To use this API users must indicate a weight, height, gender, age, waist and hip.

The Body Health API provides detailed health and body composition metrics based on user-entered data, such as weight, height, age, gender, waist and hip measurements. It provides various health metrics such as BMI, BMR, WHR, etc.

Zyla provides a wide range of integration methods for almost all programming languages. You can use these codes to integrate with your project as you need.

There are different plans to suit all tastes, including a free trial for a small number of requests, but your rate is limited to avoid abuse of the service.

This API is essential as it facilitates access to a wide range of health and body composition metrics accurately and efficiently.

The BMI Calculator endpoint returns a JSON object containing various health metrics, including BMI, BMR, WHR, ideal weight range, and more, based on user inputs like weight, height, age, gender, waist, and hip measurements.

Key fields in the response include "weight," "height," "bmi," "ideal_weight," "bmr," "whr," and "whtr." Each field provides specific health metrics, such as BMI value and status, ideal weight range, and waist-to-hip ratio.

The response data is structured in a JSON format, with nested objects for weight and height, and individual fields for metrics like BMI and BMR. This organization allows easy access to specific health metrics.

Users can input parameters such as weight, height, gender, age, waist circumference, and hip measurements. These parameters are essential for calculating accurate health metrics.

The endpoint provides information on BMI, BMR, WHR, ideal weight range, and associated health risks. This comprehensive data helps users assess their body composition and health status.

Users can analyze the returned metrics to understand their health status, set fitness goals, and create personalized diet or exercise plans based on their BMI, ideal weight, and risk assessments.

Data accuracy is maintained through established formulas for calculating health metrics, ensuring that results are based on scientifically validated methods. Regular updates and quality checks further enhance reliability.

Typical use cases include health and fitness apps for personalized metrics, medical assessment tools for healthcare professionals, and nutrition planning for creating tailored diet plans based on individual health data.

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