Okinawa's distinctive hepatitis B epidemiology reflected in 11-year seroprevalence trends among elderly long-term care residents in Japan.

Cross-cultural adaptation and validation of the Willingness to Care Scale for elder care in Chinese adults.

An AI-driven multi-criteria decision-support system for elderly care personalization

AI investment decision in digital home-based elderly care service: A mean field game analysis

To evaluate the effectiveness of the Smart Post-Discharge Elderly Health Management Platform on health-related quality of life and self-management after hospital discharge. The global aging population has increased challenges in post-discharge care for older adults, particularly due to chronic conditions and the need for continuous care. Cluster randomised controlled trial. The trial enrolled 383 elderly patients assigned to a platform-based intervention or standard telephone follow-up, with health-related quality of life, self-management, usability and economic outcomes assessed over 6 months. This study was reported in accordance with the CONSORT guidelines. Repeated measures analyses showed significantly greater improvements in HRQoL in the intervention group over 6 months (p = 0.026), with significant improvements across multiple physical and mental health domains (p = 0.002-0.021). Self-management total scores increased significantly (p < 0.001). Platform usability was rated highly by 96.35% of users, and 70.83% reported reduced healthcare costs. Providing a practical framework for integrating technology into nursing practice for aging populations, this platform shows promise for post-discharge elderly care by strengthening nurse-led digital health delivery, care continuity, self-management and health outcomes beyond hospital settings. ChiCTR2400089720.

Assessing spatial equity of elder care accessibility across income groups: Implications of the built environment

Development of liquid-core hydrogel beads to improve hydration in elderly: Pilot studies

ABSTRACT The aim of this study is to assess the influence of destination attractiveness factors, the availability of boarding houses and social infrastructure, shopping accessibility, financial capacity, and travel predisposition on the willingness of older adults to remain economically active and to engage in tourism activities, including within rural areas. The sample comprised 421 elderly individuals from China, with 70.8% falling in the 61–70 age range and 49.4% reporting an income between 410 and 684 US dollars. The analysis evaluated the impact of destination-related factors, personal characteristics, family-related factors, and social factors on the willingness of older adults to remain economically active and undertake tourist trips, including the potential selection of rural areas. The results confirm the significant influence of factors that determine the attractiveness of a destination, such as environmental cleanliness and the availability of elderly care infrastructure, as well as the impact of older tourists’ income levels in choosing a vacation destination. The practical implications include recommendations for tourism companies and policymakers aimed at enhancing the accessibility of rural tourism through targeted investment in accommodation facilities tailored to the needs of older individuals.

ObjectiveWith the intensification of population aging, the subjective well-being of older adults has become a topic of social concern. Meanwhile, artificial intelligence (AI) is penetrating various fields of society and transforming the living environment of older adults. However, research on the association between AI and the subjective well-being of older adults remains relatively scarce, and this study aims to explore the relationship between AI development and their subjective well-being.MethodsBased on the data from the China Longitudinal Aging Social Survey (CLASS), this study uses a two-way fixed effects model to examine the association between AI development and the subjective well-being of older adults. For robustness checks, it employs methods including double machine learning (DML), instrumental variable approach, variable replacement, addition of control variables, and adjustment of clustering levels.Results(1) AI development is positively correlated with the subjective well-being of older adults. (2) Older adults’ use of elderly care services and their social participation are the channels through which AI development is associated with improved subjective well-being among older adults. (3) The digital literacy of older adults plays a positive moderating role. (4) The positive association between AI development and subjective well-being is significantly larger in magnitude for the old-old group than for the young-old group.ConclusionThis study provides robust empirical evidence for a positive association between AI development and the subjective well-being of older adults. Based on this, efforts should be made to promote the in-depth integration of AI with elderly care services, expand AI-enabled channels for social participation, launch initiatives to improve older adults’ digital literacy, and refine policy safeguards for AI-assisted elderly support.

