Search Results (891)

The increase in the elderly population globally reflects a change in the population’s mindset regarding preventive health measures and necessitates a rethinking of healthcare strategies. The integration of machine learning (ML)-type algorithms in geriatrics represents a direction for optimizing prevention, diagnosis, prediction, monitoring, and treatment. This paper presents a systematic review of the scientific literature published between 1 January 2020 and 31 May 2025. The paper is based on the applicability of ML techniques in the field of geriatrics. The study is conducted using the Web of Science database for a detailed discussion. The most studied algorithms in research articles are Random Forest, Extreme Gradient Boosting, and support vector machines. They are preferred due to their performance in processing incomplete clinical data. The performance metrics reported in the analyzed papers include the accuracy, sensitivity, F1-score, and Area under the Receiver Operating Characteristic Curve. Nine search categories are investigated through four databases: WOS, PubMed, Scopus, and IEEE. A comparative analysis shows that the field of geriatrics, through an ML approach in the context of elderly nutrition, is insufficiently explored, as evidenced by the 61 articles analyzed from the four databases. The analysis highlights gaps regarding the explainability of the models used, the transparency of cross-sectional datasets, and the validity of the data in real clinical contexts. The paper highlights the potential of ML models in transforming geriatrics within the context of personalized predictive care and outlines a series of future research directions, recommending the development of standardized databases, the integration of algorithmic explanations, the promotion of interdisciplinary collaborations, and the implementation of ethical norms of artificial intelligence in geriatric medical practice. Full article

Aging is a multifactorial process that affects various physiological functions, including masticatory performance, which is crucial for oral health and nutritional well-being. Impaired masticatory function, often due to factors such as tooth loss, reduced salivation, or muscle atrophy, can lead to significant nutritional challenges and compromise the overall health of elderly individuals. Recent research has illuminated the interconnectedness of masticatory function, oral microbiota, and gut health, suggesting that altered chewing ability may disrupt oral microbial communities, which in turn affect gastrointestinal health and systemic inflammation. This commentary review provides a comprehensive analysis of the role of masticatory function in aging, exploring its impact on the oral microbiota, gut health, and broader nutritional status. We discuss the potential consequences of impaired mastication, including malnutrition, dysbiosis, and gastrointestinal disorders, and explore possible strategies for improving masticatory function and maintaining a healthy gut microbiome through interventions like dietary modifications, oral care, and rehabilitation. We aim to underscore the importance of integrating masticatory function management into the broader context of aging-related healthcare, promoting holistic, multidisciplinary approaches to support nutritional needs and quality of life in older adults. Full article

This narrative review examines the effects of caffeine on brain health in older adults, with particular attention to its potential for dependence—an often-overlooked issue in geriatric care. Caffeine acts on central adenosine, dopamine, and glutamate systems, producing both stimulating and rewarding effects that can foster tolerance and habitual use. Age-related pharmacokinetic and pharmacodynamic changes prolong caffeine’s half-life and increase physiological sensitivity in the elderly. While moderate consumption may enhance alertness, attention, and possibly offer neuroprotective effects—especially in Parkinson’s disease and Lewy body dementia—excessive or prolonged use may lead to anxiety, sleep disturbances, and cognitive or motor impairment. Chronic exposure induces neuroadaptive changes, such as adenosine receptor down-regulation, resulting in tolerance and withdrawal symptoms, including headache, irritability, and fatigue. These symptoms, often mistaken for typical aging complaints, may reflect a substance use disorder yet remain under-recognized due to caffeine’s cultural acceptance. The review explores caffeine’s mixed role in neurological disorders, being beneficial in some and potentially harmful in others, such as restless legs syndrome and frontotemporal dementia. Given the variability in individual responses and the underestimated risk of dependence, personalized caffeine intake guidelines are warranted. Future research should focus on the long-term cognitive effects and the clinical significance of caffeine use disorder in older populations. Full article

