JMIR Aging

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.

Dementia poses a significant public health challenge. Early detection is crucial for timely intervention but remains difficult in community settings, as many existing cognitive screening tools are either insufficiently sensitive to subtle decline or too burdensome for widespread use.

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.

Mental health issues such as depression, loneliness, and cognitive decline are prevalent among older adults. They are particularly pronounced in rural areas due to socioeconomic disadvantages, limited health literacy, and social isolation. These challenges have been exacerbated by the COVID-19 pandemic, highlighting the urgent need for accessible, community-based mental health strategies. Social prescribing—linking individuals to nonmedical community resources—offers a promising model, especially when coupled with culturally relevant and digitally facilitated interventions.

As telehealth has become an increasingly common mode of care delivery, older adults may face structural, technological, and interactional barriers that limit their ability to engage with video-based care. Although digital ageism, defined as the presence of age-related stereotypes, lowered expectations, or assumptions about older adults’ technology-related competence, has been described in prior literature, less is known about how such dynamics surface during real-time telehealth encounters and how they may shape participation in technology-focused clinical trials.

Falls are prevalent and serious health problems among older adults. Concerns about falling and reduced falls efficacy are common fall-related psychological impairments, representing 2 distinct emotional and cognitive constructs, respectively. The impact of digital-based exercise interventions on these specific constructs remains unclear.