
evidence-based mesocycle and deload programming
v1by FitnessGrid
Manages multi-week training blocks for high-frequency athletes using fitness-fatigue modeling and strategic deload cycles.
periodizationrecoverydeloadmesocycleprogramming
Get an AI coach that uses this skill
FitnessGrid is an AI coach that plans your week and adapts as you go. Install evidence-based mesocycle and deload programming and your coach will follow this protocol every week, learn from what you actually do, and adjust on the fly.
- Your coach builds the week from this skill
- Adapts to your actual progress, not a static template
- Free to start — no credit card, ~60 seconds to set up
Procedure
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Mesocycle Assessment:
- Call
analyze_training_historyandget_user_historyto identify the current training phase length. - Determine if the athlete is following a standard loading ratio (e.g., 3:1 or 4:1).
- Call
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Recovery Monitoring:
- Review
get_week_statsfor the past 2–4 weeks. - Check for objective markers of fatigue: nocturnal HRV drops, sleep duration consistently <7 hours, or stalled performance.
- Evaluate subjective readiness via RPE/RIR trends in recent workout logs.
- Review
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Deload Triggering (Proactive or Reactive):
- Proactive: If the athlete has reached 4–8 weeks of progressive loading (average 5.6 weeks), suggest a planned deload microcycle.
- Reactive: If performance markers have stalled or wellness indicators (mood, soreness, sleep) have declined, initiate a reactive deload regardless of the current week in the cycle.
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Deload Implementation:
- Use
apply_deload_templateorupdate_workoutto modify the upcoming week (typically 7 days). - Volume: Reduce total sets or repetitions by 30–50%.
- Intensity: Maintain load but increase Repetitions in Reserve (RIR) to move further from failure.
- Frequency: Maintain or slightly reduce session frequency to preserve habits and avoid atrophy. Do not recommend complete cessation.
- Use
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Progression Planning:
- Use
plan_weekto structure the next mesocycle block (4–12 weeks) post-deload. - Ensure incremental increases in volume or intensity to drive adaptation according to the Fitness-Fatigue Model.
- Use
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Documentation:
- Call
create_noteto record the rationale for the deload (e.g., "Reactive deload triggered by HRV drop and RPE increase") to inform futureget_insightsqueries.
- Call