Strava training load and planning with AI

Training load analysis requires more than one workout. KBrain gives Claude and ChatGPT access to your Strava activity history through MCP so the assistant can reason about cumulative stress, fatigue, form, and recovery across weeks.

Training Stress Score from heart rate

TSS can be estimated from heart rate data when power or pace-based stress is not available. KBrain can calculate an HR-based score for each activity and show how hard the session was relative to your own threshold.

ATL, CTL, and TSB: the fitness-fatigue model

Acute Training Load reflects recent stress. Chronic Training Load approximates longer-term fitness. Training Stress Balance, the gap between the two, estimates freshness or form. KBrain can surface these trends from your Strava history so Claude or ChatGPT can answer load questions with real numbers.

Overreaching detection

If acute load rises too fast, resting heart rate proxies worsen, cadence changes, or easy runs require unusually high HR, KBrain can help an assistant flag the pattern before it becomes injury or burnout.

Optimal training windows

An athlete can ask whether form tends to peak after a certain number of recovery days, whether hard workouts work better after two easy days, or whether a rest day would create more value than another session. KBrain turns training history into planning context.

Personalized recovery estimates

Recovery time becomes more personal when based on actual HR response, workout duration, intensity distribution, and recent load. One activity may need 12 hours. Another may need two days. The difference is in the data.