Qiaoqin Wan

Background: Oral frailty is a multidimensional state associated with physical decline. This study is to investigate the association between oral frailty and grip strength in older adults, focusing on the moderating role of body mass index (BMI). Methods: A multicentre cross‐sectional study was conducted in Beijing, China, between July, 2024 and March, 2026. Participants aged 60 years or older were recruited using convenience sampling from eight communities. Oral frailty was assessed using the Oral Frailty Assessment Tool (OFAT). Grip strength and oral health indicators, including oral motor skills and masticatory function, were measured. Multivariate linear regression and interaction analyses were performed. Results: Oral frailty was prevalent in 55.5% of participants. After adjusting for confounders, oral frailty ( β = −0.42, p < 0.001) and decreased oral motor skills ( β = −0.10, p = 0.001) were independently associated with reduced grip strength. A significant interaction effect was identified between oral frailty and BMI ( β = 0.11, p = 0.006). Low BMI significantly amplified the negative impact of oral frailty on grip strength. Conclusions: Oral frailty and decreased oral motor skills are core independent factors for reduced grip strength in older adults. The synergistic effect of low BMI and oral weakness suggests that the elderly have a high‐risk phenotype of muscle strength decline. Comprehensive geriatric screening should integrate oral function and nutritional status to identify vulnerable individuals early.

Background: Oral frailty is a comprehensive indicator of age‐related oral decline, yet its heterogeneity and association to depression are poorly understood. We aimed to identify latent classes of oral frailty and evaluate the association between dietary diversity and depressive symptoms in older adults with oral frailty. Methods: We conducted a cross‐sectional study enrolling adults aged 60 years or older from urban communities in Beijing and Hebei, China. Latent class analysis was used to identify oral frailty subgroups based on the Oral Frailty Assessment Tool. We used multivariate logistic regression to analyse associations with depressive symptoms (assessed via the Patient Health Questionnaire‐9) and structural equation modelling to assess the indirect association effect of dietary diversity. Results: Between August 2024 and October 2025, 525 participants were enrolled. We identified three oral frailty classes: Multiple Problems‐High Risk ( n = 92; 18%), Structural Deficit‐Functional Compensation ( n = 289; 55%), and Oral Health Advantage ( n = 144; 27%). Compared with the Oral Health Advantage group, the risk of depressive symptoms was significantly higher in the Structural Deficit‐Functional Compensation (OR: 1.736, 95% CI: 1.129–2.669) and Multiple Problems‐High Risk groups (OR: 3.039, 95% CI: 1.695–5.450). Dietary diversity partially mediated these relationships, accounting for 21.89% and 23.40% of the total effects, respectively. Conclusions: Oral frailty in Chinese older adults presents as distinct phenotypes that are associated with depression. Strategies enhancing dietary diversity might serve as a method to improve depression risk in those with oral frailty.

Key Points: A six-predictor model estimated 2-year incident cognitive impairment in maintenance hemodialysis patients with good discrimination and calibration. The final model showed incremental predictive value beyond age and education alone and may support risk stratification in routine dialysis care. An online calculator and nomogram may facilitate individualized risk estimation and prioritization of cognitive screening. Background: Cognitive impairment is common but often underrecognized in maintenance hemodialysis (MHD) patients and is associated with adverse outcomes. Short-term risk prediction tools in this population remain limited. We aimed to develop and internally validate a model for predicting 2-year incident cognitive impairment in MHD patients with normal baseline cognition. Methods: We conducted a multicenter prospective cohort study in five hospitals in Beijing and Tianjin, China. Adults with normal baseline cognition were enrolled between July and September 2022 and reassessed at 24 months (±3 months) using the Beijing version of the Montreal Cognitive Assessment. Among 625 eligible participants, 441 (71%) completed follow-up and were analyzed. Candidate predictors were prespecified on the basis of previous evidence and clinical relevance. A prediction model was developed using least absolute shrinkage and selection operator logistic regression and internally validated with 1000 bootstrap iterations. Results: At 2 years, 158 of 441 participants (36%) developed incident cognitive impairment. Six predictors were retained: age, years of education, hearing level, depression status, sleep quality, and hemoglobin level. The final model showed good discrimination and calibration, with an apparent area under the receiver operating characteristic curve (AUC) of 0.831 (95% confidence interval [CI], 0.795 to 0.866) and a Brier score of 0.145 (95% CI, 0.130 to 0.161). Compared with a baseline model including age and years of education, the final model showed better discrimination (AUC, 0.831 versus 0.797; DeLong test, P = 0.002) and improved overall fit (likelihood ratio test, P < 0.001). Bootstrap validation yielded an optimism-corrected AUC of 0.800 (95% CI, 0.724 to 0.878) and a corrected Brier score of 0.151. A predicted probability of 0.364 identified higher-risk individuals. Conclusions: This prospectively developed model estimated 2-year risk of incident cognitive impairment in MHD patients using readily obtainable indicators. It may support risk stratification, prioritized cognitive screening, and targeted assessment of potentially modifiable factors.

