Objective: Computerized cognitive-behavioural therapy (CCBT) may enhance older adults' access to evidence-based depression treatment. Our objective was to determine the extent to which adults aged 65 years and older are represented in existing studies of CCBT for depression and describe available data on recruitment, retention, and outcomes.
Methods: We retrieved all controlled and uncontrolled trials of CCBT for depression published between 2000 and 2010. We obtained data on older adults via the article text or correspondence with authors.
Results: Older adults comprised approximately 3% of study participants in reviewed studies. Authors reported that older participants may be less likely than younger adults to drop out, but more likely to experience technical challenges.
Conclusions: Older adults are under-represented in studies of CCBT for depression.
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http://dx.doi.org/10.1111/j.2044-8260.2012.02038.x | DOI Listing |
J Psychiatr Pract
November 2024
Department of Psychiatry and Behavioral Sciences, University of Louisville School of Medicine, Louisville, KY.
Purpose Of Review: Provider shortages and other barriers to traditional mental health care have led to the development of technology-based services designed to enhance access and improve the efficiency and convenience of treatment. We reviewed research on computer-assisted cognitive behavior therapy (CCBT) and mobile mental health applications to assess the effectiveness of these methods of delivering or augmenting treatment, evaluating patient and provider uptake, and making recommendations on the clinical use of these tools in the treatment of depression and anxiety.
Results: Research on CCBT has found solid evidence for efficacy when the use of a therapeutic computer program is supported by a clinician or other helping professional.
Clin Psychol Psychother
December 2024
Lyssn.io, Seattle, Washington, USA.
Although clinician-supported computer-assisted cognitive-behaviour therapy (CCBT) is well established as an effective treatment for depression and anxiety, less is known about the specific interventions used during coaching sessions that contribute to outcomes. The current study used artificial intelligence (AI) to identify specific components of clinician-supported CCBT and correlated those scores with therapy outcomes. Data from a randomized clinical trial comparing clinician-supported CCBT with treatment as usual in a primary care setting were utilized.
View Article and Find Full Text PDFJAMA Netw Open
November 2024
Department of Psychiatry and Behavioral Sciences, University of Louisville School of Medicine, Louisville, Kentucky.
Importance: Approximately 1 in 5 adults are diagnosed with depression in their lifetime. However, less than half receive help from a health professional, with the treatment gap being worse for individuals with socioeconomic disadvantage. Computer-assisted cognitive behavioral therapy (CCBT) is an effective and convenient strategy to treat depression; however, its cost-effectiveness in a sociodemographically diverse population remains unknown.
View Article and Find Full Text PDFPilot Feasibility Stud
March 2024
Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK.
Background: A serious game called SPARX (Smart, Positive, Active, Realistic, X-factor thoughts), originally developed in New Zealand and incorporating cognitive behavioural therapy (CBT) principles, has been shown to help reduce symptoms of depression and anxiety in adolescents with mild to moderate depression in studies undertaken in Australasia. However, SPARX has never been trialled in the United Kingdom (UK), and there have been issues relating to low engagement when it has been used in a real-world context.
Aims: To conduct the first pilot and feasibility randomised controlled trial (RCT) in England to explore the use of SPARX in different settings.
Front Psychiatry
February 2024
School of Electronic Science and Technology, Hainan University, Haikou, China.
Introduction: Depression is a prevalent mental illness that is primarily diagnosed using psychological and behavioral assessments. However, these assessments lack objective and quantitative indices, making rapid and objective detection challenging. In this study, we propose a novel method for depression detection based on eye movement data captured in response to virtual reality (VR).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!