Search Results (1,411)

Background: Physical activity is a key modifiable factor influencing healthy aging, yet data on activity patterns and their physiological correlates in older adults from Central Asia remain limited. Understanding these relationships is essential for informing region-specific health promotion strategies. Objectives: This study assessed physical activity levels among urban-dwelling older adults in Astana, Kazakhstan, and examined associations between activity level, body composition, visceral fat accumulation, metabolic indicators, and muscle strength. Methods: A cross-sectional study was conducted among 608 adults aged ≥60 years (median age: 68 years; 82.1% women). Physical activity was measured using the validated Physical Activity Scale for the Elderly (PASE). Anthropometric and body composition indicators, including BMI, total and visceral fat, skeletal muscle mass, and handgrip strength, were evaluated. Spearman correlation and linear regression analyses were applied. The analyses were exploratory and did not include adjustment for potential confounders such as sex, chronic disease burden, or socioeconomic status; therefore, the observed associations should be interpreted with caution. Results: The median PASE score was 55.55, with 61.8% of participants demonstrating moderate activity levels, primarily through walking and household tasks. In analyses without adjustment for potential confounding factors, PASE scores showed weak inverse associations with visceral fat (ρ = −0.214; p < 0.001) and waist-to-hip ratio (ρ = −0.154; p < 0.001), as well as weak positive associations with handgrip strength. Across the reported significant associations, correlation coefficients ranged from |ρ| = 0.103 to 0.235, and the explanatory capacity of the regression models was low, with R² values ranging from 0.6% to 8.2%. Conclusions: Higher habitual physical activity may be linked to selected bioelectrical impedance parameters, WHR, and handgrip strength among urban older adults. Given the cross-sectional design, causal interpretation should be approached with caution. These findings provide meaningful regional baseline evidence for future longitudinal and intervention studies on physical activity and healthy aging in Central Asia. Full article

Background: Cognitive impairment (CI) may be overdiagnosed in individuals with vision impairment (VI) due to the vision-dependent design of current cognitive assessment tools. This cross-sectional study evaluated the acceptability and diagnostic accuracy (pilot) of the Visually Independent Test Battery of Neurocognition (VISION-Cog) protocol, against gold-standard neurologist diagnosis. Methods: Community-dwelling older adults with near binocular presenting VI (near visual acuity [NVA] ≥0.2 logarithm of the minimum angle of resolution [LogMAR] units) were recruited from the Population Health and Eye Disease Profile in Elderly Singaporeans (PIONEER) study. Participants underwent VISION-Cog and the Singapore-validated Montreal Cognitive Assessment (MoCA-SG) testing and were referred for neurologist evaluation based on standardized referral protocols. The acceptability of the VISION-Cog was assessed through study completion rates, test duration, and the qualitative feedback. Vision-Cog’s diagnostic accuracy (pilot) against neurologist evaluation was analyzed using binary logistic regression and C-statistics to estimate area under the receiver operating curve (AUC) with corresponding sensitivity and specificity. Results: Out of forty-five participants (mean age [SD]: 73.8 [6.1 years]; mean NVA [SD]: 0.47 [0.14] LogMAR; and 54.1% female), 37 (82.2%) completed the protocol. The mean VISION-Cog completion time [SD] was 59 m 57 s (7 m 18 s). Qualitatively, participants found the testing time acceptable. The VISION-Cog achieved an AUC of 0.930 against neurologist diagnosis, with 100.0% sensitivity and 78.0% specificity. Conclusions:The VISION-Cog demonstrated satisfactory preliminary diagnostic accuracy and good acceptability indices in older Asian adults, supporting the need of larger studies to confirm its diagnostic accuracy of CI and clinical utility in those with VI.: Full article

Rapid population ageing in China urgently demands improved attention to elderly friendly community green space design. Despite national efforts toward community renovation and urban regeneration, existing projects often overlook the systematic optimisation of green spaces explicitly tailored to elderly residents, leading to environments that inadequately support their physical, psychological, and social needs. Given that home-based care remains the predominant preference for elderly populations in China, creating optimised community green spaces is essential to facilitate healthy ageing-in-place effectively. This study systematically investigates the discrepancies between elders’ observed usage patterns and their stated landscape design preferences in two residential communities in Suzhou, China. By integrating year-round observational data with subjective interviews, the research identifies critical mismatches between elderly individuals’ actual behaviours and expressed preferences, highlighting significant deficiencies in current landscape designs. Comparative analyses reveal that prioritising microclimate comfort, accessible pathways, and targeted seating arrangements significantly enhances elderly usage frequency and satisfaction. Ultimately, this study provides practical, policy-aligned recommendations for designing climate-adaptive, elderly centric community green spaces, effectively contributing to sustainable urban renewal and the Healthy China 2030 initiative. Full article

