Feasibility and Efficacy of Delivering Cognitive Behavioral Therapy Through an Online Psychotherapy Tool for Depression: Protocol for a Randomized Controlled Trial

Comparing clinical decision-making of AI technology to a multi-professional care team in an electronic cognitive behavioural therapy program for depression: protocol

Introduction

Depression is a leading cause of disability worldwide, affecting up to 300 million people globally. Despite its high prevalence and debilitating effects, only one-third of patients newly diagnosed with depression initiate treatment. Electronic cognitive behavioural therapy (e-CBT) is an effective treatment for depression and is a feasible solution to make mental health care more accessible. Due to its online format, e-CBT can be combined with variable therapist engagement to address different care needs. Typically, a multi-professional care team determines which combination therapy most benefits the patient. However, this process can add to the costs of these programs. Artificial intelligence (AI) has been proposed to offset these costs.

Methods

This study is a double-blinded randomized controlled trial recruiting individuals experiencing depression. The degree of care intensity a participant will receive will be randomly decided by either: (1) a machine learning algorithm, or (2) an assessment made by a group of healthcare professionals. Subsequently, participants will receive depression-specific e-CBT treatment through the secure online platform. There will be three available intensities of therapist interaction: (1) e-CBT; (2) e-CBT with a 15-20-min phone/video call; and (3) e-CBT with pharmacotherapy. This approach aims to accurately allocate care tailored to each patient's needs, allowing for more efficient use of resources.

Discussion

Artificial intelligence and providing patients with varying intensities of care can increase the efficiency of mental health care services. This study aims to determine a cost-effective method to decrease depressive symptoms and increase treatment adherence to online psychotherapy by allocating the correct intensity of therapist care for individuals diagnosed with depression. This will be done by comparing a decision-making machine learning algorithm to a multi-professional care team. This approach aims to accurately allocate care tailored to each patient's needs, allowing for more efficient use of resources with the convergence of technologies and healthcare.

Ethics

The study received ethics approval and began participant recruitment in December 2022. Participant recruitment has been conducted through targeted advertisements and physician referrals. Complete data collection and analysis are expected to conclude by August 2024.

Clinical trial registration

ClinicalTrials.Gov, identifier NCT04747873.

User experiences of an online therapist-guided psychotherapy platform, OPTT: A cross-sectional study

Introduction

In the last few years, online psychotherapy programs have burgeoned since they are a more accessible and scalable treatment option compared to in-person therapies. While these online programs are promising, understanding the user experience and perceptions of care is essential for program optimization.

Methods

This study investigated the experiences of end-users who had previously received online psychotherapy through a web-based platform. A 35-item multiple-choice survey was developed by the research team and distributed to past users to capture their perceptions of the program.

Results

The survey yielded 163 responses, with a 90 % completion rate. Participants were predominantly white and female, with an average age of 42 years. While most participants preferred in-person therapy, they also reported the benefits of the online psychotherapy program. Participants had positive perceptions of the platform, the quality and interaction of their therapist, and the homework assignments and skills covered. Lack of motivation to complete weekly homework assignments was cited as a common struggle.

Discussion

The findings support online psychotherapy as a beneficial digital mental health tool and highlight some areas for improvement. Scalability and accessibility are key benefits of the platform. At the same time, improvements in participant engagement, including those from equity-seeking and equity-deserving groups, may enhance the efficacy of the programs offered.

Comparison of online and in-person cognitive behavioral therapy in individuals diagnosed with major depressive disorder: a non-randomized controlled trial

Objective

The increased prevalence of major depressive disorder (MDD) amid the COVID-19 pandemic has resulted in substantial growth in online mental health care delivery. Compared to its in-person counterpart, online cognitive behavioral therapy (e-CBT) is a time-flexible and cost-effective method of improving MDD symptoms. However, how its efficacy compares to in-person CBT is yet to be explored. Therefore, the current study compared the efficacy of a therapist-supported, electronically delivered e-CBT program to in-person therapy in individuals diagnosed with MDD.

Methods

Participants (n = 108) diagnosed with MDD selected either a 12 week in-person CBT or an asynchronous therapist-supported e-CBT program. E-CBT participants (n = 55) completed weekly interactive online modules delivered through a secure cloud-based online platform (Online Psychotherapy Tool; OPTT). These modules were followed by homework in which participants received personalized feedback from a trained therapist. Participants in the in-person CBT group (n = 53) discussed sessions and homework with their therapists during one-hour weekly meetings. Program efficacy was evaluated using clinically validated symptomatology and quality of life questionnaires.

