Mobile phones are becoming an increasingly important platform for the delivery of health interventions. In recent years, researchers have used mobile phones as tools for encouraging physical activity and healthy diets, for symptom monitoring in asthma and heart disease, for sending patients reminders about upcoming appointments, for supporting smoking cessation, and for a range of other health problems. This paper provides an overview of this rapidly growing body of work. We describe the features of mobile phones that make them a particularly promising platform for health interventions, and we identify five basic intervention strategies that have been used in mobile-phone health applications across different health conditions. Finally, we outline the directions for future research that could increase our understanding of functional and design requirements for the development of highly effective mobile-phone health interventions.

Objective This paper presents an experimental design, the micro-randomized trial, developed to support optimization of just-in-time adaptive interventions (JITAIs). JITAIs are mHealth technologies that aim to deliver the right intervention components at the right times and locations to optimally support individuals’ health behaviors. Micro-randomized trials offer a way to optimize such interventions by enabling modeling of causal effects and time-varying effect moderation for individual intervention components within a JITAI. Methods The paper describes the micro-randomized trial design, enumerates research questions that this experimental design can help answer, and provides an overview of the data analyses that can be used to assess the causal effects of studied intervention components and investigate time-varying moderation of those effects. Results Micro-randomized trials enable causal modeling of proximal effects of the randomized intervention components and assessment of time-varying moderation of those effects. Conclusions Micro-randomized trials can help researchers understand whether their interventions are having intended effects, when and for whom they are effective, and what factors moderate the interventions’ effects, enabling creation of more effective JITAIs.

BackgroundThe science of implementation has offered little toward understanding how different implementation strategies work. To improve outcomes of implementation efforts, the field needs precise, testable theories that describe the causal pathways through which implementation strategies function. In this perspective piece, we describe a four-step approach to developing causal pathway models for implementation strategies.Building causal modelsFirst, it is important to ensure that implementation strategies are appropriately specified. Some strategies in published compilations are well defined but may not be specified in terms of its core component that can have a reliable and measureable impact. Second, linkages between strategies and mechanisms need to be generated. Existing compilations do not offer mechanisms by which strategies act, or the processes or events through which an implementation strategy operates to affect desired implementation outcomes. Third, it is critical to identify proximal and distal outcomes the strategy is theorized to impact, with the former being direct, measurable products of the strategy and the latter being one of eight implementation outcomes (1). Finally, articulating effect modifiers, like preconditions and moderators, allow for an understanding of where, when, and why strategies have an effect on outcomes of interest.Future directionsWe argue for greater precision in use of terms for factors implicated in implementation processes; development of guidelines for selecting research design and study plans that account for practical constructs and allow for the study of mechanisms; psychometrically strong and pragmatic measures of mechanisms; and more robust curation of evidence for knowledge transfer and use.

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Evidence-based practice is important for behavioral interventions but there is debate on how best to support real-world behavior change. The purpose of this paper is to define products and a preliminary process for efficiently and adaptively creating and curating a knowledge base for behavior change for real-world implementation. We look to evidence-based practice suggestions and draw parallels to software development. We argue to target three products: (1) the smallest, meaningful, self-contained, and repurposable behavior change modules of an intervention; (2) “computational models” that define the interaction between modules, individuals, and context; and (3) “personalization” algorithms, which are decision rules for intervention adaptation. The “agile science” process includes a generation phase whereby contender operational definitions and constructs of the three products are created and assessed for feasibility and an evaluation phase, whereby effect size estimates/casual inferences are created. The process emphasizes early-and-often sharing. If correct, agile science could enable a more robust knowledge base for behavior change.

The use and development of mobile interventions are experiencing rapid growth. In "just-in-time" mobile interventions, treatments are provided via a mobile device and they are intended to help an individual make healthy decisions "in the moment," and thus have a proximal, near future impact. Currently the development of mobile interventions is proceeding at a much faster pace than that of associated data science methods. A first step toward developing data-based methods is to provide an experimental design for testing the proximal effects of these just-in-time treatments. In this paper, we propose a "micro-randomized" trial design for this purpose. In a micro-randomized trial, treatments are sequentially randomized throughout the conduct of the study, with the result that each participant may be randomized at the 100s or 1000s of occasions at which a treatment might be provided. Further, we develop a test statistic for assessing the proximal effect of a treatment as well as an associated sample size calculator. We conduct simulation evaluations of the sample size calculator in various settings. Rules of thumb that might be used in designing a micro-randomized trial are discussed. This work is motivated by our collaboration on the HeartSteps mobile application designed to increase physical activity.

This study was registered on ClinicalTrials.gov number NCT03225521.