Nutrition Analyzer

Assessment Content:

• Macronutrient assessment:

  • Protein intake quantity and quality
  • Carbohydrate type distribution (refined vs. complex carbs)
  • Fat type distribution (saturated/monounsaturated/polyunsaturated/trans fat)
  • Dietary fiber intake

• Vitamin assessment:

  • Vitamins A, C, D, E, K
  • B-vitamins (B1, B2, B3, B6, B12, folate, pantothenic acid, biotin)
  • Comparison against RDA
  • Deficiency risk assessment

• Mineral assessment:

  • Major minerals: calcium, phosphorus, magnesium, sodium, potassium, chloride, sulfur
  • Trace minerals: iron, zinc, copper, manganese, iodine, selenium, chromium, molybdenum
  • Comparison against RDA
  • Deficiency risk assessment

• Special nutrient assessment:

  • Omega-3 fatty acids (EPA, DHA, ALA)
  • Choline
  • Coenzyme Q10
  • Phytochemicals (flavonoids, carotenoids, etc.)

Output:

• Achievement rate for each nutrient
• Deficient/insufficient/adequate/excessive classification
• Deficiency risk identification
• Priority improvement recommendations

3. Nutritional Status Assessment

Comprehensive evaluation of the user's nutritional status.

Assessment Content:

• Overall nutrition quality score:

  • Nutrient density score
  • Food diversity score
  • Balanced diet score

• Nutritional pattern identification:

  • Dietary pattern type (Mediterranean, DASH, vegetarian, etc.)
  • Eating time patterns (meal frequency, eating window)
  • Snacking patterns

• Nutritional risk identification:

  • Deficiency risks (e.g., vitamin D deficiency, iron deficiency)
  • Excess risks (e.g., vitamin A excess, sodium excess)
  • Unhealthy eating habits (high sugar, high fat, high sodium)

Output:

• Nutritional status grade (excellent/good/fair/poor)
• Key nutritional issues identified
• Risk factor list
• Improvement priority

4. Correlation Analysis

Analyzes correlations between nutrition and other health metrics.

Supported Correlation Analyses:

• Nutrition <-> Weight:

  • Relationship between calorie intake and weight change
  • Macronutrient ratios and weight management
  • Eating timing and metabolic relationship

• Nutrition <-> Exercise:

  • Impact of nutritional intake on exercise performance
  • Nutritional needs on workout days vs. rest days
  • Protein intake and muscle recovery

• Nutrition <-> Sleep:

  • Caffeine intake and sleep quality
  • Dinner timing and sleep onset time
  • Specific nutrients (e.g., magnesium, tryptophan) and sleep

• Nutrition <-> Blood Pressure:

  • Sodium intake and blood pressure
  • Potassium/sodium ratio and blood pressure
  • DASH diet adherence and blood pressure control

• Nutrition <-> Blood Glucose:

  • Carbohydrate types and blood glucose fluctuations
  • Dietary fiber and blood glucose control
  • Meal timing and blood glucose curves

Output:

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

5. Personalized Recommendation Generation

Generates personalized nutrition improvement recommendations based on user data.

Recommendation Types:

• Nutrient adjustment recommendations:

  • Increase deficient nutrients
  • Reduce excessive nutrients
  • Optimize nutrient ratios

• Food selection recommendations:

  • Recommend specific food categories
  • Food substitution suggestions (healthier alternatives)
  • Food pairing suggestions (to enhance absorption)

• Dietary habit recommendations:

  • Meal timing adjustments
  • Meal frequency adjustments
  • Cooking method suggestions

• Supplement recommendations (for reference only):

  • Supplement suggestions based on deficiency risk
  • Supplement dosage and timing
  • Interaction warnings

Recommendation Basis:

• DRIs/RDA standards
• User nutrition history data
• User health status and goals
• Evidence-based nutrition science

Usage Instructions

Trigger Conditions

This skill is triggered when the user requests:

