Addict free - A smart and connected relapse intervention mobile app

Zhou Yang, Vinay Jayachandra Reddy, Rashmi Kesidi, Fang Jin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

It is widely acknowledged that addiction relapse is highly associated with spatial-temporal factors such as some specific places or time periods. Current studies suggest that those factors can be utilized for better relapse interventions, however, there is no relapse prevention application that makes use of those factors. In this paper, we introduce a mobile app called “Addict Free", which records user profiles, tracks relapse history and summarizes recovering statistics to help users better understand their recovering situations. Also, this app builds a relapse recovering community, which allows users to ask for advice and encouragement, and share relapse prevention experience. Moreover, machine learning algorithms that ingest spatial and temporal factors are utilized to predict relapse, based on which helpful addiction diversion activities are recommended by a recovering recommendation algorithm. By interacting with users, this app targets at providing smart suggestions that aim to stop relapse, especially for alcohol and tobacco addiction users.

Original languageEnglish
Title of host publicationProceedings of the 16th International Symposium on Spatial and Temporal Databases, SSTD 2019
PublisherAssociation for Computing Machinery
Pages202-205
Number of pages4
ISBN (Electronic)9781450362801
DOIs
StatePublished - Aug 19 2019
Event16th International Symposium on Spatial and Temporal Databases, SSTD 2019 - Vienna, Austria
Duration: Aug 19 2019Aug 21 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference16th International Symposium on Spatial and Temporal Databases, SSTD 2019
CountryAustria
CityVienna
Period08/19/1908/21/19

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