In the context of global demographic aging, falls among the elderly remain a major public health concern, often leading to injury, hospitalization, and loss of autonomy. This study proposes a real-time fall detection system that combines a modern computer vision model, YOLOv11 with integrated pose estimation, and an Artificial Intelligence (AI)-based voice assistant designed to reduce false alarms and improve intervention efficiency and reliability. The system continuously monitors human posture via video input, detects fall events based on body dynamics and keypoint analysis, and initiates a voice-based interaction to assess the user’s condition. Depending on the user’s verbal response or the absence thereof, the system determines whether to trigger an emergency alert to caregivers or family members. All processing, including speech recognition and response generation, is performed locally to preserve user privacy and ensure low-latency performance. The approach is designed to support independent living for older adults. Evaluation of 200 simulated video sequences acquired by the development team demonstrated high precision and recall, along with a decrease in false positives when incorporating voice-based confirmation. In addition, the system was also evaluated on an external dataset to assess its robustness. Our results highlight the system’s reliability and scalability for real-world in-home elderly monitoring applications. Full article

Background and Objectives: Caring for an individual with dementia encompasses many challenges. This can lead to increased burden, anxiety, and mental health issues among those taking care of them. Limited research exists investigating the care of people with dementia in Kuwait, particularly regarding the mental health of caregivers. There is a need to understand the impact of caregiver burden in this population. This study aimed to assess the level of burden of care, depression, anxiety, and well-being among caregivers of the elderly with dementia in Kuwait. Methods: This study used a descriptive and cross-sectional design. To measure the burden of care, depression, anxiety, and well-being of the caregivers, we utilized the Zarit Burden Interview, the Hospital Anxiety and Depression Scale, and the World Health Organization-Five Well-Being Index. A sensitivity analysis was conducted to compare the results of the parametric and non-parametric methods. Results: This study included 180 (65%) caregivers for the elderly with dementia and 98 (35%) without dementia. The descriptive statistics showed that caregivers for the elderly with dementia and caregivers for the elderly without dementia experienced moderate burden (17.21 ± 9.09 and 14.51 ± 8.08, respectively), borderline abnormal anxiety (9.92 ± 5.15 and 8.61 ± 4.79, respectively), borderline abnormal depression (8.69 ± 4.35 and 8.06 ± 4.24, respectively), and low mental health well-being (54.40 ± 25.10 and 58.90 ± 23.42, respectively). The t-test of independent samples and Mann–Whitney U test results showed that the burden and anxiety in the caregivers for the elderly with dementia group were statistically significantly higher than those in the caregivers for the elderly without dementia group (p = 0.015 and p = 0.039; p = 0.026 and p = 0.027, respectively). The ANOVA test and Kruskal–Wallis test revealed that the caregivers for the elderly with dementia group had statistically significant differences in burden (p < 0.001; p < 0.001), anxiety (p = 0.048; p = 0.043), depression (p = 0.017; p = 0.009), and mental health well-being (p = 0.001; p = 0.002) scores across various durations of care. The multiple linear regression showed that caregiving was a significant predictor of burden of care and anxiety, indicating that caregivers of the elderly with dementia experienced a higher burden of care than those caring for the elderly without dementia. In addition, confounders with significant influence were duration of care (p < 0.001), education level (p = 0.002), employment status (p = 0.008), and gender (p = 0.02). Conclusions: Family caregivers experienced significant levels of burden of care and anxiety when caring for the elderly with dementia. A multidimensional holistic approach is needed to provide family caregivers of the elderly with dementia with valuable interventions. Full article

Background: Older adults living in aged care are at risk of poor health outcomes due to influenza, pneumococcal disease, and herpes zoster infections. Despite these conditions being vaccine-preventable, little is known about vaccine uptake rates in the residential elderly care setting in Australia. Methods: This was a retrospective cohort study examining the medical records of residents of 31 aged care homes in Australia (n = 1108). Data were extracted from medical records for the period March 2023 to September 2023. The proportion of residents vaccinated against influenza, pneumococcal disease, and herpes zoster was calculated. Univariate and multivariate logistic regressions were used to identify possible demographic and other characteristics associated with the vaccination uptake. Results: This study included 1108 residents. Two-thirds (68%) were female, and the median age was 87 years. All residents had one or more comorbidities. Most (92.6%) had received an influenza vaccine within the prior two years, but only 38.3% had received a pneumococcal vaccine, and 16.8% had received herpes zoster vaccination. In all models, receipt of the other vaccines was a significant predictor for vaccine administration. The other factor associated with influenza vaccination was non-consumption of alcohol and younger age for herpes zoster vaccination. Conclusions: While there is a high uptake of influenza vaccines, there is a low uptake of both pneumococcal and herpes zoster vaccines in residents of aged care facilities. Further research into the barriers and enablers of vaccine uptake should be undertaken, with the goal of increasing the vaccination uptake in this vulnerable population. Full article

