fitness-analyzer

Exercise Analyzer Skill

Analyze exercise data, identify exercise patterns, assess fitness progress, and provide personalized training recommendations.

Features

1. Exercise Trend Analysis

Analyze trends in exercise volume, frequency, and intensity, identifying areas of improvement or areas needing adjustment.

Analysis Dimensions：

Exercise volume trends (duration, distance, calories)
Exercise frequency trends (days of exercise per week)
Changes in intensity distribution (proportion of low/medium/high intensity)
Changes in exercise type preferences

Output：

Trend direction (improving/stable/declining)
Magnitude and percentage of change
Trend significance
Improvement recommendations

2. Exercise Progress Tracking

Track progress in specific exercise types and quantify fitness results.

Supported Progress Tracking：

Running progress: pace improvement, distance increase, heart rate improvement
Strength training progress: weight increase, volume increase, RPE changes
Endurance progress: increased exercise duration, extended distance
Flexibility progress: improvement in joint range of motion

Output：

Starting value vs current value
Improvement percentage
Progress visualization
Milestones achieved

3. Exercise Habit Analysis

Identify the user's exercise habits and patterns.

Analysis Content：

Usual exercise time (morning/afternoon/evening)
Exercise frequency pattern (how many days per week)
Exercise type preferences
Distribution of rest days
Exercise consistency score

Output：

Habit summary
Consistency score (0-100)
Optimization recommendations
Habit formation recommendations

4. Correlation Analysis

Analyze the correlation between exercise and other health indicators.

Supported Correlation Analyses：

Exercise ↔ Weight: relationship between exercise expenditure and weight changes
Exercise ↔ Blood Pressure: long-term effects of exercise on blood pressure
Exercise ↔ Blood Glucose: effects of exercise on blood glucose control
Exercise ↔ Mood/Sleep: effects of exercise on mood and sleep

Output：

Correlation coefficient (-1 to 1)
Correlation strength (weak/moderate/strong)
Statistical significance
Causal inference
Practical recommendations

5. Personalized Recommendation Generation

Generate personalized exercise recommendations based on user data.

Recommendation Types：

Exercise frequency recommendation: whether exercise frequency needs to be increased/decreased
Exercise intensity recommendation: intensity adjustment recommendations
Exercise type recommendation: recommended exercise types to try
Exercise time recommendation: optimal exercise time
Recovery recommendation: rest and recovery recommendations

Basis for Recommendations：

WHO/ACSM/AHA Exercise Guidelines
User Exercise History Data
User Health Status
User Fitness Goals

Output Format

Trend Analysis Report

# 运动趋势分析报告

## 分析周期
2025-03-20 至 2025-06-20（3个月）

## 运动量趋势

### 运动时长
- 趋势：⬆️ 上升
- 开始：平均120分钟/周
- 当前：平均180分钟/周
- 变化：+50%（+60分钟/周）
- 解读：运动量显著增加，表现优秀

### 卡路里消耗
- 趋势：⬆️ 上升
- 开始：平均960卡/周
- 当前：平均1440卡/周
- 变化：+50%
- 解读：运动消耗增加，有助于体重管理

### 运动距离
- 趋势：⬆️ 上升
- 开始：平均10公里/周
- 当前：平均20公里/周
- 变化：+100%
- 解读：耐力显著提升

## 运动频率

- 当前频率：4天/周
- 目标频率：4-5天/周
- 状态：✅ 达标
- 建议：保持当前频率

## 强度分布

| 强度 | 占比 | 变化 |
|------|------|------|
| 低强度 | 25% | +5% |
| 中等强度 | 55% | -10% |
| 高强度 | 20% | +5% |

