The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Automated Text Messaging as an Adjunct to Cognitive Behavioral Therapy for Depression: A Clinical Trial
Open accessAbstract
BACKGROUND: Cognitive Behavioral Therapy (CBT) for depression is efficacious, but effectiveness is limited when implemented in low-income settings due to engagement difficulties including nonadherence with skill-building homework and early discontinuation of treatment. Automated messaging can be used in clinical settings to increase dosage of depression treatment and encourage sustained engagement with psychotherapy. OBJECTIVES: The aim of this study was to test whether a text messaging adjunct (mood monitoring text messages, treatment-related text messages, and a clinician dashboard to display patient data) increases engagement and improves clinical outcomes in a group CBT treatment for depression. Specifically, we aim to assess whether the text messaging adjunct led to an increase in group therapy sessions attended, an increase in duration of therapy attended, and reductions in Patient Health Questionnaire-9 item (PHQ-9) symptoms compared with the control condition of standard group CBT in a sample of low-income Spanish speaking Latino patients. METHODS: Patients in an outpatient behavioral health clinic were assigned to standard group CBT for depression (control condition; n=40) or the same treatment with the addition of a text messaging adjunct (n=45). The adjunct consisted of a daily mood monitoring message, a daily message reiterating the theme of that week's content, and medication and appointment reminders. Mood data and qualitative responses were sent to a Web-based platform (HealthySMS) for review by the therapist and displayed in session as a tool for teaching CBT skills. RESULTS: Intent-to-treat analyses on therapy attendance during 16 sessions of weekly therapy found that patients assigned to the text messaging adjunct stayed in therapy significantly longer (median of 13.5 weeks before dropping out) than patients assigned to the control condition (median of 3 weeks before dropping out; Wilcoxon-Mann-Whitney z=-2.21, P=.03). Patients assigned to the text messaging adjunct also generally attended more sessions (median=6 sessions) during this period than patients assigned to the control condition (median =2.5 sessions), but the effect was not significant (Wilcoxon-Mann-Whitney z=-1.65, P=.10). Both patients assigned to the text messaging adjunct (B=-.29, 95% CI -0.38 to -0.19, z=-5.80, P<.001) and patients assigned to the control conditions (B=-.20, 95% CI -0.32 to -0.07, z=-3.12, P=.002) experienced significant decreases in depressive symptom severity over the course of treatment; however, the conditions did not significantly differ in their degree of symptom reduction. CONCLUSIONS: This study provides support for automated text messaging as a tool to sustain engagement in CBT for depression over time. There were no differences in depression outcomes between conditions, but this may be influenced by low follow-up rates of patients who dropped out of treatment.
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“The feasibility of this approach is supported by previous studies showing that receiving simple text messaging reminders increases attendance to face-to-face treatment sessions among POC [54,55].”
From App-Based Mindfulness Meditation for People of Color Who Experience Race-Related Stress: Protocol for a Randomized Controlled Trial (2022) · cites this paper as [54]
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“Text messaging reminders have proven effective in increasing treatment adherence among POC community samples [54-56,63], including those in app-based mindfulness meditation studies [49].”
From App-Based Mindfulness Meditation for People of Color Who Experience Race-Related Stress: Protocol for a Randomized Controlled Trial (2022) · cites this paper as [54]
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Efficacy of text messaging-based interventions for health promotion: A meta-analysis
Head, K. J., Noar, S. M., Iannarino, N. T., & Harrington, N. G. (2013) · Social Science & Medicine · doi:10.1016/j.socscimed.2013.08.003 · Cited by 647
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- 10.1176/appi.ps.59.11.1264 · DOI · Paper page
- Beck, AT, 1979, Cognitive Therapy of Depression: A Personal Reflection
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Ng, A., Wei, B., Jain, J., Ward, E. A., Tandon, S. D., Moskowitz, J. T., Krogh‐Jespersen, S., Wakschlag, L. S., & Alshurafa, N. I. (2021) · doi:10.2196/preprints.33850
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“Meanwhile, one study [36] used a health guide manual as a reference in the construction of text messages; however the study did not explicitly state the processes involved.”
BackgroundUnderstanding Text Messages for Anxiety Therapy Through Topic Modeling
Rahman, T. F. A., & Nayan, N. M. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-7339-2_8
Wiegmann, C., Benedyk, A., Machleid, F., & Kaminski, J. A. (2024) · Nervenheilkunde · doi:10.1055/a-2426-9877
“auch bei depressiv erkrankten Patienten positive Effekte auf The - rapieadhärenz [22 Zu ähnlichen Ergebnissen kommt eine randomisiert-kontrol-lierte Studie zu einer SMS-basierten Anwendung (T4RP, [23]) Patienten erhielten tägliche Textnachrichten zur Überprüfung der Symptome sowie eine Erinnerung…”
Hodgins, J. P., Mazzone, E., Franklin, E., Greenberg, S. R., Morrell, H. E. R., & Boustani, M. M. (2025) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-025-00492-4
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“…and reinforce positive behaviors to improve their emotional state. the data analysis builds upon prior research findings in similar studies (aguilera et al., 2017; dobson & dozois, 2014; Pfeiffer et al., (2016). the text-messaging boosters aimed to reinforce the CoPe content for personal…”
InfluentialCha, B. S., Kim, J., Borghouts, J. W., Eikey, E. V., Schneider, M. L., Schueller, S. M., Stadnick, N. A., Zheng, K., Mukamel, D. B., & Sorkin, D. H. (2025) · Psychiatric Quarterly · doi:10.1007/s11126-025-10225-8
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Metadata
- Authors
- Aguilera, A., Bruehlman-Senecal, E., Demasi, O., & Avila, P.
- Affiliations
- University of California, Berkeley (US)
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2017-05-08
- Volume
- 19
- Issue
- 5
- Pages
- e148
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC5440738
- OpenAlex
- W2612408532
- Cited by
- 122
- References
- 40
- Citations by year
- 2026: 5 · 2025: 14 · 2024: 12 · 2023: 9 · 2022: 22 · 2021: 20 · 2020: 12 · 2019: 16 · 2018: 9 · 2017: 3
- FWCI
- 12.52
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute of Mental Health (K23MH094442)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
- Keywords
- depression, cognitive behavioral therapy, text messaging, mobile health, mood tracking, treatment engagement, medication adherence, group therapy, low-income patients, clinician dashboard, PHQ-9, school dropout prevention, homework adherence
- MeSH terms
- Depression, Female, Humans, Male, Middle Aged, Cognitive Behavioral Therapy, Telemedicine, Text Messaging
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