A Novel Smoking Cessation Smartphone App Integrated With a Mobile Carbon Monoxide Checker for Smoking Cessation Treatment: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Smoking cessation treatment programs have been widely available for patients with nicotine dependence. Despite intensive programs, the continuous abstinence rate (CAR) from weeks 9-12 is still about 50%. Recently, a smartphone app emerged as a novel tool for therapeutic interventions, including nicotine dependence. In this study, we developed "CureApp Smoking Cessation" (CASC), which consists of a smartphone app for patients and a Web-based patient management software for doctors with a mobile carbon monoxide (CO) checking device to improve the efficacy of the smoking cessation treatment. OBJECTIVE: This study aims to evaluate whether the CASC app is effective for individuals with nicotine dependence in addition to standard smoking cessation programs. METHODS: This will be a randomized, sham-controlled, open-label, multicenter trial. We will recruit participants with nicotine dependence, but are otherwise healthy adults. We will randomize and allocate participants 1:1 to the CASC treatment group or a control app group. Both groups will receive a 12-week standard smoking cessation program with pharmacotherapy and counseling. In addition, participants in the treatment group will have the CASC app installed on their smartphone, which will provide video tutorials, advice from an artificial intelligence nurse, a digital diary, and measure daily exhaled CO concentration. In contrast, the control group will have the control app installed on their smartphone, where all the functions that can potentially effect smoking cessation are removed. The primary outcome will be the biochemically validated CAR from weeks 9-24. The success of smoking cessation will be defined as self-reported continuous abstinence from weeks 9-24 and exhaled CO concentration ≤10 ppm both at weeks 12 and 24. The main secondary outcomes will be the CAR from weeks 9-12, weeks 9-52, and 7-day point prevalence abstinence at weeks 4, 8, 12, 24, and 52. RESULTS: We will recruit 580 participants with nicotine dependence from October 2017 to September 2018 or until the recruitment process is complete. The final 52-week follow-up will be completed in October 2019. We expect all trial results to be available by the end of 2019. The trial is funded by CureApp, Inc. CONCLUSIONS: This is the first randomized controlled trial to evaluate the efficacy of CASC. We expect that CASC, in addition to standard smoking cessation programs, has a significantly higher CAR during weeks 9-24 than the control app. TRIAL REGISTRATION: University Hospital Medical Information Network Clinical Trials Registry UMIN000031589; https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000033555. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12252.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".