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多发性硬化症患者的强化物病理、概率性选择和药物依从性。

Reinforcer pathology, probabilistic choice, and medication adherence in patients with multiple sclerosis.

机构信息

Department of Applied Behavioral Science, University of Kansas, Lawrence, KS, USA.

Cofrin-Logan Center for Addiction Research and Treatment, University of Kansas, Lawrence, KS, USA.

出版信息

J Exp Anal Behav. 2023 Mar;119(2):275-285. doi: 10.1002/jeab.830. Epub 2023 Jan 30.

Abstract

The reinforcer pathology model posits that core behavioral economic mechanisms, including delay discounting and behavioral economic demand, underlie adverse health decisions and related clinical disorders. Extensions beyond substance use disorder and obesity, however, are limited. Using a reinforcer pathology framework, this study evaluates medical adherence decisions in patients with multiple sclerosis. Participants completed behavioral economic measures, including delay discounting, probability discounting, and a medication purchase task. A medical decision-making task was also used to evaluate how sensitivity to mild side effect risk and efficacy contributed to the likelihood of taking a hypothetical disease-modifying therapy. Less steep delay discounting and more intense (greater) medication demand were independently associated with greater adherence to the medication decision-making procedure. More generally, the pattern of interrelations between the medication-specific and general behavioral economic metrics was consistent with and contributes to the reinforcer pathology model. Additional research is warranted to expand these models to different populations and health behaviors, including those of a positive health orientation (i.e., medication adherence).

摘要

强化病理模型假设,包括延迟折扣和行为经济需求在内的核心行为经济学机制,是不良健康决策和相关临床障碍的基础。然而,这种模型的应用范围不仅限于物质使用障碍和肥胖症。本研究使用强化病理模型,评估多发性硬化症患者的医疗依从性决策。参与者完成了行为经济学测量,包括延迟折扣、概率折扣和药物购买任务。还使用了一项医疗决策任务来评估对轻度副作用风险和疗效的敏感性如何影响接受假设性疾病修正治疗的可能性。延迟折扣越不陡峭,药物需求越强烈(大),与对药物决策程序的遵从性越高呈独立相关。更一般地说,药物特异性和一般行为经济学指标之间的相互关系模式与强化病理模型一致,并有助于该模型。需要开展更多的研究,将这些模型扩展到不同的人群和健康行为,包括具有积极健康倾向的人群(即药物依从性)。

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