Personalizing a mental health texting intervention using reinforcement learning
Marvyn R. Arévalo Avalos (University of California)
Karina Rosales (University of California)
Chris Karr (Audacious Software)
Caroline A. Figueroa (TU Delft - Information and Communication Technology)
Tiffany Luo (University of California)
Suchitra Sudarshan (University of California)
Vivian Yip (University of California)
Adrian Aguilera (University of California)
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Abstract
StayWell is a 60-day CBT/DBT-based text messaging intervention which leverages reinforcement learning algorithms to support mental health. Participants were randomly assigned to receiving personalized messaging (adaptive arm), static messaging (random arm) or mood-monitoring only messages (control arm). A diverse sample of 1121 adults participated in a fully remote trial between December 2021 and July 2022. Across study arms, participants showed a 25% reduction in depression symptoms (PHQ-8) and 24% reduction in anxiety symptoms (GAD-7) following the intervention. We did not find statistically significant differences in PHQ-8 and GAD-7 reductions between intervention arms. Participants in the control arm had higher mood-monitoring messages response rates than those in other conditions. Finally, post-hoc exploratory analysis assessing outcomes by condition indicated that patients with minimal to mild depression symptoms (PHQ-8 < 10) benefitted from the reinforcement learning algorithm. The results of this trial suggest that StayWell is a promising text-messaging intervention to achieve reductions in depression and anxiety among diverse populations.
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