Results

Both treatments yielded significant improvements in depressive symptoms and quality of life from baseline to post-treatment. Participants who opted for in-person therapy presented significantly higher baseline symptomatology scores than the e-CBT group. However, both treatments demonstrated comparable significant improvements in depressive symptoms and quality of life from baseline to post-treatment. e-CBT seems to afford higher participant compliance as dropouts in the e-CBT group completed more sessions on average than those in the in-person CBT group.

Conclusion

The findings support e-CBT with therapist guidance as a suitable option to treat MDD. Future studies should investigate how treatment accessibility is related to program completion rates in the e-CBT vs. in-person group.

Clinical Trial Registration

ClinicalTrials.Gov Protocol Registration and Results System (NCT04478058); clinicaltrials.gov/ct2/show/NCT04478058.

Real-world behavioral dataset from two fully remote smartphone-based randomized clinical trials for depression

Most people with mental health disorders cannot receive timely and evidence-based care despite billions of dollars spent by healthcare systems. Researchers have been exploring using digital health technologies to measure behavior in real-world settings with mixed results. There is a need to create accessible and computable digital mental health datasets to advance inclusive and transparently validated research for creating robust real-world digital biomarkers of mental health. Here we share and describe one of the largest and most diverse real-world behavior datasets from over two thousand individuals across the US. The data were generated as part of the two NIMH-funded randomized clinical trials conducted to assess the effectiveness of delivering mental health care continuously remotely. The longitudinal dataset consists of self-assessment of mood, depression, anxiety, and passively gathered phone-based behavioral data streams in real-world settings. This dataset will provide a timely and long-term data resource to evaluate analytical approaches for developing digital behavioral markers and understand the effectiveness of mental health care delivered continuously and remotely.

Does Cyberostracism Reduce Prosocial Behaviors? The Protective Role of Psychological Resilience

To reduce the negative consequences of cyberostracism on prosocial behaviors, we developed a coping strategy based on psychological resilience, and revealed its effectiveness in combating the adverse effects of cyberostracism on prosocial behavior through two studies. Study 1 demonstrated that psychological resilience could mitigate the negative impact of cyberostracism on prosocial behaviors through experimental manipulation. By targeting continuously ostracized people with low resilience for an online self-help resilience intervention program, Study 2 confirmed that psychological resilience was effective in alleviating the detrimental effects of cyberostracism. These studies not only help us to recognize the negative effects of cyberostracism, but also extend Williams’ temporal need–threat model of ostracism in the context of online ostracism. As emerging technologies represent a promising new approach to intervention delivery, the most valuable contribution of this study is that we developed an online self-help psychological resilience intervention program that showed encouraging therapeutic effects and advantages for assisting in caring for a larger population of people who are at elevated risk for being cyberostracized.

Effects of Group and Individual Culturally Adapted Cognitive Behavioral Therapy on Depression and Sexual Satisfaction among Perimenopausal Women

Aims: Previous research has shown the efficacy of culturally adapted Cognitive Behavioral Therapy (CA-CBT) in reducing depression, yet its effect on increasing sexual satisfaction is not well documented. In this study, an embedded randomized controlled trial design was used to examine the effect of group and individual CA-CBT on depression and sexual satisfaction among perimenopausal women. Method: A total of 64 depressed Iranian perimenopausal women were randomly assigned to two formats of treatments; sixteen sessions of group CA-CBT and eight sessions of individual CA-CBT, as well as a waitlist control group. Depression and sexual satisfaction were measured using BDI-II and ENRICH, respectively, at T1 (pre-treatment), T2 (post-treatment) and T3 (follow-up). Results: Repeated measures ANOVA indicated that the women who underwent both group and individual CA-CBT had effectively reduced depression and increased sexual satisfaction between pre-treatment and post-treatment, and it was sustained after six months of follow-ups with large effect sizes of significant differences (p < 0.001), but the control group did not. Conclusion: The results showed promising evidence for the efficacy of both treatment groups of CA-CBT for depression and sexual satisfaction among perimenopausal women. The population mental health burden among perimenopausal women may likely be reduced by propagating this effective treatment.