Motivation is a critical psychological determinant of health behavior change in patients with coronary heart disease (CHD). Despite its theoretical and clinical importance, well-validated psychometric instruments specifically designed to assess motivation intensity in this population remain limited. This study aimed to develop and validate a motivation scale for health behavior change in patients with CHD. A cross-sectional study was conducted in a tertiary hospital in China between April 2019 and December 2022 using a two-phase scale development design. The conceptual framework was based on the Contemplation-Action-Maintenance model. An initial item pool was generated through a literature review, existing instruments, and expert input, followed by Delphi consultation and patient-based pretesting in 76 patients with CHD. Item selection was performed combining Classical Test Theory and Item Response Theory in 749 patients. Psychometric properties were evaluated in an independent sample of 347 patients, including 35 who completed the scale twice. Reliability and validity were assessed using internal consistency, test–retest reliability, content validity, criterion validity, concurrent validity, and confirmatory factor analysis. The final scale comprised 12 items across three dimensions, explaining 86.04

To evaluate the long-term efficacy of Chinese herbal medicine (CHM) in enhancing reproductive outcomes and reducing recurrence rates among endometriosis-associated infertility women infertile women. This study is a retrospective analysis of a randomized, double-blind, placebo-controlled clinical trial implemented from May 2014 to April 2018. The study included patients with endometriosis-associated infertility who had undergone standardized laparoscopic surgery. After surgery, the participants were randomly assigned to 2 groups: the intervention group received a CHM treatment regimen, while the control group was given placebo therapy. Both groups were subjected to the same blinding procedures and received treatment for a period of 6 months. Five years after surgery, the researchers conducted follow-up with the participants and analysed the data on pregnancy outcomes and recurrence of endometriosis. In this trial, a total of 202 patients with endometriosis-associated infertility (101 per group) were followed up at 5 years post-surgery for clinical pregnancy, live birth, and recurrence outcomes (101 patients per group). According to our data, pregnancies occurred in 83 (82.18

Despite increasing policy investment in smart home-based elderly care platforms, participation among older adults and care institutions remains limited. Existing studies have primarily examined participation-related factors from either the demand or supply side, but have paid limited attention to how these factors are interconnected within platform-based elderly care systems. Consequently, the structural relationships among participation-related factors and the factors occupying key positions within the broader care system remain insufficiently understood. To explore the interdependencies among factors related to participation in smart home-based elderly care platforms among older adults and care institutions in China. A three-stage exploratory study design was adopted. First, potential participation-related factors were identified through prior ethnographic research. Second, the identified factors were refined using expert ratings, coefficients of variation, and fuzzy set membership analysis. Third, an expert-informed directed and weighted factor network was constructed based on expert assessments of inter-factor relationships. Social network analysis was applied to examine network structure, brokerage positions, and structural prominence through network-level analysis, block modeling, and node- and edge-level metrics. Factors demonstrating high structural prominence were identified by integrating node-level indicators with block model positions, and robustness was assessed through sensitivity analyses. Initially, 21 elderly-related and 14 institution-related factors were identified. After refinement, 27 factors were retained, forming a fully interconnected expert-informed network with a clear core-periphery structure. Platform-, governance-, and institution-related factors occupied structurally central and intermediary positions, whereas older adult-related factors were more structurally dependent within the network. Twelve structurally prominent factors were identified, including trust in service provision, family support, payment power, inadequacy of existing care arrangements, care service quality, platform quality, service accessibility, service delivery costs, government subsidy support, payment and settlement mechanisms, revenue allocation arrangements, and performance incentive structures. Sensitivity analyses further supported the stability of the network structure and structurally prominent factors. Participation-related conditions in smart home-based elderly care platforms appear to be embedded in interdependent structural relationships rather than operating as isolated factors. The findings suggest that institutional arrangements, platform governance, and service delivery conditions may play central roles in shaping participation dynamics. These findings highlight the value of a system-oriented perspective and suggest that improving participation may require coordinated strategies across service delivery, platform governance, payment mechanisms, institutional incentives, and policy support.

