Automated Text Messaging as an Adjunct to Cognitive Behavioral Therapy for Depression: A Clinical Trial

Adrian Aguilera, Emma Bruehlman-Senecal, Orianna Demasi, Patricia Avila, Adrian Aguilera, Emma Bruehlman-Senecal, Orianna Demasi, Patricia Avila

Abstract

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.

Keywords: Latinos; cognitive behavioral therapy; depression; mental health; mhealth; text messaging.

Conflict of interest statement

Conflicts of Interest: None declared.

©Adrian Aguilera, Emma Bruehlman-Senecal, Orianna Demasi, Patricia Avila. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 08.05.2017.

Figures

Figure 1
Figure 1
Sample text messages received by patients in the texting condition during depression treatment translated to English.
Figure 2
Figure 2
Sample mood graph from HealthySMS used to review in between session mood.
Figure 3
Figure 3
Iterations made to the text message content during the intervention.
Figure 4
Figure 4
Condition differences in total sessions attended. Figures display intent-to-treat analyses.
Figure 5
Figure 5
Condition differences in weeks in treatment until patient dropout. Figures display intent-to-treat analyses.

References

    1. Cuijpers P, Berking M, Andersson G, Quigley L, Kleiboer A, Dobson KS. A meta-analysis of cognitive-behavioural therapy for adult depression, alone and in comparison with other treatments. Can J Psychiatry. 2013 Jul;58(7):376–85.
    1. Miranda J, Azocar F, Organista KC, Dwyer E, Areane P. Treatment of depression among impoverished primary care patients from ethnic minority groups. Psychiatr Serv. 2003 Feb;54(2):219–25. doi: 10.1176/appi.ps.54.2.219.
    1. Miranda J, Chung JY, Green BL, Krupnick J, Siddique J, Revicki DA, Belin T. Treating depression in predominantly low-income young minority women: a randomized controlled trial. JAMA. 2003 Jul 2;290(1):57–65. doi: 10.1001/jama.290.1.57.
    1. Alegría M, Chatterji P, Wells K, Cao Z, Chen C, Takeuchi D, Jackson J, Meng X. Disparity in depression treatment among racial and ethnic minority populations in the United States. Psychiatr Serv. 2008 Nov;59(11):1264–72. doi: 10.1176/appi.ps.59.11.1264.
    1. Organista KC, Muñoz RF, González G. Cognitive-behavioral therapy for depression in low-income and minority medical outpatients: description of a program and exploratory analyses. Cogn Ther Res. 1994 Jun;18(3):241–59. doi: 10.1007/BF02357778.
    1. Kazantzis N, Whittington C, Dattilio F. Meta-Analysis of homework effects in cognitive and behavioral therapy: a replication and extension. Clin Psychol: Science and Practice. 2010;17(2):144–56. doi: 10.1111/j.1468-2850.2010.01204.x.
    1. Hans E, Hiller W. A meta-analysis of nonrandomized effectiveness studies on outpatient cognitive behavioral therapy for adult anxiety disorders. Clin Psychol Rev. 2013 Dec;33(8):954–64. doi: 10.1016/j.cpr.2013.07.003.
    1. Neimeyer RA, Kazantzis N, Kassler DM, Baker KD, Fletcher R. Group cognitive behavioural therapy for depression outcomes predicted by willingness to engage in homework, compliance with homework, and cognitive restructuring skill acquisition. Cogn Behav Ther. 2008;37(4):199–215. doi: 10.1080/16506070801981240.
    1. Kazantzis N, Whittington C, Zelencich L, Kyrios M, Norton PJ, Hofmann SG. Quantity and quality of homework compliance: a meta-analysis of relations with outcome in cognitive behavior therapy. Behavior Therapy. 2016;47(5):755–72. doi: 10.1016/j.beth.2016.05.002.
    1. Mausbach BT, Moore R, Roesch S, Cardenas V, Patterson TL. The relationship between homework compliance and therapy outcomes: an updated meta-analysis. Cognit Ther Res. 2010 Oct;34(5):429–38. doi: 10.1007/s10608-010-9297-z.
    1. Hollon S. Does cognitive therapy have an enduring effect? Cognit Ther Res. 2003;27(1):71–5. doi: 10.1023/A:1022538713914.
    1. Berrouiguet S, Baca-García E, Brandt S, Walter M, Courtet P. Fundamentals for future mobile-health (mhealth): a systematic review of mobile phone and web-based text messaging in mental health. J Med Internet Res. 2016 Jun 10;18(6):e135. doi: 10.2196/jmir.5066.
    1. Aguilera A, Berridge C. Qualitative feedback from a text messaging intervention for depression: benefits, drawbacks, and cultural differences. JMIR Mhealth Uhealth. 2014 Nov 05;2(4):e46. doi: 10.2196/mhealth.3660.
    1. Ogrodniczuk JS, Joyce AS, Piper WE. Strategies for reducing patient-initiated premature termination of psychotherapy. Harv Rev Psychiatry. 2005;13(2):57–70. doi: 10.1080/10673220590956429.
    1. Aguilera A, Muñoz RF. Text messaging as an adjunct to CBT in low-income populations: a usability and feasibility pilot study. Prof Psychol Res Pr. 2011 Dec 1;42(6):472–8. doi: 10.1037/a0025499.
    1. Aguilera A, Schueller SM, Leykin Y. Daily mood ratings via text message as a proxy for clinic based depression assessment. J Affect Disord. 2015 Apr 1;175:471–4. doi: 10.1016/j.jad.2015.01.033.
