A Text Messaging Intervention for Coping With Social Distancing During COVID-19 (StayWell at Home): Protocol for a...

Area of Science:

• Digital mental health interventions
• Psychological support during pandemics
• Machine learning in healthcare

Background:

• Social distancing is essential for mitigating COVID-19 transmission but can increase depression and anxiety.
• Digital interventions, like text messaging, offer scalable support for mental well-being.
• This study addresses the need for accessible psychological tools during public health crises.

Purpose of the Study:

• To evaluate a 60-day text messaging intervention for managing depressive mood and anxiety.
• To assess the efficacy of machine learning in personalizing message frequency and content.
• To compare daily mood ratings across different message categories and time windows.

Main Methods:

• A two-arm randomized controlled trial comparing standard messaging with machine learning-adapted messaging.
• Intervention includes daily messages in behavioral activation and coping skills categories.
• Participants provide daily mood ratings and complete depression (PHQ-8) and anxiety (GAD-7) scales.

Main Results:

• Study approved by the Committee for the Protection of Human Subjects at UC Berkeley (April 2020).
• Data collection initiated in April 2020, with 229 participants enrolled by August 2020.
• Manuscripts detailing trial results will be submitted for publication and scientific meetings.

Conclusions:

• Findings will inform the development of effective digital tools for mental health.
• The study will elucidate the benefits of machine learning in personalizing digital mental health interventions.
• Results aim to mitigate the adverse psychological effects of social distancing measures.