PubMed.
- Peer Texting to Promote Quitline Use and Smoking Cessation Among Rural Participants in Vietnam: Randomized Clinical Trial.International journal of public health · 2024Trial
- Efficacy of Web-Delivered Acceptance and Commitment Therapy (ACT) for Helping Black Adults Quit Smoking.Journal of racial and ethnic health disparities · 2023Trial
- Efficacy and utilization of an acceptance and commitment therapy-based smartphone application for smoking cessation among Black adults: secondary analysis of the iCanQuit randomized trial.Addiction (Abingdon, England) · 2022Trial
- Empowering AI assisted clinical drug development: tactics to address data bias, the digital divide and missing patient populations through AI and digital solutions.Frontiers in digital health · 2026Review
- Testing a Machine Learning-Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial.JMIR research protocols · 2025Article
- An mHealth Intervention With Financial Incentives to Promote Smoking Cessation and Physical Activity Among Black Adults: Protocol for a Feasibility Randomized Controlled Trial.JMIR research protocols · 2025Article
- An Overview of Innovative Approaches to Support Timely and Agile Health Communication Research and Practice.International journal of environmental research and public health · 2022Review
- Basic behavioral science research priorities in minority health and health disparities.Translational behavioral medicine · 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
- Health recommender systems to facilitate collaborative decision-making in chronic disease management: A scoping review.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundThe Patient Experience Recommender System for Persuasive Communication Tailoring (PERSPeCT) is a machine learning recommender system with a database of messages to motivate smoking cessation. PERSPeCT uses the collective intelligence of users (ie, preferences and feedback) and demographic and smoking profiles to select motivating messages. PERSPeCT may be more beneficial for tailoring content to minority groups influenced by complex, personally relevant factors.
objectiveThe objective of this study was to describe and evaluate the use of PERSPeCT in African American people who smoke compared with white people who smoke.
methodsUsing a quasi-experimental design, we compared African American people who smoke with a historical cohort of white people who smoke, who both received up to 30 emailed tailored messages over 65 days. People who smoke rated the daily message in terms of perceived influence on quitting smoking for 30 days. Our primary analysis compared daily message ratings between the two groups using a t test. We used a logistic model to compare 30-day cessation between the two groups and adjusted for covariates.
resultsThe study included 119 people who smoke (African Americans, 55/119; whites, 64/119). At baseline, African American people who smoke were significantly more likely to report allowing smoking in the home (P=.002); all other characteristics were not significantly different between groups. Daily mean ratings were higher for African American than white people who smoke on 26 of the 30 days (P<.001). Odds of quitting as measured by 30-day cessation were significantly higher for African Americans (odds ratio 2.3, 95% CI 1.04-5.53; P=.03) and did not change after adjusting for allowing smoking at home.
conclusionsOur study highlighted the potential of using a recommender system to personalize for African American people who smoke.
trial registrationClinicalTrials.gov NCT02200432; https://clinicaltrials.gov/ct2/show/NCT02200432. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/jmir.6465.
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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.