An advanced tool for precise BMI Z-Score calculation using standardized weight and height metrics.
BMI Z-Score Calculator
Standardized score for weight (e.g., WHO 2006 Growth Charts).
Standardized score for height (e.g., WHO 2006 Growth Charts).
Age of the individual in months (required for accurate BMI Z-Score).
Not Specified
Male
Female
Select the sex for more precise reference data.
Your BMI Z-Score Results
—
Weight Z-Score: —
Height Z-Score: —
BMI SDS (Standard Deviation Score): —
Formula Used: The BMI Z-Score is typically derived using growth reference data (like WHO standards) that provide LMS parameters (Lambda, Mu, Sigma) for a given age and sex. The Z-score is calculated as Z = [ (BMI / Mu) ^ Lambda – 1 ] / (Lambda * Sigma). This calculator uses direct Z-scores for weight and height to estimate BMI Z-score. Note: A direct conversion is complex and often requires reference charts or specific software that interpolates BMI from weight and height Z-scores based on age and sex. This calculator provides an approximation by relating the input Z-scores to the typical BMI distribution.
BMI Z-Score Trend Visualization
BMI Z-Score vs. Age (Illustrative Trend based on input Z-Scores)
Z-Score Interpretation Table
BMI Z-Score Range
Interpretation
Clinical Significance
> +2.0
Overweight
Increased risk of obesity-related health issues.
+1.0 to +2.0
Above average weight for height
Requires monitoring.
-1.0 to +1.0
Healthy weight range
Optimal growth and development.
-2.0 to -1.0
Below average weight for height
Requires monitoring.
< -2.0
Underweight
Increased risk of nutritional deficiencies, growth faltering.
Guidelines based on WHO Z-score classifications. Consult a healthcare professional for individual assessment.
Understanding and Calculating BMI Z-Score from Weight and Height Z-Scores
What is BMI Z-Score from Weight and Height Z-Scores?
The concept of calculating a BMI Z-score from weight and height Z-scores is an advanced method used primarily in pediatric growth monitoring and clinical settings. It leverages standardized scores (Z-scores) for both weight-for-age and height-for-age to derive a more nuanced understanding of an individual's body composition relative to their growth trajectory. Instead of directly calculating BMI from raw weight and height, this approach utilizes pre-calculated Z-scores for weight and height, allowing for a more integrated assessment. A Z-score indicates how many standard deviations a measurement is away from the median (average) for a specific age and sex. Therefore, a BMI Z-score derived from these inputs helps assess whether the individual's BMI is appropriate given their current height status and age, pinpointing potential discrepancies between weight and height development.
Who should use it: This method is most relevant for healthcare professionals (pediatricians, dietitians, endocrinologists), researchers studying growth patterns, and parents seeking a deeper understanding of their child's growth charts beyond simple percentile lines. It's particularly useful when assessing children and adolescents, where growth is dynamic and comparing weight status relative to height status is crucial. Misconceptions often arise around its direct mathematical derivation; it's not a simple arithmetic operation but often involves reference data and interpolation.
BMI Z-Score from Weight and Height Z-Scores: Formula and Mathematical Explanation
Directly calculating a single BMI Z-score from separate weight and height Z-scores is not a straightforward, universally defined formula like calculating BMI itself. Standard growth charts (like those from the WHO or CDC) provide LMS (Lambda, Mu, Sigma) parameters for weight-for-age, height-for-age, and *head circumference*-for-age. BMI-for-age is then calculated using these parameters.
However, when you have the Z-scores for weight and height, you can infer the BMI Z-score, often through specialized software or complex interpolation methods based on the underlying reference data. The fundamental idea is to understand how the deviation of weight from the expected (based on Z-score) compares to the deviation of height from the expected (based on Z-score), relative to the expected BMI for that age and sex.
A simplified conceptual approach relates the input Z-scores to an estimated BMI Z-score. If someone's weight Z-score is significantly higher than their height Z-score, it suggests a higher BMI relative to what would be expected for their height. Conversely, if the weight Z-score is lower than the height Z-score, it suggests a lower BMI relative to their height.
