Scientific Foundation
Research Base
Our algorithms incorporate findings from over 500 peer-reviewed nutrition studies, metabolic research papers, and clinical trials. We continuously update our knowledge base as new research emerges.
Professional Guidelines
We follow evidence-based recommendations from the Academy of Nutrition and Dietetics, American Heart Association, World Health Organization, and other leading nutrition authorities.
Nutrition Databases
Food composition data is sourced from USDA National Nutrient Database, international food composition tables, and verified nutrition databases to ensure accuracy.
Built on established nutrition science
Calculation Methodologies
| Calculation | Method Used | Accuracy | Validation |
|---|---|---|---|
| BMR Calculation | Mifflin-St Jeor Equation | ±10% in 95% of population | Validated across 500+ studies |
| TDEE Estimation | Activity Factor Method + NEAT | ±15% with activity logging | Calibrated with metabolic ward studies |
| Protein Requirements | Body weight & activity-based | 0.8-2.2g/kg validated range | Sports nutrition & clinical research |
| Micronutrient Targets | DRI + bioavailability factors | Meets 97.5% population needs | Institute of Medicine standards |
Basal Metabolic Rate (BMR)
We use the Mifflin-St Jeor equation, validated as the most accurate predictor of BMR across diverse populations. For individuals with known body composition, we incorporate the Katch-McArdle formula for enhanced precision.
Total Daily Energy Expenditure (TDEE)
Activity multipliers are based on validated research from exercise physiology studies. We account for planned exercise, occupational activity, and non-exercise activity thermogenesis (NEAT).
Macronutrient Distribution
Protein requirements follow evidence-based guidelines: 0.8-2.2g/kg body weight depending on activity level and goals. Carbohydrate and fat distribution is optimized based on metabolic health, activity patterns, and dietary preferences.
Micronutrient Optimization
Vitamin and mineral recommendations align with Dietary Reference Intakes (DRIs) while accounting for bioavailability, nutrient interactions, and individual absorption factors.
AI Architecture & Training
Training Data
Our models are trained on anonymized nutrition data, successful meal plan outcomes, and expert-verified nutrition protocols. No individual user data is used in model training without explicit consent.
Model Architecture
We employ ensemble methods combining nutrition knowledge graphs, constraint optimization, and preference learning algorithms to generate personalized recommendations while maintaining nutritional adequacy.
AI Safety Measures
Multi-layer validation ensures recommendations meet minimum nutritional requirements, avoid dangerous combinations, and flag potential allergens or contraindications before presentation to users.
Validation & Quality Assurance
Clinical Validation
Our algorithms undergo testing with registered dietitians and nutrition researchers. Real-world meal plans are reviewed for nutritional adequacy, safety, and practical implementation.
Continuous Improvement
User feedback, outcome tracking, and professional review inform algorithm refinements. We maintain detailed logs of recommendation accuracy and user satisfaction metrics.
Known Limitations
Our AI cannot account for rare genetic conditions, complex drug-nutrient interactions, or rapidly changing health status. We clearly communicate these limitations and recommend professional consultation when appropriate.
Validation Statistics
Algorithm Accuracy Rate
94.2%
Professional Reviews
500+
Research Papers Referenced
800+
Research Updates
Our methodology evolves with advancing nutrition science. We review and incorporate new research quarterly, with major algorithm updates subjected to additional validation and testing before deployment.
Latest Update: Q4 2024
Enhanced protein requirement calculations based on new research from the International Society of Sports Nutrition (ISSN) position stand.