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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9499383 | PMC |
| http://dx.doi.org/10.31478/202206e | DOI Listing |
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Exploring the Needs and Preferences of Users and Parents to Design a Mobile App to Deliver Mental Health Peer Support to Adolescents With Type 1 Diabetes: Qualitative Study.
JMIR Diabetes
January 2025
Research Institute, BC Children's Hospital, Vancouver, BC, Canada.
Background: Beyond physical health, managing type 1 diabetes (T1D) also encompasses a psychological component, including diabetes distress, that is, the worries, fears, and frustrations associated with meeting self-care demands over the lifetime. While digital health solutions have been increasingly used to address emotional health in diabetes, these technologies may not uniformly meet the unique concerns and technological savvy across all age groups.
Objective: This study aimed to explore the mental health needs of adolescents with T1D, determine their preferred modalities for app-based mental health support, and identify desirable design features for peer-delivered mental health support modeled on an app designed for adults with T1D.
Barriers and Facilitators of User Engagement With Digital Mental Health Interventions for People With Psychosis or Bipolar Disorder: Systematic Review and Best-Fit Framework Synthesis.
JMIR Ment Health
January 2025
Division of Psychology and Mental Health, University of Manchester, Manchester, United Kingdom.
Background: Digital mental health interventions (DMHIs) to monitor and improve the health of people with psychosis or bipolar disorder show promise; however, user engagement is variable, and integrated clinical use is low.
Objective: This prospectively registered systematic review examined barriers and facilitators of clinician and patient engagement with DMHIs, to inform implementation within real-world settings.
Methods: A systematic search of 7 databases identified empirical studies reporting qualitative or quantitative data about factors affecting staff or patient engagement with DMHIs aiming to monitor or improve the mental or physical health of people with psychosis or bipolar disorder.
Reflections of Foster Youth Engaging in the Co-Design of Digital Mental Health Technology: Duoethnography Study.
JMIR Form Res
January 2025
Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, United States.
Background: Current research on digital applications to support the mental health and well-being of foster youth is limited to theoretical applications for transition-aged foster youth and support platforms developed without intentional input from foster youth themselves. Centering the lived expertise of foster youth in digital solutions is crucial to dismantling barriers to care, leading to an increase in service access and improving mental health outcomes. Co-design centers the intended end users during the design process, creating a direct relationship between potential users and developers.
View Article and Find Full Text PDFA randomised cross-over trial assessing the impact of AI-generated individual feedback on written online assignments for medical students.
Med Teach
January 2025
Institute of Medical Education, University Hospital Bonn, Bonn, Germany.
Purpose: Self-testing has been proven to significantly improve not only simple learning outcomes, but also higher-order skills such as clinical reasoning in medical students. Previous studies have shown that self-testing was especially beneficial when it was presented with feedback, which leaves the question whether an immediate and personalized feedback further encourages this effect. Therefore, we hypothesised that individual feedback has a greater effect on learning outcomes, compared to generic feedback.
View Article and Find Full Text PDFChallenges in standardizing preimplantation kidney biopsy assessments and the potential of AI-Driven solutions.
Curr Opin Nephrol Hypertens
January 2025
Department of Microbiology, Immunology and Transplantation, KU Leuven.
Purpose Of Review: This review explores the variability in preimplantation kidney biopsy processing methods, emphasizing their impact on histological interpretation and allocation decisions driven by biopsy findings. With the increasing use of artificial intelligence (AI) in digital pathology, it is timely to evaluate whether these advancements can overcome current challenges and improve organ allocation amidst a growing organ shortage.
Recent Findings: Significant inconsistencies exist in biopsy methodologies, including core versus wedge sampling, frozen versus paraffin-embedded processing, and variability in pathologist expertise.
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