Train Smarter with AI – How Tom Adapts His Plan to Progress and Recovery

Problem

Rigid plans ignore sleep, stress, and life. Pushing through fatigue risks injury and plateaus. Tom wants a plan that works with him.

AI Solution

The app collects training data (heart rate, pace, reps/weight, RPE*), daily metrics (sleep, steps), and optionally HRV. The AI detects patterns and adapts:

• Progressive overload: increase when sessions feel easy.
• Deload/recovery: reduce volume/intensity when fatigue signals arise.
• Auto‑scheduling: move sessions when recovery or calendar conflicts demand it.
• Technique cues: brief tips on tempo, range of motion, breathing.

*RPE: perceived effort 1–10 helps the AI estimate load realistically.

Example Flow

1. Mon: planned intervals. Poor sleep, elevated resting HR → AI suggests an easy run.
2. Wed: strength session feels light → AI adds 2.5–5% load and +1 set.
3. Fri: low HRV, high recent RPE → recovery day with mobility and a walk.

Outcome

Tom trains consistently, avoids injury, and improves endurance, strength, and well‑being. The plan remains realistic—even in busy weeks.

Benefits

• Personalized: adapts daily to performance and life.
• Safer: early overload warnings and sensible rest.
• Explainable: clear reasons for adjustments.

Limitations

• Data quality matters (reliable devices, honest RPE).
• Medical issues require a doctor; the app isn’t a diagnosis tool.
• Privacy: store health data only with trusted services.

Related Content

• Prompt: “Create an 8‑week plan with a deload week, 3× strength, 2× cardio, back focus.”
• Guide: “Recovery 101—sleep, HRV, nutrition.”
• Tools (examples): ChatGPT, Garmin/Polar, Apple Health, WHOOP, Strava.