Psychiatric Annals

CME Article 

Mobile Health Technologies to Deliver and Support Cognitive-Behavioral Therapy

Stephen M. Schueller, PhD; Elizabeth C. Adkins, MA, MS

Abstract

Advances in technology are changing the ways cognitive-behavioral therapy (CBT) can be delivered. Through mobile technologies, effective interventions exist that allow people to receive CBT without ever visiting a practitioner's office. Additionally, mobile technologies are increasingly entering practitioners' offices, combining technological and human elements to create hybrid forms of care. Although clinical research has demonstrated exciting possibilities for mobile technologies to deliver and support CBT, for the most part clinical practice has been unchanged. We provide an overview of mobile CBT tools used either to deliver or to support CBT, highlighting what works and noting current limitations of our understanding. We also discuss new avenues in mobile CBT that leverage peers, artificial intelligence and chatbots, and mobile sensing to create scalable, personalized, and context-aware interventions. The future of mobile CBT should not be confined to digitizing current practices but should leverage technological affordances to improve CBT as it exists today. [Psychiatr Ann. 2019;49(8):348–352.]

Abstract

Advances in technology are changing the ways cognitive-behavioral therapy (CBT) can be delivered. Through mobile technologies, effective interventions exist that allow people to receive CBT without ever visiting a practitioner's office. Additionally, mobile technologies are increasingly entering practitioners' offices, combining technological and human elements to create hybrid forms of care. Although clinical research has demonstrated exciting possibilities for mobile technologies to deliver and support CBT, for the most part clinical practice has been unchanged. We provide an overview of mobile CBT tools used either to deliver or to support CBT, highlighting what works and noting current limitations of our understanding. We also discuss new avenues in mobile CBT that leverage peers, artificial intelligence and chatbots, and mobile sensing to create scalable, personalized, and context-aware interventions. The future of mobile CBT should not be confined to digitizing current practices but should leverage technological affordances to improve CBT as it exists today. [Psychiatr Ann. 2019;49(8):348–352.]

Digital technologies are shaping our world in critical ways. People have become hyperconnected, sharing glimpses into their lives through social media and wearable devices, and are in nearly constant communication through email, text messages, and communication apps. Such technologies are transforming work and changing the roles and tasks of professionals. Mental health practitioners, however, have generally been slower than other fields to adopt even routinely deployed health information technologies. For example, a 2009 study found the lowest rates of adoption of electronic health records among psychiatrists at 33% with the highest being for cardiologists at 62%.1 The authors of this study noted that specialty-specific differences may stem from unique concerns and for psychiatrists this includes privacy and confidentiality as well as additional protections from the Health Insurance Portability and Accountability Act on process notes and substance abuse information. However, considerable enthusiasm exists toward the potential of digital technologies to reshape mental health services. This enthusiasm is driven by various factors. A robust body of research has demonstrated that mobile health (mHealth) interventions can lead to significant improvements in mental health and in some instances these improvements are as large as face-to-face services.2 An increasing number of mHealth tools are available with estimates suggesting that nearly 10,000 mental health apps are commercially available.3 Lastly, many practitioners report an interest in using mHealth tools in their practice.4 This creates a demand for effective and usable tools.

However, although research on mHealth applications focused on cognitive-behavioral therapy (CBT) is robust, practitioner knowledge of such applications and how to use them in their practice is lacking. Practitioners are rarely trained to identify and use mHealth tools. The sheer number of tools available makes it hard for those unacquainted with this area to get started. As such, we provide an overview of mHealth for CBT focusing on three main areas: (1) mHealth tools intended specifically to provide CBT interventions for depression and anxiety, although tools exist for nearly any disorder that can be treated through CBT but an exhaustive review is behind the scope of this article; (2) mHealth tools intended to augment or support CBT in traditional settings; and (3) innovative future directions for mobile CBT. We hope this review provides an update to the latest advances and builds the competence of those new to this area to understand its current state.

Mobile Technologies that Provide Cognitive-Behavioral Interventions

Early research established the efficacy of CBT interventions delivered via the Internet to treat a variety of issues.5 Such research has produced promising results indicating that Internet (I)-CBT is as effective as face-to-face treatment for a range of psychiatric and somatic disorders.2 With the uptick of smartphone use in more recent years, developers have begun to adapt effective I-CBT interventions into smartphone apps as well as to create new apps based on CBT principles. In general, evidence suggests that mHealth apps are also effective.6,7

Many apps with research backing, however, never make it to app stores. Despite a multitude of publicly available mHealth apps, people may have a difficult time distinguishing between the hundreds of options. Independent review of apps in app stores have demonstrated that fewer than 5% of apps likely have direct empirical support.8,9 The lack of direct empirical support is further exacerbated by the lack of indirect empirical support as well. In other words, among publicly available apps that claim to be based on CBT, adherence to CBT principles and components is relatively low. One review of apps for depression found only 12 apps that were based on CBT or behavioral activation, and only two of these apps had at least 50% of the expected CBT principles.5 Another review found that the apps that were rated most popular on app stores for anxiety and worry did not include any evidence-based treatment components.10 Instead most apps included only psychoeducation or symptom monitoring.5 As such, core CBT principles such as cognitive restructuring, activity scheduling, or behavioral experimentation tend to be lacking.

