JMIR Aging

Physical and cognitive function, both of which decline with aging, are significantly interrelated. Although numerous studies have investigated the effect of physical exercise on cognitive function, relatively few have examined the impact of pure cognitive training on physical performance.

Artificial intelligence (AI) has the potential to improve health among older adults, yet how different stakeholders decide to develop, finance, and adopt AI innovations is not well understood.

By 2050, 22% of the global population will be aged 60 years or older, with Europe experiencing rapid aging. This increase in chronic diseases and functional decline demands a shift from fragmented, hospital-centric care to continuous care models. The World Health Organization’s healthy aging framework prioritizes intrinsic capacity (IC)—defined as physical and mental abilities across the locomotion, vitality, sensory, cognition, and psychology domains. Digital health solutions enable remote monitoring but face barriers, such as low digital literacy and usability challenges among older adults.

Age-related cognitive and sensorimotor declines co-occur and reinforce one another, increasing vulnerability to functional dependence and falls. Exergaming offers integrated cognitive-motor training, but most systems lack features that support personalized care for socially vulnerable, community-dwelling older adults. Low-income older adults receiving Public Community Care services bear a disproportionate burden of cognitive and sensorimotor decline, yet remain underrepresented in digital health research. As part of a broader program to develop a digital health monitoring-to-intervention framework, we evaluated a pressure sensor–based exergame designed to deliver targeted cognitive-motor training within the existing Public Community Care infrastructure.

Multidomain dementia-prevention interventions delivered via apps have the potential to reach large populations. However, existing trials have tended to recruit more socioeconomically advantaged participants, raising concerns that the resulting interventions may be less usable for older adults from minority ethnic, lower educational, or lower socioeconomic backgrounds, who are at higher risk of dementia. The Tailored Intervention for Brain Health and Cognitive Enrichment (ENHANCE) app was designed to address this by prioritizing accessibility and engagement across diverse user groups.

The population of adults aged 65 and older is rapidly increasing, while the availability of caregivers is declining. Smart homes that provide unobtrusive, continuous monitoring and alerting on clinically relevant changes in daily activity patterns offer a potentially innovative solution for aging in place.

Many clinically relevant aspects of health are expressed through behavior, affect, and interaction, and therefore rely on observation rather than instrumental measurement; yet such information is often documented infrequently in routine care. Dementia, and particularly behavioral and psychological symptoms of dementia (BPSD), is a relevant case because symptoms fluctuate across time and context. Little is known about how structured daily symptom assessment can be integrated into routine dementia care practice.

Gamified digital training may support long-term engagement in aging populations, yet randomized evidence linking behavioral effects to convergent neuroimaging outcomes remains limited.

Caregivers of persons living with dementia are at increased risk of reporting high stress. Mindfulness-based interventions (MBIs) teach caregivers mindfulness skills and are effective at reducing stress. Digital MBIs are a feasible way to improve access to MBIs for caregivers of persons living with dementia. Yet, caregiver improvement with digital MBI utilization is less defined in the literature.

As smart older adult care shifts from basic information support to more continuous algorithm-driven care, concerns have grown about whether such systems support older adults’ independence or weaken their sense of autonomy. Although previous research has focused mainly on technology acceptance, less is known about how algorithmic care shapes perceived autonomy and through which psychological pathways this occurs.

As people age over the coming decades, demand for in-home support and other interventions, such as home modifications, to help older adults age in place successfully, is also expected to rise. Smart home technologies have the potential to enhance aging in place by complementing traditional home modifications; however, adoption within federally funded home modification programs remains limited.

The World Health Organization framework for healthy aging emphasizes that the capacity to establish and sustain relationships is a vital component of functional ability. Social robots offer valuable support for this relational aspect, although their effectiveness is contingent upon the quality of the interactions. While it is established that facial expressions and inclusive behaviors can influence rapport building, the combined effects of these elements on older adults remain unexamined.