Saturday April 15, 2023

Annual Meeting of the Education Committee of the AACR

d3c Presentation

Innovations in Methods for Adapting and Personalizing Interventions for Cancer Control

Inbal Billie Nahum-Shani, PhD – Institute for Social Research

Susan A. Murphy, PhD -Harvard University

David Wetter, PhD -University of Utah

Effective care across the continuum of cancer control demands an increasing array of evidence-based interventions to address individuals’ changing strengths, needs, and circumstances. This can be achieved via Adaptive Interventions, which explicitly guide how to modify the type/intensity of services based on information about the individual. Furthermore, advances in digital technologies, such as mobile devices and wearable sensors, have created unprecedented opportunities to deliver just-in-time adaptive interventions (JITAIs) which leverage information about the person’s state and context to adapt intervention delivery in real-time, real-world settings.

This educational session will provide an accessible introduction to recent methodological advances for optimizing adaptive interventions and JITAIs. We will begin by introducing two experimental approaches that can be used to empirically-inform the development of adaptive interventions and JITAIs: the Sequential Multiple Assignment Randomized Trial (SMART) and the Micro-Randomized Trial (MRT). We will then discuss a novel application of the SMART with community health centers serving low socioeconomic status populations. The goal is to develop an adaptive intervention that employs technology-based strategies for increasing the reach and impact of evidence-based tobacco cessation treatment. Next, we will discuss a novel application of the MRT using digital sensing to detect stress in real time among smokers attempting to quit. The goal is to develop a JITAI for improving stress-regulation and preventing smoking relapse. Throughout, we will discuss additional innovations and future directions in the development and applications of new methods for optimizing adaptive interventions.