Effective apps share some key characteristics. They tend to emphasize CBT skills. IntelliCare, for example, is a suite of publicly available apps that emphasizes skill acquisition by distilling individual CBT skills into separate apps.11 The IntelliCare apps are lean on psychoeducation, instead focusing on features that allow users to complete exercises first, with the hope that users will learn from doing. Effective apps show good engagement. For many mHealth apps sustained use tends to be poor.6 Although technical features with good user experience is an important strategy for building engagement, another strategy is to include a human element either through technical or clinical support. Supported apps have been consistently found to be more effective than self-guided apps. This support can be provided by therapists, trainees, and laypersons via text messaging, emailing, and phone calls.

Mobile Technology in Assistance of the Delivery of Cognitive-Behavioral Therapy

Although one model of mobile CBT considers the technology itself to be the primary form of treatment, either delivered as a self-guided intervention or with some human support, other apps are specifically designed as an adjunctive tool to face-to-face treatment.12 Therapy delivery models that are specifically designed to combine human and technological elements are deemed blended care. Blended care models aim to use mHealth tools to increase the efficiency or impact of therapist-delivered in-person CBT. Specific goals of blended care include reducing the number of therapy sessions needed by providing some learning outside of session or increasing the number of patients that a practitioner could support on his or her caseload by shifting some aspects of treatment to the technology. Research has demonstrated that blended care models can reduce practitioner's time and reduce dropout rates.13 The technological elements in a blended care model can be used to gather additional information and enhance patients' learning and skill usage outside of sessions. For example, one mHealth app designed to augment prolonged exposure is a PE Coach. Through this app, patients receive psychoeducation, audio record sessions, track symptom severity, record exposure homework, and get tips for breathing training.14 Other treatment support apps tend to focus on more general CBT skills and can be used with diverse CBT protocols or treatments. An example would be apps that promote cognitive restructuring or activity monitoring or provide psychoeducation around an additional content area like a CBT-insomnia app for a patient with sleep disturbances. Although most studies of mobile CBT have evaluated these tools outside of therapy, one meta-analysis study found 10 studies that compared standard treatment versus treatment plus mobile adjuncts.12 On average, these studies found significant but modest improvements from the technology-enhanced care. For example, the benefit of technology-enhanced care conditions compared to all controls was an effect size of .34, whereas technology-enhanced care compared to active treatment comparisons still produced an effect size of .27. These studies spanned a range of different conditions including anxiety, depression, eating disorders, posttraumatic stress disorder, and substance use to name a few. Thus, integrating technology into clinical practice might help improve the impact and efficiency of care.

Despite the benefits of integrating mHealth tools into practice, few tools are specifically designed to facilitate such integration. Thus, practitioners might need to find ways to leverage self-guided mHealth apps to assist in the delivery of individual and group psychotherapy, aftercare, and relapse prevention. Self-guided apps can be used to provide psychoeducation, covering topics that a practitioner might not have the time or expertise to deliver themselves. For example, a CBT-insomnia app could be recommended to target comorbid sleep disturbances for a patient seeking treatment for another problem. A self-guided app might be able to provide more opportunities to practice skills outside of sessions. A therapist might recommend a mindfulness app to facilitate a patient's meditation practice. Symptom tracking or journaling apps could encourage and help patients, accurately and in real time, record mood and physiological symptoms, note avoidant behaviors, complete worksheets (eg, thought records), and log exposures and related symptoms. Lastly, practitioners could use general purpose apps including productivity apps or to do lists to improve adherence to homework and session attendance through reminders and prompts. Indeed, many people report they use apps that are not classified as mental health apps for mental health purposes.15

mHealth apps provide significant opportunities to support face-to-face treatment, and practitioners are interested in integrating these tools; however, more research needs to be conducted to further understand how to integrate and harness the strengths of both humans and technology to provide exceptional treatment to individuals with mental illness.

The Future of Mobile Cognitive-Behavioral Therapy

Although the majority of mHealth CBT interventions to date have been web-based platforms or mobile apps, recent examples have given us a glimpse into what the future of interventions might entail. These interventions attempt to overcome limitations in previous technologies. An example would be increasing scalability by using peers rather than professional or forgoing human support altogether by leveraging recent advances in artificial intelligence and natural language processing. Future interventions might also use advances in mobile sensing and machine learning to provide interventions as the times and places they might be most impactful.

