Addict Free -- A Smart and Connected Relapse Intervention Mobile App
This addresses relapse prevention for individuals with alcohol and tobacco addiction, but it is incremental as it applies existing methods to a new application domain.
The authors tackled the problem of addiction relapse by developing a mobile app that uses spatial-temporal factors and machine learning to predict relapse and recommend diversion activities, resulting in a tool that provides smart suggestions for alcohol and tobacco addiction users.
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.