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Regression Models to Predict Corrected Height, Weight, and Obesity Indicators among University Students in Beijing, China

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Whereas data collection on subjective anthropometric measures is inexpensive and sometimes may be the only feasible option for large-scale population-based surveys, self-reported height and weight can be susceptible to measurement error and social desirability bias. In this study, we aimed to assess the level of discrepancy between self-reported and device-measured height, weight, and obesity indicators, and to construct regression models to predict corrected anthropometric measures using self-reported data.


Paper-and-pencil-based health surveys were administered to all freshmen enrolled in Tsinghua University in Beijing, China. Freshmen’s height and weight were measured by trained staff using stadiometer and digital scale within one week following survey completion. Robust regressions were performed to predict corrected height, weight, body mass index (BMI), and overweight and obesity prevalence using self-reported data (N = 16,675).


Male freshmen over-reported both height and weight, whereas female freshmen over-reported height but under-reported weight. Both resulted in underestimation of BMI and overweight prevalence. The predicted values based on robust regressions substantially reduced the discrepancy between self-reported and objectively-measured height, weight, BMI, and overweight prevalence.


Parsimonious regression models could be useful in obesity surveillance by predicting corrected anthropometric measures using self-reported data.
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Keywords: BMI; body mass index; self-reported

Document Type: Research Article

Publication date: November 1, 2018

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  • The American Journal of Health Behavior seeks to improve the quality of life through multidisciplinary health efforts in fostering a better understanding of the multidimensional nature of both individuals and social systems as they relate to health behaviors.

    The Journal aims to provide a comprehensive understanding of the impact of personal attributes, personality characteristics, behavior patterns, social structure, and processes on health maintenance, health restoration, and health improvement; to disseminate knowledge of holistic, multidisciplinary approaches to designing and implementing effective health programs; and to showcase health behavior analysis skills that have been proven to affect health improvement and recovery.

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