McAdams MA, Van Dam RM, Hu FB. Comparison of self-reported and measured BMI as correlates of disease markers in US adults. Obesity 15, 188-196

Department of Epidemiology, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115, USA.
Obesity (Impact Factor: 3.73). 02/2007; 15(1):188-96. DOI: 10.1038/oby.2007.504
Source: PubMed


The purpose of this study is to evaluate the validity of BMI based on self-reported data by comparison with technician-measured BMI and biomarkers of adiposity.
We analyzed data from 10,639 National Health and Nutrition Education Study III participants > or =20 years of age to compare BMI calculated from self-reported weight and height with BMI from technician-measured values and body fatness estimated from bioelectrical impedance analysis in relation to systolic blood pressure, fasting blood levels of glucose, high-density lipoprotein-cholesterol, triglycerides, C-reactive protein, and leptin.
BMI based on self-reported data (25.07 kg/m2) was lower than BMI based on technician measurements (25.52 kg/m2) because of underreporting weight (-0.56 kg; 95% confidence interval, -0.71, -0.41) and overreporting height (0.76 cm; 95% confidence interval, 0.64, 0.88). However, the correlations between self-reported and measured BMI values were very high (0.95 for whites, 0.93 for blacks, and 0.90 for Mexican Americans). In terms of biomarkers, self-reported and measured BMI values were equally correlated with fasting blood glucose (r = 0.43), high-density lipoprotein-cholesterol (r = -0.53), and systolic blood pressure (r = 0.54). Similar correlations were observed for both measures of BMI with plasma concentrations of triglycerides and leptin. These correlations did not differ appreciably by age, sex, ethnicity, or obesity status. Correlations for percentage body fat estimated through bioelectrical impedance analysis with these biomarkers were similar to those for BMI.
The accuracy of self-reported BMI is sufficient for epidemiological studies using disease biomarkers, although inappropriate for precise measures of obesity prevalence.

