Search Results (2,845)

This study aimed to explore the key factors to consider when developing and sharing open-source assistive technology (AAT). To this end, the sip-and-puff mouse was selected from development requests submitted to the Korean Assistive Technology Open Platform. In this study, the sip-and-puff mouse was developed, applied, and shared as an open-source AAT for individuals with disabilities. The research was conducted based on usability feedback collected from five participants through three usability evaluations. During the usability evaluations, task performance scores and time taken were measured, and questionnaires were completed. Following the second usability evaluation, a diary study on long-term usage was conducted. Descriptive statistics, frequency analysis, and paired t-tests were performed using SPSS 22.0. Thematic analysis was conducted using ATLAS.ti. Based on the integrated qualitative and quantitative findings, practical design directions for user-centered open-source assistive technology were identified, including user and environmental matching, customization, durability, supporting documentation, use-error management, and participatory development. The results of this study are expected to contribute to improving the usability of customized open-source assistive technology (AAT) through a participatory design approach. Highly usable open-source AATs will help to make AATs more widely utilized in the daily living activities of the elderly and people with disabilities. Full article

This article describes a new hardware/software alert system designed to assist elderly people and prevent falls due to bed-exit events at night. To assist the elderly during the night, it is important to implement automatic lighting activation in the bedroom. This helps reduce the risk of falls when they need to, for example, go to the bathroom. The caregiver can be alerted immediately via Wi-Fi, during the night, providing immediate assistance to the elderly person. The developed HW/SW system combines a passive infrared motion sensing device, a light sensor, and dedicated hardware based on the ESP32-C6 RISC-V microcontroller that communicates via Wi-Fi with a developed Android dedicated App, which the caregiver can access using a tablet or smartphone. Nighttime falls remain one of the most serious health problems for older people. The main innovative contribution of this work is the development of a low-cost preventive battery-free assistive system that does not require an internet access contract, preserves the elderly person’s privacy, and promptly alerts the caregiver whenever the elderly person gets out of bed during the night. The system is directly integrated with automated lighting, preventing the elderly person from walking in the dark and without appropriate aid. The system also supports the caregiver by generating alerts through an open-source mobile App. Full article

Urban–rural public facility disparity is a systemic challenge that reflects dysfunctional feedback loops within and across social and technical subsystems. Traditional administrative-unit-based analyses cannot capture such systemic alignments. This study applies the marker-controlled watershed segmentation (MWS) algorithm, the Two-Step Floating Catchment Area (2SFCA) method, and spatial clustering methods sequentially to dynamically separate urban from rural areas and assess the urban–rural disparity in public facility provisions within the Chengdu–Chongqing economic circle (CCEC). The results indicate: (1) The distribution of public facilities in the CCEC is characterized by significant spatial inequity, predominantly manifested as a substantial disparity between urban and rural areas. (2) A significant misalignment exists between the supply of public facilities and the demands of vulnerable groups. Rural residents, low-income groups, and elderly people are at a disadvantage in accessing public facilities. (3) Rural residents are concentrated in low-income areas and have the most limited ability to obtain public facility services and resources, especially in terms of healthcare facilities. By replacing predefined administrative boundaries with image-derived functional urban–rural patches, the proposed framework enables a more spatially realistic assessment of urban–rural public facility disparities. This approach provides a transferable tool for system-level decision-making in regional planning, particularly for targeting resource allocation in underserved areas. Full article

