A Preliminary Exploration of Former Smokers Enrolled in an Internet Smoking Cessation Program
Bibliographic record
Abstract
BACKGROUND: Internet interventions may have an important role to play in helping self-quitters maintain an initial period of abstinence. Little is known about the characteristics and utilization patterns of former smokers who use Internet cessation programs. OBJECTIVE: The overarching aim of this preliminary study was to establish the feasibility of a subsequent randomized trial of the effectiveness of Internet interventions in preventing relapse. Specifically, this study sought to determine the number of former smokers that register on a smoking cessation website, the characteristics of former smokers and their website utilization patterns, and potential predictors of sustained abstinence. METHODS: Participants were self-identified former smokers who registered on a free smoking cessation website. Recruitment occurred immediately following site registration. Participants completed Web-based baseline and 1-month follow-up assessments. Website utilization metrics were extracted at 1 month. Descriptive statistics were used to characterize the full sample. Baseline differences were examined between recent quitters (≤7 days of abstinence at enrollment) and more established quitters (8+ days of abstinence at enrollment) using chi-square tests and t tests. Univariate logistic regression examined demographic, smoking, psychosocial characteristics, and website utilization metrics as predictors of 1-month abstinence. RESULTS: During the 10-month study period, 1141 former smokers were recruited to participate: 494 accepted the invitation, 395 were eligible, 377 provided informed consent, and 221 completed the baseline and fully enrolled (56% of those eligible). At 1 month, 55.7% (123/221) of participants completed the follow-up survey. Mean age was 44.25 years (SD 12.78) and the sample was primarily female (174/221, 78.7%), white (196/221, 88.7%), and had at least some college education (177/221, 80.1%). Slightly more than half of participants (123/221, 55.7%) reported quitting more than a week prior to website registration and 43.9% (97/221) had quit within 7 days of registration. The website features most likely to be used were an interactive Quit Date tool (166/221, 75.1%) and the Community (134/221, 60.6%). Univariate regression models showed that recent quitters, those with higher motivation to remain abstinent, and those who used cessation medication in the past year were more likely to use the Community. Older age, longer duration of abstinence at registration, better health status, and health care provider advice to quit were associated with 1-month abstinence. Website utilization metrics did not predict abstinence, though odds ratios suggested higher utilization was associated with greater odds of abstinence. CONCLUSIONS: This exploratory study demonstrated the feasibility of recruiting former smokers to a research study and documented the uptake of an Internet cessation intervention among this group of self-quitters. Results also showed higher levels of website utilization and greater likelihood of community use among smokers early in their quit attempt compared to those with a longer period of abstinence at enrollment. Important areas for future research include identifying former smokers who may be more susceptible to relapse and determining which components of an Internet intervention are most helpful to prevent relapse in the early and later stages of a quit attempt.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".