ArticleAmerican journal of preventive medicine2013
Crowdsourced peer- versus expert-written smoking-cessation messages.
Article in American journal of preventive medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Cited by 29 papers, 2 of them syntheses that pooled 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.
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.
QUIT-PRIMO: Web-Delivered Clinical Microsystem Intervention for Tobacco Control
Hygienists Internet Tobacco Cessation Study (HiQuit)
Effectiveness of WhatsApp Online Group Discussion for Smoking Relapse Prevention: a Pragmatic Randomized Controlled Trial
Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 71 citations in OpenAlex.
- Mapping of Crowdsourcing in Health: Systematic Review.Journal of medical Internet research · 2018Pooled it
- Internet-based interventions for smoking cessation.The Cochrane database of systematic reviews · 2017Pooled it
- Feasibility and Engagement of a Peer-Driven Mobile Intervention for Adolescent E-Cigarette Cessation: Cluster Randomized Pilot Study.JMIR formative research · 2026Trial
- The role of mood in shaping reactions to smoking cessation messages among adults who smoke: a multimodal investigation.BMC public health · 2024Trial
- Peer Texting to Promote Quitline Use and Smoking Cessation Among Rural Participants in Vietnam: Randomized Clinical Trial.International journal of public health · 2024Trial
- Effect of a Machine Learning Recommender System and Viral Peer Marketing Intervention on Smoking Cessation: A Randomized Clinical Trial.JAMA network open · 2023Trial
- Impact of a Collective Intelligence Tailored Messaging System on Smoking Cessation: The Perspect Randomized Experiment.Journal of medical Internet research · 2016Trial
- Evaluating the QUIT-PRIMO clinical practice ePortal to increase smoker engagement with online cessation interventions: a national hybrid type 2 implementation study.Implementation science : IS · 2015Trial
- Leveraging Social Media to Achieve Population-Level Reach of Lung Cancer Screening-Eligible Individuals: A RE-AIM Framework Perspective.Journal of medical Internet research · 2026Article
- Perceptions of User-Generated Content as a Source of Health Messages in Smoking Cessation Mobile Interventions: Focus Group Study.JMIR human factors · 2025Article
- Testing a Machine Learning-Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial.JMIR research protocols · 2025Article
- Stopping use of E-cigarettes and smoking combustible cigarettes: findings from a large longitudinal digital smoking cessation intervention study in the United States.BMC research notes · 2024Article
- Developing Mood-Based Computer-Tailored Health Communication for Smoking Cessation: Feasibility Randomized Controlled Trial.JMIR formative research · 2023Article
- Video-based communication assessment for weight management counseling training in medical residents: a mixed methods study.BMC medical education · 2022Article
- Use of a Rapid Qualitative Method to Inform the Development of a Text Messaging Intervention for People With Serious Mental Illness Who Smoke: Formative Research Study.JMIR formative research · 2022Article
- Involving Crowdworkers with Lived Experience in Content-Development for Push-Based Digital Mental Health Tools: Lessons Learned from Crowdsourcing Mental Health Messages.Proceedings of the ACM on human-computer interaction · 2022Article
- mHealth Messaging to Motivate Quitline Use and Quitting: Protocol for a Community-Based Randomized Controlled Trial in Rural Vietnam.JMIR research protocols · 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
- Written Advice Given by African American Smokers to Their Peers: Qualitative Study of Motivational Messages.JMIR formative research · 2021Article
- Agreement and Disparities between Women and Stop-Smoking Specialists about How to Promote Adherence to Nicotine Replacement Therapy in Pregnancy.International journal of environmental research and public health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundTailored, web-assisted interventions can reach many smokers. Content from other smokers (peers) through crowdsourcing could enhance relevance. PURPOSE: To evaluate whether peers can generate tailored messages encouraging other smokers to use a web-assisted tobacco intervention (Decide2Quit.org).
methodsPhase 1: In 2009, smokers wrote messages in response to scenarios for peer advice. These smoker-to-smoker (S2S) messages were coded to identify themes. Phase 2: resulting S2S messages, and comparison expert messages, were then e-mailed to newly registered smokers. In 2012, subsequent Decide2Quit.org visits following S2S or expert-written e-mails were compared.
resultsPhase 1: a total of 39 smokers produced 2886 messages (message themes: attitudes and expectations, improvements in quality of life, seeking help, and behavioral strategies). For not-ready-to-quit scenarios, S2S messages focused more on expectations around a quit attempt and how quitting would change an individual's quality of life. In contrast, for ready-to-quit scenarios, S2S messages focused on behavioral strategies for quitting. Phase 2: In multivariable analysis, S2S messages were more likely to generate a return visit (OR=2.03, 95% CI=1.74, 2.35), compared to expert messages. A significant effect modification of this association was found, by time-from-registration and message codes (both interaction terms p<0.01). In stratified analyses, S2S codes that were related more to "social" and "real-life" aspects of smoking were driving the main association of S2S and increased return visits.
conclusionsS2S peer messages that increased longitudinal engagement in a web-assisted tobacco intervention were successfully collected and delivered. S2S messages expanded beyond the biomedical model to enhance relevance of messages.
trial registrationThis study is registered at www.clinicaltrials.gov NCT00797628 (web-delivered provider intervention for tobacco control [QUIT-PRIMO]) and NCT01108432 (DPBRN Hygienists Internet Quality Improvement in Tobacco Cessation [HiQuit]).
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.