Much of the enthusiasm for mental mHealth interventions comes from their potential to scale to provide resources for those otherwise unable to access care. Although technologies themselves might be scalable, the human professionals supporting the technologies are not. One way to overcome this challenge is to create mHealth platforms that facilitate peer-to-peer collaboration while maintaining effective elements from CBT. Panoply was a crowdsourcing-based intervention that reduced cognitive restructuring into elemental components to allow users to learn, practice, and support others in restructuring negative automatic thoughts.16 A randomized trial of Panoply compared to expressive writing showed that both interventions could lead to reductions in depressive symptoms, but that Panoply had specific impacts on negative thinking that mediated decreases in depression.16

From a scalability perspective the best mHealth interventions would be those that could forgo humans altogether. Using advances in artificial intelligence and natural language processing a few CBT “conversational agents” have been developed and evaluated.17,18 Conversational agents, also referred to as chatbots, are digital services that use natural language (ie, speech or text) as the primary means of user interaction. Woebot is a therapy chatbot that provides CBT through chats on either a messenger platform or mobile app. A randomized pilot study in university college students who were stressed found Woebot produced significant reductions in depressive symptoms.17 Current conversational agents are innovative and an exciting blending of state-of-the-art technology with CBT interventions; however, they are likely far away from being able to provide the type of support one is able to receive in therapy. Thus, this latter approach of building a conversational agent to supplement therapy is a likely important case for the future.

Mobile CBT interventions have the potential to fit into the context of people's daily lives—to create on-the-go interventions that can be deeply personalized and contextualized. Recent research is laying the groundwork for such interventions and early proof-of-concept studies illustrate how such interventions might operate. These interventions require the ability to (1) automatically collect information about one's mental health state or context, (2) process that information to determine whether to deliver an intervention and which intervention to deliver, and (3) provide an intervention to an individual in the moment. An early example of such an intervention was “Mobilyze!,” a smartphone app capable of drawing sensor-based information from a person such as location and physical activity to provide in-the-moment prompts reinforcing CBT skills such as behavioral activation.18 Other examples have illustrated additional ways in which such technologies can provide CBT skills. For example, the Challenger app addresses social anxiety by using location services to create exposure-type exercises posed to people as challenges.19 In this app, a user who reports difficulty making phone calls might be challenged to call a local business with the business selected based on proximity and the user's personal goals.

These examples demonstrate the potential of mHealth CBT applications to improve with advances in novel technologies as well as a better understanding by practitioners and clinical scientists as how to use technologies to support and advance evidence-based CBT practices. The resultant tools will likely provide new ways to deliver and augment CBT with the potential to increase its impact and efficiency. Technologies must still address the best way to adapt to the variety of disorders that CBT is able to address either through incorporating screening and assessment into intervention delivery, focusing on transdiagnostic CBT elements, or combining with human practitioners to guide people to the proper technological treatments.

Conclusions

mHealth CBT applications are effective for nearly any issue that traditional CBT is used for. Nevertheless, several current challenges exist. First, many mHealth tools advertised as CBT-based bear little resemblance to the core strategies of CBT. Although this may not be that different than practitioners who claim to use CBT but fail to implement these strategies in their practice, it is easier to determine the fidelity to CBT of an app than a practitioner because we can download and evaluate an app directly and so we are more aware of this issue. Second, even mHealth tools with demonstrated effectiveness in clinical research may face significant barriers to adoption and use in real-world settings. Engagement is a major issue facing mHealth research that appears to be mitigated somewhat by the inclusion of human support, but this support in turn significantly impacts the scalability. Lastly, our current clinical workforce is unprepared to integrate these tools into their workflows and effective models of care that combine technological and human components for mental health services still need to be developed. The American Psychiatric Association has developed a framework that assists practitioners by providing questions to ask to evaluate mobile mental health apps.20 Additionally, PsyberGuide is a repository of information including reviews of credibility, user experience, and transparency related to privacy and data security of mobile mental health apps.21 Such resources are useful tools for practitioners looking to use mHealth in their clinical practice.

Despite these challenges, health systems and private industry continue to invest effort to development, deploy, and evaluate mHealth tools for mental health. Researchers and practitioners of CBT can enhance their work currently and in the future by finding ways to contribute to such efforts. This article provides an overview of where mobile CBT is now, where it is going, and where practitioners can find some information to help them use it in their work.

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Authors

Stephen M. Schueller, PhD, is an Assistant Professor, Department of Psychological Science, University of California, Irvine. Elizabeth C. Adkins, MA, MS, is a Clinical Psychology Doctoral Student, Department of Preventive Medicine, Northwestern University.

Address correspondence to Stephen M. Schueller, PhD, Department of Psychological Science, School of Social Ecology, 4201 Social and Behavioral Sciences Gateway, Irvine, CA 92697-7085; email: s.schueller@uci.edu.

Disclosure: The authors have no relevant financial relationships to disclose.

10.3928/00485713-20190717-02

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