ABSTRACT Population aging has increased the demand for intelligent smart home systems for elderly care. Although Internet of Things (IoT) sensors enable unobtrusive residential monitoring, existing human activity recognition methods often rely on centralized processing and have limited ability to model heterogeneous sensing sources and long‐range temporal dependencies. To address this issue, this paper proposes a Distributed Adaptive Multi‐Agent Transformer (DAMAT) framework for smart home activity recognition. DAMAT models heterogeneous sensing streams as collaborative agents, captures long‐range temporal and cross‐agent contextual dependencies through transformer‐based interaction, and employs adaptive coordination attention to regulate agent contributions under different activity contexts. Experiments on the CASAS and UCI HAR datasets show that DAMAT consistently outperforms representative deep learning baselines. In particular, the CASAS results directly support the effectiveness of the proposed framework in distributed smart home sensing environments, while the UCI HAR results provide auxiliary evidence that the temporal modeling and adaptive coordination mechanisms remain effective on wearable inertial sensor data.

ABSTRACT The evolving landscape of societal values, living habits, cognitive frameworks, and behavioral patterns across different generations shapes preferences for elder care services. This study explores generational diversities in elder care preferences among Chinese older adults. By utilizing Latent Class Analysis, four distinct types of elder care service demands were identified: Low Demand, Daily Convenience Service Demand, Social and Mental Health Care Demand, and High Demand. A multi-state Markov model was employed to construct a micro-simulation predicting the population size of older adults with varying care preferences in urban and rural areas from 2020 to 2040. Results indicate that living in rural areas and lacking a pension decreases the likelihood of moving from Low Demand to High Demand for elder care services. This study highlights the pivotal impact of generational and regional factors on elder care demand in China. Findings underscore the need to account for the growing demand for services, especially in social and mental health care, and tailored service provision.

Amidst accelerated urbanisation and population ageing in China, urban solo-dwelling elders face escalating social isolation-manifested through diminished physical networks and profound psychological loneliness-severely compromising their well-being. To address gaps in existing studies (such as short-term evaluations and insufficient heterogeneity exploration), this research employs a quasi-natural experiment leveraging regional disparities in smart elderly care policy implementation within the Yangtze River Delta urban agglomeration. By constructing treatment and control groups through multi-level matching of individual behavioural data and city-level indicators across technology-advanced and technology-lagging regions, we assess the technology's sustained impact. Findings reveal that smart elderly care interventions significantly alleviate social isolation, yet effects exhibit pronounced regional stratification and age-based differentials. Elders residing in areas with strong infrastructure and policy enforcement, alongside younger cohorts (exemplified by the 65-75 age group), gain substantially greater benefits. The technology operates via dual mechanisms: intensifying community participation and optimising health self-management behaviours. Crucially, individual education attainment, family support networks, and regional resource allocation efficiency emerge as key moderators of impact magnitude. This study not only deciphers the dynamic process of technology-facilitated social capital accumulation but also warns that unevenly distributed technological dividends may deepen marginalisation of vulnerable subgroups (such as the oldest-old or low-literacy elders). These insights provide theoretical grounding for designing inclusive age-friendly technologies and formulating spatially calibrated health resource allocation policies in ageing societies.

Trust divergence in elderly care robot acceptance in middle-aged and older adults: A latent profile and network analysis.

IntroductionThailand′s rapidly aging population highlights the urgent need for sustainable strategies to support older adults′ independence in activities of daily living (ADLs). Occupational therapists (OTs) play a vital role; however, their limited availability restricts community‐based service delivery. Village Health Volunteers (VHVs), embedded within local communities, provide ongoing support but lack structured guidance in ADL‐focused care.ObjectiveThis study is aimed at developing occupational therapy–informed practice guidelines that translate professional expertise into culturally relevant recommendations for community‐based eldercare.MethodsA qualitative design using semistructured interviews was employed with 10 OTs in Chiang Mai, Northern Thailand. Participants were recruited through stratified and purposive sampling across urban, semiurban, and suburban settings. The inclusion criteria required a minimum of 5 years of clinical experience with older adults and direct involvement in ADL training. Data were collected between April and June 2024, transcribed verbatim, and analyzed thematically. Guiding questions were interpreted using the Occupational Therapy Practice Framework: Domain and Process, 4th Edition (OTPF‐4), while additional inquiry explored broader community and cultural themes. Credibility was ensured through rapport building, audio recording, and member checking.ResultsOTs recommended adaptive techniques, environmental modifications, caregiver education, and assistive device integration. Culturally adapted solutions, such as Thai garments, bamboo ramps, and gradual modification of bathing practices, were emphasized to enhance acceptance and sustainability.ConclusionThis study provides structured, occupational therapy–informed guidelines to support ADLs among community‐dwelling Thai older adults. Empowering VHVs through these recommendations may strengthen community‐based eldercare, reduce caregiver burden, and promote active, dignified aging in place.

