Medical Disclaimer

CapyCal is a wellness tracking application designed to help you build mindful habits around food, fasting, and movement. CapyCal is not a medical device.

The insights, calculations, and recommendations provided within the app are for informational purposes only and are not intended to diagnose, treat, cure, or prevent any disease or medical condition.

We do not provide specific medical guidance for individuals with health conditions such as eating disorders, diabetes, or pregnancy. Always consult with a qualified healthcare professional before making significant changes to your diet, fasting schedule, or exercise routine.

Our Data & Methodologies

CapyCal relies on publicly available, peer-reviewed scientific guidelines and standard metabolic formulas to provide our insights. We are not affiliated with the organizations listed below, but we utilize their data to ensure accuracy.

1. Metabolic Calculations

We use industry-standard equations to estimate your energy needs.

Basal Metabolic Rate (BMR)

We use the Mifflin-St Jeor Equation, widely considered the most accurate standard for non-clinical settings.

Source: Mifflin, M. D., et al. (1990). "A new predictive equation for resting energy expenditure in healthy individuals." The American Journal of Clinical Nutrition. Link to PubMed

Total Daily Energy Expenditure (TDEE)

Activity multipliers are based on standard metabolic equivalent (MET) values.

Source: Hussain, et al. (2024). "Comprehensive Review on BMI, TDEE, BMR, and Calories." International Research Journal on Advanced Engineering. Link to Source

Daily Calorie Intake Recommendation

Based on the BMR, activity level and goal we recommend the user a daily calorie intake amount.

Females: (10 x weight in kg) + (6,25 x height in cm) – (5,0 x age in years) – 161

Males: (10 x weight in kg) + (6,25 x height in cm) – (5,0 x age in years) + 5

Body Mass Index (BMI)

Classifications are based on standards established by the World Health Organization (WHO) and CDC.

Source: Centers for Disease Control and Prevention (CDC) - BMI

Diet Strategies for Weight Loss and Weight Gain

Kim JY. Optimal Diet Strategies for Weight Loss and Weight Loss Maintenance. J Obes Metab Syndr. 2021 Mar 30;30(1):20-31. Link to PubMed

Bray GA, et al. Effect of Overeating Dietary Protein at Different Levels on Circulating Lipids and Liver Lipid: The PROOF Study. Nutrients. 2020 Dec 11;12(12):3801. Link to PubMed

Romieu I, et al. Energy balance and obesity: what are the main drivers? Cancer Causes Control. 2017 Mar;28(3):247-258. Link to PubMed

Ostendorf DM, et al. Physical Activity Energy Expenditure and Total Daily Energy Expenditure in Successful Weight Loss Maintainers. Obesity (Silver Spring). 2019 Mar;27(3):496-504. Link to PubMed

2. Nutritional Guidelines

Our food grading and macro-nutrient suggestions align with major public health guidelines:

General Healthy Eating

3. Nutrient Highlights

The meal highlights content is based on these public sources of information:

Rich in Complex Carbs, Simple Carbs

Healthy Fats, Saturated Fats

Possible Trans Fats

Protein-Rich, Lack of Protein, Diverse Protein

Rich in Fiber

Rich in Vitamins & Minerals, Diverse in Nutrition, Natural

Macros-Balanced

We use this macronutrient ratio: carbohydrates 40-50%, protein 25-35%, fat 20-30%

Greens Booster, Lack of Greens

Processed Meat

Added Sugar

High in Salt

4. Fasting Protocols

Our fasting windows are based on research regarding circadian rhythms and metabolic health.

Source: de Cabo, R., & Mattson, M. P. (2019). "Effects of Intermittent Fasting on Health, Aging, and Disease." The New England Journal of Medicine. Link to Source

5. AI Body Scan & Composition Estimates

Our body composition features use Computer Vision (Visual Anthropometry) to estimate body metrics based on 2D images. These estimates are trained on population datasets comparing visual markers to clinical measurements.

Methodology:

Body Fat %

Visual Volumetric Analysis: Our AI estimation methodology is supported by research validating the use of single-image smartphone photography for body composition assessment.

Source: Digital Single-Image Smartphone Assessment of Total Body Fat and Abdominal Fat Using Machine Learning

Muscle Mass

Estimated using anthropometric formulas that correlate limb circumference and volume with lean tissue mass.

Source: Total-body skeletal muscle mass: development and cross-validation of anthropometric prediction models

Body Age

This is a "Metabolic Age" calculation. It compares your calculated Basal Metabolic Rate (BMR) to the average BMR of different chronological age groups within the general population. If your BMR matches the average 25-year-old, your Body Age is 25.

⚠️ Accuracy Disclaimer (Important)

Please note that AI Visual Analysis is an estimation tool, not a diagnostic instrument.

Margin of Error: Visual estimation typically has a variance of ±3-5% compared to Gold Standard clinical methods like DEXA (Dual-Energy X-ray Absorptiometry) or Hydrostatic Weighing.

Source: Digital Single-Image Smartphone Assessment of Total Body Fat and Abdominal Fat Using Machine Learning

Factors Affecting Accuracy: Lighting, clothing tightness, camera angle, and hydration levels can significantly impact results.

Recommendation: Use these results to track trends (changes over time) rather than focusing on the absolute number.