Digital health technologies, apps, and informatics for patient education, medicine and nursing, preventative interventions, and clinical care / home care for elderly populations
Editor-in-Chief: Yun Jiang, PhD, MS, RN, FAMIA, Assistant Professor, Department of Systems, Populations, and Leadership, University of Michigan School of Nursing
Jinjiao Wang, PhD, RN, MPhil, Assistant Professor, Postdoctoral Program Director, Elaine C. Hubbard Center for Nursing Research on Aging, School of Nursing, University of Rochester
Impact Factor 2023
Yun Jiang, PhD, MS, RN, FAMIA, Assistant Professor, Department of Systems, Populations, and Leadership, University of Michigan School of Nursing
JMIR Aging (JA) is an open access journal focusing on technologies, medical devices, apps, engineering, informatics applications and patient education for medicine and nursing, education, preventative interventions and clinical care / home care for elderly populations. In addition, aging-focused big data analytics using data from electronic health record systems, health insurance databases, federal reimbursement databases (e.g. U.S. Medicare and Medicaid), and other large databases are also welcome.
This journal is read by clinicians, nurses/allied health professionals, informal caregivers and patients alike and have (as all JMIR journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies. We publish original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews).
Assessing cognitive constructs affected by Alzheimer disease, such as processing speed (PS), is important to screen for potential disease and allow for early detection. Digital PS assessments have been developed to provide widespread, efficient cognitive testing, but all have been validated only based on the correlation between test scores. Best statistical practices dictate that concurrent validity should be assessed for agreement or equivalence rather than using correlation alone.
Amid a worldwide pandemic in the setting of an era of rapidly developing technologies, we turn now to the novel and exciting endeavor of pioneering the metaverse. Described as the conglomeration of augmented reality, virtual reality, and artificial intelligence, the metaverse has widespread applications in multiple settings, including revolutionizing health care. It also holds the potential to transform geriatric medicine, introducing new dimensions through which we can prevent social isolation, encourage health and well-being, and offer a new dimension through which we manage chronic disease. Although it is still a futuristic and novel technology, the metaverse’s realization may indeed be closer than we think.
The increased use of wearable sensor technology has highlighted the potential for remote telehealth services such as rehabilitation. Telehealth services incorporating wearable sensors are most likely to appeal to the older adult population in remote and rural areas, who may struggle with long commutes to clinics. However, the usability of such systems often discourages patients from adopting these services.
During the COVID-19 epidemic, opportunities for social interaction and physical activity among older people are decreasing, which may have a negative impact on their health. As a solution, a web-based group exercise program provided through a videoconferencing platform would be useful. As a web-based exercise program that older adults can easily, safely, and enjoyably perform at home, we developed a short-duration, light-intensity aerobic dance exercise program. Before studying the effectiveness of this exercise program, its characteristics, such as feasibility, safety, enjoyment, and system usability, should be examined among older adults.
Virtual reality (VR) is a promising and cost-effective tool that has the potential to reduce the prevalence of falls and locomotor impairments in older adults. However, we believe that existing VR-based approaches to prevent falls do not mimic the full breadth of perceptual, cognitive, and motor demands that older adults encounter in daily life. Researchers have not yet fully leveraged VR to address affective factors related to fall risk, and how stressors such as anxiety influence older adult balance and real-world falls. In this perspective paper, we propose developing VR-based tools that replicate the affective demands of real-world falls (eg, crossing the street) to enhance fall prevention diagnostics and interventions by capturing the underlying processes that influence everyday mobility. An effort to replicate realistic scenarios that precipitate falls in VR environments will inform evidence-based diagnostics and individualize interventions in a way that could reduce falls in older adults in daily life.
Two fields of research and development targeting the needs of the aging population of the world are flourishing, successful aging and assistive information and communication technologies (A-ICTs). The risks of ageist stereotypes emerging from how we communicate in both discourses are long known. This raises questions about whether using specific age criteria in the context of “aging deficits” can bias participation in, or compliance with, the research process by older adults who try to avoid age-related stigma.
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