Characterising patterns of alcohol use among heavy drinkers: A cluster analysis utilising alcohol biosensor data.

ABSTRACT:

Introduction

Previous research has predominately relied on person-level or single characteristics of drinking episodes to characterise patterns of drinking that may confer risk. This research often relies on self-report measures. Advancements in wearable alcohol biosensors provide a multi-faceted objective measure of drinking. The current study aimed to characterise drinking episodes using data derived from a wearable alcohol biosensor.

Methods

Participants (n = 45) were adult heavy drinkers who wore the Secure Continuous Remote Alcohol Monitoring (SCRAM) bracelet and reported on their drinking behaviours. Cluster analysis was used to evaluate unique combinations of alcohol episode characteristics. Associations between clusters and self-reported person and event-level factor

SUBMITTER: Gunn RL 

PROVIDER: S-EPMC9972297 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature