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Integrating technology into cognitive behavior therapy for adolescent depression: a pilot study

An Erratum to this article was published on 15 January 2016



Rapid advances in information technology and telecommunications have resulted in a dramatic increase in the use of mobile devices and the internet to enhance and facilitate access to treatment. Cognitive behavior therapy (CBT) is an empirically based treatment that is well suited for enhancement by new technologies, particularly with youth. To facilitate the dissemination of this evidence-based treatment, we developed a technology-enhanced CBT intervention for the treatment of adolescent depression consisting of (1) online therapist training (2) in-session use of tablets for teaching clients CBT concepts and skills, and (3) text messaging for between session homework reminders and self-monitoring.


Eighteen licensed clinicians (social workers n = 7, psychologists n = 9) were randomized to have their patients receive either the intervention (CBT) or treatment as usual (TAU). Each clinician treated four adolescents for 12 weeks. Clinicians in the CBT arm completed an online tutorial on CBT treatment of adolescent depression, then received an iPad with access to patient education materials for teaching CBT concepts to patients during sessions. Individualized text messages were integrated into treatment for homework reminders, support, and outcomes measurement. Outcome measures included a 49-item multiple choice test for tutorial effectiveness; the system usability scale (SUS) for user satisfaction; quick inventory of depressive symptomatology–adolescent version (QIDS-A-Pat); and clinician and patient ratings on the therapeutic alliance scale for adolescents (TASA).


A significant increase in knowledge of CBT concepts was found after completing the tutorial, t(8) = 7.02, p < 0.001. Clinician and patient ratings of user satisfaction were high for both the iPad teaching tools, and the text messaging. Ninety-five percent of teens said reviewing their text messages with their therapist was helpful, and all said they would use text messaging in treatment again. Ratings of the therapeutic alliance were higher in the CBT arm t(131) = 4.03, p = 0.001. A significant reduction in depression was found in both groups [t(34) = 8.453, p < 0.001 and t(29) = 6.67, p < 0.001 for CBT and TAU, respectively). Clinical ratings of improvement were greater on all outcome measures for the CBT arm; however, none reached statistical significance. Effect sizes (Cohen’s d) ranged from small (QIDS-A) to large (TASA).


Results support the feasibility of this technology-enhanced CBT intervention as a means of improving CBT treatment of adolescent depression and may help address the critical shortage of therapists trained on empirically based treatments.


The use of technology for the psychological treatment of mental disorders is on a rapid ascent. While the potential ways of using technology to enhance treatment have been discussed for decades [1, 2], the recent explosion in information technology and telecommunications, and the widespread use of mobile devices have resulted in a dramatic increase in the use of both mobile devices and the internet to enhance and facilitate access to treatment. Several review articles have been published summarizing the bourgeoning body of data being generated [37]. Results have generally been supportive of both efficacy and feasibility, though several issues have been identified, such as confidentiality, privacy, crisis management, technological competence, and ethical issues [3, 8]. As with all innovations, new practice guidelines have been developed to address the unique challenges presented [911].

Cognitive behavior therapy (CBT) is an empirically based treatment that is uniquely suited to enhancement by new technologies [5, 12]. It is highly structured, typically manualized, follows a sequential progression, emphasizes self-responsibility, self-monitoring and homework, and includes ongoing outcome measurements. A variety of technology-enhanced CBT applications across a range of mental disorders have been reported. These include computer-administered CBT self-treatment (stand alone, no therapist contact), computer-assisted CBT treatment (computer-administered with some clinician guidance or contact), mobile monitoring and communication, psychoeducation, remote live treatment via videoconference, and online therapist training [1218].

The use of technology is particularly well suited for psychological interventions with youth and teens. Nine in ten teens in the USA (93 %) have access to a computer, 78 % have cell phones, and 74 % have mobile access to the internet via a cell phone, tablet or other device [19]. Text messaging has become the preferred mode of communication among teens, with two-thirds reporting they are more likely to use their cell phones to text their friends than to talk with them. Half of teens in the USA send 50 or more texts per day [20]. Mobile phone use by teens cuts across socio-demographic backgrounds, as more US families replace traditional land lines with mobile phones (e.g., 41 % of households have only wireless according to a 2013 survey by the National Center for Health Statistics; among poor households, the figure is 56 %) [21]. Teens in both the USA and abroad have both the technical expertise with these technologies, and a favorable attitude toward their use in mental health care [4, 22]. Three quarters of lifetime mental disorders begin in adolescence and young adulthood, making it a critical target age for prevention and intervention efforts [2325].

Given the compatibility between CBT and new technologies, and the affinity for new technologies by youth, the integration of new technologies into CBT treatment of youth has been rapidly increasing [5, 7]. Applications have been developed for the treatment of a variety of disorders, including simple phobias, social anxiety disorder, generalized anxiety disorder, obsessive–compulsive disorder, encopresis, autism, eating disorders, depression, and substance abuse [2639].