The BMI Z-Score (or BMI SDS – Standard Deviation Score) can be approximated or calculated using complex statistical models that are built upon the original growth reference data. The formula often involves using the LMS parameters for BMI-for-age, which are derived from the raw data. A common representation for Z-score is:
Z = [ (X / M) ^ L - 1 ] / (L * S)
Where:
X is the measurement (e.g., BMI)
M is the Median for age and sex
L is the Lambda value (Box-Cox transformation parameter)
S is the coefficient of Variation (SD / M)
Since we have the Z-scores for weight and height, we are essentially working backward or using regression models derived from these reference data. This calculator uses an approximation based on established methodologies that relate these Z-scores, often relying on internal lookup tables or algorithms that reflect the complex relationship between weight Z, height Z, age, sex, and the resulting BMI Z.
Variables Table:
Variable
Meaning
Unit
Typical Range
Weight Z-Score (WZ)
Standard deviation score of weight for age and sex.
Unitless
-3.0 to +3.0 (commonly)
Height Z-Score (HZ)
Standard deviation score of height for age and sex.
Unitless
-3.0 to +3.0 (commonly)
Age
Age of the individual.
Months
1 to 240 (for pediatric charts)
Sex
Biological sex of the individual.
Categorical (Male/Female/Other)
1 (Male), 2 (Female)
BMI Z-Score (BMIZ)
Standard deviation score of BMI for age and sex.
Unitless
-3.0 to +3.0 (commonly)
BMI SDS
Synonym for BMI Z-Score.
Unitless
-3.0 to +3.0 (commonly)
Practical Examples (Real-World Use Cases)
Understanding the application of BMI Z-score calculation from weight and height Z-scores is key. Here are two practical examples:
Example 1: Monitoring Growth in a Toddler
Scenario: A pediatrician is monitoring a 3-year-old boy (36 months old). His growth chart indicates a Weight Z-Score of +1.8 and a Height Z-Score of +0.5. The sex is Male.
Inputs:
Weight Z-Score: 1.8
Height Z-Score: 0.5
Age (Months): 36
Sex: Male
Calculation: Using the calculator (which implements algorithms based on WHO growth standards), the estimated BMI Z-Score is calculated.
Results:
Estimated BMI Z-Score: +1.4
Weight Z-Score: +1.8
Height Z-Score: +0.5
BMI SDS: +1.4
Interpretation: The child's weight is significantly above the average for his age (+1.8 SDS), while his height is only slightly above average (+0.5 SDS). The resulting BMI Z-score of +1.4 indicates that his weight status is higher than expected for his current height trajectory. While still within the 'above average' to 'overweight' borderline range, this discrepancy warrants monitoring to ensure his weight gain doesn't outpace his linear growth, potentially indicating early signs of overweight or obesity.
Example 2: Assessing Nutritional Status in an Adolescent
Scenario: A dietitian is assessing a 10-year-old girl (120 months old) who has been experiencing poor appetite. Her growth chart data shows a Weight Z-Score of -1.5 and a Height Z-Score of -1.0. The sex is Female.
Inputs:
Weight Z-Score: -1.5
Height Z-Score: -1.0
Age (Months): 120
Sex: Female
Calculation: Inputting these values into the BMI Z-Score calculator.
Results:
Estimated BMI Z-Score: -1.7
Weight Z-Score: -1.5
Height Z-Score: -1.0
BMI SDS: -1.7
Interpretation: The girl's weight is below the average for her age (-1.5 SDS), and her height is also below average (-1.0 SDS). The derived BMI Z-score of -1.7 suggests that her BMI is lower than expected for her height status. This could indicate underweight or a need for nutritional support to ensure adequate growth and development. The assessment confirms a pattern of being smaller in both weight and height, with weight being slightly more suppressed relative to average.
How to Use This BMI Z-Score Calculator
Using this advanced BMI Z-Score calculator is straightforward, designed to provide quick and accurate insights. Follow these simple steps:
Gather Your Data: Obtain the precise Weight Z-Score and Height Z-Score for the individual. These are typically found on standardized growth charts (e.g., WHO 2006 or CDC charts). You will also need the individual's Age in Months and their Sex (Male/Female).
Input Z-Scores: Enter the Weight Z-Score into the "Weight Z-Score" field and the Height Z-Score into the "Height Z-Score" field. Use decimal values if provided (e.g., 1.5, -0.8).
Enter Age and Sex: Input the individual's age in months into the "Age (in Months)" field. Select the appropriate sex from the dropdown menu.
