JMIR Aging
Using technological innovations and data science to inform and improve health care services and health outcomes for older adults.
Editor-in-Chief:
Yun Jiang, PhD, MS, RN, FAMIA, University of Michigan School of Nursing, USA; and Jinjiao Wang, PhD, RN, MPhil, University of Texas Health Science Center, USA
Impact Factor 4.8 CiteScore 6.6
Recent Articles
Undiagnosed cognitive impairment poses a global challenge, prompting recent interest in ultra-brief screening questionnaires (comprising <2–3 items) to efficiently identify individuals needing further evaluation. However, evidence on ultra-brief questionnaires remains limited, particularly regarding their validity across diverse literacy levels.
Falls are one of the leading causes of injury or death among older adults. Falls occurring in individuals during hospitalization, as an adverse event, are a key concern for healthcare institutions. Identifying older adults at high risk of falls in clinical settings enables early interventions, thereby reducing the incidence of falls.
The theory of complexity in aging indicates that the complexity of sensor-derived physiological and behavioral signals reflects an older adult’s adaptive capacity and, in turn, their frailty. Smart homes with ambient sensors offer a unique opportunity to longitudinally explore the complexity of older adults’ indoor movement in a real-world setting. Here, we introduce a computational method to estimate behavior complexity from sensor data. We further conduct a multiple-methods case series to explore the relationship between entropy-measured smart home data complexity and older adult frailty.
Mild cognitive impairment (MCI) is an intermediate state between normal aging and dementia, characterized by subjective cognitive decline and objective memory impairment. Cognitive training has consistently shown short-term benefits for individuals with MCI, but evidence on the long-term effectiveness is extremely limited. Given the progressive nature of MCI and the need for sustainable strategies to delay cognitive decline, research on the long-term impact of cognitive training is necessary and timely. Mobile-based platforms offer a promising solution by enhancing accessibility and adherence, but their durability of effect over extended periods remains underexplored.
As people with HIV (PWH) age, more than half are now over 50 years old and face approximately a 60% higher risk of developing dementia compared to the general population. In recent years, the application of artificial intelligence, particularly machine learning, combined with the growing availability of large datasets has opened new avenues for developing prediction models that improve dementia detection, monitoring, and management.
AI has demonstrated superior diagnostic accuracy compared to medical practitioners, highlighting its growing importance in healthcare. SMART-Pred (Shiny Multi-Algorithm R Tool for Predictive Modeling) is an innovative AI-based application for Alzheimer's disease (AD) prediction using handwriting analysis.
The health and economic burden of dementia has led the World Health Organization to recognize it as a public health priority. Although there currently does not exist a cure for dementia, there are multiple interventions aimed at preventing the risk of dementia and improving the quality of life of people with dementia. Voice assistants (VAs), particularly those using large language models (LLMs), have emerged as promising tools to deliver these interventions to older adults due to their accessible and natural interface.
