Background: Intrinsic capacity (IC) can better reflect the physical functioning of older adults. However, few studies have been able to systematically and thoroughly examine its influencing factors and provide limited evidence for the improvement of intrinsic capacity. The objective of this study was to provide a comprehensive description of the overall decline in intrinsic capacity among older persons in the community. Additionally, the study aimed to analyze the composition of the five domains of reduction, compare the rate of decline among older adults and investigate the factors that influence this decline. Methods: This was a cross-sectional study conducted in the Chinese community. The self-designed general characteristics questionnaire was created based on the healthy aging framework and a systematic review. Intrinsic capacity was assessed with the Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS-15), Community Health Record Management System (CHRMS), Mini Nutritional Assessment Brief Form (MNA-SF), and Short Physical Performance Battery (SPPB). The influencing factors of intrinsic capacity were investigated using stepwise logistic regression. Results: A total of 968 older adults with a mean age of 71.00 (68.00, 76.75) were examined, and 704 older adults (72.7%) showed a decline in intrinsic capacity. There was a decline in at least one domain in 39.3% of older adults, with reductions in each domain ranging from 5.3% (psychological) to 52.4% (sensory). The study examined the composition of domains that experienced a decline in intrinsic capacity. It was found that a combination of sensory and locomotor domains showed the most significant decrease in 44.5% (n = 106) of individuals who experienced a decline in the two domains. Furthermore, a combination of sensory, cognitive, and locomotor domains exhibited a significant decrease in 51.3% (n = 44) of individuals who experienced a reduction in three domains. Lastly, a combination of sensory, vitality, cognitive, and locomotor domains showed the most significant decline in four domains, accounting for 60.0% (n = 15) of the population. Older adults had a higher risk of intrinsic capacity decline if they were older (95% CI:1.158–2.310), had lower education, lived alone (95% CI: 1.133–3.216), smoked (95% CI: 1.163–3.251), high Charlson Comorbidity Index (95% CI: 1.243–1.807) scores, did not regular exercise (95% CI:1.150–3.084), with lower handgrip strength (95% CI: 0.945–0.982). Conclusions: We found a relatively high prevalence of intrinsic capacity; more attention should be paid to older adults who are older, less educated, live alone, and have more comorbidities. It is imperative to prioritize a healthy lifestyle among older persons who exhibit smoking habits, lack regular exercise, and possess inadequate handgrip strength.

Developing a nursing undergraduate blended teaching quality evaluation tool based on the Context, Input, Process and Product model and evaluating its reliability and validity. Blended teaching is a commonly used teaching method in medical education, but there are limited tools available to effectively measure the quality of blended teaching. A Delphi study and cross-sectional study. Using the Context, Input, Process and Product model as the theoretical framework, a questionnaire was developed through literature review, expert consultation and pre-survey. From April to July 2023, 448 students from a certain university were selected as the research subjects and the questionnaire was examined for reliability and validity through a survey method. The blended teaching quality evaluation scale with 35 items includes four dimensions Context, Input, Process and Product. The content validity and reliability of the blended teaching quality evaluation scale are both good, with a content validity index of 0.934 for the total scale and a content validity index of 0.750-1.00 for each item. The SEM shows that χ2/df = 6.89, RMSEA = 0.115, CFI = 0.882, NFI=0.865, RFI= 0.855, IFI = 0.882, TLI = 0.873. The Cronbach's α coefficient of the total scale is 0.991 and the Cronbach's α coefficient for each dimension is 0.944-0.984. The scale is based on the characteristics of blended learning and quality evaluation covers all aspects of teaching. It can accurately evaluate the quality of teaching, evaluate the problems in the teaching process based on the teaching quality score and propose reasonable teaching improvement suggestions based on the weak links in the teaching process.

