Nahum-Shani Inbal, Dziak John J, Wetter David W
Insitute for Social Research, University of Michigan, Ann Arbor, MI, United States.
Edna Bennett Pierce Prevention Research Center, The Pennsylvania State University, State College, PA, United States.
Front Digit Health. 2022 Mar 9;4:798025. doi: 10.3389/fdgth.2022.798025. eCollection 2022.
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.
数字技术的进步为提供有效且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含多个组成部分,即干预措施的几个方面,这些方面可加以区分以便进行系统研究。近年来,已开发出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成部分干预措施所需的实证证据。这些方法包括析因设计、序贯多重分配随机试验(SMARTs)和微随机试验(MRTs)。研究人员面临的一个重要挑战是选择合适的设计类型以匹配其科学问题。在此,我们提出多组成部分试验选择框架(MCMTC)——一个实用的框架,可用于指导有兴趣开发数字干预措施的研究人员决定选择哪种实验方法。该框架包括五个问题,鼓励研究人员在选择最合适的设计过程中回答:(1)多组成部分干预:目标是开发一种包含多个组成部分的干预措施吗;(2)组成部分选择:对于选择纳入干预措施的特定组成部分,是否存在尚未解决的科学问题;(3)不止一个组成部分:对于在干预措施中纳入不止一个组成部分,是否存在尚未解决的科学问题;(4)时间安排:对于组成部分的交付时间,即何时交付特定组成部分,是否存在尚未解决的科学问题;(5)变化:所讨论的组成部分旨在解决变化相对缓慢(例如,数月或数周内)还是迅速(例如,每天、数小时、数分钟)的情况。在整个过程中,我们使用戒烟数字干预措施的例子来说明通过回答这些问题来选择设计的过程。为简单起见,我们仅关注四种实验方法——标准的双臂或多臂随机试验、经典析因设计、SMARTs和MRTs——同时承认开发数字干预措施的可能实验方法并不局限于这些设计。