Internet Editor’s Note: Drs. Newman and Taylor recently published an article titled “A randomized controlled trial of ecological momentary intervention plus brief group therapy for generalized anxiety disorder” in Psychotherapy.
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In the June issue of Psychotherapy, Newman, Przeworski, Consoli, and Taylor presented a study on the use of a Palmtop computer-assisted therapy for generalized anxiety disorder (GAD) (Newman et al., 2014). Since developing prototypes for this computer program in the early 1990s, there have been significant advancements in the use of technology in health and psychotherapy.
In fact, the program described is easily adaptable to current technologies like Internet-delivered treatments and smartphone apps. Although some people suggest that technology could replace 80% of what doctors do (Khosla, 2012), many psychotherapists feel strongly that they cannot replace the human touch. No computer program can yet capture the faltering voice or averted gaze that psychotherapists rely on to deduce an individual’s affect and to personalize treatment.
However, there is one benefit that technology can definitely deliver better than individual therapists: access.
Through technology, barriers to treatment such as cost, stigma, and physical distance are reduced. This is particularly true of mobile software, such as the computer program used in Newman and colleagues’ study.
What We've Done: Integrating Technology with Psychotherapy
Newman and colleagues chose to develop computer-assisted CBT for GAD for 2 reasons:
- GAD is one of the most frequently diagnosed anxiety disorders with an estimated lifetime prevalence of 5.7% (Kessler & Wang, 2008), and it has a tendency toward chronic course and persistent symptoms unless successfully treated (Yonkers et al., 2003).
- CBT is an effective treatment for GAD (Newman et al., 2011, Newman et al., 2010), however it is costly and time-consuming (Newman, 2000).
Coupling these insights, they saw a clear need for a cost-effective treatment for this chronic disorder. Beyond program development, their evaluation of computer-assisted CBT for GAD was equally critical to establishing this new treatment.
Newman and colleagues proposed that adding an ambulatory treatment program to brief group therapy could generalize the impact of treatment, reduce costs and increase efficiency. The software they developed, “The Stress Manager” ©, was based on the basic principles of CBT for GAD specified by Borkovec et al. (2002). The program has 3 modules:
- “Diary only” module that prompts users to enter anxiety levels 4 times per day for 2 weeks to obtain baseline anxiety measures.
- “Recognizing triggers” module that prompts users hourly for 2 days to identify current anxiety cues
- “Therapy” module that helps users self-manage their anxiety. Whereas the therapy module uses algorithmic anxiety assessments to suggest specific treatment techniques, the program overall is designed to complement in-person therapy, during which the therapist evaluates the user’s computer data to gauge progress and guide the next week’s prescription.
To isolate the added value of the technology, they compared a 6-session palmtop Computer-Assisted Group CBT for GAD (CAGT6), a 6-session Group without the computer (CBGT6), and a typical 12-session Group (CBGT12). As hypothesized, the computer-assisted group CBT proved more efficacious than the group alone, suggesting the value of coupling traditional therapy with mobile technology.
Computer-assisted group CBT was not more efficacious than 12 weeks of group therapy. However, these findings did support Newman’s theory that the integration of technology can reduce the amount of therapist time necessary to achieve results. Finally, by demonstrating no statistically significant difference between conditions at 6- and 12-month follow-ups, they established that the impact of technology-enhanced interventions is also lasting.
When The StressManager © was developed, there were no apps and few therapy programs for hand-held computers. The marketplace has since been flooded with products that compete for consumers’ attention. By June 2013, 43,000+ mobile healthcare applications had been developed, 16,275 of which were considered patient-oriented, 1,980 of which targeted specific therapy areas, and 558 of which targeted mental health. In this crowded marketplace, it has become difficult to attract users. Of those healthcare apps, over 50% achieve fewer than 500 downloads (Aitken & Gauntlett, 2013).
