E-cigarette support for smoking cessation: Identifying the effectiveness of intervention components in an on-line randomized optimization experiment.
rct · Level II
Where this comes from
- Record sourced from PubMed, PMID 37455014.
- Also identified by DOI 10.1111/add.16294 and PMC identifier 10952247.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
The aim of this study was to determine which combination(s) of five e-cigarette-orientated intervention components, delivered on-line, affect smoking cessation. An on-line (UK) balanced five-factor (2 × 2 × 2 × 2 × 2 = 32 intervention combinations) randomized factorial design guided by the multi-phase optimization strategy (MOST) was used. A total of 1214 eligible participants (61% female; 97% white) were recruited via social media. The five on-line intervention components designed to help smokers switch to exclusive e-cigarette use were: (1) tailored device selection advice; (2) tailored e-liquid nicotine strength advice; (3): tailored e-liquid flavour advice; (4) brief information on relative harms; and (5) text message (SMS) support. The primary outcome was 4-week self-reported complete abstinence at 12 weeks post-randomization. Primary analyses were intention-to-treat (loss to follow-up recorded as smoking). Logistic regressions modelled the three- and two-way interactions and main effects, explored in that order. In the adjusted model the only significant interaction was a two-way interaction, advice on flavour combined with text message support, which increased the odds of abstinence (odds ratio = 1.55, 95% confidence interval = 1.13-2.14, P = 0.007, Bayes factor = 7.25). There were no main effects of the intervention components. Text-message support with tailored advice on flavour is a promising intervention combination for smokers using an e-cigarette in a quit attempt.
Medical subject headings
- Smoking Cessation
- Electronic Nicotine Delivery Systems