Experiences using an online therapist-guided psychotherapy platform (OPTT) in correctional workers with depression, anxiety, and PTSD.

Introduction: Correctional workers (CWs) are frequently exposed to potentially traumatic events in the workplace, leading to an increased prevalence of mental health concerns. Online psychotherapy can address many of the barriers CWs face when seeking adequate mental health care. Despite their benefits, CWs' experience using digital mental health interventions is relatively unknown. This information could be valuable in developing enhanced care delivery to improve recruitment, retention, satisfaction, and treatment outcomes. Methods: This study investigated the experiences of a sample of CWs enrolled in a clinical trial evaluating the efficacy of the Online Psychotherapy Tool (OPTT) in this population. Participants were surveyed and interviewed to capture their opinions and feedback on the program. Survey analysis was conducted through Qualtrics statistical analysis software. The interview transcripts and open-ended survey questions were analyzed using thematic analysis methods in NVivo. Results: Participants (n=14) were cis-gender, predominantly white, with an average age of 38 years. While most respondents preferred in-person therapy, they also reported the benefits of the online psychotherapy program. Specifically, they expressed positive perceptions of the platform, the quality and interaction of their care provider, and the homework assignments and skills learned. Lack of motivation to complete weekly homework assignments was a frequently cited challenge. Unhelpful aspects of the therapy noted issues with the online format and frustration with certain program elements. Discussion: Participants expressed a positive outlook on the program, the platform, and treatment outcomes. A preference for in-person therapy was still indicated, demonstrating the need to focus on engagement in digital mental health interventions. In addition, the findings of this study shed light on the factors that can influence help-seeking in this population, including stigma in the work environment, demanding work schedules, workplace perceptions, and previous experiences accessing mental health services.

Comparing the efficacy of electronic cognitive behavioral therapy to medication and combination therapy for generalized anxiety disorder: a quasi-experimental clinical trial.

Background: Generalized anxiety disorder (GAD) is a debilitating mental health disorder with first-line treatments include cognitive behavioral therapy (CBT) and pharmacotherapy. CBT is costly, time-consuming, and inaccessible. Electronic delivery (e-CBT) is a promising solution to address these barriers. However, due to the novelty of this intervention, more research testing the e-CBT efficacy independently and in conjunction with other treatments is needed. Objective: This study investigated the efficacy of e-CBT compared to and in conjunction with pharmacotherapy for GAD. Methods: This study employed a quasi-experimental design where patients selected their preferred treatment modality. Patients with GAD were enrolled in either e-CBT, medication, or combination arms. The 12-week e-CBT program was delivered through a digital platform. The medications followed clinical guidelines. The efficacy of each arm was evaluated using questionnaires measuring depression, anxiety, and stress severity, as well as quality of life. Results: There were no significant differences between arms (N e-CBT = 41; N Medication = 41; N Combination = 33) in the number of weeks completed or baseline scores. All arms showed improvements in anxiety scores after treatment. The medication and combination arms improved depression scores. The e-CBT and Combination arms improved quality of life, and the combination arm improved stress scores. There were no differences between the groups in depression, anxiety, or stress scores post-treatment. However, the combination arm had a significantly larger improvement in quality of life. Gender and treatment arm were not predictors of dropout, whereas younger age was. Conclusion: Incorporating e-CBT on its own or in combination with pharmaceutical interventions is a viable option for treating GAD. Treating GAD with e-CBT or medication appears to offer significant improvements in symptoms, with no meaningful difference between the two. Combining the treatments also offer significant improvements, while not necessarily superior to either independently. The findings suggest that all options are viable. Taking the patient's preferred treatment route based on their lifestyle, personality, and beliefs into account when deciding on treatment should be a priority for care providers.

Online cognitive behavioral therapy (eCBT) for the management of depression symptoms in unipolar and bipolar spectrum disorders, a systematic review and network meta-analysis.

BACKGROUND: Face-to-face cognitive behavioral therapy (CBT) is effective in the management of depression symptoms in unipolar and bipolar spectrum disorders. Though, compared to electronic adaptations of CBT (eCBT), it carries several accessibility limitations. Furthermore, unlike eCBT for depression symptoms (eCBTg), eCBT specific for bipolar depression (eCBT-Bipol) remains largely understudied. Thus, supplementing this gap, this systematic review and network meta-analysis (NMA) synthesized the available literature on eCBT for the treatment of unipolar and bipolar depression symptoms. METHOD: MEDLINE, CINAHL, PsycINFO, EMBASE, and Cochrane were searched for relevant randomized controlled trials (RCTs) on eCBTg and eCBT-Bipol The review followed PRISMA guidelines and used the Cochrane risk of bias tool and GRADE criteria for quality assessment. Effect sizes were summarized using standardized mean differences (SMDs) and risk ratios (RRs). RESULTS: eCBT-Bipol was comparable to eCBTg (SMD: 0.05, 95 % CI: -0.18; 0.28) and other psychotherapeutic interventions (SMD: 0.14, 95 % CI: -0.07; 0.35) for the management of mild to moderate depression symptoms. eCBT-Bipol was significantly more effective than attention controls (SMD: 0.35, 95 % CI: 0.11; 0.59), treatment as usual (SMD: 0.55, 95 % CI: 0.21; 0.90) and no intervention controls (SMD: 0.66, 95 % CI: 0.40; 0.93) in mitigating symptoms. LIMITATIONS: The scarcity of eCBT-Bipol studies impacted the quality of the evidence in terms of risk of bias and imprecision. CONCLUSIONS: The findings of this systematic review suggest that eCBT-Bipol has comparable effectiveness to eCBTg in managing depressive symptoms of unipolar and bipolar spectrum disorder. Though, they also highlighted the need for more studies on eCBT-Bipol.