Modern technology users have also become easily disengaged and distracted. 22% of downloaded apps are used only once (Aronica, 2013). Google’s Mobile Planet survey, which tracks smartphone usage across 48 countries, reported that the average smartphone user downloads 25 apps (Fox, 2013). However, Flurry, a mobile analytics company that tracks 500,000 apps on 1.3 billion devices, discovered that the average smartphone user launches apps 10 times per day (Khalef, 2014). The very technology that has increased access has also made it difficult for any one program to capture a user’s interest.
Designing programs that sustain engagement
Sustaining engagement is critical to treatment efficacy, as treatment compliance has been linked to improved outcomes (Celio et al., 2002). One driver of engagement is user motivation (Donkin & Glozier, 2012). A group of researchers in the UK demonstrated that greater user motivation, represented by self-referral, may predict the need for less time in treatment (Gyani et al., 2013). In a literature review, Newman et al. (2011a) confirmed and extended the benefit of user motivation, showing that self-administered interventions for anxiety and depressive disorders are also most effective for motivated clients.
One motivating factor linked to improved engagement in an online psychological intervention was feeling in control whereas an inverse factor was viewing the program as a poor fit to oneself (Donkin & Glozier, 2012).
Coupling these, personalization appears to be a key driver of sustained engagement and, subsequently, improved outcomes. This need for individualized treatment is highly relevant to anxiety. The way in which one experiences anxiety is extremely personal – for some, a certain level of anxiety can be motivating whereas others find it debilitating.
So, how can we design one program that is both personal and can meet individuals’ unique needs?
Allow users to customize their experience.
For example, in Newman’s GAD program, users could select the time of day they wanted to complete the daily assessment and dictate the pace at which they completed sessions. As suggested by Donkin and Glozier (2012), these personalization capabilities permit a perception of flexibility and control, which predicts enhanced engagement and consequently improved motivation to persist during a course of treatment.
Take time to learn about the user and then suggest a unique solution.
Mobile devices contain intimate data on user activity (e.g., the average mobile consumer checks in 150 times per day (Ahonen, 2013)) and are currently the best platform on which to personalize programming. Researchers at Cornell’s Interaction Design Lab are developing MyBehavior, a mobile application that first monitors a user’s physical activity and dietary behavior and then provides personalized suggestions (Rabbi, 2013). Although the program provides suggestions, it also allows for feedback, which factors into the program’s recommendation algorithm, empowering the user to feel in control and understood.
Preparing for program scalability
Beyond maintaining individual engagement, a psychotherapy program should be able to address the diverse needs of a population. One way to do this is to move from in-person psychotherapy to a self-help model. A self-help model has the potential to provide a very cost-effective means of implementing and disseminating CBT.
Research suggests that brief, in-person therapy plus computer-assisted interventions may save as much as a thousand dollars per client in treatment costs (Newman et al., 1999, Newman et al., 1996).
By moving to self-help models, there is potential to reduce cost even further. However, for this model to be successful, users must stay engaged for the prescribed treatment length thought to be efficacious, which is most sessions based on accepted guidelines for clinical trials (e.g., Taylor et al., 2006, Titov et al., 2013). Achieving this level of adherence for recent mobile and on-line programs has proven difficult, with research suggesting 90% of users of self-guided online programs for anxiety and depressive disorders withdraw after 2 sessions (Christensen et al., 2006, Farvolden et al., 2009).
One opportunity for achieving scale without compromising efficacy is adding a guide or “coach.” In fact, 55% of patients in pure-self interventions stepped up to a higher intensity intervention vs. only 26% of those in guided self-help (Gyani, 2013). A meta-analysis of 21 randomized controlled trials found the effect size of the difference between guided self-help and face-to-face psychotherapies for depression and anxiety disorders at post-test to be small-to-moderate (d = -0.02), in favor of guided self-help (Cuijpers et al., 2010).
Although a coach has more limited communication with the patient, he or she monitors the user’s progress, provides tailored feedback and, in other ways, may facilitate psychotherapy in a way that self-guided programs have not yet been able to do.