Mobile applications such as text messaging [i.e., short messaging services (SMS)] are particularly well suited for youth and can help clinicians implement CBT treatment more effectively through the use of homework reminders, real-time self-monitoring and between session communication and feedback [17]. Among mental health patients, text messaging is the most popular feature, and a higher percentage of mental health patients text compared to the general population [40, 41]. Self-monitoring in particular has been found to improve treatment outcomes, both by itself and when added to therapy [42, 43] and accounts for a significant portion of the variance in treatment outcomes [44]. Text messaging may help overcome non-compliance (a primary reason for lack of treatment efficacy) by enabling encouragement and support between sessions. Interacting with each adolescent on a daily basis to encourage compliance with homework assignments, evaluate progress, monitor side effects, etc., would be prohibitively expensive if clinicians were required to personally send and receive the messages themselves. Fortunately it is not necessary, given the demonstrated feasibility of automating those functions. There is a large body of literature on the efficacy of text messaging for improving heath behavior and treatment outcomes in other areas of health care (e.g., diabetes, asthma, hypertension, obesity), with positive outcomes in 93 % of the published studies [45]. Text messaging is also used in the treatment of psychiatric and substance use disorders in adults [46, 47]. Data on the use of SMS in the psychological treatment of youth and young adults are beginning to emerge [3, 4852]. Teens have generally reacted favorably to use of SMS technology in treatment and prevention programs, with good compliance rates [22, 53].

In response to the National Institute of Mental Health’s call for research on the use of technology to facilitate the dissemination of evidence-based treatments [54], we developed a technology-enhanced intervention protocol to facilitate CBT treatment of adolescent depression. The program consists of three components, each using technology for a particular purpose: (1) online therapist training, (2) in-session use of tablets for teaching clients CBT concepts and skills, and (3) text messaging for between session homework reminders and self-monitoring. These three components help disseminate training to therapists, help therapists implement CBT with patients more effectively, and improve CBT treatment outcomes, respectively. The goal of this study was to evaluate the feasibility, user satisfaction, and effectiveness of this technology-enhanced approach for treating adolescent depression.



Eighteen licensed clinicians who work with depressed adolescents participated in the study. Clinicians were recruited through advertisements in professional journals and through direct mail (i.e., Psychology today listing of clinicians working with depressed adolescents). Clinicians came from 13 states and various disciplines, including social work (n = 7), clinical or counseling psychology (n = 9), educational psychology (n = 1) and behavioral mental health (n = 1). Fifteen had master’s degrees and three doctoral degrees. The mean age was 44.2 years (range 31–58 years, SD = 8.4), and 56 % (n = 10) were female. Thirteen were Caucasian, four African American, and one was multiracial. Fifteen (83 %) reported some prior exposure to CBT, primarily through group lectures (78 %). None were accredited or formally trained as CBT practitioners. Mean number of years working with adolescents was 12.2 (range 2–20 years, SD = 5.59).


Sixty-five adolescents, aged 12–17 (mean age = 15.4, SD = 1.52) with a DSM-5 mood disorder [major depressive disorder (n = 31), persistent depressive disorder (n = 20), both major and persistent depressive disorders (n = 3), other specified depressive disorder (n = 6), unspecified depressive disorder (n = 5)] and a minimum score of 11 on the quick inventory of depressive symptomatology–adolescent-patient report (QIDS-A-Pat) (mean = 14.5, SD = 3.28, range 10–22) [55] were recruited. Subjects were excluded if they had bipolar disorder, severe conduct disorder, substance dependence, pervasive developmental disorders, thought disorder, severe suicidal/homicidal ideation or behavior requiring inpatient treatment. Diagnoses were determined via clinical interview using a DSM-5 symptom checklist. Non-English speakers and adolescents without daily access to a cell phone were also excluded. Patients represented diverse races and ethnicities, including Caucasian (n = 27), African American (n = 24), American Indian (n = 3), Asian (n = 1), Biracial (n = 5) and other (n = 5). Fifteen percent (n = 10) were Hispanic and two-thirds (n = 43) were female.


Clinicians were randomly assigned to have all their subjects receive either the technology-enhanced CBT intervention arm (CBT), or treatment as usual (TAU). Each clinician recruited four adolescents from their clinical practice who were initiating treatment for depression. Three clinicians dropped out of the study before completing enrollment and were replaced. Clinicians in the CBT arm completed a pre-test on CBT knowledge and then took the online tutorial on CBT treatment for adolescent depression. After completing the tutorial, clinicians took a post-test, then received an iPad containing a link to the online CBT interactive teaching materials and text-messaging system. A brief (1 h) orientation session was held with each clinician to review how to use the iPad for teaching CBT concepts to patients and for setting up text messages. Each patient was treated for 12 weeks, using the skills learned in the tutorial, and the in-session teaching tools. Individualized text messages were integrated into treatment. Clinicians in the TAU arm also recruited patients initiating treatment for depression from their clinical practice, and treated them for 12 weeks using usual care. After completing the study, clinicians in the TAU arm were offered access to the CBT training and intervention tools. Since both patients and therapists were considered research subjects, each signed informed consent statements approved by the Allendale Institutional Review Board. Patient flow and study completion rates by treatment arm are shown in Table 1.

