Background: The Pulvers’ silhouette showcards provide a non-invasive, easy-to-use, and possibly cross-culturally acceptable way of assessing an individual’s perception of their body size. This study examined, in three different populations: 1) the relationship between silhouettes and body mass index (BMI), 2) the predictive performance of silhouettes to predict dichotomous adiposity categories, and 3) whether silhouette ranking performed similarly in predicting BMI, waist circumference (WC), and waist-to-height ratio (WHR). Methods: This study included 751 participants of African-origin from the United States of America (USA), the Republic of Seychelles, and Ghana, from the ongoing cohort Modeling the Epidemiological Transition Study. We assessed the mean BMI for each silhouette rank by country and sex and performed a least-squares linear regression for the silhouette’s performance by country and sex. The performance of the silhouettes to predict overweight and obesity (BMI ³ 25 kg/m2), and obesity alone (BMI ³ 30 kg/m2) was examined through a receiver operator curve (ROC) analysis with corresponding sensitivities and specificities. Finally, a ROC analysis area under the curve (AUC) was also performed for the detection of elevated waist circumference (men ≥ 94 cm; women ≥ 80 cm) and waist-to-height ratio (> 0.5) by country and sex.Results: Mean measured BMI (kg/m2) in men/women differed largely across countries: 28.9/35.8 in the USA, 28.3/30.5 in Seychelles, and 23.9/28.5 in Ghana. The slope of the relation between silhouette ranking and BMI (i.e., linear regression coefficient and 95% confidence intervals) was similar between sexes of the same country but differed between countries: 3.65 [95% CI: 3.34-3.97 BMI units/silhouette unit] in the USA, 3.23 [2.93-3.74] in Seychelles, and 1.99 [1.72-2.26] in Ghana. Different silhouette cut-offs predicted dichotomous adiposity categories differently in the three countries. For example, a silhouette ³ 5 had sensitivity/specificity of 77.3%/90.6% to predict BMI ≥ 25 kg/m2 in the USA, but 77.8%/85.9% in Seychelles and 84.9%/71.4% in Ghana. Finally, silhouettes predicted BMI, WC, and WHR similarly, within each country and sex, based on Spearman correlations coefficients (continuous scale) and c-statistic (dichotomous classification).Conclusion: Our data suggest that Pulvers’ silhouette showcards can be a useful tool to objectively predict different adiposity measures in different populations when direct measurement cannot be performed. However, population-specific differences in the slopes of the associations, which possibly partly reflect differences in perceptions of one’s body size according to country adiposity prevalence, stress the need to calibrate silhouette showcards when using them as a survey tool.

The Pulvers’ silhouette showcards provide a non-invasive and easy-to-use way of assessing an individual’s body size perception using nine silhouette shapes. However, their utility across different populations has not been examined. This study aimed to assess: 1) the relationship between silhouette perception and measured anthropometrics, i.e., body mass index (BMI), waist circumference (WC), waist-height-ratio (WHtR), and 2) the ability to predict with silhouette showcards anthropometric adiposity measures, i.e., overweight and obesity (BMI ≥ 25 kg/m2), obesity alone (BMI ≥ 30 kg/m2), elevated WC (men ≥ 94 cm; women ≥ 80 cm), and WHtR (> 0.5) across the epidemiological transition. 751 African-origin participants, aged 20–68 years old, from the United States (US), Seychelles, and Ghana, completed anthropometrics and selected silhouettes corresponding to their perceived body size. Silhouette performance to anthropometrics was examined using a least-squares linear regression model. A receiver operator curve (ROC) was used to investigate the showcards ability to predict anthropometric adiposity measures. The relationship between silhouette ranking and BMI were similar between sexes of the same country but differed between countries: 3.65 [95% CI: 3.34–3.97] BMI units/silhouette unit in the US, 3.23 [2.93–3.74] in Seychelles, and 1.99 [1.72–2.26] in Ghana. Different silhouette cutoffs predicted obesity differently in the three countries. For example, a silhouette ≥ five had a sensitivity/specificity of 77.3%/90.6% to predict BMI ≥ 25 kg/m2 in the US, but 77.8%/85.9% in Seychelles and 84.9%/71.4% in Ghana. Ultimately, silhouettes predicted BMI, WC, and WHtR similarly within each country and sex but not across countries. Our data suggest that Pulvers’ silhouette showcards may be a helpful tool to predict anthropometric and adiposity measures in different populations when direct measurement cannot be performed. However, no universal silhouette cutoff can be used for detecting overweight or obesity status, and population-specific differences may stress the need to calibrate silhouette showcards when using them as a survey tool in different countries.