SCH: INT: Collaborative Research: Enhancing Context-Awareness and Personalization for Intensively Adaptive Smoking Cessation Messaging Interventions

University Of Memphis, Memphis TN

Investigators

Abstract

According to the Centers for Disease Control, tobacco use remains the leading preventable cause of death in the US, causing approximately 480,000 deaths each year, and incurring over $150 billion in healthcare costs. As a result, scalable, low cost, and effective smoking cessation interventions are clearly needed. This research aims to develop and validate new messaging-based smoking cessation support intervention systems that will leverage recent advances in smart wearable technologies to significantly enhance efficacy. The system will integrate wearable sensors that continuously estimate an individual's level of stress and craving as well as the occurrence of smoking. This information will be used to enhance the context awareness of the intervention system, allowing it to continuously adapt both the content and delivery timing of intervention components for each individual. By developing scalable messaging-based smoking cessation support interventions with improved personal relevance, this research has the potential to lead to direct benefits to society by more effectively helping individuals to quit smoking. To accomplish the goal of providing effective, personalized smoking cessation interventions, this research will develop and evaluate the models, algorithms, and wearable-phone-cloud computational infrastructures required to support the context inferences, personalization, and delivery timing optimizations required. Starting from the team's extensive prior work, this research will contribute to (1) advances in mobile health sensing and context inference with low-cost, low-power sensors; (2) advances in real-time, stream-based active learning for personalizing context inference models; (3) advances in contextualized recommender systems to personalize message selection based on inferred contexts; and (4) advances in robust, real-time wearable-phone-cloud data analytics systems. This work will also make substantial contributions to enhancing research infrastructure through open source software releases that can be leveraged by the research community to yield benefits in other high-profile health areas including heart disease, obesity, and addiction.