Blood glucose estimation accuracy in children with diabetes: An investigation of repeated practice with latent growth curve modeling
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. ^
Julie Ann Wagner,
"Blood glucose estimation accuracy in children with diabetes: An investigation of repeated practice with latent growth curve modeling"
Dissertations and Master's Theses (Campus Access).