A Smartphone App for Attentional Bias Retraining in Smokers: Mixed Methods Pilot Study
Bibliographic record
Abstract
BACKGROUND: Smoking is a global health threat. Attentional bias influences smoking behaviors. Although attentional bias retraining has shown benefits and recent advances in technology suggest that attentional bias retraining can be delivered via smartphone apps, there is a paucity of research on this topic. OBJECTIVE: This study aims to address this gap by exploring the use of attentional bias retraining via a novel smartphone app using a mixed methods pilot study. In the quantitative phase, it is hypothesized that participants in the training group who undertake attentional bias retraining via the app should have decreased levels of attentional bias, subjective craving, and smoking frequency, compared with those in the control group who do not undertake attentional bias retraining. The qualitative phase explores how the participants perceive and experience the novel app. METHODS: In all, 10 adult smokers (3 females and 7 males) between the ages of 26 and 56 years (mean 34.4 years, SD 9.97 years) were recruited. The participants were randomly allocated to the training and control groups. In weeks 1 and 3, participants from both groups attempted the standard visual probe task and rated their smoking frequency and subjective craving. In week 2, the participants in the training group attempted the modified visual probe task. After week 3, participants from both groups were interviewed about their views and experiences of the novel app. RESULTS: The results of the quantitative analysis did not support this study's hypothesis. The qualitative data were analyzed using thematic analysis. The results yielded 5 themes: ease, helpfulness, unhelpful aspects, barriers, and refinement. CONCLUSIONS: Findings from the qualitative study were consistent with those from previous studies on health-related smartphone apps. The qualitative results were helpful in understanding the user perspectives and experiences of the novel app, indicating that future research in this innovative area is necessary.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".