A comparison of electronically-delivered and face to face cognitive behavioural therapies in depressive disorders: A systematic review and meta-analysis

Candice Luo, Nitika Sanger, Nikhita Singhal, Kaitlin Pattrick, Ieta Shams, Hamnah Shahid, Peter Hoang, Joel Schmidt, Janice Lee, Sean Haber, Megan Puckering, Nicole Buchanan, Patsy Lee, Kim Ng, Sunny Sun, Sasha Kheyson, Douglas Cho-Yan Chung, Stephanie Sanger, Lehana Thabane, Zainab Samaan, Candice Luo, Nitika Sanger, Nikhita Singhal, Kaitlin Pattrick, Ieta Shams, Hamnah Shahid, Peter Hoang, Joel Schmidt, Janice Lee, Sean Haber, Megan Puckering, Nicole Buchanan, Patsy Lee, Kim Ng, Sunny Sun, Sasha Kheyson, Douglas Cho-Yan Chung, Stephanie Sanger, Lehana Thabane, Zainab Samaan

Abstract

Background: Cognitive behavioural therapy (CBT) is a widely used treatment for depression. However, limited resource availability poses several barriers to patients seeking access to care, including lengthy wait times and geographical limitations. This has prompted health care services to introduce electronically delivered CBT (eCBT) to facilitate access. Although previous reviews have compared the effects of eCBT to face-to-face CBT, there is an overall lack of adequately powered and up-to-date evidence in the literature to provide a reliable comparison between the two modes of administration. The purpose of this study is to evaluate the effects of eCBT compared to face-to-face CBT through a systematic review of the literature.

Methods: To be eligible for this review, studies needed to be randomized controlled trials evaluating the clinical effectiveness of any form of eCBT compared to face-to-face CBT. These encompassed studies evaluating a wide range of outcomes including severity of symptoms, adverse outcomes, clinically relevant outcomes, global functionality, participant satisfaction, quality of life, and affordability. There were no restrictions on participant age or sex.We searched MEDLINE, EMBASE, Psych Info, Cochrane CENTRAL and CINAHL databases from inception to February 20th, 2020 using a comprehensive search strategy. All stages of literature screening and data extraction were completed independently in duplicate. Data extraction and risk of bias analyses, including GRADE ratings, were conducted on studies meeting inclusion criteria. Qualitative measures are reported in a narrative summary. We pooled quantitative data in meta-analyses to provide an estimated summary effect. This review adheres to PRISMA reporting guidelines.

Findings: In total, we included 17 studies in our analyses. Our results demonstrated that eCBT was more effective than face-to-face CBT at reducing depression symptom severity (Standardized mean difference [SMD]: -1.73; 95% confidence interval [CI]: -2.72, -0.74; GRADE: moderate quality of evidence). There were no significant differences between the two interventions on participant satisfaction (SMD 0.13 95%; CI -0.32, 0.59; GRADE: low quality of evidence). One RCT reported eCBT to be less costly than face-to-face CBT (GRADE: low quality of evidence). Results did not differ when stratified by subgroups such as participant age and study location.

Interpretation: Although we found eCBT to have moderate evidence of effectiveness in reducing symptoms of depression, high heterogeneity among studies precludes definitive conclusions for all outcomes. With the current reliance and accessibility of technology to increasing number of people worldwide, serious consideration in utilizing technology should be given to maximize accessibility for depression treatments. Our results found eCBT is at least as effective as face to face CBT, thus eCBT should be offered if preferred by patients and therapists.

Funding: This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Keywords: Behavior; CBT; Cognitive behavioural therapy; Depression; Electronic; MDD; Major depressive disorders; Systematic review.

Conflict of interest statement

The authors have no conflicts of interest to report.

© 2020 The Authors.

Figures

Fig. 1
Fig. 1
PRISMA Flowchart of study screening and inclusion.
Fig. 2
Fig. 2
Risk of Bias within Studies. *other biases include self-report biases and niche study populations.
Fig. 3
Fig. 3
Forest plot for symptom severity (k = 14).
Fig. 4
Fig. 4
Forest plot for global functionality (k = 2).
Fig. 5
Fig. 5
Forest plot for participants’ satisfaction (k = 3).
Fig. 6
Fig. 6
Risk of bias across studies.
Fig. 7
Fig. 7
Symptom severity funnel plot.

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