Thank you for your interest in CCO content. As a guest, please complete the following information fields. These data help ensure our continued delivery of impactful education.
Become a member (or login)? Member benefits include accreditation certificates, downloadable slides, and decision support tools.
Biomedical Data Science, Psychiatry, and Computer Science
Geisel School of Medicine
Lebanon, New Hampshire
Nicholas Jacobson, PhD, has no relevant financial relationships to disclose.
Each year, major depressive disorder (MDD) affects approximately 3% of the global population. It is one of the leading causes of disability worldwide and is associated with a significant increase in all-cause mortality. Because this is such a prevalent disorder—and one that is associated with significant distress and impairment—receiving adequate treatment is of utmost importance.
Unfortunately, only approximately one half of patients with MDD receive minimally adequate treatment in any given year. Many factors play into this treatment inadequacy. Namely, the traditional mental healthcare system does not have enough manpower to scale to the immense number of patients with mental health disorders: Annually, more than 1500 patients with common mental health disorders need treatment from a mental healthcare professional. Even if there were an increased number of available healthcare professionals, lack of reliable and accessible transportation poses another significant barrier to traditional mental healthcare—such that people in rural regions are approximately 20 times more likely to delay seeking care compared with people in urban locations. Time limitations also decrease access to care, as many mental healthcare visits fall within the workday, and this often requires taking time off work and/or finding childcare.
Advances in technology over the past 30 years have allowed for new, previously unforeseen opportunities to treat mental health on a larger scale. For example, a majority of people worldwide now own mobile devices, and rates of ownership continue to grow. Already, there are more mobile connections than there are people in the world. As such, digital therapeutics—software used to prevent, treat, or manage medical conditions—may remedy these mental health access issues. Digital therapeutics can offer mental healthcare anytime and anywhere. They can be designed to be accessible for 24 hours a day and 7 days a week, giving on-demand support via mobile or Internet-connected devices. With such immense accessibility, digital therapeutics can greatly help address the current gaps in mental healthcare.
Digital therapeutics for MDD have been developing in waves. The first broadly accessible wave was in the form of Internet-based care. Although there are many Internet-based deployments, the most widely tested content is based on cognitive–behavioral therapy, some with and some without a form of human support. Meta-analytic evidence suggests that both guided and unguided forms of Internet-based care outperform usual treatment (mostly referrals to in-person care or waitlist controls). Moreover, meta-analytic evidence suggests that guided and unguided care perform similarly for those at subclinical levels of depression. Nevertheless, for those at clinical levels of depression, guided care is associated with significantly larger effects.
The second wave of digital therapeutics has come in the form of smartphone-based care, which can allow for greater flexibility and native integration within a person’s daily life. Evidence from a systematic meta-review suggests that smartphone-based interventions work for MDD symptoms as a whole (study efficacy gauged by sample size [N >1000] and statistical significance [P <.000001]), but there is wide heterogeneity in both the type of treatment and impact of treatment on MDD symptoms. This suggests that mobile interventions have promising early efficacy, but more work is needed to evaluate and compare treatment types and impact on symptoms.
In addition, digital therapeutics offer a greater ability to personalize treatment based on active baseline assessments, patterns of engagement with digital therapeutics, and passively collected data from patients’ devices. In other words, not all people benefit to the same degree from digital therapeutics, but the level of change and benefit can be accurately forecast using both active and passive information. Passive data also can be used to predict the severity of and changes in mental health symptoms, opening the door for just-in-time adaptive interventions.
In sum, despite the relative infancy of the field, the evidence base for the use of digital therapeutics in MDD is relatively robust. Digital therapeutics for MDD have the potential to scale to the massive portion of the population who often receive no or inadequate treatment. However, many outstanding questions and opportunities remain to enhance the delivery of personalized care within digital therapeutics.
How do you feel about incorporating digital therapeutics into management plans for your patients with MDD? For more information on the role of digital therapeutics in MDD, register for Clinical Care Options’ complimentary Mood Disorders Summit, being held November 11-12, 2022.
Goldberg SB, Lam SU, Simonsson O, et al. Mobile phone-based interventions for mental health: a systematic meta-review of 14 meta-analyses of randomized controlled trials. PLOS Digit Health. 2022;1:e0000002.
Jacobson NC, Weingarden H, Wilhelm S. Using digital phenotyping to accurately detect depression severity. J Nerv Ment Dis. 2019;207:893-896.
Jacobson NC, Chen W, Huang R. Standalone apps for anxiety and depression show promising early efficacy: synthesis of meta-analytic results. Meta-Psychol. 2020;[Preprint].
Jacobson NC, Lekkas D, Huang R, et al. Deep learning paired with wearable passive sensing data predicts deterioration in anxiety disorder symptoms across 17–18 years. J Affect Disord. 2021;282:104-111.
Jacobson NC, Kowatsch T, Marsch LA. Digital therapeutics for mental health and addiction: the state of the science and vision for the future. Volume 1. Elsevier; 2022.
Karyotaki E, Efthimiou O, Miguel C, et al. Internet-based cognitive behavioral therapy for depression: a systematic review and individual patient data network meta-analysis. JAMA Psychiatry. 2021;78:361-371.
Price GD, Heinz MV, Zhao D, et al. An unsupervised machine learning approach using passive movement data to understand depression and schizophrenia. J Affect Disord. 2022;316:132-139.
Puyat JH, Kazanjian A, Goldner EM, et al. How often do individuals with major depression receive minimally adequate treatment? a population-based, data linkage study. Can J Psychiatry. 2016;61:394-404.
Turner A. How many people have smartphones worldwide (September 2022). bankmycell.com/blog/how-many-phones-are-in-the-world. Accessed September 30, 2022.
WHO. Depression. www.who.int/news-room/fact-sheets/detail/depression. Accessed September 30, 2022.
Wilhelm S, Weingarden H, Ladis I, et al. Cognitive-behavioral therapy in the digital age: presidential address. Behav Ther. 2020;51:1-14.