A Text Messaging Intervention for Coping With Social Distancing During COVID-19 (StayWell at Home): Protocol for a Randomized Controlled Trial.
Caroline Astrid Figueroa, Rosa Hernandez-Ramos, Claire Elizabeth Boone, Laura Gómez-Pathak, Vivian Yip, Tiffany Luo, Valentín Sierra, Jing Xu, Bibhas Chakraborty, Sabrina Darrow, Adrian Aguilera
Author Information
Caroline Astrid Figueroa: School of Social Welfare, University of California Berkeley, Berkeley, CA, United States. ORCID
Rosa Hernandez-Ramos: School of Social Welfare, University of California Berkeley, Berkeley, CA, United States. ORCID
Claire Elizabeth Boone: School of Public Health, University of California Berkeley, Berkeley, CA, United States. ORCID
Laura Gómez-Pathak: School of Social Welfare, University of California Berkeley, Berkeley, CA, United States. ORCID
Vivian Yip: School of Social Welfare, University of California Berkeley, Berkeley, CA, United States. ORCID
Tiffany Luo: School of Social Welfare, University of California Berkeley, Berkeley, CA, United States. ORCID
Valentín Sierra: School of Social Welfare, University of California Berkeley, Berkeley, CA, United States. ORCID
Jing Xu: Centre for Quantitative Medicine, Duke-National University of Singapore Medical School, Singapore, Singapore. ORCID
Bibhas Chakraborty: Centre for Quantitative Medicine, Duke-National University of Singapore Medical School, Singapore, Singapore. ORCID
Sabrina Darrow: Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, United States. ORCID
Adrian Aguilera: School of Social Welfare, University of California Berkeley, Berkeley, CA, United States. ORCID
BACKGROUND: Social distancing is a crucial intervention to slow down person-to-person transmission of COVID-19. However, social distancing has negative consequences, including increases in depression and anxiety. Digital interventions, such as text messaging, can provide accessible support on a population-wide scale. We developed text messages in English and Spanish to help individuals manage their depressive mood and anxiety during the COVID-19 pandemic. OBJECTIVE: In a two-arm randomized controlled trial, we aim to examine the effect of our 60-day text messaging intervention. Additionally, we aim to assess whether the use of machine learning to adapt the messaging frequency and content improves the effectiveness of the intervention. Finally, we will examine the differences in daily mood ratings between the message categories and time windows. METHODS: The messages were designed within two different categories: behavioral activation and coping skills. Participants will be randomized into (1) a random messaging arm, where message category and timing will be chosen with equal probabilities, and (2) a reinforcement learning arm, with a learned decision mechanism for choosing the messages. Participants in both arms will receive one message per day within three different time windows and will be asked to provide their mood rating 3 hours later. We will compare self-reported daily mood ratings; self-reported depression, using the 8-item Patient Health Questionnaire; and self-reported anxiety, using the 7-item Generalized Anxiety Disorder scale at baseline and at intervention completion. RESULTS: The Committee for the Protection of Human Subjects at the University of California Berkeley approved this study in April 2020 (No. 2020-04-13162). Data collection began in April 2020 and will run to April 2021. As of August 24, 2020, we have enrolled 229 participants. We plan to submit manuscripts describing the main results of the trial and results from the microrandomized trial for publication in peer-reviewed journals and for presentations at national and international scientific meetings. CONCLUSIONS: Results will contribute to our knowledge of effective psychological tools to alleviate the negative effects of social distancing and the benefit of using machine learning to personalize digital mental health interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT04473599; https://clinicaltrials.gov/ct2/show/NCT04473599. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/23592.
