Abstract
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.
Highlights
Various types of experimental approaches have been developed in recent years to enable researchers to obtain the empirical evidence necessary for the development and/or evaluation of effective multiple-component interventions
An important challenge facing researchers interested in developing digital interventions concerns the selection of an appropriate experimental design to achieve their scientific goals
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 based on their scientific questions
Summary
The widespread use, acceptability and convenience of digital technologies (e.g., mobile and wearable devices) have the potential to reduce structural barriers to treatment, making possible the delivery of behavioral interventions anytime and anywhere [1–3]. Consider Example D discussed above and suppose there is not yet sufficient evidence to determine (a) whether delivering a prompt that recommends a brief self-regulatory strategy is beneficial on average in preventing a lapse in the 3 h, when individuals are not driving; and (b) what is the level of urge at which the prompt would be most beneficial These questions concern the best component to deliver at different points in time in an intervention that intends to address conditions that change relatively rapidly. The MRT is a relatively new experimental design, various types of MRT designs and analytic methods have been developed, allowing investigators to address scientific questions about the development of JITAIs. Examples include studies in which the proximal outcome is continuous [52] and binary [51], and studies in which the randomization probabilities are stratified to provide sufficient data to detect an interaction between the intervention options and a time-varying covariate (e.g., urge to smoke) [53].
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