Evaluation of the effects of health education interventions for hypertensive patients based on the health belief model

BACKGROUND Hypertension is a major risk factor for cardiovascular disease and stroke,and its prevalence is increasing worldwide.Health education interventions based on the health belief model(HBM)can improve the knowledge,attitudes,and behaviors of patients with hypertension and help them control their blood pressure.AIM To evaluate the effects of health education interventions based on the HBM in patients with hypertension in China.METHODS Between 2021 and 2023,140 patients with hypertension were randomly assigned to either the intervention or control group.The intervention group received health education based on the HBM,including lectures,brochures,videos,and counseling sessions,whereas the control group received routine care.Outcomes were measured at baseline,three months,and six months after the intervention and included blood pressure,medication adherence,self-efficacy,and perceived benefits,barriers,susceptibility,and severity.RESULTS The intervention group had significantly lower systolic blood pressure[mean difference(MD):-8.2 mmHg,P<0.001]and diastolic blood pressure(MD:-5.1 mmHg,P=0.002)compared to the control group at six months.The intervention group also had higher medication adherence(MD:1.8,P<0.001),self-efficacy(MD:12.4,P<0.001),perceived benefits(MD:3.2,P<0.001),lower perceived barriers(MD:-2.6,P=0.001),higher perceived susceptibility(MD:2.8,P=0.002),and higher perceived severity(MD:3.1,P<0.001)than the control group at six months.CONCLUSION Health education interventions based on the HBM effectively improve blood pressure control and health beliefs in patients with hypertension and should be implemented in clinical practice and community settings.

Effect of health education based on information-motivationbehavioral skills model on patients with unilateral vestibular dysfunction

BACKGROUND Vestibular dysfunction(VH)is a common concomitant symptom of late peri-pheral vestibular lesions,which can be trauma,poisoning,infection,heredity,and neurodegeneration,but about 50%of the causes are unknown.The study uses the information-motivation-behavioral skills(IMB)model for health education,effectively improve the quality of life,increase their self-confidence,reduce anxiety and depression,and effectively improve the psychological state of patients.AIM To explore the effect of health education based on the IMB model on the degree of vertigo,disability,anxiety and depression in patients with unilateral vestibular hypofunction.METHODS The clinical data of 80 patients with unilateral vestibular hypofunction from January 2019 to December 2021 were selected as the retrospective research objects,and they were divided into the control group and the observation group with 40 cases in each group according to different nursing methods.Among them,the control group was given routine nursing health education and guidance,and the observation group was given health education and guidance based on the IMB model.The changes in self-efficacy,anxiety and depression,and quality of life of patients with unilateral VH were compared between the two groups.RESULTS There was no significant difference in General Self-Efficacy Scale(GSES)scale scores between the two groups of patients before nursing(P>0.05),which was comparable;after nursing,the GSES scale scores of the two groups were higher than those before nursing.The nursing group was higher than the control group,and the difference was statistically significant(P<0.05).There was no significant difference in the scores of Hospital Anxiety and Depression Scale(HADS)and anxiety and depression subscales between the two groups before nursing(P>0.05).After nursing,the HADS score,anxiety,and depression subscale scores of the two groups of patients were lower than those before nursing,and the nursing group was lower than the control group,and the difference was statistically significant(P<0.05).After nursing,the Dizziness Handicap Inventory(DHI)scale and DHI-P,DHI-E and DHI-F scores in the two groups were decreased,and the scores in the nursing group were lower than those in the control group,and the difference was statistically significant(P<0.05).CONCLUSION Health education based on the IMB model can effectively improve patients'quality of life,increase self-efficacy of patients with unilateral vestibular hypofunction,enhance patients'confidence,enable patients to resume normal work and life as soon as possible,reduce patients'anxiety and depression,and effectively improve patients'psychological status.