**分析**：强度分布合理，中等强度占主导，符合有氧运动建议。

## 运动类型分布

| 运动类型 | 占比 |
|---------|------|
| 跑步 | 50% |
| 瑜伽 | 25% |
| 力量训练 | 25% |

**建议**：可以适当增加力量训练比例至30-40%。

## 洞察与建议

### 优势
1. ✅ 运动量稳定增长，(+50%)
2. ✅ 运动频率稳定，每周4天
3. ✅ 休息日充足，恢复良好

### 改进建议
1. 📈 每周增加2次力量训练
2. 📈 尝试不同运动类型避免单调
3. 📈 适当增加高强度间歇训练(HIIT)

### 警示
1. ⚠️ 注意运动强度不宜过高，控制在中等强度为主

Correlation Analysis Report

# 运动与血压相关性分析

## 数据来源
- 运动数据：fitness-logs (2025-03-20 至 2025-06-20)
- 血压数据：hypertension-tracker (同期)

## 分析结果

### 相关系数
- 变量：每周运动时长 ↔ 收缩压
- 相关系数：r = -0.68
- 相关性强度：**强负相关**
- 统计显著性：p < 0.01 **高度显著**

### 解读
运动时长与收缩压呈强负相关，意味着：
- 运动越多，血压越低
- 每增加30分钟运动，收缩压平均下降3-5 mmHg

### 实践建议
1. ✅ 继续保持规律运动，每周5-7天
2. ✅ 每次运动30-60分钟，中等强度
3. ✅ 优先选择有氧运动（快走、慢跑、骑行）
4. ⚠️ 避免憋气动作和突然爆发性运动

### 医学参考
- AHA声明：规律有氧运动可降低收缩压5-7 mmHg
- 您的运动效果：降低约10 mmHg，效果显著！

Progress Tracking Report

# 跑步进步追踪

## 分析周期
2025-01-01 至 2025-06-20（6个月）

## 配速进步

| 指标 | 开始 | 当前 | 改善 |
|------|------|------|------|
| 平均配速 | 7:30 min/km | 6:00 min/km | +20% ⬆️ |
| 最快配速 | 7:00 min/km | 5:30 min/km | +22% ⬆️ |
| 5公里用时 | 37:30 | 30:00 | +20% ⬆️ |

**趋势**：配速持续稳定提升，进步显著！

## 距离进步

| 指标 | 开始 | 当前 | 改善 |
|------|------|------|------|
| 最长单次距离 | 3 km | 12 km | +300% ⬆️ |
| 月度总距离 | 40 km | 86 km | +115% ⬆️ |
| 平均距离 | 5 km | 6 km | +20% ⬆️ |

**趋势**：耐力大幅提升，可以完成更长距离。

## 心率改善

| 指标 | 开始 | 当前 | 改善 |
|------|------|------|------|
| 静息心率 | 78 bpm | 72 bpm | -6 bpm ⬇️ |
| 相同配速心率 | 155 bpm | 145 bpm | -10 bpm ⬇️ |

**分析**：心肺功能显著改善，相同配速下心率降低。

## 里程碑

- ✅ 2025-03-15：首次完成5公里跑
- ✅ 2025-05-20：首次完成10公里跑
- ✅ 2025-06-10：配速突破6:00 min/km

## 下一步目标

- 🎯 完成半程马拉松（21公里）
- 🎯 配速提升至5:30 min/km
- 🎯 尝试间歇训练提升速度

Data Source

Primary Data Sources

Exercise Log
- Path:data/fitness-logs/YYYY-MM/YYYY-MM-DD.json
- Content: Exercise records (type, duration, intensity, heart rate, distance, etc.)
- Frequency: Updated after each exercise session
User Profile
- Path:data/fitness-tracker.json
- Content: User profile, fitness goals, statistics
- Update: Periodic updates
Health Data Correlation
- data/hypertension-tracker.json(Blood pressure data)
- data/diabetes-tracker.json(Blood glucose data)
- data/profile.json(Weight, BMI, etc.)

Data Quality Check

Data completeness: Check whether required fields are present
Data reasonableness: Check whether values are within a reasonable range
Temporal consistency: Check whether timestamps are reasonable
Duplicate data: Detect and handle duplicate records

Algorithm Description

1. Linear Regression Trend Analysis

Use linear regression to analyze the temporal trend of exercise data.

