Informed decision making on the uptake of evidence-based smoking cessation assistance: A needs assessment among end users and experts to inform decision aid development

Gültzow, Thomas; Smit, Eline Suzanne; Hudales, Raesita; Cd, Dirksen; Hoving, Ciska

doi:10.1101/2021.04.09.21255012

Cited by 3 publications

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“…The study did not fall under the scope of the Medical Research Involving Human Subjects Act as indicated by the Medical Ethics Committee Zuyderland, the Netherlands (16-N-227), and the development of VISOR and the accompanying studies (e.g., the aforementioned needs assessment [268]) were funded by the Dutch Cancer Society, UM2015-7744. Study materials (including the underlying data and statistical scripts) can be found on the on the open science framework (OSF) website [269].…”

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confidence: 99%

When informed decision making meets health promotion

Gültzow¹

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Section: Methodsmentioning

confidence: 99%

When informed decision making meets health promotion

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Effects of an Explicit Value Clarification Method With Computer-Tailored Advice on the Effectiveness of a Web-Based Smoking Cessation Decision Aid: Findings From a Randomized Controlled Trial

Gültzow¹,

Smit²,

Crutzen³

et al. 2022

J Med Internet Res

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Background Smoking continues to be a driver of mortality. Various forms of evidence-based cessation assistance exist; however, their use is limited. The choice between them may also induce decisional conflict. Offering decision aids (DAs) may be beneficial; however, insights into their effective elements are lacking. Objective This study tested the added value of an effective element (ie, an “explicit value clarification method” paired with computer-tailored advice indicating the most fitting cessation assistance) of a web-based smoking cessation DA. Methods A web-based randomized controlled trial was conducted among smokers motivated to stop smoking within 6 months. The intervention group received a DA with the aforementioned elements, and the control group received the same DA without these elements. The primary outcome measure was 7-day point prevalence abstinence 6 months after baseline (time point 3 [t=3]). Secondary outcome measures were 7-day point prevalence of abstinence 1 month after baseline (time point 2 [t=2]), evidence-based cessation assistance use (t=2 and t=3), and decisional conflict (immediately after DA; time point 1). Logistic and linear regression analyses were performed to assess the outcomes. Analyses were conducted following 2 (decisional conflict) and 3 (smoking cessation) outcome scenarios: complete cases, worst-case scenario (assuming that dropouts still smoked), and multiple imputations. A priori sample size calculation indicated that 796 participants were needed. The participants were mainly recruited on the web (eg, social media). All the data were self-reported. Results Overall, 2375 participants were randomized (intervention n=1164, 49.01%), of whom 599 (25.22%; intervention n=275, 45.91%) completed the DAs, and 276 (11.62%; intervention n=143, 51.81%), 97 (4.08%; intervention n=54, 55.67%), and 103 (4.34%; intervention n=56, 54.37%) completed time point 1, t=2, and t=3, respectively. More participants stopped smoking in the intervention group (23/63, 37%) than in the control group (14/52, 27%) after 6 months; however, this was only statistically significant in the worst-case scenario (crude P=.02; adjusted P=.04). Effects on the secondary outcomes were only observed for smoking abstinence after 1 month (15/55, 27%, compared with 7/46, 15%, in the crude and adjusted models, respectively; P=.02) and for cessation assistance uptake after 1 month (26/56, 46% compared with 18/47, 38% only in the crude model; P=.04) and 6 months (38/61, 62% compared with 26/50, 52%; crude P=.01; adjusted P=.02) but only in the worst-case scenario. Nonuse attrition was 34.19% higher in the intervention group than in the control group (P<.001). Conclusions Currently, we cannot confidently recommend the inclusion of explicit value clarification methods and computer-tailored advice. However, they might result in higher nonuse attrition rates, thereby limiting their potential. As a lack of statistical power may have influenced the outcomes, we recommend replicating this study with some adaptations based on the lessons learned. Trial Registration Netherlands Trial Register NL8270; https://www.trialregister.nl/trial/8270 International Registered Report Identifier (IRRID) RR2-10.2196/21772

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Effects of an Explicit Value Clarification Method With Computer-Tailored Advice on the Effectiveness of a Web-Based Smoking Cessation Decision Aid: Findings From a Randomized Controlled Trial (Preprint)

Gültzow¹,

Smit²,

Crutzen³

et al. 2021

Preprint

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BACKGROUND 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. OBJECTIVE 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). METHODS 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. RESULTS 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). CONCLUSIONS 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. CLINICALTRIAL Netherlands Trial Register NL8270; https://www.trialregister.nl/trial/8270 INTERNATIONAL REGISTERED REPORT RR2-10.2196/21772

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Informed decision making on the uptake of evidence-based smoking cessation assistance: A needs assessment among end users and experts to inform decision aid development

Cited by 3 publications

References 49 publications

When informed decision making meets health promotion

When informed decision making meets health promotion

Effects of an Explicit Value Clarification Method With Computer-Tailored Advice on the Effectiveness of a Web-Based Smoking Cessation Decision Aid: Findings From a Randomized Controlled Trial

Effects of an Explicit Value Clarification Method With Computer-Tailored Advice on the Effectiveness of a Web-Based Smoking Cessation Decision Aid: Findings From a Randomized Controlled Trial (Preprint)

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