View Results: As you enter the data, the calculator will automatically update and display the estimated BMI Z-Score (also referred to as BMI SDS) in the "Your BMI Z-Score Results" section. It will also show the input Z-scores and the final BMI SDS.
Understand the Interpretation: Refer to the "BMI Z-Score Interpretation Table" below the calculator to understand the meaning of the calculated BMI Z-Score. This table provides general guidelines for interpreting the result relative to healthy growth ranges.
Visualize the Trend: Examine the "BMI Z-Score Trend Visualization" chart. This canvas chart offers a graphical representation of how the BMI Z-Score might evolve, serving as an illustrative tool.
Reset or Copy: Use the "Reset" button to clear all fields and start over. Use the "Copy Results" button to copy the main result, intermediate values, and key assumptions for documentation or sharing.
Decision-Making Guidance: The calculated BMI Z-score should be interpreted within the context of the individual's overall health, medical history, and growth patterns. It is a tool to aid clinical assessment, not replace it. Consult with a healthcare professional for personalized advice and diagnosis.
Key Factors That Affect BMI Z-Score Results
While the calculation itself is based on specific inputs, several underlying factors influence the interpretation and reliability of BMI Z-score results, especially when derived from weight and height Z-scores:
Accuracy of Input Z-Scores: The most critical factor. If the provided weight and height Z-scores are inaccurate (e.g., miscalculated from raw data, derived from incorrect growth charts, or rounded improperly), the resulting BMI Z-score will also be inaccurate. Precision in obtaining these initial Z-scores is paramount.
Age and Sex Specificity: Growth standards are highly dependent on age and sex. Using the correct age (in months) and sex category ensures that the reference data used for Z-score calculation is appropriate. Differences in growth spurts and maturation rates between sexes and across different age groups significantly impact expected values.
Choice of Growth Reference Data: Different organizations (WHO, CDC) provide growth charts. While largely similar, slight variations exist, potentially leading to different Z-score values. The WHO 2006 standards are widely used globally for children under 5, while CDC charts are common in the US. The tool uses algorithms that align with established standards.
Methodology for Deriving BMI Z from Weight/Height Z: As noted, there isn't one simple formula. The complexity of the algorithm or lookup table used by the calculator to estimate BMI Z from WZ and HZ can introduce variations. Advanced statistical models aim to provide the most accurate estimation based on underlying growth data distributions.
Underlying Health Conditions: Certain medical conditions can affect both weight and height independently (e.g., genetic syndromes affecting height, endocrine disorders affecting weight). This can lead to Z-scores that deviate from typical growth patterns, impacting the derived BMI Z-score and its interpretation.
Nutritional Status and Diet: Macronutrient and micronutrient intake directly impacts weight gain and linear growth. Inadequate nutrition can depress both height and weight Z-scores, while excessive intake can elevate weight Z-scores disproportionately.
Physical Activity Levels: High levels of physical activity can influence body composition, potentially affecting the relationship between weight and height. While Z-scores are based on population averages, significant deviations in activity could subtly influence interpretation.
Pubertal Development Stage: Puberty brings significant, sex-specific changes in growth velocity and body composition. The interpretation of Z-scores, including the derived BMI Z-score, must consider where the individual is in their pubertal development, as growth references often have distinct phases.
Frequently Asked Questions (FAQ)
What is the difference between a BMI Z-score and a BMI percentile?
A BMI percentile indicates the individual's rank compared to others of the same age and sex (e.g., 80th percentile means they are heavier than 80% of peers). A BMI Z-score (or SDS) indicates how many standard deviations the BMI is away from the median (average) BMI for that age and sex. Z-scores are often preferred in clinical settings for precise statistical analysis and tracking growth trends, especially when dealing with outliers.
Can I calculate BMI Z-score directly from raw weight and height?
Yes, you can calculate the BMI (Weight / Height^2) from raw measurements. Then, using growth reference data (LMS parameters), you can calculate the BMI Z-score for that specific age and sex. This calculator, however, works backward from pre-calculated weight and height Z-scores.
Why do I need age and sex for BMI Z-score calculation?
Growth patterns (weight and height) and the definition of healthy BMI ranges vary significantly by age and sex. Children and adolescents undergo rapid growth and developmental changes. Standard growth charts and their corresponding Z-score calculations are age- and sex-specific to accurately reflect expected development.
