ArticleJournal of medical Internet research2013
Who participates in Web-assisted tobacco interventions? The QUIT-PRIMO and National Dental Practice-Based Research Network Hi-Quit studies.
Article in Journal of medical Internet research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 25 citations in OpenAlex.
- 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
- Impact of seasonality on recruitment, retention, adherence, and outcomes in a web-based smoking cessation intervention: randomized controlled trial.Journal of medical Internet research · 2013Trial
- 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
- Comparing recruitment strategies for a digital smoking cessation intervention: Technology-assisted peer recruitment, social media, ResearchMatch, and smokefree.gov.Contemporary clinical trials · 2021Article
- Impact of population tobacco control interventions on socioeconomic inequalities in smoking: a systematic review and appraisal of future research directions.Tobacco control · 2020Review
- Methodological Challenges in Web-Based Trials: Update and Insights From the Relatives Education and Coping Toolkit Trial.JMIR mental health · 2020Article
- Secure Asynchronous Communication Between Smokers and Tobacco Treatment Specialists: Secondary Analysis of a Web-Assisted Tobacco Intervention in the QUIT-PRIMO and National Dental PBRN Networks.Journal of medical Internet research · 2020Article
- Dissemination and Effectiveness of the Peer Marketing and Messaging of a Web-Assisted Tobacco Intervention: Protocol for a Hybrid Effectiveness Trial.JMIR research protocols · 2019Article
- Website Analytics of a Google Ads Campaign for a Men's Mental Health Website: Comparative Analysis.JMIR mental health · 2018Article
- Exploring Online Asynchronous Counseling With Tobacco Treatment Specialists in the QUIT-PRIMO and National Dental PBRN HI-QUIT Studies: Who Uses It and What Do They Say?American journal of health promotion : AJHP · 2018Article
- Share2Quit: Online Social Network Peer Marketing of Tobacco Cessation Systems.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2017Article
- Online tobacco websites and online communities-who uses them and do users quit smoking? The quit-primo and national dental practice-based research network Hi-Quit studies.Translational behavioral medicine · 2016Article
- Young Adult Utilization of a Smoking Cessation Website: An Observational Study Comparing Young and Older Adult Patterns of Use.JMIR research protocols · 2016Article
- Crave-Out: A Distraction/Motivation Mobile Game to Assist in Smoking Cessation.JMIR serious games · 2016Article
- Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century.Journal of medical Internet research · 2016Review
- Share2Quit: Web-Based Peer-Driven Referrals for Smoking Cessation.JMIR research protocols · 2013Article
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 3 institutions in 1 country.
Funding
Abstract
introductionSmoking is the most preventable cause of death. Although effective, Web-assisted tobacco interventions are underutilized and recruitment is challenging. Understanding who participates in Web-assisted tobacco interventions may help in improving recruitment.
objectivesTo understand characteristics of smokers participating in a Web-assisted tobacco intervention (Decide2Quit.org).
methodsIn addition to the typical Google advertisements, we expanded Decide2Quit.org recruitment to include referrals from medical and dental providers. We assessed how the expanded recruitment of smokers changed the users' characteristics, including comparison with a population-based sample of smokers from the national Behavioral Risk Factors Surveillance Survey (BRFSS). Using a negative binomial regression, we compared demographic and smoking characteristics by recruitment source, in particular readiness to quit and association with subsequent Decide2Quit.org use.
resultsThe Decide2Quit.org cohort included 605 smokers; the 2010 BRFSS dataset included 69,992. Compared to BRFSS smokers, a higher proportion of Decide2Quit.org smokers were female (65.2% vs 45.7%, P=.001), over age 35 (80.8% vs 67.0%, P=.001), and had some college or were college graduates (65.7% vs 45.9%, P=.001). Demographic and smoking characteristics varied by recruitment; for example, a lower proportion of medical- (22.1%) and dental-referred (18.9%) smokers had set a quit date or had already quit than Google smokers (40.1%, P<.001). Medical- and dental-referred smokers were less likely to use Decide2Quit.org functions; in adjusted analysis, Google smokers (predicted count 17.04, 95% CI 14.97-19.11) had higher predicted counts of Web page visits than medical-referred (predicted count 12.73, 95% CI 11.42-14.04) and dental-referred (predicted count 11.97, 95% CI 10.13-13.82) smokers, and were more likely to contact tobacco treatment specialists.
conclusionsRecruitment from clinical practices complimented Google recruitment attracting smokers less motivated to quit and less experienced with Web-assisted tobacco interventions.
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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.