Integrating the health belief model into health education programs in a clinical setting

The article demonstrates that health belief model(HBM)-based health education in hypertensive patients effectively improves blood pressure control and medication adherence at 3 months and 6 months.The HBM addresses perceived barriers,benefits,susceptibility,severity,and self-efficacy,leading to better health behaviors.HBM-based education has been effective in various contexts,including managing chronic diseases,promoting cancer screenings,and preventing infectious diseases.However,the model has limitations,such as cultural applicability and addressing complex health behaviors influenced by environmental factors.Future research should integrate HBM with other theories and conduct longitudinal studies to assess long-term impacts.Despite these limitations,HBM-based education significantly improves patient outcomes,highlighting its potential in health education and promotion when appropriately adapted and implemented.This reinforces the model's value in designing effective health interventions and advancing public health.

Assessment of public perspectives and barriers towards dengue preventive practices using the Health Belief Model in Puducherry,India:A cross-sectional study

Objective:To assess the perspectives and barriers towards dengue preventive practices among the residents of Puducherry,India.Methods:A cross-sectional survey was conducted in 300 households in Puducherry,using a population-proportionate(7:3)distribution from urban and rural areas by grid sampling.One adult interview per household was conducted and the participants were selected using a KISH grid.A semi-structured questionnaire based on the Health Belief Model(HBM)with additional questions on knowledge assessment was used.Knowledge was assessed based on the correctness of answers and the HBM scores were calculated on a 5-point Likert scale.Participants were categorized based on the median score under each domain.Logistic regression was used for adjusted analysis and models were built to predict the performances in each domain.Results:Four percent of the participants lacked basic knowledge regarding dengue transmission.While 208(69.3%)participants did not consider themselves at risk of contracting dengue within the next year,majority perceived dengue as a disease with low severity.Around 49.3%(148)were skeptical about the benefit of time and money spent on dengue prevention.Inadequate government efforts were stated as the major barrier(47.0%)and frequent reminders(142,47.3%)as the major cue to action.Age above 50 years(aOR 1.78,95%CI 1.04-3.06,P=0.037)and rural locality(aOR 2.68,95%CI 1.52-4.71,P=0.001)were found to be significantly associated with poor knowledge scores.Urban participants had a significantly higher chance to perceive low susceptibility as compared to the rural counterparts(aOR 1.74,95%CI 1.05-2.9,P=0.03).Participants with less than a high school education had low perceived benefits(aOR 2.46,95%CI 1.52-3.96,P<0.001)and low self-efficacy scores(aOR 2.66,95%CI 1.61-4.39,P<0.001).Conclusions:This study identifies key gaps in dengue prevention,including low perceived susceptibility,mild disease perception,limited knowledge of breeding sites,and overreliance on government efforts.Tailoring interventions to community needs,stratified to factors influencing the community perspectives can significantly improve dengue prevention efforts.

Comparative Analysis of ARIMA and LSTM Model-Based Anomaly Detection for Unannotated Structural Health Monitoring Data in an Immersed Tunnel

Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.

Health education intervention and its impact on patients with unilateral vestibular dysfunction:An information-motivation-behavioral model approach

Unilateral vestibular dysfunction is a one-sided impairment of vestibular function in one ear.Incorporating health education in treatment and rehabilitation plans can improve vestibular function,keep negative emotions at bay,and reduce the extent of the condition.This letter investigates the impact of the informationmotivation-behavioral skills model as a medium for health education on patient outcomes.While offering encouraging observations,there are certain limitations,such as the study’s retrospective design,small sample size,use of subjective measures,and lack of longer follow-ups that challenge the cogency of the study.The study is a step toward transforming vestibular dysfunction treatment through health education.

Big Model Strategy for Bridge Structural Health Monitoring Based on Data-Driven, Adaptive Method and Convolutional Neural Network (CNN) Group

This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge data.The CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency analysis.By integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge health.Utilizing diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter tuning.This approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure.

