A P R E D I C T I O N M O D E L T O P R E V E N T A B N O R M A L W E I G H T I N C H I L D H O O D Discussion The longitudinal growth study from 1995-‐1999 (n= 1234) in the South of the Neth-‐ erlands17made it possible not only to construct velocity curves for the total child population, but also to analyze the individual growth patterns during a certain pe-‐ riod of life. Depending on its starting position, a child tends to decelerate or accel-‐ erate its growth velocity. This is known as the regression to the mean. Based on the individual growth patterns, we could estimate a PI or BMI after a certain period of growth. If the measured value is > 2 SD above the expected value, the child runs the risk of developing obesity. These are the children with an unexpected change of body composition and possibly at risk for an abnormal metabolic development. If the measured value is < 2 SD below the expected value, the child runs the risk of developing underweight or may be suffering from gastrointestinal problems like celiac disease. By means of the mentioned method, for each of the 372 children the prediction model was applied at birth to estimate the PI at any age between birth and the age of one year and applied at the age of one year to estimate the BMI at any age be-‐ tween one and four years. To estimate the accuracy of the prediction model the differences between the actual PI or BMI at the age of 1 year and 4 years respectively and the expected values at both ages were taken. The results for PI and BMI show that the majority (95.2 % and 96.8 % respectively) of the values fit within 2 SD, as shown in Figure 3 and Figure 4. This supports the internal validity, which means that the prediction model works correctly for our study population and research artifacts can be excluded. The value of this prediction model is, that it gives the possibility to estimate if a child tends to deviate from its personal expected PI or BMI value already at a mo-‐ ment at which the PI or BMI is still normal. In other words, although a child still has a normal weight in relation to its height, its growth pattern indicates the develop-‐ ment of under-‐ or overweight and needs to be investigated to prevent abnormal growth. At the moment, by calculation of BMI, health care physicians can determine if a child suffers from overweight or underweight. For example in case of over-‐ weight, parents can be counseled, mostly directed to life style behaviors. By using our prediction model, parents can be warned before the problem has been estab-‐ lished. If this prediction model can be integrated in the electronic child record, this can be an important step forward in the prevention of abnormal growth, like obesity. By adding the age and starting values of length and weight (values at birth for PI pre-‐ diction and values at the age of 1 year for BMI prediction) of a child into the formu-‐ 83
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