{"title":"Effect of shared decision-making model on the management of diabetes high-risk groups.","authors":"Qiu-Shi Wang, Xiao-Dong Yue, Yan Ma, Zhi-Guang Zhou, Fen Li, Yi-Ling Zhang, Wei-Yu Duan","doi":"10.1111/jep.14158","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>A shared decision-making (SDM) model-based intervention programme was implemented for a population at high risk for diabetes to explore its effectiveness in intervening with blood glucose levels in this population.</p><p><strong>Methods: </strong>One hundred residents were selected according to the principle of voluntary participation and divided into the intervention group (n = 50) and the control group (n = 50) by using multistage cluster sampling. The control group received only brief diabetes knowledge education through a disease brochure issued by the hospital; the intervention group implemented a SDM model based on large classroom and individualised education for 4 months. Univariate analysis and generalised estimating equation fitting model were used to analyse the effect of intervention on blood glucose parameters in the study subjects.</p><p><strong>Results: </strong>Univariate analysis showed that after 4 months of intervention, fasting blood glucose was lower in the intervention group than in the control group (5.57 ± 0.56 vs. 6.07 ± 0.77, F = 45.721, p < 0.001); glycosylated hemoglobin was lower in the intervention group than in the control group (5.91 ± 0.28 vs. 6.02 ± 0.24, F = 25.998, p < 0.001), decreased by 0.26% in the intervention group and increased by 0.01% in the control group. One-way analysis of variance (ANOVA) showed that fasting blood glucose and glycosylated hemoglobin in the intervention group decreased to different extents from baseline. The generalised estimation equation was fitted with the intervention programme, gender, hypertension, smoking, alcohol consumption, physical activity, age, waist circumference, body mass index, baseline fasting blood glucose, and baseline glycosylated hemoglobin as independent variables, and fasting blood glucose and baseline glycosylated hemoglobin as dependent variables. Results showed that compared with the control group, fasting blood glucose and glycosylated hemoglobin levels were significantly different between the two groups (p < 0.001).</p><p><strong>Conclusion: </strong>Applying an intervention programme based on SDM model to people at high risk of diabetes can improve patients' adherence to self-management and establish a good lifestyle, thus contributing to their good glycemic control.</p>","PeriodicalId":15997,"journal":{"name":"Journal of evaluation in clinical practice","volume":" ","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of evaluation in clinical practice","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/jep.14158","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: A shared decision-making (SDM) model-based intervention programme was implemented for a population at high risk for diabetes to explore its effectiveness in intervening with blood glucose levels in this population.
Methods: One hundred residents were selected according to the principle of voluntary participation and divided into the intervention group (n = 50) and the control group (n = 50) by using multistage cluster sampling. The control group received only brief diabetes knowledge education through a disease brochure issued by the hospital; the intervention group implemented a SDM model based on large classroom and individualised education for 4 months. Univariate analysis and generalised estimating equation fitting model were used to analyse the effect of intervention on blood glucose parameters in the study subjects.
Results: Univariate analysis showed that after 4 months of intervention, fasting blood glucose was lower in the intervention group than in the control group (5.57 ± 0.56 vs. 6.07 ± 0.77, F = 45.721, p < 0.001); glycosylated hemoglobin was lower in the intervention group than in the control group (5.91 ± 0.28 vs. 6.02 ± 0.24, F = 25.998, p < 0.001), decreased by 0.26% in the intervention group and increased by 0.01% in the control group. One-way analysis of variance (ANOVA) showed that fasting blood glucose and glycosylated hemoglobin in the intervention group decreased to different extents from baseline. The generalised estimation equation was fitted with the intervention programme, gender, hypertension, smoking, alcohol consumption, physical activity, age, waist circumference, body mass index, baseline fasting blood glucose, and baseline glycosylated hemoglobin as independent variables, and fasting blood glucose and baseline glycosylated hemoglobin as dependent variables. Results showed that compared with the control group, fasting blood glucose and glycosylated hemoglobin levels were significantly different between the two groups (p < 0.001).
Conclusion: Applying an intervention programme based on SDM model to people at high risk of diabetes can improve patients' adherence to self-management and establish a good lifestyle, thus contributing to their good glycemic control.
期刊介绍：
The Journal of Evaluation in Clinical Practice aims to promote the evaluation and development of clinical practice across medicine, nursing and the allied health professions. All aspects of health services research and public health policy analysis and debate are of interest to the Journal whether studied from a population-based or individual patient-centred perspective. Of particular interest to the Journal are submissions on all aspects of clinical effectiveness and efficiency including evidence-based medicine, clinical practice guidelines, clinical decision making, clinical services organisation, implementation and delivery, health economic evaluation, health process and outcome measurement and new or improved methods (conceptual and statistical) for systematic inquiry into clinical practice. Papers may take a classical quantitative or qualitative approach to investigation (or may utilise both techniques) or may take the form of learned essays, structured/systematic reviews and critiques.
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