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ObjectivesEarly identification of fracture risk in patients with osteoporosis is essential. Machine learning (ML) has emerged as a promising technique to predict the risk, whereas its predictive performance remains controversial. Therefore, we conducted this systematic review and meta-analysis to explore the predictive efficiency of ML for the risk of fracture in patients with osteoporosis.MethodsRelevant studies were retrieved from four databases (PubMed, Embase, Cochrane Library and Web of Science) until 31 May 2023. A meta-analysis of the C-index was performed using a random-effects model, while a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. In addition, subgroup analysis was performed according to the types of ML models and fracture sites.ResultsFifty-three studies were included in our meta-analysis, involving 15 209 268 patients, 86 prediction models specifically developed for the osteoporosis population and 41 validation sets. The most commonly used predictors in these models encompassed age, BMI, past fracture history, bone mineral density T-score, history of falls, BMD, radiomics data, weight, height, gender and other chronic diseases. Overall, the pooled C-index of ML was 0.75 (95% CI: 0.72, 0.78) and 0.75 (95% CI: 0.71, 0.78) in the training set and validation set, respectively; the pooled sensitivity was 0.79 (95% CI: 0.72, 0.84) and 0.76 (95% CI: 0.80, 0.81) in the training set and validation set, respectively; and the pooled specificity was 0.81 (95% CI: 0.75, 0.86) and 0.83 (95% CI: 0.72, 0.90) in the training set and validation set, respectively.ConclusionsML has a favourable predictive performance for fracture risk in patients with osteoporosis. However, most current studies lack external validation. Thus, external validation is required to verify the reliability of ML models.PROSPERO registration numberCRD42022346896.

Given the global demographic shifts and rapid digitalization, digital engagement has emerged as a critical determinant of healthy aging. While previous research has linked digital engagement to cognitive outcomes, the underlying mechanisms remain underexplored among Chinese older adults. This study aimed to analyze the relationships between digital engagement and cognitive function among older adults in China through a moderated mediation model guided by the technological reserve hypothesis, with digital health literacy (DHL) and social support as mediators and living arrangements as a moderator. We conducted a cross-sectional questionnaire survey using stratified multistage sampling from June to November 2024, including 8123 participants aged 55 years and older. Digital engagement, defined as older adults' use of contemporary digital technologies to support routine daily activities, autonomy, independence, and social inclusion, was assessed using a multidimensional questionnaire. The Chinese eHealth Literacy Scale, the 3-item short version of the Perceived Social Support Scale, and the Mini-Cog test were used to assess DHL, social support, and cognitive function. Guided by a directed acyclic graph based on the technological reserve hypothesis, mediation and moderated mediation analyses were performed using the PROCESS macro in SPSS (IBM Corp) with 5000 bootstrap resamples. Digital engagement was positively associated with cognitive function among older adults (β=0.241, 95% CI 0.216-0.265). This association was partially mediated by DHL (β=0.059, 95% CI 0.049-0.069) and social support (β=0.012, 95% CI 0.008-0.016), with the combined indirect effects accounting for 29.5% of the total effect (β=0.071, 95% CI 0.061-0.082). Additionally, living arrangements significantly moderated the associations between digital engagement and cognitive function (β=0.109, 95% CI 0.052-0.166), digital engagement and DHL (β=0.063, 95% CI 0.014-0.112), and digital engagement and social support (β=0.151, 95% CI 0.089-0.212). These effects were stronger among older adults living alone. This study contributes to the understanding of cognitive aging in the digital environment from the perspective of the technological reserve hypothesis and digital engagement. Digital engagement influenced cognitive function via DHL and social support, and these associations of digital engagement with cognitive function, DHL, and social support were stronger among older adults living alone. Digital health interventions and public health policies should target both DHL and social support among older populations and prioritize older adults living alone.

