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: (Acting) Tiffany Leung, MD, MPH, FACP, FAMIA, FEFIM, Adjunct Clinical Associate Professor, Department of Internal Medicine, Southern Illinois University School of Medicine, USA; Scientific Editor, JMIR Publications
Impact Factor 2023
(Acting) Tiffany Leung, MD, MPH, FACP, FAMIA, FEFIM, Adjunct Clinical Associate Professor, Department of Internal Medicine, Southern Illinois University School of Medicine, USA; Scientific Editor, JMIR Publications
JMIR Aging (JA, Editor-in-chief (Acting): Tiffany Leung, MD, MPH, FAMIA, Adjunct Clinical Associate Professor, Department of Internal Medicine, Southern Illinois University School of Medicine, USA; Scientific Editor, JMIR Publications; Founding Editor-in-chief: Jing Wang, PhD, MPH, RN, FAAN, Dean and Professor, Florida State University College of Nursing, Tallahassee, FL, USA) 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).
Heart failure is a leading cause of death among older adults. Digital health can increase access to and awareness of palliative care for patients with advanced heart failure and their caregivers. However, few palliative care digital interventions target heart failure or patients’ caregivers, family, and friends, termed here as the social convoy. To address this need, the Social Convoy Palliative Care (Convoy-Pal) mobile intervention was developed to deliver self-management tools and palliative care resources to older adults with advanced heart failure and their social convoys.
Story recall is a simple and sensitive cognitive test that is commonly used to measure changes in episodic memory function in early Alzheimer disease (AD). Recent advances in digital technology and natural language processing methods make this test a candidate for automated administration and scoring. Multiple parallel test stimuli are required for higher-frequency disease monitoring.
Identifying caregiver availability, particularly for patients with dementia or those with a disability, is critical to informing the appropriate care planning by the health systems, hospitals, and providers. This information is not readily available, and there is a paucity of pragmatic approaches to automatically identifying caregiver availability and type.
Speech data for medical research can be collected noninvasively and in large volumes. Speech analysis has shown promise in diagnosing neurodegenerative disease. To effectively leverage speech data, transcription is important, as there is valuable information contained in lexical content. Manual transcription, while highly accurate, limits the potential scalability and cost savings associated with language-based screening.
Frail older adults and caregivers need support from their home care teams in making difficult housing decisions, such as whether to remain at home, with or without assistance, or move into residential care. However, home care teams are often understaffed and busy, and shared decision-making training is costly. Nevertheless, overall awareness of shared decision-making is increasing. We hypothesized that distributing a decision aid could be sufficient for providing decision support without the addition of shared decision-making training for home care teams.
Sensor-based remote health monitoring can be used for the timely detection of health deterioration in people living with dementia with minimal impact on their day-to-day living. Anomaly detection approaches have been widely applied in various domains, including remote health monitoring. However, current approaches are challenged by noisy, multivariate data and low generalizability.
More than 6 million people in the United States have Alzheimer disease and related dementias, receiving help from more than 11 million family or other informal caregivers. A range of traditional interventions has been developed to support family caregivers; however, most of them have not been implemented in practice and remain largely inaccessible. While recent studies have shown that family caregivers of people with dementia use Twitter to discuss their experiences, methods have not been developed to enable the use of Twitter for interventions.
Older adults with chronic illnesses or dependency on care who strive to age in place need support and care depending on their illness. Digital technology has enabled the possibility of supporting older adults in their wishes to age in place. However, current studies have mainly focused on the solitary evaluation of individual technologies or on evaluating technologies for specific illnesses.
As the global burden of dementia continues to plague our healthcare systems, efficient, objective, and sensitive tools to detect neurodegenerative disease and capture meaningful changes in everyday cognition are increasingly needed. Emerging digital tools present a promising option to address many drawbacks of current approaches, with contexts of use that include early detection, risk stratification, prognosis, and outcome measurement. However, conceptual models to guide hypotheses and interpretation of results from digital tools are lacking and are needed to sort and organize the large amount of continuous data from a variety of sensors. In this viewpoint, we propose a neuropsychological framework for use alongside a key emerging approach—digital phenotyping. The Variability in Everyday Behavior (VIBE) model is rooted in established trends from the neuropsychology, neurology, rehabilitation psychology, cognitive neuroscience, and computer science literature and links patterns of intraindividual variability, cognitive abilities, and everyday functioning across clinical stages from healthy to dementia. Based on the VIBE model, we present testable hypotheses to guide the design and interpretation of digital phenotyping studies that capture everyday cognition in vivo. We conclude with methodological considerations and future directions regarding the application of the digital phenotyping approach to improve the efficiency, accessibility, accuracy, and ecological validity of cognitive assessment in older adults.
Digital interventions have been shown to be effective for a variety of mental health disorders and problems. However, few studies have examined the effects of digital interventions in older adults; therefore, little is known about how older adults engage with or benefit from these interventions. Given that adoption rates for technology among people aged ≥65 years remain substantially lower than in the general population and that approximately 20% of older adults are affected by mental health disorders, research exploring whether older adults will use and benefit from digital interventions is needed.
Robot pets may assist in the challenges of supporting an aging population with growing dementia prevalence. Prior work has focused on the impacts of the robot seal Paro on older adult well-being, but recent studies have suggested the good acceptability and implementation feasibility of more affordable devices (Joy for All [JfA] cats and dogs).
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