Pilot Study of a Mobile, Virtual Reality–Based Digital Therapeutic for Smoking Cessation: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Smoking cessation remains a global challenge, with traditional treatments showing limited long-term success due to low adherence and accessibility issues. Digital therapeutics, such as mobile apps and virtual reality (VR)-based interventions, could offer innovative solutions for smoking cessation treatment. NICO-THERA is a digital therapeutic program integrating cognitive behavioral therapy (CBT) and motivational enhancement therapy (MET) to address nicotine dependence. OBJECTIVE: This study aims to evaluate the safety and efficacy of NICO-THERA, a digital therapeutic intervention combining VR and mobile apps, for supporting smoking cessation among individuals with nicotine dependence. The primary focus was on smoking abstinence, nicotine dependence reduction, and motivation to quit smoking over a 12-week intervention period. METHODS: This open-label, exploratory, randomized controlled trial involved 30 participants randomly assigned to the digital therapeutic group (DTG; n=15; mean age 43.1 years; mean number of daily cigarettes: 9.5) or basic treatment group (BTG; n=15; mean age 48.7 years; mean number of daily cigarettes: 9.2). The DTG received the NICO-THERA program, involving VR sessions (relaxation training, craving coping, and refusal skills) and mobile app-based CBT and MET modules for daily therapeutic exercises. The BTG received basic care including video education and printed materials. The primary outcomes were the 7-day point prevalence abstinence (PPA) and 30-day PPA at 8 weeks and 12 weeks. Secondary outcomes included nicotine dependence (Fagerström Test for Nicotine Dependence [FTND]) and motivation to change (Stages of Change Readiness and Treatment Eagerness Scale-Smoking [SOCRATES-S]). RESULTS: There were no significant differences in 7-day and 30-day PPA between the DTG and BTG. However, in within-group analyses, the DTG showed significant improvements in both 7-day and 30-day PPA at week 8 (z=-2.00, P=.046), along with consistent reductions in smoking days and cigarette consumption across all time points. The BTG only significantly decreased cigarette consumption at week 8. Additionally, the DTG exhibited a significant increase in taking steps of motivation at week 12 (U=19.00, P=.048) compared with the BTG. No adverse device effects were reported. Adherence to smoking cessation diaries and medication logs was higher in the DTG (mean 83 of 84 days, 99%; 9 participants) than in the BTG (mean 74 of 84 days, 88%; 12 participants), based on the 12-week average adherence among those who completed the study. CONCLUSIONS: The NICO-THERA digital therapeutic program demonstrated preliminary effectiveness at reducing nicotine dependence. Additionally, participants in the DTG group exhibited a progressive improvement in their motivation to quit smoking, as reflected by a significant reduction in ambivalence at week 8 and increase in proactive cessation efforts at week 12. These results suggest that the structured integration of MET and CBT within the NICO-THERA program effectively reinforced psychological readiness for smoking cessation, fostering a sustained commitment to behavioral change. TRIAL REGISTRATION: Clinical Research Information Service, Republic of Korea KCT0009801; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=28285&status=5&seq_group=28285&search_page=M.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".