The purpose of this preliminary study was to determine smartphone usage, expressed level of interest, and intent to use mHealth apps among adults with comorbid type 2 diabetes (T2D) and depression. A convenience sample of adults (N=35) completed a Demographic and Mobile App Survey and the CESD-R-10. A majority reported using mobile apps (n=23, 65.7%) and felt comfortable or very comfortable using mobile apps (n=14, 46.7%). However, few respondents used a health app (n=6, 17.1%) or a diabetes-specific app for diabetes management (n=3, 8.6%). Adjusted, age and education were the two variables that independently impacted app use; those aged less than 55 years as well as those with a graduate degree were more likely to use apps. Being younger and having an advanced degree increased the odds of using a diabetes-specific app. The findings suggest that adults with T2D are amenable to using mHealth apps to manage diabetes.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1177/0193945920988791 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A Medication Management App (Smart-Meds) for Patients After an Acute Coronary Syndrome: Pilot Pre-Post Mixed Methods Study.
JMIR Cardio
January 2025
Medicine Faculty, University of Geneva, Geneva, Switzerland.
Background: Medication nonadherence remains a significant challenge in the management of chronic conditions, often leading to suboptimal treatment outcomes and increased health care costs. Innovative interventions that address the underlying factors contributing to nonadherence are needed. Gamified mobile apps have shown promise in promoting behavior change and engagement.
View Article and Find Full Text PDFHeart rate variability biofeedback in a global study of the most common coherence frequencies and the impact of emotional states.
Sci Rep
January 2025
HeartMath Institute, Boulder Creek, CA, 95006, USA.
This global study analyzed data from the largest dataset ever studied in the Heart Rate Variability (HRV) biofeedback field, comprising 1.8 million user sessions collected from users of a mobile app during 2019 and 2020. We focused on HRV Coherence, which is linked to improved emotional stability and cognitive function.
View Article and Find Full Text PDFLiquid-based encapsulation for implantable bioelectronics across broad pH environments.
Nat Commun
January 2025
Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, CT, 06269, USA.
Wearable and implantable bioelectronics that can interface for extended periods with highly mobile organs and tissues across a broad pH range would be useful for various applications in basic biomedical research and clinical medicine. The encapsulation of these systems, however, presents a major challenge, as such devices require superior barrier performance against water and ion penetration in challenging pH environments while also maintaining flexibility and stretchability to match the physical properties of the surrounding tissue. Current encapsulation materials are often limited to near-neutral pH conditions, restricting their application range.
View Article and Find Full Text PDFDevelopment of mobile educational applications designed for nurses: A narrative review.
Nurse Educ Today
January 2025
School of Nursing and Midwifery, Deakin University, Burwood, Victoria 3125, Australia; Centre for Quality and Patient Safety Research in the Institute for Health Transformation, Victoria, Australia.
Objective: To identify and synthesise existing literature about the use of mobile educational applications (apps) designed to enhance the learning experience of nurses and midwives.
Design: A narrative review using a systematic, structured and comprehensive search of the literature.
Data Sources: Medline Complete (EBSCO), CINAHL (EBSCO), ERIC (EBSCO) and Embase (OVID) electronic databases.
Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning.
Comput Biol Med
January 2025
Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia; Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia. Electronic address:
- Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmentation, yet misdiagnoses yield effective medical responses, impacting patient survival rates. Recent technological advancements have popularized deep learning-based medical image analysis, leveraging transfer learning to reuse pre-trained models for various applications.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!