Moreover, an introductory assessment could sort users into risk classes, which could dictate the program and level of coaching (and cost) necessary (Beintner et al., 2011, Wilfley et al., 2013). More than 20% of doctors already engage in remote patient monitoring, averaging 22 patients monitored per month, suggesting the transition to this psychotherapy model is feasible (Comstock, 2014).
A shift from a disorder-specific to a transdiagnostic approach may also enable scaling. Given the limited reach of single-disorder interventions and the high comorbidity between common mental health problems, some have even lauded targeting underlying vulnerability factors rather than only disorder-specific symptoms (Musiat et al., 2014). Within anxiety, in particular, more than half of people with one anxiety disorder have another anxiety disorder (Brown et al., 2001). Additionally, there is considerable overlap among the active components of treatments for different anxiety disorders (e.g., increasing awareness and decreasing avoidance) (Norton, 2006). Transdiagnostic programs could contain components and procedures to address a wider range of presentations of anxiety symptoms, and research has already demonstrated they can be successfully administered via the Internet (Titov, 2010).
The next generation Newman program will be a mobile app and should include a mechanism for classing a population, a transdiagnostic treatment program, and a guided self-help structure.
Summary: Ensuring Sustainability with Adaptive Designs
Although development of the program is supported by research on benefits of coaching and transdiagnostic approaches, sustaining user engagement and, ultimately, achieving widespread adoption demands more – it requires continual monitoring and intervention adaptation.
Given that studies report between 20 and 57% of therapy clients do not return after their initial visit (Baekeland & Lundwall, 1975, Olfson, 2010), developers should prepare for re-engagement in ways similar to how clinicians might re-engage absent patients (e.g., substitute a phone call for a reminder from the app).
For example, a recent study found that participants who had received automated emails along with self-guided, internet-delivered treatment for anxiety and depression obtained higher rates of completion than those who did not (57% vs. 35%) (Titov et al., 2013). New opportunities for connecting with users via personal mobile devices vs. a universal online platform provide hope that we can customize and improve re-engagement strategies.
Overall, technology-enabled psychotherapy should be designed in a way that allows for evolution of the product with the technology curve. Mobile technology is spreading globally, however, with that expansion comes a changing marketplace and evolving user expectations. Psychotherapists will need to consider both how to deliver effective therapy programs online as well as how to deliver engaging product experiences.
Without thoughtful design of the evaluation of these new technology-supported interventions, the true benefit – and how it might be integrated into other therapies – can be lost. Although the evolution of technology will only accelerate, by using an iterative model of evaluation, psychotherapists can ensure the evidence-base is not lost in translation to new mediums.
In striking this balance between development and evaluation, psychotherapists can begin to distinguish what can become more automated and programmatic…and what still requires the human touch.
Cite This Article
Kanuri, N., Newman, M. G., & Taylor, C. B. (2014, October). Think globally, treat locally: Technology’s integration with psychotherapy. [Web article]. Retrieved from https://societyforpsychotherapy.org/think-globally-treat-locally-technologys-integration-with-psychotherapy
Ahonen, T. T. (2013, March 6). The Annual Mobile Industry Numbers and Stats Blog. Communities Dominate Brand. Retrieved from http://communities-dominate.blogs.com/brands/2013/03/the-annual-mobile-industry-numbers-and-stats-blog-yep-this-year-we-will-hit-the-mobile-moment.html
Aitken, M., Gauntlett, C. (2013, October). Patient Apps for Improved Healthcare: From Novelty to Mainstream. The IMS Institute for Healthcare Informatics. Retrieved from http://www.imshealth.com/deployedfiles/imshealth/Global/Content/Corporate/ IMS%20Health%20Institute/Reports/Patient_Apps/IIHI_Patient_Apps_Report.pdf
Aronica, J. (2013). App User Retention Up by 10%, Up Nearly 50% on Android. Localytics. Retrieved from http://www.localytics.com/blog/2013/localytics-app-user-retention-data/
Baekeland F., Lundwall L. (1975). Dropping out of treatment: A critical review. Psychological Bulletin, 82, 738–783.
