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Influencing Factors On Diabetes Distress In Elderly Hospitalized Patients And Latent Classification Analysis

Posted on:2021-05-01Degree:MasterType:Thesis
Country:ChinaCandidate:D LiFull Text:PDF
GTID:2404330602976261Subject:Care
Abstract/Summary:PDF Full Text Request
ObjectiveTo investigated the current situation and influential factors on diabetes distress in elderly hospitalized patients with diabetes mellitus,and analyzed the effect of type D personality,social support and coping styles on diabetes distress.Subsequently,latent classification model was used to reclassify diabetes distress and explore characteristics of each category,so as to provide a theoretical basis for diabetes distress interventions for elderly hospitalized patients with diabetes in clinical practice.Methods253 elderly hospitalized patients with diabetes mellitus who met the inclusion criteria in the endocrinology department of a third-class A hospital in Henan Province were assessed with the General Information Questionnaire,17-item Diabetes Distress Scale(DDS-17),the Social Support Rating Scale(SSRS),14-intem D-Type Personality Scale(DS-14),and the Simple Coping Style Scale(SCSQ)from January 2019 to May 2019.Firstly,descriptive statistical methods are used to map the current status of diabetes distress.Secondly,the influential factors of diabetes distress were analyized by the t-test,one-way analysis of variance,Pearson correlation,and multiple linear regression.Subsequently,Amos17.0 and latent gold 5.0 software was used for structural equation model and latent classification model.Results1.The overall score and average score of diabetes distress of elderly patients with diabetes was(37.92±16.49)and(2.23±0.97),respectively.Of Them,the average scores of emotional burden(EB),regimen related distress(RE),interpersonal distress(ID),and physician related distress(PR)in four dimensions were(13.27±5.67),(12.54±5.39),(5.96±2.43),and(6.14±2.93),respectively.2.T test and one-way analysis of variance showed that education level,marital status,diabetes duration,complications,treatment modalities,and type D personality played a significant impact on diabetes distress.Based on pre-screening variables from Pearson correlation,complications,type D personality,subjective support,support utilization and negative coping style were indicated as the independent variable,diabetes distress was selected as the dependent variable,thereby accounting for 47.69%total variance3.Structural equation model showed that type D personality can directly affect their diabetes distress,and can also indirectly affect diabetic distress through the three paths of social support,negative coping style,and social support and negative coping,which together explain 33.78%of the total variation of diabetes distress.4.Based on the latent classification model,the 253 elderly hospitalized patients with diabetes in this study were divided into three categories,including high-level diabetes distress,mid-level diabetes distress with regimen related distress,and low-level diabetes distress with interpersonal distress.Conclusions1.The diabetes distress of elderly hospitalized patients with diabetes mellitus was in moderately high level.2.The complications,negative coping,and type D personality could positively predict diabetes distress.However,subjective support and support utilization could negatively predict diabetes distress.3.Type D personality can directly affect their diabetic distress,and it can also indirectly affect diabetic distress through three paths of social support,negative coping,and social support and negative coping.4.The three categories of diabetes distress displayed themselves specific characteristics.It is implied that we should pay more attention to the characteristics of various types of diabetic distress in elderly patients with diabetes in the future,and formulate intervention strategies for diabetes distress in different types of groups to improve intervention effect.
Keywords/Search Tags:Diabetes Mellitus, Elderly, Diabetes Distress, Influencing, Factors Functioning Path, Latent Class Analysis
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