C H A P T E R 5 las, runs the risk of developing an abnormal growth can be calculated in a quick and easy way. Early detection that can help to reverse the increase in overweight and obesity incidence is urgently needed. Excess body weight is a major public health concern worldwide18. The costs to health services, the losses to society and the burdens carried by the individuals involved are great4. It seems beneficial if the youth health care providers would use this prediction model in their health care program to prevent abnormal growth in childhood. Conclusion This study adds a new method of considering the growth of children. Our research has shown that it is possible to create prediction models for early prevention of abnormal growth, by using longitudinal data. The model can easily be used in the health care practice by adding data of age, weight and length in the electronic child record. A digital implementation calculates if a child runs the risk of developing an abnormal growth. In this case, an alarm signal (e.g. a red colour in case of obesity and a yellow colour in case of under-‐ weight) will appear on the computer screen. Collecting anthropometric data from electronic child records from birth onwards can make it possible to update prediction models in the future. Acknowledgement This study has been supported by a grant of the Netherlands Organisation for Health Research and Development, ZonMw, to develop the Academic Collaborative Centre for Public Health Limburg (projectnumber 7125.0001). 84
Proefschrift binnenwerk Manon Ernst_DEF.indd
To see the actual publication please follow the link above