BACKGROUND: Despite validation studies demonstrating substantial bias, epidemiologic studies typically use self-reported height and weight as primary measures of body mass index because of feasibility and resource limitations. PURPOSE: To demonstrate a method for calculating accurate and precise estimates that use body mass index when objectively measuring height and weight in a full sample is not feasible. METHODS: As part of a longitudinal study of adolescent health, 1,840 adolescents (ages 12-18) self-reported their height and weight during telephone surveys. Height and weight was measured for 407 of these adolescents. Sex-specific, age-adjusted obesity status was calculated from self-reported and from measured height and weight. Prevalence and predictors of obesity were estimated using self-reported data, measured data, and multiple imputation (of measured data). RESULTS: Among adolescents with self-reported and measured data, the obesity prevalence was lower when using self-report compared with actual measurements (p < .001). The obesity prevalence from multiple imputation (20%) was much closer to estimates based solely on measured data (20%) compared with estimates based solely on self-reported data (12%), indicating improved accuracy. In multivariate models, estimates of predictors of obesity were more accurate and approximately as precise (similar confidence intervals) as estimates based solely on self-reported data. CONCLUSIONS: The two-method measurement design offers researchers a technique to reduce the bias typically inherent in self-reported height and weight without needing to collect measurements on the full sample. This technique enhances the ability to detect real, statistically significant differences, while minimizing the need for additional resources.
BACKGROUND: Despite validation studies demonstrating substantial bias, epidemiologic studies typically use self-reported height and weight as primary measures of body mass index because of feasibility and resource limitations. PURPOSE: To demonstrate a method for calculating accurate and precise estimates that use body mass index when objectively measuring height and weight in a full sample is not feasible. METHODS: As part of a longitudinal study of adolescent health, 1,840 adolescents (ages 12-18) self-reported their height and weight during telephone surveys. Height and weight was measured for 407 of these adolescents. Sex-specific, age-adjusted obesity status was calculated from self-reported and from measured height and weight. Prevalence and predictors of obesity were estimated using self-reported data, measured data, and multiple imputation (of measured data). RESULTS: Among adolescents with self-reported and measured data, the obesity prevalence was lower when using self-report compared with actual measurements (p < .001). The obesity prevalence from multiple imputation (20%) was much closer to estimates based solely on measured data (20%) compared with estimates based solely on self-reported data (12%), indicating improved accuracy. In multivariate models, estimates of predictors of obesity were more accurate and approximately as precise (similar confidence intervals) as estimates based solely on self-reported data. CONCLUSIONS: The two-method measurement design offers researchers a technique to reduce the bias typically inherent in self-reported height and weight without needing to collect measurements on the full sample. This technique enhances the ability to detect real, statistically significant differences, while minimizing the need for additional resources.
Authors: A M Adachi-Mejia; M R Longacre; J J Gibson; M L Beach; L T Titus-Ernstoff; M A Dalton Journal: Int J Obes (Lond) Date: 2006-09-12 Impact factor: 5.095
Authors: Madeline A Dalton; Anna M Adachi-Mejia; Meghan R Longacre; Linda T Titus-Ernstoff; Jennifer J Gibson; Susan K Martin; James D Sargent; Michael L Beach Journal: Pediatrics Date: 2006-11 Impact factor: 7.124
Authors: William T Dalton; Liang Wang; Jodi L Southerland; Karen E Schetzina; Deborah L Slawson Journal: South Med J Date: 2014-06 Impact factor: 0.954