Table 1 Patient recruitment and study completion by treatment arm

Description of the technology-enhanced CBT intervention

Online therapist training tutorial

The online training tutorial was developed as a way to address the critical shortage of clinicians trained in CBT, due in large part to a lack of training available [56, 57]. Putting the training online makes the training more accessible, cost-effective, and obviates the need for travel to one of the limited number of centers that offer CBT training. Trainees are not bound by time limitations, and can work at their own pace and schedule (a recent study found time and cost the strongest predictor of unwillingness to obtain training on empirically based treatments) [57]. The quality of the training is also enhanced using principles of instructional design to deliver multi-modal, interactive learning, both of which have been found to increase knowledge retention [58]. Standardizing the training helps insure the quality of the instruction, which is important as several studies have found that much of the CBT that is being delivered is not being administered properly [59, 60]. The tutorial was modeled after the cognitive behavior therapy manual used in the NIMH funded treatment of adolescents with depression study [61] and consisted of nine modules (overview, theoretical principals of CBT, explaining the nature of depression to Clients and the therapeutic relationship, explaining treatment rationale to clients, mood monitoring, goal setting, behavioral activation, problem solving, and cognitive restructuring). Trainees worked at their own pace, and could email us with any questions. The tutorial took about 5.5 h to complete (see for examples of tutorial content). Animations, graphical illustrations, interactive exercises, and video illustrations of an expert clinician (Dr. Kennard) applying the techniques were used as teaching tools. Session agendas and a treatment protocol were provided to assist clinicians in treatment implementation.

Online interactive patient educational materials

The second component consisted of online instructional materials to help clinicians explain CBT concepts to patients. Patient understanding of treatment rationale and treatment concepts is a critical part of effective treatment, as the more sense a treatment makes to a client, the more likely they are to comply with it [62, 63]. In CBT, there is a collaborative relationship between the therapist and client, with the client seen as both capable of, and responsible for, change. To empower clients with the skills necessary for change, it is critical that both (the client and the depressed adolescents parents) have an basic understanding of the nature of depression, the CBT treatment rationale, and, CBT concepts and skills, such as mood monitoring, identifying and challenging automatic thoughts, and activity scheduling. Therapists typically teach this using a combination of verbal instruction and paper and pencil forms. We created a series of online, interactive education materials to (1) help novice CBT therapists structure sessions, (2) insure that the concepts are covered thoroughly and accurately, (3) engage and involve the youth and personalize the material, and (4) create personalized goals and homework assignments. For example, in teaching clients about automatic thoughts, the therapist first displays a hypothetical scenario on the tablet PC (in our case, an iPad), to teach the relationship between thoughts, emotions, and behaviors. The teen then generates two or three possible thoughts they might have in that situation and the different feelings associated with those thoughts. Once the concept is understood, i.e., that different thoughts lead to different feelings, the therapist goes through the process again using a situation from the client’s real life. Finally, homework is collaboratively set up, e.g., to monitor one’s mood and thinking at specific intervals during the day. In another example, the client may be learning problem-solving skills. In this case, the tablet plays a pre-recorded scenario of a typical teen problem, after which the client goes through the problem-solving process using the tablet. Finally, the process is repeated with a real-life problem the client has, followed by setting up problem-solving practice between sessions.

Interactive text messaging

The third part of the intervention consists of text messages the client receives between sessions to remind them of their homework goals, and to record results of homework practice (see for an illustration). These are set up as the final step in the patient education process previously described. Typically, the client would receive two texts each day: a reminder text call in the morning and a text later in the day to record results. For example, if the goal was to increase pleasant activities, the morning text would say “remember to do at least one pleasant activity today’. Do you remember the activity you were going to do?” If they said no, they would receive a text back reminding them what the activity was. In the evening, they would receive a text asking them if they did the pleasant activity, get a reinforcing message if they did, and a text back asking them to describe what they did and how it affected their mood. If they did not do the activity, they would receive a text back saying “Making yourself do something when you do not feel like it is hard. Sometimes just doing something nice helps you feel better” followed by “Tell me what kept you from doing the activity today. We can talk about it next session.” A report of all texts sent and received is sent to the therapist for review with the client at their next session, as a way to process together how the homework went and to troubleshoot problems and reinforce learning. Timing and frequency of texts are determined collaboratively by the therapist and teen. For example, the teen may say that 9 pm is the best time to receive evening texts, as that is when he has some down time. Or a therapist may want to increase or decrease the frequency of mood monitoring, depending on the clinical status of the teen. Examples of texts are shown in Table 2.

Table 2 Examples of text messages and response options by therapeutic module

Outcome measures

Online tutorial

Effectiveness of the online tutorial in improving clinician’s knowledge of CBT concepts was evaluated using a 49-item multiple choice pre- and post-test covering the tutorial content. The test had good internal consistency reliability (coefficient alpha = 0.821). Technical feasibility of the tutorial was evaluated with the system usability scale (SUS) [64, 65]. The SUS is a reliable, well-validated 10-item scale designed to evaluate the usability and user satisfaction with web-based applications and other technologies. The SUS has good internal consistency reliability (coefficient alpha: r = 0.86 in our sample) in assessing usability across diverse types of user interfaces (e.g., web, interactive voice response, cell phone, etc.) It provides quantitative feedback on a 0–100 scale. In a cross-validation study of the SUS using an anchored adjective scale, systems with “Good” usability had mean score of 71.4. [66] This criterion was used for successful system design in the current study. In addition to the SUS, ratings were also obtained on whether the stated learning objectives of the tutorial were met, and a set of questions evaluating satisfaction with the clinical content of the tutorial.

