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Multimodal and Multilevel Adaptive Interventions: Innovations in Intervention, Implementation and Experimental Designs


DECEMBER 2, 2022

Inbal Billie Nahum-Shani, PhD
Research Associate Professor, Survey Research Center, Institute For Social Research
Co-Director, Data-science for Dynamic Decision-making Center (d3c)
University of Michigan

Daniel Almirall, PhD
Research Associate Professor, Survey Research Center, Institute for Social Research
and Research Associate Professor, Statistics, College of Literature, Science, and the Arts
Co-Director, Data-science for Dynamic Decision-making Center (d3c)
University of Michigan

About the Presentation: Effective prevention, treatment, and recovery services for substance use disorders (SUD)—and their implementation—often requires a sequence of intervention components that is capable of detecting and responding to an individual’s (or organization’s) changing strengths, needs, and circumstances. This can be achieved via Adaptive Interventions, which explicitly guide how to modify the type/‌intensity of interventions based on accruing information about an individual (or organization). Further, advances in digital technologies, such as mobile devices and electronic health records (EHRs) have created unprecedented opportunities to adapt interventions at different time scales (e.g., monthly, many times a day) and levels (e.g., individual, clinic, health system).

This seminar will provide an introduction to recent methodological advances for optimizing adaptive interventions for SUD. We will introduce two novel types of adaptive interventions: (1) Multimodal Adaptive Interventions (MADIs); and (2) Multilevel Adaptive Implementation Strategies (MAISYs). We will also discuss, briefly, new experimental designs for optimizing each of these new types of adaptive interventions, including the Hybrid Experimental Design (HED) and the Clustered Sequential, Multiple-Assignment Randomized Trial (SMART) design.

About the Presenters:   Daniel Almirall is a statistician who develops methods to form evidence-based adaptive interventions.  Adaptive interventions are used to guide individualized intervention decisions for the on-going management of chronic illnesses or disorders such as drug abuse, depression, anxiety, autism, obesity, or HIV/AIDS. More recently, Mr. Almirall has been developing methods to inform the construction of optimized multilevel adaptive implementation interventions (MAISYs) using Multilevel Implementation SMARTs (MI-SMARTs).  He is particularly interested in applications in mental health and substance use.

Billie Nahum-Shani is a behavioral scientist who develops behavioral theory and novel methodology to construct adaptive interventions; these interventions modify the type, timing, dose, or delivery mode of support in order to address the unique and changing needs of individuals. Of particular interest are interventions that leverage digital technology to adapt intervention delivery to individuals in real-time, in their daily lives. Her work includes developing and extending experimental designs that can inform the development of adaptive interventions, including Sequential Multiple Assignment Randomized Trials (SMARTs), Micro-Randomized Trials (MRTs) and Hybrid Experimental Designs (HEDs). She is providing leadership to several NIH funded projects, including an NIH/NIDA funded P50 Center of Excellence to develop novel experimental designs and data analytic methods for adapting and personalizing services for drug use and HIV.