Decision aids to facilitate decision making around behavior change in the field of health promotion: A scoping review

Gültzow, Thomas; Zijlstra, Daniëlle N; Bolman, Catherine; Vries, Hein de; Dirksen, Carmen D.; Muris, Jean; Smit, Eline Suzanne; Hoving, Ciska

doi:10.1016/j.pec.2021.01.015

Cited by 9 publications

(7 citation statements)

References 62 publications

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“…Therefore, they should respond similarly to the elements typically used in DAs. However, given the substantial amount of heterogeneity in the literature on DAs aimed at primary prevention [ 74 ], it may be worth investigating further whether prevention-related decisions really respond to DA elements, as one would expect based on the wider DA literature; for example, by replicating our work with a larger sample. In addition, more research could be conducted on perceived differences between prevention-related decisions and other health-related decisions (eg, treatment decisions); for example, by conducting in-depth interviews with participants who recently took both to discover perceived differences in their decision-making.…”

Section: Discussionmentioning

confidence: 87%

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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Section: Discussionmentioning

confidence: 87%

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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“…Decision aids (DAs) are interventions specifically meant to provide unbiased information in order to increase users' knowledge, and to support these users in choosing a healthcare option that best reflects their personal values and preferences (Stacey et al, 2017) -in other words: DAs support informed decision making. DAs have predominantly been developed to support people in making decisions about medical treatments and screening programs (Stacey et al, 2017), but are also increasingly used to help smokers make informed decisions regarding preventive health-related behaviours, such as smoking cessation (Gültzow et al, 2021;Moyo et al, 2018). In various studies it has been shown that DAs can have positive effects on smoking cessation outcomes (e.g., BinDhim et al, 2018;Cupertino et al, 2010;Willemsen et al, 2006), however, as far as we are aware, in all of those studies assessments of needs and viewpoints of potential end users in initial phases of DA development were not reported.…”

Section: Introductionmentioning

confidence: 99%

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

Gültzow¹,

Smit

Hudales³

et al. 2021

Preprint

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Introduction: Evidence-based cessation assistance is known to increase cessation rates. Activating personal preferences as part of the decision for smoking cessation assistance tools could further improve tools' effectiveness. Decision aids (DAs) help individuals to choose amongst the various options by taking these preferences into account and, therefore, could have a positive effect on cessation rates. To develop attractive and effective DAs, potential end users' needs, and experts' viewpoints should be considered during development processes. Therefore, the aim of this study was: (1) To explore smokers' needs and viewpoints regarding a smoking cessation assistance DA, and (2) to obtain consensus among smoking cessation counsellors and scientific experts about the content and format of such a DA. Materials and methods: Data was gathered via two approaches applied across three studies: (1) 20 semi-structured interviews with potential end users, (2) two three-round Delphi studies with 61 smoking cessation counsellors and 44 scientific experts. Data from the interviews and the first round of the Delphi studies were analysed qualitatively using the Framework method, while data from the second and third round of the Delphi studies were analysed quantitatively using medians and interquartile ranges. Results: Potential end users reported to acquire information in different ways: Via own experiences, their social environment, and the media. Important characteristics to decide between tools also varied, however effectiveness and costs were commonly reported as the most important characteristics. The experts reached consensus on 38 and 40 statements (respectively) regarding important cessation assistance tools' characteristics and their viewpoints on a smoking cessation assistance DA, e.g., that a tool should be appropriate for users' level of addiction. Discussion and conclusion: Some clear trends emerged among the potential end users (especially regarding important characteristics). Experts also reached consensus among a number of statements. However, there was some variation in the needs and wishes among the (different) stakeholders. The combination of these studies highlights that a 'one size fits all' approach is not desirable. In the development of DAs, this heterogeneity should be taken into account, e.g., by enabling users to customize a DA based on their personal preferences while safeguarding essential elements.

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“…In other words: DAs support informed decision making among DAs' end users. DAs have predominantly been developed to support people in making decisions about medical treatments and screening programs (Stacey et al, 2017), but are also increasingly used to help individuals make informed decisions regarding preventive health-related behaviours, such as smoking cessation (Gültzow et al, 2021;Moyo et al, 2018). In various studies it has been shown that DAs can have positive effects on smoking cessation outcomes (e.g., BinDhim et al, 2018;Cupertino et al, 2010;Willemsen et al, 2006), however, as far as we are aware, in all those studies assessments of needs and viewpoints of potential end users in initial phases of DAdevelopment were not reported.…”

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confidence: 99%

Informed Decision Making on the Uptake of Evidence-Based Smoking Cessation Assistance by Individuals Motivated to Quit Smoking

Gültzow

Smit

Hudales

et al. 2022

EJHC

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Evidence-based cessation assistance increases cessation rates. Activating preferences during decision making could improve effectiveness further. Decision aids (DAs) facilitate deciding by taking preferences into account. To develop effective DAs, potential end users' (i.e., individuals motivated to quit) needs and experts' viewpoints should be considered. Therefore, the aim of this needs assessment was: (1) To explore end users' needs and (2) to obtain consensus among smoking cessation counsellors and scientific experts to develop a self-administered DA to support end users in choosing cessation assistance. Data was gathered via two approaches: (1) twenty semi-structured interviews with potential end users and (2) two three-round Delphi studies with 61 counsellors and 44 scientific experts. Interview data and the first Delphi rounds were analysed qualitatively, the other Delphi rounds were analysed quantitatively. Potential end users acquired information in different ways, e.g., via own experiences. Important characteristics to decide between tools varied, however effectiveness and costs were commonly reported. Experts reached consensus on 38 and 40 statements, e.g., tools should be appropriate for users' addiction level. Although some trends emerged, due to the variation among stakeholders, a 'one size fits all'-approach is undesirable. This heterogeneity should be considered, e.g., by enabling users to customise the DA.

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Decision aids to facilitate decision making around behavior change in the field of health promotion: A scoping review

Cited by 9 publications

References 62 publications

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

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

Informed Decision Making on the Uptake of Evidence-Based Smoking Cessation Assistance by Individuals Motivated to Quit Smoking

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