Technology and the future of cognitive-behavioral interventions

Our field has accumulated a lot of empirical support for the use of cognitive behavioral therapy (CBT) in treating a wide range of mental and behavioral health problems. However, these gold-standard CBT treatments remain extremely difficult to access, leaving wide gaps between those who need psychological care and those who actually receive it (Wang et al., 2005). Technology offers exciting and promising solutions to address both patient- and system-level barriers to accessing mental health treatment.

One of the first prominent advances in technology-based interventions is Internet-delivered CBT (I-CBT), which can easily be disseminated, reduces the time needed from trained clinicians dramatically compared to face-to-face treatments (Enander et al., 2016), and has garnered strong initial efficacy data over the years (Andrews et al., 2018; Carlbring, Andersson, Cuijpers, Riper, & Hedman-Lagerlöf, 2018). That said, I-CBT is already becoming antiquated as the ubiquity and convenience of smartphones take hold. Smartphones are now owned by 81% of adults in the U.S., and ownership rates largely extend across socioeconomic and demographic groups (Pew Research Center, 2019), highlighting the immense potential reach of smartphone-delivered treatments. Moreover, smartphones have the advantage over I-CBT that they allow users to access content on-the-go, as opposed to only when at their home computer. This could aid in better generalization of skills, prompted by practicing skills more often when symptoms arise in the real world. Smartphones also offer the potential additional advantage of having the ability to collect large amounts of data about the user passively (i.e., without burdening the user), via sensors such as global position system (GPS), accelerometer, and call or text message logs. Passive sensor data may one day help us to assess mental health more frequently, and to use assessments to deliver or adapt technology-based interventions in real-time. To accomplish that vision, a first step in this relatively new domain is to develop accurate and reproducible algorithms based on sensor data (Mohr, Zhang, & Schueller, 2017).

While smartphone-delivered treatments hold exciting promise, issues of efficacy, engagement, and privacy will be critical for our field to address if smartphone-based treatments are going to make a truly meaningful impact on access to mental healthcare. In particular, whereas very early efficacy studies of smartphone-delivered treatments suggest modest, promising results (e.g., superiority compared to controls with small to moderate effects; Firth, Torous, Nicholas, Carney, Pratap, et al., 2017; Firth, Torous, Nicholas, Carney, Rosenbaum, et al., 2017), most smartphone-based interventions that are publicly available lack efficacy data (e.g., Leigh & Flatt, 2015). Additionally, users struggle to stick with smartphone-delivered interventions – often dropping out shortly after downloading a new app (Owen et al., 2015). Low engagement is especially likely to occur when smartphone-based treatments are delivered without any human support (Mohr, Cuijpers, & Lehman, 2011; Torous et al., 2018). Poor engagement is compounded by a lack of collaboration between clinician-scientists, industry, and key stakeholders (Schueller, Muñoz, & Mohr, 2013) – a partnership that, if pursued, can effectively marry clinical and scientific rigor with expertise in user-interface design and engagement, as well as insights into real-world needs. Finally, few guidelines currently exist related to data privacy, and consumers and clinicians alike are under-educated about how to vet a technology-delivered intervention in terms of data privacy and security. Taken together, for smartphone-based interventions to truly impact access to care, it is essential that we address these areas as we move forward.

Altogether, technology has the potential to help to address one of the major problems faced by the field of psychology today – a lack of access to gold-standard treatments such as CBT. In order for technology to make a meaningful impact on the access gap, we must embrace technology-based advances in assessment and treatment with a cautious optimism, bearing in mind both their promise as well as their limitations at this early stage.

Discussion Questions

  1. What are the major problems that our field faces, which technology-based assessments and interventions could help to address?
  2. What are the key limitations to technology-based treatments, at the current early stage?
  3. How do I-CBT and smartphone-delivered CBT compare? What might be the pros and cons of each?
  4. What might a vetting process for app-delivered mental treatments look like? How could this be implemented and enforced?

Reference Article

Wilhelm, S., Weingarden, H., Ladis, I., Braddick, V., Shin, J., & Jacobson, N. C. (2020). Cognitive-Behavioral Therapy in the Digital Age: Presidential Address. Behavior Therapy, 51(1), 1-14.

