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Tytuł:
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A Text Messaging Intervention for Coping With Social Distancing During COVID-19 (StayWell at Home): Protocol for a Randomized Controlled Trial
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Autorzy:
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Figueroa, Caroline Astrid
Hernandez-Ramos, Rosa
Boone, Claire Elizabeth
Gómez-Pathak, Laura
Yip, Vivian
Luo, Tiffany
Sierra, Valentín
Xu, Jing
Chakraborty, Bibhas
Darrow, Sabrina
Aguilera, Adrian
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Temat:
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Medicine
Computer applications to medicine. Medical informatics
R858-859.7
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Źródło:
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JMIR Research Protocols, Vol 10, Iss 1, p e23592 (2021)
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Wydawca:
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JMIR Publications, 2021.
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Rok publikacji:
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2021
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Kolekcja:
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LCC:Medicine
LCC:Computer applications to medicine. Medical informatics
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Typ dokumentu:
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article
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Opis pliku:
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electronic resource
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Język:
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English
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ISSN:
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1929-0748
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Relacje:
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http://www.researchprotocols.org/2021/1/e23592/; https://doaj.org/toc/1929-0748
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DOI:
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10.2196/23592
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Dostęp URL:
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https://doaj.org/article/5a00f06aa831409c93f03e55035b688b  Link otwiera się w nowym oknie
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Numer akcesji:
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edsdoj.5a00f06aa831409c93f03e55035b688b
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BackgroundSocial 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. ObjectiveIn 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. MethodsThe 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. ResultsThe 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. ConclusionsResults 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 RegistrationClinicalTrials.gov NCT04473599; https://clinicaltrials.gov/ct2/show/NCT04473599 International Registered Report Identifier (IRRID)DERR1-10.2196/23592
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