Introduction Optometrists, as primary eye care professionals working in the community, routinely provide consultations for older adults. Impaired vision significantly increases falls risk, positioning optometrists as important health service providers to address primary falls prevention. However, there is scarce evidence for how optometrists implement evidence-based falls prevention management. This study explored Australian community optometrists' understanding of falls prevention management and their perspectives on the barriers and enablers for implementing falls prevention management in their clinical practice. Methods The study employed a qualitative descriptive design, using purposeful sampling to recruit Western Australian optometrists across metropolitan and rural primary eye care settings. Focus group discussions explored participants' knowledge of falls prevention, implementation experiences, and educational opportunities to improve implementation. Data were analysed using inductive content analysis. Results Four focus groups were conducted (N = 23, females = 12 and males = 11). Participants were predominantly early career optometrists (52% with 1 - 5 years of experience), 87% were aged under 40 and primarily practicing in metropolitan areas. The main category identified a substantial evidence-to-practice gap in optometrists' falls prevention management for older adults within primary eye care settings. Four generic categories contributed; perceived low levels of knowledge and awareness about falls prevention, sporadic integration into clinical practice, the need for an improved approach to collaboration within multidisciplinary teams, and hesitancy to undertake falls prevention management. Conclusion Community optometrists had perceived low levels of knowledge about falls prevention and identified limited opportunities to implement falls prevention management. Suggestions to bridge this implementation gap included providing further education and promoting optometrists' engagement in multidisciplinary teams. Further research investigating how community optometrists can translate falls prevention evidence into practice is required.

Introduction: With the growing population of older adults, predicting survival through clinical examinations has gained an increased interest. We aimed to evaluate the relationship between balance measures, such as postural sway and voluntary stepping performance and survival. Methods: This study analyzed balance parameters, collected from previous studies conducted between 2005 and 2011, including postural sway, stabilogram diffusion analysis (SDA) in standing, and voluntary step execution. Cox proportional hazards regression analyses were performed to examine associations between balance parameters and mortality. Results: Voluntary stepping performance, especially under dual-task (DT) condition, was associated with survival. Slower step initiation time was associated with higher mortality risk; with every 0.100 s increase, the survival hazard rate ratios decreased by 28%. Postural sway parameters, particularly increase in anterior-posterior sway velocity, demonstrated predictive ability but less effective than voluntary stepping test parameters. Among SDA measures, higher critical displacement tended to show an association with lower survival in older adults. Conclusion: These findings align with prior research regarding physical performance indicators, such as gait speed, to survival. Voluntary stepping under DT conditions extends this understanding by capturing both physical and cognitive functions, offering a more integrated survival assessment. Voluntary stepping performance, in DT conditions, may be a valuable predictor of survival in older adults. Incorporating DT-based assessments into clinical evaluations could improve survival prediction and guide early interventions targeting cognitive-motor health. Future studies should explore the longitudinal impact of voluntary stepping on survival, assessing whether cognitive-motor training for balance rehabilitation can enhance survival rates.

Introduction: This study aimed to assess the within- and between-states inequality in hypertension management and cardiovascular disease (CVD) risk across sociodemographic groups in India and the correlation between states/UTs expenditure on health, hypertension management, and CVD risk. Methods: This study utilized cross-sectional data from 2017/2018 Longitudinal Aging Study in India (LASI), with a total sample of 58,848 respondents aged ≥45 years. Hypertension management was assessed based on hypertension awareness, treatment, and control, while 10-year CVD risk was measured using the 2019 WHO CVD risk. We used multilevel logistic regression models to estimate the socioeconomic inequality in hypertension management and 10-year CVD risk, measured by the relative index of inequality (RII). Results: Rural areas had a substantially poorer coverage of hypertension awareness, treatment, and control compared with urban areas. Richest socioeconomic groups were more likely to be aware of their hypertensive status (RII 1.28, 95% CI: 1.16–1.42), receive antihypertensive treatment (RII 1.47, 95% CI: 1.28–1.69), have their blood pressure controlled (RII 1.60, 95% CI: 1.34–1.90), and have 10-year CVD risk <10% (1.06, 95% CI: 1.01–1.12) compared to the poorest. The variance partition coefficient ranged from 1.5% for 10-year CVD risk to 9.9% for hypertension control. There was no significant correlation between socioeconomic inequality in hypertension management, 10-year CVD risk, and the per capita public health expenditure of states/UTs. Conclusion: Differences in state-level health system capacity may disproportionately affect socioeconomically disadvantaged populations, underscoring the need for more equitable hypertension management and cardiovascular health strategies across India.