• Nutrition trend analysis
• Nutrient intake assessment
• Nutritional status assessment
• Nutrition improvement recommendations
• Nutrition and other health metric correlation analysis

Execution Steps

Step 1: Determine Analysis Scope

Clarify the analysis type and time range requested by the user:

• Analysis type: trend/assessment/correlation/recommendations
• Time range: week/month/quarter/custom
• Analysis depth: macronutrients/micronutrients/comprehensive analysis

Step 2: Read Data

Primary data sources:

1. data-example/nutrition-tracker.json - Main nutrition tracking data
2. data-example/nutrition-logs/YYYY-MM/YYYY-MM-DD.json - Daily diet records

Related data sources:

1. data-example/profile.json - Weight, BMI, and other baseline data
2. data-example/fitness-tracker.json - Exercise data
3. data-example/sleep-tracker.json - Sleep data
4. data-example/hypertension-tracker.json - Blood pressure data
5. data-example/diabetes-tracker.json - Blood glucose data

Step 3: Data Analysis

Execute the appropriate analysis algorithms based on the analysis type:

Trend analysis algorithm:

• Linear regression to calculate trend slope
• Moving average to smooth fluctuations
• Statistical significance tests

RDA achievement rate calculation:

rda_achievement = (actual_intake / rda_value) * 100

status_classification:
- < 50%: Severe deficiency
- 50-75%: Insufficient
- 75-100%: Approaching target
- 100-150%: Adequate (ideal range)
- > 150%: Excess (check tolerable upper intake level, UL)

Nutrient density score:

nutrient_density_score = (
    (vitamins_achieved / total_vitamins) * 40 +
    (minerals_achieved / total_minerals) * 30 +
    (fiber_achieved / fiber_rda) * 30
)

Correlation analysis algorithm:

• Pearson correlation coefficient calculation
• Lag correlation analysis (accounting for time-delay effects)
• Multivariate regression analysis

Step 4: Generate Report

Output the analysis report in the standard format (see "Output Format" section).

Output Format

Nutrition Trend Analysis Report

# Nutrition Intake Trend Analysis Report

## Analysis Period
2025-03-20 to 2025-06-20 (3 months, 90 days recorded)

## Macronutrient Trends

### Calorie Intake
- **Trend**: Down
- **Start**: avg 2100 cal/day
- **Current**: avg 1950 cal/day
- **Change**: -150 cal/day (-7.1%)
- **Interpretation**: Moderate calorie reduction, consistent with weight loss goal

**Trend Line**:

2100 | .. 2050 | . . 2000 +-- . 1950 | . 1900 +---------- Mar Apr May Jun

### Protein
- **Trend**: Stable
- **Average**: 82g/day (range: 70-95g)
- **Target**: 80g/day
- **Achievement rate**: 93% (84/90 days on target)
- **Interpretation**: Protein intake is stable and largely on target

### Dietary Fiber
- **Trend**: Improving
- **Start**: avg 18g/day
- **Current**: avg 22g/day
- **Change**: +4g/day (+22%)
- **Target**: 30g/day
- **Interpretation**: Significant fiber increase, but continued effort needed

### Fat
- **Trend**: Down
- **Start**: avg 75g/day
- **Current**: avg 68g/day
- **Change**: -7g/day (-9.3%)
- **Target**: <=65g/day
- **Interpretation**: Fat intake decreasing, approaching target

**Fat Type Distribution Changes**:
| Fat Type | Start | Current | Target | Trend |
|----------|-------|---------|--------|-------|
| Saturated fat | 25g | 20g | <20g | Improving |
| Monounsaturated | 30g | 32g | >35g | Slightly up |
| Polyunsaturated | 15g | 12g | 15-20g | Needs increase |
| Trans fat | 2g | 0.5g | 0g | Improving |

