Twenty-Four-Hour Low Energy Availability Induced by Diet or Exercise Exhibits Divergent Influences on Sleep and Recovery Indices among Female and Male Cyclists
Bibliographic record
Abstract
PURPOSE: To examine the effects of 24-h manipulations in energy availability (EA), through altering energy intake (EI) or exercise energy expenditure (EEE), on sleep characteristics and nocturnal heart rate variability (HRV). METHODS: Twenty endurance athletes (10 females, 10 males) undertook five 24-h periods of EA manipulation, separated by 8 d, in a randomized Latin square design. Following 24 h of standardized high EA (45 kcal·kg -1 FFM·d -1 ), athletes then followed a diet providing low EA (15 kcal·kg -1 FFM·d -1 ) either with/without exercise (LEA EX/REST ), high EA (45 kcal·kg -1 FFM·d -1 ) with/without exercise (HEA EX/REST ), and excess EA (75 kcal·kg -1 FFM·d -1 ) without exercise (GEA). Sleep and overnight HRV were assessed using Somfit and Oura ring devices, respectively, pre- and post-EA manipulation. RESULTS: Total sleep duration was extended by 53-61 min following LEA EX versus non-exercise conditions: GEA ( P = 0.002), HEA REST ( P = 0.003), and LEA REST ( P = 0.020). Time in bed was also increased during LEA EX compared with GEA ( P = 0.006) and HEA REST ( P = 0.023), but without alterations to sleep efficiency between conditions ( P = 0.613). Sleep onset latency increased by 8 min following LEA REST compared with GEA ( P = 0.012), whereas there were no other alterations to objective/subjective sleep characteristics between conditions (all P > 0.05). There was no difference in overnight HRV following the 24-h EA manipulation ( P > 0.05). However, mean overnight HR was lower following LEA REST and HEA REST (-4 ± 3 and -2 ± 3 bpm), compared with LEA EX (+1 ± 3 bpm), HEA EX (0 ± 3 bpm), and GEA (0 ± 3 bpm, all P < 0.01). There was no difference between sexes in the response to EA manipulation across all variables. CONCLUSIONS: Twenty-four hours of exercise-induced LEA extended total sleep time, whereas diet-induced LEA reduced mean overnight HR. The responses to EA manipulations did not differ between sexes.
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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) | 0.001 | 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".