Health Benefits of a Pulse‐Based Diet for Soccer Players During Regular Season Play
Bibliographic record
Abstract
Pulses are considered a low glycemic index food and may be beneficial for athletic performance. We have shown that acute consumption of lentils is beneficial for improving laboratory‐based endurance performance during simulated soccer games (Little et al., 2009; 2010; Bennett et al. 2012) and a pulse diet (2 months) is beneficial for lowering cholesterol levels (Abeysekara et al. 2012). PURPOSE: We hypothesize that chronic consumption of pulses by soccer players will improve measures of health throughout the soccer season. METHODS: Soccer players were randomized to receive a calorie adjusted, pulse‐based diet (250g of cooked pulses) for one month or consume their regular diet. Participants then crossed over to the opposite diet. Pre and post each month‐long diet phase, blood samples were collected to determine fasting glucose, insulin and lipid levels. RESULTS: Consuming pulses did not significantly affect blood insulin, total cholesterol, HDL or LDL and VLDL levels (n=9). Total cholesterol (TC) expressed as a ratio to HDL resulted in athletes consuming the pulse based diet after 4 weeks (2.04 ±2.49) having a lower TC:HDL ratio compared to when they consumedtheir regular diets (3.43±2.29) (p=0.03). Cholesterol ratios also decreased over time after consuming the pulse diet (Baseline: 2.74±2.49, Post diet intervention: 2.04±1.00). CONCLUSION Consuming a diet composed of pulses provides the beneficial effect of reducing cholesterol ratios in healthy adult athletes. The reduction in cholesterol ratios over time in healthy individuals may indicate a beneficial role of consuming pulses to reduce cardiovascular disease risk.
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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 | 0.001 |
| 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".