This research investigates the development of a biophilic conceptual design proposal tailored to dementia care environments in South Korea, responding to the country’s rapidly aging population and the projected rise in dementia prevalence. The study integrates spatial and aesthetic strategies grounded in established biophilic design principles, including visual and non-visual connections with nature, thermal and airflow variability, dynamic and diffuse lighting, and the presence of water. Drawing on comparative case study analysis, the research emphasizes the therapeutic potential of nature-oriented environments in reducing stress, enhancing mood, improving physical health, and supporting cognitive function among residents with dementia. Emphasizing a human-centric perspective, the study also considers the experimental and behavioral needs of elderly users within the design process. In addition, it critically examines the challenges and limitations associated with implementing biophilic design strategies in architectural practice. Full article

Background: With increasing life expectancy, older adult populations worldwide are growing rapidly. Falls are among the most prominent problems that older adults face. This study aimed to assess the educational components of the Stopping Elderly Accidents, Deaths, & Injuries (STEADI) program for improving knowledge, skills, and behavioral intentions for fall prevention among older adults. Methods: A quasi-experimental study was conducted with a non-equivalent control group pretest–posttest design, involving 128 older women (≥60 years) in a community center in Riyadh. Data were collected using a structured questionnaire. Descriptive statistics were used to summarize the data. Pearson’s chi-square test was performed to compare demographic and physical characteristics between the groups. Independent-sample t-tests, effect size calculation (Cohen’s d), and ANCOVA-adjusted analyses were used to compare post-intervention outcomes between groups. Within-group changes were compared using a paired t-test. Additionally, one-way analysis of variance (ANOVA) was performed to compare the demographic, health, and physical characteristics of the participants. Statistical significance was set at p ≤ 0.05. Results: The intervention group showed improved knowledge (t = 11.654), skills (t = 7.961), and intention to perform preventive behaviors (t = 3.785), with a significant p-value of <0.0001. Large intervention effects were observed for knowledge (Cohen’s d = 2.30) and skills (Cohen’s d = 1.57). ANCOVA-adjusted analyses confirmed significant intervention effects for knowledge (adjusted mean difference = 5.06, 95% CI 4.46–5.66, p < 0.001) and skills (adjusted mean difference = 1.87, 95% CI 1.56–2.18, p < 0.001). Conclusions: The results indicate that the STEADI program produces significant short-term improvements in knowledge, skills, and behavioral intentions related to fall prevention. The findings emphasize the importance of integrating prevention programs into community settings and activating the role of families in supporting preventive practices. Full article

Medication dispensing errors remain a significant concern in healthcare systems, particularly in elderly care and long-term medication management, where incorrect medication delivery may compromise patient safety and treatment outcomes. This study presents the design and experimental validation of a cyber–physical medication dispensing platform integrating robotic manipulation, edge AI-based visual verification, distributed motion control, and cloud synchronization. The platform combines a rotary medication storage mechanism, vacuum-based pill handling, a Klipper-based control framework, and a YOLOv8 perception subsystem deployed on a Hailo AI accelerator for real-time edge inference. Experimental evaluation was conducted under controlled laboratory conditions. Using an environment-specific validation dataset, the perception subsystem achieved a precision of 0.627, recall of 0.739, and mAP@0.5 of 0.786. An adaptive verification strategy was subsequently evaluated to improve dispensing verification under varying pill occupancy conditions. End-to-end system testing comprising 80 dispensing trials achieved an overall dispensing success rate of 86.25%, with no incorrect dispensing events observed. The results demonstrate the feasibility of integrating edge AI verification, distributed control, and cloud connectivity within a cyber–physical medication dispensing platform. The presented system provides a foundation for future research on perception-assisted medication dispensing, long-term deployment, and clinical validation in smart healthcare environments. Full article