AI-powered health assistants offer promising opportunities to enhance health management among older adults. However, real-world uptake remains limited, not only due to individual hesitation, but also because of complex interactions among users, platforms, and public policies. This study investigates the dynamic behavioral mechanisms behind adoption in aging populations using a tripartite evolutionary game model. Based on replicator dynamics, the model simulates the strategic behaviors of older adults, platforms, and government. It identifies evolutionarily stable strategies, examines convergence patterns, and evaluates parameter sensitivity through a Jacobian matrix analysis. Results show that when adoption costs are high, platform trust is low, and government support is limited, the system tends to converge to a low-adoption equilibrium with poor service quality. In contrast, sufficient policy incentives, platform investment, and user trust can shift the system toward a high-adoption state. Trust coefficients and incentive intensity are especially influential in shaping system dynamics. This study proposes a novel framework for understanding the co-evolution of trust, service optimization, and institutional support. It emphasizes the importance of coordinated trust-building strategies and layered policy incentives to promote sustainable engagement with AI health technologies in aging societies. Full article

Smart home technology enhances the quality of life, particularly with respect to in-home care and health monitoring. While video-based methods provide accurate behavior analysis, privacy concerns drive interest in non-visual alternatives. This study proposes a Bluetooth Low Energy (BLE)-enabled indoor positioning and behavior recognition system, integrating machine learning techniques to support sustainable and privacy-preserving health monitoring. Key optimizations include: (1) a vertically mounted Data Collection Unit (DCU) for improved height positioning, (2) synchronized data collection to reduce discrepancies, (3) Kalman filtering to smooth RSSI signals, and (4) AI-based RSSI analysis for enhanced behavior recognition. Experiments in a real home environment used a smart wristband to assess BLE signal variations across different activities (standing, sitting, lying down). The results show that the proposed system reliably tracks user locations and identifies behavior patterns. This research supports elderly care, remote health monitoring, and non-invasive behavior analysis, providing a privacy-preserving solution for smart healthcare applications. Full article

Introduction: Urinary tract infections (UTIs) remain a significant public health issue worldwide, particularly affecting the geriatric population with increased morbidity and mortality. Aging-related immune changes, comorbidities, and urogenital abnormalities contribute to the higher incidence and complexity of UTIs in elderly patients. Antimicrobial resistance, especially extended-spectrum beta-lactamase (ESBL) production and carbapenem resistance, poses a major challenge in managing UTIs in this group. Methods: This retrospective, multicenter study included 776 patients aged 65 and older, hospitalized with a diagnosis of urinary tract infection between January 2019 and August 2024. Clinical, laboratory, and microbiological data were collected and analyzed. Urine samples were obtained under sterile conditions and pathogens identified using conventional and automated systems. Antibiotic susceptibility testing was performed according to CLSI standards. Logistic regression analyses were conducted to identify factors associated with ESBL production, carbapenem resistance, and mortality. Results: Among the patients, the median age was 78.9 years, with 45.5% female. ESBL production was detected in 56.8% of E. coli isolates and carbapenem resistance in 1.2%. Klebsiella species exhibited higher carbapenem resistance (37.8%). Independent predictors of ESBL production included the presence of urogenital cancer and antibiotic use within the past three months. Carbapenem resistance was associated with recent hospitalization, absence of kidney stones, and infection with non-E. coli pathogens. Mortality was independently associated with intensive care admission at presentation, altered mental status, Gram-positive infections, and comorbidities such as chronic obstructive pulmonary disease and urinary incontinence. Discussion: Our findings suggest that urinary pathogens and resistance patterns in elderly patients are similar to those in younger adults reported in the literature, highlighting the need for age-specific awareness in empiric therapy. The identification of risk factors for multidrug-resistant organisms emphasizes the importance of targeted antibiotic stewardship, especially in high-risk geriatric populations. Multicenter data contribute to regional understanding of resistance trends, aiding clinicians in optimizing management strategies for elderly patients with UTIs. Conclusions: This study highlights that E. coli and Klebsiella species are the primary causes of UTIs in the elderly, with resistance patterns similar to those seen in younger adults. The findings also contribute important data on risk factors for ESBL production and carbapenem resistance, supported by a robust patient sample. Full article