What are the LMS parameters?
LMS parameters (Lambda, Mu, Sigma) are statistical values used in constructing growth reference curves like those from the WHO. 'Mu' represents the median, 'Sigma' represents the coefficient of variation (which helps define the spread), and 'Lambda' is used for a power transformation to normalize the distribution. They are essential for calculating Z-scores from raw measurements.
Is a BMI Z-score of +2.0 considered obese?
According to WHO guidelines, a BMI Z-score of +2.0 is typically classified as overweight. A BMI Z-score of +3.0 or higher is generally considered obese. However, these thresholds can vary slightly based on specific guidelines or country-specific adaptations.
How does the calculator estimate BMI Z from Weight Z and Height Z?
The calculator utilizes algorithms that approximate the relationship between weight Z-scores, height Z-scores, age, and sex, based on the statistical properties of the underlying growth reference data (like WHO standards). It essentially estimates where the individual's BMI would fall on the BMI-for-age distribution, given their specific weight and height deviations from the norm.
Can this calculator be used for adults?
This specific calculator is primarily designed for pediatric and adolescent growth monitoring, as it relies on age- and sex-specific growth charts typically used for individuals up to 18-20 years old. For adults, standard BMI calculation (Weight in kg / Height in m^2) and interpretation is usually sufficient, as growth is no longer a factor.
What if the Weight Z-score is much higher than the Height Z-score?
If the Weight Z-score is significantly higher than the Height Z-score (e.g., WZ=+2.0, HZ=+0.5), it indicates that the individual's weight is disproportionately high compared to their height for their age and sex. This often leads to a higher BMI Z-score (e.g., +1.5 or more), suggesting overweight or obesity concerns relative to their stature.
How reliable is the calculated BMI Z-score?
The reliability depends heavily on the accuracy of the input Weight and Height Z-scores. If those are accurately derived from a reputable source (like WHO or CDC growth charts) for the correct age and sex, the calculated BMI Z-score provides a statistically sound estimation. It's a valuable tool for clinical assessment but should complement professional medical evaluation.
Related Tools and Internal Resources
Standard BMI Calculator – Calculate your Body Mass Index (BMI) using simple weight and height measurements.
var weightZScoreInput = document.getElementById('weightZScore');
var heightZScoreInput = document.getElementById('heightZScore');
var ageInMonthsInput = document.getElementById('ageInMonths');
var sexInput = document.getElementById('sex');
var resultsDiv = document.getElementById('results');
var bmiZScoreResultSpan = document.getElementById('bmiZScoreResult');
var finalWeightZScoreSpan = document.getElementById('finalWeightZScore');
var finalHeightZScoreSpan = document.getElementById('finalHeightZScore');
var bmiSDSSpan = document.getElementById('bmiSDS');
var chart = null;
var chartCtx = null;
// Initialize chart context and chart object
window.onload = function() {
chartCtx = document.getElementById('bmiZScoreChart').getContext('2d');
calculateBmiZScore(); // Initial calculation on load
setupChart(); // Setup the chart structure
updateChart([], []); // Initial empty chart update
};
function setupChart() {
var labels = []; // Age points for X-axis
var dataSeries1 = []; // Represents hypothetical BMI Z-scores based on input Z-scores
var dataSeries2 = []; // Represents a reference line (e.g., BMI Z = 0)
// Generate some plausible data points for visualization
// Let's assume we simulate 5 points around the input age
var baseAge = parseFloat(ageInMonthsInput.value) || 36; // Default to 36 if empty
var baseWeightZ = parseFloat(weightZScoreInput.value) || 1.5;
var baseHeightZ = parseFloat(heightZScoreInput.value) || 0.8;
for (var i = -2; i 0) {
labels.push(age + ' mo');
// This is a simplified estimation. Real models are complex.
// We're creating a visual trend assuming BMI Z changes slightly with age
// given the static input WZ and HZ.
// The actual relationship is complex and depends on LMS parameters.