Community nurses play a pivotal role in palliative care but face barriers in managing complex symptoms, such as fragmented knowledge and a lack of community-tailored evidence-based guidance, impairing clinical efficiency. The aim of this study was to develop and evaluate a knowledge graph-based question-answering system for symptom management in community palliative care. A three-phase codesign study guided by the Knowledge-to-Action framework was conducted. Phase 1 (Knowledge Creation): A Symptom Management Knowledge Base (Knowledge Product I) was developed through a codesign process involving a multidisciplinary expert panel. This panel adapted a knowledge base created by researchers through systematic evidence synthesis, employing FAME criteria for contextual adaptation. Phase 2 (Action Cycle: Implementation): A semantically structured knowledge graph (Knowledge Product II) was constructed via automated extraction by software developers, followed by manual verification by researchers. Based on this graph, a question-answering system was created and implemented as a WeChat mini-program, resulting in a practical KG-QA system (Knowledge Product III). Phase 3 (Action Cycle: Evaluation): The system's acceptability, usability, and perceived usefulness and ease of use were assessed among experts and community nurses during a two-week evaluation period using the Clinical Nursing Information System Effectiveness Evaluation Scale and the Post-Study System Usability Questionnaire, which is grounded in the Technology Acceptance Model. The knowledge base comprises 225 evidence items for nine symptoms; the knowledge graph integrates ten entity types, 11 relationship categories, 442 entities and 668 relationships, with the system supporting four query interfaces and three search methods. The evaluations demonstrated high perceived usefulness and ease of use, with strong scores for acceptability (102.25 ± 16.21; 110.56 ± 9.90) and usability (2.47 ± 1.98; 2.23 ± 1.93). The question-answering system bridges the evidence-practice gap via a nursing-process paradigm, offering a potentially scalable model that aligns with national policies pending further validation. However, these findings are based on a small‑scale, single‑region, short‑term evaluation relying largely on subjective measures. Future research should explore its long-term clinical outcomes and cross-setting scalability.

