The BMI Analysis Data Capture API offers a comprehensive analysis of a person's physical and metabolic status based on basic body measurements. This API uses parameters such as weight, height, waist, hips, neck, age, gender, and physical activity level to generate a wide range of health indicators, delivering results in different systems of units, such as metric or imperial.
Its main function is to calculate Body Mass Index (BMI), a standard indicator that classifies an individual's weight status (underweight, normal, overweight, or different degrees of obesity). However, the API goes far beyond BMI, integrating complementary metrics that allow for a more accurate and personalized assessment of physical well-being.
Overall, the BMI Analysis Data Capture API is a comprehensive solution for users looking to integrate a reliable body assessment system into their applications or platforms, with multilingual support and clear, practical, interpretive results based on scientifically validated formulas.
{"lang":"en","output_system":"metric","bmi":{"value":27.61,"prime":1.1,"category":"Overweight (pre-obesity)","risk":"Increased cardiometabolic risk"},"ideal_weight":{"min":70.3,"max":94.7},"whr":{"value":0.94,"risk":"Moderate"},"whtr":{"value":48.21,"risk":"Healthy"},"body_fat":{"value":18.8,"method":"US Navy"},"bmr":{"value":2073.75,"formula":"Mifflin-St Jeor"},"tdee":{"value":3214,"activity_level":"moderate"},"ponderal_index":14.16,"body_surface_area":2.38,"display_measurements":{"weight":105,"height":195,"waist":94,"hip":100,"neck":40},"sex":"m","age":40,"units":{"weight":"kg","height":"cm","waist":"cm","hip":"cm","neck":"cm","system":"metric"},"source":"NA"}
curl --location --request POST 'https://zylalabs.com/api/10981/bmi+analysis+data+capture+api/20707/bmi+calculation' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"weight": {
"value": 105,
"unit": "kg"
},
"height": {
"value": 195,
"unit": "cm"
},
"waist": {
"value": 94,
"unit": "cm"
},
"hip": {
"value": 100,
"unit": "cm"
},
"neck": {
"value": 40,
"unit": "cm"
},
"sex": "m",
"age": 40,
"activity_level": "moderate",
"lang": "en",
"output_system": "metric"
}'
| Header | Description |
|---|---|
Authorization
|
[Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed. |
No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.
The BMI Calculation endpoint returns a comprehensive set of health indicators, including BMI, body fat percentage, basal metabolic rate (BMR), total daily energy expenditure (TDEE), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), and ideal weight range, among others.
Key fields in the response include "bmi" (value, category, risk), "body_fat" (value, method), "bmr" (value, formula), "tdee" (value, activity level), and "ideal_weight" (min, max). Each field provides critical insights into an individual's health status.
Users can input parameters such as weight, height, waist, hips, neck, age, gender, and physical activity level to customize their health assessment and receive tailored results.
The response data is structured in a JSON format, with nested objects for specific metrics like BMI and body fat. Each metric includes relevant values and categories, making it easy to parse and utilize in applications.
The API utilizes scientifically validated formulas and methods, such as the Mifflin-St Jeor equation for BMR and the US Navy method for body fat calculation, ensuring high accuracy and reliability in the results.
Data accuracy is maintained through the use of scientifically validated formulas for calculations, such as the Mifflin-St Jeor equation for BMR and the US Navy method for body fat estimation, ensuring reliable results.
Typical use cases include health and fitness applications, personalized wellness programs, and medical assessments, where users can track and analyze their physical and metabolic health indicators.
For example, "bmi.category" indicates weight status (e.g., "Overweight"), while "bmr.value" shows the daily calorie requirement at rest. Understanding these fields helps users interpret their health metrics effectively.
Users can leverage the returned data to monitor health trends, set fitness goals, and make informed lifestyle choices based on their BMI, body fat percentage, and other metrics provided in the response.
Users can expect consistent data patterns, such as BMI categories (underweight, normal, overweight, obesity) and risk assessments (e.g., increased cardiometabolic risk), which help in interpreting health status effectively.
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