A comparison of direct vs. self‐report measures for assessing height, weight and body mass index: a systematic review
Bibliographic record
Abstract
Obesity is a rapidly increasing public health problem, with surveillance most often based on self-reported values of height and weight. We conducted a systematic review to determine what empirical evidence exists regarding the agreement between objective (measured) and subjective (reported) measures in assessing height, weight and body mass index (BMI). Five electronic databases were searched to identify observational and experimental studies on adult populations over the age of 18. Searching identified 64 citations that met the eligibility criteria and examined the relationship between self-reported and directly measured height or weight. Overall, the data show trends of under-reporting for weight and BMI and over-reporting for height, although the degree of the trend varies for men and women and the characteristics of the population being examined. Standard deviations were large indicating that there is a great deal of individual variability in reporting of results. Combining the results quantitatively was not possible because of the poor reporting of outcomes of interest. Accurate estimation of these variables is important as data from population studies such as those included in this review are often used to generate regional and national estimates of overweight and obesity and are in turn used by decision makers to allocate resources and set priorities in health.
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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 | 0.022 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.011 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".