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder worldwide, affecting approximately 56.9 million people in 2021 and projected to reach 152 million by 2050. Its defining pathological features, amyloid-beta plaques and neurofibrillary tangles, accumulate for up to two decades before cognitive symptoms emerge, placing the preclinical and mild cognitive impairment (MCI) stages at the centre of the early detection problem. Despite this, current diagnostic practice in routine clinical settings remains unreliable, with post-mortem studies placing the specificity of clinical AD diagnosis between 44.3 and 70.8% even in specialist memory clinics. Machine learning (ML) and deep learning (DL) applied to neuroimaging and electrophysiological data have emerged as candidate tools for closing this diagnostic gap, yet whether the accuracy figures reported in published studies translate into clinically useful performance on independent data remains unresolved. This study presents a structured comparative review of machine learning and deep learning methods reported across four publicly available Alzheimer’s disease datasets, namely the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Open Access Series of Imaging Studies (OASIS), the OpenNeuro ds004504 electroencephalography (EEG) dataset, and the Kaggle Alzheimer’s magnetic resonance imaging (MRI) dataset. Thirteen model families are examined through the published literature rather than through new experiments, and for each model and dataset combination, the best accuracy reported in the source study is recorded alongside the model’s mathematical formulation. All performance figures reported in this abstract and throughout the paper are taken from the published studies reviewed, not from new experiments conducted by the authors. Across the reviewed studies, deep learning architectures pre-trained on ImageNet and fine-tuned on neuroimaging data are reported to produce the highest accuracy on MRI classification tasks. Residual Network (ResNet)-101 is reported at 98.21 percent on ADNI and 97.45 percent on OASIS, while the IncepRes fusion architecture reaches 98.35% on OASIS by combining multi-scale feature extraction from InceptionV3 with residual connectivity from ResNet152V2. Traditional machine learning classifiers remain competitive on tabular clinical and biomarker data, with Extreme Gradient Boosting (XGBoost) reaching 91% on ADNI multiclass features. Logistic Regression achieves 82 to 85% on binary ADNI classification and is the only classifier in this review that provides explicit per-feature prediction contributions without post hoc tooling. Gaussian Naïve Bayes achieves 80 to 83% on the same task. On the OpenNeuro EEG dataset, K-nearest neighbours (KNN) with singular value decomposition (SVD) entropy features achieves 91% binary accuracy, with feature engineering quality determining performance more reliably than classifier architecture. Eight principal findings emerge from the cross-dataset analysis. Binary classification consistently outperforms multiclass by 10 to 30% across all datasets, reflecting the genuine biological ambiguity of the mild cognitive impairment category. Dataset size and augmentation predict reported accuracy more reliably than model architecture. Ensemble methods outperform individual classifiers by 5 to 8% in nearly every imaging study. Deeper architectures can overfit small clinical cohorts. EEG models trail MRI models by approximately 10 to 15% on comparable binary tasks. Cross-dataset generalisation has not been systematically evaluated in most studies, and the few that have tested it report accuracy drops of 5 to 10% or more when models encounter data from different scanners or cohorts. Eight recurring limitations constrain the clinical utility of these findings. Small sample sizes and limited demographic diversity, severe class imbalance inflating raw accuracy metrics, poor cross-dataset generalisation driven by scanner heterogeneity, limited deep learning interpretability, the dominance of binary over multiclass tasks, the absence of longitudinal modelling despite available datasets, inadequate standardisation of preprocessing and evaluation protocols, and the signal-to-noise ratio constraints specific to EEG recordings of elderly patients collectively define the gap between benchmark performance and clinical readiness. Future work must prioritise multi-centre training cohorts, multimodal fusion architectures, longitudinal progression modelling, and standardised interpretability evaluation as non-optional requirements for any system intended for clinical deployment. Full article

Health literacy (HL), recognized as a modifiable social determinant, influences the autonomy and clinical outcomes of the elderly population, so that low levels of HL contribute to worse health outcomes, including frailty. The objective was to analyze the factors associated with frailty, with a focus on HL, in elderly people from two municipalities in the interior of São Paulo. In this cross-sectional study of 219 elderly individuals (≥60 years) from Jundiaí and Araçatuba, frailty was measured using the FRAIL-BR scale and the Clinical-Functional Vulnerability Index (IVCF-20), and HL was measured using the 14-item Health Literacy Scale (HLS-14). Sociodemographic, clinical, and behavioral variables were included. Bivariate analyses and multinomial logistic regression were performed, with a significance level of 5%. Women predominated (64.8%), with an average age of 71.8 (±7.55). Low HL remained associated with frailty according to the FRAIL scale (OR = 3.66; 95% CI: 1.51–8.85) and the IVCF-20 (OR = 5.65; 95% CI: 1.83–17.44). Other factors common to both scales were advanced age (FRAIL: OR = 4.24; 95% CI: 1.26–14.28; IVCF-20: OR = 28.67; 95% CI: 5.31–154.89) and family dysfunction (FRAIL: OR = 3.67; 95% CI: 1.06–12.68; IVCF-20: OR = 6.81; 95% CI: 1.72–27.06). Living alone and diabetes were associated with the IVCF-20, while depression was associated with the FRAIL scale. This research constitutes the first empirical study conducted in Brazil to provide evidence that low levels of health literacy are significantly associated with frailty in older adults. Full article