## Vitamin Status Trends

### Vitamin D
- **Intake trend**: Increasing (supplementation started)
- **Start**: avg 2 mcg/day (dietary sources)
- **Current**: avg 52 mcg/day (including 2000 IU supplement)
- **RDA**: 15 mcg/day
- **Serum level change**:
  - Baseline (2025-05): 18 ng/mL
  - Current (2025-06): 22 ng/mL
  - Target: 30-100 ng/mL
- **Interpretation**: Supplement is taking effect, but continued monitoring needed

### Vitamin C
- **Trend**: Improving
- **Start**: avg 65 mg/day
- **Current**: avg 85 mg/day
- **RDA**: 100 mg/day
- **Achievement rate**: from 65% to 85%
- **Recommendation**: Increase citrus fruits, kiwi, strawberries

### B-Vitamins
- **Vitamin B12**: Adequate (avg 2.5 mcg, RDA 2.4 mcg)
- **Folate**: Insufficient (avg 320 mcg, RDA 400 mcg)
- **B6**: Adequate (avg 1.5 mg, RDA 1.3 mg)

## Mineral Trends

### Calcium
- **Trend**: Stable
- **Average**: 850 mg/day
- **RDA**: 1000 mg/day
- **Achievement rate**: 85%
- **Main sources**: Dairy 40%, tofu 25%, leafy greens 20%

### Iron
- **Trend**: Adequate
- **Average**: 12 mg/day
- **RDA**: 8 mg/day (male)
- **Achievement rate**: 150%
- **Main sources**: Meat, eggs, legumes, leafy greens

### Sodium
- **Trend**: Improving
- **Start**: avg 2800 mg/day
- **Current**: avg 2100 mg/day
- **Target**: <2300 mg/day (ideal <1500 mg)
- **Interpretation**: General target met; ideal target still needs effort

### Potassium
- **Trend**: Improving
- **Start**: avg 2800 mg/day
- **Current**: avg 3200 mg/day
- **Target**: 3500-4700 mg/day
- **Potassium/sodium ratio**: from 1.0 to 1.5 (target >2)
- **Recommendation**: Continue increasing fruits and vegetables

## Special Nutrient Trends

### Omega-3
- **Trend**: Increasing (fish oil supplement)
- **Start**: avg 150 mg/day
- **Current**: avg 850 mg/day (including supplement)
- **Recommended**: 500-1000 mg/day
- **Status**: On target

### Choline
- **Trend**: Stable
- **Average**: 350 mg/day
- **AI (Adequate Intake)**: 425 mg/day
- **Achievement rate**: 82%
- **Main sources**: Eggs (60%), meat (25%), legumes (15%)

## Dietary Pattern Analysis

### Food Category Distribution
| Food Category | Proportion | Change | Rating |
|--------------|-----------|--------|--------|
| Fruits & vegetables | 35% | +8% | Increased |
| Whole grains | 20% | +5% | Improved |
| Refined grains | 15% | -7% | Reduced |
| Protein sources | 20% | Stable | Adequate |
| Added fats | 8% | -3% | Reduced |
| Added sugars | 2% | -2% | Reduced |

### Eating Time Patterns
- **Average eating window**: 12.5 hours (07:30 - 20:00)
- **Eating frequency**: avg 4.2 times/day
- **Most common meal times**:
  - Breakfast: 07:30 (90% of days)
  - Lunch: 12:15 (95% of days)
  - Dinner: 18:45 (98% of days)
  - Snack: 15:30 (60% of days)

### Diet Quality Score
- **Nutrient density score**: 7.2/10 (up from 6.5)
- **Food diversity score**: 6.8/10
- **Balanced diet score**: 7.5/10
- **Overall score**: 7.2/10 - **Good**

## Insights & Recommendations

### Key Insights

1. **Dietary fiber continues to improve but remains insufficient**
   - Increased from 18g to 22g, but still below target of 30g
   - Impact: satiety, gut health, blood glucose control
   - Recommendation: Include at least 5g of fiber per meal