Wearable-sensor human activity recognition (HAR) plays a key role in health monitoring, elderly care, and human–computer interaction. Deep learning dominates the field, but two limitations remain. CNNs with fixed kernels cannot capture cross-scale temporal events such as gait cycles and postural transitions in a single layer, and softmax attention on small sensor datasets is often diluted by common-mode background responses across the sequence. We propose DMSCNet, an end-to-end framework with two modules. The Dilated Multi-Scale Branch Block (DMSB) combines a shared bottleneck, parallel dilated convolutions, a pooling bypass, and SE-based channel recalibration to widen the temporal receptive field under a controlled parameter budget. The Contrastive Temporal Attention (CTA) module adopts a dual-path differential design, in which the two paths learn overlapping but non-identical attention patterns and their subtraction suppresses shared low-level responses while preserving the discriminative positions each path locks onto, encoded with opposite signs. DMSB and CTA are cascaded into a DMSC Block and stacked residually. On UCI-HAR, USC-HAD, and RealWorld, DMSCNet reaches F1-scores of 97.65%, 91.80%, and 99.05%, outperforming nine baselines. Ablations confirm that SE acts along the channel axis and CTA along the temporal axis, and visualization reveals a dynamic–static dichotomy together with a signed bipolar encoding pattern produced by the dual-path subtraction. Full article

Mobile health (mHealth) systems in rural communities require rigorous software engineering methodology and empirical validation of end-user acceptance. A gap exists in applying structured System Development Life Cycle (SDLC) frameworks to community-facing mHealth platforms with embedded technology acceptance evaluation. This study presents the design, architecture, and iterative development of the “Smart Daily Life Care” cross-platform mobile application using a six-phase SDLC framework, targeting rural elderly communities in Thailand. The system architecture employed a microservices design with age-friendly UI engineering, conforming to WCAG 2.1 AA. Technology acceptance was evaluated post-deployment using the Technology Acceptance Model (TAM) with 200 participants (elderly users, caregivers, and health personnel). System efficiency was rated at $\overline{\mathrm{x}}$ = 4.58 and user satisfaction at $\overline{\mathrm{x}}$ = 4.64. TAM regression identified perceived usefulness as the dominant predictor of behavioral intention (β = 0.412), followed by perceived ease of use (β = 0.318) and social influence (β = 0.268), with R² = 0.682. Integrating TAM evaluation within SDLC phases enables iterative remediation of acceptance barriers before deployment. Village Health Volunteer networks function as indispensable sociotechnical enablers of adoption. The SDLC–TAM integration provides a structured methodological approach suitable for replication in age-sensitive health information systems in low-resource settings. Full article

Background/Objectives: Halitosis in geriatric patients is multifactorial, but the joint contribution of prosthetic rehabilitation type and polypharmacy after routine dental procedures has rarely been quantified. We investigated how prosthesis type, polypharmacy, and salivary function were associated with volatile sulfur compound (VSC) burden and self-perceived halitosis in elderly dental patients. Methods: This cross-sectional study enrolled 88 patients aged ≥65 years, four weeks after completing routine dental procedures. Participants were stratified into three groups: complete denture wearers (n = 30), partial removable denture wearers (n = 28), and fixed prostheses/implants (n = 30). We measured unstimulated salivary flow rate (uSFR), tongue coating index (TCI), denture biofilm index, total VSCs (Halimeter^®), organoleptic score (0–5), and self-perceived halitosis. Polypharmacy, comorbidities, and the Geriatric Oral Health Assessment Index (GOHAI) were recorded. Analyses included one- and two-way ANOVA, Spearman correlations, theory-informed multivariable linear and logistic regression, exploratory mediation analysis, and ROC curves. Results: Forty-two participants (47.7%) reported halitosis. Mean VSC differed across groups (complete dentures 278.2 ± 38.6 ppb; partial 211.2 ± 46.3 ppb; fixed 164.4 ± 43.9 ppb; ANOVA p < 0.001). uSFR correlated inversely with VSC (ρ = −0.61, p < 0.001) and TCI correlated positively (ρ = 0.56, p < 0.001). A significant prosthesis × polypharmacy interaction was observed (F = 3.74, p = 0.029, η²_p = 0.082): polypharmacy was associated with higher VSC most clearly among partial and fixed prostheses wearers, whereas complete denture wearers showed high VSC levels regardless of polypharmacy status. Exploratory mediation findings were consistent with partial indirect association, with 45.9% of the polypharmacy–VSC association statistically explained by reduced uSFR; however, the cross-sectional design precludes causal or temporal interpretation. The full multivariable model showed apparent discrimination for self-perceived halitosis (AUC = 0.92), while the simplified four-item chairside composite model showed AUC = 0.89; neither estimate was optimism-corrected or externally validated. Conclusions: In elderly post-procedure patients, complete denture wearing, polypharmacy, and salivary hypofunction were independently and jointly associated with higher halitosis burden. Reduced salivary flow was consistent with a partial indirect statistical pathway in the polypharmacy–VSC association, supporting hydration counseling and meticulous prosthesis hygiene as low-cost geriatric interventions. Sensitivity analyses excluding implant-supported restorations, participants with MMSE scores of 24–26, and expanded mediation models including TCI and biofilm/plaque did not materially change the main inference. Full article

