Using ‘Smart’ Technology to Aid in Cigarette Smoking Cessation: Examining an Innovative Way to Monitor and Improve Quit Attempt Outcomes
Bibliographic record
Abstract
Abstract Introduction Recently, smartphone applications (apps) have been used as smoking cessation aids. Interactive apps appear to more effective than non-interactive apps. SmokeBeat, a smartphone app used in conjunction with a smartwatch, aims to detect smoking events, interact with the user as they occur and potentially stop smoking events before they occur in the future. Aims The purpose of this feasibility study was to determine the sensitivity and specificity of SmokeBeat in detecting smoking events. Methods The feasibility of using the app as a smoking cessation aid was tested over a 2-week period by daily, dependent smokers. SmokeBeat's cigarette detection rate was measured in laboratory sessions both before and after the 2-week period. Fisher's exact test was used to compare detection rates from each session. Results/Findings The detection rate was 22.5% during session 1 and 41.7% during session 2. Once technological issues were controlled for (i.e., signal loss between smartphone and smartwatch), SmokeBeat's detection rate improved over the 2-week period, resulting in a 100% detection rate. Conclusions Apps which can detect smoking events in real time present an opportunity for a proactive and interactive smoking cessation aid – a potentially useful tool for individuals attempting to quit smoking.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".