Online teaching materials and text messaging

Technical feasibility with the online teaching materials and text-messaging system was evaluated with the SUS. Open-ended feedback was also solicited on user satisfaction with the system from both clinicians and patients.

Clinical outcomes

Clinical outcome measures were obtained at the end of 6 and 12 weeks of treatment. The primary clinical outcome measure was pre-to-post treatment changes in patient ratings of depression on the quick inventory of depressive symptomatology–adolescent version (QIDS-A-Pat) [55]. Secondary outcomes included clinician global ratings of improvement (CGI-I) and severity (CGI-S) [67], and clinician and patient ratings on the therapeutic alliance scale for adolescents (TASA) [68].

Statistical analyses

Categorical and ordinal variables, such as gender and percent responders were tested by Chi-square tests of distributional independence. Interval and ratio level measurements, such as age, and depression severity scores were compared with two-tailed, between group t tests for equivalence of means. When the sample size in each of two groups is 32, a 0.05 level Chi-square test will have power of 0.7–0.97 to distinguish between the groups when the proportions in the two categories are characterized by effect sizes of 0.1 to 0.25. Samples of 32 per group have statistical power of 0.50–0.88 to detect moderate to large mean differences (effect sizes of 0.5–0.8) in two group t tests using two-sided alphas of 0.05. The sample size estimate was based on the QIDS-A-Pat.


Clinicians and patients

There were no significant differences between clinicians randomized to CBT and TAU in terms of age [t(16) = 0.42, p = 0.678), gender (X2(1) = 1.90, p = 0.168], or years’ experience [t(16) = 0.10, p = 916]. There were also no significant differences between patients in the CBT and TAU arms on age [t(63) = 0.076, p = 0.940], gender (X2(1) = 0.94, p = 0.432), or baseline depression severity (QIDS-A-Pat) [t(63) = 0.27, p = 0.787).

Online tutorial

Increase in didactic knowledge

We examined changes in scores on the 49-item pre-and post-tests of knowledge of CBT concepts covered in the tutorial. A significant increase was found in the number of correct items from the pre-test (24.4, SD = 4.42) to the post-test (33.9, SD = 5.11), t(8) = 7.02, p < 0.001.

Learning objectives

Twenty-three learning objectives were identified a priori as learning goals for the online tutorial (Table 3). After completing the tutorial, 97 % of the learning objectives were rated as met. The mean rating of how much they learned as a result of taking the tutorial was 4.4 (rated on a 1–5 scale (1 = very little and 5 = a great deal).

Table 3 Learning objectives: CBT tutorial

User satisfaction: technical aspects

The means score on the SUS for the online tutorial was 78.4 (SD = 20.44) (Table 4). This corresponds to a score of good user satisfaction on the SUS. Mean global rating of user-friendliness (rated scale range from 1 (worst imaginable) to 7 (best imaginable)) was 5.6, which is halfway between “good” and “excellent”.

Table 4 System usability scale scores for the online tutorial and teaching/text system

User satisfaction: clinical content

Descriptive statistics were obtained on user satisfaction with the online tutorial (Table 5). All that subjects agreed or strongly agreed that the material was presented in an interesting manner, was clearly presented and easy to understand, and was useful and relevant to treating adolescent depression. All would recommend the online tutorial to others.

Table 5 Mean satisfaction ratings on tutorial scale clinical content

Online teaching materials and text messaging

User satisfaction: clinicians

The means score on the SUS for the online CBT teaching materials and text-messaging system was 84.4 (SD = 13.80). This corresponds to a score between good and excellent. Ratings on individual SUS items are presented in Table 6. The mean rating for all items was between “agree” and “strongly agree”. Clinicians found the system ‘user friendly’ in terms of understanding how to utilize the system for teaching CBT skills, setting up text messages, and receiving text reports.

Table 6 Mean ratings on system usability scale items: online teaching materials and text messaging

User satisfaction: patients

Feedback was also solicited from adolescents on how helpful the teaching and text message system was. Eighty-five percent of patients felt the teaching materials presented on the iPad during sessions were helpful in learning new skills, 90 % felt the text messages between sessions were helpful, and 95 % said reviewing their text message responses on their homework and mood at the next session with their therapist were helpful. All patients said they would be willing to use text messaging again to communicate their feelings to their clinician between sessions.

Clinical outcomes

Both treatment groups significantly improved with treatment, with mean improvements on the QIDS-A of 6.09 (SD = 4.26) and 5.73 (SD = 4.71) for the CBT and TAU groups [t(34) = 8.453, p < 0.001 and t(29) = 6.67, p < 0.001 respectively]. Clinical outcome measures comparing the CBT and TAU groups are presented in Table 7. Therapist ratings of the therapeutic alliance (TASA) were significantly higher in the CBT intervention arm than in the TAU arm, t(131) = 4.03, p = 0.001. Measures of symptomatic improvement were greater on all other outcome measures for the CBT arm; however, none reached statistical significance. Effect sizes (Cohen’s d) [69] ranged from small (QIDS-A) to large (TASA).

