A text-messaging study to help cope with social distancing: clinical trial protocol for the Stay Well at Home study (Preprint)
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, like 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 days text-messaging intervention. Additionally, we aim to assess if 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 are 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 Patient Health Questionnaire depression scale 8-item (PHQ-8) and Generalized Anxiety Disorder 7-item (GAD-7) at baseline and at intervention completion. RESULTS The Institutional Review Board at the University of California Berkeley approved this study (CPHS: 2020-04-13162) in April 2020. Data collection runs from April 2020 to April 2021. As of August 24th 2020, we have enrolled 229 participants. We plan to submit manuscripts describing the main results of the trial and results from the micro-randomized trial for publication in peer-reviewed journals and presentations at (inter)-national 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. CLINICALTRIAL Clinicaltrials.gov: NCT04473599; pre-results.