Real-Time Tracking Identified What Predicted Cannabis Relapse in Young Adults | Research

The takeaway

Using smartphone-based ecological momentary assessment, researchers found that negative effect expectancies, family support, confidence to abstain, and situational permissibility during use predicted lapse during attempted cannabis abstinence.

Young adults trying to reduce cannabis use, addiction counselors, digital health researchers.

73.5% of young daily cannabis users relapsed during two weeks of attempted abstinence; real-time smartphone data predicted who would lapse.

What the researchers found

Nearly 3 in 4 participants (73.5%) lapsed during attempted abstinence. The combination of negative effect expectancies, perceived family support, confidence to abstain, and situational permissibility during use was highly accurate in predicting who would lapse. Greater percent of days with use, easy accessibility, and situational permissibility were each associated with lapse.

Why it matters

Most young heavy cannabis users who try to quit relapse on their own. Understanding the real-time factors that predict relapse could enable personalized digital interventions that provide support at the moments when people are most vulnerable.

The numbers in context

34 participants, ages 18-25, using 5+ days/week. 73.5% lapsed during attempted abstinence. Smartphone assessments captured affect, craving, accessibility, permissibility, and motivation multiple times daily.

How the study worked

34 young adults aged 18-25 using marijuana 5+ days/week completed ecological momentary assessment (EMA) via smartphone multiple times daily for two weeks of regular use, then two weeks of attempted abstinence.

What this study cannot tell us

Small sample (n=34). Self-selected participants willing to attempt abstinence and use monitoring technology. Two-week abstinence period is short. EMA compliance may vary.

How to read the evidence

Moderate - novel methodology with real-time data collection, but small sample size limits generalizability.

When this study was published

Published in 2018. Smartphone-based interventions for substance use have expanded since.

The bigger picture

This study demonstrates that smartphone-based real-time monitoring can capture meaningful predictors of cannabis relapse that traditional surveys miss. As digital health tools become more sophisticated, this approach could enable just-in-time interventions.

Questions still open

Could real-time EMA data trigger automated interventions at high-risk moments? Would addressing the identified predictors (family support, confidence, permissibility) improve quit rates? How do these momentary factors interact with longer-term predictors?

Common questions

Why is it so hard for young people to quit cannabis?

This study found that 73.5% of young heavy users lapsed within two weeks of trying to quit. Key predictors of relapse included easy access to cannabis, environments where use felt acceptable, low confidence in ability to abstain, and lack of family support.

Can smartphone apps help people quit cannabis?

This study used smartphone monitoring to identify real-time predictors of relapse. The approach could potentially be adapted into intervention apps that provide support during high-risk moments, though this study only tracked patterns rather than testing interventions.

Read the original research

Momentary factors during marijuana use as predictors of lapse during attempted abstinence in young adults.

Addictive behaviors, 83, 167-174

View on PubMed ↗Publisher’s paper ↗

Citation

Shrier, Lydia A; Sarda, Vishnudas; Jonestrask, Cassandra; Harris, Sion Kim. (2018). Momentary factors during marijuana use as predictors of lapse during attempted abstinence in young adults.. Addictive behaviors, 83, 167-174. https://doi.org/10.1016/j.addbeh.2017.12.032