Table 7 Clinical outcome measures: CBT vs. TAU

Text messaging

A total of 9,613 text messages requiring a response were sent. Of these, 3658 (38.1 %) were responded to. The correlation between improvement on the QIDS-A and percent of texts responded to was not significant (r = 0.165, p = 0.343).

Dropout rate

Seven subjects dropped out prior to week 12 in the TAU arm, compared to 4 subjects in the CBT arm.


Results of this study provide support for the feasibility of this technology-enhanced CBT Intervention as a means of improving CBT treatment of adolescent depression. User satisfaction, a critical component of feasibility, was high for both adolescents and therapists on all components. The program was successful in increasing therapists’ knowledge of CBT concepts and principles. Teens found the online teaching tools useful for learning CBT concepts and skills. They also found the text messaging between sessions helpful, particularly for reviewing work done between sessions with their therapist. All teens indicated they would be willing to use the system again. Rather than put a barrier between the teen and the therapist, the technology improved the therapeutic bond, a critical factor in treatment outcomes. Improving the therapeutic relationship may help to keep teens in treatment, a critical factor for successful outcomes.

From a system delivery perspective, the use of this technology-enhanced intervention is designed to augment rather than replace existing one to one clinician care. As such it is not a low intensity intervention (i.e., an intervention designed to limit therapist time) [70]—and keeps the same number and lengths of session as usual. This approach contrasts recent “stepped-care” models of treatment, which start with the least restrictive treatment with minimal therapist support. Future research can examine the use of this (and similar) technologies within a stepped-care model. This could include examining factors such as length of treatment, use of online self-help combined with therapist and non-therapist support, both with and without text-messaging augmentation.

Effect sizes on the clinical outcomes in the current study were small to medium. According to Cohen, a small effect size is one in which there is a real effect, but can only be seen through careful study. The current study used community clinicians (vs. academic research centers) to see how well the intervention works in a sample of community therapists that not had formal training in CBT. Taken in this light, small effects are encouraging. As the training continues to be evaluated and refined, the impact of additional follow-up training, or live applied training may further improve results. Prior studies with remote CBT training found the addition of live remote observation through a videoconference of trainees conducting CBT, with immediate feedback in real time significantly improved clinical skills [16]. The addition of this applied training component may have improved clinical outcomes. A follow-up study is underway to examine the impact of the addition of live training on post-training treatment outcomes with community patients.

The current program utilized technology to integrate three components as part of a single intervention: therapist training, client education, and treatment implementation and outcomes. As the use of technology continues to be adopted and integrated into clinical treatment, more empirical evidence will help shed light on which components are useful and under what circumstances. At a minimum, the current intervention helps address the critical shortage of training on empirically based treatments. The potential ways in which technology such as text messaging and use of interactive educational tools can enhance treatment are at the start of a new era of clinical research. New possibilities are rapidly emerging, and to some extent, are outpacing our ability to empirically evaluate these new innovations [71]. Some recent data suggest that the explosion of mental health apps has resulted apps of poor quality, or apps that do not reflect clinical practice guidelines or evidence-based practices [72, 73]. However, while presenting many challenges, they also present exciting opportunities. Continued research should continue to generate empirical data to help guide both clinical practice as well as future research in this area.


In the current study, a technology-enhanced CBT Intervention was effective in improving symptoms of depression in adolescents. User satisfaction with the technology was high for both therapists and patients. The therapeutic alliance was stronger in the cohort receiving the technology-enhanced intervention. Effect sizes comparing clinical outcomes between CBT and TAU were small.


  1. 1.

    Greist JH, Klein MH, Van Cura LJ. A computer interview for psychiatric patient target symptoms. Arch Gen Psychiatry. 1973;29(2):247–53.

    PubMed  Article  CAS  Google Scholar 

  2. 2.

    Greist JH. Clinical computing: computers and psychiatry. Psychiatri Serv. 1995;46(10):989–91.

    Article  CAS  Google Scholar 

  3. 3.

    Seko Y, et al. Youth mental health interventions via mobile Phones: a scoping review. Cyberpsychol Behav Soc Netw. 2014;17:591–602.

    PubMed  Article  Google Scholar 

  4. 4.

    Boydell KM, et al. Using technology to deliver mental health services to children and youth: a scoping review. J Can Acad Child Adolesc Psychiatry. 2014;23(2):87–99.

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Berry RR, Lai B. The emerging role of technology in cognitive-behavioral therapy for anxious youth: a review. J Rationale-Emotive Cogn Behav Ther. 2014;32:57–66.

    Article  Google Scholar 

  6. 6.

    Boschen MJ. Mobile telephones and psychotherapy: II a review of empirical research. Behav Therapist. 2009;32:175–81.

    Google Scholar 

  7. 7.

