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养老机构老年人低舌压的影响因素分析及决策树模型的构建OA

Analysis of influencing factors on the decline of tongue pressure in elderly people in elderly care institutions and research on establishing a decision tree model

中文摘要英文摘要

目的:探讨养老机构老年人低舌压的影响因素并建立决策树模型.方法:便利选取2025年4月—7月居住于江西省南昌市2所养老机构内符合纳入与排除标准的206例老年人为研究对象,根据调查对象最大舌压的中位数(28.13 kPa)分为舌压正常组(n=104例)和低舌压组(n=102例),分析养老机构老年人低舌压的影响因素.采用IBM SPSS Modeler构建养老机构老年人低舌压影响因素的决策树模型.结果:本研究养老机构老年人低舌压发生率为49.5%.Logistic回归模型与决策树模型均显示年龄、是否存在咀嚼困难、EAT-10得分及每秒钟发[ta]音的次数与养老机构老年人低舌压有关,2种模型的受试者工作特征曲线下面积分别为0.829,0.821.De-Long检验结果显示,2种模型差异无统计学意义(Z=0.471,P=0.677).结论:高龄、存在咀嚼困难、EAT-10得分越高、每秒钟连续发[ta]音次数<6次是养老机构老年人发生低舌压的危险因素,本研究构建的Logistic回归模型及决策树模型均具有较好的预测效能,有助于临床早期识别老年人低舌压的风险并开展个性化干预.

Objective:To investigate the factors influencing the maximum tongue pressure of elderly residents in nursing homes and to construct a decision tree model.Methods:A convenience sample of 206 elderly residents meeting the inclusion and exclusion criteria was recruited from two nursing homes in Nanchang,Jiangxi province,between April and July 2025.Participants were divided into a normal tongue pressure group(n=104)and a low tongue pressure group(n=102)based on the median maximum tongue pressure(28.13 kPa).Factors influencing reduced maximum tongue pressure were analyzed.IBM SPSS Modeler was used to construct a decision tree model for these factors.Results:The prevalence of low tongue pressure among the elderly residents was 49.5%.Both the logistic regression model and the decision tree model indicated that age,presence of chewing difficulties,EAT-10 scores,and the number of"ta"repetitions per second were associated with maximum tongue pressure.The areas under the receiver operating characteristic(ROC)curves for the two models were 0.829 and 0.821,respectively.DeLong's test showed no statistically significant difference between the two models(Z=0.471,P=0.677).Conclusion:Advanced age,chewing difficulties,higher EAT-10 scores,and fewer than six"ta"repetitions per second are risk factors for low tongue pressure in elderly residents of long-term care facilities.Both the logistic regression model and the decision tree model constructed in this study demonstrate good predictive performance,facilitating the early clinical identification of the risk of reduced tongue pressure and the implementation of personalized interventions.

张文静;付志霞;胡静;占莉琳

330000,南昌大学附属口腔医院||南昌大学护理学院南昌大学护理学院南昌大学护理学院330000,南昌大学附属口腔医院

舌压Logistic回归模型决策树模型影响因素

tongue pressureLogistic regression modeldecision tree modelinfluencing factors

《全科护理》 2026 (15)

2815-2820,6

江西省卫生健康委科研项目,编号:202610464.

10.12104/j.issn.1674-4748.2026.15.004

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