Sleep disturbances, frailty, and body pain are widespread in middle-aged and older adults with depression, and have been identified as depression risk factors. However, there is a scarcity of research on the benefits of sleep improvement, frailty amelioration, and pain management on incident depression. A total of 8895 respondents aged above 45 years were derived from the China Health and Retirement Longitudinal Study (CHARLS) between 2011 and 2018. The parametric g-formula was used to estimate the 7-year risks of depression under independent hypothetical interventions on nighttime sleep duration (NSD), daytime napping duration (DND), perceived sleep quality (PSQ), frailty, and pain, as well as their various combinations. The observed depression risk was 41.77 %. The independent intervention on frailty was the most effective in lowering incident depression, with a risk ratio (RR) of 0.61 (95 % CI: 0.57-0.64), followed by PSQ (RR: 0.75, 95 % CI: 0.73-0.78), pain (RR: 0.90, 95 % CI: 0.87-0.91), and NSD (RR: 0.96, 95 % CI: 0.93-0.98). In subgroup analysis, intervention on NSD was more effective in men, PSQ was more effective in middle-aged individuals, and frailty and pain were more effective in older persons. The combined intervention of NSD, PSQ, frailty, and pain lowered the risk the greatest (RR: 0.35, 95 % CI: 0.32-0.37). Generalizing our results to other populations should be possible if they have the same distribution of effect modifiers and interference patterns because of the calculation principle of the parametric g-formula. Interventions for sleep disturbances, frailty, and body pain can minimize the risk of depression.

Background: Exposure to green spaces is associated with many health benefits across various stages of life. However, no comprehensive synthesis currently exists to consolidate this evidence into a systematic body of knowledge that captures the overall impact of exposure to green space on the health of older adults. This systematic review seeks to address this gap by generating a thorough, integrated evidence base and offering valuable insights for future research directions and practical applications. Methods: We adopted an extensive search strategy, drawing from multiple electronic databases as well as the National Institute for Health and Clinical Excellence (United Kingdom) and the Guidelines International Network. The electronic databases searched included the Cochrane Library, Medline, Embase, CINAHL, PubMed, Web of Science, Scopus, Global Health, and CNKI, using the PEOS search framework. The search encompassed publications from the inception of each database to November 2024. A total of 5,749 records were initially identified, and, following a dual-reviewer screening and selection process, 27 studies were ultimately included in the review. Results: Exposure to green spaces was associated with effects on circulatory system disorders, mental health conditions, nervous system diseases, cognitive decline, metabolic disorders, and overall life satisfaction in older adults. Evidence regarding cardiovascular outcomes was mixed, with some studies indicating a U-shaped relationship for hypertension. Exposure to green space demonstrated a protective role against metabolic diseases and was positively associated with enhanced life satisfaction among older adults. Meta-analyses revealed that exposure to green space was associated with a lower risk of Alzheimer’s disease ( OR= 0.856, 95% CI: 0.769–0.943) and depression (OR= 0.724, 95% CI: 0.549–0.900). Conclusions: This systematic review synthesizes the multifaceted health effects of green environments on ageing populations, critically evaluating empirical evidence on exposure to green space and health outcomes in older adults. The findings present a robust, evidence-based framework that underscores consistent associations between the accessibility and quality of green spaces and key indicators of geriatric health, thereby laying the groundwork for designing targeted, nature-based behavioral interventions. Trial registration: PROSPERO (CRD42024619700).

Emerging evidence links metabolic dysfunction-associated fatty liver disease (MAFLD) with increased all-cause and circulatory system disease (CSD) mortality in adults, yet survival machine learning studies are limited. This study analyzed 4415 NHANES participants with MAFLD to predict mortality using five survival models, and further, the optimal models were selected to identify the most significant predictors of mortality. Machine learning models proved highly effective in prediction. The Gradient Boosted Survival (GBS) model performed best for all-cause mortality, while Extra Survival Trees (EST) excelled for CSD mortality. The Shapley Additive Explanations (SHAP) analyses revealed that the five clinical factors most strongly associated with all-cause mortality were age, gender, platelet count, high-density lipoprotein cholesterol, and smoking status. For CSD mortality, the key factors associated with increased risk were age, blood urea nitrogen, systolic blood pressure, history of heart attack, and gender. Subgroup analyses confirmed GBS and Cox proportional hazard (CoxPH) were optimal for middle-aged and older all-cause mortality, whereas Elastic Net-regularized Cox proportional hazard (CoxNet) was best for older CSD mortality. The findings demonstrate that survival machine learning models effectively predict mortality risk in MAFLD patients. Integrating these models with permutation importance and SHAP provides transparent insights into individual risk profiles, enabling clinicians to clearly interpret how key variables contribute to predictions and improve risk stratification.