Does an Explicit Value Clarification Method with Computer-Tailored Advice Increase the Effectiveness of a Web-based Decision Aid to Assist Smokers in Choosing Evidence-Based Cessation Assistance? Findings From a Randomized Controlled Trial. (Preprint)

Abstract

Smoking continues to be a driver of mortality. Various forms of evidence-based cessation assistance exist, but their use is limited. The decision between them may also induce decisional conflict. Offering decision aids (DAs) may be beneficial, but insights into their effective elements are lacking.

To test the added value of an effective element (ie, an 'explicit value clarification method' [VCM] paired with computer-tailored advice) of a web-based DA focusing on cessation assistance. The computer-tailored advice indicated the most fitting cessation assistance. The primary outcome measure was 7-day point prevalence abstinence 6 months post baseline (t=3). Secondary outcome measures were 7-day point prevalence abstinence 1 month post baseline (t=2), evidence-based cessation assistance use (t=2 and t=3), and decisional conflict (immediately post DA, t=1).

A randomized controlled trial (RCT) was conducted. The intervention group received a DA with an explicit VCM with computer-tailored advice, the control group received the same DA without these elements. Participants were mainly recruited online (eg, social media). All data was self-reported. Logistic and linear regression analyses (crude and adjusted for covariates) were performed to assess the outcomes. To test the robustness, analyses were conducted following 2 (decisional conflict) and 3 (smoking cessation outcomes) different scenarios: (1) Complete cases, (2) worst-case scenario (dropout respondents are considered to smoke, smoking outcomes only), and (3) multiple imputations. According to an a priori sample size calculation (α=.05; β=.20), 796 participants were needed.

2375 participants were randomized (n = 1164 intervention), 599 participants completed the DAs (n = 275 intervention), 276 (n = 143 intervention), 97 (n = 54 intervention), and 103 (n = 56 intervention) participants completed t=1, t=2 and t=3, respectively. Effects in favor of the intervention group on the primary outcome were only observed in the worst-case scenario (P = .02 [crude]; P = .04 [adjusted]). Effects on the secondary outcomes were only observed regarding smoking abstinence after 1 month (P = .02 in the crude and adjusted model), cessation assistance uptake after 1 months (only in the crude model, P = .04) and after 6 months (P = .01 [crude]; P = .02 [adjusted]), but also only in the worst-case scenario. Non-usage attrition was 34.19% higher in the intervention group than in the control group (P < .001).

We cannot confidently recommend the inclusion of explicit VCMs and computer-tailored advice at this point. In fact, they might result in higher attrition rates during DA completion, thereby limiting their potential. However, because a lack of statistical power may influenced our findings regarding the outcomes, we recommend replicating this study, taking our lessons learned into account. For example, we found indications that a stronger emphasis on usage times is justified in relation to digital DAs.

Netherlands Trial Register NL8270; https://www.trialregister.nl/trial/8270

RR2-10.2196/21772