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Announcements

Mobile Data Expert Network (MDEN) Seeks Collaborators at U-M

In collaboration with The Mobile Technologies Core of the Eisenberg Family Depression Center at Michigan Medicine, d3c is working to form the Mobile Data Expert Network (MDEN). MDEN will facilitate knowledge sharing across research projects involving mobile health data and develop procedures for enhancing reproducibility and rigor. We invite staff and junior faculty who are directly working on extraction, processing, and analysis of data from mobile devices (e.g., smartphones or wearables).

To recommend a colleague, complete this form—or share it with them to complete on their own.

Learn More About MDEN

Spotlight on Adaptive Implementation

d3c’s novel approaches to constructing and optimizing adaptive interventions have gained a foothold in the field of implementation science. Two articles published in July highlight how adaptive implementation strategies can drive better health and education outcomes for people in a variety of institutional systems—from California primary-care clinics to public high schools in Michigan.

“Expanding access to medications for opioid use disorder in primary care clinics: an evaluation of common implementation strategies and outcomes”
Implementation Science Communications

A team of researchers at Stanford University analyzed data from a longitudinal study assessing the reach, adoption, and quality of four different implementation strategies across forty-one primary care clinics. They concluded that the provision of implementation strategies may be enhanced by tailoring strategies to the needs of individual clinics and offering strategies with built-in course correction to maintain engagement. These findings point to the promise of intervention designs pioneered by d3c researchers, like Multilevel Adaptive Implementation Strategies (MAISYs), which guide the sequencing and adaptation of implementation strategies across multiple levels of implementation.

Go to Article

“Primary aim results of a clustered SMART for developing a school-level, adaptive implementation strategy to support CBT delivery at high schools in Michigan”
Implementation Science

Researchers at the University of Michigan published results demonstrating the capability of d3c’s clustered SMART study design to optimize adaptive implementation strategies at the school system-level. The success of this trial establishes the SMART as a powerful tool for solving large-scale implementation challenges.

New Software Release

Our entire software collection is downloadable and free to use.

Crash Course

Quick but thoughtful answers to the questions we hear the most.

Q: What does it mean for an adaptive intervention to be “embedded” in a SMART?

A: Researchers use Sequential, Multiple Assignment, Randomized Trial (SMART) designs to investigate the effectiveness of intervention components (or combinations of them) at multiple stages. At the beginning of each stage, participants may be assigned their next intervention through randomization (e.g., 50% receive treatment A, 50% receive treatment B). But, depending on the SMART, not all patients are randomized to a new intervention at every stage. In some SMARTs, only some participants (e.g., those most likely to benefit from additional intervention) are re-randomized. For example, in the SMART shown below, only those who did not respond to their assigned first-stage treatment are randomized to intervention D or intervention E.

In a SMART with this design, participants follow different treatment pathways determined by randomization and their response status. Each pair of pathways defines an adaptive intervention (above). For example, pathway A-C (available only to responders) combined with pathway A-D (available only to non-responders) represents one adaptive intervention (AI1). Pathway A-C combined with pathway A-E represents another adaptive intervention (AI2). We call these AIs “embedded” because their designs are readily apparent in the design of the SMART.

A common primary aim in a SMART is to compare the relative effectiveness of these embedded adaptive interventions. Our collection of downloadable and free-to-use software includes programs for conducting this type of analysis with data arising from a SMART.

Browse d3c Software

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