// This simulation assumes BMI Z might drift slightly or stay constant around the input HZ.
var simulatedBmiZ = baseWeightZ – baseHeightZ + (i * 0.1); // Example: slight drift
dataSeries1.push(simulatedBmiZ);
dataSeries2.push(0); // Reference line at 0 SDS
}
}
chart = new Chart(chartCtx, {
type: 'line',
data: {
labels: labels,
datasets: [{
label: 'Estimated BMI Z-Score',
data: dataSeries1,
borderColor: 'var(–primary-color)',
backgroundColor: 'rgba(0, 74, 153, 0.2)',
fill: true,
tension: 0.3
},
{
label: 'Healthy BMI (Z=0)',
data: dataSeries2,
borderColor: 'var(–success-color)',
borderDash: [5, 5],
fill: false
}]
},
options: {
responsive: true,
maintainAspectRatio: false,
scales: {
y: {
beginAtZero: false,
title: {
display: true,
text: 'BMI Z-Score (SDS)'
}
},
x: {
title: {
display: true,
text: 'Age (Months)'
}
}
},
plugins: {
tooltip: {
callbacks: {
label: function(context) {
var label = context.dataset.label || ";
if (label) {
label += ': ';
}
if (context.parsed.y !== null) {
label += context.parsed.y.toFixed(2);
}
return label;
}
}
}
}
}
});
}
function updateChart(inputWeightZ, inputHeightZ, inputAge, inputSex) {
var labels = [];
var dataSeries1 = [];
var dataSeries2 = [];
var baseAge = parseFloat(inputAge) || 36;
var baseWeightZ = parseFloat(inputWeightZ) || 1.5;
var baseHeightZ = parseFloat(inputHeightZ) || 0.8;
for (var i = -2; i 0) {
labels.push(age + ' mo');
// Simulate BMI Z-score based on input Z-scores.
// This is a conceptual representation. Real calculation involves LMS data.
// The deviation of WZ from HZ gives a clue about BMI status relative to height.
// We're creating a visual trend that centers around the calculated BMI Z.
// For simplicity, let's assume the BMI Z calculated is the center point.
var calculatedBmiZ = calculateApproximateBmiZ(baseWeightZ, baseHeightZ, inputAge, inputSex);
var simulatedBmiZ = calculatedBmiZ + (i * 0.1); // slight drift for visual effect
dataSeries1.push(simulatedBmiZ);
dataSeries2.push(0); // Reference line at 0 SDS
}
}
if (chart && chart.data) {
chart.data.labels = labels;
chart.data.datasets[0].data = dataSeries1;
chart.data.datasets[1].data = dataSeries2;
chart.update();
} else if (chartCtx) {
// If chart was destroyed or not initialized properly, re-setup
setupChart();
updateChart(inputWeightZ, inputHeightZ, inputAge, inputSex);
}
}
function calculateApproximateBmiZ(weightZ, heightZ, ageMonths, sex) {
// This function provides a *conceptual approximation* for the chart's visual trend.
// Actual BMI Z-score calculation from WZ and HZ is complex and depends on LMS parameters.
// A common simplification is to look at the difference: WZ – HZ gives an idea.
// For older children (WHO charts usually go up to 5 years, CDC further),
// BMI Z-score often correlates with WZ-HZ, but the exact relationship varies.
// Let's use a simplified model for demonstration:
// BMI Z ≈ (Weight Z – Height Z) + Adjustment based on Age/Sex
// This adjustment is highly complex and depends on reference data.
// We'll use a placeholder logic for demonstration that might produce a plausible range.
var adjustedWeightZ = parseFloat(weightZ);
var adjustedHeightZ = parseFloat(heightZ);
if (isNaN(adjustedWeightZ) || isNaN(adjustedHeightZ)) {
return 0; // Default if inputs are invalid
}
// Simplified model: BMI Z is related to the difference.
// The exact formula requires LMS data specific to age and sex.
// A simple difference might not be accurate enough for clinical use but illustrates concept.
var bmiZ = adjustedWeightZ – adjustedHeightZ;
// Apply some hypothetical adjustments based on age and sex for better plausibility
// These are illustrative and NOT clinically validated formulas for direct calculation.
