Athlytics

Athlytics processes Strava API data within R to enable longitudinal exercise physiology analysis through standardized calculation of physiological indicators and time-series visualization.

Key Features:

• Data Acquisition Integration: Seamlessly integrates Strava API data acquisition with analytical functions for longitudinal exercise physiology research.
• Physiological Indicator Calculation: Calculates key physiological indicators including ACWR (acute-to-chronic workload ratio reflecting training stress balance), EF (efficiency factor assessing aerobic efficiency), and physiological decoupling (indicating cardiovascular stability).
• Composite Load Metric Approximation: Addresses necessary approximations for composite load metrics such as TSS (Training Stress Score) and HRSS (Heart Rate Stress Score) based on activity summary data.
• Time-Series Visualization: Provides multidimensional time-series visualization capabilities for longitudinal physiological data.
• Standardized Function Interfaces: Offers standardized, reproducible function interfaces that enable efficient hypothesis testing regarding dynamic interplay between training stimuli, physiological efficiency, and stress responses.

Scientific Applications:

• Exercise Physiology Research: Quantifies physiological indicators related to adaptation and fatigue from dense longitudinal wearable sensor data collected in real-world settings.
• Training Load Monitoring: Analyzes training stress balance and workload ratios to assess athlete readiness and injury risk.
• Aerobic Efficiency Assessment: Evaluates changes in cardiovascular efficiency and aerobic performance over time through efficiency factor metrics.
• Cardiovascular Stability Analysis: Detects physiological decoupling patterns indicating alterations in cardiovascular stability during sustained exercise.

Methodology:

Athlytics processes Strava API data directly within R, calculating physiological indicators from activity summary data with approximations for composite load metrics.