MCMTC: A Pragmatic Framework for Selecting an Experimental Design to Inform the Development of Digital Interventions.
MCMTC: A Pragmatic Framework for Selecting an Experimental Design to Inform the Development of Digital Interventions.
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DOI：
10.3389/fdgth.2022.798025
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发表时间：
2022
影响因子：
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通讯作者：
Wetter DW
中科院分区：
文献类型：
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作者：
Nahum-Shani I;Dziak JJ;Wetter DW
数字技术的进步为提供有效和可扩展的行为改变干预措施创造了前所未有的机会。许多数字干预措施包括多个组成部分，即干预措施的几个方面，可以进行系统调查。近年来开发了各种类型的实验方法，使研究人员能够获得开发有效的多组分干预措施所需的经验证据。这些方法包括析因设计、序贯多重分配随机试验（SMART）和微型随机试验（MRT）。研究人员面临的一个重要挑战是选择合适的设计类型来匹配他们的科学问题。在这里，我们提出了MCMTC -一个实用的框架，可用于指导有兴趣开发数字干预的研究人员决定选择哪种实验方法。该框架包括鼓励研究者在选择最合适的设计过程中回答的五个问题：（1）多组分干预：目标是开发包括多个组分的干预;（2）组分选择：是否存在关于选择纳入干预的特定组分的开放性科学问题;（3）不止一个组分：是否存在关于在干预措施中纳入一个以上组成部分的开放性科学问题;（4）时间：是否存在关于组成部分交付时间的开放性科学问题，即何时交付特定组成部分;以及（5）变化：所讨论的组成部分是否旨在解决变化相对缓慢的条件（例如，数月或数周）或快速（例如，每天，小时，分钟）。在整个过程中，我们使用戒烟数字干预的例子来说明通过回答这些问题来选择设计的过程。为了简单起见，我们只专注于四种实验方法-标准的两组或多组随机试验，经典析因设计，SMART和MRT-承认开发数字干预的可能实验方法的阵列并不限于这些设计。
Advances in digital technologies have created unprecedented opportunities to deliver effective and scalable behavior change interventions. Many digital interventions include multiple components, namely several aspects of the intervention that can be differentiated for systematic investigation. Various types of experimental approaches have been developed in recent years to enable researchers to obtain the empirical evidence necessary for the development of effective multiple-component interventions. These include factorial designs, Sequential Multiple Assignment Randomized Trials (SMARTs), and Micro-Randomized Trials (MRTs). An important challenge facing researchers concerns selecting the right type of design to match their scientific questions. Here, we propose MCMTC – a pragmatic framework that can be used to guide investigators interested in developing digital interventions in deciding which experimental approach to select. This framework includes five questions that investigators are encouraged to answer in the process of selecting the most suitable design: (1) Multiple-component intervention: Is the goal to develop an intervention that includes multiple components; (2) Component selection: Are there open scientific questions about the selection of specific components for inclusion in the intervention; (3) More than a single component: Are there open scientific questions about the inclusion of more than a single component in the intervention; (4) Timing: Are there open scientific questions about the timing of component delivery, that is when to deliver specific components; and (5) Change: Are the components in question designed to address conditions that change relatively slowly (e.g., over months or weeks) or rapidly (e.g., every day, hours, minutes). Throughout we use examples of tobacco cessation digital interventions to illustrate the process of selecting a design by answering these questions. For simplicity we focus exclusively on four experimental approaches—standard two- or multi-arm randomized trials, classic factorial designs, SMARTs, and MRTs—acknowledging that the array of possible experimental approaches for developing digital interventions is not limited to these designs.