主管:国家卫生健康委员会
主办:国家卫生健康委医院管理研究所
中国科技核心期刊(中国科技论文统计源期刊)
中国科学引文数据库(CSCD)核心库期刊
《中文核心期刊要目总览》核心期刊

中国护理管理 ›› 2023, Vol. 23 ›› Issue (12): 1888-1893.doi: 10.3969/j.issn.1672-1756.2023.12.024

• 数智化护理 • 上一篇    下一篇

社区居家老年人智慧养老接纳度预测模型的构建与验证

张明明 吴碧琳 胡慧玲 吴雪   

  1. 北京大学护理学院,100191 北京市
  • 出版日期:2023-12-15 发布日期:2023-12-15
  • 通讯作者: 吴雪,博士,博士生导师,副教授,E-mail:wuxue@bjmu.edu.cn
  • 作者简介:张明明,硕士在读
  • 基金资助:
    国家重点研发计划(2020YFC2008805,2020YFC2008800)

Establishment and validation of a prediction model for acceptance of smart care for the elderly in the community

ZHANG Mingming, WU Bilin, HU Huiling, WU Xue   

  1. School of Nursing, Peking University, Beijing, 100191, China
  • Online:2023-12-15 Published:2023-12-15
  • Contact: E-mail:wuxue@bjmu.edu.cn

摘要: 目的:基于技术接受整合模型建立并验证智慧养老接纳度决策树预测模型,为预测居家老年人智慧养老接纳度提供依据。方法:通过方便抽样法,于2022年5月—12月在北京市4个社区、山东省立第三医院社区卫生服务中心及山东省聊城市社区卫生服务中心招募1?031位老年人参与研究,采用一般资料调查问卷、老年人居家智慧养老接纳度影响因素问卷和技术焦虑量表收集资料。应用LASSO回归筛选变量,按照7?:3比例将研究对象随机分为训练组(723例)和测试组(308例),训练组建立决策树模型,测试组采用准确率、灵敏度、特异度及受试者工作特征曲线评价模型。结果:绩效期望、努力期望、社会影响、职业、健康管理需求、智慧养老产品使用经验是社区居家老年人智慧养老接纳度的影响因素。决策树深度为7层,共10个叶节点。决策树的准确率为80.2%,灵敏度为80.2%,特异度为77.7%,受试者工作特征曲线下面积为0.812。结论:构建的社区居家老年人智慧养老接纳度模型预测效能良好,可为评估并促进老年人对智慧养老的接纳提供科学指导。

关键词: 智慧养老;接纳度;决策树;技术接受整合模型;社区居家老年人

Abstract: Objective: To establish the prediction model of the acceptance of smart care for the elderly in the community based on the Unified Theory of Acceptance and Use of Technology, and provide reference for predicting the acceptance of smart care for elderly people. Methods: From May to December 2022, a total of 1031 elderly from 4 communities in Beijing, the Community Health Service Center of Shandong provincial Third Hospital and the Community Health Service Center of Liaocheng were recruited. Data were collected using a general information questionnaire, a questionnaire on influencing factors of the acceptance of smart care for the elderly, and Technophobia Scale. The variables were screened by LASSO regression, and the sample was randomly divided into training group (n=723) and test group (n=308) according to the ratio of 7:3. A decision tree model was established in the training group, and the accuracy, sensitivity, specificity and Receiver Operating Characteristic (ROC) curve were used to test the model in the test group. Results: Performance expectancy, effort expectancy, social factors, occupation, health management needs, and experience in using smart elderly care products were the influencing factors of the acceptance of smart care for the elderly in the community. The depth of the decision tree containing 7 layers and a total of 10 leaf nodes. The accuracy, sensitivity, specificity and AUC of the decision tree were 80.2%, 80.2%, 77.7% and 0.812. Conclusion: The decision tree of the acceptance of smart care for the elderly in the community has good prediction efficiency, which can provide scientific guidance for evaluating and promoting the acceptance of smart care for the elderly.

Key words: smart care for the elderly; acceptance; decision tree; Unified Theory of Acceptance and Use of Technology; community-dwelling elderly

中图分类号:  R47;R197