The prevalence of diabetes and heart failure rises sharply with age, and their coexistence amplifies cardiovascular and renal risk. Elderly patients display unique clinical and biological profiles characterised by frailty, multimorbidity, and pharmacodynamic variability that challenge conventional treatment strategies. Sodium–glucose co-transporter-2 inhibitors (SGLT2i) have emerged as a cornerstone of cardio–renal–metabolic protection, with the most consistent cardiovascular benefit being the reduction in heart failure hospitalisation, whereas effects on cardiovascular death and major adverse cardiovascular events vary according to baseline cardiovascular risk, heart failure phenotype, diabetic status, and trial design. However, real-world use among the elderly remains limited due to concerns about tolerability, polypharmacy, and cost. This review analyses the pharmacological rationale and evidence base for SGLT2i therapy in older adults, highlighting mechanisms beyond glucose control, quantitative data from pivotal trials, and practical issues for geriatric implementation. Full article

Background/Objectives: Determining chronological age is important in several domains, including forensic identification, clinical decision-making, legal matters, and immigration procedures. Dental tissues are widely recognized as reliable indicators of age because they undergo gradual and measurable structural changes throughout life. Nevertheless, most conventional dental methods show limited reliability when applied to adults and elderly individuals. The objective of this study was to investigate an automated deep learning-based approach for age-group classification in adults and seniors using panoramic dental radiographs. Methods: Panoramic dental radiographs were analyzed using a custom-designed Convolutional Neural Network (CNN) along with several established pre-trained deep learning architectures. The dataset consisted of 1469 radiographic images obtained from Egyptian individuals aged between 25 and 70 years. Images were classified into five predefined age categories using a classification-based framework, and the models were trained to learn age-related dental patterns from radiographic images. Results: The proposed Custom CNN achieved the highest accuracy of 85.2%, outperforming YOLOv8 (79.1%) and all other evaluated models, with the lowest prediction error (MAE = 1.92 years; RMSE = 5.46 years). Overall, the deep learning models demonstrated strong performance in classifying dental age groups, particularly within adult and senior populations, where conventional methods often show reduced reliability. Conclusions: The findings suggest that deep learning analysis of panoramic dental radiographs may serve as a supportive tool for age-group classification in adult populations, complementing rather than replacing traditional assessment methods. These results, while promising, are limited to the dataset and experimental conditions of this study, and broader applicability requires further validation across diverse populations and settings. Full article

In this study, we analyzed the physical load and postural needs of the elderly during the waiting process in a hospital. Based on the results, a waiting chair with supportive and assistive functions was designed. The design was created using ergonomic principles, focusing on two main aspects: head support and non-electric standing assistance. The design was validated through interviews with older adult users to ensure that it addresses their actual needs and enhances overall usability. The design benefits older adults when using the chair. Full article