BackgroundTaiwan’s status as a super-aged society underscores the need for efficient strategies to promote healthy aging. While the benefits of mindfulness-based interventions for sleep and mental health are evident, there is a shortage of cultural adaptations for Taiwan’s older adults. Current courses mainly focus on mindfulness-based stress reduction, while neglecting mindfulness-based elder care, and mindful sustainable aging principles. However, the abstract nature of some mindfulness concepts, combined with limited digital support and localized resources, makes it challenging for older adults to engage effectively.ObjectiveTo enhance healthy aging outcomes in older community-dwelling adults in Taiwan, this study developed an 8-week theory-based mindfulness curriculum that combined the mindfulness-based stress reduction, mindfulness-based elder care, and mindful sustainable aging frameworks with digital health support.MethodsThis research employed a mixed methods feasibility study design. We used the modified Delphi method in phase 1 to reach agreement on curriculum content and digital material selection. Ten older adults participated in the pilot study, which comprised phase 2. We used quantitative measurements to assess mindfulness, sleep quality, cognition, heart rate variability, perceptions of aging, healthy aging perspectives, and physical function and activity at baseline, at weeks 4 and 8, and at a 1-month follow-up. Qualitative interviews were conducted to gather insights into participants’ experiences.ResultsParticipants achieved significant improvements in mindfulness, sleep quality, aging perception, healthy aging outlook, and physical function during the study (all P<.05). Cognition, heart rate variability, and physical activity levels showed no significant changes throughout the study. The qualitative data supported these findings, as participants selected simple practices, such as mindful breathing, which they practiced daily to enhance their emotional well-being and social connections. Participants valued the digital learning materials for aiding their practice; however, some older adults with reading difficulties faced challenges accessing the content.ConclusionsUsing the Delphi method resulted in an acceptable and feasible theory-based digitally supported mindfulness program that improved several indicators of healthy aging in older adults in Taiwan. Our findings need to be validated using longer trials to assess cognitive and physiological effects. Furthermore, digital accessibility requires further development.

As China's population aging rapidly, long-term care insurance (LTCI) has emerged as a critical policy instrument to address the care needs of older adults. While previous studies have examined the effects of LTCI on service utilization, less is known about its effect on assistive device utilization-a key component in supporting functional independence and reducing caregiver burden. This study examines the association between LTCI benefit design and assistive device use among older adults in China. We use cross-sectional data from 1,460 older adults residing in elderly care institutions located in non-pilot areas, general LTCI pilot areas (covering care services only), and special LTCI pilot areas (covering both care services and assistive devices). Logistic regression models were used to estimate the associations between LTCI benefit types and assistive device use. Robustness checks were conducted using propensity score matching (PSM), and subgroup analyses were performed by age, education, and household registration. General LTCI programs that cover care service only were significantly associated with a lower probability of assistive device use, consistent with a potential substitution effect in which subsidized formal care services may replace the need for assistive devices. In contrast, LTCI programs that explicitly include assistive device benefits were associated with a higher probability of assistive device use. These associations were more pronounced among the oldest-old, individuals with urban household registration, and those with lower education levels. Additional analyses indicate that the observed associations are primarily driven by mobility-related devices, whereas estimates for ADL-related devices and total device counts are not statistically significant. The findings suggest that the design of LTCI benefit packages may influence older adults' care choices and technology adoption. While causal interpretation remains limited, the results highlight the potential role of benefit design in shaping the balance between formal care services and assistive technology use. Expanding assistive device coverage and improving accessibility may help support functional independence among older adults in ageing societies, particularly in underserved rural regions.

