ChatGPT, an advanced natural language processing model, holds significant promise in diabetes self-management and education. ChatGPT excels in providing personalized educational experiences by tailoring information to meet individual patient needs and preferences. It aids patients in developing self-management skills and strategies, fostering proactive disease management. Additionally, ChatGPT addresses healthcare access disparities by enabling patients to access educational resources irrespective of their geographic location or physical limitations. However, it is important to acknowledge and address the deficiencies of ChatGPT, such as its limited medical expertise, contextual understanding, and emotional support capabilities. Strategies for optimizing ChatGPT include regular training and updating, integration of healthcare professionals' expertise, improvement in contextual comprehension, and enhancing emotional support. By addressing these limitations and striking a balance between the benefits and limitations, ChatGPT can play a significant role in empowering patients to better understand and manage diabetes. Further research and development are needed to refine ChatGPT's capabilities and address ethical considerations, but its integration in patient education holds the potential to transform healthcare delivery and create a more informed and engaged patient population.
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http://dx.doi.org/10.1007/s10439-023-03317-8 | DOI Listing |
Midwifery
December 2024
Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.
Problem And Background: Gestational diabetes mellitus (GDM) is a common medical complication of pregnancy, and the emerging evidence demonstrates how GDM online communities have a positive impact on promoting self-management and improving outcomes. Further analysis of such groups can increase understanding of how peer support in GDM online communities is enabled and enacted.
Aim: To examine women's experiences of GDM online communities on Facebook, their motivations for participation, and perceptions of dynamics within the community.
J Am Pharm Assoc (2003)
January 2025
Arizona Department of Health Services, Phoenix, AZ, USA. Electronic address:
Background: Pharmacist-provided Medication Therapy Management (MTM) services have demonstrated improved clinical outcomes for patients. MTM services could incorporate additional lifestyle and wellness counseling to potentially enhance healthcare for underserved patients.
Objective: To report the outcomes of a new pharmacist-provided MTM lifestyle and wellness counseling program for underserved rural Arizonans with diabetes and/or hypertension.
Health Place
January 2025
Department of Forest Resources Management, Faculty of Forestry, University of British Columbia, Vancouver, V6T 1Z4, Canada. Electronic address:
The engagement of senior citizens with urban nature has been shown to provide multiple health benefits and mitigate health issues associated with demographic aging. This review utilized the PRISMA methodology to systematically analyze the relationship between monitoring tools, seniors' behaviors in urban nature, and influencing factors. The main findings are as follows: (1) 4 main types, including self-reports, on-site observations, sensors, and third-party data, and 24 sub-types of measurement tools: ranging from questionnaires to crowdsourced imagery services.
View Article and Find Full Text PDFPLoS One
January 2025
Endocrine Unit, Department of Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
Gestational Weight Gain (GWG) modulates pregnancy outcomes and long-term offspring metabolic health. The 2009 Institute of Medicine (IOM) GWG recommendations have largely been validated in Caucasian and mono-ethnic East Asian cohorts. Asians are at higher metabolic risk at a lower body mass index (BMI), and this has prompted the World Health Organization (WHO) to identify lower BMI cut-offs for risk evaluation amongst Asians.
View Article and Find Full Text PDFDiabetes Technol Ther
January 2025
Children's Mercy Kansas City, Endocrinology, Kansas City, Missouri, USA.
To use electronic health record (EHR) data to develop a scalable and transferrable model to predict 6-month risk for diabetic ketoacidosis (DKA)-related hospitalization or emergency care in youth with type 1 diabetes (T1D). To achieve a sharable predictive model, we engineered features using EHR data mapped to the T1D Exchange Quality Improvement Collaborative's (T1DX-QI) data schema used by 60+ U.S.
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