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BACKGROUND: Generalized anxiety disorder (GAD) is a prevalent anxiety disorder characterized by uncontrollable worry, trouble sleeping, muscle tension, and irritability. Cognitive behavioural therapy (CBT) is one of the first-line treatments that has demonstrated high efficacy in reducing symptoms of anxiety. Electronically delivered CBT (e-CBT) has been a promising adaptation of in-person treatment, showing comparable efficacy with increased accessibility and scalability. Finding further scalable interventions that can offer benefits to patients requiring less intensive interventions can allow for better resource allocation. Some studies have indicated that weekly check-ins can also lead to improvements in GAD symptoms. However, there is a lack of research exploring the potential benefits of online check-ins for patients with GAD. OBJECTIVE: This study aims to investigate the effects of weekly online asynchronous check-ins on patients diagnosed with GAD and compare it with a group receiving e-CBT. METHODS: Participants (n e-CBT = 45; n check-in = 51) with GAD were randomized into either an e-CBT or a mental health check-in program for 12 weeks. Participants in the e-CBT program completed pre-designed modules and homework assignments through a secure online delivery platform where they received personalized feedback from a trained care provider. Participants in the mental health check-in condition had weekly asynchronous messaging communication with a care provider where they were asked structured questions with a different weekly theme to encourage conversation. RESULTS: Both treatments demonstrated statistically significant reductions in GAD-7-item questionnaire (GAD-7) scores over time, but when comparing the groups there was no significant difference between the treatments. The number of participants who dropped out and baseline scores on all questionnaires were comparable for both groups. CONCLUSIONS: The findings support the effectiveness of e-CBT and mental health check-ins for the treatment of GAD.
Comparing the Effectiveness of Electronically Delivered Therapy (e-CBT) to Weekly Online Mental Health Check-ins for Generalized Anxiety DisorderA Randomized Controlled TrialPlain Language SummaryGeneralized anxiety disorder (GAD) is a prevalent psychiatric condition that leads to symptoms like uncontrollable worry, trouble sleeping, muscle tension, and irritability. Cognitive behavioural therapy (CBT) is a common psychotherapy used for GAD since it has been shown to reduce symptoms. However, traditional CBT that is in person can have barriers such as being inaccessible and costly, and therefore electronically delivered CBT (e-CBT) is a viable alternative since previous studies have shown its efficacy in reducing symptoms and being similar compared to face-to-face CBT. Previous studies have also shown reductions in GAD symptomology through the use of checking in on people and their mental health. Therefore, this study aimed to compare e-CBT to a check-in condition and had a total of 45 individuals in e-CBT and 51 participants in the check-in condition. Participants in the e-CBT condition completed 12 weeks of predesigned e-CBT modules, homework and received personalized feedback from a care provider. In contrast, individuals in the check-in condition completed 12 weeks of unstructured asynchronous messaging with a care provider. Results from the study showed that both the e-CBT and check-in condition demonstrated statistically significant improvements in GAD-7 across time, but when comparing the groups there was no significant difference. The results show the efficacy of e-CBT and checking in on people's mental health to reduce GAD and future research should examine the 2 conditions combined.
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Introduction: Online mental healthcare has gained significant attention due to its effectiveness, accessibility, and scalability in the management of mental health symptoms. Despite these advantages over traditional in-person formats, including higher availability and accessibility, issues with low treatment adherence and high dropout rates persist. Artificial intelligence (AI) technologies could help address these issues, through powerful predictive models, language analysis, and intelligent dialogue with users, however the study of these applications remains underexplored. The following mixed methods review aimed to supplement this gap by synthesizing the available evidence on the applications of AI in online mental healthcare. Method: We searched the following databases: MEDLINE, CINAHL, PsycINFO, EMBASE, and Cochrane. This review included peer-reviewed randomized controlled trials, observational studies, non-randomized experimental studies, and case studies that were selected using the PRISMA guidelines. Data regarding pre and post-intervention outcomes and AI applications were extracted and analyzed. A mixed-methods approach encompassing meta-analysis and network meta-analysis was used to analyze pre and post-intervention outcomes, including main effects, depression, anxiety, and study dropouts. We applied the Cochrane risk of bias tool and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) to assess the quality of the evidence. Results: Twenty-nine studies were included revealing a variety of AI applications including triage, psychotherapy delivery, treatment monitoring, therapy engagement support, identification of effective therapy features, and prediction of treatment response, dropout, and adherence. AI-delivered self-guided interventions demonstrated medium to large effects on managing mental health symptoms, with dropout rates comparable to non-AI interventions. The quality of the data was low to very low. Discussion: The review supported the use of AI in enhancing treatment response, adherence, and improvements in online mental healthcare. Nevertheless, given the low quality of the available evidence, this study highlighted the need for additional robust and high-powered studies in this emerging field. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=443575, identifier CRD42023443575.
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BACKGROUND: Bipolar disorder (BD) is a highly prevalent psychiatric condition that can significantly impact every aspect of a person's life if left untreated. A subtype of BD, bipolar disorder type II (BD-II), is characterized by long depressive episodes and residual depression symptoms, with short-lived hypomanic episodes. Medication and psychotherapy, such as cognitive behavioral therapy (CBT), are the main treatment options for BD-II. CBT specific for BD-II involves the recognition of warning signs, potentially triggering stimuli, and the development of coping skills to increase euthymic periods and improve global functioning. However, access to in-person CBT may be limited by several barriers, including low availability, high costs, and geographical limitations. Thus, web-based adaptations of CBT (e-CBT) have become a promising solution to address these treatment barriers. Nevertheless, e-CBT for the treatment of BD-II remains understudied. OBJECTIVE: The proposed study aims to establish the first e-CBT program specific for the treatment of BD-II with residual depressive symptoms. The primary objective of this study will be to determine the effect of e-CBT in managing BD symptomatology. The secondary objective will be to assess the effects of this e-CBT program on quality of life and resilience. The tertiary objective will involve gathering user feedback using a posttreatment survey to support the continuous improvement and optimization of the proposed program. METHODS: Adult participants (N=170) with a confirmed diagnosis of BD-II experiencing residual depressive symptoms will be randomly assigned to either the e-CBT and treatment as usual (TAU; n=85) group or the TAU (n=85) control group. Participants in the control group will be able to participate in the web-based program after the first 13 weeks. The e-CBT program will consist of 13 weekly web-based modules designed following a validated CBT framework. Participants will complete module-related homework and receive asynchronous personalized feedback from a therapist. TAU will consist of standard treatment services conducted outside of this research study. Depression and manic symptoms, quality of life, and resiliency will be assessed using clinically validated symptomatology questionnaires at baseline, week 6, and week 13. RESULTS: The study received ethics approval in March 2020, and participant recruitment is expected to begin in February 2023 through targeted advertisements and physician referrals. Data collection and analysis are expected to conclude by December 2024. Linear and binomial regression (continuous and categorical outcomes, respectively) will be conducted along with qualitative interpretive methods. CONCLUSIONS: The findings will be the first on the effectiveness of delivering e-CBT for patients with BD-II with residual depressive symptoms. This approach can provide an innovative method to address barriers to in-person psychotherapy by increasing accessibility and decreasing costs. TRIAL REGISTRATION: ClinicalTrials.gov NCT04664257; https://clinicaltrials.gov/ct2/show/NCT04664257. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/46157.
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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.
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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.