Background/Objectives: Colorectal cancer is a leading cause of cancer-related mortality worldwide, and early detection of gastrointestinal (GI) polyps through endoscopy is critical for improving patient outcomes. However, access to specialist gastroenterology care remains severely limited in Federal Health Professional Shortage Areas (HPSAs), particularly for high-acuity geriatric patients. This study proposes GeriAIGastroNet, a clinically oriented deep learning framework designed to support AI-assisted tele-gastroenterology workflows in resource-limited settings, with the primary objective of enabling AI-powered risk stratification and colonoscopy referral triage for elderly patients who lack on-site gastroenterology access. Methods: The framework integrates an EfficientNet-B4 backbone with multi-scale attention fusion and a geriatric severity-aware classification head to enable accurate GI polyp segmentation and automated clinical risk stratification from endoscopic images. Patients identified as high-risk are referred to colonoscopy-capable centers; such centers typically offer diagnostic colonoscopy with polypectomy capability for smaller and intermediate-complexity polyps, while patients with larger, sessile, or morphologically complex lesions requiring advanced endoscopic resection (e.g., endoscopic mucosal resection or endoscopic submucosal dissection) are further referred to tertiary endoscopy centers with specialized expertise. The model was trained and evaluated on the publicly available HyperKvasir dataset (1000 annotated polyp images). Results: GeriAIGastroNet achieved a classification accuracy of 96.77%, F1-score of 96.90%, Dice coefficient of 89.18%, and Intersection over Union (IoU) of 80.80%, outperforming established baselines, including U-Net, Attention U-Net, TransUNet, and Hybrid CNN-Transformer architectures. The integrated tele-gastroenterology decision support layer enables severity-based patient triage and automated referral triggering. Conclusions: These results demonstrate the potential of AI-powered polyp analysis to strengthen equitable access to GI care by facilitating risk stratification and specialist referral in HPSAs where direct endoscopy is unavailable, making the system deployable in telehealth infrastructures serving underserved elderly populations. Full article

This study examines the risk of accidental subscription among elderly consumers in micro-drama platforms, addressing a critical gap in digital consumer behavior research as aging populations increasingly engage with subscription-based digital services. Using a quantitative approach, data were collected from 780 Thai respondents aged 60 and above through a structured online questionnaire. The data were analyzed using binary logistic regression to assess the effects of demographic factors (age, gender, education, and income) and behavioral factors (platform usage frequency, time spent per session, prior subscription experience, and impulse clicking behavior) on the likelihood of accidental subscription. The findings reveal that age, gender, platform usage frequency, time spent per session, and prior subscription experience significantly influence accidental subscription, while education, income, and impulse clicking behavior do not. Notably, frequent platform use and prior experience increase risk, whereas longer session duration reduces it, suggesting nuanced engagement effects. These results confirm that accidental subscription is a systematic and predictable outcome shaped by user characteristics and interaction patterns. The study contributes by extending consumer behavior research to unintended outcomes and offers practical implications for user-centered platform design, consumer protection policies, and targeted digital literacy initiatives, particularly in emerging digital economies. Full article

Background/Objectives: Anxiety disorders in people over 65 y of age are common. Treatment of those disorders is often based on studies involving much younger patients. Experience shows that those treatments are regularly ineffective in the elderly, due to differences in physiology and the disparate etiology of the disease. Here, we examine current trends in research to generate data for evidence-based approaches to treat anxiety disorders in the elderly. Our objective was to evaluate the scope, methodological characteristics, and therapeutic focus of current clinical trials for anxiety disorders in the elderly, and to determine whether the existing evidence pipeline is likely to meet the substantial unmet need for effective and well-tolerated treatments. Methods: We searched clinicaltrials.gov for studies addressing “Anxiety disorder” and related readouts and selected those studies that included patients older than 65 y, and that had anxiety measures as primary or secondary endpoints. Results: We find that over 99% of clinical “anxiety” trials exclude patients older than 65 y. Sixty-six trials fulfilled our inclusion criteria. Trials specifically recruiting the elderly are a rare exception. Unexpectedly, only 10 “anxiety” trials are sponsored by the pharmaceutical industry, despite the potential rewards in such investments. Discussion and Conclusions: Although most clinical trials are registered in clinicaltrials.gov., our work is limited by the fact that not all clinical trials carried out world-wide are included in that database. Our findings indicate that ongoing clinical research supporting evidence-based recommendations for the treatment of anxiety in the elderly is scarce. Detailed secondary analysis of clinical trial results for the efficacy and safety of anxiolytics in various age cohorts may at least be a useful instrument for hypothesis generation, to trigger additional clinical research specifically designed to address anxiety treatment in the elderly. Full article