2. **Fat quality improving**
   - Saturated fat decreased, trans fat nearly eliminated
   - Polyunsaturated fat slightly low, need to increase Omega-3 foods
   - Recommendation: Increase deep-sea fish, nuts, flaxseed

3. **Sodium improved but potassium/sodium ratio still low**
   - Sodium decreased 33%, potassium increased 14%
   - Potassium/sodium ratio from 1.0 to 1.5, still below target 2.0
   - Recommendation: Continue increasing high-potassium foods (bananas, oranges, potatoes, spinach)

4. **Vitamin D supplementation is effective**
   - Serum level from 18 to 22 ng/mL (+4 ng in 4 weeks)
   - Expected to reach target range in 3-4 months
   - Recommendation: Continue supplementation, monitor periodically

### Priority Action Plan

#### Priority 1: Increase dietary fiber to 30g/day (2 weeks)

**Specific Actions**:
1. Breakfast: Whole grains (oats/whole wheat bread) + fruit (9g)
2. Lunch: Brown rice/whole wheat noodles + 2 servings of vegetables (8g)
3. Dinner: Sweet potato/mixed grains + 2 servings of vegetables (8g)
4. Snack: Fruit + nuts (5g)
**Total**: 30g

#### Priority 2: Optimize potassium/sodium ratio to 2.0 (4 weeks)

**Specific Actions**:
1. Reduce processed foods (primary sodium source)
2. 2-3 servings of high-potassium fruits daily (bananas, oranges, kiwi)
3. Choose spinach, potatoes, mushrooms, tomatoes for vegetables
4. Use herbs and spices instead of salt for seasoning

#### Priority 3: Maintain vitamin D supplementation (long-term)

**Monitoring Plan**:
- Recheck serum levels in 3 months
- Target: 40-60 ng/mL
- Adjust dosage based on results

## Nutrition Goal Progress

| Goal | Start | Current | Target | Progress | Status |
|------|-------|---------|--------|----------|--------|
| Calories | 2100 | 1950 | 1800-2000 | 100% | On target |
| Protein | 75g | 82g | 80g | 100% | On target |
| Dietary fiber | 18g | 22g | 30g | 73% | In progress |
| Vitamin D | 18 ng/mL | 22 ng/mL | 30-100 | 20% | Improving |
| Sodium | 2800 mg | 2100 mg | <2300 | 100% | On target |
| Omega-3 | 150 mg | 850 mg | 500-1000 mg | 100% | On target |

---

**Report generated**: 2025-06-20
**Analysis period**: 2025-03-20 to 2025-06-20 (90 days)
**Days recorded**: 90
**Nutrition analyzer version**: v1.0

Data Structures

Diet Record Data

{
  "date": "2025-06-20",
  "meals": [
    {
      "type": "breakfast",
      "time": "07:30",
      "foods": ["eggs", "milk", "whole wheat bread"],
      "calories": 450,
      "macronutrients": {
        "protein_g": 20,
        "carbs_g": 55,
        "fat_g": 15,
        "fiber_g": 5,
        "saturated_fat_g": 5,
        "monounsaturated_fat_g": 6,
        "polyunsaturated_fat_g": 3,
        "trans_fat_g": 0.1
      },
      "micronutrients": {
        "vitamin_a_mcg": 150,
        "vitamin_c_mg": 5,
        "vitamin_d_mcg": 1.5,
        "vitamin_e_mg": 1,
        "vitamin_k_mcg": 5,
        "thiamine_mg": 0.3,
        "riboflavin_mg": 0.4,
        "niacin_mg": 4,
        "vitamin_b6_mg": 0.1,
        "folate_mcg": 30,
        "vitamin_b12_mcg": 0.6,
        "calcium_mg": 250,
        "iron_mg": 2,
        "magnesium_mg": 40,
        "phosphorus_mg": 200,
        "zinc_mg": 2,
        "selenium_mcg": 10,
        "potassium_mg": 350,
        "sodium_mg": 300
      },
      "special_nutrients": {
        "omega_3_g": 0.1,
        "choline_mg": 150
      }
    }
  ],
  "daily_summary": {
    "total_calories": 2000,
    "total_macronutrients": {
      "protein_g": 80,
      "carbs_g": 250,
      "fat_g": 65,
      "fiber_g": 30
    },
    "rda_achievement": {
      "protein": 100,
      "vitamin_c": 85,
      "vitamin_d": 35,
      "calcium": 90,
      "iron": 75
    },
    "goal_achieved": true
  }
}