The cardiovascular risk conferred by BMI-metabolic phenotypes is dynamic. However, a comprehensive understanding of how longitudinal transitions influence distinct cardiovascular disease (CVD) subtypes remains limited. We aimed to systematically investigate the associations of BMI-metabolic phenotype transitions with the specific risks of incident heart disease and stroke, and to explore possible sex differences. This longitudinal study included 6,274 participants aged ≥ 50 years and free of CVD from two prospective cohorts (CHARLS and ELSA). We defined 16 phenotype transitions based on changes in BMI-metabolic status between a baseline (T0) and a four-year follow-up assessment (T1). Using a landmark approach with follow-up commencing from T1, we used adjusted, stratified Cox proportional hazards models to estimate hazard ratios (HRs) for incident composite CVD, heart disease, and stroke over a median of 5.0 years. The stable metabolically healthy normal weight (MHNW→MHNW) group was the reference. Phenotype transitions were frequent. In the pooled six-group analysis, compared with the stable MHNW group, persistent metabolic unhealthiness and deterioration were the most consistent predictors of elevated composite CVD risk (HR 2.04, 95

Background:Inference-time retrieval augmentation is increasingly used to improve the traceability and verifiability of large language model (LLM) applications in health care. Evaluation practices for text-based retrieval-augmented generation (RAG) and graph-structured RAG (GraphRAG) systems remain heterogeneous, which limits comparison across studies and complicates judgments about clinical readiness. Objective:This review mapped evaluation methods for inference-time retrieval-augmented and graph-structured retrieval-augmented LLM systems in health care and characterized how evaluation constructs are defined, operationalized, and reported across system layers and evaluation-setting categories. Methods:We conducted a scoping review in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews), with search reporting informed by PRISMA-S (PRISMA literature search extension). Searches were conducted through May 14, 2026, in PubMed (MEDLINE), Web of Science Core Collection, IEEE Xplore, ACM Digital Library, arXiv, and medRxiv, with backward and forward citation tracking of included studies. Eligible records described health care-relevant LLM systems using inference-time RAG and reported at least 1 evaluation component. Data were charted on study characteristics, system design, retrieval-layer evaluation, evidence linkage, safety-related and GraphRAG-specific evaluation, and selected reporting and governance characteristics. We also constructed an evidence-and-gap map cross-classifying evaluation-setting categories with key evaluation domains. Results:A total of 157 studies met the inclusion criteria. Clinical question answering was the most frequently represented application (89/157, 56.7%), followed by clinical decision support (70/157, 44.6%). Most evaluations were conducted in offline-only settings (140/157, 89.2%), whereas 17/157 (10.8%) studies reported workflow-facing, prospective, or deployment-level evaluation. Independent retrieval-layer evaluation was reported in 47/157 (29.9%) studies. Grounding and faithfulness evaluation was reported in 41/157 (26.1%) studies, and fine-grained evidence verification was reported in 22/157 (14%) studies. Human evaluation was reported in 94/157 (59.9%) studies, but interrater reliability was reported in 26/94 (27.7%) studies. LLM-as-judge evaluation was reported in 41/157 (26.1%) studies, with bias-control measures reported in 15/41 (36.6%) studies. Formal safety-related evaluation was reported in 45/157 (28.7%) studies. Among 27 (17.2%) GraphRAG studies, intermediate-artifact evaluation was reported in 11/27 (40.7%) studies, and graph construction evaluation was reported in 6/27 (22.2%) studies. The evidence-and-gap map showed limited coverage of fine-grained verification, contradiction handling, safety evaluation, LLM-as-judge safeguards, GraphRAG construction evaluation, and GraphRAG intermediate-artifact evaluation in workflow-facing, prospective, or deployment-level settings. Conclusions:Evaluation of health care RAG and GraphRAG systems has expanded rapidly, yet reporting and operational definitions remain inconsistent across evaluation layers. Current evidence remains concentrated in offline evaluation, with limited workflow-facing, prospective, or deployment-level assessment of retrieval quality, fine-grained evidence linkage, safety, LLM-as-judge safeguards, GraphRAG construction quality, and GraphRAG intermediate artifacts. This review maps these gaps across evaluation-setting categories and translates them into synthesis-informed evaluation considerations. These findings suggest that future evaluation may need to move beyond end-to-end benchmark performance toward more transparent, layer-specific, safety-oriented, and clinically contextualized assessment before workflow-facing implementation.