The quality of living environments for older adults represents a critical component of social sustainability in an ageing society. Among various housing options, nursing homes are the most institutionalised form of elderly care and demand special attention regarding architectural design. This paper explores the impact of architectural and open space features on social sustainability in living environments for older adults. A comparative analysis of three Slovenian nursing homes is presented. The first two, built in the post-war period, were based on elevated architectural criteria inspired by Swedish human-centred housing design. The third was completed in 2021 in the post-COVID-19 era, which revealed the vulnerability of such housing typologies. An integrated methodological framework was developed by applying two complementary assessment tools: the Safe and Connected, developed by the authors to evaluate the spatial, health, and social resilience of nursing home environments, and the Well-being and Integration, addressing the role of open space with a particular emphasis on the identification of older adults with their environment and the facilitation of social resilience. Both tools evaluate indicators linked to the social dimension of quality of life for older adults. The results show a gradual improvement in architectural quality from the 1960s to the 1980s, followed by a partial decline in the contemporary case. The Swedish example scored highest across both tools. In contrast, the newest Slovenian facility scored surprisingly low in social integration, highlighting critical gaps in current nursing home design. This study demonstrates the value of applying interdisciplinary, tool-based evaluations in identifying design strategies that foster resilient and inclusive LTC environments, and the proposed framework may serve as a decision-making aid for architects, planners, and policymakers. This research highlights the importance of reintroducing human-oriented design principles to support socially sustainable nursing home environments. Full article

Background/Objectives: With aging linked to increased oral health conditions, the communication skills of dental professionals are vital to ensure patient satisfaction and improve the quality of geriatric dental care. This cross-sectional study evaluated geriatric patients’ satisfaction with communication at a university dental clinic, comparing interactions with dentists and fifth-year dental students. Methods: A self-report questionnaire was administered to 111 patients, assessing sociodemographic data, general health, and satisfaction across six communication dimensions: verbal and nonverbal communication, empathy, respect, problem-solving, and support materials. The data were analyzed using Mann–Whitney U tests (α = 0.05). Results: The geriatric patients exhibited higher levels of satisfaction when interacting with dentists compared to students, particularly in the domains of nonverbal communication (p = 0.007), empathy (p = 0.035), and respect (p = 0.017). However, no statistically significant differences (p > 0.05) were observed in terms of verbal communication, problem-solving, and support materials. Conclusions: The geriatric patients demonstrated greater satisfaction with interactions with practicing dentists. These findings indicate that an attending dentist’s clinical experience enhances interpersonal interactions with geriatric patients. Therefore, developing the interpersonal skills of future dentistry professionals, as well as adapting communication to the needs of the elderly, is essential to provide more satisfactory experiences in geriatric dental care. Full article

Many studies have focused on ageing and gut microbiota, but the correlation between gut microbiota and physical function in older adults, especially those with longevity, remains obscure and deserves further exploration. In this study we investigated changes in the gut microbiota and the association between gut microbiota and physical function in adults with longevity. This is a prospective observational study. Fifty-one older adults aged ≥ 60 years (including 27 participants aged 90 years and above) were enrolled. Information on clinical data, physical function including intrinsic capacity by Integrated Care for Older People (ICOPE) tool, and dietary habits of participants was collected and analysed. Gut microbiota structure and functional pathways were analysed by Metagenomics. Intrinsic capacity (measured as ICOPE scores) of adults’ longevity (aged 90–98, longe group) was significantly lower than older adults aged 60–89 years (CON group) (5.44 ± 2.15 vs. 6.71 ± 1.46, p = 0.017). Gut microbiota of the longe group is enriched in Akkermansia and Bifidobacterium, which may be beneficial to health. Gut microbiota was closely related to daily milk (including plain milk, flavoured milk with a content of cow’s milk or reconstituted milk of ≥80%, or reconstituted milk or fermented milk with a content of cow’s milk or milk powder of ≥80%) consumption, anxiety, and physical function including grip strength by the Short Physical Performance Battery (SPPB). Bacteroides plebeius and Bacteroides eggerthii were increased in long-living adults with better physical function. Escherichia coli was more abundant in frail young-old adults. Grip strength is positively correlated with the abundance of Roseburia hominis, Eubacterium rectale, Eubacterium eligens, and Roseburia intestinalis (p < 0.05). Pathways related to amino acid synthesis that include L-isoleucine, L-valine, and L-threonine were over-presented in long-living adults of better physical function. Adults with longevity showed comparable gut microbiota abundance to younger elderly individuals. The gut microbiota of long-living adults showed higher abundance of potentially beneficial bacteria, and the altered bacteria are closely associated with physical function. Changes in the gut microbiota may precede clinical indicators during the process of ageing. Gut microbiota may be a potential biomarker for longevity and healthy ageing. Nutrition and emotional state can be important influencing factors. Full article

