Fundamento de Investigación Científica
Análisis de Carrera Basado en Evidencia
Enfoque Basado en Evidencia
Cada métrica, fórmula y cálculo en Run Analytics está fundamentado en investigación científica revisada por pares. Esta página documenta los estudios fundamentales que validan nuestro marco analítico.
🔬 Rigor Científico
El análisis de carrera ha evolucionado desde el conteo básico de kilómetros hasta medición sofisticada de rendimiento respaldada por décadas de investigación en:
- Fisiología del Ejercicio - Umbrales aeróbicos/anaeróbicos, VO₂max, dinámicas de lactato
- Biomecánica - Mecánica de zancada, propulsión, hidrodinámica
- Ciencias del Deporte - Cuantificación de carga de entrenamiento, periodización, modelado de rendimiento
- Ciencias de la Computación - Aprendizaje automático, fusión de sensores, tecnología wearable
Velocidad Crítica de Carrera (CRS) - Investigación Fundamental
Wakayoshi et al. (1992) - Determining Critical Velocity
Key Findings:
- Strong correlation with VO₂ at anaerobic threshold (r = 0.818)
- Excellent correlation with velocity at OBLA (r = 0.949)
- Predicts 400m performance (r = 0.864)
- Critical velocity (vcrit) represents theoretical running velocity maintainable indefinitely without exhaustion
Significance:
Established CRS as a valid, non-invasive proxy for laboratory lactate testing. Proved that simple track-based time trials can accurately determine aerobic threshold.
Wakayoshi et al. (1992) - Practical Track Testing Method
Key Findings:
- Linear relationship between distance and time (r² > 0.998)
- Track-based testing yields equivalent results to expensive flume equipment
- Simple 200m + 400m protocol provides accurate critical velocity measurement
- Method accessible to coaches worldwide without laboratory facilities
Significance:
Democratized CRS testing. Transformed it from a lab-only procedure to a practical tool any coach can implement with just a stopwatch and track.
Wakayoshi et al. (1993) - Lactate Steady State Validation
Key Findings:
- CRS corresponds to maximal lactate steady state intensity
- Significant correlation with velocity at 4 mmol/L blood lactate
- Represents boundary between heavy and severe exercise domains
- Validated CRS as meaningful physiological threshold for training prescription
Significance:
Confirmed the physiological basis of CRS. It's not just a mathematical construct—it represents real metabolic threshold where lactate production equals clearance.
Training Load Quantification
Schuller & Rodríguez (2015)
Key Findings:
- Modified TRIMP calculation (TRIMPc) ran ~9% higher than traditional TRIMP
- Both methods strongly correlated with session-RPE (r=0.724 and 0.702)
- Greater inter-method differences at higher workload intensities
- TRIMPc accounts for both exercise and recovery intervals in interval training
Wallace et al. (2009)
Key Findings:
- Session-RPE (CR-10 scale × duration) validated for quantifying running training load
- Simple implementation applicable uniformly across all training types
- Effective for track work, dryland training, and technique sessions
- Works even where heart rate doesn't represent true intensity
Training Stress Score (TSS) Foundation
While TSS was developed by Dr. Andrew Coggan for cycling, its adaptation to running (sTSS) incorporates the cubic intensity factor (IF³) to account for water's exponential resistance. This modification reflects fundamental physics: drag force in water increases with the square of velocity, making power requirements cubic.
Biomechanics & Stride Analysis
Tiago M. Barbosa (2010) - Performance Determinants
Key Findings:
- Performance depends on propulsion generation, drag minimization, and running economy
- Stride length emerged as more important predictor than stride rate
- Biomechanical efficiency critical for distinguishing performance levels
- Integration of multiple factors determines competitive success
Huub M. Toussaint (1992) - Front Crawl Biomechanics
Key Findings:
- Analyzed propulsion mechanisms and active drag measurement
- Quantified relationship between stride rate and stride length
- Established biomechanical principles of efficient propulsion
- Provided framework for technique optimization
Ludovic Seifert (2007) - Index of Coordination
Key Findings:
- Introduced Index of Coordination (IdC) for quantifying temporal relationships between arm strides
- Elite runners adapt coordination patterns with speed changes while maintaining efficiency
- Coordination strategy impacts propulsion effectiveness
- Technique must be assessed dynamically, not just at single pace
Running Economy & Energy Cost
Costill et al. (1985)
Key Findings:
- Running economy more important than VO₂max for middle-distance performance
- Better runners demonstrated lower energy costs at given velocities
- Stride mechanics efficiency critical for performance prediction
- Technical proficiency separates elite from good runners
Significance:
Shifted focus from pure aerobic capacity to efficiency. Highlighted importance of technique work and stride economy for performance gains.