    Slone NC, Reese RJ, McClellan MJ. Telepsychology outcome research with children and adolescents: a review of the literature. Psychol Serv. 2012;9(3):272–92.

    PubMed  Article  Google Scholar 

  8. 8.

    Luxton DD, et al. mHealth for mental health: integrating smartpohone technology in behavioral healthcare. Prof Psychol Res Pract. 2011;42(6):505–12.

    Article  Google Scholar 

  9. 9.

    Doarn CR, Merrell RC. Standards and guidelines for telemedicine—an evolution. Telemed J E Health. 2014;20(3):187–8.

    PubMed  Article  Google Scholar 

  10. 10.

    Turvey C, et al. ATA practice guidelines for video-based online mental health services. Telemed J E Health. 2013;19(9):722–30.

    PubMed  Article  Google Scholar 

  11. 11.

    Joint Task Force for the Development of Telepsychology, Guidelines for Psychologists. Guidelines for the practice of telepsychology. Am Psychol. 2013;68(9):791–800.

    Article  Google Scholar 

  12. 12.

    Boschen MJ, Casy LM. The use of mobile telephones as adjuncts to cognitive behavioral psychotherapy. Prof Psychol Res Pract. 2008;39(5):546–52.

    Article  Google Scholar 

  13. 13.

    Donker T, et al. Smartphones for smarter delivery of mental health programs: a systematic review. J Med Internet Res. 2013;15(11):e247.

    PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Kaltenthaler E, et al. Computerised cognitive-behavioural therapy for depression: systematic review. Br J Psychiatry. 2008;193(3):181–4.

    PubMed  Article  Google Scholar 

  15. 15.

    Spek V, et al. Internet-based cognitive behaviour therapy for symptoms of depression and anxiety: a meta-analysis. Psychol Med. 2007;37(3):319–28.

    PubMed  Article  Google Scholar 

  16. 16.

    Kobak KA, et al. Web-based therapist training on cognitive behavior therapy for anxiety disorders: a pilot study. Psychotherapy (Chic). 2013;50(2):235–47.

    Article  Google Scholar 

  17. 17.

    Aguilera A, Muench F. There’s an app for that: the information technology applications for cognitive behavioral practitioners. Behav Ther. 2012;35(4):65–73.

    Google Scholar 

  18. 18.

    Roy-Byrne P, et al. Delivery of evidence-based treatment for multiple anxiety disorders in primary care: a randomized controlled trial. JAMA. 2010;303(19):1921–8.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  19. 19.

    Madden M, et al. Teens and technology 2013. In: Pew Research Center’s Internet & American Life Project. Washington, DC: Pew Research Center; 2013. Accessed 31 Oct 2015.

  20. 20.

    Lenhart A, et al. Teens and mobile phones, P.I.A.L. project, editor. Washington: Pew Research Center; 2010.

    Google Scholar 

  21. 21.

    Blumberg SJ. Wireless substitution: early release of estimates from the National health interview survey, July–December 2013. Washington, DC: National Center for Health Statistics, U.S. Department of Health and Human Services; 2014. Accessed 30 Oct 2015.

  22. 22.

    Whittaker R, et al. MEMO–a mobile phone depression prevention intervention for adolescents: development process and postprogram findings on acceptability from a randomized controlled trial. J Med Internet Res. 2012;14(1):e13.

    PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Kessler RC, et al. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey replication. Arch Gen Psychiatry. 2005;62(6):593–602.

    PubMed  Article  Google Scholar 

  24. 24.

    Kessler RC, et al. Lifetime prevalence and age-of-onset distributions of mental disorders in the World Health Organization’s World Mental Health Survey Initiative. World Psychiatry. 2007;6(3):168–76.

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Kessler RC, et al. Age of onset of mental disorders: a review of recent literature. Curr Opin Psychiatry. 2007;20(4):359–64.

    PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Nelson EL, Barnard M, Cain S. Treating childhood depression over videoconferencing. Telemed J E Health. 2003;9(1):49–55.

    PubMed  Article  Google Scholar 

  27. 27.

    Dewis LM, et al. Computer-aided vicarious exposure versus live graded exposure for spider phobia in children. J Behav Ther Exp Psychiatry. 2001;32(1):17–27.

    PubMed  Article  CAS  Google Scholar 

  28. 28.

    Gladstone T, et al. Understanding adolescent response to a technology-based depression prevention program. J Clin Child Adolesc Psychol. 2014;43(1):102–14.

    PubMed  Article  Google Scholar 

  29. 29.

    Merry SN, et al. The effectiveness of SPARX, a computerised self help intervention for adolescents seeking help for depression: randomised controlled non-inferiority trial. BMJ. 2012;344:e2598.

    PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Sarver NW, Beidel DC, Spitalnick JS. The feasibility and acceptability of virtual environments in the treatment of childhood social anxiety disorder. J Clin Child Adolesc Psychol. 2014;43(1):63–73.

    PubMed  Article  Google Scholar 

  31. 31.

    Ritterband LM, et al. An Internet intervention as adjunctive therapy for pediatric encopresis. J Consult Clin Psychol. 2003;71(5):910–7.

    PubMed  Article  Google Scholar 

  32. 32.

