Evaluating the Impact of Pharmacotherapy in Augmenting Quit Rates Among Hispanic Adults in an App-Delivered Smoking Cessation Intervention: Secondary Analysis of a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Hispanic adults receive less advice to quit smoking and use fewer evidence-based smoking cessation treatments compared to their non-Hispanic counterparts. Digital smoking cessation interventions, such as those delivered via smartphone apps, provide a feasible and within-reach treatment option for Hispanic adults who smoke and want to quit smoking. While the combination of pharmacotherapy and behavioral interventions are considered best practices for smoking cessation, its efficacy among Hispanic adults, especially alongside smartphone app-based interventions, is uncertain. OBJECTIVE: This secondary analysis used data from a randomized controlled trial that compared the efficacy of 2 smoking cessation apps, iCanQuit (based on acceptance and commitment therapy) and QuitGuide (following US clinical practice guidelines), to explore the association between pharmacotherapy use and smoking cessation outcomes among the subsample of 173 Hispanic participants who reported on pharmacotherapy use. Given the randomized design, we first tested the potential interaction of pharmacotherapy use and intervention arm on 12-month cigarette smoking abstinence. We then examined whether the use of any pharmacotherapy (ie, nicotine replacement therapy [NRT], varenicline, or bupropion) and NRT alone augmented each app-based intervention efficacy. METHODS: Participants reported using pharmacotherapy on their own during the 3-month follow-up and cigarette smoking abstinence at the 12-month follow-up via web-based surveys. These data were used (1) to test the interaction effect of using pharmacotherapy to aid smoking cessation and intervention arm (iCanQuit vs QuitGuide) on smoking cessation at 12 months and (2) to test whether the use of pharmacotherapy to aid smoking cessation augmented the efficacy of each intervention arm to help participants successfully quit smoking. RESULTS: The subsample of Hispanic participants was recruited from 30 US states. They were on average 34.5 (SD 9.3) years of age, 50.9% (88/173) were female, and 56.1% (97/173) reported smoking at least 10 cigarettes daily. Approximately 22% (38/173) of participants reported using pharmacotherapy to aid smoking cessation at the 3-month follow-up, including NRT, varenicline, or bupropion, with no difference between intervention arms. There was an interaction between pharmacotherapy use and intervention arm that marginally influenced 12-month quit rates at 12 months (P for interaction=.053). In the iCanQuit arm, 12-month missing-as-smoking quit rates were 43.8% (7/16) for pharmacotherapy users versus 28.8% (19/16) for nonusers (odds ratio 2.21, 95% CI 0.66-7.48; P=.20). In the QuitGuide arm, quit rates were 9.1% (2/22) for pharmacotherapy users versus 21.7% (15/69) for nonusers (odds ratio 0.36, 95% CI 0.07-1.72; P=.20). Results were similar for the use of NRT only. CONCLUSIONS: Combining pharmacotherapy to aid smoking cessation with a smartphone app-based behavioral intervention that teaches acceptance of cravings to smoke (iCanQuit) shows promise in improving quit rates among Hispanic adults. However, this combined approach was not effective with the US clinical guideline-based app (QuitGuide). TRIAL REGISTRATION: ClinicalTrials.gov NCT02724462; https://clinicaltrials.gov/study/NCT02724462. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1001/jamainternmed.2020.4055.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.016 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".