Presleep α-Lactalbumin Consumption Does Not Improve Sleep Quality or Time-Trial Performance in Cyclists
Bibliographic record
Abstract
We tested the hypothesis that presleep consumption of α-lactalbumin (LA), a fraction of whey with a high abundance of tryptophan, would improve indices of sleep quality and time-trial (TT) performance in cyclists relative to an isonitrogenous collagen peptide (CP) supplement lacking tryptophan. Using randomized, double-blind, crossover designs, cyclists consumed either 40 g of LA or CP 2 hr prior to sleep. In Study 1, six elite male endurance track cyclists (age 23 ± 6 years, V˙O2peak 70.2 ± 4.4 ml·kg-1·min-1) consumed a supplement for three consecutive evenings before each 4-km TT on a velodrome track, whereas in Study 2, six well-trained cyclists (one female; age 24 ± 5 years, V˙O2peak 66.9 ± 8.3 ml·kg-1·min-1) consumed a supplement the evening before each 4-km TT on a stationary cycle ergometer. Indices of sleep quality were assessed with wrist-based actigraphy. There were no differences between the CP and LA supplements in terms of total time in bed, total sleep time, or sleep efficiency in Study 1 (LA: 568 ± 71 min, 503 ± 67 min, 88.3% ± 3.4%; CP: 546 ± 30 min, 479 ± 35 min, 87.8% ± 3.1%; p = .41, p = .32, p = .74, respectively) or Study 2 (LA: 519 ± 90 min, 450 ± 78 min, 87.2% ± 7.6%; CP: 536 ± 62 min, 467 ± 57 min, 87.3% ± 6.4%; p = .43, p = .44, p = .97, respectively). Similarly, time to complete the 4-km TT was unaffected by supplementation in Study 1 (LA: 274.9 ± 7.6 s; CP: 275.5 ± 7.2 s; p = .62) and Study 2 (LA: 344.3 ± 22.3 s; CP: 343.3 ± 23.0 s; p = .50). Thus, relative to CP, consuming LA 2 hr prior to sleep over 1-3 days did not improve actigraphy-based indices of sleep quality or 4-km TT performance in cyclists.
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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) | 0.001 | 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) | 0.001 | 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".