Combining RUSLE model and the vegetation health index to unravel the relationship between soil erosion and droughts in southeastern Tunisia

Droughts and soil erosion are among the most prominent climatic driven hazards in drylands,leading to detrimental environmental impacts,such as degraded lands,deteriorated ecosystem services and biodiversity,and increased greenhouse gas emissions.In response to the current lack of studies combining drought conditions and soil erosion processes,in this study,we developed a comprehensive Geographic Information System(GIS)-based approach to assess soil erosion and droughts,thereby revealing the relationship between soil erosion and droughts under an arid climate.The vegetation condition index(VCI)and temperature condition index(TCI)derived respectively from the enhanced vegetation index(EVI)MOD13A2 and land surface temperature(LST)MOD11A2 products were combined to generate the vegetation health index(VHI).The VHI has been conceived as an efficient tool to monitor droughts in the Negueb watershed,southeastern Tunisia.The revised universal soil loss equation(RUSLE)model was applied to quantitatively estimate soil erosion.The relationship between soil erosion and droughts was investigated through Pearson correlation.Results exhibited that the Negueb watershed experienced recurrent mild to extreme drought during 2000–2016.The average soil erosion rate was determined to be 1.8 t/(hm2•a).The mountainous western part of the watershed was the most vulnerable not only to soil erosion but also to droughts.The slope length and steepness factor was shown to be the most significant controlling parameter driving soil erosion.The relationship between droughts and soil erosion had a positive correlation(r=0.3);however,the correlation was highly varied spatially across the watershed.Drought was linked to soil erosion in the Negueb watershed.The current study provides insight for natural disaster risk assessment,land managers,and stake-holders to apply appropriate management measures to promote sustainable development goals in fragile environments.

Investigation of contemporary college students’mental health status and construction of a risk prediction model

BACKGROUND Due to academic pressure,social relations,and the change of adapting to independent life,college students are under high levels of pressure.Therefore,it is very important to study the mental health problems of college students.Developing a predictive model that can detect early warning signals of college students’mental health risks can help support early intervention and improve overall well-being.AIM To investigate college students’present psychological well-being,identify the contributing factors to its decline,and construct a predictive nomogram model.METHODS We analyzed the psychological health status of 40874 university students in selected universities in Hubei Province,China from March 1 to 15,2022,using online questionnaires and random sampling.Factors influencing their mental health were also analyzed using the logistic regression approach,and R4.2.3 software was employed to develop a nomogram model for risk prediction.RESULTS We randomly selected 918 valid data and found that 11.3%of college students had psychological problems.The results of the general data survey showed that the mental health problems of doctoral students were more prominent than those of junior college students,and the mental health of students from rural areas was more likely to be abnormal than that of urban students.In addition,students who had experienced significant life events and divorced parents were more likely to have an abnormal status.The abnormal group exhibited significantly higher Patient Health Questionnaire-9(PHQ-9)and Generalized Anxiety Disorder-7 scores than the healthy group,with these differences being statistically significant(P<0.05).The nomogram prediction model drawn by multivariate analysis includ-ed six predictors:The place of origin,whether they were single children,whether there were significant life events,parents’marital status,regular exercise,intimate friends,and the PHQ-9 score.The training set demonstrated an area under the receiver operating characteristic(ROC)curve(AUC)of 0.972[95%confidence interval(CI):0.947-0.997],a specificity of 0.888 and a sensitivity of 0.972.Similarly,the validation set had a ROC AUC of 0.979(95%CI:0.955-1.000),with a specificity of 0.942 and a sensitivity of 0.939.The H-L deviation test result was χ^(2)=32.476,P=0.000007,suggesting that the model calibration was good.CONCLUSION In this study,nearly 11.3%of contemporary college students had psychological problems,the risk factors include students from rural areas,divorced parents,non-single children,infrequent exercise,and significant life events.

Intelligent Student Mental Health Assessment Model on Learning Management System

A learning management system(LMS)is a software or web based application,commonly utilized for planning,designing,and assessing a particular learning procedure.Generally,the LMS offers a method of creating and delivering content to the instructor,monitoring students’involvement,and validating their outcomes.Since mental health issues become common among studies in higher education globally,it is needed to properly determine it to improve mental stabi-lity.This article develops a new seven spot lady bird feature selection with opti-mal sparse autoencoder(SSLBFS-OSAE)model to assess students’mental health on LMS.The major aim of the SSLBFS-OSAE model is to determine the proper health status of the students with respect to depression,anxiety,and stress(DAS).The SSLBFS-OSAE model involves a new SSLBFS model to elect a useful set of features.In addition,OSAE model is applied for the classification of mental health conditions and the performance can be improved by the use of cuckoo search optimization(CSO)based parameter tuning process.The design of CSO algorithm for optimally tuning the SAE parameters results in enhanced classifica-tion outcomes.For examining the improved classifier results of the SSLBFS-OSAE model,a comprehensive results analysis is done and the obtained values highlighted the supremacy of the SSLBFS model over its recent methods interms of different measures.

