Physical Activity of Adults with Mental Retardation: Review and Research Needs
Bibliographic record
Abstract
OBJECTIVE: To characterize physical activity levels of adults with mental retardation and identify limitations in published research. DATA SOURCES: Key word searches for "mental retardation," "intellectual disability," "learning disability," or "developmental disability" combined with "physical activity" or "habitual exercise" identified articles from MEDLINE, Academic Search Elite, Psych Articles, Health Source, and SPORT Discus. This produced a total of 801 citations. STUDY INCLUSION AND EXCLUSION CRITERIA: Published English-language literature that quantitatively measured physical activity levels of adults with mental retardation was included in this review. Fourteen articles met this criterion. DATA EXTRACTION: Characteristics of participants, study design, outcome measures, methods of analyses, and findings in terms of percentages, step counts, and accelerometer output were extracted. DATA SYNTHESIS: Data were synthesized to identify the percentage of adults with mental retardation who met published health-related physical activity criteria and compare them with adults without mental retardation and to examine study limitations. RESULTS: The studies with the greatest rigor indicate that one-third of adults or fewer with mental retardation were sufficiently active to achieve health benefits. However, data are insufficient to determine whether adults with mental retardation are less active than the general community. CONCLUSIONS: Future research would be enhanced by including appropriately powered representative samples, by including comparison groups, by validating physical activity questionnaires, and by determining the accuracy of proxy respondents.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".