  Alcohol consumption has a significant impact on individuals' health, with
even more pronounced consequences when consumption becomes excessive. One
approach to promoting healthier drinking habits is implementing just-in-time
interventions, where timely notifications indicating intoxication are sent
during heavy drinking episodes. However, the complexity or invasiveness of an
intervention mechanism may deter an individual from using them in practice.
Previous research tackled this challenge using collected motion data and
conventional Machine Learning (ML) algorithms to classify heavy drinking
episodes, but with impractical accuracy and computational efficiency for mobile
devices. Consequently, we have elected to use Hyperdimensional Computing (HDC)
to design a just-in-time intervention approach that is practical for
smartphones, smart wearables, and IoT deployment. HDC is a framework that has
proven results in processing real-time sensor data efficiently. This approach
offers several advantages, including low latency, minimal power consumption,
and high parallelism. We explore various HDC encoding designs and combine them
with various HDC learning models to create an optimal and feasible approach for
mobile devices. Our findings indicate an accuracy rate of 89\%, which
represents a substantial 12\% improvement over the current state-of-the-art.
