# Accuracy of Assessing Weight Status in Adults by Structured Observation
## 3 Results
### Validity of Estimating BMI by Trained Observers  
As shown in Table 2, sensitivity was higher for estimating overweight/obesity status than for identifying obesity status alone (72.8% vs. 41.4%). Specificity was higher for estimating obesity than for identifying overweight/obese subjects (96.4% vs. 78.4%). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Participants’ Characteristics** & **Sensitivity** & **Specificity** & **Likelihood Ratio** \\ \hline  & & & Positive (LR+) & Negative (LR\(-\)) \\ Weight status & & & & \\ Obesity & 41.4\% & 96.4\% & 11.5 & 0.61 \\ Overweight/obesity & 72.8\% & 78.4\% & 3.4 & 0.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sensitivity, specificity, and likelihood ratios of estimated measures: obesity, overweight/obesity (n = 254 observations). Figure 1: Distributions of measured weight (**a**) and height (**b**), according to the respective categories of estimation (n = 254 observations). For obesity, correct classification of obese subjects was more than 11 times more likely than misclassification of the non-obese (LR+ = 11.5), while the probability of misclassification of the obese was nearly 40% lower than the probability of correct classification of the non-obese (LR\(-\) = 0.61). For the combined status of overweight and obesity, the probability of correct classification of the overweight/obese subjects was more than three-fold higher than that of misclassification of the non-overweight/obese (LR+ = 3.4), while the probability of misclassification of the overweight/obese was nearly one-third the probability of correct classification of the non-overweight/obese (LR\(-\) = 0.35). In Table 3, gender of observer, gender of participant, and age of participant were shown to be associated with the estimation of obesity and overweight. Women observers classified obesity with higher sensitivity than male observers (56.8% vs. 14.3%, \(p\) = 0.002). When considering overweight/obesity, sensitivity increased for both genders, mainly for male observers, but it remained lower than for female observers, although not statistically significant (76.6% for female observers vs. 66.7% for male observers, \(p\) = 0.146). Specificity and positive LR were higher for obesity, whereas negative LR was always lower for obesity/overweight. Regarding the gender of the participant, sensitivity to detect overweight/obesity was higher among male participants (80.0% vs. 65.6%, \(p\) = 0.029). Specificity for obesity was also higher among men (100% vs. 92.9%, \(p\) = 0.014), whereas specificity for overweight/obesity was higher among women (100% vs. 68%, \(p\) = 0.002). The LR\(-\) was similar among men and women, and lower for overweight/obesity (LR\(-\)\({}_{\rm women}\) = 0.34; LR\(-\)\({}_{\rm men}\) = 0.29) than for obesity (LR\(-\)\({}_{\rm women}\) = 0.54; LR\(-\)\({}_{\rm men}\) = 0.62). Although not statistically significant, the older age of the participant increased sensitivity to detect obesity (from 20.0% for 18-34 years old to 54.2% for 55 years old or more). When age was regrouped into only two categories (<50 years and \(\geq\)50 years), the sensitivity to detect obesity and overweight/obesity together was similar to the sensitivity values when three age categories were considered. For obesity, the LR+ ranged between 7.1 and 15.6, and for overweight/obesity, it was highest for participants aged 18-34 years (LR+ = 9.5), ranging between 2.0 and 4.7 in the remaining groups.