Relationship between physical activity, physical performance, and iron status in adult women
Bibliographic record
Abstract
Iron deficiency affects approximately 16% of US females 18-45 years old. Iron is a key component of heme-containing proteins, which are essential for oxygen transport throughout the body. With low iron levels, performance and intense physical activity may be compromised. Thus, the purpose of this study was to examine the relationship between iron status, physical performance, and physical activity in 18- to 45-year-old females. Participants (N = 109) were screened for iron status using a venous blood sample, had their height and mass measured, and self-reported their physical activity level. The screening was used to match iron-depleted nonanemic females (hemoglobin, Hgb > 120 g·L(-1); serum ferritin, sFer < 20 µg·L(-1)) to females with normal iron levels. After participant matching, they had their body composition measured, performed three cycle ergometer tests (maximal, endurance, and efficiency), and wore an ActiGraph GT1M accelerometer for five consecutive days, except when sleeping or during water activities. The final sample consisted of 25 iron-depleted participants and 24 with normal iron levels. Key findings were as follows: (i) after controlling for fat-free mass and vigorous physical activity, iron-depleted females had a significantly lower [Formula: see text]O(2) at ventilatory threshold compared with those with normal iron levels (P < 0.05); and (ii) after controlling for age, iron-depleted females spent significantly more time in sedentary behaviors and significantly less time in light physical activity than those with normal iron levels (P < 0.05). The increased sedentary time in iron-depleted females may contribute to excess mass gain over time; however, further investigation is needed to confirm these results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".