The Validity of Obesity Based on Self-reported Weight and Height: Implications for Population Studies*

Skaraborg Institute, Skovde, Sweden.
Obesity (Impact Factor: 3.73). 02/2007; 15(1):197-208. DOI: 10.1038/oby.2007.536
Source: PubMed


To validate self-reported information on weight and height in an adult population and to find a useful algorithm to assess the prevalence of obesity based on self-reported information.
This was a cross-sectional survey consisting of 1703 participants (860 men and 843 women, 30 to 75 years old) conducted in the community of Vara, Sweden, from 2001 to 2003. Self-reported weight, height, and corresponding BMI were compared with measured data. Obesity was defined as measured BMI > or = 30 kg/m2. Information on education, self-rated health, smoking habits, and physical activity during leisure time was collected by a self-administered questionnaire.
Mean differences between measured and self-reported weight were 1.6 kg (95% confidence interval, 1.4; 1.8) in men and 1.8 kg (1.6; 2.0) in women (measured higher), whereas corresponding differences in height were -0.3 cm (-0.5; -0.2) in men and -0.4 cm (-0.5; -0.2) in women (measured lower). Age and body size were important factors for misreporting height, weight, and BMI in both men and women. Obesity (measured) was found in 156 men (19%) and 184 women (25%) and with self-reported data in 114 men (14%) and 153 women (20%). For self-reported data, the sensitivity of obesity was 70% in men and 82% in women, and when adjusted for corrected self-reported data and age, it increased to 81% and 90%, whereas the specificity decreased from 99% in both sexes to 97% in men and 98% in women.
The prevalence of obesity based on self-reported BMI can be estimated more accurately when using an algorithm adjusted for variables that are predictive for misreporting.