Author Bios

Hilary Weingarden, PhD. is an Instructor in the Department of Psychiatry at Harvard Medical School and a Psychologist at Massachusetts General Hospital. Her research is focused on emotion-based risk for adverse outcomes such as suicidal ideation in obsessive compulsive related disorders (OCRDs), and on using technology to enhance assessment and interventions for OCRDs. Her research is funded by the National Institute of Mental Health (NIMH), the President and Fellows of Harvard College, and industry collaborators.

Anna Schwartzberg, B.A., is a clinical research coordinator in the Obsessive Compulsive Disorder (OCD) and Related Disorders program at Massachusetts General Hospital. She plans to pursue a Ph.D. in clinical psychology and is interested in studying psychophysiology and multimodal assessment of anxiety disorders.


Sabine Wilhelm, PhD. is a Professor at Harvard Medical School, Chief of Psychology at the Massachusetts General Hospital, and Director of the Obsessive Compulsive Disorder (OCD) and Related Disorders Program at Massachusetts General Hospital. She has published over 265 articles, with a particular focus on treatment development, predictors, and mechanisms, including development and testing of technology-based treatments. Her research has been funded by the NIMH, industry, foundation grants, and private donors.

Works Cited

Andrews, G., Basu, A., Cuijpers, P., Craske, M. G., McEvoy, P., English, C. L., & Newby, J. M. (2018). Computer therapy for the anxiety and depression disorders is effective, acceptable and practical health care: An updated meta-analysis. Journal of Anxiety Disorders, 55, 70–78.

Carlbring, P., Andersson, G., Cuijpers, P., Riper, H., & Hedman-Lagerlöf, E. (2018). Internet-based vs. face-to-face cognitive behavior therapy for psychiatric and somatic disorders: An updated systematic review and meta-analysis. Cognitive Behaviour Therapy, 47, 1–18.

Enander, J., Andersson, E., Mataix-Cols, D., Lichtenstein, L., Alström, K., Andersson, G., … Rück, C. (2016). Therapist guided internet based cognitive behavioural therapy for body dysmorphic disorder: Single blind randomised controlled trial. BMJ, 352. i241.

Firth, J., Torous, J., Nicholas, J., Carney, R., Pratap, A., Rosenbaum, S., & Sarris, J. (2017). The efficacy of smartphone-based mental health interventions for depressive symptoms: A meta-analysis of randomized controlled trials. World Psychiatry, 16, 287–298. 10.1002/wps.20472.

Firth, J., Torous, J., Nicholas, J., Carney, R., Rosenbaum, S., & Sarris, J. (2017). Can smartphone mental health interventions reduce symptoms of anxiety? A meta-analysis of randomized controlled trials. Journal of Affective Disorders, 218, 15–22.

Leigh, S., & Flatt, S. (2015). App-based psychological interventions: Friend or foe? Evidence-Based Mental Health, 18, 97–99.

Mohr, D. C., Cuijpers, P., & Lehman, K. (2011). Supportive accountability: A model for providing human support to enhance adherence to eHealth interventions. Journal of Medical Internet Research, 13. e30.

Mohr, D. C., Zhang, M., & Schueller, S. M. (2017). Personal sensing: Understanding mental health using ubiquitous sensors and machine learning. Annual Review of Clinical Psychology, 13, 23-47.

Owen, J. E., Jaworski, B. K., Kuhn, E., Makin-Byrd, K. N., Ramsey, K. M., & Hoffman, J. E. (2015). mHealth in the wild: Using novel data to examine the reach, use, and impact of PTSD Coach. JMIR Mental Health, 2. e7.

Pew Research Center (2019). Mobile fact sheet. Retrieved from

Schueller, S. M., Muñoz, R. F., & Mohr, D. C. (2013). Realizing the potential of behavioral intervention technologies. Current Directions in Psychological Science, 22, 478–483.

Torous, J., Nicholas, J., Larsen, M. E., Firth, J., & Christensen, H. (2018). Clinical review of user engagement with mental health smartphone apps: Evidence, theory and improvements. Evidence-Based Mental Health, 21, 116–119.

Wang, P. S., Berglund, P., Olfson, M., Pincus, H. A., Wells, K. B., & Kessler, R. C. (2005). Failure and delay in initial treatment contact after first onset of mental disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62, 603–613.