TY - GEN
T1 - Addict free - A smart and connected relapse intervention mobile app
AU - Yang, Zhou
AU - Reddy, Vinay Jayachandra
AU - Kesidi, Rashmi
AU - Jin, Fang
N1 - Funding Information:
This work was supported by the U.S. National Science Foundation under Grant CNS-1737634.
Publisher Copyright:
© 2019 Copyright held by the owner/author(s).
PY - 2019/8/19
Y1 - 2019/8/19
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85071632190&partnerID=8YFLogxK
U2 - 10.1145/3340964.3340986
DO - 10.1145/3340964.3340986
M3 - Conference contribution
AN - SCOPUS:85071632190
T3 - ACM International Conference Proceeding Series
SP - 202
EP - 205
BT - Proceedings of the 16th International Symposium on Spatial and Temporal Databases, SSTD 2019
PB - Association for Computing Machinery
Y2 - 19 August 2019 through 21 August 2019
ER -