Effect of Seat Tube Angle and Exercise Intensity on Muscle Activity Patterns in Cyclists
Bibliographic record
Abstract
International Journal of Exercise Science 10(8): 1145-1156, 2017. Previous studies have reported improved efficiency at steeper seat tube angle (STA) during ergometer cycling; however, neuromuscular mechanisms have yet to be fully determined. The current study investigated effects of STA on lower limb EMG activity at varying exercise intensities. Cyclists (n=11) were tested at 2 workloads; 160W and an individualised workload (IWL) equivalent to lactate threshold (TLac) minus 10%δ (derived from maximal incremental data), using 3 STA (70, 75 and 80°). Electromyographic data from Vastus Medialis (VM), Rectus Femoris (RF), Vastus Lateralis (VL) and Biceps Femoris (BF) were assessed. The timing and magnitude of activation were quantified and analysed using a two-way ANOVA. STA had significant (P < 0.05) effects on timing of onset and offset of VM, timing of offset of VL, and angle at peak for RF, all occurring later at 80 vs. 70° STA at IWL. In RF, increased activity occurred during the first 108° of the crank cycle at 80 vs. 70° at IWL (P < 0.01). As most of the power in the pedal stroke is generated during the mid-section of the down-stroke, movement of the activation range of knee extensors into the predominantly power phase of the pedal stroke would potentially account for increased efficiency and decreased cardio-respiratory costs. Greater activity of bi-articular RF, in the first 108º of the crank cycle at IWL (80 vs. 70º) may more closely resemble the pelvic stabilising activity of RF in running biomechanics; and potentially explain the more effective transition from cycling to running reported in triathletes using steeper STA.
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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) | 0.002 | 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".