Objectives Two-sample MR methods were employed to analyze the impact of smoking and bitter beverage consumption on the risk of osteoporosis and osteoporosis with pathological fractures, in order to assess the causal association. Methods Publicly available genome-wide association study summary data were analyzed using MR methods. The exposures investigated were smoking (smoking per day, smoking initiation, and lifetime smoking index) and bitter beverages (coffee, tea, bitter alcoholic beverages, bitter non-alcoholic beverages, and total bitter beverages). The outcomes examined were the risk of osteoporosis and osteoporosis with pathological fractures. The inverse-variance weighted (IVW) method was used as the main statistical model. The stability and reliability of the results were verified by the Cochran’s Q test, the Egger-intercept test, and the leave-one-out analysis. Results Smoking per day was causally associated with the risk of osteoporosis OR = 1.417, 95% CI = 1.119–1.794, P  = 0.003), and lifetime smoking index had a possible genetic causal association with the risk of osteoporosis with pathological fractures (OR = 4.187, 95% CI = 1.909–9.184, P  < 0.001). No genetic causal association was found between smoking initiation or lifetime smoking index and the risk of osteoporosis ( P  > 0.05). No genetic causal association was identified between smoking per day or smoking initiation and the risk of osteoporosis with pathological fractures ( P  > 0.05). Total and bitter non-alcoholic beverage consumption showed a potential effect on the risk of osteoporosis (OR = 3.687, 95% CI = 1.535–8.858, P  = 0.003 and OR = 3.040, 95% CI = 1.466–6.304, P  = 0.002, respectively). Conclusions This study found smoking raises the risk of osteoporosis and osteoporosis with pathological fractures based on genetics. Certain bitter beverages are linked to an increased osteoporosis risk.

Background The health literacy among older adults deserves further investigation. This study aimed to conduct a multistage research to investigate the current status and key determinants of health literacy among Chinese older adults and evaluate the effectiveness of an active learning intervention in enhancing their health literacy. Methods In the first phase, a cross-sectional study surveyed 608 elderly residents. The second phase was a two-arm parallel randomized controlled trial (RCT) in which 120 older adults were randomly assigned to a three-month intervention ( n  = 60) or control group ( n  = 60). The active learning program intervention included health lectures, active discussions, heuristic questioning, and family homework, while the control group only received health literacy pamphlets. Health literacy scores were the primary outcome and were evaluated from five dimensions. The RCT data was collected at baseline and the completion of the intervention. Results In the cross-sectional study, the median (IQR) health literacy score was 4.355 (4.030, 4.647) (range: 0–5) Quantile regression showed that sex, education, number of children, self-reported health, chronic disease and insurance significantly affected health literacy. The intervention group showed significant improvement in all dimensions ( P  < 0.05), with significant group × time interactions in health knowledge, health behaviours, health skills, health intentions and total health literacy. Multiple linear regression indicated that marriage status related to health knowledge, education level related to health behaviours and total health literacy, chronic diseases and insurance factors related to health skills, and sex and insurance factors related to health intentions have significant effects. Conclusion The health literacy of older adults is influenced by individuals, families, and societal factors. The active learning program effectively enhances comprehensive health literacy and is a valuable strategy for advancing China’s proactive health strategy by mobilizing the roles of the individual, family, and society. Trial registration The trial has been retrospectively registered on April 8, 2025, at the Chinese Clinical Trial Registry (ChiCTR2500100396|| http://www.chictr.org.cn/ ), which is a primary registry of the International Clinical Trial Registry Platform of the World Health Organization.

Objectives: The longitudinal relationship between sarcopenia, depression, and cognitive impairment has been insufficiently studied in China. This study aimed to characterize the association between sarcopenia and cognitive impairment and the mediating role of depression using nationally representative data. Methods: 7091 middle-aged and older adults were analyzed from the China Health and Retirement Longitudinal Study (CHARLS) across three waves (2011, 2013, and 2015). Cognitive trajectories were modeled using a group-based trajectory model (GBTM), while multivariable ordinal logistic regression was employed to evaluate the associations with cognitive trajectories. The mediating role of depressive symptoms was assessed through bootstrap mediation analysis and cross-lagged panel modeling (CLPM). Results: Trajectory analysis identified four distinct cognitive function patterns: “High and Stable” trajectory (n = 2563, 36.73%), “Middle and Stable” group (n = 2860, 38.76%), “Middle and Decline” group (n = 1280, 18.62%), and “Low and Decline” group (n = 388, 5.90%). Sarcopenia and depressive symptoms were associated with the “Low and Decline” trajectory of cognitive function [Overall: OR (95%CI) of 0.315 (0.259, 0.382) and 0.417 (0.380, 0.459)]. Mediation analysis indicated that depressive symptoms accounted for 11.78% of the relationship between sarcopenia and cognitive trajectories. The cross-lagged panel modeling demonstrated a significant mediation pathway of “T1 cognitive function → T2 depression → T3 sarcopenia”, with T2 depression mediating 5.31% of the total effect. Conclusions: Our study identified four distinct cognitive trajectories, with sarcopenia and depressive symptoms significantly associated with worse cognitive trajectories over time. Depressive symptoms mediated the relationship between sarcopenia and cognitive function. This highlights the importance of integrating mental health and physical health interventions to address the interconnected risks associated with aging.

