JMIR Aging

The COVID-19 pandemic highlights how restrictions on in-person interactions within long-term care homes (LTCHs) severely compromised social connectedness among older adults and their families. Post-pandemic, despite policies changes supporting greater in-person family engagement, frequent outbreaks continue to disrupt face-to-face interactions and factors such as geography, life circumstances, and health can constrain family members’ ability to make regular in-person visits. Research suggests that web-based videoconferencing technology (WVT) may be a practical solution to help older adults within LTCH to maintain social connection in the absence of physical gathering. However, increased understanding of end user experiences is lacking and more information on LTCHs readiness to support and sustain WVT will be needed if this modality is to be successfully and widely implemented.

Background：As the global aging population accelerates, mobile health applications (mHealth apps) have emerged as critical tools in elderly health management. However, the promotion of mHealth apps has faced multiple obstacles, including insufficient technological adaptation to aging, digital resistance, and ageism. The impact of ageism on technology usage experiences among older adults is influenced by mechanisms such as stereotypes and biases. Notably, extant research has not adequately explored the subjective experiences of older adults in the context of mHealth app usage scenarios.

Conventional methods of functional assessment include subjective self- or informant report, which may be biased by personal characteristics, cognitive abilities, and lack of standardization (eg, influence of idiosyncratic task demands). Traditional performance-based assessments offer some advantages over self- or informant reports but are time-consuming to administer and score.

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.

Cardiovascular disease (CVD) is the main cause of death in middle-aged and elderly people in China.The interplay between sarcopenia and insulin resistance (IR) in driving CVD risk has not been fully understood, particularly regarding sarcopenia severity and IR heterogeneity.

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.

Dementia presents significant challenges for informal caregivers. A gap remains in technology-driven personalized support tailored to caregivers' needs.

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.

Older adults make up the largest proportion of nonusers of the internet. With the increasing digitalization of services, it is important to identify what interventions are effective at reducing digital exclusion in older adults.