ArticleBMJ open2023
Does the use of prediction equations to correct self-reported height and weight improve obesity prevalence estimates? A pooled cross-sectional analysis of Health Survey for England data.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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15 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Pooled it
- Long-Term Obesity Trends in Northern Sweden: Cross-Sectional Data From the MONICA Study (1986-2022).Obesity science & practice · 2026Article
- Sleep and workload mediate the relationship between digital engagement and burnout: a national cross-sectional study of university faculty in India.Archives of public health = Archives belges de sante publique · 2026Article
- Long-term trends in central obesity in England: an age-period-cohort approach.International journal of obesity (2005) · 2026Article
- Development and Validation of a Simple Model to Predict Patient Height.medRxiv : the preprint server for health sciences · 2025Article
- Six-year follow-up of an abstinence-based, food addiction recovery approach to weight management.Frontiers in psychiatry · 2025Article
- Functional disabilities and adverse well-being by COVID-19 and Long COVID history and employment status: 2022 Behavioral Risk Factor Surveillance System.American journal of industrial medicine · 2024Article
- Factors associated with reported need of weight loss support among adults with overweight or obesity: results from a cross-sectional population study in 2022 in Sweden.Public health nutrition · 2024Article
- Sex differences in the secular change in waist circumference relative to BMI in five countries from 1997 to 2020.Obesity (Silver Spring, Md.) · 2024Article
- Article
- Agreement between parent-report and EMR height, weight, and BMI among rural children.Frontiers in nutrition · 2024Article
- Sex differences in the secular change in waist circumference relative to body mass index in the Americas and England from 1997 to 2020.medRxiv : the preprint server for health sciences · 2023Article
- Association of Place With Adolescent Obesity.JAMA pediatrics · 2023Article
- Health literacy in female patients affected by anorexia nervosa and bulimia nervosa: a cross-sectional study with pair-matched controls.Eating and weight disorders : EWD · 2023Article
- The association between cognitive ability and body mass index: A sibling-comparison analysis in four longitudinal studies.PLoS medicine · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveAdults typically overestimate height and underestimate weight compared with directly measured values, and such misreporting varies by sociodemographic and health-related factors. Using self-reported and interviewer-measured height and weight, collected from the same participants, we aimed to develop a set of prediction equations to correct bias in self-reported height and weight and assess whether this adjustment improved the accuracy of obesity prevalence estimates relative to those based only on self-report.
designPopulation-based cross-sectional study.
participants38 940 participants aged 16+ (Health Survey for England 2011-2016) with non-missing self-reported and interviewer-measured height and weight.
main outcome measuresComparisons between self-reported, interviewer-measured (gold standard) and corrected (based on prediction equations) body mass index (BMI: kg/m
resultsOn average, men overestimated height more than women (1.6 cm and 1.0 cm, respectively; p<0.001), while women underestimated weight more than men (2.1 kg and 1.5 kg, respectively; p<0.001). Underestimation of BMI was slightly larger for women than for men (1.1 kg/m
conclusionsCompared with self-reported data, applying prediction equations improved the accuracy of obesity prevalence estimates and increased sensitivity of being classified as obese. Including additional sociodemographic variables did not improve obesity classification enough to justify the added complexity of including them in prediction equations.
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