{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T13:52:03Z","timestamp":1791899523751,"version":"4.3.4"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"vor","delay-in-days":34,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Health Resources & Services Administration (HRSA) Coronavirus Aid","id":[{"id":"https:\/\/ror.org\/0121dpf30","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Relief, and Economic Security","award":["#T1NHP39159"],"award-info":[{"award-number":["#T1NHP39159"]}]},{"name":"4TU Federation in the Netherlands"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Digital mental health interventions could improve population health. However, our understanding of how to improve their effectiveness through personalization is limited. We describe the outcomes of a micro-randomized trial (MRT) of a 60-day text messaging program, delivered during the final stages of the COVID-19 pandemic. We analyze the effects of 6 different types of cognitive behavioral therapy (CBT) and dialectical behavioral therapy (DBT) messages, and examine whether socio-demographic, health-related, and contextual factors (eg, timing of sending a message) moderate effectiveness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This secondary analysis included 373 adults aged 18 years or older (mean\u2009=\u200938.07), mostly English-speaking female, highly educated White individuals who reported being in good health, but having high stress and struggling to get their basic needs met. Participants were recruited between December 2021 and September 2022 through social media and a recruitment service (Health Match). We used the weighted and centered least squares method to examine the proximal effects of the text-message categories on daily mood, and whether sociodemographic, health-related factors, and the timing of message delivery moderated effectiveness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>None of the 6 CBT\/DBT-message categories significantly improved mood ratings marginally. However, their effectiveness varied based on the time of day and self-reported health. Overall, sending a message in the afternoon was more effective. However, certain message categories showed significant effects when sent in the morning among participants with poorer self-reported health, while some showed lower effectiveness when sent in the late afternoon for participants with better health.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our analysis underscores the importance of considering the time of day and individual health status when developing digital mental health interventions. These insights can help in designing more personalized, adaptive, and effective digital interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamiaopen\/ooag156","type":"journal-article","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T11:48:14Z","timestamp":1791632894000},"source":"Crossref","is-referenced-by-count":0,"title":["Personalizing digital mental health: a micro-randomized trial analysis of cognitive behavioral therapy\/dialectical behavioral therapy text messages to improve mood"],"prefix":"10.1093","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0692-2244","authenticated-orcid":false,"given":"Caroline A","family":"Figueroa","sequence":"first","affiliation":[{"name":"Faculty of Technology, Policy, and Management, Delft Technical University , Delft, 2628,","place":["The Netherlands"]},{"name":"School of Social Welfare, University of California Berkeley , Berkeley, CA, 94720,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Statistics and Business Analytics, College of Business and Economics, United Arab Emirates University , Al Ain, Abu Dhabi, 15551,","place":["United Arab Emirates"]},{"name":"Centre for Biomedical Data Science, Duke-NUS Medical School, National University of Singapore , Singapore, 169857,","place":["Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marvyn R","family":"Ar\u00e9valo Avalos","sequence":"additional","affiliation":[{"name":"School of Social Welfare, University of California Berkeley , Berkeley, CA, 94720,","place":["United States"]},{"name":"Department of Psychiatry and Behavioral Sciences, University of California\u2013San Francisco , San Francisco, CA, 94107,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7366-0478","authenticated-orcid":false,"given":"Bibhas","family":"Chakraborty","sequence":"additional","affiliation":[{"name":"Centre for Biomedical Data Science, Duke-NUS Medical School, National University of Singapore , Singapore, 169857,","place":["Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrian","family":"Aguilera","sequence":"additional","affiliation":[{"name":"School of Social Welfare, University of California Berkeley , Berkeley, CA, 94720,","place":["United States"]},{"name":"Department of Psychiatry and Behavioral Sciences, University of California\u2013San Francisco , San Francisco, CA, 94107,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,10,13]]},"reference":[{"key":"2026101309105192700_ooag156-B1","doi-asserted-by":"crossref","first-page":"1700","DOI":"10.1016\/S0140-6736(21)02143-7","article-title":"Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic","volume":"398","author":"Santomauro","year":"2021","journal-title":"Lancet"},{"key":"2026101309105192700_ooag156-B2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1136\/bmjment-2023-300670","article-title":"Digital mental health: challenges and next steps","volume":"26","author":"Smith","year":"2023","journal-title":"BMJ Ment Health"},{"key":"2026101309105192700_ooag156-B3","doi-asserted-by":"crossref","first-page":"523","DOI":"10.3389\/fpsyt.2020.00523","article-title":"The need for a mental health technology revolution in the COVID-19 pandemic","volume":"11","author":"Figueroa","year":"2020","journal-title":"Front Psychiatry"},{"key":"2026101309105192700_ooag156-B4","doi-asserted-by":"crossref","first-page":"921982","DOI":"10.3389\/fpsyt.2022.921982","article-title":"Text messages in the field of mental health: rapid review of the reviews","volume":"13","author":"Shalaby","year":"2022","journal-title":"Front Psychiatry"},{"key":"2026101309105192700_ooag156-B5","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1177\/0017896920959368","article-title":"Text messages to reduce depressive symptoms: Do they work and what makes them effective? 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