JMIR Aging

Timely medical follow-up after a diagnosis of cognitive impairment, such as mild cognitive impairment (MCI) or dementia, is imperative for initiating appropriate medical treatment and accessing comprehensive care management and psychosocial support. However, many community-dwelling older adults who receive a positive case-finding result default on their medical follow-up appointments. This persistent challenge undermines early detection and active case-finding efforts and increases the risk of early institutionalization. Understanding the determinants is important for developing effective interventions in community-based case-finding.

Older adults face increased crash risk due to age-related declines in cognitive, visual, and physical functioning; yet, many Australians in their 70s are continuing to drive. Web-based platforms are increasingly used to deliver health and mobility information to older adults and may support safer driving; however, existing online resources on driving safety often lack age-specific guidance, have usability limitations, and may not be designed with older adults in mind.

Older adults who have fallen are at an increased risk of future falls. Training cognitive and physical functions simultaneously, known as dual-task (DT) training, has been shown to improve mobility and reduce fall risks. With appropriate digital tools, such as smartphones and mobile apps, it is possible to deliver DT training in unsupervised, home-based settings, thereby increasing accessibility beyond the clinical environment.

Cognitive decline in older adults is often accompanied by subtle motor alterations. Digital handwriting analysis has emerged as a promising noninvasive approach for detecting these changes, but its usefulness in community-based settings remains unclear.

This research letter proposes a novel model design leveraging natively multimodal large language models to identify fall risks and generate visualizations of recommended home environmental modifications, aiming to improve the accessibility and impact of personalized fall prevention advice for older adults. Through a pilot rating study, this work demonstrates that multimodal large language models can generate safe and actionable advice to reduce fall risk in lived spaces of older adults, and also generate realistic edits based on original images. While this concept needs further testing and clinical comparison, it highlights a promising avenue for further innovation of fall prevention tactics.

While older adults’ social media use has been widely studied for its instrumental benefits, such as accessing health information or maintaining family ties, research has largely focused on identity-based platforms that mirror offline social networks, leaving pseudonymous, interest-driven environments such as Reddit underexplored. Although older adults actively participate in these spaces to share personal narratives and engage beyond their existing social roles, the literature has yet to center their own voices, with most existing work focusing on caregivers or younger users discussing older adults rather than older adults speaking for themselves.

Loneliness is a growing public health challenge among older adults and is associated with a wide range of adverse health outcomes. The role of eHealth literacy in shaping the relationship between loneliness and quality of life remains unclear.

With the aging of the global population, preventing the onset of mobility limitations is considered a worldwide public health priority.

Depressive symptoms in older adults are amplified by social isolation and limited access to clinic-based mental health care. Ecological momentary assessment (EMA) enables remote self-monitoring and unobtrusively captures response times (RTs), which may serve as indicators of psychomotor and cognitive functioning.

The risk of falls escalates with advancing age, a consequence of the concomitant degeneration of multiple physiological systems, altered sensory processing capabilities, and reduced postural control. Multisensory integration (MSI) training has been demonstrated to enhance the brain’s processing of multisensory information. However, existing studies show considerable variability in intervention duration and training modalities, limiting comparability across studies and contributing to inconsistent findings.

Maintaining balance is essential for older adults to preserve independence and reduce fall risk. However, empirical evidence linking trunk stability with balance and gait is scarce, partly due to the lack of accessible field tests. Smartphone-based accelerometry field tests offer a promising approach to assess trunk stability outside the laboratory.

Globally, the older population is increasing rapidly, becoming one of the most significant demographic trends of the 21st century. This growth poses important social, health, and technological challenges for societies that must adapt their environments and services to promote independent and healthy aging. In Spain, the population aged 65 years and older reached 18% of the total population in 2020, and projections indicate that this proportion will continue to rise in the coming decades. Within this context, smart homes have emerged as one of the most promising avenues to support aging in place and improve the quality of life. Smart homes encompass a wide variety of functions, including environmental control, safety monitoring, communication, and other assistive technologies, that may help older people stay healthy, safe, and independent in their own homes. However, older people are not a homogeneous group. Their lifestyles, health conditions, and technological experiences differ substantially, which means that, as with any assistive technology, smart home functions must match the real and perceived needs of the target users to ensure acceptance, adoption, and long-term use.