Introduction: Older adults with mild cognitive impairment (MCI) are at elevated risk for falls and related complications. However, longitudinal data on fall incidence and functional recovery in high-risk individuals following a severe fall remain limited. This study aimed to compare 12-month fall incidence, cognitive and physical function, and physical activity in older adults with and without probable MCI who presented to the emergency department (ED) after a severe fall but were not hospitalized. Methods: Data were collected from the SeFallED study, a prospective observational study of community-dwelling older adults (≥60 years) presenting to the ED due to a fall and discharged within 72 h of a fall. Participants were classified into older adults with and without probable MCI based on Montreal Cognitive Assessment scores (cut-off ≤24). Primary outcomes were recurrent falls and those demanding medical attention, assessed monthly via telephone interviews over 12 months. Secondary outcomes included cognitive performance, physical function (Short Physical Performance Battery, gait speed, postural sway), life-space mobility, and physical activity (accelerometry, self-report). Results: Older adults with probable MCI (n = 116) were significantly more likely to experience a recurrent fall (52.6% vs. 36.5%; p = 0.012) and a fall requiring medical attention (33.6% vs. 11.9%; p < 0.001) than older adults without MCI (n = 126), as shown by chi-square tests. RM-ANCOVA indicated that cognitive performance improved in older adults with probable MCI over time, but no significant group differences emerged in physical function or physical activity. Life-space mobility remained consistently lower in older adults with probable MCI. Conclusion: Despite comparable physical function, older adults with probable MCI are at substantially higher risk of experiencing recurrent falls, including falls requiring medical attention. Further research should investigate whether incorporating a cognitive screening in the ED and initiating secondary falls prevention strategies at the earliest opportunity can reduce fall incidence, particularly among older adults with MCI.

Introduction: Falls occur in all age-groups and represent a significant public health concern. Previous studies have implemented artificial intelligence, including machine learning (ML) and deep learning (DL) algorithms for fall risk prediction, but the comparative performance between models and the applicability for younger populations remains unclear. This study aims to develop and compare different ML/DL models and identify key predictive features across age-groups. Methods: We enrolled 1,441 community-dwelling adults aged over 20 years in southern Taiwan and collected demographic, clinical, and physical performance data. Participants were categorized based on fall history. Five ML models (KNN, RF, GBDT, XGBoost, and CatBoost) and two DL models (GRU, AGRU) were trained and evaluated using accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (AUROC). Feature importance was interpreted using SHapley Additive exPlanations values in the best-performing model. Age-stratified subgroup analyses were conducted for groups aged 20–45, 46–65, and >65 years. Results: The AGRU model achieved the highest accuracy (91.39%) and AUROC (0.934) in the overall group and outperformed other models across all subgroups. Feature importance analysis revealed pulse rate, living alone, systolic blood pressure, 5-times Sit-to-Stand test, and sex as major predictors of falls in the overall group. The top five predictive factors varied across age-groups. Conclusion: We developed a robust and interpretable DL model to identify fall risk across different age-groups. Age-specific risk factors highlight the need for tailored preventive strategies. External validation using an independent dataset demonstrated moderate generalizability. Validating the model on larger, more diverse datasets and integration of sequential or sensor-based data are essential for practical applications.

Introduction: The global aging population poses significant challenges to healthcare, with frailty, balance impairment, and fall risks being prominent issues. However, the conventional clinical assessments often fail to detect early signs of these conditions. This study aimed to explore the potential of metabolomics in early identification of biomarkers related to frailty, poor balance, and fall risks in older adults. Methods: We analyzed plasma samples from 110 participants aged 25–98 years using untargeted metabolomic analysis. Clinical assessments, including Instrumental Activities of Daily Living (IADL), Morse Fall Risk Scale, Timed Up and Go (TUG), and Fried Frailty Criteria, were performed. We examined the correlation between metabolomic results, aging-related blood tests, and clinical assessments. Statistical analysis and pathway analysis were used to identify key metabolic alterations. Results: The metabolomics analysis identified 914 metabolites matching in the human metabolome database, with 293 metabolites significantly correlated with age. Metabolomic profiles showed distinct alterations in older adults, with significant metabolic changes observed in the old-old group, particularly in pathways related to lipid metabolism, sphingolipid signaling, and fatty acid metabolism. A new age classification based on metabolic profiles revealed significant differences in frailty risks across groups, with metabolic signatures linked to poor balance and fall risks. Conclusion: Metabolomics offers a promising approach to identify early biomarkers of frailty, balance impairment, and fall risks in older adults. The integration of metabolic profiles with clinical assessments could lead to more precise and personalized healthcare interventions, improving fall prevention strategies and frailty management. Future studies with larger cohorts are needed to validate these findings and explore the clinical utility of Metabolomics in aging-related healthcare.