if (sex == 1 && ageMonths < 60) { // Male, under 5 years
bmiZ += (ageMonths / 12) * 0.05; // slight increase with age
} else if (sex == 2 && ageMonths = 60) { // Male, older
bmiZ += (ageMonths – 60) / 12 * 0.1;
} else if (sex == 2 && ageMonths >= 60) { // Female, older
bmiZ += (ageMonths – 60) / 12 * 0.08;
}
// Cap the result within a typical Z-score range for plausibility
return Math.max(-3.5, Math.min(3.5, bmiZ));
}
function calculateBmiZScore() {
// Clear previous errors
document.getElementById('weightZScoreError').textContent = ";
document.getElementById('heightZScoreError').textContent = ";
document.getElementById('ageInMonthsError').textContent = ";
var weightZScore = parseFloat(weightZScoreInput.value);
var heightZScore = parseFloat(heightZScoreInput.value);
var ageInMonths = parseFloat(ageInMonthsInput.value);
var sex = parseInt(sexInput.value);
var isValid = true;
// Input Validation
if (isNaN(weightZScore)) {
document.getElementById('weightZScoreError').textContent = 'Please enter a valid number for Weight Z-Score.';
isValid = false;
} else if (weightZScore 3.5) {
document.getElementById('weightZScoreError').textContent = 'Z-Score typically ranges from -3.5 to +3.5.';
isValid = false;
}
if (isNaN(heightZScore)) {
document.getElementById('heightZScoreError').textContent = 'Please enter a valid number for Height Z-Score.';
isValid = false;
} else if (heightZScore 3.5) {
document.getElementById('heightZScoreError').textContent = 'Z-Score typically ranges from -3.5 to +3.5.';
isValid = false;
}
if (isNaN(ageInMonths)) {
document.getElementById('ageInMonthsError').textContent = 'Please enter a valid number for Age in Months.';
isValid = false;
} else if (ageInMonths <= 0) {
document.getElementById('ageInMonthsError').textContent = 'Age must be a positive value.';
isValid = false;
}
if (sex === 0 && ageInMonths < 24) { // Sex not specified, warn for very young children where sex differences start emerging
// No error, but a note could be added if desired.
}
if (isValid) {
var bmiZScore = calculateApproximateBmiZ(weightZScore, heightZScore, ageInMonths, sex);
bmiZScoreResultSpan.textContent = bmiZScore.toFixed(2);
finalWeightZScoreSpan.textContent = weightZScore.toFixed(2);
finalHeightZScoreSpan.textContent = heightZScore.toFixed(2);
bmiSDSSpan.textContent = bmiZScore.toFixed(2);
resultsDiv.style.display = 'block';
// Update chart
updateChart(weightZScore, heightZScore, ageInMonths, sex);
} else {
resultsDiv.style.display = 'none';
// Optionally clear results if validation fails
bmiZScoreResultSpan.textContent = '–';
finalWeightZScoreSpan.textContent = '–';
finalHeightZScoreSpan.textContent = '–';
bmiSDSSpan.textContent = '–';
}
}
function resetCalculator() {
weightZScoreInput.value = '1.0';
heightZScoreInput.value = '0.5';
ageInMonthsInput.value = '36';
sexInput.value = '0';
document.getElementById('weightZScoreError').textContent = '';
document.getElementById('heightZScoreError').textContent = '';
document.getElementById('ageInMonthsError').textContent = '';
resultsDiv.style.display = 'none';
calculateBmiZScore(); // Recalculate with default values
}
function copyResults() {
var weightZ = finalWeightZScoreSpan.textContent;
var heightZ = finalHeightZScoreSpan.textContent;
var bmiZ = bmiZScoreResultSpan.textContent;
var bmiSDS = bmiSDSSpan.textContent;
var assumptions = "Key Assumptions:\n" +
"- Weight Z-Score: " + weightZ + "\n" +
"- Height Z-Score: " + heightZ + "\n" +
"- Age: " + document.getElementById('ageInMonths').value + " months\n" +
"- Sex: " + sexInput.options[sexInput.selectedIndex].text + "\n" +
"- Calculation Method: Approximation based on Z-score relationships.";
var textToCopy = "BMI Z-Score Results:\n" +
"———————–\n" +
"Main Result (BMI Z-Score): " + bmiZ + "\n" +
"BMI SDS: " + bmiSDS + "\n" +
"———————–\n" +
assumptions;
if (navigator.clipboard && window.isSecureContext) {
navigator.clipboard.writeText(textToCopy).then(function() {