Beintner I., Jacobi C., Taylor C. B. (2011). Effects of an Internet-based Prevention Programme for Eating Disorders in the USA and Germany – A Meta-analytic Review. European Eating Disorders Review, 20(1), 1-8. doi: 10.1002/erv.1130
Borkovec, T. D., Newman, M. G., Pincus, A. L. & Lytle, R. (2002). A component analysis of cognitive-behavioral therapy for generalized anxiety disorder and the role of interpersonal problems. Journal of Consulting and Clinical Psychology, 70, 288–298. doi:10.1037/0022-006X.70.2.288
Brown, T. A., Campbell, L. A., Lehman, C. L., Grisham, J. R., and Mancill, R. B. (2001). Current and Lifetime Comorbidity of the DSM-IV Anxiety and Mood Disorders in a Large Clinical Sample. Journal of Abnormal Psychology, 110 (4), 585–99.
Celio, A. A., Winzelberg, A. J., Dev, P., Taylor, C. B. (2002). Improving Compliance in On-line, Structured Self-help Programs: Evaluation of an Eating Disorder Prevention Program. Journal of Psychiatric Practice, 8 (1), 14-20.
Christensen H., Griffiths K. M., Mackinnon A. J., Brittliffe K. (2006) Online randomized controlled trial of brief and full cognitive behaviour therapy for depression. Psychol Med, 36, 1737–46. doi: 10.1017/s0033291706008695
Comstock, J. (2014, May 29). Survey: One third of docs recommended a health app to patients. MobiHealthNews. Retrieved from http://mobihealthnews.com/33594/survey-one-third-of-docs-recommended-a-health-app-to-patients/
Cuijpers, P., Donker, T., van Straten, A., Li, J., & Andersson, G. (2010). Is guided self-help as effective as face-to-face psychotherapy for depression and anxiety disorders? A systematic review and meta-analysis of comparative outcome studies. Psychological Medicine, 40 (12), 1943-57. doi:http://dx.doi.org/10.1017/S0033291710000772
Donkin, L. & Glozier, N. (2012). Motivators and motivations to persist with online psychological interventions: a qualitative study of treatment completers. J Med Internet Res, 14 (3): e91. doi: 10.2196/jmir.2100.
Farvolden P., Cunningham J., Selby P. (2009) Using e-health programs to overcome barriers to the effective treatment of mental health and addiction problems. J Technol Hum Serv, 27, 5–22. doi: 10.1080/15228830802458889
Fox, Z. (2013, September 5). The Average Smartphone User Downloads 25 Apps. Mashable. Retrieved from http://mashable.com/2013/09/05/most-apps-download-countries/
Gyani A., Shafran R., Layard R., Clark D. M. (2013). Enhancing recovery rates: lessons from year one of IAPT. Behav Res Ther, 51(9), 597-606. doi:10.1016/j.brat.2013.06.004. Epub 2013 Jul 4.
