David S Freedman1, Amy J Goodwin Davies2, Thao-Ly Tam Phan3, F Sessions Cole4, Nathan Pajor5, Suchitra Rao6, Ihuoma Eneli7, Lyudmyla Kompaniyets1, Samantha J Lange1, Dimitri A Christakis8, Christopher B Forrest9. 1. Division of Nutrition, Physical Activity, and Obesity, Centers for Disease Control and Prevention, Atlanta, Georgia, USA. 2. Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. 3. Department of Pediatrics, Nemours/Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA. 4. Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine, St Louis, Missouri, USA. 5. Department of Pediatrics, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, Ohio, USA. 6. Department of Pediatrics, University of Colorado, Colorado, Aurora, USA. 7. Nationwide Children's Hospital, Columbus, Ohio, USA. 8. Seattle Children's Research Institute, University of Washington, Seattle, Washington, USA. 9. Department of Pediatrics, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Abstract
BACKGROUND: Weight control programs for children monitor BMI changes using BMI z-scores that adjust BMI for the sex and age of the child. It is, however, uncertain if BMIz is the best metric for assessing BMI change. OBJECTIVE: To identify which of 6 BMI metrics is optimal for assessing change. We considered a metric to be optimal if its short-term variability was consistent across the entire BMI distribution. SUBJECTS: 285 643 2- to 17-year-olds with BMI measured 3 times over a 10- to 14-month period. METHODS: We summarized each metric's variability using the within-child standard deviation. RESULTS: Most metrics' initial or mean value correlated with short-term variability (|r| ~ 0.3 to 0.5). The metric for which the within-child variability was largely independent (r = 0.13) of the metric's initial or mean value was the percentage of the 50th expressed on a log scale. However, changes in this metric between the first and last visits were highly (r ≥ 0.97) correlated with changes in %95th and %50th. CONCLUSIONS: Log %50 was the metric for which the short-term variability was largely independent of a child's BMI. Changes in log %50th, %95th, and %50th are strongly correlated.
BACKGROUND: Weight control programs for children monitor BMI changes using BMI z-scores that adjust BMI for the sex and age of the child. It is, however, uncertain if BMIz is the best metric for assessing BMI change. OBJECTIVE: To identify which of 6 BMI metrics is optimal for assessing change. We considered a metric to be optimal if its short-term variability was consistent across the entire BMI distribution. SUBJECTS: 285 643 2- to 17-year-olds with BMI measured 3 times over a 10- to 14-month period. METHODS: We summarized each metric's variability using the within-child standard deviation. RESULTS: Most metrics' initial or mean value correlated with short-term variability (|r| ~ 0.3 to 0.5). The metric for which the within-child variability was largely independent (r = 0.13) of the metric's initial or mean value was the percentage of the 50th expressed on a log scale. However, changes in this metric between the first and last visits were highly (r ≥ 0.97) correlated with changes in %95th and %50th. CONCLUSIONS: Log %50 was the metric for which the short-term variability was largely independent of a child's BMI. Changes in log %50th, %95th, and %50th are strongly correlated.
Authors: R Glenn Weaver; Bridget Armstrong; Elizabeth Adams; Michael Beets; James White; Kate Flory; Dawn Wilson; Alex Mclain; Brianna Tennie Journal: Res Sq Date: 2022-03-29