Relapse Prevention provides technology that monitors user behavior for indicators of potential relapse events. The platform delivers personalized guidance and support to help individuals proactively manage their recovery journey.
Funding
Funding not disclosed
Founders
Product
Problem
Individuals recovering from alcohol and drug addiction often experience subtle, unnoticed changes in behavior and environment that can lead to relapse. Recognizing these early warning signs is critical, but individuals may not always be aware of these personal triggers. The inability to connect with support networks during moments of crisis further compounds the risk of relapse.
Solution
Upendo provides Prelapse, a mobile-first application that uses patent-pending quantified-self technology and machine learning algorithms to monitor and identify personalized relapse triggers. By collecting and processing emotional, psychological, geographical, and social data points, the application detects patterns, trends, and anomalies that may indicate an imminent relapse. The app provides real-time alerts to the individual and their designated support network, facilitating timely intervention and support. Prelapse aims to empower individuals in recovery with data-driven insights and community support, helping them proactively manage their sobriety.
Target Audience
The primary target audience includes individuals in recovery from alcohol and drug addiction, as well as their support networks, including family, friends, therapists, and support groups.
Features
- Mobile application for iOS and Android devices
- Patent-pending technology to identify personalized relapse triggers
- Collection and processing of emotional, psychological, geographical, and social data
- Machine learning algorithms to detect patterns, trends, and anomalies
- Real-time alerts to the individual and their support network
- Configurable settings to customize the app to individual needs
- Integration of national statistics and addiction recovery best practices
- Geospatial technology to identify location-based triggers