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Machine Learning for the Early Prediction of Delayed Cerebral Ischemia in Patients With Subarachnoid Hemorrhage: Systematic Review and Meta-Analysis.
J Med Internet Res
January 2025
Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Background: Delayed cerebral ischemia (DCI) is a primary contributor to death after subarachnoid hemorrhage (SAH), with significant incidence. Therefore, early determination of the risk of DCI is an urgent need. Machine learning (ML) has received much attention in clinical practice.
View Article and Find Full Text PDFExploring 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.
Psychological and Behavioral Insights From Social Media Users: Natural Language Processing-Based Quantitative Study on Mental Well-Being.
JMIR Form Res
January 2025
Smith School of Business, Queen's University, Kingston, ON, Canada.
Background: Depression significantly impacts an individual's thoughts, emotions, behaviors, and moods; this prevalent mental health condition affects millions globally. Traditional approaches to detecting and treating depression rely on questionnaires and personal interviews, which can be time consuming and potentially inefficient. As social media has permanently shifted the pattern of our daily communications, social media postings can offer new perspectives in understanding mental illness in individuals because they provide an unbiased exploration of their language use and behavioral patterns.
View Article and Find Full Text PDFDevelopment of an eHealth Intervention Including Self-Management for Reducing Sedentary Time in the Transition to Retirement: Participatory Design Study.
JMIR Form Res
January 2025
Division of Psychology, School of Health, Care and Social Welfare, Mälardalen University, Västerås/Eskilstuna, Sweden.
Background: Having a great amount of sedentary time is common among older adults and increases with age. There is a strong need for tools to reduce sedentary time and promote adherence to reduced sedentary time, for which eHealth interventions have the potential to be useful. Interventions for reducing sedentary time in older adults have been found to be more effective when elements of self-management are included.
View Article and Find Full Text PDFIdentifying Fraudulent Responses in a Study Exploring Delivery Options for Pregnancies Impacted by Gestational Diabetes: Lessons Learned From a Web-Based Survey.
J Med Internet Res
January 2025
Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Current literature is unclear on the safety and optimal timing of delivery for pregnant individuals with gestational diabetes mellitus, which inspired our study team to conduct a web-based survey study exploring patient and provider opinions on delivery options. However, an incident of fraudulent activity with survey responses prompted a shift in the focus of the research project. Unfortunately, despite the significant rise of web-based surveys used in medical research, there remains very limited evidence on the implications of and optimal methods to handle fraudulent web-based survey responses.
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