Measuring Body Composition in Individuals with Intellectual Disability: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Research shows obesity to be more prevalent amongst individuals with intellectual disability (ID) making correct measurement of body composition crucial. This study reviewed the validity and reliability of methods used for assessing body composition in individuals with ID. METHODS: Authors conducted electronic searches through PubMed (1990 to present) and PsycINFO (1990 to present) and assessed relevant articles independently based on scoping review guidelines. Reviewers included primary research related to the validity and reliability of body composition measures on individuals with ID. RESULTS: Searches identified six articles assessing body composition methods used on individuals with ID including body mass index (BMI), skinfold thickness, bioelectrical impedance analysis (BIA), waist circumference, tibia length, and anthropometric girth measurements. BMI and waist circumference appear suitable measures but skinfold thickness measurements may not be advisable due to participants' noncompliance resulting in a lack of precision and inaccurate results. CONCLUSIONS: The current literature contains too few well-conducted studies to determine the precision and validity of body composition measures on individuals with ID. There may be a need to devise further regression equations that apply to individuals with specific types of ID in order to increase the reliability and validity of body composition measurements.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".