Mokslinių tyrimų pagrindas
Bėgimo analitika pagrįsta įrodymais
Įrodymais pagrįstas požiūris
Kiekvienas rodiklis, formulė ir skaičiavimas „Run Analytics“ programėlėje yra pagrįstas recenzuojamais moksliniais tyrimais. Šiame puslapyje dokumentuojami pagrindiniai tyrimai, patvirtinantys mūsų analitinę sistemą.
🔬 Mokslinis griežtumas
Bėgimo analitika išsivystė nuo paprasto kilometrų skaičiavimo iki sudėtingų veiklos matavimų, paremtų dešimtmečiais trukusių tyrimų šiose srityse:
- Pratimų fiziologija – aerobiniai / anaerobiniai slenksčiai, VO₂max, laktato dinamika
- Biomechanika – žingsnių mechanika, varymas (propulsija), bėgimo efektyvumas
- Sporto mokslas – treniruočių krūvio kiekybinis įvertinimas, periodizacija, veiklos modeliavimas
- Kompiuterių mokslas – mašininis mokymasis, jutiklių sintezė, dėvimoji technologija
Critical Run Speed (CRS) - Foundational Research
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)
- Simple 5K + 3K 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, road running, and technical trail sessions
- Works even where heart rate doesn't represent true intensity
Running Stress Score (rTSS) Foundation
While TSS was developed by Dr. Andrew Coggan for cycling, its adaptation to running (rTSS) incorporates a quadratic intensity factor (IF²) to reflect running's physiological requirements. Unlike other endurance sports, running biomechanics follow a squared relationship where physiological load scales with the square of intensity due to impact forces and gravitational work.
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
Nummela et al. (2007) - Running Economy Determinants
Key Findings:
- Analyzed relationship between stride length, rate, and metabolic cost
- Quantified impact of ground contact time on running efficiency
- Established biomechanical principles of efficient forward propulsion
- Provided framework for form optimization in endurance events
Derrick et al. (2002) - Impact Shock and Attenuation
Key Findings:
- Introduced methods for quantifying impact shock and attenuation during running
- Elite runners adapt leg stiffness patterns with speed changes while maintaining efficiency
- Biomechanical strategy impacts injury risk and propulsion effectiveness
- Technique must be assessed across various speeds and fatigue states
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, COROS) to provide lab-quality metrics outdoors.
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.
Įrodymais pagrįstas požiūris
Kiekvienas rodiklis, formulė ir skaičiavimas „Run Analytics“ programėlėje yra pagrįstas recenzuojamais moksliniais tyrimais. Šiame puslapyje dokumentuojami pagrindiniai tyrimai, patvirtinantys mūsų analitinę sistemą.
🔬 Mokslinis griežtumas
Bėgimo analitika išsivystė nuo paprasto kilometrų skaičiavimo iki sudėtingų veiklos matavimų, paremtų dešimtmečiais trukusių tyrimų šiose srityse:
- Pratimų fiziologija – aerobiniai / anaerobiniai slenksčiai, VO₂max, laktato dinamika
- Biomechanika – žingsnių mechanika, varymas (propulsija), bėgimo efektyvumas
- Sporto mokslas – treniruočių krūvio kiekybinis įvertinimas, periodizacija, veiklos modeliavimas
- Kompiuterių mokslas – mašininis mokymasis, jutiklių sintezė, dėvimoji technologija
Modern Platform Implementations
Apple Watch Running Analytics
Apple engineers recorded thousands of runners across various terrains and skill levels. This diverse training dataset enables algorithms to analyze torso and limb dynamics using gyroscope and accelerometer working in tandem, achieving high accuracy in power and efficiency metrics across all skill levels.
COROS POD 2 Advanced Metrics
The COROS POD 2 uses a waist-mounted sensor to provide superior stride detection by capturing torso movement more accurately than wrist-mounted devices. Their custom-trained ML models process hundreds of hours of labeled running data, enabling real-time pace and form feedback with ±1% accuracy.
Garmin Multi-Band GPS Innovation
Dual-frequency satellite reception (L1 + L5 bands) provides 10X greater signal strength, dramatically improving pace accuracy in "urban canyons" and dense forests. Reviews praise multi-band Garmin models as producing "scary-accurate" tracking on technical trails and track sessions, addressing the historical challenge of GPS drift for runners.
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.