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| 楼NO.1062 发布时间：2025/9/27 12:08:55 |
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| 楼NO.1063 发布时间：2025/9/27 12:08:26 |
First Dbol Cycle Help
When designing a training program it is useful to think in terms of two overlapping time scales:
the long_term picture that shows how volume and intensity change over weeks or months, and the short_term picture that guides daily
workouts. The long_term view is often called the macrocycle (sometimes just "macro") while the short_term
view is a microcycle.
**Macrocycle _ the overall arc**
| Phase | Typical goal | Volume vs Intensity |
|-------|--------------|---------------------|
| Preparation / base building | Build aerobic capacity and general strength | High volume, low to moderate intensity |
| Development | Increase specific work rates (speed, power) | Moderate_high volume, rising intensity |
| Peak | Deliver maximum performance | Low volume, high intensity |
| Recovery | Allow the body to rest and repair | Very low volume, low intensity |
Most programs fit into a 6_12 month window. In that time you gradually
shift from "do more work" to "do harder work", then back to light work for
recovery.
---
### 3. Training Blocks
Below are typical training blocks. Use them as templates and adapt the exact numbers (distance, pace, etc.) to your own data.
| Block | Focus | Typical Workouts |
|-------|-------|------------------|
| **Base** | Aerobic endurance, fat metabolism | Long runs at 60_70_% HRmax; easy tempo rides <_90_% HRmax. |
| **Build** | Lactate threshold & VO_max | Threshold runs (85_95_% HRmax), interval sessions: 4?00_m @ 4_5_kph faster than race pace. |
| **Peak** | Race_specific speed, pacing | Race_pace repeats; tempo runs at or just above target race pace. |
| **Taper** | Recovery & mental readiness | Reduce volume but keep intensity; focus on rest and nutrition. |
---
## 2. How to Use the Data for Training
| Step | What to Do | Example (Using Your Dataset) |
|------|------------|------------------------------|
| 1__ Calculate key statistics for each session:
_ Total distance
_ Average speed
_ Speed distribution (percent of time above/below race pace)
_ Time in target zones | Use Python/pandas to add columns. | `df'dist' = df.groupby('session_id')'distance'.transform('sum')` |
| 2__ Build a *training load* profile:
_ Accumulate weekly distance, average speed, and intensity distribution. | Plot with matplotlib/seaborn. | `weekly = df.groupby(pd.Grouper(freq='W')).agg('dist':'sum', 'avg_speed':'mean')` |
| 3__ Compare against a *desired training plan* (e.g., 5_week macrocycle). | Use rolling averages or a "target curve" overlay. | `plt.plot(weekly'dist', label='actual'); plt.plot(target_curve, '--', label='goal')` |
| 4__ Identify gaps:
_ If weekly distance is below target but intensity high _ need more mileage;
_ If intensity too low _ consider interval sessions. | Use conditional statements or a simple dashboard. | `if actual'dist' _10% _ significant.
| Gives a quick, evidence_based assessment.
|
| 4 | Adjust training plan accordingly (increase volume/intensity,
add rest, or refine pacing). | Optimizes performance while preventing overtraining.
|
By following this structured approach, athletes can objectively evaluate their training
status and make informed decisions to reach peak performance.
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| 楼NO.1064 发布时间：2025/9/27 12:08:17 |
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