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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6394428 | PMC |
| http://dx.doi.org/10.1007/s40596-018-1007-2 | 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.
Developing a Machine Learning-Based Automated Patient Engagement Estimator for Telehealth: Algorithm Development and Validation Study.
JMIR Form Res
January 2025
Department of Computer Science, Purdue University, West Lafayett, IN, United States.
Background: Patient engagement is a critical but challenging public health priority in behavioral health care. During telehealth sessions, health care providers need to rely predominantly on verbal strategies rather than typical nonverbal cues to effectively engage patients. Hence, the typical patient engagement behaviors are now different, and health care provider training on telehealth patient engagement is unavailable or quite limited.
View Article and Find Full Text PDFResponses of biological characteristics and detoxification enzymes in the fall armyworm to methoxyfenozide stress.
J Econ Entomol
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Hubei Engineering Technology Center of Forewarning and Management of Agricultural and Forestry Pests, Yangtze University, Jingzhou 434000, PR China.
Methoxyfenozide is an insecticide with a unique mode of action on the insect ecdysone receptor and has been registered for the control of insect pests all over the world. In the present work, Spodoptera frugiperda was exposed to sublethal and lethal concentrations of methoxyfenozide to determine its impact on specific biological traits, metabolic enzyme activity, and the expression of detoxification enzymes. The result showed that 72-h posttreatment with LC50 and LC70 of methoxyfenozide significantly reduced the fecundity (eggs/female) of the F0 generation compared to those of the control group.
View Article and Find Full Text PDFReflections of Foster Youth Engaging in the Co-Design of Digital Mental Health Technology: Duoethnography Study.
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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 PDFMachine learning models for predicting postoperative peritoneal metastasis after hepatocellular carcinoma rupture: a multicenter cohort study in China.
Oncologist
January 2025
Department of Hepatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
Background: Peritoneal metastasis (PM) after the rupture of hepatocellular carcinoma (HCC) is a critical issue that negatively affects patient prognosis. Machine learning models have shown great potential in predicting clinical outcomes; however, the optimal model for this specific problem remains unclear.
Methods: Clinical data were collected and analyzed from 522 patients with ruptured HCC who underwent surgery at 7 different medical centers.
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