Comparing the Efficacy of an Electronically Delivered Cognitive Behavioral Therapy Program to a Mental Health Check-In Program for Generalized Anxiety Disorder: Protocol for a Randomized Trial.

BACKGROUND: Generalized anxiety disorder (GAD) is a prevalent anxiety disorder, with cognitive behavioral therapy (CBT) being the gold standard treatment. However, it is inaccessible and costly to many, as the mental health industry is overwhelmed by the demand for treatment. This means effective, accessible, and time-saving strategies must be developed to combat these problems. Web-based interventions for mental health disorders are an innovative and promising way to address these barriers. While electronically delivered CBT (e-CBT) has already proved productive and scalable for treating anxiety, other less resource-intensive interventions can be innovated. Checking up on mental health face-to-face has been shown to provide similar benefits to patients with anxiety disorders previously, but more research is needed to evaluate the efficacy of web-based delivery of this intervention. OBJECTIVE: This study will compare the efficacy of e-CBT and a web-based mental health check-in program to treat GAD. These programs will both be delivered through a secure, web-based care delivery platform. METHODS: We will randomly allocate participants (N=100) who are 18 years or older with a confirmed diagnosis of GAD to either an e-CBT program or a mental health check-in program over 12 weeks to address their anxiety symptoms. Participants in the e-CBT arm will complete predesigned modules and homework assignments while receiving personalized feedback and asynchronous interaction with a therapist through the platform. Participants in the mental health check-in arm will be contacted weekly through the web-based platform's written chat feature (messaging system). Therapists will ask the participants a series of predesigned questions that revolve around a different theme each week to prompt conversation. Using clinically validated questionnaires, the efficacy of the e-CBT arm will be compared to the mental health check-in arm. These questionnaires will be completed at baseline, week 6, and week 12. RESULTS: The study received ethics approval in April 2021, and participant recruitment began in May 2021. Participant recruitment has been conducted through targeted advertisements and physician referrals. Complete data collection and analysis are expected to conclude by August 2023. Linear and binomial regression (continuous and categorical outcomes, respectively) will be conducted. CONCLUSIONS: To the research team's knowledge, this will be the first study to date comparing the efficacy of e-CBT with a web-based mental health check-in program to treat GAD. The findings from this study can help progress the development of more scalable, accessible, and efficacious mental health treatments. TRIAL REGISTRATION: ClinicalTrials.gov NCT04754438; https://classic.clinicaltrials.gov/ct2/show/NCT04754438. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48899.

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.

Developing and Implementing a Web-Based Relapse Prevention Psychotherapy Program for Patients With Alcohol Use Disorder: Protocol for a Randomized Controlled Trial.

BACKGROUND:  Alcohol use disorder (AUD) is characterized by problematic alcohol use accompanied by clinically substantial distress. Patients with AUD frequently experience high relapse rates, and only 1 in 5 remain abstinent 12 months post treatment. Traditional face-to-face relapse prevention therapy (RPT) is a form of cognitive behavioral therapy (CBT) that examines one's situational triggers, maladaptive thought processes, self-efficacy, and motivation. However, access to this treatment is frequently limited due to its high cost, long waitlists, and inaccessibility. A web-based adaptation of RPT (e-RPT) could address these limitations by providing a more cost-effective and accessible delivery method for mental health care in this population. OBJECTIVE:  This study protocol aims to establish the first academic e-RPT program to address AUD in the general population. The primary objective of this study is to compare the efficacy of e-RPT to face-to-face RPT in decreasing relapse rates. The secondary objective is to assess the effects of e-RPT on quality of life, self-efficacy, resilience, and depressive symptomatology. The tertiary objective is to evaluate the cost-effectiveness of e-RPT compared to face-to-face RPT. METHODS:  Adult participants (n=60) with a confirmed diagnosis of AUD will be randomly assigned to receive 10 sessions of e-RPT or face-to-face RPT. e-RPT will consist of 10 predesigned modules and homework with asynchronous, personalized feedback from a therapist. Face-to-face RPT will comprise 10 one-hour face-to-face sessions with a therapist. The predesigned modules and the face-to-face sessions will present the same content and structure. Self-efficacy, resilience, depressive symptomatology, and alcohol consumption will be measured through various questionnaires at baseline, amid treatment, and at the end of treatment. RESULTS:  Participant recruitment is expected to begin in October 2022 through targeted advertisements and physician referrals. Completed data collection and analysis are expected to conclude by October 2023. Outcome data will be assessed using linear and binomial regression (for continuous and categorical outcomes, respectively). Qualitative data will be analyzed using thematic analysis methods. CONCLUSIONS:  This study will be the first to examine the effectiveness of e-RPT compared to face-to-face RPT. It is posited that web-based care can present benefits in terms of accessibility and affordability compared to traditional face-to-face psychotherapy. TRIAL REGISTRATION: ClinicalTrials.gov NCT05579210; https://clinicaltrials.gov/ct2/show/NCT05579210. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/44694.

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.