Effects of Resistance Exercise on the Sleep Patterns of Sedentary Individuals
Bibliographic record
Abstract
Abstract The past decades have seen the conduction of a great number of studies that have attempted to assess the effects of physical exercise on sleep. However, the majority of these studies have specifically evaluated the effects of aerobic exercise. To the best of our knowledge, only one study has evaluated the effects of resistance exercise (RE) on sleep patterns. The aim of the current investigation is to verify the effect of one session of RE on the sleep patterns of sedentary individuals with good sleep quality. Twenty-seven sedentary men with good sleep quality between the ages of 20 and 40 participated in this investigation. The experimental protocol consisted of one session of RE, which was comprised of 3 sets of 15 repetitions with a load equivalent to 50% of the one-repetition maximum test (1-RM) with 90 sec intervals between each set. The sleep parameters were analyzed by means of a t-test, with significance defined as p < 0.05. No significant differences were found between sleep parameters when RE was performed during different periods of the day. However, the sleep onset latency and the sleep efficiency in the evening group showed a trend toward alteration (p = 0.06). Sedentary individuals with good sleep quality did not display significant alterations in their sleep parameters after performing one session of RE with a load equivalent to 50% of 1-RM.
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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 | 0.000 | 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".