An Empirical Study on the Coupling and Coordination of Health Investment, Resident Health and Economic Growth in Sichuan Province —Based on a Modified Coupling Model

We should calculate the coupling degree of medical investment, resident health and economic growth in Sichuan Province, and make clear the coordinated development of the aforementioned three factors. In that, the government was able to formulate policies that feature the positive interaction and coordinated development of regional medical investment, health and economy. Methods on index system for the evaluation of health investment, resident health and economic growth were constructed, and the coupling and coordination degree of the three systems were empirically studied based on the entropy weight method, the coupling coordination model and the gray correlation method. From the perspective of time series, the overall coupling and coordination level of Sichuan Province is relatively low, and the comprehensive development level of health investment and economic growth system has lagged behind the resident health system;from the perspective of spatial distribution characteristics, in 2019, the coordinated development level of health investment resident health and economic growth coupling in western Sichuan, southern Sichuan, northern Sichuan, eastern Sichuan and northern Sichuan is in the primary coordination stage, but there is a lag in the development of the health investment system between western Sichuan and southern Sichuan, and there is a lag in the development of the economic growth system between northern Sichuan and eastern Sichuan. From the analysis of gray correlation degree, the main correlation factors are diverse. All in all, the overall coordination level of health investment, resident health and economic growth in Sichuan Province is relatively low, and in order to achieve its coordinated development, it is necessary to narrow regional differences, formulate coordinated development strategies according to local conditions, and improve the overall coordination level.

Effects of health concept model-based detailed behavioral care on mood and quality of life in elderly patients with chronic heart failure

BACKGROUND With the intensification of social aging,the susceptibility of the elderly population to diseases has attracted increasing attention,especially chronic heart failure(CHF)that accounts for a large proportion of the elderly.AIM To evaluate the application value of health concept model-based detailed behavioral care in elderly patients with CHF.METHODS This study recruited 116 elderly CHF patients admitted from October 2018 to October 2020 and grouped them according to the nursing care that they received.The elderly patients who underwent health concept model-based detailed behavioral care were included in a study group(SG;n=62),and those who underwent routine detailed behavioral nursing intervention were included as a control group(CG;n=54).Patients’negative emotions(NEs),quality of life(QoL),and nutritional status were assessed using the self-rating anxiety/depression scale(SAS/SDS),the Minnesota Living with Heart Failure Questionnaire(MLHFQ),and the Modified Quantitative Subjective Global Assessment(MQSGA)of nutrition,respectively.Differences in rehabilitation efficiency,NEs,cardiac function(CF)indexes,nutritional status,QoL,and nursing satisfaction were comparatively analyzed.RESULTS A higher response rate was recorded in the SG vs the CG after intervention(P<0.05).After care,the left ventricular ejection fraction was higher while the left ventricular end-diastolic dimension and left ventricular end systolic diameter were lower in the SG compared with the CG(P<0.05).The post-intervention SAS and SDS scores,as well as MQSGA and MLHFQ scores,were also lower in the SG(P<0.05).The SG was also superior to the CG in the overall nursing satisfaction rate(P<0.05).CONCLUSION Health concept model-based detailed behavioral care has high application value in the nursing care of elderly CHF patients,and it can not only effectively enhance rehabilitation efficiency,but also mitigate patients’NEs and improve their CF and QoL.

