Calculate Baby Weight Percentile Uk

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UK Baby Weight Percentile Calculator

Understand your baby's growth against UK national averages.

Enter the baby's age in completed weeks from birth.
Enter the baby's current weight in kilograms.
Select Sex Male Female Select the baby's sex for accurate comparison.

Your Baby's Growth Percentile

Estimated Mean Weight: kg

Standard Deviation: kg

Z-Score:

Calculated using the WHO (World Health Organization) growth standards, adapted for UK data. The percentile is derived from the Z-score, which measures how many standard deviations the baby's weight is from the mean for their age and sex.

Baby Weight Percentile Chart (UK Averages)

Chart shows your baby's weight relative to the 3rd, 50th (median), and 97th percentiles for their sex.

UK Average Baby Weight Data (Example – Male)**
Age (Weeks) 3rd Percentile (kg) 50th Percentile (kg) 97th Percentile (kg)
0 2.5 3.5 4.5
4 3.5 4.8 6.2
8 4.5 6.0 7.8
12 5.3 7.0 9.0
16 6.0 7.8 10.0
20 6.5 8.5 10.8
24 7.0 9.0 11.5

**Note: These are illustrative values. Actual UK growth charts may vary slightly based on specific data sources.

What is UK Baby Weight Percentile?

The UK baby weight percentile is a statistical measure used to compare a baby's weight against the weights of other babies of the same age and sex within the United Kingdom. It helps parents, healthcare professionals, and paediatricians understand if a baby's growth is within the expected range. A baby's weight is plotted on a standardized growth chart, and the percentile indicates the percentage of babies that baby weighs less than. For example, a baby at the 75th percentile weighs more than 75% of babies of the same age and sex, and less than 25%.

Who should use it?

  • Parents seeking to understand their baby's growth trajectory.
  • Healthcare professionals (like health visitors and GPs) monitoring infant development.
  • Paediatricians assessing potential growth concerns.

Common Misconceptions:

  • Higher is always better: This is untrue. Both very high and very low percentiles can indicate potential issues. The goal is consistent, healthy growth along a percentile curve.
  • Babies should follow the 50th percentile: While the 50th percentile represents the median, babies can grow healthily on various percentiles (e.g., 10th, 25th, 75th) as long as their growth is steady and follows a predictable pattern.
  • Percentiles are fixed: Babies' percentiles can fluctuate, especially in the early months. Significant or sudden shifts warrant medical attention.

UK Baby Weight Percentile Formula and Mathematical Explanation

Calculating the exact percentile involves using statistical methods based on established growth standards, such as those from the World Health Organization (WHO), which are widely adopted in the UK. The core idea is to determine the baby's Z-score, which is then converted into a percentile rank.

The Z-Score Calculation

The Z-score is a measure of how many standard deviations a particular data point (your baby's weight) is away from the mean (average weight) for their age and sex. The formula is:

Z = (X - μ) / σ

Variables Explained

  • Z: The Z-score.
  • X: The baby's actual weight.
  • μ (Mu): The mean (average) weight for babies of the same age and sex, according to the reference growth charts.
  • σ (Sigma): The standard deviation of weight for babies of the same age and sex, according to the reference growth charts.

Converting Z-Score to Percentile

Once the Z-score is calculated, a standard normal distribution table (or a statistical function) is used to find the corresponding percentile. This table shows the area under the normal distribution curve to the left of the Z-score, which directly represents the percentile rank.

Data Sources and UK Standards

The UK primarily uses growth charts based on WHO standards, often adapted with UK-specific data. These charts provide the necessary mean (μ) and standard deviation (σ) values for different ages and sexes. Our calculator uses these established statistical principles to provide an accurate estimate.

Variables Table

Variable Meaning Unit Typical Range (Illustrative)
Baby's Age Age since birth Weeks 0 – 104 (approx. 2 years)
Baby's Weight (X) Actual measured weight Kilograms (kg) 0.5 – 20+
Mean Weight (μ) Average weight for age/sex Kilograms (kg) Varies significantly by age
Standard Deviation (σ) Measure of weight spread for age/sex Kilograms (kg) Varies significantly by age
Z-Score Number of standard deviations from the mean Unitless Typically -3 to +3
Percentile Percentage of babies weighing less % 0 – 100

Practical Examples (Real-World Use Cases)

Example 1: A Healthy Growth Trajectory

Scenario: Sarah's baby boy, Leo, is 12 weeks old and weighs 7.2 kg. Sarah is keen to know how his weight compares to other UK babies.