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    • "This requires that they not only be aware of their actual weight, but also have a good sense of what their ideal body weight should be. Previous studies have already demonstrated the lack of accuracy with patients' self-reported weight [10] [11] [12]. The focus of this study was to measure patients' insight to their target ideal weight. "
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    ABSTRACT: Objective: Despite much effort, obesity remains a significant public health problem. One of the main contributing factors is patients' perception of their target ideal body weight. This study aimed to assess this perception. Methods: The study took place in an urban area, with the majority of participants in the study being Hispanic (65.7%) or African-American (28.0%). Patients presented to an outpatient clinic were surveyed regarding their ideal body weight and their ideal BMI calculated. Subsequently they were classified into different categories based on their actual measured BMI. Their responses for ideal BMI were compared. Results: In 254 surveys, mean measured BMI was 31.71 ± 8.01. Responses to ideal BMI had a range of 18.89-38.15 with a mean of 25.96 ± 3.25. Mean (±SD) ideal BMI for patients with a measured BMI of <18.5, 18.5-24.9, 25-29.9, and ≥30 was 20.14 ± 1.46, 23.11 ± 1.68, 25.69 ± 2.19, and 27.22 ± 3.31, respectively. These differences were highly significant (P < 0.001, ANOVA). Conclusions: Most patients had an inflated sense of their target ideal body weight. Patients with higher measured BMI had higher target numbers for their ideal BMI. Better education of patients is critical for obesity prevention programs.
    Journal of obesity 12/2014; 2014:491280. DOI:10.1155/2014/491280
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    • "Paternal height and the weights of both parents were self reported, so subject to bias (Gorber et al., 2007; McAdams et al., 2007). However, BMI based on selfreported measurements may be sufficiently accurate for epidemiological studies (McAdams et al., 2007). Our analyses also did not include several other factors that have been previously shown to relate to neonatal anthropometry, including maternal pregnancy weight gain (Kramer, 1987; Catalano et al., 1995; Goldenberg et al., 1997; Frederick et al., 2008; Roland et al., 2012; Tikellis et al., 2012), maternal and paternal birthweight (Kramer, 1987; Little, 1987; Emanuel et al., 1992; Magnus et al., 2001), maternal micronutrient status (Kramer, 1987; Mathews et al., 1999; Leffelaar et al., 2010), placental weight (Kramer, 1987; Roland et al., 2012; Tikellis et al., 2012) and maternal glucose metabolism before or during pregnancy (Catalano et al., 2003; HAPO Study Cooperative Research Group, 2009; Catalano et al., 2012; Roland et al., 2012). "
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    ABSTRACT: The patterns of association between maternal or paternal and neonatal phenotype may offer insight into how neonatal characteristics are shaped by evolutionary processes, such as conflicting parental interests in fetal investment and obstetric constraints. Paternal interests are theoretically served by maximizing fetal growth, and maternal interests by managing investment in current and future offspring, but whether paternal and maternal influences act on different components of overall size is unknown. We tested whether parents' prepregnancy height and body mass index (BMI) were related to neonatal anthropometry (birthweight, head circumference, absolute and proportional limb segment and trunk lengths, subcutaneous fat) among 1,041 Australian neonates using stepwise linear regression. Maternal and paternal height and maternal BMI were associated with birthweight. Paternal height related to offspring forearm and lower leg lengths, maternal height and BMI to neonatal head circumference, and maternal BMI to offspring adiposity. Principal components analysis identified three components of variability reflecting neonatal “head and trunk skeletal size,” “adiposity,” and “limb lengths.” Regression analyses of the component scores supported the associations of head and trunk size or adiposity with maternal anthropometry, and limb lengths with paternal anthropometry. Our results suggest that while neonatal fatness reflects environmental conditions (maternal physiology), head circumference and limb and trunk lengths show differing associations with parental anthropometry. These patterns may reflect genetics, parental imprinting and environmental influences in a manner consistent with parental conflicts of interest. Paternal height may relate to neonatal limb length as a means of increasing fetal growth without exacerbating the risk of obstetric complications.
    American Journal of Physical Anthropology 12/2014; Early view(4). DOI:10.1002/ajpa.22680 · 2.38 Impact Factor
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    • "We will explore whether one or more of the following variables have an effect on the relationship of interest, and if so, include them in the analyses as confounder: (1) Pregnancy duration and birth weight (from YHC files), (2) Parents’ BMI using self-report (self-report of adult height and weight has proven to be sufficiently accurate for epidemiological research [28]), (3) Parents’ health-related quality of life and wellbeing (SF-12), (4) Age, educational level and ethnic background of the parents. "
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    ABSTRACT: Two overweight prevention interventions were developed to be offered by preventive Youth Health Care (YHC) in addition to the currently applied overweight prevention protocol to parents of 0-3 year old children. The two interventions aim to support parents of preschool children to realize healthy child nutrition and activity behaviors of their young child. The aim of this study is to assess the effects of the two overweight prevention interventions with regard to child health behaviors and child Body Mass Index.Methods/design: A cluster randomized controlled trial was conducted among parents and their preschool children who attend one of 51 participating YHC teams. The teams were randomly allocated to one of the two intervention groups, or to the control group (care as usual).The 'BBOFT+' intervention focuses on effective child rearing by parents from birth onwards by enlarging parental skills concerning healthy behavioural life-style habits. Parents who are allocated to the 'E-health4Uth Healthy toddler' intervention group, at the child age of circa 18 and 24 months old, are invited to complete an online E-health module providing tailored health education regarding healthy child nutrition and activity behaviors. The E-health messages are discussed and reinforced during the subsequent regularly scheduled visits by YHC professionals, and were repeated after 4 weeks.The primary outcome measures at child age 3 years are: overweight inducing/reducing behaviors, (for 'BBOFT+' only) healthy sleep, Body Mass Index and prevalence of overweight and obesity. Secondary outcome measures are attitudes and other cognitive characteristics of the parents regarding the overweight-related behaviors of their child, parenting styles and practices, and health-related quality of life of the children. We hypothesize that the use of the additional interventions will result in a healthier lifestyle of preschool children and an improved BMI and less development of overweight and obesity compared to usual care.Trial registration: Current Controlled Trials ISRCTN 1831.
    BMC Public Health 10/2013; 13(1):974. DOI:10.1186/1471-2458-13-974 · 2.26 Impact Factor
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