    Silver M, Oakes P. Evaluation of a new computer intervention to teach people with autism or Asperger syndrome to recognize and predict emotions in others. Autism. 2001;5(3):299–316.

    PubMed  Article  CAS  Google Scholar 

  33. 33.

    Wuthrich VM, et al. A randomized controlled trial of the Cool Teens CD-ROM computerized program for adolescent anxiety. J Am Acad Child Adolesc Psychiatry. 2012;51(3):261–70.

    PubMed  Article  Google Scholar 

  34. 34.

    Comer JS, et al. A pilot feasibility evaluation of the CALM Program for anxiety disorders in early childhood. J Anxiety Disord. 2012;26(1):40–9.

    PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Comer JS, et al. Internet-delivered, family-based treatment for early-onset OCD: a preliminary case series. J Clin Child Adolesc Psychol. 2014;43(1):74–87.

    PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Pretorius N, et al. Cognitive-behavioural therapy for adolescents with bulimic symptomatology: the acceptability and effectiveness of internet-based delivery. Behav Res Ther. 2009;47(9):729–36.

    PubMed  Article  CAS  Google Scholar 

  37. 37.

    Gulec H, et al. A randomized controlled trial of an internet-based posttreatment care for patients with eating disorders. Telemed J E Health. 2014.

  38. 38.

    Hoek W, et al. Effects of Internet-based guided self-help problem-solving therapy for adolescents with depression and anxiety: a randomized controlled trial. PLoS One. 2012;7(8):e43485.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  39. 39.

    Schwinn TM, Schinke SP, Di Noia J. Preventing drug abuse among adolescent girls: outcome data from an internet-based intervention. Prev Sci. 2010;11(1):24–32.

    PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Campbell B, et al. Cell phone ownership and use among mental health outpatients. Pers Ubiquit Comput. 2015;19:367–78.

    Article  Google Scholar 

  41. 41.

    Khazaal Y, et al. Internet use by patients with psychiatric disorders in search for general and medical informations. Psychiatr Q. 2008;79(4):301–9.

    PubMed  Article  Google Scholar 

  42. 42.

    Kazantzis N. Power to detect homework effects in psychotherapy outcome research. J Consult Clin Psychol. 2000;68(1):166–70.

    PubMed  Article  CAS  Google Scholar 

  43. 43.

    Kazdin AE. Reactive self-monitoring: the effects of response desirability, goal setting, and feedback. J Consult Clin Psychol. 1974;42(5):704–16.

    PubMed  Article  CAS  Google Scholar 

  44. 44.

    Michie S, et al. Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychol. 2009;28(6):690–701.

    PubMed  Article  Google Scholar 

  45. 45.

    Fjeldsoe BS, Marshall AL, Miller YD. Behavior change interventions delivered by mobile telephone short-message service. Am J Prev Med. 2009;36(2):165–73.

    PubMed  Article  Google Scholar 

  46. 46.

    Agyapong VI, et al. Supportive text messaging for depression and comorbid alcohol use disorder: single-blind randomised trial. J Affect Disord. 2012;141(2–3):168–76.

    PubMed  Article  Google Scholar 

  47. 47.

    Free C, et al. Smoking cessation support delivered via mobile phone text messaging (txt2stop): a single-blind, randomised trial. Lancet. 2011;378(9785):49–55.

    PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Matthews M, et al. Mobile phone mood charting for adolescents. Br J Guid Couns. 2008;36(2):113–29.

    Article  Google Scholar 

  49. 49.

    Suffoletto B, et al. A text message alcohol intervention for young adult emergency department patients: a randomized clinical trial. Ann Emerg Med. 2014.

  50. 50.

    Bauer S, et al. Technology-enhanced maintenance of treatment gains in eating disorders: efficacy of an intervention delivered via text messaging. J Consult Clin Psychol. 2012;80(4):700–6.

    PubMed  Article  Google Scholar 

  51. 51.

    Furber GV, et al. How adolescents use SMS (short message service) to micro-coordinate contact with youth mental health outreach services. J Adolesc Health. 2011;48(1):113–5.

    PubMed  Article  Google Scholar 

  52. 52.

    Branson CE, Clemmey P, Mukherjee P. Text message reminders to improve outpatient therapy attendance among adolescents: a pilot study. Psychol Serv. 2013;10(3):298–303.

    PubMed  Article  Google Scholar 

  53. 53.

    Kauer SD, et al. Investigating the utility of mobile phones for collecting data about adolescent alcohol use and related mood, stress and coping behaviours: lessons and recommendations. Drug Alcohol Rev. 2009;28(1):25–30.

    PubMed  Article  Google Scholar 

  54. 54.

    U.S. Department of Health and Human Services. PHS 2011-2, Omnibus Solicitation of the National Institutes of Health, Centers for Disease Control and Prevention, Food and Drug Aadministration, and Administration for Children and Families for Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) Grant applications. Washington, DC. 2011. Accessed 30 Oct 2015.

  55. 55.

    Moore HK, et al. A pilot study of an electronic, adolescent version of the quick inventory of depressive symptomatology. J Clin Psychiatry. 2007;68(9):1436–40.

