Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Dec 18, 2019
Open Peer Review Period: Dec 18, 2019 - Dec 30, 2019
Date Accepted: Mar 21, 2020
(closed for review but you can still tweet)
A Digital Therapeutics Solution Complementing Psychopharmacology-Supported Smoking Cessation: Randomized Controlled Trial
Smoking cessation is a persistent, leading public health challenge. Digital therapeutic solutions are emerging to improve smoking cessation treatments. Previous approaches have proposed supporting cessation with tailored motivational messages. Some managed to prove short-term improvements in smoking cessation. Yet, these approaches were either rather static in terms of personalization or human-based non-scalable solutions. Additionally, long-term effects were not presented and they were not combined with existing psychopharmacological therapies.
To analyze the long-term efficacy of a digital therapeutic mobile application supporting psychopharmacological therapy for smoking cessation and complementarily assess the involved innovative technology.
A 12-month, randomized, open-label, parallel-group trial comparing smoking cessation rates was performed at the Virgen del Rocío University Hospital in Seville (Spain). Smokers were randomly allocated into a control group (CG) receiving usual care (psychopharmacological treatment, n = 120) or an intervention group (IG) (receiving psychopharmacological treatment and using a digital therapeutics mobile application sending artificial intelligence-generated 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 measured as presence of adverse events related to the pharmacological therapies. Analyses were conducted on per-protocol and intention-to-treat basis. Incomplete data and multinomial regression analyses were performed to assess the variables influencing participant cessation probability. Additionally, the technical solution was assessed using the precision of the tailored motivational smoking cessation messages and user engagement with the system. We conducted t-tests to compare the cessation and no-cessation subgroups. Additionally, a voluntary satisfaction questionnaire concerning the system was administered at the end of the intervention to all participants who completed the trial.
In the IG, abstinence was 2.75 times higher (3.45 adjusted odds ratio [OR] for other variables) in the per-protocol and 2.15 times higher (3.13 adjusted OR by other variables) in the intention-to-treat analysis. Lost-data analysis and multinomial logistic models showed different patterns in participants who dropped out. IG smokers had 19 adverse events compared to 23 among CG smokers. None of the clinical secondary objective measures were significantly different between the groups. The system was able to learn and tailor the messages for improved effectiveness in supporting smoking cessation, but not to reduce the time between a message being sent and being opened. In either case, there was no significant difference between the cessation and no-cessation subgroups. However, statistical significance was found in system engagement after 6 months, but not at all subsequent months. High levels of appreciation of the system were reported at the end of the study.
The proposed digital therapeutics solution complementing psychopharmacological therapy provided significantly greater efficacy in achieving 1-year tobacco abstinence than psychopharmacological therapy alone and set the basis for artificial intelligence-based future approaches. Clinical Trial: ClinicalTrials.gov NCT03553173
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