    1. Lambert MJ, Harmon C, Slade K, Whipple JL, Hawkins EJ. Providing feedback to psychotherapists on their patients' progress: clinical results and practice suggestions. J Clin Psychol. 2005 Feb;61(2):165–74. doi: 10.1002/jclp.20113.
    1. Furber G, Jones GM, Healey D, Bidargaddi N. A comparison between phone-based psychotherapy with and without text messaging support in between sessions for crisis patients. J Med Internet Res. 2014;16(10):e219. doi: 10.2196/jmir.3096.
    1. Clough BA, Casey LM. Using SMS reminders in psychology clinics: a cautionary tale. Behav Cogn Psychother. 2014 May;42(3):257–68. doi: 10.1017/S1352465813001173.
    1. Broom MA, Ladley AS, Rhyne EA, Halloran DR. Feasibility and perception of using text messages as an adjunct therapy for low-income, minority mothers with postpartum depression. JMIR Ment Health. 2015;2(1):e4. doi: 10.2196/mental.4074.
    1. Agyapong VI, Ahern S, McLoughlin DM, Farren CK. Supportive text messaging for depression and comorbid alcohol use disorder: single-blind randomised trial. J Affect Disord. 2012 Dec 10;141(2-3):168–76. doi: 10.1016/j.jad.2012.02.040.
    1. Kroenke K, Spitzer RL. The PHQ-9: a new depression diagnostic and severity measure. Psychiatr Ann. 2002 Sep 01;32(9):509–15. doi: 10.3928/0048-5713-20020901-06.
    1. Rosal MC, White MJ, Borg A, Scavron J, Candib L, Ockene I, Magner R. Translational research at community health centers: challenges and successes in recruiting and retaining low-income Latino patients with type 2 diabetes into a randomized clinical trial. Diabetes Educ. 2010;36(5):733–49. doi: 10.1177/0145721710380146.
    1. Burlingame GM, McClendon DT, Alonso J. Cohesion in group therapy. Psychotherapy (Chic) 2011 Mar;48(1):34–42. doi: 10.1037/a0022063.
    1. Miranda J, Woo S, Lagomasino I, Hepner K, Wiseman S, Muñoz R. i4health. 2006. [2017-04-05]. BRIGHT CBT depression group manuals .
    1. Muñoz RF, Mendelson T. Toward evidence-based interventions for diverse populations: The San Francisco General Hospital prevention and treatment manuals. J Consult Clin Psychol. 2005 Oct;73(5):790–9. doi: 10.1037/0022-006X.73.5.790.
    1. Cohen JS, Edmunds JM, Brodman DM, Benjamin CL, Kendall PC. Using self-monitoring: implementation of collaborative empiricism in cognitive-behavioral therapy. Cogn Behav Prac. 2013 Nov;20(4):419–28. doi: 10.1016/j.cbpra.2012.06.002.
    1. Head KJ, Noar SM, Iannarino NT, Grant HN. Efficacy of text messaging-based interventions for health promotion: a meta-analysis. Soc Sci Med. 2013 Nov;97:41–8. doi: 10.1016/j.socscimed.2013.08.003.
    1. Mohr DC, Schueller SM, Riley WT, Brown CH, Cuijpers P, Duan N, Kwasny MJ, Stiles-Shields C, Cheung K. Trials of intervention principles: evaluation methods for evolving behavioral intervention technologies. J Med Internet Res. 2015;17(7):e166. doi: 10.2196/jmir.4391.
    1. Baker TB, Gustafson DH, Shah D. How can research keep up with eHealth? ten strategies for increasing the timeliness and usefulness of eHealth research. J Med Internet Res. 2014;16(2):e36. doi: 10.2196/jmir.2925.
    1. Löwe B, Unützer J, Callahan CM, Perkins AJ, Kroenke K. Monitoring depression treatment outcomes with the patient health questionnaire-9. Med Care. 2004 Dec;42(12):1194–201.
    1. Huang FY, Chung H, Kroenke K, Delucchi KL, Spitzer RL. Using the patient health questionnaire-9 to measure depression among racially and ethnically diverse primary care patients. J Gen Intern Med. 2006 Jun;21(6):547–52. doi: 10.1111/j.1525-1497.2006.00409.x.
    1. Lehmann E, D'Abrera HJ. Nonparametrics: statistical methods based on ranks. San Francisco: Holden-Day; 1975.
    1. Beck AT, Rush AJ, Shaw BF, Emery G. Cognitive Therapy of Depression: A Personal Reflection. New York: Guilford Press; 1979.
    1. Mohr DC, Cuijpers P, Lehman K. Supportive accountability: a model for providing human support to enhance adherence to eHealth interventions. J Med Internet Res. 2011;13(1):e30. doi: 10.2196/jmir.1602.
    1. Krishna S, Boren SA, Balas EA. Healthcare via cell phones: a systematic review. Telemed J E Health. 2009 Apr;15(3):231–40. doi: 10.1089/tmj.2008.0099.
    1. Akter S, Ray P. mHealth - an Ultimate Platform to Serve the Unserved. Yearb Med Inform. 2010:94–100.
    1. Downer SR, Meara JG, Da Costa AC, Sethuraman K. SMS text messaging improves outpatient attendance. Aust Health Rev. 2006 Aug;30(3):389–96.
    1. Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME, Kording KP, Mohr DC. Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J Med Internet Res. 2015;17(7):e175. doi: 10.2196/jmir.4273.
    1. Walsh S, Golden E, Priebe S. Systematic review of patients' participation in and experiences of technology-based monitoring of mental health symptoms in the community. BMJ Open. 2016 Jun 21;6(6):e008362. doi: 10.1136/bmjopen-2015-008362.
    1. Bruehlman-Senecal E, Aguilera A, Schueller SM. Mobile phone–based mood ratings prospectively predict psychotherapy attendance. Behavior Therapy. 2017 Sep;48(5):614–23. doi: 10.1016/j.beth.2017.01.002.

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