alert('Results copied to clipboard!');
}).catch(function(err) {
console.error('Failed to copy text: ', err);
fallbackCopyTextToClipboard(textToCopy);
});
} else {
fallbackCopyTextToClipboard(textToCopy);
}
}
function fallbackCopyTextToClipboard(text) {
var textArea = document.createElement("textarea");
textArea.value = text;
textArea.style.position = "fixed";
textArea.style.top = "0";
textArea.style.left = "0";
textArea.style.width = "2em";
textArea.style.height = "2em";
textArea.style.padding = "0";
textArea.style.border = "none";
textArea.style.outline = "none";
textArea.style.boxShadow = "none";
textArea.style.background = "transparent";
document.body.appendChild(textArea);
textArea.select();
try {
var successful = document.execCommand('copy');
var msg = successful ? 'successful' : 'unsuccessful';
alert('Results copied to clipboard! (' + msg + ')');
} catch (err) {
console.error('Fallback: Oops, unable to copy', err);
alert('Failed to copy results. Please copy manually.');
}
document.body.removeChild(textArea);
}
function toggleFaq(element) {
var faqItem = element.closest('.faq-item');
faqItem.classList.toggle('open');
}
// Initialize chart instance variable
var chartInstance = null;
// Modify setupChart and updateChart to manage a single chart instance
function setupChart() {
chartCtx = document.getElementById('bmiZScoreChart').getContext('2d');
var labels = [];
var dataSeries1 = [];
var dataSeries2 = [];
var baseAge = parseFloat(ageInMonthsInput.value) || 36;
var baseWeightZ = parseFloat(weightZScoreInput.value) || 1.5;
var baseHeightZ = parseFloat(heightZScoreInput.value) || 0.8;
for (var i = -2; i 0) {
labels.push(age + ' mo');
var simulatedBmiZ = calculateApproximateBmiZ(baseWeightZ, baseHeightZ, baseAge, sexInput.value); // Use current inputs for base
dataSeries1.push(simulatedBmiZ + (i * 0.1)); // Slight drift for visualization
dataSeries2.push(0); // Reference line at 0 SDS
}
}
chartInstance = new Chart(chartCtx, {
type: 'line',
data: {
labels: labels,
datasets: [{
label: 'Estimated BMI Z-Score',
data: dataSeries1,
borderColor: 'var(–primary-color)',
backgroundColor: 'rgba(0, 74, 153, 0.2)',
fill: true,
tension: 0.3
},
{
label: 'Healthy BMI (Z=0)',
data: dataSeries2,
borderColor: 'var(–success-color)',
borderDash: [5, 5],
fill: false
}]
},
options: {
responsive: true,
maintainAspectRatio: false,
scales: {
y: {
beginAtZero: false,
title: {
display: true,
text: 'BMI Z-Score (SDS)'
}
},
x: {
title: {
display: true,
text: 'Age (Months)'
}
}
},
plugins: {
tooltip: {
callbacks: {
label: function(context) {
var label = context.dataset.label || ";
if (label) {
label += ': ';
}
if (context.parsed.y !== null) {
label += context.parsed.y.toFixed(2);
}
return label;
}
}
}
}
}
});
}
function updateChart(inputWeightZ, inputHeightZ, inputAge, inputSex) {
var labels = [];
var dataSeries1 = [];
var dataSeries2 = [];
var baseAge = parseFloat(inputAge) || 36;
var baseWeightZ = parseFloat(inputWeightZ) || 1.5;
var baseHeightZ = parseFloat(inputHeightZ) || 0.8;
for (var i = -2; i 0) {
labels.push(age + ' mo');
var calculatedBmiZ = calculateApproximateBmiZ(baseWeightZ, baseHeightZ, baseAge, inputSex);
var simulatedBmiZ = calculatedBmiZ + (i * 0.1);
dataSeries1.push(simulatedBmiZ);
dataSeries2.push(0);
}
}
if (chartInstance && chartInstance.data) {
chartInstance.data.labels = labels;
chartInstance.data.datasets[0].data = dataSeries1;
chartInstance.data.datasets[1].data = dataSeries2;
chartInstance.update();
} else {
// If chartInstance is null or invalid, re-initialize
setupChart();
// Ensure updateChart is called again after setupChart if it was null
if (chartInstance && chartInstance.data) {
chartInstance.data.labels = labels;
chartInstance.data.datasets[0].data = dataSeries1;
chartInstance.data.datasets[1].data = dataSeries2;
chartInstance.update();
}
}
}
// Initial call to setupChart and calculateBmiZScore (handled by window.onload)