• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Blood glucose estimation accuracy in children with diabetes: An investigation of repeated practice with latent growth curve modeling.
【24h】

Blood glucose estimation accuracy in children with diabetes: An investigation of repeated practice with latent growth curve modeling.

机译：糖尿病患儿的血糖估算准确性：使用潜在生长曲线建模进行反复练习的调查。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Blood glucose (BG) estimation is an important part of maintaining euglycemia in individuals with diabetes. Interventions to increase accuracy were designed without taking into account the natural effects of practice. The current study investigated the effects of naturalistic practice on BG estimation accuracy. Latent growth curve modeling (LGCM) was used to analyze data from 43 children with insulin dependent diabetes. LGCM consists of two stages. In the first stage, a growth curve was fit to the repeated measures of each individual in the sample. Results indicated that the true growth trajectory was curvilinear, and best represented by a quadratic function. Fit of the quadratic model to the data was good (X2 = 18.52 (15), p = .24, CFI = .99, RMSEA = .08). Overall, participants' accuracy improved initially, and then deteriorated over time, in some cases even surpassing baseline levels of inaccuracy. In the second stage, the parameters for an individual's growth curve were predicted by a set of explanatory variables. Addition of age, anxiety, and psychological adjustment to diabetes as predictors also resulted in adequate model fit (X2 = 31.61 (30), p = .39, CFI = .99, RMSEA = .04). Results indicate that older children were more likely to improve and then deteriorate. Younger children were more anxious and were more likely to improve and sustain those improvements. Results of this investigation can be used to create and implement effective interventions to increase blood glucose estimation accuracy.
机译：血糖（BG）估计是维持糖尿病患者血糖正常的重要部分。设计提高准确性的干预措施时并未考虑实践的自然影响。当前的研究调查了自然实践对BG估计准确性的影响。使用潜伏生长曲线建模（LGCM）分析来自43名胰岛素依赖型糖尿病儿童的数据。 LGCM由两个阶段组成。在第一阶段，生长曲线适合样品中每个个体的重复测量。结果表明，真实的增长轨迹是曲线的，最好用二次函数表示。二次模型对数据的拟合良好（X2 = 18.52（15），p = .24，CFI = .99，RMSEA = .08）。总体而言，参与者的准确性最初有所提高，然后随着时间的推移而下降，在某些情况下甚至超过了基线的不准确度。在第二阶段，通过一组解释变量预测个人成长曲线的参数。年龄，焦虑和对糖尿病的心理适应性的增加也可以作为预测模型的依据（X2 = 31.61（30），p = 0.39，CFI = .99，RMSEA = .04）。结果表明，年龄较大的儿童更有可能改善然后恶化。年幼的孩子更加焦虑，更有可能改善和维持这些改善。该调查的结果可用于创建和实施有效的干预措施，以提高血糖估计的准确性。

著录项

  • 作者

    Wagner, Julie Ann.;

  • 作者单位

    University of Rhode Island.;

  • 授予单位 University of Rhode Island.;
  • 学科 Quantitative psychology.
  • 学位 Ph.D.
  • 年度 2000
  • 页码 66 p.
  • 总页数 66
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号