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

Study Overview
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Brief Summary
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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
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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: a uniform random messaging group (micro-randomized trial design). 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

Official Title
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Stay Well at Home: A Text-messaging Study to Improve Mood and Help Cope With Social Distancing

Conditions
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Depressive Symptoms, Anxiety, COVID-19

Intervention / Treatment
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* Behavioral: Uniform random message delivery
* Behavioral: Reinforcement learning message delivery
* Behavioral: Mood ratings only


Participation Criteria
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Eligibility Criteria
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Inclusion criteria: Over 18 years old Own a mobile phone Speak English or Spanish Exclusion criteria: Not owning a mobile phone Under 18 years old

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Prevention
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: Uniform Random<br>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. | Behavioral: Uniform random message delivery<br>* In this arm, the categories and timings of text-messages will be delivered to participants using a random schedule<br>|
| Experimental: Reinforcement Learning<br>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. | Behavioral: Reinforcement learning message delivery<br>* In this arm, the categories and timings of text-messages will be chosen by a reinforcement learning algorithm<br>|
| Active Comparator: Mood ratings only<br>In this arm, participants will monitor their mood and receive random feedback based on mood responses. | Behavioral: Mood ratings only<br>* In this arm, participants will monitor their mood daily and receive feedback on that mood randomly<br>|

What is the study measuring?
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Primary Outcome Measures

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

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Self reported mood ratings | 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. | 8 weeks | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Mobile health, Text-messaging, Covid-19, Mental health, Coping