To investigate the influencing factors of frailty among rural elderly people,and to construct,verify and compare frailty risk prediction models based on multiple machine learning algorithms,so as to provide a scientific basis for the early identification and precise prevention of frailty.

From July to September 2023,using a multi-stage stratified cluster random sampling strategy,a total of 1 366 elderly people from 13 rural areas were selected as the research subjects.They were randomly divided into the training set(n=957) and the internal validation set(n=409) at a ratio of 7∶3.In July 2024,a total of 491 rural elderly people were selected as the external validation set.In this study,whether frailty occurs was used as the dependent variable.Variable selection was completed through single-factor analysis and LASSO regression.Then five machine learning methods including random forest(RF),extreme gradient boosting algorithm(XGBoost),artificial neural network(ANN),support vector machine(SVM),and Logistic regression(LR) were used to construct the prediction model.The core evaluation indicators were sensitivity,specificity,area under the receiver operating characteristic curve,and classification accuracy rate.The clinical practicality of the model was evaluated by the decision curve analysis.The performance of each frailty risk prediction model was comprehensively evaluated to determine the optimal model.

The incidence rate of frailty among rural elderly people was 27.4%.LASSO regression ultimately included sleep quality,depression status,anxiety status,fall risk and physical condition as key predictors.After internal and external verification and comprehensive evaluation by multiple indicators,the XGBoost model performed the best.

The incidence of frailty among rural elderly people is relatively high.There are numerous influencing factors.The frailty risk prediction model based on XGBoost algorithm has the best performance.It could provide a scientific and effective tool for frailty risk screening and precise intervention.It has high clinical application value.

To construct a prediction model for social frailty in elderly patients undergoing maintenance hemodialysis.To explore the predictive value of modifiable factors such as dialysis symptom burden,family function,depression,and physical activity for the model.

By using the convenience sampling method,451 elderly patients undergoing maintenance hemodialysis in the hemodialysis room of a tertiary grade A hospital in Chengde City from November 2024 to April 2025 were selected as the research subjects for the construction group.Another 181 elderly patients undergoing maintenance hemodialysis in the hemodialysis rooms of two tertiary grade A hospitals from May to July 2025 were selected as the external validation group for the research.A cross-sectional survey was conducted using general information questionnaires,5-Item Geriatric Depression Scale(GDS-5),Dialysis Symptom Index(DSI),Brief Aging Perceptions Questionnaire(B-APQ),Family APGAR Index(APGAR),and the Score of the Chinese Version of the International Physical Activity Questionnaire-Short Form.Logistic regression analysis was used to screen the influencing factors of social frailty.The ROC curve was drawn using R language and internal and external validations were conducted.The distinguishability,calibration,and clinical practicability of the model were evaluated.Based on the predictive factors,the basic model and the full model were constructed.The predictive value of the modifiable factors was quantified.

The incidence rates of social frailty among elderly maintenance hemodialysis patients in the construction group and the validation group were 33.7% and 35.9% respectively.Logistic results showed that dialysis duration,burden of dialysis symptoms,family function,physical activity,and depression were the influencing factors of social frailty in elderly maintenance hemodialysis patients.Based on the above five factors,the area under the receiver operating characteristic curve of the Nomogram for internal and external validation was 0.888 and 0.876 respectively.The Hosmer-Lemeshow test results showed that both P>0.05.The calibration curves and decision curves of internal and external validation both showed that the model had good calibration and high clinical practicability.The modifiable factors significantly contributed to the predictive value of the model.

The constructed prediction model for social frailty in elderly patients undergoing maintenance hemodialysis has good predictive efficacy and clinical applicability.It could provide a tool for the early identification of social frailty in elderly maintenance hemodialysis patients.Medical staff could formulate corresponding intervention measures based on factors that can be intervened,such as the burden of dialysis symptoms,family function,depression,and physical activity,so as to delay the occurrence and development of social frailty in elderly patients undergoing maintenance hemodialysis.

To explore the multidimensional fatigue and motor perception of patients with acute myocardial infarction at different time points,and to construct a cross-lag model to analyze the dynamic correlation between multi-dimensional fatigue and motor perception.

A total of 308 patients with acute myocardial infarction admitted to our hospital from May 2023 to January 2025 were selected.The general information questionnaire,the Chinese version of the Exercise Sensitivity Qustionnaire(ESQ),and the Chinese version of the Multidimensional Fatigue Inventory(MFI-20) were used to investigate the levels of multidimensional fatigue and motor perception of the patients 1 day before discharge,1 month after discharge,and 3 months after discharge.

