# LEVERAGING PREDICTIVE ANALYTICS WITHIN SOCIAL NETWORKS TO MAXIMIZE DRUG ANDALCOHOL TREATMENT EFFICACY AND RELAPSE PREVENTION

> **NIH NIH R44** · SOBER GRID, INC. · 2020 · $471,700

## Abstract

PROJECT SUMMARY/ABSTRACT
Sober Grid™ has developed a smartphone-based mobile application currently in use by over
120,000 individuals worldwide who are in, or seeking, recovery from drug and alcohol addiction.
The “Grid”, as it is known, is a mobile-based, social recovery community providing rapid context-
specific peer support, efficient help seeking, motivational enhancement exercises, and member
ratings of support content – all aimed to prevent relapse. The overarching goal of this phase II
project is to extend the current capabilities of the Sober Grid app to achieve a comprehensive
social recovery support app featuring intelligent, context appropriate resource matching and 24/7
rapid response peer-coaching that is effective in reducing disordered substance use and is cost
effective. We hypothesize that providing this functionality to high-risk members will be acceptable,
feasible, increase access to and engagement with resources, and have a positive effect in
increasing time to relapse and days of consecutive abstinence. The company’s priority
commercial goal is to leverage the insights gained from its large database of information and
behaviors recorded in its existing addiction recovery-focused social network, combined with its
recently completed SBIR phase I work, to identify users at risk for relapse and deliver tailored lay
and professional peer support, telehealth, and provider interventions prompted by near real-time
automated risk indicators built into an Enhanced Sober Grid (ESG) solution. The specific goals of
this proposed phase II project include: (1) implementing the relapse risk scoring algorithm
successfully developed in phase I in a production environment, (2) developing a ML resource
relevancy matching algorithm, and (3) using this system to pilot test the feasibility and
acceptability as well as to estimate the effect on abstinence of providing peer-coaching and
resource information to a national sample of Sober Grid members in recovery from non-
alcohol/nicotine substances who are predicted to be at high risk for relapse. We will accomplish
our objectives by: (Aim 1) Implementing an accurate, near real-time production risk prediction
system; (Aim 2) Developing an enhanced, in-app substance-addiction recovery resource locator;
and, (Aim 3) Determining the feasibility, acceptability and estimating the effect size of the
proposed ESG among high relapse-risk participants in recovery from opioid, stimulant, or
cannabis use. The expected impact of this project is to substantially enhance Sober Grid's ability
to deliver effective and timely interventions to the right individuals, at the right time – including
those high-need and underserved populations that would otherwise lack access to care and draw
considerable resources from the healthcare system. This capability holds the promise to reduce
drug and alcohol relapse and the associated negative health impacts at both an individual and
societal level while reducing the total cos...

## Key facts

- **NIH application ID:** 9897501
- **Project number:** 5R44DA044062-03
- **Recipient organization:** SOBER GRID, INC.
- **Principal Investigator:** Wendy Warrington
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $471,700
- **Award type:** 5
- **Project period:** 2017-07-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9897501

## Citation

> US National Institutes of Health, RePORTER application 9897501, LEVERAGING PREDICTIVE ANALYTICS WITHIN SOCIAL NETWORKS TO MAXIMIZE DRUG ANDALCOHOL TREATMENT EFFICACY AND RELAPSE PREVENTION (5R44DA044062-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9897501. Licensed CC0.

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