The effect of sitting and calf exercise on fluid accumulation below the knees (1156.13)
Bibliographic record
Abstract
Background: Overnight rostral fluid shift from the legs to the neck correlates with the severity of obstructive sleep apnea (OSA) and time spent sitting. Sitting causes gravity‐dependent fluid accumulation in the legs that is counteracted by walking. However most office workers are required to sit for long periods and walking to preventing fluid accumulation in the legs is impractical. We hypothesized that sitting would cause fluid accumulation below the knees that could be attenuated by calf exercise while sitting. Methods: We measured fluid volume (FV) below both knees using bioelectrical impedance in 10 healthy subjects (8 men, age 50.8±6.2 yrs, BMI 23.7±2.4 kg/m2). Measurements were made in the supine, erect and seated postures and while sitting for 4 hours. Subjects were studied twice, a week apart, in a randomised double cross‐over design: control ‐ no calf exercise, intervention ‐ 15 calf muscle contractions against a pedal resistance (Step‐It) every 15 min. Paired t‐tests were used to analyze changes in FV and the effect of the intervention. Findings: Relative to the supine posture, FV below the knees increased in the erect (64.7±28.9 ml, p<0.0001) and seated postures (135.8.7±59.2 ml, p<0.0001) and after 4 hours of sitting (251.0±65.7 ml, p<0.0001). Modest exercise using the Step‐It device while sitting did not reduce fluid accumulation (control 115.2±47.0 ml, intervention 120.0±28.0 ml, p=0.72). Conclusions: Sitting for 4 hours results in substantial fluid accumulation in the legs of healthy subjects and this is not attenuated by modest intermittent calf exercise using a Step‐It device. This fluid could serve as a reservoir for redistribution to the neck and lungs during sleep, and contribute to OSA in predisposed individuals. A higher level of calf activity may be required to reduce fluid accumulation in the legs when sitting.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".