One day before discharge,1 month after discharge,and 3 months after discharge,the MFI-20 scores of patients with acute myocardial infarction were 57.12±6.42,51.69±4.93,and 42.39±5.07 respectively.The ESQ scores were 40.24±5.68,36.57±4.74,and 29.13±4.16 respectively.There were statistically significant differences in terms of MFI-20 and ESQ scores among patients with different NYHA classifications and comorbidities at the three time points(all P<0.05).Pearson correlation analysis showed that there was a positive correlation between MFI-20 and ESQ scores at the three time points(all P<0.01).The results of the cross-lag model showed that the model had a good fitting effect.The multidimensional fatigue of patients 1 day before discharge could positively predict their movement perception 1 month after discharge(P<0.01).The multi-dimensional fatigue of patients 1 month after discharge could positively predict their movement perception 3 months after discharge(P<0.01).The movement perception of patients 1 day before discharge could also positively predict the multi-dimensional fatigue 1 month after discharge(P<0.05).The movement perception of patients 1 month after discharge could also positively predict the multi-dimensional fatigue of them 3 months after discharge(P<0.01).

Within 3 months after discharge,multidimensional fatigue and the level of movement perception in patients with acute myocardial infarction showed a downward trend.However,multidimensional fatigue and movement perception were mutually causal.There was a dynamic interactive influence relationship between them.Clinical nurses should pay attention to the collaborative intervention of fatigue relief and improvement of movement perception,so as to promote the recovery of patients.

To explore the quality of life and management needs of patients with cervical cancer during concurrent chemoradiotherapy,so as to provide a reference for clinical interventions.

An explanatory sequential mixed-methods design was adopted.A total of 355 cervical cancer patients undergoing concurrent chemoradiotherapy in the oncology department of a tertiary grade A hospital in Luzhou city,Sichuan province,were surveyed using questionnaires.Additionally,26 patients were selected through purposive sampling for semi-structured interviews.

During concurrent chemoradiotherapy,the highest score in quality of life among cervical cancer patients was observed in the domain of disease acceptance.The lowest score was in fertility concerns.Network analysis revealed that physical discomfort was the core node affecting quality of life,whereas satisfaction with disease management served as a bridge node connecting emotional status and functional status in the network.Qualitative interviews identified four themes:symptom management needs,disease knowledge needs,continuity of care needs,and psychosocial needs.

The quality of life of cervical cancer patients during concurrent chemoradiotherapy is influenced by multiple factors.Their management needs exhibit multidimensional characteristics.Quality of life could be improved through precise symptom management,full-cycle health education and follow-up,as well as multidisciplinary interventions.

To explore the latent classes of aging perceptions for the elderly in the communities,and to analyze the centrality indicators of depressive and anxiety symptoms across different classes.

Cluster sampling method was used to recruit 1 019 older adults from five communities in Xinxiang city,Henan province.They were surveyed using general information questionnaire,the Brief Aging Perceptions Questionnaire(B-APQ),the Patient Health Questionnaire-9(PHQ-9),and the Generalized Anxiety Disorder-7 scale(GAD-7).Latent profile analysis was performed to identify latent classes of aging perceptions.Network analysis of depressive and anxiety symptoms was conducted for each class,and core symptoms were identified by expected influence.

Three latent classes of aging perceptions for the elderly in the communities were identified:the low aging perception group(43.1%),the high aging awareness-adaptation group(43.6%),and the high aging awareness-distress group(13.3%).Age and comorbidity status were influencing factors of these classes(P<0.05).Core symptoms differed across the three classes.The low aging perception group showed "feeling down or sad", "excessive worry", and "uncontrollable worry".The high aging awareness-adaptation group showed "uncontrollable worry", "fatigue", and "irritability".The high aging awareness-distress group showed "uncontrollable worry", "restlessness", and "feelings of worthlessness".

Aging perceptions for the elderly in the communities exhibit significant heterogeneity.The core depressive and anxiety symptoms vary across different classes.Nursing staff should provide targeted psychological care based on aging perceptions of the elderly and their specific psychological symptoms.

To explore the latent profile of health self-help behaviors among empty-nest elderly patients with hypertension,and to analyze its influencing factors.

Using stratified cluster sampling method,a total of 339 empty-nest elderly patients with hypertension in Fenyang city were selected as the survey subjects from January to May 2025.General information questionnaires,Health Self-Help Behavior Scale,Perceived Ageism Questionnaire,and Self-efficacy Scale for Hypertensive Patients were used for the investigation.

The health self-help behaviors of empty-nest elderly patients with hypertension could be divided into three potential profile groups,namely the isolated abandonment-low self-help behavior group(29.2%),the passive dependence-moderate self-help behavior group(57.5%),and the active and proactive-high self-help behavior group(13.3%).The results of multivariate analysis showed that age,educational level,living condition,having other chronic diseases,experiencing age discrimination,and hypertension self-efficacy were the influencing factors of potential profile classification for health self-help behaviors among empty-nest elderly patients with hypertension.

The health self-help behaviors of empty-nest elderly patients with hypertension showed group heterogeneity.Healthcare professionals should adopt corresponding intervention measures for different groups to improve their health self-help behaviors and enhance their quality of life.