The association of atrial fibrillation (AF) and neck of femur fractures (NOF) are common in old people, creating a complex clinical scenario with significant implications for morbidity, mortality, and healthcare systems. This narrative review explores the bidirectional relationship between AF and NOF, focusing on shared risk factors, pathophysiological links, and challenges in clinical management, and also reviews the benefit of an orthogeriatric model. Advanced age, frailty, osteoporosis, polypharmacy, and cardiovascular comorbidities predispose patients to both conditions, while AF itself increases fall risk through haemodynamic instability, syncope, and adverse effects of rate- or rhythm-controlling medications. Importantly, the physiological stress of hip fracture and subsequent surgery can precipitate new-onset or worsening AF via inflammatory, neurohormonal, and metabolic mechanisms. The main challenge for ortho-geriatricians lies in anticoagulation management and preoperative and postoperative management. While anticoagulation reduces thromboembolic risk in AF, it increases perioperative bleeding risk in patients with NOF, often leading to delays in surgery that are independently associated with poorer outcomes. This review examines the current evidence regarding perioperative anticoagulation strategies, timing of surgery, and postoperative resumption of therapy. In addition, the review examines important outcome parameters such as mortality, stroke, bleeding, length of hospital stay, and functional recovery. This highlights the importance of not only improving multidisciplinary care involving orthopaedics, cardiology, geriatrics, and anaesthesia to optimise outcomes, but also enhancing risk stratification. Standardised perioperative pathways and integrated geriatric–cardiac assessment may help mitigate complications. Therefore, understanding how AF and NOF interact is key to delivering holistic, patient-centred care for an increasingly elderly population in orthogeriatric wards. Full article

Autoimmune diseases comprise a broad group of conditions that affect virtually any organ or tissue and share mechanisms of chronic, autoimmune-based inflammation. Once rare or unknown, they have become increasingly common in recent years, affecting individuals of all ages, including the elderly, due to the growing number of older people. According to evolutionary medicine, if the frequency of a disease, or group of diseases, increases sharply over a few decades, the primary cause cannot be the effect of genetic alterations but rather the consequence of one or more alterations in the living conditions of the species. For autoimmune diseases, there is no environmental, dietary, or infectious factor that appears to correlate with their strong increased frequency. On the contrary, the epidemic of autoimmune diseases is likely correlated with serious alterations of our holobiont (i.e., our organism, the host species, plus the myriad of species coexisting with us). In particular, the critical factor appears to be the decreasing incidence of macroparasite (i.e., parasitic worm) infestations, without, however, excluding the effects of profound alterations in the bacterial ecosystems that are also part of our holobiont. The macroparasites modulate and curb the intensity of immune responses in order to survive in our bodies. In the coevolution of host organism and other species of the holobiont, a delicate balance has developed that is severely altered by the eradication of parasitic worms. Therefore, it is necessary to move beyond the concept of macroparasites as harmful species by definition and therefore to be eliminated without hesitation. Alternatively, it is essential to study our holobiont as a whole and consider the balances of its ecosystems before modern alterations. Furthermore, the evaluation of the effects of reintroducing macroparasite species with which we have coevolved into our holobiont would be enlightening or helpful to understanding autoimmune diseases and implementing effective prevention and treatment. Full article

This paper describes the design, fabrication, and successful evaluation of a wristband developed for alerting users in critical situations. The target group is single people, especially the elderly, but also people with various disabilities. A low-cost wristband, which, when these people fall, immediately triggers a Wi-Fi-connected alert system on the gadgets of the staff or the person belonging to them. The system is implemented as a wearable forearm sleeve that integrates multiple sensors and electronic components, capable of sending instant alerts to family members or caregivers via the Blynk application. Full article

Background/Objectives: Traffic accidents are a significant public health issue. Pedestrians are considered the most vulnerable victims, showing the highest mortality rates. Thus, mortality rate indicators reflect the effectiveness of policies and safety measures applied to urban mobility. Therefore, the study objective is to identify trends in pedestrian mortality to foster an understanding of the local reality and to propose effective interventions for the safety and mobility of this population. Methods: This ecological time-series study used secondary data from the Unified Health System Information System (DATASUS) on all traffic accident-related deaths in Espírito Santo, Brazil, from 2009 to 2020. Data were classified according to the 10th Revision of the International Classification of Diseases (ICD-10). Variables included sex (male; female), age group (in years: 0 to 80+), and victim type (pedestrian). Mortality rates were logarithmically transformed (base 10), and the Prais–Winsten regression model was employed using STATA 13.0. Results: A total of 1969 traffic accident-related deaths were recorded. Males accounted for 75% of deaths, individuals of mixed race (pardo) represented 57%, and 72% were unmarried. A significant reduction in mortality rates was observed across age groups, especially among individuals aged 0–24 years. Mortality trends remained stationary only among individuals aged 80 years and older. Overall, the mortality rate decreased throughout the study period, from 5.51 to 1.69 deaths per 100,000 inhabitants. Conclusion: Pedestrian mortality rates from traffic accidents in Espírito Santo showed a decreasing trend, particularly among children and young people, while remaining stable among the elderly. Full article

