Suppr超能文献

数字健康干预措施的剂量反应研究:概念、考虑因素和挑战。

Dose-response research in digital health interventions: Concepts, considerations, and challenges.

机构信息

Department of Health Education and Behavior.

Department of Psychology and Neuroscience.

出版信息

Health Psychol. 2019 Dec;38(12):1168-1174. doi: 10.1037/hea0000805. Epub 2019 Oct 3.

Abstract

To optimize digital health interventions, intervention creators must determine what intervention dose will produce the most substantial health behavior change-the dose-response relationship-while minimizing harms or burden. In this article we present important concepts, considerations, and challenges in studying dose-response relationships in digital health interventions. We propose that interventions make three types of prescriptions: (1) , prescriptions to receive content from the intervention, such as to read text or listen to audio; (2) , prescriptions to produce and provide content to the intervention, such as to send text messages or post intervention-requested photos on social media; and (3) , prescriptions to engage in behaviors outside the intervention, such as changing food intake or meditating. Each type of prescription has both an intended dose (i.e., what the intervention actually prescribes) and an enacted dose (i.e., what portion of the intended dose is actually completed by the participant). Dose parameters of duration, frequency, and amount can be applied to each prescription type. We consider adaptive interventions and interventions with ad libitum prescriptions as examples of tailored doses. Researchers can experimentally manipulate the intended dose to determine the dose-response relationship. The enacted dose cannot be directly manipulated; however, we consider the applicability of "controlled concentration" research design to the study of enacted dose. We consider challenges in dose-response research in digital health interventions, including characterizing amount with self-paced activities and combining doses across modality. The presented concepts and considerations may help contribute to the optimization of digital health interventions. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

摘要

为了优化数字健康干预措施,干预措施的创建者必须确定产生最大健康行为改变的干预剂量——剂量反应关系——同时将危害或负担降至最低。在本文中,我们介绍了数字健康干预措施中研究剂量反应关系的重要概念、考虑因素和挑战。我们提出干预措施有三种类型的处方:(1)接收干预内容的处方,例如阅读文本或收听音频;(2)产生和提供干预内容的处方,例如发送短信或在社交媒体上发布干预请求的照片;(3)在干预措施之外参与行为的处方,例如改变饮食或冥想。每种类型的处方都有一个预期剂量(即干预实际规定的剂量)和一个实施剂量(即参与者实际完成的预期剂量的一部分)。剂量参数持续时间、频率和数量可以应用于每种处方类型。我们以自适应干预措施和自由处方干预措施为例,探讨了定制剂量。研究人员可以通过实验来操纵预期剂量,以确定剂量反应关系。实施剂量不能直接操纵;然而,我们考虑将“控制浓度”研究设计应用于实施剂量的研究。我们考虑了数字健康干预措施中剂量反应研究的挑战,包括用自我调节活动来描述剂量以及将不同模式的剂量结合起来。所提出的概念和考虑因素可能有助于优化数字健康干预措施。(PsycINFO 数据库记录(c)2019 APA,保留所有权利)。

相似文献

引用本文的文献

本文引用的文献

10

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验