A secondary analysis examining the concordance of self-perception of weight and actual measurement of body fat percentage: The CRONICAS Cohort Study
Bibliographic record
Abstract
Individuals’ self-perceptions of weight often differ from objective measurements of body fat. This study aimed to 1) measure agreement between self-perceptions of weight and objective measurement of body fat by bioelectric impedance analysis (BIA) among Peruvian adults; and 2) quantify the association between body fat and a) baseline self-perceptions of weight and b) whether a participant underestimated their weight status. Longitudinal data from the CRONICAS Cohort Study of 3181 Peruvian adults aged 35-years and older were used. BIA measurements of body fat were categorized across four nominal descriptions: low weight, normal, overweight, and obese. Kappa statistics were estimated to compare BIA measurements with baseline self-perceptions of weight. To quantify the association between body fat over time with both baseline self-perceptions of weight and underestimation of weight status, random effects models, controlling for socioeconomic and demographic covariates, were employed. Of the 3181 participants, 1111 (34.9%) were overweight and 649 (20.4%) were obese at baseline. Agreement between self-perceived and BIA weight status was found among 43.1% of participants, while 49.9% underestimated and 6.9% overestimated their weight status. Weighted kappa statistics ranged from 0.20 to 0.31 across settings, suggesting poor agreement. Compared to perceiving oneself as normal, perceiving oneself as underweight, overweight, or obese was associated with − 4.1 (p < 0.001), + 5.2 (p < 0.001), and + 8.1 (p < 0.001) body fat percentage points, respectively. Underestimating one’s weight status was associated with having 2.4 (p < 0.001) body fat percentage points more than those not underestimating only after adjusting for demographic and socioeconomic covariates. Half of study participants were overweight or obese. There was poor agreement between self-perceptions of weight with BIA measurements of body fat, indicating that individuals often believe they weigh less than they actually do. Underestimating one’s weight status was associated with having more body fat percentage points, but was only statistically significant after adjusting for demographic and socioeconomic characteristics. Further research should be conducted to investigate how self-perceptions of weight can support clinical and public health interventions to curb the obesity epidemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".