BACKGROUND:To compare the effectiveness of multifactorial and exercise programs in preventing falls among older adults, with a specific focus on evaluating the individual and combined contributions of their key intervention components. METHODS:This study was a systematic review and component network meta-analysis. PubMed, Embase, and Web of Science were searched from inception to February 2025 for randomized controlled trials, focusing on four primary outcomes: fallers, recurrent fallers, injurious fallers, and fractured fallers. Risk of bias was evaluated using the Cochrane tool, and additive component network meta-analysis compared intervention group and component efficacy. RESULTS:69 randomized controlled studies were included. In multifactorial interventions, traditional health education could increase fall risk (iRR: 1.10, 95% CI [1.03; 1.67]) and recurrent fall risk (iRR: 1.25, 95% CI [1.06; 1.48]). Medication management can increase recurrent fall risk (iRR: 1.35, 95% CI [1.09; 1.67]) and fracture risk (iRR: 2.11, 95% CI [1.48; 3.00]). Exercise (iRR: 1.24, 95% CI [1.01; 1.53]) increased fracture risk, and environment modification (iRR: 0.56, 95% CI [0.61; 0.79]) reduced it. The additive effect of risk assessment and advice, exercise, and environment modification reduced fall risk. In exercise programs, gait and balance (iRR: 0.58, 95% CI [0.36; 0.93]) can reduce recurrent fall risk. An intervention containing two components (gait and balance + strength and resistance) reduced the risk of falls and fall-related injuries. LINKING EVIDENCE TO ACTION:Environment modification reduced fracture risk, emphasizing the value of creating safe living spaces. The combination of risk assessment, advice, exercise, and environment modification reduced fall risk, suggesting a holistic approach may be effective in preventing falls. Traditional methods of health education and medication management are in urgent need of updating to synergize with other exercise components and enhance the effectiveness of fall prevention. Prospective clinical trials are needed to optimize combinations of exercise components, particularly integrating gait and balance training with strength and resistance exercises. TRIAL REGISTRATION:The review was registered online in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number (CRD42025643530).

BACKGROUND:Asthma affects an estimated 260 million people worldwide. Adherence to inhaled corticosteroids ranges from 22% to 63% and accounts for about 24% of exacerbations. Feedback-enhanced electronic monitoring couples electronic recording of inhaler use with active feedback, unlike passive monitoring. Randomized trial evidence for this approach has not been synthesized. OBJECTIVE:To evaluate the effect of feedback-enhanced electronic monitoring on medication adherence, exacerbations, disease control, health-related quality of life, safety and acceptability in people with asthma. DESIGN:Systematic review and meta-analysis of randomized controlled trials. DATA SOURCES:PubMed/MEDLINE, the Cochrane Central Register of Controlled Trials, EMBASE, CINAHL, Web of Science and Scopus were searched from inception to October 2025 without language restriction, supplemented by reference lists and trial registries. REVIEW METHODS:Trials comparing electronic monitoring combined with at least one feedback modality against standard care were eligible. Two reviewers independently screened records, extracted data and appraised risk of bias with the Cochrane risk of bias tool for randomized trials, version 2. Random-effects meta-analyses were performed and certainty of evidence was rated with the Grading of Recommendations Assessment, Development and Evaluation approach. RESULTS:Twenty-eight trials with 4410 participants were included. Feedback-enhanced electronic monitoring improved adherence relative to standard care (mean difference 18.43%, 95% confidence interval 13.02 to 23.84; 21 trials, 2567 participants; moderate certainty), with consistent benefit in children (22.21%), adults (14.23%) and mixed populations (23.18%). Exacerbation rates fell (mean difference -2.65, 95% confidence interval -4.39 to -0.91; 10 trials, 1569 participants; low certainty) and the proportion with well-controlled asthma rose (mean difference 6.67%, 95% confidence interval 4.47 to 8.86; 5 trials, 1709 participants; low certainty). Quality of life improved (Mini Asthma Quality of Life Questionnaire mean difference 0.26, 95% confidence interval 0.13 to 0.40; 5 trials, 787 participants; moderate certainty), whereas the change in Asthma Control Test score did not reach statistical significance (mean difference 0.53, 95% confidence interval -0.24 to 1.31; 9 trials). Adverse events were comparable and acceptability was high (System Usability Scale 69.4 to 80.1). CONCLUSIONS:Feedback-enhanced electronic monitoring improves inhaler adherence in asthma across age groups, and this is accompanied by fewer exacerbations, a higher proportion of patients reaching disease control and better quality of life. The benefit appears to arise from converting passive monitoring into active behavior change. Device durability, user training, integration with health services and digital equity require attention at implementation. SOCIAL MEDIA ABSTRACT:Adding feedback to electronic inhaler monitoring raises asthma medication adherence by 18% and improves control and quality of life.