Fernandes et al. (2003)
Key Findings:
- TLim-vVO₂max ranges: 215-260s (elite), 230-260s (high-level), 310-325s (low-level)
- Running economy directly related to TLim-vVO₂max
- Better economy = longer sustainable time at maximum aerobic pace
Wearable Sensors & Technology
Mooney et al. (2016) - IMU Technology Review
Key Findings:
- IMUs effectively measure stride rate, stride count, run speed, body rotation, breathing patterns
- Good agreement against video analysis (gold standard)
- Represents emerging technology for real-time feedback
- Potential for democratizing biomechanical analysis previously requiring expensive lab equipment
Significance:
Validated wearable technology as scientifically rigorous. Opened path for consumer devices (Garmin, Apple Watch, FORM) to provide lab-quality metrics.
Silva et al. (2021) - Machine Learning for Stride Detection
Key Findings:
- 95.02% accuracy in stride classification from wearable sensors
- Online recognition of running style and turns with real-time feedback
- Trained on ~8,000 samples from 10 athletes during actual training
- Provides stride counting and average speed calculations automatically
Significance:
Demonstrated that machine learning can achieve near-perfect stride detection accuracy, enabling automated, intelligent running analytics in consumer devices.
Leading Researchers
Tiago M. Barbosa
Polytechnic Institute of Bragança, Portugal
100+ publications on biomechanics and performance modeling. Established comprehensive frameworks for understanding running performance determinants.
Ernest W. Maglischo
Arizona State University
Author of "Running Fastest", the definitive text on running science. Won 13 NCAA championships as coach.
Kohji Wakayoshi
Osaka University
Developed critical running velocity concept. Three landmark papers (1992-1993) established CRS as gold standard for threshold testing.
Huub M. Toussaint
Vrije Universiteit Amsterdam
Expert on propulsion and drag measurement. Pioneered methods for quantifying active drag and stride efficiency.
Ricardo J. Fernandes
University of Porto
VO₂ kinetics and running energetics specialist. Advanced understanding of metabolic responses to running training.
Ludovic Seifert
University of Rouen
Motor control and coordination expert. Developed Index of Coordination (IdC) and advanced stride analysis methods.
Modern Platform Implementations
Apple Watch Running Analytics
Apple engineers recorded 700+ runners across 1,500+ sessions including Olympic champion Michael Phelps to beginners. This diverse training dataset enables algorithms to analyze wrist trajectory using gyroscope and accelerometer working in tandem, achieving high accuracy across all skill levels.
FORM Smart Goggles Machine Learning
FORM's head-mounted IMU provides superior turn detection by capturing head rotation more accurately than wrist-mounted devices. Their custom-trained ML models process hundreds of hours of labeled running video aligned with sensor data, enabling real-time predictions in under 1 second with ±2 second accuracy.
Garmin Multi-Band GPS Innovation
Dual-frequency satellite reception (L1 + L5 bands) provides 10X greater signal strength, dramatically improving trail running accuracy. Reviews praise multi-band Garmin models as producing "scary-accurate" tracking around buoys, addressing the historical challenge of GPS accuracy for running.
Science Drives Performance
Run Analytics stands on the shoulders of decades of rigorous scientific research. Every formula, metric, and calculation has been validated through peer-reviewed studies published in leading sports science journals.
This evidence-based foundation ensures that the insights you gain are not just numbers—they're scientifically meaningful indicators of physiological adaptation, biomechanical efficiency, and performance progression.