A Mobile Health Solution Complementing Psychopharmacology-Supported Smoking Cessation: Randomized Controlled Trial
Open accessPublished version of a preprint
Abstract
Background: Smoking cessation is a persistent leading public health challenge. Mobile health (mHealth) solutions are emerging to improve smoking cessation treatments. Previous approaches have proposed supporting cessation with tailored motivational messages. Some managed to provide short-term improvements in smoking cessation. Yet, these approaches were either static in terms of personalization or human-based nonscalable solutions. Additionally, long-term effects were neither presented nor assessed in combination with existing psychopharmacological therapies. Objective: This study aimed to analyze the long-term efficacy of a mobile app supporting psychopharmacological therapy for smoking cessation and complementarily assess the involved innovative technology. Methods: A 12-month, randomized, open-label, parallel-group trial comparing smoking cessation rates was performed at Virgen del Rocío University Hospital in Seville (Spain). Smokers were randomly allocated to a control group (CG) receiving usual care (psychopharmacological treatment, n=120) or an intervention group (IG) receiving psychopharmacological treatment and using a mobile app providing artificial intelligence–generated and tailored smoking cessation support messages (n=120). The secondary objectives were to analyze health-related quality of life and monitor healthy lifestyle and physical exercise habits. Safety was assessed according to the presence of adverse events related to the pharmacological therapy. Per-protocol and intention-to-treat analyses were performed. Incomplete data and multinomial regression analyses were performed to assess the variables influencing participant cessation probability. The technical solution was assessed according to the precision of the tailored motivational smoking cessation messages and user engagement. Cessation and no cessation subgroups were compared using t tests. A voluntary satisfaction questionnaire was administered at the end of the intervention to all participants who completed the trial. Results: In the IG, abstinence was 2.75 times higher (adjusted OR 3.45, P=.01) in the per-protocol analysis and 2.15 times higher (adjusted OR 3.13, P=.002) in the intention-to-treat analysis. Lost data analysis and multinomial logistic models showed different patterns in participants who dropped out. Regarding safety, 14 of 120 (11.7%) IG participants and 13 of 120 (10.8%) CG participants had 19 and 23 adverse events, respectively (P=.84). None of the clinical secondary objective measures showed relevant differences between the groups. The system was able to learn and tailor messages for improved effectiveness in supporting smoking cessation but was unable to reduce the time between a message being sent and opened. In either case, there was no relevant difference between the cessation and no cessation subgroups. However, a significant difference was found in system engagement at 6 months (P=.04) but not in all subsequent months. High system appreciation was reported at the end of the study. Conclusions: The proposed mHealth solution complementing psychopharmacological therapy showed greater efficacy for achieving 1-year tobacco abstinence as compared with psychopharmacological therapy alone. It provides a basis for artificial intelligence–based future approaches. Trial Registration: ClinicalTrials.gov NCT03553173; https://clinicaltrials.gov/ct2/show/NCT03553173 International Registered Report Identifier (IRRID): RR2-10.2196/12464
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Of the 15 studies, 4 (27%) assessed efficacy of AI chatbots in smoking cessation.
“Out of the 15 studies, 4 (27%) studies [23-25,27] assessed the efficacy of AI chatbots in smoking cessation.”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [23]
The Abstract (Methods) describes a randomized trial assessing smoking cessation with an AI-supported mobile app. The Introduction identifies the AI as a health recommender system that selects and sends tailored motivational messages, not an AI chatbot. This source supports counting the paper as a smoking-cessation efficacy study, but not specifically as a chatbot study; the 4-of-15 tally belongs to the citing review, not this trial. (LLM, conf=0.88)
Experimental — accuracy not yet measuredNone of the secondary clinical measures (health-related quality of life, healthy lifestyle, and physical activity) showed any differences between the groups.
“However, none of the secondary clinical measures (health-related quality of life, healthy lifestyle, and physical activity) showed any differences between the groups.”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [23]
Carrasco-Hernandez et al reported that smoking abstinence (exhaled carbon monoxide and urine cotinine test) was 2.15 times (P=.02) higher in the intervention group than in the control group.
“Carrasco-Hernandez et al [23] reported that smoking abstinence (exhaled carbon monoxide and urine cotinine test) was 2.15 times (P=.02) higher in the intervention group than in the control group.”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [23]
The need for greater interactivity can also be associated with the fluctuations in user engagement found in 13% (2/15) of studies.
“The need for greater interactivity can also be associated with the fluctuations in user engagement found in 13% (2/15) of studies [22,23].”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [23]
In 20% (3/15) of studies, the humanistic yet nonhumanistic construct of AI chatbots provided a safe space for the users to discuss, share, and ask for information on sensitive issues.
“Third, in 20% (3/15) of studies, the humanistic yet nonhumanistic construct of AI chatbots provided a safe space for the users to discuss, share, and ask for information on sensitive issues [5,22,23,35].”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [23]
The Introduction describes an AI recommender that sends tailored smoking-cessation messages, and Methods > Intervention and Control Groups describes users rating those messages. Searches for a chatbot, a safe space, and discussion or questions about sensitive issues found no passage addressing the attributed claim. The paper provides no page numbers for these passages. (LLM, conf=0.75)
Experimental — accuracy not yet measured
In one of these studies, the engagement rates decreased gradually as the intervention progressed, and in the other study, the engagement rates decreased significantly by midprogram but increased to 70% in the final week, that is, the 12th week.
“In one of these studies, the engagement rates decreased gradually as the intervention progressed [23], and in the other study, the engagement rates decreased significantly by midprogram but increased to 70% in the final week, that is, the 12th week [22].”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [23]
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
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- Authors
- Carrasco-Hernandez, L., Jódar-Sánchez, F., Núñez-Benjumea, F., Moreno Conde, J., Mesa González, M., Civit-Balcells, A., Hors-Fraile, S., Parra-Calderón, C. L., Bamidis, P. D., & Ortega-Ruiz, F.
- Affiliations
- Consejo Superior de Investigaciones Científicas (ES)
- Aristotle University of Thessaloniki (GR)
- ORCID
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2020-04-27
- Volume
- 8
- Issue
- 4
- Pages
- e17530
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC7215523
- OpenAlex
- W3012838529
- Cited by
- 65
- References
- 87
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 5 · 2023: 15 · 2022: 14 · 2021: 3 · 2020: 3
- FWCI
- 5.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RISalud Institutional Health Repository of Andalucía (submittedVersion, cc-by)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, cc-by)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Deposito de Investigacion Universidad de Sevilla (University of Seville) (submittedVersion, cc-by-nc-nd)
- Repositorio Institucional de la Universidad de Málaga (University of Málaga) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Physiology › Smoking Behavior and Cessation
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Keywords
- smoking cessation, mobile health, artificial intelligence, psychopharmacological treatment, nicotine replacement therapy, bupropion, varenicline, mobile application, user engagement, health-related quality of life, physical activity, long-term abstinence, adverse events, user satisfaction, predictors of dropout, smoking cessation services, digital health interventions, user-generated content
- MeSH terms
- Artificial Intelligence, Humans, Psychopharmacology, Quality of Life, Spain, Smoking Cessation, Telemedicine
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