Impact Of Blood Flow Restriction Training Performed At Different Times Of Day On Performance
Bibliographic record
Abstract
Recent data suggest skeletal muscles have an internal clock that dictates health-related adaptations. Therefore, exercise training at different times of the day may generate different health benefits and myokine release. However, little data exists on how the time of day of exercise may impact males and females, specifically using blood flow restriction (BFR) training. PURPOSE: To investigate the impact of a 6-week BFR exercise intervention performed at different times of day on body composition, isokinetic measures, and irisin expression in young, healthy adults. METHODS: A single-arm intervention of 6-week BFR resistance training was performed three times per week. Participants (n = 31; aged 19-30) were categorized into morning (05:00-11:00, n = 16) or afternoon (11:00-17:00, n = 15) group. A sub-analysis of responders and non-responders (top and bottom 25% of muscle strength) was performed on a random sample of individuals from the morning and afternoon groups. Primary outcomes were changes in body composition (dual-energy X-ray Absorptiometry), total work, average power, peak torque (Humac Norm Isokinetic Dynamometer) and irisin expression (western blot). RESULTS: No significant differences between groups were observed at baseline. A significant improvement was observed for both groups for change in lean mass (AM: p = 0.006, PM: p = 0.011) and lean mass/height2 (AM: p = 0.005, PM: p = 0.013). A significant improvement was observed for the morning group for trunk lean mass (p = 0.012), total work of flexors (p = 0.02) and extensors (p = 0.012), average power of flexors (p = 0.003) and extensors (p = 0.004), and peak torque of flexors (p = 0.004) and extensors (p = 0.038). A significant decrease was observed in the afternoon group for body fat percentage (p = 0.049). Responders’ who performed BFR in the morning had approximately a 1.2-fold (p = 0.012) increase in irisin compared to non-responders. CONCLUSION: This study demonstrates that there is no difference in performance outcomes when young, healthy individuals exercise with BFR at different times of day. People who respond to BFR in the morning have a greater irisin expression. Healthy Seniors Pilot Project, CIHR, Public Health Agency of Canada
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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) | 0.002 | 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".