A Smoking Cessation App for Nondaily Smokers (Version 2 of the Smiling Instead of Smoking App): Acceptability and Feasibility Study
Bibliographic record
Abstract
Background Recent evidence highlights the significant detrimental impact of nondaily smoking on health and its disproportionate prevalence in underserved populations; however, little work has been done to develop treatments specifically geared toward quitting nondaily smoking. Objective This study aims to test the feasibility, acceptability, and conceptual underpinnings of version 2 of the Smiling Instead of Smoking (SiS2) smartphone app, which was developed specifically for nondaily smokers and uses a positive psychology approach. Methods In a prospective, single-group study, nondaily smokers (N=100) were prescribed use of the SiS2 app for 7 weeks while undergoing a quit attempt. The app assigned daily positive psychology exercises and behavioral tasks every 2 to 3 days, which guided smokers through using the smoking cessation tools offered in the app. Participants answered surveys at baseline and at 2, 6, 12, and 24 weeks postquit. Feasibility was evaluated based on app use and acceptability based on survey responses. The underlying conceptual framework was tested by examining whether theorized within-person changes occurred from baseline to end of treatment on scales measuring self-efficacy, desire to smoke, and processing of self-relevant health information (ie, pros and cons of smoking, importance of the pros and cons of quitting, and motivation). Results Participants used the SiS2 app on an average of 24.7 (SD 13.8) days out of the 49 prescribed days. At the end of treatment, most participants rated the functions of the app as very easy to use (eg, 70/95, 74% regarding cigarette log and 59/95, 62% regarding happiness exercises). The average score on the System Usability Scale was 79.8 (SD 17.3; A grade; A+ ≥84.1, B+ <78.8). Most participants reported that the app helped them in their quit attempt (83/95, 87%), and helped them stay positive while quitting (78/95, 82%). Large effects were found for within-person decreases in the desire to smoke (b=−1.5, 95% CI −1.9 to −1.1; P<.001; gav=1.01), the importance of the pros of smoking (b=-20.7, 95% CI −27.2 to −14.3; P<.001; gav=0.83), and perceived psychoactive benefits of smoking (b=−0.8, 95% CI −1.0 to −0.5; P<.001; gav=0.80). Medium effects were found for increases in self-efficacy for remaining abstinent when encountering internal (b=13.1, 95% CI 7.6 to 18.7; P<.001; gav=0.53) and external (b=11.2, 95% CI 6.1 to 16.1; P<.001; gav=0.49) smoking cues. Smaller effects, contrary to expectations, were found for decreases in motivation to quit smoking (P=.005) and the perceived importance of the pros of quitting (P=.009). Self-reported 30-day point prevalence abstinence rates were 40%, 56%, and 56% at 6, 12, and 24 weeks after the quit day, respectively. Conclusions The SiS2 app was feasible and acceptable, showed promising changes in constructs relevant to smoking cessation, and had high self-reported quit rates by nondaily smokers. The SiS2 app warrants testing in a randomized controlled trial.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".