BACKGROUND:Patient-facing large language model (LLM) systems are increasingly proposed as scalable tools for chronic disease self-management support. In this setting, safety depends not only on factual accuracy but also on whether outputs are interpretable, trusted, and used safely over time. OBJECTIVE:To explain how safety guardrails shape outcomes in patient-facing LLM-supported self-management across different task-risk, user, and interaction contexts. METHODS:We conducted a realist review of LLM-based or LLM-enabled generative conversational systems used for self-management of long-term physical health conditions. Searches of PubMed, Web of Science Core Collection, IEEE Xplore, ACM Digital Library, and arXiv covered all indexed years to 1 April 2026 and used terms for chronic disease or self-management, patient-facing conversational systems, and LLMs or generative AI, identifying 1,154 records before deduplication. The core evidence base comprised 21 studies and 38 context-mechanism-outcome configurations (CMOCs). The patient-facing self-management task, rather than disease label, was the unit of synthesis. RESULTS:At the study level, the dominant patient-facing task was coded as low risk in 6 studies, moderate risk in 11, and high risk in 4; 17 studies evaluated mainly single-turn interactions and 4 included multi-turn, sequential, or simulated-consultation elements. Most evidence concerned simulated or expert-judged patient-facing tasks rather than sustained real-world deployment. Three patterns recurred. Provenance-related safeguards improved transparency and checkability more consistently than they ensured safe downstream action. Communication-oriented safeguards improved readability or perceived comprehensibility while leaving recurring gaps in completeness or actionability. Boundary-control strategies, including source-bounded retrieval, clinician deferral, and escalation support, became more important as task actionability and interaction complexity increased. CONCLUSIONS:In patient-facing chronic disease self-management, safety cannot be judged adequately by answer plausibility alone. This review develops a refined programme theory and a risk-linked, theory-generating heuristic framework, but many proposed mechanisms remain indirect and require real-world, longitudinal, multi-turn testing before deployment.

Introduction Postoperative delirium (POD) is a common and serious complication after cardiac surgery, particularly in elderly patients, and is associated with adverse short- and long-term outcomes. Effective preventive strategies remain limited. Liraglutide, a glucagon-like peptide-1 receptor agonist, has demonstrated potential neuroprotective, anti-inflammatory and metabolic benefits, which may reduce the incidence of POD.Methods and analysis This is a single-centre, randomised, double-blind, placebo-controlled trial in elderly patients undergoing elective cardiac surgery. Participants will be randomised in a 1:1 ratio to receive liraglutide or placebo from the day before surgery until postoperative day 3. A total of 260 patients are planned to be enrolled in this study. The primary endpoint is the incidence of POD within 7 days, assessed using the Confusion Assessment Method (CAM) or CAM-intensive care unit. Secondary outcomes include delirium severity, neurocognitive and psychological function, cardiac function, clinical outcomes, major adverse cardiovascular events within 1 year and perioperative biomarker changes. Exploratory outcomes include functional MRI in selected subgroups and additional biomarker analyses.Ethics and dissemination The protocol has been approved by the Medical Ethics Committee of Nanjing Drum Tower Hospital. Written informed consent will be obtained from all participants. Findings will be disseminated in peer-reviewed journals and academic conferences.Trial registration number ChiCTR2500106943.

