Stay Well at Home: a Text-messaging Study Social Distancing

Not Applicable

Completed

• Conditions: Depressive Symptoms; COVID-19; Anxiety
• Interventions: Behavioral: Uniform random message delivery; Behavioral: Mood ratings only; Behavioral: Reinforcement learning message delivery

• Registration Number: NCT04473599
• Lead Sponsor: University of California, Berkeley

Brief Summary

The investigators have developed supportive text-messages in English and Spanish to help people cope with the stress and anxiety of COVID-19 social distancing. The purpose of this study is to examine if automated text-messages will improve depression and anxiety symptoms and enhance positive mood.

Additionally, the investigators will compare the effectiveness of sending messages on a random schedule (using a micro-randomized trial design) or sent by a reinforcement learning policy on overall change in depression and anxiety symptoms and daily mood during the 8-week study.

Detailed Description

The investigators will send participants supportive text-messages for a period of 2 months. These text-messages will include tips about behavioral activation and coping skills to deal with worries and stress. The investigators generated a message bank balanced such that 50% of all messages are related to behavioral activation (BA) and 50% messages involve different coping skills. Participants will receive one of these messages per day between 9:00 am and 6:00 pm. Participants will also receive a message asking them to rate their mood on a scale of 1-9 once a day 3 hours after the BA or coping message.

Participants will be randomized to:

1. a uniform random messaging group (micro-randomized trial design).

2. a reinforcement learning group with a learned decision mechanism for the timing and type of text-message. The algorithm learns from previous data (which messages were sent, what was the participants' mood) to maximize an increase in participants mood. No other data are collected from participants' phones.

The investigators will compare the effect of sending text-messages by a random schedule, and text-messaging chosen by the RL algorithm. This allows to both evaluate the effect of the individual intervention components over time within a micro-randomized trial design, and assess the added value of using RL to adapt the messaging scheme.

The investigators hypothesize that:

* Participant will show improvements in depression, anxiety symptoms and mood during the 60 day study.

* The participants in the group receiving reinforcement learning will have a greater improvement in depressive symptoms, anxiety and positive mood during the study than participants in the micro-randomized group.

* The investigators will find differential effects on mood ratings for the two categories of messages

Study Timeline

• Study Start: 2020-04-17
• Study First Submitted: 2020-07-13
• Study First Submitted QC: 2020-07-13
• Study First Posted: 2020-07-16
• Primary Completion: 2022-07-25
• Study Completion: 2023-01-10
• Status Verified: 2023-05
• Last Update Submitted: 2023-05-19
• Last Update Posted: 2023-05-23

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 1000

Inclusion Criteria

Not provided

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Exclusion Criteria

Not provided

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Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
Uniform Random	Uniform random message delivery	Participants will receive supportive text-messages for a period of 2 months. These text-messages have two categories: behavioral activation (BA) and coping skills. In this arm, participants will receive one of these types of messages daily on a random schedule in random time periods throughout the day.
Mood ratings only	Mood ratings only	In this arm, participants will monitor their mood and receive random feedback based on mood responses.
Reinforcement Learning	Reinforcement learning message delivery	In this arm we will test a reinforcement learning (RL) algorithm with a learned decision mechanism for the timing and type of text-messages. The algorithm learns from previous data (which messages were sent, what was the participants' mood) to maximize an increase in participants' mood.

Primary Outcome Measures

Name	Time	Method
Anxiety scores	Change from baseline to 8 week follow-up	General Anxiety Disorder 7 item (GAD-7). The GAD-7 has scores from 0 to 21. Higher scores mean a worse outcome.
Depression scores	Change from baseline to 8 week follow-up	Patient Health Questionnaire 9 item (PHQ-9). The PHQ-9 has scores from 0 to 27. Higher scores mean a worse outcome.

Secondary Outcome Measures

Name	Time	Method
Self reported mood ratings	8 weeks	The self-reported mood rating 3 hours after receiving a message. The score is from 0-9. A higher mood rating indicates a better outcome.

Trial Locations

Locations (1)

University of California Berkeley; 🇺🇸 Berkeley, California, United States