Identifying determinants of varenicline adherence using the Theoretical Domains framework: a rapid review
Bibliographic record
Abstract
Abstract Background Adhering to varenicline has been shown to significantly improve the chances of successfully quitting smoking, with studies indicating a twofold increase in 6-month quit rates. However, despite its potential benefits, many individuals struggle with maintaining good adherence to varenicline; thus there is a need to develop scalable strategies to help people adhere. As a first step to inform the development of an intervention to improve adherence to varenicline, we conducted a rapid literature review to identify: 1) modifiable barriers and facilitators to varenicline adherence, and 2) behaviour change techniques associated with increased adherence to varenicline. Methods We searched MEDLINE, Embase, APA PsycINFO, CINAHL, and the Cochrane Central Register of Controlled Trials for relevant studies published between 2006 and 2022. Search terms included “varenicline,” “smoking cessation,” and "adherence," and their respective subject headings and synonyms. We screened and included studies reporting modifiable determinants of adherence to varenicline and then assessed quality, extracted modifiable determinants and mapped them to the Theoretical Domains Framework version 2 and the Behaviour Change Technique Taxonomy version 1. Results A total of 1,221 titles were identified through the database searches; 61 met the eligibility criteria. Most of the studies were randomized controlled trials and predominantly focused on barriers to varenicline. Only nine studies explicitly mentioned behaviour change techniques used to help varenicline adherence. Eight domains were identified as barriers to varenicline adherence (behavioural regulation, memory, goals, intentions, beliefs about capabilities, beliefs about consequences, optimism/pessimism, and environmental context) and five as facilitators (knowledge, behavioural regulation, beliefs about capabilities, social influences, and environmental context). Conclusions This study identifies barriers and facilitators that should be addressed when developing a complex adherence intervention tailored to patients’ needs based on modifiable determinants of medication adherence, some of which are under- used by existing adherence interventions. The findings from this review will inform the design of a theory-based healthbot planned to improve varenicline adherence in people undergoing smoking cessation treatment. Systematic review registration This study was registered with PROSPERO (# CRD42022321838).
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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 | 0.028 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.036 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".