Abstract BackgroundLarge language models (LLMs) are increasingly embedded in conversational agents for cardiometabolic care. These systems could support self-management, but their behavior change content, delivery mechanisms, and implementation transparency are poorly understood. ObjectiveThis scoping review mapped behavior change techniques (BCTs) used in LLM-driven conversational agents for cardiometabolic prevention and management, described how these techniques are delivered across static, rule-based, and generative mechanisms, examined LLM design, personalization, and safety reporting, and summarized user experience and behavioral or clinical outcomes. MethodsWe searched PubMed, Web of Science, Embase, CINAHL, APA PsycInfo, IEEE Xplore, ACM Digital Library, arXiv, ClinicalTrials.gov, and the WHO International Clinical Trials Registry Platform for records published from January 1, 2020, to November 30, 2025. The final search was run on March 25, 2026, using this publication-date limit. Eligible studies reported a patient-facing text- or voice-based cardiometabolic conversational agent using an LLM or other transformer-based generative model. Two reviewers independently screened records and extracted data. BCTs were coded using the Behavior Change Technique Taxonomy v1; selected self-management BCTs were classified as static, rule-based or templated, or generative or context-aware. Empirical human-participant– or evaluator-based studies were appraised with the Mixed Methods Appraisal Tool, and a study-specific checklist assessed LLM implementation reporting transparency. ResultsThirty-eight studies were included; 19 involved empirical human-participant– or evaluator-based assessments, whereas 19 were technical and system-level evaluations, including framework-development, simulated-output, and proof-of-concept studies. Studies were concentrated in 2024‐2025. Instruction on how to perform behavior was identified in 30 of 38 (79%) studies, information about health consequences in 27 of 38 (71%) studies, and feedback and monitoring techniques in 19 of 38 (50%) studies. Most agents were positioned as educators or coaches targeting type 2 diabetes, obesity, or related cardiometabolic risk, and GPT-family models embedded in hybrid architectures with retrieval-augmented generation or rule-based components predominated. Generative outputs were used mainly for tailored explanations, risk information, and socioemotional responses, whereas self-monitoring, reminders, and structured interactions were more often rule-based or mixed-mode. Only 13 of 38 (34%) studies fully reported prompts or system messages, and 16 of 38 (42%) studies fully reported safety or oversight mechanisms. User evaluations reported good usability and perceived helpfulness, but behavioral or physiological outcomes were sparse and usually limited to pilot, short-term, or single-case designs. ConclusionsLLM-driven conversational agents for cardiometabolic care are proliferating but remain early-stage and methodologically heterogeneous. Current systems primarily use LLMs as educational and explanatory layers with “synthetic empathy” over rule-based data capture and safety functions, while behavior change content remains dominated by information provision and simple feedback. More rigorous comparative studies with longer follow-up are needed before firm conclusions can be drawn about sustained behavioral or clinical benefit.

BACKGROUND Emergence delirium (ED) is a common postoperative complication in older adult patients, posing a significant burden on both patients and medical staff. Despite its prevalence, there is a notable lack of research focused on identifying predictive factors and constructing models for ED in the post-anesthesia care unit. Therefore, developing a risk prediction model for ED in older adult patients is imperative. We anticipate that such a model would demonstrate strong predictive efficacy and be applicable in clinical settings.AIM To develop and validate an ED risk-prediction model for early intervention in older adults. METHODS This study enrolled 705 older surgical patients (January 2024 to October 2024) for modeling and 115 (November 2024 to December 2024) for validation. Using least absolute shrinkage and selection operator and multivariable logistic regression, we developed a predictive model with an online dynamic nomogram. Internal (10-fold cross-validation) and external validation demonstrated strong discrimination, calibration, and clinical utility. RESULTS The incidence of ED in older adult patients postoperatively was found to be 17.16%. Independent risk factors for postoperative ED included preoperative Mini-Mental State Examination score, preoperative albumin level, surgical duration, surgical risk score, number of indwelling catheters, and extubation time (all P < 0.05). The model demonstrated the area under curve (AUC) of 0.924 [95% confidence interval (CI): 0.897-0.951], with the calibration curve closely aligning with the ideal curve. The Hosmer-Lemeshow test yielded chi 2 = 7.934, P = 0.541, indicating good clinical utility. Internal validation resulted in an AUC of 0.920 (95%CI: 0.571-0.959), while external validation showed an AUC of 0.931 (95%CI: 0.866-0.997). The calibration curve for the validation cohort closely matched the ideal curve, with the Hosmer-Lemeshow test showing chi 2 = 5.772, P = 0.763, further supporting its clinical applicability. CONCLUSION The dynamic nomogram accurately predicts ED risk in older adults, aiding early identification and clinical intervention.