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Available from: Juan Merlo, Oct 13, 2014
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    • "This resulted in our sample size being reduced by approximately 15%. Furthermore, the collection of self-reported height and weight may have resulted in poor estimates of BMI, as has been demonstrated in previous studies (Gosse, 2014; Nyholm et al., 2007). Although our analysis controlled for confounding variables it would have been beneficial to collect and adjust for other individual factors known to correlate with PA, such as self-efficacy or health status (Trost et al., 2002). "
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    ABSTRACT: Family members have the capacity to influence each other's health behaviours. This study examined whether there were associations in the objectively assessed physical activity and Body Mass Index (BMI) of mothers and fathers. Methods: Recruitment took place in Bristol (UK) during 2012/13. Participants were 272 pairs of parents (dyads) that wore an accelerometer for at least 500. min on 3 or more days. Parents provided demographic information and self-reported height and weight. Multi-variable linear and logistic regression models examined the relationships between parents' moderate-to-vigorous physical activity (MVPA) and BMI. Results: MVPA minutes (r = 0.26, p < 0.001) and Body Mass Index (r = 0.20, p = 0.002) of parents were correlated. Logistic regression analysis showed that mothers were almost twice (OR 1.87, p <. 0.05) as likely to be overweight or obese when fathers were. Linear regression models showed that at the weekend every 9. min of paternal MVPA was associated with 3. min of maternal MVPA (r = 0.34, p < 0.001). Conclusions: Both physical activity and BMI of parenting partners were associated. Since parents tend to share home environments and often perform activities together or as a family, then behavioural changes in one parent may have a ripple effect for other family members.
    12/2015; 2:473-477. DOI:10.1016/j.pmedr.2015.06.007
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    • "The smoking and drinking variables were then dummy coded using the " not at all " category as the reference group. Finally, following standard procedures (Nyholm et al., 2007), responses to an overall self-rated general health questionnaire item (5-point scale from very good to very bad) were dummy coded using the lowest health rating as the reference group. "
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    ABSTRACT: Individuals often overestimate their height while concomitantly underestimating their weight; this results in lower obesity prevalence rates when self-report data are used to calculate BMI, a pattern that has been observed in both sexes (Gorber et al., 2007; Nyholm et al., 2007). This misclassification of obesity due to inaccurate self-reported (SR) BMI values has considerable public health implications, especially given that global obesity levels have reached epidemic-proportions. Thus, accurately identifying obese individuals, in particular, is crucial to the interpretation of lifestyle factors that increase obesity risk (Flegal et al., 2013). Individual characteristics, such as true (measured) weight and age, also appear to influence the accuracy of SR BMI values. It appears that actual body weight may influence the extent to which an individual underestimates their weight. For example, Hill and Roberts (1998) documented a ~0.1 increase in BMI underestimation for every unit increase in measured BMI. Furthermore, previous research has documented that the overestimation of height and underestimation of weight significantly increases with age, leading to the increased misclassification of overweight and obese older individuals (Lawlor et al., 2002; Dahl et al., 2010). This misclassification may preclude enrollment in weight reduction programs designed to reduce the health complications associated with obesity, thereby putting these older individuals at risk. Still, studies assessing the accuracy of SR BMI have largely been restricted to wealthier nations. The few studies examining these relationships in lower income countries have produced conflicting results. Weight underestimation and height overestimation (similar to Western populations) has been documented in Mexico, Thailand, and China (Santillan and Camargo, 2003; Lim et al., 2009; Zhou et al., 2010), while studies in Brazil and Mexico have observed no significant difference between self-report and measured BMI (Osuna-Ramírez et al., 2006; Rech et al., 2008). Thus, further work is required to determine if the discrepancies between SR and measured BMI are shared or differ by culture. The present study assesses whether SR and measured BMI values differ cross-culturally in older adults using data from World Health Organization's Study on global AGEing and adult health (SAGE) Wave 1 (Kowal et al., 2012). Data from six middle income countries (China, Ghana, India, Mexico, the Russian Federation, and South Africa) are used to examine how discrepancies in SR and actual BMI among older adults varies cross-culturally. Three hypotheses are tested. First, BMI calculated from SR height and weight will significantly differ from BMI calculated from height and weight measured by SAGE interviewers. Second, heavier individuals will be more likely to underreport their weight, decreasing their SR BMI value and resulting in a negative difference between SR and measured BMI. Third, age will be inversely correlated with discrepancies in SR and measured BMI (calculated by subtracting measured BMI from SR BMI); thus, older adults will be more likely to misreport their height and weight).
    Population Association of America 2015 Annual Meeting, San Diego, CA, USA; 05/2015
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    • "The regression equations of interest, (3) and (6), were run on the model generating group for males and females separately to obtain the necessary parameters for the correction equations. Male- and female-specific correction equations were developed separately to allow for known different trends in misreporting weight for males and females [12,13], and to match the convention used in other Canadian corrections [20,21] and international corrections [14,23,24]. This stratification by sex was maintained for all analyses. "
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    ABSTRACT: Background National data on body mass index (BMI), computed from self-reported height and weight, is readily available for many populations including the Canadian population. Because self-reported weight is found to be systematically under-reported, it has been proposed that the bias in self-reported BMI can be corrected using equations derived from data sets which include both self-reported and measured height and weight. Such correction equations have been developed and adopted. We aim to evaluate the usefulness (i.e., distributional similarity; sensitivity and specificity; and predictive utility vis-à-vis disease outcomes) of existing and new correction equations in population-based research. Methods The Canadian Community Health Surveys from 2005 and 2008 include both measured and self-reported values of height and weight, which allows for construction and evaluation of correction equations. We focused on adults age 18–65, and compared three correction equations (two correcting weight only, and one correcting BMI) against self-reported and measured BMI. We first compared population distributions of BMI. Second, we compared the sensitivity and specificity of self-reported BMI and corrected BMI against measured BMI. Third, we compared the self-reported and corrected BMI in terms of association with health outcomes using logistic regression. Results All corrections outperformed self-report when estimating the full BMI distribution; the weight-only correction outperformed the BMI-only correction for females in the 23–28 kg/m2 BMI range. In terms of sensitivity/specificity, when estimating obesity prevalence, corrected values of BMI (from any equation) were superior to self-report. In terms of modelling BMI-disease outcome associations, findings were mixed, with no correction proving consistently superior to self-report. Conclusions If researchers are interested in modelling the full population distribution of BMI, or estimating the prevalence of obesity in a population, then a correction of any kind included in this study is recommended. If the researcher is interested in using BMI as a predictor variable for modelling disease, then both self-reported and corrected BMI result in biased estimates of association.
    BMC Public Health 05/2014; 14(1):430. DOI:10.1186/1471-2458-14-430 · 2.26 Impact Factor
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