A systematic review of physical activity levels in <scp>N</scp>ative <scp>A</scp>merican populations in <scp>C</scp>anada and the <scp>U</scp>nited <scp>S</scp>tates in the last 50 years
Bibliographic record
Abstract
Physical activity is beneficial for many chronic conditions. However, activity levels of Native Americans are not well known. This systematic review investigated if Native American populations achieve the recommended physical activity levels, compared current and past activity levels, and assessed the ability of exercise training programmes to improve health outcomes among this population. Electronic databases (e.g. MEDLINE, EMBASE) were searched and citations were cross-referenced. Included articles reported physical activity levels or investigations among Native Americans. This search identified 89 articles: self-report (n = 61), accelerometry and pedometry (n = 10), metabolic monitoring (n = 10) and physical activity interventions (n = 17). Few adults were found to meet the physical activity recommendations (27.2% [95% confidence interval = 26.9-27.5%] self-report, 9% [4-14%] accelerometry). Among children/youth, 26.5% (24.6-28.4%) (self-report) to 45.7% (42.3-49.1%) (pedometry/accelerometry) met the recommendations. Adults and children/youth were generally identified as physically inactive (via doubly labelled water). Overall, Native American adults reported lower activity levels since 2000, compared to 1990s, although similar to 1980s. Few physical activity interventions employed strong methodologies, large sample sizes and objective outcome measures. There is a clear need to increase Native American populations' physical activity. Additional research is required to evaluate exercise training programmes among this population.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".