Longitudinal
Longitudinal.
Same athlete. A longer story.
Fitness software has traditionally separated people into activities. Running. Cycling. Strength. Recovery. Workouts. Metrics. But the athlete does not exist in separate databases.
Longitudinal.
Information gains meaning across time. A session can matter because of what came before and what happens after.
Individual.
Athletes respond differently. With enough relevant history, a system can begin comparing training stimulus with observed response.
Training.
The model exists to support real training decisions, not to collect data for its own sake.
Useful patterns may include how capability changes, how quickly an athlete adapts, what preceded strong or poor performance, which patterns repeatedly appear and how training response changes over time.