Aims: Heart failure (HF) is characterized by high morbidity and frequent hospital readmissions, highlighting the need for scalable out-of-hospital monitoring to support post-discharge nursing care. We aimed to (i) develop a HF-tailored, user-friendly voice task set suitable for remote monitoring; (ii) identify HF-related acoustic markers and build explainable voice-based models; and (iii) evaluate the accuracy of smartphone recordings for clinical monitoring. Methods and results: In this multicentre observational study, patients hospitalized with acute heart failure (AHF) were recruited at four centres. Voice recordings were obtained with professional devices and common smartphones (Apple, Huawei, Vivo). Eleven speech tasks were evaluated, and 11 acoustic feature categories were extracted [e.g. Mel frequency cepstrum coefficients (MFCCs), chroma, spectral, glottal features]. Two XGBoost models were trained to classify clinical status from admission to discharge and from mid-hospitalization to discharge; models were interpreted with Shapley Additive Explanations (SHAP). Four tasks were selected for the final model. The admission-to-discharge model achieved 0.76 accuracy, 0.86 sensitivity, 0.65 specificity, and an area under the ROC curve (AUC) of 0.77. The mid-hospitalization-to-discharge model showed 0.81 accuracy, 0.89 sensitivity, 0.71 specificity, and an AUC of 0.80. Shapley Additive Explanations analysis revealed vocal improvements corresponding to clinical recovery, characterized by transitions from unstable, noisy voice patterns to more stable, harmonic-rich, brighter, and stronger vocal expressions. Cross-device comparison demonstrated high consistency among recordings across different smartphone brands, supporting the feasibility of mobile-based voice data collection. Conclusion: We developed an explainable, high-performing voice-based HF monitoring model deployable via smartphones. The approach is non-invasive, low-burden, and feasible for integration into nurse-coordinated post-discharge monitoring and remote triage.

The effectiveness of robot-assisted arthroplasty has been paid more attention, however, the question whether robotic technology could serve as a cost-effec

Objective: The Hospice Comfort Questionnaire-Patients (HCQ-P) is widely used to assess patient’s comfort levels in hospice care. This research aimed to culturally adapt the HCQ-P for the Chinese context and validate its psychometric properties to ensure its applicability and effectiveness in China. Methods: This research was conducted in two main phases: (1) translation and cross-cultural adaptation of the HCQ-P into Chinese, (2) evaluation of the psychometric properties through expert consultations and a cross-sectional survey among 360 hospice care patients. The evaluation included determining floor and ceiling effects, evaluating internal consistency using Cronbach’s α, and testing test-retest reliability with the intra-class correlation coefficient (ICC). Content validity was assessed using the content validity index (CVI), and construct validity was tested through confirmatory factor analysis (CFA). Results: The HCQ-P was successfully translated and culturally adapted into Chinese, with no significant floor or ceiling effects detected. The overall Cronbach’s α for the HCQ-P was 0.94, demonstrating excellent internal consistency, while dimension-specific alphas ranged from 0.77 to 0.84. The overall ICC was 0.93, indicating high test-retest reliability, with individual dimensions ranging from 0.77 to 0.81. Both item-level and scale-level CVIs reached 1, reflecting unanimous expert agreement on content relevance. Significant factor loadings in the CFA confirm that the HCQ-P is statistically robust and well-aligned with the cross-cultural and clinical contexts of Chinese hospice care. Conclusions: The Chinese version of the HCQ-P exhibits robust psychometric properties, making it a valid and reliable instrument for assessing patient comfort in Chinese hospice care settings.

Self-administered, unsupervised computerized tools for community-based early cognitive screening are gradually being developed and applied. This study aims to evaluate the diagnostic performance and system usability of those tools in detecting MCI and dementia in older adults. Five electronic databases were systematically searched from inception to 17 August 2023. The included studies were reported using the PRISMA 2020 guideline, the risk of bias and applicability was assessed using QUADAS-2. 28 articles were included in the systematic review and 21 studies in the meta-analysis. The pooled sensitivity and specificity for unsupervised computerized tools were 0.84 (95% CI: 0.81 - 0.87) and 0.86 (95% CI: 0.82 - 0.89) respectively. Meta-regression showed that mean age, cognitive status, cognitive domains and administration time might be responsible for heterogeneity. Notably, tools taking less than 5 min had a pooled sensitivity of 0.85 (95% CI: 0.81-0.89). However, only 3 studies evaluated feasibility and system usability in the intended clinical settings. Unsupervised computerized cognitive screening tools demonstrate good diagnostic performance for MCI/dementia, and tools taking less than 5 min appear particularly suitable for large-scale cognitive screening. Future research should combine various cognitive data to develop multimodal screening tools with better diagnostic performance, evaluate these tools' usability and improve age-friendly designs.