Algorithm Descriptions

RDA Achievement Rate Calculation

def calculate_rda_achievement(actual_intake, rda_value, ul_value=None):
    """
    Calculate RDA achievement rate and status.

    Parameters:
    - actual_intake: Actual intake amount
    - rda_value: Recommended Dietary Allowance
    - ul_value: Tolerable Upper Intake Level (optional)

    Returns:
    - achievement_rate: Achievement rate percentage
    - status: Status label
    """
    achievement_rate = (actual_intake / rda_value) * 100

    if ul_value and actual_intake > ul_value:
        status = "exceeds_ul"
        category = "Excess (dangerous)"
    elif achievement_rate < 50:
        status = "severe_deficiency"
        category = "Severe deficiency"
    elif achievement_rate < 75:
        status = "insufficient"
        category = "Insufficient"
    elif achievement_rate < 100:
        status = "approaching_target"
        category = "Approaching target"
    elif achievement_rate <= 150:
        status = "adequate"
        category = "Adequate"
    else:
        status = "high_intake"
        category = "High"

    return {
        'achievement_rate': round(achievement_rate, 1),
        'status': status,
        'category': category
    }

Nutrient Density Score

def calculate_nutrient_density_score(meal_data):
    """
    Calculate food nutrient density score (0-10 scale).

    Factor weights:
    - Vitamin achievement rate: 40%
    - Mineral achievement rate: 30%
    - Dietary fiber: 20%
    - Restrictive nutrients (saturated fat, sodium, added sugars): 10%
    """
    score = 0

    # Vitamin score
    vitamin_achievements = [
        meal_data['micronutrients'][v] / RDA[v]
        for v in ['vitamin_a', 'vitamin_c', 'vitamin_d', 'vitamin_e', 'vitamin_k']
    ]
    vitamin_score = min(sum(vitamin_achievements) / len(vitamin_achievements), 1.5) * 10
    score += min(vitamin_score, 10) * 0.40

    # Mineral score
    mineral_achievements = [
        meal_data['micronutrients'][m] / RDA[m]
        for m in ['calcium', 'iron', 'magnesium', 'zinc']
    ]
    mineral_score = min(sum(mineral_achievements) / len(mineral_achievements), 1.5) * 10
    score += min(mineral_score, 10) * 0.30

    # Dietary fiber score
    fiber_score = min(meal_data['macronutrients']['fiber_g'] / 5, 2) * 10
    score += min(fiber_score, 10) * 0.20

    # Restrictive nutrient penalty
    penalty = 0
    if meal_data['macronutrients']['saturated_fat_g'] > 10:
        penalty += 2
    if meal_data['micronutrients']['sodium_mg'] > 600:
        penalty += 2
    if meal_data.get('added_sugars_g', 0) > 10:
        penalty += 2

    score = max(0, score - penalty * 0.10)

    return round(score, 1)

Healthy Eating Index Score

def calculate_healthy_eating_index(daily_data):
    """
    Calculate Healthy Eating Index (adapted from HEI-2015).