Background With the rapid advancement of digital transformation, digital health literacy (DHL) has emerged as a crucial determinant influencing health outcomes and health inequalities among older adults. However, empirical evidence on how DHL affects health inequalities remains relatively limited. This study aims to investigate the association between DHL and health inequalities while analyzing the potential mechanisms through which DHL exerts its influence via mechanism testing among older adults in China. Methods From June to November 2024, a multicenter cross-sectional study was conducted by five research groups from four universities in China. DHL was assessed using the eHEALS scale, while health inequalities were measured using an index of relative deprivation. Multivariate regression models examined the association between DHL and health inequalities. The Baron and Kenny stepwise regression method was used to examine the mediating effect, and bias-corrected bootstrap resampling with 5000 iterations was applied to calculate 95% confidence intervals (95% CI) to confirm the significance of the mediating effect. Results Overall, 8696 valid individuals were included. DHL demonstrated a significant positive association with self-rated health (SRH) (coef = 0.015, P < .01) and a significant negative association with health inequalities (coef = −0.016, P < .01). DHL indirectly reduced health inequalities through the mediating effects of alleviating depressive symptoms (coef = −0.005, 95%CI: −0.0066, −0.0045) and promoting physical activity (coef = −0.001, 95%CI: −0.0020, −0.0011), respectively. However, the role in promoting health service utilization and regulating unhealthy behaviors was not significant. Bootstrap tests confirmed the significance of the mediating role. Conclusion DHL was associated with health inequalities and mitigates them by alleviating depressive symptoms and promoting physical activity. It is recommended that while bridging the digital divide, more attention could be paid to DHL and the translation of competencies among older adults to reduce health disparities and promote equitable aging.

Hepatitis B virus (HBV) infection is a major problem for public health; timely antiviral treatment can significantly prevent the progression of liver damage from HBV by slowing down or stopping the virus from reproducing. In the study we applied Bayesian approach to cost-effectiveness analysis, using Markov Chain Monte Carlo (MCMC) simulation methods for the relevant evidence input into the model to evaluate cost-effectiveness of entecavir (ETV) and lamivudine (LVD) therapy for chronic hepatitis B (CHB) in Jiangsu, China, thus providing information to the public health system in the CHB therapy. Eight-stage Markov model was developed, a hypothetical cohort of 35-year-old HBeAg-positive patients with CHB was entered into the model. Treatment regimens were LVD100mg daily and ETV 0.5 mg daily. The transition parameters were derived either from systematic reviews of the literature or from previous economic studies. The outcome measures were life-years, quality-adjusted lifeyears (QALYs), and expected costs associated with the treatments and disease progression. For the Bayesian models all the analysis was implemented by using WinBUGS version 1.4. Expected cost, life expectancy, QALYs decreased with age. Cost-effectiveness increased with age. Expected cost of ETV was less than LVD, while life expectancy and QALYs were higher than that of LVD, ETV strategy was more cost-effective. Costs and benefits of the Monte Carlo simulation were very close to the results of exact form among the group, but standard deviation of each group indicated there was a big difference between individual patients. Compared with lamivudine, entecavir is the more cost-effective option. CHB patients should accept antiviral treatment as soon as possible as the lower age the more cost-effective. Monte Carlo simulation obtained costs and effectiveness distribution, indicate our Markov model is of good robustness.