Research on Condenser Deterioration Evolution Trend Based on ANP-EWM Fusion Health Degree

This study presents a proposed method for assessing the condition and predicting the future status of condensers operating in seawater over an extended period.The aim is to address the problems of scaling and corrosion,which lead to increased loss of cold resources.The method involves utilising a set of multivariate feature parameters associated with the condenser as input for evaluation and trend prediction.This methodology offers a precise means of determining the optimal timing for condenser cleaning,with the ultimate goal of improving its overall performance.The proposed approach involves the integration of the analytic network process with subjective expert experience and the entropy weightmethod with objective big data analysis to develop a fusion health degreemodel.The mathematical model is constructed quantitatively using the improved Mahalanobis distance.Furthermore,a comprehensive prediction model is developed by integrating the improved Informer model and Markov error correction.This model takes into account the health status of the equipment and several influencing factors,includingmultivariate feature characteristics.This model facilitates the objective examination and prediction of the progression of equipment deterioration trends.The present study involves the computation and verification of the field time series data,which serves to demonstrate the accuracy of the condenser health-related models proposed in this research.These models effectively depict the real condition and temporal variations of the equipment,thus offering a valuable method for determining the precise cleaning time required for the condenser.

Discussion on the Impact of“Shift System”Teaching Model of Physical Education on Students’Mental Health

The“shift system”teaching model of physical education is an emerging education model that aims to improve students’independent choice and personalized development.However,there are also some challenges in the practical application of this model.For example,there are mental health issues for some students including difficulty in adaptation,social interaction,high psychological pressure,etc.Based on this,this article analyzes the impact of the“shift system”teaching model of physical education on students’mental health and explores the optimization path of the physical education“shift system”teaching model in order to promote students’mental health and all-round development.

Research on the Intervention of Internet+ Health Education Model on Hemodialysis Patients with Psychological Distress

Objective:To observe the status quo of patients’psychological distress,and to explore the effect of Internet+health education model(IHEM)on patients who experienced psychological distress during their first hemodialysis treatment,with the goal of reducing their psychological distress and improving their quality of life.Methods:IHEM was conducted on 120 first-time hemodialysis patients for 3 months while a distress thermometer and a list of questionnaires were used to screen patients and provide corresponding psychological intervention.The incidence rate of psychological distress was analyzed statistically to explore the difference in psychological distress at various periods.Results:The incidence rate(score≥4)of psychological distress in first-time hemodialysis patients was 46.67%,and their distress was mainly rooted in physical,emotional,practical problems(economy,time,and energy),etc.Through IHEM,the psychological distress scores of the patients decreased to 3.29±1.02 at one month after their discharge,and the incidence rate was 32.14%;the psychological distress scores of the patients were 2.29±1.02 at 3 months after their discharge,and the incidence rate was 21.14%.The difference before and after the intervention was statistically significant(P<0.05).Conclusion:A psychological distress thermometer can timely detect the degree and causes of psychological distress among first-time hemodialysis patients,and the use of IHEM may significantly alleviate the psychological distress among hemodialysis patients.

Large multimodal models assist in psychiatry disorders prevention and diagnosis of students

Students are considered one of the groups most affected by psychological pro-blems.Given the highly dangerous nature of mental illnesses and the increasing-ly serious state of global mental health,it is imperative for us to explore new me-thods and approaches concerning the prevention and treatment of mental illne-sses.Large multimodal models(LMMs),as the most advanced artificial intelligen-ce models(i.e.ChatGPT-4),have brought new hope to the accurate prevention,diagnosis,and treatment of psychiatric disorders.The assistance of these models in the promotion of mental health is critical,as the latter necessitates a strong foundation of medical knowledge and professional skills,emotional support,stigma mitigation,the encouragement of more honest patient self-disclosure,reduced health care costs,improved medical efficiency,and greater mental health service coverage.However,these models must address challenges related to health,safety,hallucinations,and ethics simultaneously.In the future,we should address these challenges by developing relevant usage manuals,accountability rules,and legal regulations;implementing a human-centered approach;and intelligently upgrading LMMs through the deep optimization of such models,their algorithms,and other means.This effort will thus substantially contribute not only to the maintenance of students’health but also to the achievement of global sustainable development goals.

