Creatine Improves Total Sleep Duration Following Resistance Training Days versus Non-Resistance Training Days among Naturally Menstruating Females
Bibliographic record
Abstract
Females historically experience sleep disturbances and overall poor sleep compared to males. Creatine has been proposed to impact sleep; however, the effects are not well known. The purpose of this study was to examine the effects of creatine supplementation on sleep among naturally menstruating females. Twenty-one participants completed a double-blind, randomized controlled trial in which they consumed 5 g creatine + 5 g maltodextrin or placebo, 10 g maltodextrin, daily for 6 weeks. Participants completed resistance training 2x/week using the TONAL® (Tonal Systems Inc., San Francisco, CA, USA) at-home gym. Pre- and post-testing assessed body composition, Pittsburgh Sleep Quality Index (PSQI), dietary intake, and muscular strength. Sleep was assessed nightly using an ŌURA® (Oulu, Finland) ring. Compared to the placebo group, those consuming creatine experienced significant increases in total sleep on training days (p = 0.013). No significant changes in chronic sleep and PSQI (pre–post) were observed. There was a significant increase in TONAL® strength score over time (p < 0.001), with no between-group differences. Participants reduced their total calorie (kcal) (p = 0.039), protein (g/kg) (p = 0.009), carbohydrate (g/kg) (p = 0.023), and fat (g) (p = 0.036) intake over time. Creatine supplementation increases sleep duration on resistance training days in naturally menstruating females.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.002 | 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".