Background: Sarcopenia and osteoporosis are common age-related conditions that frequently coexist, forming a syndrome known as osteosarcopenia. Sarcopenia is defined by the progressive loss of muscle mass, strength, and function, while osteoporosis is characterized by reduced bone mineral density and altered bone quality. Together, they significantly increase the risk of falls, fractures, and functional decline in older adults. Summary: Osteosarcopenia arises from shared pathophysiological mechanisms, including chronic low-grade inflammation, oxidative stress, hormonal changes, and nutritional deficiencies. These factors contribute to a cycle of musculoskeletal deterioration. In addition to systemic pathways, local muscle-bone crosstalk mediated by myokines and osteokines plays a critical role. Management requires a comprehensive approach. Nutritional interventions such as adequate protein, calcium, and vitamin D intake, along with anti-inflammatory nutrients like omega-3 fatty acids and polyphenols, support musculoskeletal health. Resistance and weight-bearing exercises are essential to maintain muscle and bone mass. Pharmacological treatments for osteoporosis include bisphosphonates, denosumab, and anabolic agents, while investigational therapies for sarcopenia, such as selective androgen receptor modulators and myostatin inhibitors, offer potential benefits. Key Messages: Sarcopenia and osteoporosis share risk factors and often coexist in older adults. Early diagnosis and integrated treatment strategies are essential. Nutrition, exercise, and pharmacologic interventions can mitigate the burden of osteosarcopenia. A combined approach is more effective than treating each condition separately and may reduce falls and improve quality of life.

Background: Osteoporosis is a frequent age-related skeletal disorder, wielding an enormous influence on the quality of life of many women but also men. Osteoporosis results from a shifted ratio between the activity of osteoblasts and osteoclasts resulting in an increased degradation of bone. This leads to a systemic impairment of bone mass and microarchitecture. Currently, only a small number of patients with osteoporosis is treated adequately. This implies the urgent need of developing new treatment strategies and application mechanisms as osteoporosis is associated with an enormous economic burden. Therefore, animal models are of great importance for the investigation and treatment of bone diseases. Summary: In this narrative review, we focus on animal models specifically used for age-related osteoporosis including mice, selected non-rodent small and large animal models, and fish. Key Message: With this manuscript, we want to contribute to the identification of novel treatment strategies for the treatment of age-related osteoporosis.

Plain Language SummaryCognitive frailty is a condition where older adults experience both mild cognitive impairment (MCI) and physical frailty, increasing their risk of poor health outcomes. Traditional tests for identifying this condition are often time-consuming and require specialized equipment, making them unsuitable for routine use or remote settings. This study explored a simple 20-s video-based Upper Frailty Meter (vFM) test as an alternative way to identify cognitive frailty, even from a distance. The study included 413 participants aged 50–79 years. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), and physical frailty was measured through the vFM test. During the test, participants repeatedly bent and straightened their elbows for 20 s under two conditions: a physical task alone (single task) and a physical task combined with a mental exercise (dual task). Using video analysis powered by artificial intelligence, researchers evaluated physical frailty phenotypes like slowness, weakness, and exhaustion. The results showed that individuals classified as cognitively frail (12.8% of participants) had higher frailty index compared to those without the condition. The dual-task slowness measure was the most effective individual marker for identifying cognitive frailty. Combining single-task and dual-task results improved accuracy, achieving sensitivity and specificity rates exceeding 85%. The 20-s vFM test is practical, easy to use, and suitable for telehealth. It could help identify individuals at risk of cognitive frailty early, enabling timely interventions to support healthy aging. Future research will explore its long-term effectiveness.