This study continues on the foundation established by Pulver and colleagues in creating the silhouette showcards and subsequent validation in populations of African-origin.25–27 Our data suggest that the Pulvers’ silhouette showcards may be a useful tool for predicting objective body size such as BMI, WC, and WHR, in different populations of mainly African-origin. However, the relationship between silhouettes and adiposity markers differed according to the country. Overall, our data suggest that silhouettes may be a useful tool to predict actual adiposity measures, conditional to adequate calibration for a specific population.
BMI and other adiposity measures correlated strongly with silhouette ranking in all populations. However, the magnitude of the linear regression coefficients between silhouette ranking and actual adiposity markers differed between the three countries in this study. For example, an increase of 1 silhouette unit was associated with an increase of 3–4 BMI units (kg/m2) in the USA and Seychelles but only 1–2 BMI units in Ghana. This difference suggests varying perceptions of one’s body shape according to mean population BMI. One may speculate that in the USA and Seychelles, where mean population BMI is high, individuals with adiposity are more inclined to view a large body shape as normal compared to populations (e.g., Ghana) where mean population BMI is lower. Again, this altered view suggests that silhouette showcards need to be specific (i.e., calibrated) to different populations when used for predicting individuals’ actual adiposity. From a prevention perspective, the differences in perceptions of one’s body size across populations may suggest larger tolerance for larger body shapes in populations with high adiposity levels. Overall, this underlies that silhouettes can have a role for assessing adiposity in populations when direct measurements cannot be made (i.e., for surveillance purposes, as evaluated in this study), but also for assessing perceptions and attitudes of people for weight control programs.
The relationship between silhouettes and adiposity markers can differ according to sex in the same population. Using different silhouette showcards, regression coefficients for the relationship between silhouettes and BMI (kg/m2 per silhouette unit) were, for examples, 0.73 for men and 0.81 for women among white Americans (with mean BMI of 25.5 kg/m2 in men and 24.1 kg/m2 in women) and 0.73 for men and 0.80 in Japanese women (with mean BMI of 23.3 kg/m2 in men and 21.5 kg/m2 in women) and 0.80 for men and 0.81 for women in Seychelles (with mean BMI of 26.4 kg/m2 in men and 29.3 kg/m2 in women).21,22,26 It is therefore likely that the same linear regression models can be used in men and women for calibration of the association between silhouettes and BMI (or other adiposity markers) within the same population, as long as mean BMI in the population is similar in both sexes. Inversely, as our data in Ghana suggest, different predictive models may need to be developed in men and women when mean BMI markedly differs between men and women in the same population. Differences in the slopes of the associations between silhouettes and BMI (and other adiposity markers) may also partly depend on different sex-specific perceptions of body shape, and this question necessitates further studies.
The country and sex-specific associations between silhouettes and adiposity markers were quite similar when using BMI, WC, and WHR. This relationship is not unexpected as BMI, WC, and WHR quite strongly and similarly inter-correlate with each other, e.g., correlation coefficients of 0.77 to 0.96 in our study, which is consistent with correlations found in other studies.42 However, the fact that these associations between silhouettes and BMI, WC, and WHR (and the associations between these adiposity markers and objectively measured fat mass) are still not extremely strong, implies that silhouettes would not be a reliable tool to predict adiposity at the individual level (sensitivity and specificity are not optimal), but they can be useful when assessing adiposity levels (e.g., the prevalence of obesity, mean BMI) at a population level, conditional on appropriate calibration in a specific population. More generally, our data suggest that a subjective two-dimensional pictorial body size assessment (silhouette drawings) can be a useful tool for predicting a volumetric dimension (adiposity), at least at the population level.
This study’s main strength was the use of the identical methodology in the three countries, allowing us to make direct comparisons between populations of the same racial origin and that the three populations differed largely according to mean adiposity levels and socioeconomic development stages. However, the study also has limitations. First, although the study was designed to include participants of African-origin in all sites, in order to control for ethnic differences, persons from mixed origin were also included in varying but small proportions, particularly in Seychelles. Second, the study included middle-aged adults, and the findings may not necessarily extend to older or younger individuals. Third, Pulver’s silhouette tool presents body size silhouettes from thinnest to heaviest, which could lead to reporting bias. Future studies should examine if presenting the silhouettes in random order would gather different results. Fourth, survey administrators presented silhouettes to the participants; further studies should assess if results would differ if participants had assessed their silhouettes in the absence of assisting personal. Finally, our analysis, according to sex, was limited because of the limited sample size.