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Background: Multiple chronic conditions (multimorbidity) are becoming more prevalent among aging populations. Digital health technologies have the potential to assist in the self-management of multimorbidity, improving the awareness and monitoring of health and well-being, supporting a better understanding of the disease, and encouraging behavior change.
Objective: The aim of this study was to analyze how 60 older adults (mean age 74, SD 6.4; range 65-92 years) with multimorbidity engaged with digital symptom and well-being monitoring when using a digital health platform over a period of approximately 12 months.
Methods: Principal component analysis and clustering analysis were used to group participants based on their levels of engagement, and the data analysis focused on characteristics (eg, age, sex, and chronic health conditions), engagement outcomes, and symptom outcomes of the different clusters that were discovered.
Results: Three clusters were identified: the typical user group, the least engaged user group, and the highly engaged user group. Our findings show that age, sex, and the types of chronic health conditions do not influence engagement. The 3 primary factors influencing engagement were whether the same device was used to submit different health and well-being parameters, the number of manual operations required to take a reading, and the daily routine of the participants. The findings also indicate that higher levels of engagement may improve the participants' outcomes (eg, reduce symptom exacerbation and increase physical activity).
Conclusions: The findings indicate potential factors that influence older adult engagement with digital health technologies for home-based multimorbidity self-management. The least engaged user groups showed decreased health and well-being outcomes related to multimorbidity self-management. Addressing the factors highlighted in this study in the design and implementation of home-based digital health technologies may improve symptom management and physical activity outcomes for older adults self-managing multimorbidity.
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http://dx.doi.org/10.2196/46287 | DOI Listing |
Alzheimers Res Ther
September 2025
Motor Control and Learning Group, Institute of Human Movement Sciences and Sport, Department of Health Sciences and Technology, ETH Zurich, Leopold-Ruzicka-Weg 4, Zurich, 8093, Switzerland.
Int J Oral Maxillofac Surg
September 2025
School of Dentistry, Department of Health Science, Magna Graecia University of Catanzaro, Catanzaro, Italy.
This study was performed to evaluate the amount of bone implant engagement (BIE) of zygomatic implants (ZIs) at the malar bone level and its correlation with the ZAGA classification (zygoma anatomy-guided approach). One hundred ZIs placed in 32 patients with severe maxillary atrophy using a fully digital protocol were assessed: 80 placed in pairs (40 anterior (AI), 40 posterior (PI)) and 20 as single ZIs (SI). The ZAGA classification was determined preoperatively.
View Article and Find Full Text PDFJ Affect Disord
September 2025
Department of Medicine, Kyung Hee University College of Medicine, Seoul, South Korea; Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; D
Background: Major depressive disorder (MDD), anxiety disorders, and self-harm are substantial contributors to the global disease burden, exacerbated by the COVID-19 pandemic.
Methods: We used Global Burden of Diseases Study (GBD) 2021 to estimate global, regional, and national prevalence, mortality, and disability-adjusted life years (DALYs) for MDD, anxiety disorders, and self-harm from 1990 to 2021. Annual percentage changes were calculated for pre-pandemic (1990-2019) and pandemic (2019-2021) periods.
Int J Biol Macromol
September 2025
College of pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China; Shandong Key Laboratory of Digital Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China. Electronic address:
Hepatocellular carcinoma (HCC) poses a serious threat to human life and health. Nowadays, liver-targeting drug delivery systems have been proven as a promising strategy in treating HCC. Angelica sinensis polysaccharide (ASP), a plant polysaccharide with good biocompatibility, has excellent aqueous solubility and intrinsic liver-targeted capability.
View Article and Find Full Text PDFTrends Cardiovasc Med
September 2025
Department of Cardiology, NYU Langone Health and NYU School of Medicine, New York, NY.
Cardio-obstetrics is a growing sub-specialty focused on the prevention, diagnosis, and management of high-risk pregnancies in women with cardiac disease, a condition affecting 1-4% of pregnancies and a leading cause of indirect maternal mortality in developed countries. The prevalence of maternal cardiac disease is rising due to factors such as increasing maternal age, obesity, comorbidities, and improved survival of individuals with congenital heart disease. Artificial intelligence (AI) is increasingly used in cardiology to enhance early diagnosis, risk stratification, and treatment planning, offering promising tools to support the diagnostic and therapeutic complexities of maternal cardiac disease.
View Article and Find Full Text PDF