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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.
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:
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:
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Uniform Random | Active Comparator | 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. |
|
| Reinforcement Learning | Experimental | 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. |
|
| Mood ratings only | Active Comparator | In this arm, participants will monitor their mood and receive random feedback based on mood responses. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Uniform random message delivery | Behavioral | In this arm, the categories and timings of text-messages will be delivered to participants using a random schedule |
|
| 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 |
| 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 |
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Inclusion criteria:
Exclusion criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Adrian Aguilera, PhD | UC Berkeley | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of California Berkeley | Berkeley | California | 94709 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 37146444 | Derived | Haro-Ramos AY, Rodriguez HP, Aguilera A. Effectiveness and implementation of a text messaging intervention to reduce depression and anxiety symptoms among Latinx and Non-Latinx white users during the COVID-19 pandemic. Behav Res Ther. 2023 Jun;165:104318. doi: 10.1016/j.brat.2023.104318. Epub 2023 Apr 16. | |
| 34543230 | Derived |
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Individual participant data that underlies the results reported in the articles will be made available to researchers on request after deidentification.
After publication of the data, no end date
Anyone with a methodologically sound proposal.
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| ID | Term |
|---|---|
| D003863 | Depression |
| D001008 | Anxiety Disorders |
| D000086382 | COVID-19 |
| D000092862 | Psychological Well-Being |
| ID | Term |
|---|---|
| D001526 | Behavioral Symptoms |
| D001519 | Behavior |
| D001523 | Mental Disorders |
| D011024 | Pneumonia, Viral |
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Participants are randomized to receive messages according to a random schedule (within a mico-randomized trial), delivered via a reinforcement learning policy or a control with mood ratings only.
| Reinforcement learning message delivery | Behavioral | In this arm, the categories and timings of text-messages will be chosen by a reinforcement learning algorithm |
|
| Mood ratings only | Behavioral | In this arm, participants will monitor their mood daily and receive feedback on that mood randomly |
|
| Aguilera A, Hernandez-Ramos R, Haro-Ramos AY, Boone CE, Luo TC, Xu J, Chakraborty B, Karr C, Darrow S, Figueroa CA. A Text Messaging Intervention (StayWell at Home) to Counteract Depression and Anxiety During COVID-19 Social Distancing: Pre-Post Study. JMIR Ment Health. 2021 Nov 1;8(11):e25298. doi: 10.2196/25298. |
| 33370721 | Derived | Figueroa CA, Hernandez-Ramos R, Boone CE, Gomez-Pathak L, Yip V, Luo T, Sierra V, Xu J, Chakraborty B, Darrow S, Aguilera A. A Text Messaging Intervention for Coping With Social Distancing During COVID-19 (StayWell at Home): Protocol for a Randomized Controlled Trial. JMIR Res Protoc. 2021 Jan 14;10(1):e23592. doi: 10.2196/23592. |
| D011014 |
| Pneumonia |
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
| D014777 | Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D010549 | Personal Satisfaction |