Disturbed sleep patterns are common among older adults and may contribute to cognitive and physical declines. However, evidence for the relationship between sleep duration and cognitive frailty, a concept combining physical frailty and cognitive impairment in older adults is lacking. The objective of our study was to examine the associations of sleep duration and its changes with cognitive frailty. We analyzed data from the 2008-2018 waves of the Chinese Longitudinal Healthy Longevity Survey. Cognitive frailty was rendered based on the modified Fried frailty phenotype and Mini-Mental State Examination. Sleep duration was categorized as short (<6 h), moderate (6-9 h), and long (>9 h). We examined the association of sleep duration with cognitive frailty status at baseline using logistic regressions and with future incidence of cognitive frailty using Cox proportional hazards models. Restricted cubic splines were employed to explore potential non-linear associations. Among 11,303 participants, 1,298 (11.5%) had cognitive frailty at baseline. Compared to participants who had moderate sleep duration, the odds of having cognitive frailty were higher in those with long sleep duration (odds ratio [OR] =1.71, 95% confidence interval [CI] =1.48-1.97, p<0.001). A J-shaped association between sleep duration and cognitive frailty was also observed (p<0.001). Additionally, during a median follow-up of 6.7 years among 5,201 participants who were not cognitively frail at baseline, 521 (10.0%) developed cognitive frailty. A higher risk of cognitive frailty was observed in participants with long sleep duration (hazard ratio [HR] =1.32, 95% CI =1.07-1.62, p=0.008). Long sleep duration was associated with cognitive frailly in older Chinese adults. These findings provide insights into the relationship between sleep duration and cognitive frailty, with potential implications for public health policies and clinical practice.

Background To construct two prognostic models to predict survival in breast cancer patients; to compare the efficacy of the two models in the whole group and the advanced human epidermal growth factor receptor-2-positive (HER2+) subgroup of patients; to conclude whether the Hybrid Bayesian Network (HBN) model outperformed the logistics regression (LR) model. Methods In this paper, breast cancer patient data were collected from the SEER database. Data processing and analysis were performed using Rstudio 4.2.0, including data preprocessing, model construction and validation. The L_DVBN algorithm in Julia0.4.7 and bnlearn package in R was used to build and evaluate the HBN model. Data with a diagnosis time of 2018(n = 23,384) were distributed randomly as training and testing sets in the ratio of 7:3 using the leave-out method for model construction and internal validation. External validation of the model was done using the dataset of 2019(n = 8128). Finally, the late HER2 + patients(n = 395) was selected for subgroup analysis. Accuracy, calibration and net benefit of clinical decision making were evaluated for both models. Results The HBN model showed that seventeen variables were associated with survival outcome, including age, tumor size, site, histologic type, radiotherapy, surgery, chemotherapy, distant metastasis, subtype, clinical stage, ER receptor, PR receptor, clinical grade, race, marital status, tumor laterality, and lymph node. The AUCs for the internal validation of the LR and HBN models were 0.831 and 0.900; The AUCs for the external validation of the LR and HBN models on the whole population were 0.786 and 0.871; the AUCs for the external validation of the two models on the subgroup population were 0.601 and 0.813. Conclusion The accuracy, net clinical benefit, and calibration of the HBN model were better than LR model. The predictive efficacy of both models decreased and the difference was greater in advanced HER2 + patients, which means the HBN model had higher robustness and more stable predictive performance in the subgroup.

To develop and validate an interactive nomogram to predict healthcare-associated infections (HCAIs) in the intensive care unit (ICU). A multicenter retrospective study was conducted to review 2017 data from six hospitals in Guizhou Province, China. A total of 1,782 ICU inpatients were divided into either a training set (n = 1,189) or a validation set (n = 593). The patients' demographic characteristics, basic clinical features from the previous admission, and their need for bacterial culture during the current admission were extracted from electronic medical records of the hospitals to predict HCAI. Univariate and multivariable analyses were used to identify independent risk factors of HCAI in the training set. The multivariable model's performance was evaluated in both the training set and the validation set, and an interactive nomogram was constructed according to multivariable regression model. Moreover, the interactive nomogram was used to predict the possibility of a patient developing an HCAI based on their prior admission data. Finally, the clinical usefulness of the interactive nomogram was estimated by decision analysis using the entire dataset. The nomogram model included factor development (local economic development levels), length of stay (LOS; days of hospital stay), fever (days of persistent fever), diabetes (history of diabetes), cancer (history of cancer) and culture (the need for bacterial culture). The model showed good calibration and discrimination in the training set [area under the curve (AUC), 0.871; 95% confidence interval (CI), 0.848-0.894] and in the validation set (AUC, 0.862; 95% CI, 0.829-0.895). The decision curve demonstrated the clinical usefulness of our interactive nomogram. The developed interactive nomogram is a simple and practical instrument for quantifying the individual risk of HCAI and promptly identifying high-risk patients.