Robot therapy, with PARO being a prominent, has become an effective intervention in elderly care, where caregivers play an important role in patient engagement during therapy. This study analyzes caregiver behavior to reveal behavioral parameters associated with caregiving skill in delivering robot therapy using PARO. The analysis parameters included physical activity of caregivers and patients during therapy, facial gaze, eye level between caregiver and patient, and PARO face orientation. Using a vision-based activity recognition system, observational data were collected from 24 novice caregivers (12 trained, 12 untrained) and 6 elderly participants, with statistical analysis (Mann-Whitney U tests) confirming significant differences in interaction behavior between trained and untrained caregivers. The results showed that trained caregivers demonstrated specific technical skills, including holding PARO longer during introductions, delaying the initial placement of PARO, sustaining facial gaze, maintaining longer eye contact, and dynamically adjusting PARO's position based on patient cues. Although trained caregivers tended to maintain a smaller eye-level difference with patients on average, this difference was not statistically significant. These findings indicate that formal caregiving education is associated with systematic differences in interaction behavior, even at a pre-professional stage. The study contributes a quantitative framework for identifying foundational behavioral markers related to caregiving skill in robot therapy, rather than evaluating clinical efficacy.

Although enhancing rural human settlements is fundamental to sustainable rural development, inadequate farmers' participation remains a core governance challenge. As digital technology becomes a vital instrument for rural governance, a critical gap remains in systematically understanding its impact on farmers' participation and the mechanisms involved. This study employs the Ordered Probit model, the instrumental variable method, and the mediation effect model to empirically investigate the impact, mechanisms, and heterogeneity of digital technology on farmers' participation in the governance of rural human settlements. The findings are as follows. First, digital technology significantly promotes farmers' participation, and this conclusion remains robust after a series of robustness tests and addressing endogeneity issues. Second, mechanism tests indicate that digital technology primarily operates through increasing farmers' economic cognition and reinforcing social norm constraints. This reveals the dual logic of its mechanism, which integrates both "economic rationality" and "social rationality". Third, heterogeneity analysis indicates that the promoting effect of digital technology is more pronounced among farmers with elderly care responsibilities and in small-scale villages, reflecting the contextual dependence of its empowering effects. Accordingly, this study proposes recommendations such as optimizing the functional design of digital platforms and implementing differentiated, targeted digital empowerment strategies.

Under the "Internet Plus" model, this study aims to develop a proof‑of‑concept intelligent decision‑support system for elderly disease risk assessment, with a specific focus on Alzheimer's Disease (AD). The primary objective is to evaluate whether blood‑based transcriptomic biomarkers can be translated into an interpretable and scalable risk stratification framework suitable for home‑care and community health settings. Whole‑blood transcriptomic and associated clinical data were obtained from a publicly available microarray dataset comprising 329 individuals (144 AD, 104 MCI, and 81 cognitively normal controls). Transcriptomic features were used exclusively for model training, while clinical variables-including cognitive scores, frailty index, depression scores, and medication adherence-were used post hoc for risk annotation and alert simulation. Rigorous preprocessing and quality control were applied, followed by differential gene expression analysis using the limma framework and biologically informed biomarker selection. A Random Forest classifier trained on selected transcriptomic biomarkers achieved an accuracy of 91.2%, with sensitivity of 88.5% and specificity of 93.6% under stratified five‑fold cross‑validation. Model interpretability was supported through permutation‑based feature importance and SHAP analysis, enabling identification of key genes contributing to risk prediction. Based on probabilistic outputs, subjects were categorized into simplified alert states such as "Alzheimer's Risk Flagged" and "Monitoring Recommended." Although real‑time home monitoring data were not collected, the proposed system simulates how omics‑driven risk scores could be integrated into EMR‑compatible and IoT‑enabled care platforms. This study demonstrates a scalable and interpretable computational framework that bridges transcriptomic research and practical decision support, highlighting the potential of non‑invasive, individualized, and deployable risk assessment tools for elderly care, particularly in resource‑limited or home‑based environments.

Mechanism and control strategy design of a multi-position nursing bed for bedridden elderly care.