    Score range: 0-100 points.
    """
    score = 0

    # Adequacy components (max 50 points)
    # 1. Fruits (5 points)
    fruit_servings = daily_data['fruit_servings']
    score += min(fruit_servings, 2.5) * 2

    # 2. Vegetables (5 points)
    veg_servings = daily_data['vegetable_servings']
    score += min(veg_servings, 3) * 1.67

    # 3. Whole grains (10 points)
    whole_grains_oz = daily_data['whole_grains_oz']
    score += min(whole_grains_oz, 3) * 3.33

    # 4. Dairy (10 points)
    dairy_servings = daily_data['dairy_servings']
    score += min(dairy_servings, 3) * 3.33

    # 5. Protein (5 points)
    protein_oz = daily_data['protein_oz']
    score += min(protein_oz, 5) * 1

    # 6. Seafood/plant protein (5 points)
    plant_protein_oz = daily_data['plant_protein_oz']
    score += min(plant_protein_oz, 2) * 2.5

    # 7. Fatty acid ratio (10 points)
    fat_ratio = daily_data['unsaturated_fat_g'] / max(daily_data['saturated_fat_g'], 1)
    score += min(fat_ratio, 2.5) * 4

    # Moderation components (max 40 points, reverse scored)
    # 8. Refined grains (10 points, less is better)
    refined_grains_oz = daily_data['refined_grains_oz']
    score += max(10 - refined_grains_oz * 2, 0)

    # 9. Sodium (10 points, less is better)
    sodium_g = daily_data['sodium_mg'] / 1000
    score += max(10 - sodium_g * 2, 0)

    # 10. Added sugars (10 points, less is better)
    added_sugars_pct = daily_data['added_sugars_g'] / (daily_data['total_calories'] / 100)
    score += max(10 - added_sugars_pct * 10, 0)

    # 11. Saturated fat (10 points, less is better)
    saturated_fat_pct = daily_data['saturated_fat_g'] / (daily_data['total_calories'] / 100)
    score += max(10 - saturated_fat_pct * 10, 0)

    return round(score, 1)

Medical Safety Boundaries

Important Disclaimer

This analysis is for health reference only and does not constitute medical diagnosis or a nutrition prescription.

Scope of Analysis

Can do:

• Nutrition data statistics and analysis
• Trend identification and visualization
• RDA achievement rate calculation
• Nutritional deficiency risk assessment
• General nutrition recommendations
• Supplement interaction checks

Cannot do:

• Diagnose nutritional deficiency diseases
• Prescribe supplements
• Replace a registered dietitian
• Handle severe malnutrition
• Assess food allergies

Danger Signal Detection

The following danger signals are detected during analysis:

1. Nutrient excess:

   • Vitamin A > 3000 mcg (long-term)
   • Vitamin D > 100 mcg (long-term)
   • Iron > 45 mg (long-term)
   • Selenium > 400 mcg
   • Sodium > 2300 mg (persistent)

2. Nutrient deficiency:

   • Vitamin D < 10 mcg/day (serum < 12 ng/mL)
   • Vitamin B12 < 1.5 mcg/day (vegetarians)
   • Iron < 6 mg/day (women of childbearing age)
   • Calcium < 500 mg/day

3. Abnormal energy intake:

   • Persistent < 1200 cal/day (potential malnutrition)
   • Persistent > 3500 cal/day (potential overweight)

4. Abnormal dietary patterns:

   • Dietary fiber < 10g/day
   • Added sugars > 25% of calories
   • Saturated fat > 15% of calories

Recommendation Levels

Level 1: General recommendations

• Based on DRIs/RDA standards
• Applicable to the general population
• No medical supervision required

Level 2: Reference recommendations

• Based on user data and health status
• Should be combined with personal circumstances
• Recommend consulting a dietitian

Level 3: Medical recommendations

• Involves disease management or supplements
• Requires physician confirmation
• Do not self-adjust medication dosages

Reference Resources

• Chinese Dietary Reference Intakes (DRIs) (per Chinese clinical guidelines): http://www.cnsoc.org/
• US Dietary Guidelines: https://www.dietaryguidelines.gov/
• USDA FoodData Central: https://fooddatacentral.usda.gov/
• WHO Nutrition Recommendations: https://www.who.int/nutrition/
• Supplement Interaction Database: https://naturalmedicines.therapeuticresearch.com/