Designing Multi-modal Timeline Interfaces to Make Conscious Decisions About Alcohol Intake

Abstract

At a time when voice and chat technologies continue to find niches in digital, what tools can be available to people who seek support with reducing alcohol intake? Recent research on smartphone sensing to track alcohol recommends taking a social context approach to address public health hazards of drinking, while many current digital services require self-reporting. Focusing on two audiences, one who wants abstinence, and one that desires to reduce drinking, it is clear that many solutions focus on an abstinence focused approach. Medical practitioners recommend solutions that take a more phased approach to reducing alcohol usage. Traditional programs to reduce drinking in digital technologies focus on cessation and quitting permanently over a phased or reflective approach to reducing alcohol intake. Understanding how communities that experience minority stress, specifically bisexual individuals, can inform ideas of how to create new social spaces that decrease interpersonal stress. What can happen with a timeline, which typically focuses on quantitative metrics, to a solution that focuses more on modulating and thinking through alcohol usage. And, as AI assistants now can get built in code with tools like Claude and ChatGPT. Traditional biometrics like breathalyzers give personalized feedback; how can this evolve into a digital interface that tracks this data over time, and in a private and safe way in a digital interface?