Kessler, R. C., & Wang, P. S. (2008). The descriptive epidemiology of commonly occurring mental disorders in the United States. Annual Review of Public Health, 29, 115–129. doi:10.1146/annurev.publhealth.29.020907.090847
Khalaf, S. (2014, April 22). The Rise of the Mobile Addict. Flurry. Retrieved from http://www.flurry.com/bid/110166/the-rise-of-the-mobile-addict#.U2FYcOZdVz4
Khosla, V. (2012, December 4). Technology will replace 80% of what doctors do. Retrieved from http://tech.fortune.cnn.com/2012/12/04/technology-doctors-khosla/
Musiat P., Conrod P., Treasure J., Tylee A., Williams C., et al. (2014). Targeted Prevention of Common Mental Health Disorders in University Students: Randomised Controlled Trial of a Transdiagnostic Trait-Focused Web-Based Intervention. PLoS ONE, 9 (4): e93621. doi:10.1371/journal.pone.0093621
Newman, M. G. (2000). Recommendations for a cost-offset model of psychotherapy allocation using generalized anxiety disorder as an example. Journal of Consulting and Clinical Psychology, 68, 549–555. doi:10.1037/0022-006X.68.4.549
Newman, M. G., Castonguay, L. G., Borkovec, T. D., Fisher, A. J., Boswell, J., Szkodny, L. E., & Nordberg, S. S. (2011). A randomized controlled trial of cognitive-behavioral therapy for generalized anxiety disorder with integrated techniques from emotion-focused and interpersonal therapies. Journal of Consulting and Clinical Psychology, 79, 171–181. doi:10.1037/a0022489
Newman, M. G., Consoli, A. J., & Taylor, C. B. (1999). A palmtop computer program for the treatment of generalized anxiety disorder. Behavior Modification, 23, 597–619. doi:10.1177/0145445599234005
Newman, M. G., Kenardy, J., Herman, S., & Taylor, C. B. (1996). The use of hand-held computers as an adjunct to cognitive-behavior therapy. Computers in Human Behavior, 12, 135–143. doi:10.1016/0747-5632(95)00024-0
Newman, M. G., Przeworski, A., Consoli, A. J., & Taylor, C. B. (2014). A Randomized Controlled Trial of Ecological Momentary Intervention Plus Brief Group Therapy for Generalized Anxiety Disorder. Psychotherapy, 51(2), 198-206. doi: 10.1037/a0032519 Special Section: Technology and Psychotherapy
Newman, M. G., Przeworski, A., Fisher, A. J., & Borkovec, T. D. (2010). Diagnostic comorbidity in adults with generalized anxiety disorder: Impact of comorbidity on psychotherapy outcome and impact of psychotherapy on comorbid diagnoses. Behavior Therapy, 41, 59–72. doi:10.1016/j.beth.2008.12.005
Newman, M. G., Szkodny, L. E., Llera, S. J., Przeworksi, A. (2011a). A review of technology-assisted self help and minimal contact therapies for anxiety and depression: Is human contact necessary for therapeutic efficacy? Clinical Psychology Review, 31 (1), 89-103.
Norton, P. J. (2006). Toward a Clinically-Oriented Model of Anxiety Disorders. Cognitive Behaviour Therapy, 35 (2), 88–105.
Olfson, M., & Marcus, S. C. (2010). National Trends in Outpatient Psychotherapy. American Journal of Psychiatry, 167, 1456-1463.
Rabbi, M. (2013, November 20). MyBehavior: Automating Personalized Health Feedback Using a Multiarm Bandit Model. HCI Seminar. Lecture conducted from the School of Engineering and Applied Sciences in University of Rochester, Rochester, NY.
Taylor C. B., Bryson S., Luce K. H., et al. (2006). Prevention of Eating Disorders in At-Risk College-Age Women. Arch Gen Psychiatry, 63 (8), 881-888. doi:10.1001/archpsyc.63.8.881.
Titov, N., Andrews, G., Johnston, L., Robinson, E., Spence, J. (2010). Transdiagnostic Internet treatment for anxiety disorders: A randomized controlled trial. Behaviour Research and Therapy, 48 (9), 890-899. doi: 10.1016/j.brat.2010.05.014
Titov N., Dear B. F., Johnston L., Lorian C., Zou J., et al. (2013). Improving adherence and clinical outcomes in self-guided internet treatment for anxiety and depression: randomised controlled trial. PLoS ONE, 8 (7): e62873. doi: 10.1371/journal.pone.0062873
Wilfley, D. E., Agras, W. S., Taylor, C. B. (2013). Reducing the Burden of Eating Disorders: A Model for Population-Based Prevention and Treatment for University and College Campuses. Int J Eat Disord, 2013, 46, 529–532.
Yonkers, K. A., Bruce, S. E., Dyck, I. R., & Keller, M. B. (2003). Chronicity, relapse, and illness-course of panic disorder, social phobia, and generalized anxiety disorder: Findings in men and women from 8 years of follow-up. Depression and Anxiety, 17, 173–179. doi:10.1002/da.10106