JMIR Aging

Falls and fractures are a major clinical concern for older adults. International clinical guidelines recommend annual fall risk screening for all adults aged 60 years and older. However, existing falls risk–screening algorithms have shown limited sensitivity and specificity in detecting future falls. Emerging health technologies, including wearable sensors, in-silico finite element models (FEMs), and virtual reality (VR) technology, show promise in enhancing fall and fracture risk assessments by providing more personalized objective data to support targeted primary prevention. However, large-scale longitudinal studies are required to evaluate their predictive accuracy and cost-effectiveness for systematic community–based screening.

Digital technology is increasingly being used to deliver interventions and initiatives to support the well-being of older adults. However, few studies have conducted needs assessments to identify the future well-being service requirements of an older adult population and their preferred modes of delivery, whether via digital technology or, in- person, or a combination of both (ie, a hybrid model).

Knee osteoarthritis (KOA) lacks disease-modifying treatment, making early identification of modifiable risk factors in asymptomatic older adults a priority. Patellar tendon stiffness (PTS) and knee extensor strength (KESt) may independently influence KOA development, yet their prognostic value in individuals free of knee symptoms is unestablished.

Alzheimer disease (AD) is a leading cause of dementia, and there is growing interest in scalable approaches for early screening using speech-based tasks. While prior work has demonstrated promising results using either transcript-based language features or acoustic cues, most approaches remain unimodal or rely on simple fusion strategies that do not explicitly consider interactions across modalities.

Early mortality after long-term care facility (LTCF) admission is common; yet, prognostic tools are often derived from Western minimum dataset-based cohorts or require hospital electronic health record linkages that are unavailable at intake in many LTCFs. There is also limited evidence on explainable, admission-feasible machine learning–based prognostication in Asian LTCF settings.

Community exergaming may help older adults remain physically active by combining movement, enjoyment, and social interaction, but less is known about whether observed changes persist after supervised delivery ends in real-world community settings.

Keep-on-Keep-up Nutrition (KOKU-Nut) is a free, tablet-based digital platform that focuses on increasing physical activity and improving the dietary intake of older adults.

Mobility limitations in older adults are associated with an increased physiological cost of walking, reduced functional independence, and elevated fall risk. Conventional wheeled walkers improve stability but lack real-time physiological monitoring and safety feedback. Digital health–enabled assistive devices integrating physiological monitoring and fall detection may enhance mobility performance and safety.

Digital technologies have the potential to support physical, cognitive, and social activity among older adults, but many small- and medium-sized enterprises (SMEs) lack the resources to conduct meaningful co-design with older adults. Toolkits derived from rigorous co-design processes may offer a scalable mechanism for translating end-user priorities into real-world product development.

Demoralization negatively affects the quality of life, especially in people with mental disorders.

The use of emergency medical services in Germany has significantly increased, particularly for nonemergency cases. This issue becomes especially relevant for nursing homes, where staff is confronted with resource shortages and organizational challenges, making emergency medical services involvement a common course of action. This leads to potentially avoidable hospitalizations for nursing home residents and exposes them to severe health risks, such as nosocomial infections. To address this, we developed the Optimal@NRW project, implementing an innovative intersectoral telemedicine intervention that includes an early warning system and mobile nonphysician medical assistants in order to enable outpatient treatment in acute medical cases for nursing home residents. We hypothesize that hospitalizations could be significantly reduced, as patients could be treated on-site in the absence of a life-threatening emergency.

Older adults often express positive attitudes toward digital health technologies in surveys, yet adoption remains low. Self-report measures may not capture automatic affective reactions such as anxiety or distrust. Implicit paradigms such as the affect misattribution procedure (AMP) can reveal these automatic attitudes, but parameters optimized for younger adults may not be suitable for older adults because of age-related slowing and changes in visual processing.