Use of Adaptive Conjoint Analysis-Based Values Clarification in a Patient Decision Aid Is Not Associated with Better Perceived Values Clarity or Reduced Decisional Conflict but Enhances Values Congruence.

IF 3.1 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES Medical Decision Making Pub Date : 2024-11-18 DOI:10.1177/0272989X241298630
Nida Gizem Yılmaz, Arwen H Pieterse, Danielle R M Timmermans, Annemarie Becker, Birgit Witte-Lissenberg, Olga C Damman
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Abstract

Background: Evidence is lacking on the most effective values clarification methods (VCMs) in patient decision aids (PtDAs). We tested the effects of an adaptive conjoint analysis (ACA)-based VCM compared with a ranking-based VCM and no VCM on several decision-related outcomes, with the decisional conflict and its subscale "perceived values clarity" as primary outcomes.

Design: Online experimental study with 3 conditions: no VCM versus ranking-based VCM versus ACA-based VCM (N = 282; Mage = 63.11 y, s = 12.12), with the latter 2 conditions including attributes important for a lung cancer treatment decision. We assessed 1) decisional conflict, 2) perceived values clarity (decisional conflict subscale), 3) perceived cognitive load, 4) anticipated regret, 5) ambivalence, 6) preparedness for decision making, 7) hypothetical treatment preference, and 8) values congruence (proxy). We performed analysis of covariance and linear regression. Age and level of deliberation were included as potential moderators, and we controlled for subjective numeracy (covariate). We exploratively tested the moderating effects of subjective numeracy and health literacy (without covariates).

Results: We found no significant effect of type of VCM on overall decisional conflict or perceived values clarity. Age had a moderating effect: in younger participants, no VCM (v. ranking-based VCM) led to more values clarity, while in older participants, a ranking-based VCM (v. no VCM) led to more values clarity. Completing the ACA-based VCM, compared with no VCM, resulted in more values congruence.

Limitations: The hypothetical choice situation might have induced lower levels of cognitive/affective involvement in the decision.

Conclusions: This study found mixed effects of an ACA-based VCM. It did not decrease decisional conflict or increase perceived values clarity, yet it did improve values congruence.

Implications: Completion of an ACA-based VCM in a PtDA may increase values congruence.

Highlights: An adaptive conjoint analysis or a ranking-based values clarification method did not decrease analog patients' decisional conflict nor did it increase their perceived values clarity.In younger participants, no VCM (v. ranking-based VCM) led to more values clarity, while in older participants, a ranking-based VCM (v. no VCM) led to more values clarity.An adaptive conjoint analysis task for values clarification resulted in more values congruence.

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在患者决策辅助工具中使用基于自适应联合分析的价值观澄清与更好地感知价值观清晰度或减少决策冲突无关,但能增强价值观一致性。
背景:关于患者决策辅助工具(PtDA)中最有效的价值澄清方法(VCM),目前还缺乏证据。我们测试了基于自适应联合分析(ACA)的 VCM 与基于排序的 VCM 和无 VCM 相比对几种决策相关结果的影响,其中决策冲突及其子量表 "感知价值清晰度 "为主要结果:设计:在线实验研究,包括 3 个条件:无 VCM 与基于排序的 VCM 与基于 ACA 的 VCM(N = 282;Mage = 63.11 y,s = 12.12),后 2 个条件包括对肺癌治疗决策很重要的属性。我们评估了 1) 决策冲突、2) 感知到的价值观清晰度(决策冲突分量表)、3) 感知到的认知负荷、4) 预期后悔、5) 矛盾、6) 决策准备、7) 假设治疗偏好和 8) 价值一致性(代理)。我们进行了协方差分析和线性回归分析。我们将年龄和审议水平作为潜在的调节因素,并对主观计算能力(协变量)进行了控制。我们探索性地测试了主观计算能力和健康素养(不含协变量)的调节作用:结果:我们发现,虚拟医疗模型的类型对总体决策冲突或感知价值清晰度没有明显影响。年龄具有调节作用:对于年轻的参与者来说,没有虚拟医疗模型(与基于排名的虚拟医疗模型相比)会使其价值观更加清晰,而对于年长的参与者来说,基于排名的虚拟医疗模型(与没有虚拟医疗模型相比)会使其价值观更加清晰。完成基于 ACA 的 VCM 与不完成 VCM 相比,价值一致性更高:局限性:假设的选择情境可能会降低决策中的认知/情感参与度:本研究发现,基于 ACA 的 VCM 效果参差不齐。结论:本研究发现,基于 ACA 的 VCM 效果参差不齐,它既没有减少决策冲突,也没有增加感知价值的清晰度,但却提高了价值一致性:在 PtDA 中完成基于 ACA 的 VCM 可能会提高价值一致性:适应性联合分析或基于排序的价值观澄清方法既没有减少模拟患者的决策冲突,也没有增加他们感知到的价值观清晰度。在年轻的参与者中,不进行 VCM(v. 基于排序的 VCM)会导致更多的价值观清晰度,而在年长的参与者中,基于排序的 VCM(v. 不进行 VCM)会导致更多的价值观清晰度。
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来源期刊
Medical Decision Making
Medical Decision Making 医学-卫生保健
CiteScore
6.50
自引率
5.60%
发文量
146
审稿时长
6-12 weeks
期刊介绍: Medical Decision Making offers rigorous and systematic approaches to decision making that are designed to improve the health and clinical care of individuals and to assist with health care policy development. Using the fundamentals of decision analysis and theory, economic evaluation, and evidence based quality assessment, Medical Decision Making presents both theoretical and practical statistical and modeling techniques and methods from a variety of disciplines.
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