Background/Objectives: Age-related hearing loss (ARHL) is a common sensory impairment in the elderly, and its early prediction and intervention are crucial for improving the quality of life in older adults. This study aims to develop and validate an interpretable machine learning model based on routine blood biomarkers to predict the risk of ARHL occurrence. Methods: A total of 542 participants were selected from the National Health and Nutrition Examination Survey (NHANES) database, including 271 ARHL patients and 271 healthy controls. The samples were randomly divided into a training set (50%) and two independent internal validation sets (25% each). Through systematic comparison of 113 machine learning algorithm combinations, the optimal predictive model (glmBoost+Stepglm[forward]) was constructed, and the SHAP method was employed for feature interpretation. To evaluate the model’s generalization ability, external validation was further performed using a cohort of 92 cases from Chongqing People’s Hospital. Additionally, an openly accessible interactive prediction web page was developed based on the R Shiny framework, supporting real-time clinical risk assessment and visual interpretation. Results: The model achieved an AUC of 0.948 in the training set, with AUCs of 0.893 and 0.945 in two internal validation sets, respectively, and an overall accuracy rate of 86.3%. In the external validation cohort (albeit with a limited sample size of 92 from a single center), the model maintained good performance with an AUC of 0.839 (95% CI: 0.750–0.918) and an accuracy of 77.2%. The model identified nine key predictive features, with the top three being glycated hemoglobin (HbA1c), mean corpuscular volume (MCV), and blood glucose according to SHAP interpretability analysis. Conclusions: This study successfully developed and validated an interpretable machine learning model based on routine blood biomarkers for community-based risk stratification of age-related hearing loss. The model demonstrated robust performance in internal and external validations, including an age-matched elderly subgroup. An interactive web tool was developed to facilitate real-time risk assessment. While the model is intended as a prescreening tool for large-scale populations rather than a diagnostic test for age-matched individuals, it provides a novel approach for early identification of individuals at higher risk of ARHL and offers insights into its systemic pathogenesis. Full article

Background and Objectives: A relationship between cognitive decline (CD) and chronic coronary syndrome (CCS), common among the elderly population, has not yet been clearly established. Our study aims to evaluate the link between severe cognitive impairment and cognitive impairment, as measured by various neuropsychological tests in patients with or without CCS. In addition, we sought to identify cardiovascular risk factors (CVRFs) that influence the severity of CD and severe cognitive impairment. Materials and Methods: This observational study was conducted on 264 people with CVRFs. Of the 264, 132 were classified as patients with CCS and 132 as control subjects without CCS. Neuropsychological assessment tools included the Instrumental Activities of Daily Living (IADL) and Activities of Daily Living (ADL) scales, the Montreal Cognitive Assessment (MoCA), the Mini-Mental State Examination (MMSE), and the Geriatric Depression Scale (GDS-15). Clinical characteristics, echocardiographic measures, and vascular parameters of all subjects were also evaluated. Results: Patients with CCS had significantly lower cognitive performance (MMSE, p = 0.010; MoCA, p = 0.021), reduced functional status (IADL, p = 0.030; ADL, p = 0.012), and higher depression scores (p = 0.004) compared with controls. They also had worse cardiovascular profiles, including lower left ventricular ejection fraction (LVEF) (p = 0.001), higher NT-proBNP levels (p = 0.005), and increased carotid intima-media thickness (IMT) (p < 0.05). IMT and blood pressure values were negatively correlated with cognitive and functional scores and positively correlated with depression severity (p < 0.001). Multivariate analysis identified systolic and diastolic blood pressure, age, body mass index, heart rate, reduced daily activity, and depression as independent predictors of cognitive decline in patients with CCS. In the GDS-15 score, each unit increase was associated with a 32.1% higher risk of cognitive decline and a 37.1% higher risk of MMSE-defined severe cognitive impairment, while improved ADL scores significantly reduced this risk. Conclusions: CCS is associated with an increased risk of severe cognitive impairment and also with cognitive decline, influenced by hypertension, subclinical atherosclerosis, depression, and reduced functional status. These findings emphasize the importance of early identification and multidisciplinary management of cognitive impairment in patients with CCS to prevent progression to severe cognitive impairment. Full article