Background:The study explored the relationship between body mass index (BMI) and activities of daily living (ADL) disability among Chinese older adults. Methods:Using 2011-2020 data from the China Health and Retirement Longitudinal Study, we included 3975 older individuals and assessed their baseline BMI, ADL disability, other covariates, and ADL disability over the follow-up period. Cox proportional hazards regression, restricted cubic spline, and two-piecewise linear regression models were performed. We also conducted subgroup analyses to explore effect heterogeneity across different subpopulations and sensitivity analyses to confirm the robustness of our findings. Results:During a median follow-up of seven years, 2003 participants developed ADL disability. The Cox proportional hazards models demonstrated a significant association between BMI and the risk of ADL disability. When BMI was categorised into groups, only obese older adults exhibited a significantly higher risk of ADL disability compared to those with normal weight. The restricted cubic spline model further revealed a nonlinear U-shaped relationship between continuous BMI and ADL disability risk, indicating that the risk of ADL disability initially decreased and then increased with rising BMI. Subgroup analyses revealed that the U-shaped relationship was observed only among individuals aged 60-69 years and female older adults, while sensitivity analyses consistently confirmed the robustness of this U-shaped association between BMI and ADL disability risk. Conclusions:A nonlinear U-shaped relationship between BMI and ADL disability risk was observed among Chinese adults aged 60-69 and older female adults, suggesting that both high and low BMI are associated with increased ADL disability risk. Despite limitations such as baseline-only BMI measurements, observational study design, potential residual confounding, and limited generalisability beyond Chinese older adults, these findings highlight the importance of routine BMI screening and targeted weight management strategies to help prevent or delay the onset of ADL disability in older adults.

Background: Coronary heart disease (CHD), the global leading cause of death (8.9 million annual fatalities), requires combined lifestyle and medication management. We developed iCARE - a personalized mHealth system delivering visualized recommendations to improve health behaviors and medication adherence. Purpose: To investigate health behavior trajectories and evaluate effectiveness of iCARE in CHD patients. Methods: This multicenter randomized controlled trial enrolled CHD patients (≥18 years, angiography-confirmed, Android smartphone owners) from two Chinese tertiary hospitals, randomizing them (1:1:1) to: 1) iCARE group receiving personalized mHealth interventions via visualized content (comics/videos/images) plus usual care, 2) text message group receiving text-based interventions, or 3) control group with usual care and daily logging. Primary outcomes were 12-month health behavior trajectories (medication adherence, physical activity, diet, smoking cessation) assessed at discharge, 3-, 6-, and 12-month intervals using validated scales and digital biomarkers, analyzed via generalized linear mixed models (GLMM) to identify temporal patterns and group×time interactions. Results: Between September 2019 and May 2024, we screened 3,000 patients, enrolling 1,149 participants: 378 in the iCARE intervention group, 383 in the text message group, and 388 in the control group. In this 12-month randomized controlled trial, trajectory analyses using generalized linear mixed models revealed significant Group × Time interactions for all continuous behavioral outcomes (all p<0.001). The iCARE group demonstrated greater monthly increases in healthy diet rate (β=0.41, 95%CI[0.25-0.57]) and medication adherence rate (β=0.38, 95%CI[0.22-0.54] ), while the text message group showed superior improvement in regular exercise rate (β=0.35, 95%CI[0.20-0.50]). Both interventions significantly reduced smoking rates versus control (iCARE: β=-0.43, 95%CI[-0.58,-0.28]; text: β=-0.31, 95%CI[-0.46,-0.16]). Peak improvements occurred at 6 months (e.g., maximum between-group difference in medication adherence: 12.4 percentage points, 95%CI[9.1-15.7]). Substantial between-individual variability was observed (slope variances: 0.12-0.19), particularly in smoking trajectories. Conclusion: The iCARE system effectively improves dietary, exercise, and medication adherence in CHD patients, with the greatest behavior change typically occurring at 6 months.

