Frecuencia de zancada durante la carrera de resistencia en tapiz rodante y al aire libre (Stride frequency patterns during both treadmill and outdoor running in endurance runners)
Bibliographic record
Abstract
Los objetivos fueron i) estudiar la concordancia entre un reloj deportivo (Suunto Ambit2) y un sistema fotoeléctrico (Optogait) como instrumento referencia para medir la frecuencia de zancada (FZ) y la longitud de zancada (LZ); ii) observar las FZ y LZ durante la carrera al aire libre; y iii) analizar el efecto de la manipulación de la FZ en la economía de carrera monitorizada por un analizador de gases en función de las FZ encontradas en el análisis observacional. Ciento-sesenta corredores fueron analizados entre 8-14 km·h-1. El dispositivo Suunto Ambit2 concordó con el sistema de referencia en la medición de la FZ y la LZ [r=0.99 (0.99-1.00); Error Típico de la Estimación=0.58 zancadas∙min-1 y 0.02m]. Los corredores mantuvieron una FZ constante [Coeficiente de Variación (CV)=2.4%] aun cuando hubo variaciones en la velocidad (CV=6.8%), y dependieron de la LZ (CV=6.5%) durante las carreras al aire libre. Por último, los corredores mantuvieron un bajo coste de carrera con su FZ autoseleccionada (media=81.3 zancadas∙min-1), aunque un incremento hasta el 12% podría ser beneficioso cuando la velocidad varía sin detrimento en el coste de carrera. Palabras clave: Cadencia, Economía de Carrera, Velocidad, Exterior Abstract. This study aimed i) to study the agreement between a sports watch (Suunto Ambit2) with a photoelectric device (Optogait) as a reference instrument on measuring stride frequencies (SF) and stride lengths (SL); ii) to observe the stride patterns during outdoor running; and iii) to analyse the effect of SF manipulations on running economy monitored by a gas analyser and based on the observational analysis. One hundred and sixty recreational runners were analysed at speeds between 8-14 km·h-1. The Suunto Ambit2 agreed with the reference system [r=0.99 (0.99-1.00); Typical Error of the Estimate=0.58 strides∙min-1 and 0.02m]. Runners tended to maintain SF constant [Coefficient of Variation (CV)=2.4%]) during variations in speed (CV=6.8%) while relied on SL (CV=6.5%) adjustments during outdoor running. Finally, runners seemed to maintain a low running cost with their auto-selected SF (average=81.3 strides∙min-1), but an increase of up to 12% could be benefit when speed changes, without running cost detriment. Key words: Cadence, Running Economy, Velocity, Outdoor.
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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 | 0.001 | 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".