Trial reportJMIR mHealth and uHealth2022
Effectiveness of a Conversational Chatbot (Dejal@bot) for the Adult Population to Quit Smoking: Pragmatic, Multicenter, Controlled, Randomized Clinical Trial in Primary Care.
Trial report in JMIR mHealth and uHealth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 25 papers, 3 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.
Effectiveness of a Chat Bot for Smoking Cessation: a Pragmatic Trial in Primary Care.
Effect of a Behavioral Intervention Delivered Via Telegram Messenger on Smoking Cessation in Patients After Myocardial Infarction: The TELEGRAM-MI Randomized Controlled Trial
Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it, 43 citations in OpenAlex.
- Korean Clinical Practice Guideline of Korean Society for Research on Nicotine and Tobacco (KSRNT) and National Evidence-based Healthcare Collaborating Agency (NECA) on Treatment of Tobacco Use 2024.Journal of Korean medical science · 2025Guideline
- Natural Language Processing Chatbot-Based Interventions for Improvement of Diet, Physical Activity, and Tobacco Smoking Behaviors: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Reporting Quality of AI Intervention in Randomized Controlled Trials in Primary Care: Systematic Review and Meta-Epidemiological Study.Journal of medical Internet research · 2025Pooled it
- Conversational Chatbot for Cigarette Smoking Cessation: Results From the 11-Step User-Centered Design Development Process and Randomized Controlled Trial.JMIR mHealth and uHealth · 2024Trial
- The Analytical Framework of Clinical Trials Evaluating Clinical Outcomes of Artificial Intelligence-Based Digital Health Interventions: A Systematic Literature Review.Journal of market access & health policy · 2026Review
- Review
- The Development and Use of AI Chatbots for Health Behavior Change: Scoping Review.Journal of medical Internet research · 2026Article
- A Telegram-based behavioral intervention for smoking cessation after myocardial infarction: A study protocol of a pragmatic, single-blind, randomized trial.Tobacco prevention & cessation · 2026Article
- AI-Enabled Personalized Smoking Cessation Intervention With the Aipaca Chatbot: Mixed Methods Feasibility Study.JMIR formative research · 2025Observational
- Artificial Intelligence in Health Promotion and Disease Reduction: Rapid Review.Journal of medical Internet research · 2025Review
- Artificial Intelligence in Nursing Support for Families: A Rapid Review.JMA journal · 2025Article
- Machine Learning in Primary Health Care: The Research Landscape.Healthcare (Basel, Switzerland) · 2025Review
- Psychological, economic, and ethical factors in human feedback for a chatbot-based smoking cessation intervention.NPJ digital medicine · 2025Article
- The role of chatbots and virtual assistants in enhancing tobacco cessation counselling.Frontiers in digital health · 2025Review
- Alter egos alter engagement: perspective-taking can improve disclosure quantity and depth to AI chatbots in promoting mental wellbeing.Frontiers in digital health · 2025Article
- Efficacy of a conversational chatbot for cigarette smoking cessation: Protocol of the QuitBot full-scale randomized controlled trial.Contemporary clinical trials · 2024Article
- The role of nurses in smoking cessation interventions for patients: a scoping review.BMC nursing · 2024Article
- Evolutionary Trends in the Adoption, Adaptation, and Abandonment of Mobile Health Technologies: Viewpoint Based on 25 Years of Research.Journal of medical Internet research · 2024Article
- Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review.Journal of medical Internet research · 2024Review
- Evaluation framework for conversational agents with artificial intelligence in health interventions: a systematic scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTobacco addiction is the leading cause of preventable morbidity and mortality worldwide, but only 1 in 20 cessation attempts is supervised by a health professional. The potential advantages of mobile health (mHealth) can circumvent this problem and facilitate tobacco cessation interventions for public health systems. Given its easy scalability to large populations and great potential, chatbots are a potentially useful complement to usual treatment.
objectiveThis study aims to assess the effectiveness of an evidence-based intervention to quit smoking via a chatbot in smartphones compared with usual clinical practice in primary care.
methodsThis is a pragmatic, multicenter, controlled, and randomized clinical trial involving 34 primary health care centers within the Madrid Health Service (Spain). Smokers over the age of 18 years who attended on-site consultation and accepted help to quit tobacco were recruited by their doctor or nurse and randomly allocated to receive usual care (control group [CG]) or an evidence-based chatbot intervention (intervention group [IG]). The interventions in both arms were based on the 5A's (ie, Ask, Advise, Assess, Assist, and Arrange) in the US Clinical Practice Guideline, which combines behavioral and pharmacological treatments and is structured in several follow-up appointments. The primary outcome was continuous abstinence from smoking that was biochemically validated after 6 months by the collaborators. The outcome analysis was blinded to allocation of patients, although participants were unblinded to group assignment. An intention-to-treat analysis, using the baseline-observation-carried-forward approach for missing data, and logistic regression models with robust estimators were employed for assessing the primary outcomes.
resultsThe trial was conducted between October 1, 2018, and March 31, 2019. The sample included 513 patients (242 in the IG and 271 in the CG), with an average age of 49.8 (SD 10.82) years and gender ratio of 59.3% (304/513) women and 40.7% (209/513) men. Of them, 232 patients (45.2%) completed the follow-up, 104/242 (42.9%) in the IG and 128/271 (47.2%) in the CG. In the intention-to-treat analysis, the biochemically validated abstinence rate at 6 months was higher in the IG (63/242, 26%) compared with that in the CG (51/271, 18.8%; odds ratio 1.52, 95% CI 1.00-2.31; P=.05). After adjusting for basal CO-oximetry and bupropion intake, no substantial changes were observed (odds ratio 1.52, 95% CI 0.99-2.33; P=.05; pseudo-R
conclusionsA treatment including a chatbot for helping with tobacco cessation was more effective than usual clinical practice in primary care. However, this outcome was at the limit of statistical significance, and therefore these promising results must be interpreted with caution.
trial registrationClinicaltrials.gov NCT03445507; https://tinyurl.com/mrnfcmtd. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12911-019-0972-z.
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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.