The Lazarus phenomenon (LP), also called auto-resuscitation, may happen after the end of ineffective cardiopulmonary resuscitation (CPR), or after death is confirmed in a person who did not undergo CPR, and heart activity returns spontaneously. The aim of the study was to focus on older individuals (aged >60) experiencing the LP and to analyse distractors that cause this phenomenon. Methods. PubMed, Scopus, and Web of Science electronic databases were searched to find cases of LP from the year 1982 until 31 December 2025. Of the 81 total cases found, 48 patients were included in the study. For the analysis they were divided into two subgroups dependent on age: No 1 (60–79), No 2 (≥80). Results. Based on the descriptive analysis, the causes of cardiac arrest were divided almost equally between cardiac and non-cardiac causes (47.6% and 52.3% respectively). Cardiac arrest occurred equally in the IH and OH. In 16 out of 37 cases where such data were reported, a return to consciousness was confirmed, representing 43.2%. Conclusions. In older people, even those of very advanced age, the Lazarus phenomenon may occur. Based on the analysis carried out and given the lack of available data and the small sample size (48 individuals), it is not possible at this stage to definitively identify the causes of LP in the elderly population. As a potential cause of LP, age-related changes should be taken into account. Given that LP also occurs in the older population, consideration should be given to the need for extended monitoring of vital signs following the declaration of death. With a view to raising awareness of LP, it seems appropriate to include information on this phenomenon in the CPR guidelines. Full article

Neuropharmacology has emerged in recent years as a field of active research, driven by the need to address increasing challenges posed by clinical conditions such as neurodegenerative disorders, which affect more and more elderly people worldwide as the average life expectancy extends progressively [...] Full article

Background/Objectives: Community hospitals can be a valuable and cost-effective resource for elderly people, especially in rural areas. Their aim is to promote self-reliance, prevent unnecessary hospital admissions, and facilitate rapid recovery after acute illness. The widespread adoption of intermediate care facilities helps alleviate hospital overcrowding by preventing clinical deterioration through advanced and continuous nursing care. An intermediate care unit was established in a rural area of central Italy. This study aims to describe the impact of a community hospital on patients’ functional status from admission to discharge, describing a real-life model. Methods: This single-center descriptive study examines trends in the quality of care provided. Data were retrieved from anonymized electronic clinical records. Statistical analyses were performed using descriptive statistics, paired t-tests, and Pearson correlation coefficients. Results: A total of 532 residents (mean age 80.7 ± 13.2 years; 61% female) were admitted to the community hospital between January 2022 and September 2025. The mean length of stay was 15.2 ± 7.6 days, with a mean improvement in Modified Barthel Index score of 5.24 ± 7.95 (p < 0.05). Most patients (81.8%) were discharged home, while 6.0% required hospitalization. No readmissions were recorded in 2025. Clinical risk events occurred only in 1.2% of the total. Nursing specialization increased during the study period, correlating with improved patient outcomes (R = 0.88). Conclusions: This descriptive cross-sectional study in a rural nurse-led intermediate care unit found relatively short lengths of stay, high rates of home discharges and modest, but statistically significant, improvements in functional autonomy. Full article

Home healthcare has recently been promoted in response to the increase in vulnerable people, such as elderly patients who can have difficulty accessing clinics and hospitals in Japan. A characteristic specific to home healthcare is that laboratory tests using specimens are conducted by transport from home to laboratory centers or by point-of-care testing at home. In this case, several issues can lead to inaccurate test values. This narrative literature review summarizes issues in the preanalytical process, a critical phase for ensuring the accuracy of laboratory tests. Specimen collection may not always be smooth in the pathological conditions of some elderly patients and/or in the non-clinic/hospital environments. The preservation of specimens, considering prolonged pre-centrifugation time and storage temperature, can alter the values of various analytes, including blood glucose, potassium, and lactate dehydrogenase. In addition, hemolytic phenomenon caused by insufficient specimen collection, vibration during specimen transport, and excessive milking during fingertip blood sampling can also be an issue. Awareness of the preanalytical process in testing specimens is important for obtaining accurate laboratory tests in home healthcare settings. This comprehensively summarized paper will be helpful in securing test quality and patient care. Full article