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Related Experiment Video

Updated: Jan 9, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Personalizing a mental health texting intervention using reinforcement learning.

Marvyn R Arévalo Avalos1, Karina Rosales1, Chris Karr2

  • 1School of Social Welfare, University of California Berkeley, Berkeley, CA, USA.

Npj Mental Health Research
|December 2, 2025
PubMed
Summary
This summary is machine-generated.

StayWell, a 60-day text messaging program, effectively reduced depression and anxiety symptoms in adults. Personalized messaging showed promise for individuals with mild depression.

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Area of Science:

  • Digital mental health
  • Behavioral science
  • Machine learning in healthcare

Background:

  • Mental health interventions are increasingly delivered via digital platforms.
  • Text messaging interventions offer scalable solutions for mental health support.
  • Cognitive Behavioral Therapy (CBT) and Dialectical Behavior Therapy (DBT) principles are effective in managing depression and anxiety.

Purpose of the Study:

  • To evaluate the efficacy of a 60-day CBT/DBT-based text messaging intervention called StayWell.
  • To compare personalized (adaptive) messaging, static messaging, and mood-monitoring only messages for mental health support.
  • To investigate the impact of reinforcement learning algorithms in a text messaging intervention.

Main Methods:

  • A randomized controlled trial involving 1121 diverse adults conducted remotely.
  • Participants were assigned to adaptive, random, or control (mood-monitoring only) arms.
  • Depression (PHQ-8) and anxiety (GAD-7) symptoms were assessed before and after the 60-day intervention.

Main Results:

  • Overall, participants experienced significant reductions in depression (25%) and anxiety (24%) symptoms.
  • No statistically significant differences in symptom reduction were observed between the adaptive and random messaging arms.
  • Participants in the control arm showed higher response rates to mood-monitoring messages.

Conclusions:

  • The StayWell intervention demonstrates potential for reducing depression and anxiety symptoms in diverse populations.
  • Reinforcement learning algorithms may offer specific benefits for individuals with minimal to mild depression.
  • Further research is warranted to optimize personalized messaging strategies in digital mental health interventions.