JMIR Aging

Machine learning clustering offers an unbiased approach to better understand the interactions of complex social and clinical variables via integrative subphenotypes, an approach not studied in out-of-hospital cardiac arrest (OHCA).

Delirium, an acute confusional state highlighted by inattention, has been reported to occur in 10% to 50% of patients with COVID-19. People hospitalized with COVID-19 have been noted to present with or develop delirium and neurocognitive disorders. Caring for patients with delirium is associated with more burden for nurses, clinicians, and caregivers. Using information in electronic health record data to recognize delirium and possibly COVID-19 could lead to earlier treatment of the underlying viral infection and improve outcomes in clinical and health care systems cost per patient. Clinical data repositories can further support rapid discovery through cohort identification tools, such as the Informatics for Integrating Biology and the Bedside tool.

In recent years, researchers have been advocating for the integration of ambulatory gait monitoring as a complementary approach to traditional fall risk assessments. However, current research relies on dedicated inertial sensors that are fixed on a specific body part. This limitation impacts the acceptance and adoption of such devices.

During the COVID-19 pandemic, government-mandated social distancing prevented the spread of the disease but potentially exacerbated social isolation and loneliness for older people, especially those already vulnerable to isolation. Older adults may have been able to draw from their personal resources such as psychological resilience (PR) and technology use (TU) to combat such effects. Educational attainment (EA) or early-life EA may potentially shape later-life personal resources and their impact on the effects of the pandemic lockdown on outcomes such as loneliness. The developmental adaptation model allows for the supposition that social isolation, TU, and PR may be affected by early EA in older adults.

Despite the availability of physical activity (PA) interventions, many older adults are still not active enough. This might be partially explained by the often-limited effects of PA interventions. In general, health behavior change interventions often do not focus on contextual and time-varying determinants, which may limit their effectiveness. However, before the dynamic tailoring of interventions can be developed, one should know which time-dependent determinants are associated with PA and how strong these associations are.

Among older adults, nursing home admissions (NHAs) are considered a significant adverse outcome and have been extensively studied. Although the volume and significance of electronic data sources are expanding, it is unclear what predictors of NHA are systematically identified in the literature using EHR and administrative/claims data.

A number of real-world digital literacy training programs exist to support engagement with mobile devices, but these have been understudied.

Prediction models are increasingly utilized in clinical practice with some requiring patient-reported outcomes (PROs). The optimal approach to collecting the needed inputs is unknown.

The COVID-19 infodemic has imposed a disproportionate burden on older adults who face increased challenges in accessing and assessing public health information, but little is known about factors influencing older adults’ trust in public health information during COVID-19.

Various eldercare settings have embraced the utilization of the life story approach to enhance the development of comprehensive care plans. However, organizing life stories and extracting useful information is labor-intensive, primarily due to the repetitive, fragmented, and redundant nature of life stories gathered from everyday communication scenarios. Existing life story systems, while available, do not adequately fulfill the requirements of users, especially in the application of care services.

Older Chinese immigrants constitute the largest older Asian ethnic population in New Zealand. Aging in a foreign land can be complex, presenting increasing challenges for gerontology scholars, practitioners, and policymakers. Older Chinese immigrants are more susceptible to experiencing loneliness and social isolation compared to native older people, primarily due to language, transportation and cultural barriers. These factors subsequently impact their physical and mental health. With advancement in robotic technology, aged care robots are being applied to support older people with their daily living needs. However, studies of using robots with older immigrants living in the community are sparse. Their preferences of appearance and function of aged care robots are unclear. This unclear information impacts the acceptance and usability of robots, highlighting the need for a user-centered design approach.

Identifying persons with a high risk of developing osteoporosis and preventing the occurrence of the first fracture is a healthcare priority. Most existing osteoporosis screening tools have high sensitivity but relatively low specificity.