    PubMed  Article  Google Scholar 

  56. 56.

    Shafran R, et al. Mind the gap: improving the dissemination of CBT. Behav Res Ther. 2009;47(11):902–9.

    PubMed  Article  CAS  Google Scholar 

  57. 57.

    Stewart RE, Chambless DL, Baron J. Theoretical and practical barriers to practitioners’ willingness to seek training in empirically supported treatments. J Clin Psychol. 2012;68(1):8–23.

    PubMed  Article  Google Scholar 

  58. 58.

    Gardner H. Multiple intelligences: the theory in practice. New York: Basic Books; 1993.

    Google Scholar 

  59. 59.

    Kessler RC, Merikangas KR, Wang PS. Prevalence, comorbidity, and service utilization for mood disorders in the United States at the beginning of the twenty-first century. Annu Rev Clin Psychol. 2007;3:137–58.

    PubMed  Article  Google Scholar 

  60. 60.

    Stobie B, et al. Contents may vary: a pilot study of treatment histories of OCD patients. Behav Cogni Psychother. 2007;35:273–82.

    Article  Google Scholar 

  61. 61.

    Curry JF, et al. Treatment for Adolescents with Depression Study (TADS) cognitive behavior therapy manual. Durham: Duke University Medical Center; 2005.

    Google Scholar 

  62. 62.

    Street RL Jr, et al. How does communication heal? Pathways linking clinician-patient communication to health outcomes. Patient Educ Couns. 2009;74(3):295–301.

    PubMed  Article  Google Scholar 

  63. 63.

    Raue PJ, et al. Patients’ depression treatment preferences and initiation, adherence, and outcome: a randomized primary care study. Psychiatr Serv. 2009;60(3):337–43.

    PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Brooke J. SUS: a ‘quick and dirty’ usability scale. In: Jordan PW, et al., editors. Usability evaluation in industry. London: Taylor and Francis; 1996. p. 189–94.

    Google Scholar 

  65. 65.

    Bangor A, Kortum P, Miller J. The System Usability Scale (SUS): an Empirical evaluation. Int J Human-Computer Interact. 2008;24(6):574–92.

    Article  Google Scholar 

  66. 66.

    Bangor A, Kortum P, Miller J. Determining what individual SUS scores mean: adding an adjective rating scale. J Usability Stud. 2009;4(3):114–23.

    Google Scholar 

  67. 67.

    Guy W. ECDEU assessment manual for psychopharmacology, revised. Rockville MD: National Institute of Mental Health, US Department of Health, Education, and Welfare publication ADM; 1976. p. 76–338.

  68. 68.

    Shirk SR, et al. Alliance and outcome in cognitive-behavioral therapy for adolescent depression. J Clin Child Adolesc Psychol. 2008;37(3):631–9.

    PubMed  Article  Google Scholar 

  69. 69.

    Cohen J, editor. Statistical power analysis for the behavioral sciences, 2nd. Hillside: Erlbaum; 1988.

    Google Scholar 

  70. 70.

    Bennett-Levy J, et al. The Oxford guide to low intensity CBT interventions. Oxford: Oxford University Press; 2010.

    Book  Google Scholar 

  71. 71.

    Jones DJ. Future directions in the design, development, and investigation of technology as a service delivery vehicle. J Clin Child Adolesc Psychol. 2014;43(1):128–42.

    PubMed  PubMed Central  Article  Google Scholar 

  72. 72.

    Abroms LC, et al. A content analysis of popular smartphone apps for smoking cessation. Am J Prev Med. 2013;45(6):732–6.

    PubMed  Article  Google Scholar 

  73. 73.

    Van Singer M, Chatton A, Khazaal Y. Quality of Smartphone Apps Related to Panic Disorder. Front Psychiatry. 2015;6:96.

    PubMed  PubMed Central  Google Scholar 

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Authors’ contributions

JCM and KAK were responsible for developing study design, instrument development, data analysis, and manuscript development. BK provided oversight of clinical content of tutorial, provided input into study design, and manuscript development. All authors read and approved the final manuscript.


The authors would like to acknowledge the contribution of Tracy Reyes for project management, general support, critical review, and subject recruitment, Alison Deep for managing and developing the technology utilized in text messaging and online teaching tools, Rich DeVuono for filming vignettes and photography, and Hal Stokes and Illumnia for tutorial development.

Study funding

This study was supported in part by a Grant from the National Institute of Mental Health, National Institutes of Health, Department of Health and Human Services, under Small Business Innovation Research (SBIR) Grant Number R44MH086152.

Competing interests

KAK, JCM, and BK have a proprietary interest in the computer-assisted technology that is under study in the manuscript, and will receive royalties on sales of the program.

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Corresponding author

Correspondence to Kenneth A. Kobak.

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Kobak, K.A., Mundt, J.C. & Kennard, B. Integrating technology into cognitive behavior therapy for adolescent depression: a pilot study. Ann Gen Psychiatry 14, 37 (2015).

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  • Cognitive therapy
  • Internet
  • Training
  • Depressive disorder
  • Adolescent
  • Dissemination
  • Evidence based