BackgroundIntegrated care models enhanced by the clinical decision support system offer innovative approaches to managing the growing global burden of older adult care. However, their efficacy remains uncertain. ObjectiveThis study aimed to evaluate the efficacy of an intelligent and integrated older adult care model, termed the SMART (Sensors and scales [receptor], a Mobile phone autonomous response system [central nervous system in the spinal cord], a Remote cloud management center [central nervous system in the brain], and a Total care system [effector]) system, in improving the quality of life (QOL) for home-dwelling older adults. MethodsIn this stratified randomized controlled trial, we consecutively recruited older adults aged 65 years or older from November 1, 2020, to December 31, 2020. Eligible participants were randomly allocated 1:1 to either the SMART group, receiving routine discharge instructions and personalized integrated care interventions across 11 domains (decreased or lost self-care ability, falls, delirium, dysphagia, incontinence, constipation, urinary retention, cognitive decline, depression, impaired skin integrity, and common diseases) generated by the SMART system, or the usual care group, receiving only routine discharge instructions. The intervention lasted for 3 months. The primary end point was the percent change in QOL from baseline to the 3-month follow-up, assessed using the World Health Organization Quality of Life Instrument - Older Adults Module. Secondary end points included functional status at the 3-month follow-up and percent changes in health self-management ability, social support, and confidence in avoiding falling from baseline to the 3-month follow-up. Data were analyzed following the intention-to-treat principle, using covariance or logistic regression models, as appropriate. Subgroup and sensitivity analyses were conducted to assess result consistency and robustness. ResultsIn total, 94 participants were recruited, with 48 assigned to the SMART group. The personalized and integrated care by the SMART system significantly improved the QOL among the older adults, with an estimated intervention difference of 11.97% (95% CI 7.2%-16.74%, P<.001), and social support and health self-management ability as well, with estimated intervention differences of 6.75% (95% CI 3.19%-10.3%, P<.001) and 4.95% (95% CI 0.11%-10%, P=.003), respectively, while insignificantly improving in the Modified Falls Efficacy Scale score. Similarly, the SMART system had a 66% reduction in instrumental activities of daily living disability (odds ratio [OR] 0.34, 95% CI 0.11-0.83, P=.02). However, the SMART system did not significantly affect activities of daily living disability or the Modified Falls Efficacy Scale score. The subgroup and sensitivity analyses confirmed the robustness of the findings. ConclusionsThe personalized and integrated older adult care by the SMART system demonstrated significant efficacy in improving QOL, health self-management ability, and social support, while reducing instrumental activities of daily living disability among home-dwelling older adults. Trial RegistrationChinese Clinical Trial Registry ChiCTR-IOR-17010368; https://tinyurl.com/2zax24xr

Ulinastatin is a protease-inhibiting drug with anti-inflammatory and other pharmacological properties. Little is known regarding its role following acute type A aortic dissection (ATAAD) surgery. We perform a randomized controlled trial to investigate the protective effect of ulinastatin against negative inflammatory response and organ dysfunction in ATAAD surgery (PANDA). The primary outcome of mean daily Sequential Organ Failure Assessment (SOFA) score from baseline to 7 days of surgery is 8.80 (SD, 4.11) in the ulinastatin group and 8.61 (SD, 4.47) in the control group (mean difference between groups was 0.04; 95% confidence interval [CI], -0.24 to 0.33; p = 0.765). Systemic inflammatory response syndrome (SIRS) within 7 days of surgery is lower in the ulinastatin group than in the control group (p < 0.001). Additional ulinastatin to standard treatment is likely to reduce SIRS rates instead of preventing organ dysfunction, highlighting the potential importance of the benefits of anti-inflammatory pharmacotherapeutics. The trial is registered on clinicaltrials.org (NCT04711889).

Background: The Cardiovascular–Kidney–Metabolic (CKM) syndrome, recently proposed by the American Heart Association, underscores the interplay among metabolic, renal, and cardiovascular conditions. Early risk identification is essential for effective prevention. Although diet is central to metabolic health, the impact of daily nutrient intake on CKM progression remains unclear. Objective: The aim of this study was to develop and validate a machine learning (ML) model for predicting the progression of CKM syndrome from early (Stages 1-3) to advanced stage (Stage 4). Methods: The National Health and Nutrition Examination Survey 2005-2018 dataset was used for the analysis. Daily dietary nutrients were selected as primary features, with demographic and lifestyle factors incorporated to improve model performance. Feature preprocessing involved VIF-based removal of multicollinearity, class balancing via Synthetic Minority Over-sampling Technique (SMOTE), and predictor selection using the Boruta algorithm. Subsequently, six ML algorithms, namely Random Forest (RF), light gradient boosting machine (LightGBM), Naive Bayes (NB), Support Vector Machine (SVM), eXtreme Gradient Boost (XGBoost), and K-Nearest Neighbors (KNN) were employed to train ML models using 10-fold cross-validation. Results: A total of 12,376 participants were enrolled in the study. The negative Weighted Quantile Sum regression index demonstrated a statistically significant inverse association between the dietary nutrient mixture and CKM syndrome progression (OR = 0.68, 95% CI: 0.62-0.74, P < 0.01). After excluding multicollinear variables and selecting important predictors, the ML model retained 29 daily dietary nutrients features and 5 baseline characteristics. The RF model demonstrated superior performance compared to alternative ML algorithms, achieving an accuracy of 91.1%, a sensitivity of 93.7%, a specificity of 87.4%, an F1 score of 92.6%, and an AUC of 0.971[95%CI(0.969-0.974)]. SHAP analysis indicated that among the dietary variables, niacin, copper, and vitamin E were identified as the most important nutritional predictors. Among demographic features, age and sex were the most influential factors. Conclusions: RF exhibited the best performance for predicting the progression of CKM syndrome from early to advanced stages. SHAP value interpretation revealed that niacin played a dominant role in prediction, with copper, vitamin E, age and sex also emerging as key contributing factors.

