As the global population ages and the demand for high-quality care rises, smart senior care, underpinned by digital and information technologies, has garnered increasing attention as a vital approach to elderly health management. This paper conducts a systematic bibliometric analysis of smart senior care from 1981 to 2023, examining 3 dimensions: publication output and academic collaboration, intellectual basis and structure, alongside research frontiers and evolving trends. International research efforts in smart senior care have grown increasingly diverse and interconnected. However, significant regional disparities persist, particularly in knowledge production and academic impact. The research frontiers and intellectual basis of smart senior care demonstrate interdisciplinary characteristics, with a focus on 2 primary directions: technological innovation and humanistic care. Research priorities in smart senior care vary across countries, while research hotspots have undergone notable shifts in recent years.
The integration of artificial intelligence (AI) into nursing education is becoming an increasingly important issue. However, little is known about the experiences of nurse educators about AI in nursing. This was a qualitative phenomenological study. The population consisted of 12 nurse educators from T & uuml;rkiye and USA. The maximum variation sampling method was used. Educators' reflections demonstrated 4 main themes: "Perception of AI," "Benefits of using AI in nursing," "Restrictions of AI," and "Suggestions for AI usage." All of the 13 subthemes were also identified. AI offers the potential to improve the quality of education by supporting critical thinking and decision-making skills. However, reduced human interaction and ethical concerns were concerns.
Falls cause millions of injuries and deaths annually, making prevention a key priority in home health care (HHC). Traditional fall risk assessments often overlook the complex interaction of personal, environmental, and behavioral factors. This study addresses these limitations by introducing a novel approach that leverages multimodal data, specifically visual frames and structured prompts, to assess fall risk in in-home patients. Using the multimodal large language model (MLLM), LLaVA-NeXTVideo-7B-hf, we analyze simulated in-home patients’ video data to enable a more comprehensive and dynamic evaluation of fall risk, paving the way for intelligent, video-based fall prevention in home health care. Preliminary validation using simulated video data demonstrates the feasibility of using MLLMs for such tasks. Simulated in-home patient video data were processed into 24 equally spaced frames. Twelve visually observable fall risk factors extracted from the literature search, categorized as intrinsic, extrinsic, or behavioral, guided the creation of prompts for the MLLM. Standardized prompts were developed by testing the model with concise prompts for simple inferences and elaborated prompts for complex ones. Each prompt was run 3 times, and consensus results were compared with expert evaluations. The model achieved 85.71% accuracy with concise prompts on 7 simple risk factors and 100% accuracy with elaborated prompts on two complex ones. However, the model consistently failed for 2 risk factors that required clinical judgment or had limited visual data. MLLMs like LLaVA-NeXTVideo 7B-hf show strong potential for augmenting fall risk assessment in HHC when guided by well-structured prompts. The approach focuses on visually inferable factors and is intended to complement, rather than replace, clinical evaluation. This proof-of-concept feasibility study shows that MLLMs can support preliminary fall risk analysis using simulated home health care video data and lays the groundwork for future video-based research in this setting, where existing work remains limited. To our knowledge, this is the first study to evaluate the feasibility of MLLM-based video analysis for fall risk assessment in home health care.
This study explores public perceptions of palliative and hospice care through analysis of user comments on Korean-language YouTube videos. A total of 12,155 comments were collected from 54 videos retrieved using the keywords “palliative care” and “hospice.” The search was conducted between September 11 and October 31, 2024. Sentiment analysis using a Korean-language sentiment lexicon revealed that the majority of comments were neutral (57.8%), followed by negative (23.0%) and positive (19.2%) sentiments. Topic modeling using the Latent Dirichlet Allocation method identified 13 recurring themes: religious beliefs, diagnosis and treatments, pain control, family caregiving, death with dignity, emotional farewell, social support and policy, life reflection, meaning of end-of-life care, hope, appreciation, decision-making, and remembrance and mourning. Nurses play an important role in addressing the public misconceptions and emotional concerns regarding end-of-life care that are frequently expressed on online platforms. Insights from this study can support the development of tailored communication strategies, promote early advance care planning, and inform informatics-based interventions to improve equitable access to hospice and palliative care. These findings also highlight the potential of social media analysis as a tool for understanding public needs and guiding data-informed nursing practice.
The aim of this study is to examine the effect of different discharge education methods on readiness for discharge and self-efficacy in individuals undergoing coronary artery bypass graft surgery. This quasi-experimental study included 72 CABG patients in a university hospital in the Aegean Region, Turkey, from October 2019 to March 2022. The Standard Care Group (n=24) received no intervention, while the Education Booklet Group (n=24) received discharge education via a booklet, and the Mobile Application Group (n=24) via a mobile app. There was no statistically significant difference between the Education Booklet Group and the Mobile Application Group in terms of readiness for discharge and self-efficacy scores, but the scores of the Education Booklet Group and the Mobile Application Group were statistically higher than those of the Standard Care Group. While readiness for discharge and self-efficacy showed no significant difference between the 2 intervention groups, the mobile app was found to be at least as effective, usable, and highly recommended as the education traditionally given with the education booklet.