Examining the Economic Value of Investing in Health

This review article examines the relationship between health and economic development,highlighting the economic benefits of investing in health.The rise of non-communicable diseases(NCDs)and the COVID-19 pandemic have exposed high demand for increased investment in health as well as critical gaps in the global health system,particularly in low-and middle-income countries,where investments in primary healthcare and innovations in health technologies are lacking.The article emphasizes the importance of examining the economic impact of health,providing a summary of the different pathways through which health impacts the economy and reviewing various economic analyses,including a novel methodology called the health-augmented macroeconomic model(HMM)for evaluating the macroeconomic value of investing in health.The article suggests that reducing disease burdens can effectively generate sizable economic returns,and it is vital to integrate the concept of economic value in health policies and interventions.

A Study on the Role of Tourism in Enhancing Personal Mental Health in the Post-Epidemic Era

With the advent of the post-epidemic era,a great wave of tourism has been ushered in everywhere.The relationship between tourism and mental health has become a hot topic in society.This paper investigates the enhancement of people’s mental health after tourism through social survey.Using Hangzhou as the sample collection site,this paper conducted a study on the role of tourism in enhancing personal mental health through descriptive analysis,factor analysis and structural equation modeling,and further specifically analyzed the role of mediating variables.The results showed that:(1)The purpose of tourism is to relax and relieve stress,and the effectiveness of tourism is mainly reflected in the alleviation of emotional conditions;(2)Factor Analysis reduced the dimensionality of personal mental health indicators,and finally obtained four factors,among which the comprehensive behavioral ability and physiological manifestation had the best improvement effect after tourism;(3)The structural equation model shows that the enhancing effect of tourism on mental health originates from the factor of inner psychological characteristic,and this factor works through two paths:Inner Psychological Characteristic-Social Adaptability-Physiological Manifestations-Enhancement of Mental Health by Tourism,and Inner Psychological Characteristic-Comprehensive Behavioral Ability-Enhancement of Mental Health by Tourism;(4)Tourism has an enhancing effect on personal mental health,and the enhancing effect is most significant among the middle-aged and young people who are unmarried and do not have children yet.These results have been reasonably analyzed and explained,and relevant suggestions are put forward.

Study on Influencing Factors of Mental Health of Mobile Young White-Collar Workers in China

Mobile young white-collar workers not only have the characteristics of mobile young people,but also have the characteristics of general white-collar workers.Under the influence of both,their mental health may be suffering from“double disadvantage”.So,based on an ecological model of the stress process,this paper tries to use the data of the questionnaire on the mental health of mobile young white-collar workers in Zhejiang Province to explore the influence of some factors in the middle workplace and residence place on the mental health of micro individuals.The results show that:(1)The working environment with high control and low freedom and the workplace discrimination against the mobile status will have a negative impact on the mental health of mobile young white-collar workers;(2)Financial anxiety in daily life will lead to a decline in the mental health level of mobile young white-collar workers;(3)Good organizational support and neighborhood social relations can significantly relieve life pressure,so as to effectively improve the mental health of mobile young white-collar workers.It can be seen that we also need to pay more attention to the mental health of mobile young white-collar workers in order to improve their situation.

Predicting depression in patients with heart failure based on a stacking model

BACKGROUND There is a lack of literature discussing the utilization of the stacking ensemble algorithm for predicting depression in patients with heart failure(HF).AIM To create a stacking model for predicting depression in patients with HF.METHODS This study analyzed data on 1084 HF patients from the National Health and Nutrition Examination Survey database spanning from 2005 to 2018.Through univariate analysis and the use of an artificial neural network algorithm,predictors significantly linked to depression were identified.These predictors were utilized to create a stacking model employing tree-based learners.The performances of both the individual models and the stacking model were assessed by using the test dataset.Furthermore,the SHapley additive exPlanations(SHAP)model was applied to interpret the stacking model.RESULTS The models included five predictors.Among these models,the stacking model demonstrated the highest performance,achieving an area under the curve of 0.77(95%CI:0.71-0.84),a sensitivity of 0.71,and a specificity of 0.68.The calibration curve supported the reliability of the models,and decision curve analysis confirmed their clinical value.The SHAP plot demonstrated that age had the most significant impact on the stacking model's output.CONCLUSION The stacking model demonstrated strong predictive performance.Clinicians can utilize this model to identify highrisk depression patients with HF,thus enabling early provision of psychological interventions.