Background/Objectives: Against the backdrop of increasing global aging and the trans-formation of family structures, grandparental caregiving has become commonplace, and its impact on the mental health of older persons is of great concern. Methods: Based on data from the 2023 Xi’an Jiaotong University Urban and Rural Elderly Family Support and Psychological Condition Survey, this study analyzed the impact of grandparental care-giving behaviors on the mental health of the elderly through the Psychological Condition Measurement Scale (PCMS), and comprehensively assessed the presence, intensity, and heterogeneous impact of grandparental caregiving behaviors by gender by applying linear regression modeling, the Propensity Score Matching (PSM) method, and the Instrumental Variables Method (IVM). Results: Grandparental care has a significant positive effect on the mental health of older people, but this positive effect diminishes as the intensity of care increases. The results of the sub-sample estimation show that grandparental caregiving has a positive effect on men’s and low-intensity caregiving on women’s mental health, but high-intensity caregiving has a negative effect on women’s mental health. In addition, in-tergenerational financial support and intergenerational emotional comfort play an im-portant mediating role between grandparental caregiving and the mental health of older persons, in line with the explanatory framework of intergenerational exchange theory. Conclusions: It is recommended that the Government support grandparental care for the elderly at the financial, social security, and policy levels; that society build a diversified system of elderly care services and strengthen public childcare services; and that families establish a value identity of two-way support. Full article

This study presents the findings of an academic extension project focused on promoting oral health and diagnosing oral lesions in institutionalized elderly individuals. The project involved visits by students and faculty to two nursing homes in southern Brazil. Data collection included extraoral and intraoral clinical examinations and educational activities such as lectures and the distribution of printed materials on oral and denture hygiene. According to caregiving staff, oral hygiene, including denture cleaning, was generally performed once daily during morning showers. A total of 118 older adults (68 males and 50 females; mean age 76.1 ± 8.6 years) were examined. Forty-nine used dentures, of whom only 24 (49%) reported satisfaction with their prostheses. In total, 42 oral lesions were identified, mainly angular cheilitis (8), inflammatory fibrous hyperplasia (7), irritation fibroma (7), frictional hyperkeratosis (7), prosthetic stomatitis (5), actinic cheilitis (3), traumatic ulcers (3), and leukoplakia (2). Educational sessions also targeted caregivers, offering practical guidance for improving hygiene practices. The results underscore the need for better oral care and improved access to dental services for institutionalized elderly populations. Academic extension activities play a valuable role in health promotion and in training future professionals in elderly care. Full article

With the rapid advancement of urbanization in China, the demand for community-based elderly care facilities (CECFs) has been increasing. One pressing challenge is the question of how to provide CECFs that not only meet the health needs of the elderly but also make efficient use of limited urban land resources. This study addresses this issue by adopting an integrated multi-method research framework that combines multi-objective optimization (MOO) algorithms, Spearman rank correlation analysis, ensemble learning methods (Random Forest combined with SHapley Additive exPlanations (SHAP), where SHAP enhances the interpretability of ensemble models), and Self-Organizing Map (SOM) neural networks. This framework is employed to identify optimal building configurations and to examine how different architectural parameters influence key daylight performance indicators—Useful Daylight Illuminance (UDI) and Daylight Factor (DF). Results indicate that when UDI and DF meet the comfort thresholds for elderly users, the minimum building area can be controlled to as little as 351 m² and can achieve a balance between natural lighting and spatial efficiency. This ensures sufficient indoor daylight while mitigating excessive glare that could impair elderly vision. Significant correlations are observed between spatial form and daylight performance, with factors such as window-to-wall ratio (WWR) and wall thickness (WT) playing crucial roles. Specifically, wall thickness affects indoor daylight distribution by altering window depth and shading. Moreover, the ensemble learning models combined with SHAP analysis uncover nonlinear relationships between various architectural parameters and daylight performance. In addition, a decision support method based on SOM is proposed to replace the subjective decision-making process commonly found in traditional optimization frameworks. This method enables the visualization of a large Pareto solution set in a two-dimensional space, facilitating more informed and rational design decisions. Finally, the findings are translated into a set of practical design strategies for application in real-world projects. Full article