Sleep disturbance is one of the most prevalent health issues among community-dwelling older adults. This systematic review aims to assess the prevalence of sleep disturbances among these adults living in the community and identify associated risk factors. A comprehensive literature search was performed using PubMed, Web of Science, Embase, and the Cochrane Library databases. We screened studies focusing on the prevalence of sleep disturbances in community-dwelling older adults (≥ 60 years). A random-effects model was used to calculate the pooled prevalence of sleep disturbances. Sensitivity and subgroup analyses were conducted to investigate sources of heterogeneity, and funnel plots were used to assess publication bias. Our systematic review included 41 articles, encompassing a total sample of 71,607 participants from 13 countries. The pooled prevalence of sleep disturbances, measured by PSQI, was found to be 45

Although integrated care has been proposed as a promising approach to the challenges of fragmented geriatric care, a universally accepted implementation framework for integrated care for older adults living at home remains elusive. This study aimed to address the gap by developing an integrated geriatric care model (SMART system) using a knowledge-based clinical decision support system (CDSS) architecture inspired by the principle of neural reflex and evaluate the usability of the SMART system. The development of our SMART system was guided by the knowledge-based Clinical Decision Support System architecture and the principle of neural reflexes, which included 5 phases: (1) functional design; (2) architecture and database design; (3) security measures design; (4) user interface and visualization design; (5) prototypes development and iteratively testing. Subsequently, a cross-sectional study was conducted from December 2020 to February 2021, collecting older Chinese adults aged 60 years old and above consecutively to evaluate their usability perception of the Care Receiver App within the SMART system via the Health Information Technology Usability Evaluation Scale (Health-ITUES) version designed for older adults. The SMART system consisted of a Care Receiver App, a Professional Care Provider App, and a Cloud Platform. According to the assessment results and daily monitoring data, the SMART system can diagnose care problems and tailor interventions and implementation approaches to address the multifaceted care needs of older individuals. The personalized interventions and implementation approaches generated by the SMART system, after being reviewed and adjusted by professional geriatric nurses, will be sent to the corresponding care providers to facilitate coordinated care services. A total of 110 eligible older individuals were included in the usability testing. The Care Receiver App was perceived as useful and acceptable among older individuals with the mean scores for each item of the Health-ITUES version designed for older adults exceeding 3.00. This study successfully developed an integrated geriatric care model using a knowledge-based CDSS architecture inspired by the principle of neural reflex. Furthermore, the study indicated acceptable usability perception of the SMART system among older population. The study was registered in the Chinese Clinical Trial Registry (Registration number: ChiCTR-IOR-17010368) on 12/01/2017.

To investigate the level of eHealth literacy among patients scheduled for combined orthodontic and orthognathic treatment, and to explore its association with anxiety and depression, providing a basis for clinical interventions. A cross-sectional study was conducted with a final sample of 111 patients. Questionnaires including the eHealth Literacy Scale (eHEALS), Generalized Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-9 (PHQ-9) were administered to patients scheduled for combined orthodontic and orthognathic treatment at a tertiary stomatological hospital from June 2023 to April 2025. The mean eHealth literacy score among patients was 25.76 ± 5.46. The incidence rates for anxiety and depression symptoms were 16.2% and 13.5%, respectively. Logistic regression analysis revealed that lower per capita annual household income was significantly associated with an increased risk of anxiety (OR = 9.16, 95% CI [2.30-36.52], p = 0.002) and depression symptoms (OR = 8.83, 95% CI [1.08-72.46], p = 0.042). Additionally, eHealth literacy scores were negatively correlated with anxiety (OR = 0.82, 95% CI [0.73-0.93], p < 0.001) and depression symptoms (OR = 0.64, 95% CI [0.47-0.86], p = 0.003). Our findings demonstrate that patients scheduled for combined orthodontic and orthognathic treatment experience significant psychological distress, with lower-income individuals exhibiting more severe symptoms. Better eHealth literacy appears to reduce emotional issues. These findings indicate that healthcare providers should assess patients' ehealth literacy before treatment and focus on low-income patients.