Formula： y = a + bx

Where:

y: Exercise metric (duration, calories, distance, etc.)
x: Time
a: Intercept
b: Slope (direction and speed of trend)

Interpretation：

b > 0: Upward trend
b < 0: Downward trend
b ≈ 0: Stable

2. Pearson Correlation Coefficient

Used to analyze the linear correlation between two variables.

Formula： r = Σ[(xi - x̄)(yi - ȳ)] / √[Σ(xi - x̄)² × Σ(yi - ȳ)²]

Range：-1 ≤ r ≤ 1

Interpretation：

r = 1: Perfect positive correlation
r = -1: Perfect negative correlation
r = 0: No linear correlation

Strength Assessment：

|r| < 0.3: Weak correlation
0.3 ≤ |r| < 0.7: Moderate correlation
|r| ≥ 0.7: Strong correlation

3. Pace Calculation

Pace = Exercise duration / Distance

Unit: min/km or min/mile

Example：

30-minute run of 5 kilometers
Pace = 30 / 5 = 6 min/km

4. MET Energy Metabolism Calculation

Calorie expenditure = MET × Body weight (kg) × Time (hours)

MET Values for Common Exercises：

Walking (3-5 km/h): 3.5-5 MET
Jogging (8 km/h): 8 MET
Running (10 km/h): 10 MET
Swimming: 6-10 MET
Cycling (leisure): 4 MET
Strength training: 5 MET
Yoga: 3 MET

Medical Safety Boundaries

⚠️ Important Statement This analysis is for health reference only and does not constitute medical advice.

Scope of Analytical Capabilities

✅ What can be done：

Exercise data statistics and analysis
Trend identification and visualization
Correlation calculation and interpretation
General exercise recommendations

❌ What cannot be done：

Disease Diagnosis
Exercise Risk Assessment
Specific Exercise Prescription Design
Exercise Injury Diagnosis and Treatment

Red Flag Detection

During the analysis process, detect the following red flags:

Abnormal Heart Rate
- Exercise heart rate > 95% of maximum heart rate
- Resting heart rate > 100 bpm
Abnormal Blood Pressure
- Systolic blood pressure ≥ 180 mmHg
- Diastolic blood pressure ≥ 110 mmHg
Signs of Overtraining
- 7 consecutive days of high-intensity exercise
- Exercise perception continues to decline (RPE > 17)
Rapid Weight Loss
- Weight loss > 1kg per week (may be unhealthy)

Recommendation Grading

Level 1: General Recommendations

Based on WHO/ACSM guidelines
Applicable to the general population

Level 2: Reference Recommendations

Based on user data
Needs to be combined with individual circumstances

Level 3: Medical Recommendations

Involves disease management
Requires physician confirmation

Usage Examples

Example 1: Generate Exercise Trend Report

/fitness trend 3months

Output:

3-month exercise trend analysis
Changes in exercise volume, frequency, and intensity
Insights and recommendations

Example 2: Track Running Progress

/fitness analysis progress running

Output:

Pace progress
Distance progress
Heart rate improvement
Milestone achievement

Example 3: Analyze the Correlation Between Exercise and Blood Pressure

/fitness analysis correlation blood_pressure

Output:

Correlation coefficient
Correlation strength
Significance test
Practical recommendations

Skill Version: v1.0 Last Updated: 2026-01-02 Maintainer: WellAlly Tech

About this skill

Features

1. Exercise Trend Analysis

2. Exercise Progress Tracking

3. Exercise Habit Analysis

4. Correlation Analysis

5. Personalized Recommendation Generation

Output Format

Trend Analysis Report

Correlation Analysis Report

Progress Tracking Report

Data Source

Primary Data Sources

Data Quality Check

Algorithm Description

1. Linear Regression Trend Analysis

2. Pearson Correlation Coefficient

3. Pace Calculation

4. MET Energy Metabolism Calculation

Medical Safety Boundaries

Scope of Analytical Capabilities

Red Flag Detection

Recommendation Grading

Usage Examples

Example 1: Generate Exercise Trend Report

Example 2: Track Running Progress

Example 3: Analyze the Correlation Between Exercise and Blood Pressure