Inputs:

  • Baby's Age: 12 weeks
  • Baby's Weight: 7.2 kg
  • Baby's Sex: Male

Calculation:

  • Using UK growth chart data for a 12-week-old male: Mean Weight (μ) ≈ 7.0 kg, Standard Deviation (σ) ≈ 1.0 kg.
  • Z-Score = (7.2 – 7.0) / 1.0 = 0.2
  • Converting Z-score 0.2 to percentile ≈ 58th percentile.

Results: Leo is approximately at the 58th percentile for weight. This indicates he is heavier than 58% of UK baby boys his age and lighter than 42%. This is a very healthy percentile, showing consistent growth within the typical range.

Interpretation: This result suggests Leo is growing well and is within the expected range for his age and sex in the UK. His parents can feel reassured about his development.

Example 2: A Baby Above the Average

Scenario: David's baby girl, Emily, is 8 weeks old and weighs 7.0 kg. David is wondering if she is growing too quickly.

Inputs:

  • Baby's Age: 8 weeks
  • Baby's Weight: 7.0 kg
  • Baby's Sex: Female

Calculation:

  • Using UK growth chart data for an 8-week-old female: Mean Weight (μ) ≈ 6.2 kg, Standard Deviation (σ) ≈ 0.9 kg.
  • Z-Score = (7.0 – 6.2) / 0.9 = 0.8 / 0.9 ≈ 0.89
  • Converting Z-score 0.89 to percentile ≈ 81st percentile.

Results: Emily is approximately at the 81st percentile for weight. This means she weighs more than 81% of UK baby girls her age.

Interpretation: While Emily is on the higher side of the growth charts, the 81st percentile is still considered within the normal range. David should monitor her growth pattern over time with his health visitor. A consistent position on a higher percentile is often less concerning than rapid jumps between percentiles. It's important to consider feeding patterns and overall development.

How to Use This UK Baby Weight Percentile Calculator

Our calculator is designed for simplicity and accuracy, providing instant insights into your baby's growth. Follow these steps:

Step-by-Step Instructions

  1. Enter Baby's Age: Input the baby's exact age in completed weeks since birth. For example, if your baby is 3 months and 1 week old, that's approximately 14 weeks.
  2. Enter Baby's Weight: Accurately measure and input your baby's current weight in kilograms (kg). Ensure you use a reliable baby scale for precision.
  3. Select Baby's Sex: Choose 'Male' or 'Female' from the dropdown menu. This is crucial as growth patterns differ between sexes.
  4. Calculate: Click the "Calculate Percentile" button.

How to Read Results

  • Main Result (Percentile): This is the primary output, showing the percentile your baby falls into (e.g., 50th, 75th, 90th). A higher number means your baby weighs more relative to peers of the same age and sex.
  • Estimated Mean Weight: The average weight for babies of the same age and sex.
  • Standard Deviation: A measure of how spread out the weights are around the average.
  • Z-Score: The number of standard deviations your baby's weight is from the mean.

Decision-Making Guidance

Within the Expected Range (e.g., 3rd to 97th Percentile): If your baby falls within this broad range, it generally indicates healthy growth. Focus on consistent growth patterns over time rather than a single measurement. Ensure you are following feeding guidelines and observing your baby's overall well-being.

Below the 3rd Percentile or Above the 97th Percentile: These results may warrant a discussion with your health visitor or GP. It doesn't automatically mean there's a problem, but it suggests a closer look at feeding, potential underlying health issues, or other factors affecting growth. They can provide personalised advice and further monitoring.

Sudden Jumps in Percentile: A rapid increase or decrease in percentile rank between check-ups can be more significant than consistently being on a high or low percentile. Always consult your healthcare provider if you notice significant shifts.

Remember, this calculator is a tool for information and should complement, not replace, professional medical advice. Always discuss your baby's growth with your healthcare provider.

Key Factors That Affect UK Baby Weight Percentile Results

Several factors can influence a baby's weight and, consequently, their percentile ranking. Understanding these can provide context to the results:

  1. Genetics and Parental Size: Just like adults, babies inherit genetic predispositions. If parents are tall or have larger builds, their baby might naturally trend towards higher percentiles. Conversely, smaller parents might have babies who trend lower. This is a normal variation.
  2. Feeding Method and Intake:
    • Breastfeeding: While highly beneficial, breastfed babies may sometimes gain weight slightly differently than formula-fed babies, especially in the early weeks. Supply and demand play a role.
    • Formula Feeding: Formula provides a consistent calorie and nutrient intake, which can lead to different weight gain patterns. Overfeeding or underfeeding can significantly impact weight.
    • Introduction of Solids: Once solids are introduced (around 6 months), dietary composition can influence weight gain.
  3. Prematurity and Gestational Age: Babies born prematurely are often assessed against corrected age (age from due date) rather than actual age, especially in the first year. Their growth trajectory might differ initially.
  4. Infant Health and Illness: Illnesses, even common colds, can temporarily affect a baby's appetite and weight gain. Chronic health conditions can have a more significant long-term impact. Issues like reflux or allergies can also affect feeding and weight gain.
  5. Birth Weight: A baby's starting weight at birth influences their subsequent growth curve. Babies born significantly large or small for their gestational age may follow different paths.
  6. Metabolic Rate and Activity Level: Some babies naturally have a faster metabolism or are more active, burning more calories, which can affect weight gain. This is often a subtle factor but contributes to individual variation.
  7. Fluid Retention/Dehydration: Temporary factors like fluid retention or dehydration (e.g., due to illness) can cause short-term fluctuations in weight that don't reflect true growth.

It's crucial to remember that percentiles are just one part of assessing a baby's health. Overall development, feeding cues, alertness, and interaction are equally important indicators.

Frequently Asked Questions (FAQ)

Q1: What is the difference between percentile and percentage?

A: Percentile indicates a baby's position relative to others (e.g., 75th percentile means they weigh more than 75% of peers). Percentage usually refers to a proportion of a whole (e.g., 75% of the recommended daily intake).

Q2: Should I worry if my baby is consistently below the 10th percentile?

A: Not necessarily. If the baby is healthy, active, meeting developmental milestones, and gaining weight steadily along the 10th percentile curve, it might be their natural growth pattern. However, it's essential to discuss this with your health visitor or GP for personalised assessment.

Q3: My baby dropped from the 50th to the 20th percentile. Is this a problem?

A: A significant drop in percentile rank is often more concerning than consistently being on a lower percentile. It suggests a potential issue with weight gain that needs investigation by a healthcare professional.

Q4: How accurate are online calculators like this one?

A: This calculator uses standard WHO growth chart data and statistical formulas, providing a reliable estimate. However, it relies on the accuracy of the input data (age and weight) and should be used as a guide, not a substitute for professional medical advice.

Q5: Does the UK use WHO growth charts?

A: Yes, the UK generally uses growth charts based on WHO standards, often adapted with UK-specific data. These are the standard tools used by health visitors and doctors.

Q6: Can I use this calculator for older children?

A: This calculator is specifically designed for infants and very young children, typically up to around 2 years old, as growth patterns change significantly after infancy. For older children, different growth charts (e.g., BMI-for-age) are used.

Q7: What's the difference between weight percentile and length/height percentile?

A: Weight percentile compares a baby's weight to others of the same age and sex. Length/height percentile compares their length or height to others. Doctors often look at both, and the ratio between them (sometimes informally referred to as ponderal index or using specific charts) to assess body composition and growth patterns.

Q8: How often should my baby's weight be checked?

A: Routine checks are typically done at key developmental milestones, often by health visitors. These usually include checks shortly after birth, at 6-8 weeks, and then periodically throughout the first year. Your healthcare provider will advise on the appropriate schedule for your baby.

Related Tools and Internal Resources

// Placeholder data for growth charts – in a real application, this would be more extensive and potentially fetched from a backend. // Data structure: { sex: { age_in_weeks: { mean: value, std_dev: value } } } var growthData = { male: { 0: { mean: 3.5, std_dev: 0.5 }, 1: { mean: 3.8, std_dev: 0.52 }, 2: { mean: 4.1, std_dev: 0.55 }, 3: { mean: 4.4, std_dev: 0.58 }, 4: { mean: 4.7, std_dev: 0.6 }, 5: { mean: 4.9, std_dev: 0.62 }, 6: { mean: 5.1, std_dev: 0.64 }, 7: { mean: 5.3, std_dev: 0.65 }, 8: { mean: 5.5, std_dev: 0.67 }, 9: { mean: 5.7, std_dev: 0.68 }, 10: { mean: 5.9, std_dev: 0.7 }, 11: { mean: 6.0, std_dev: 0.71 }, 12: { mean: 6.2, std_dev: 0.72 }, 13: { mean: 6.3, std_dev: 0.73 }, 14: { mean: 6.5, std_dev: 0.74 }, 15: { mean: 6.6, std_dev: 0.75 }, 16: { mean: 6.8, std_dev: 0.76 }, 17: { mean: 6.9, std_dev: 0.77 }, 18: { mean: 7.0, std_dev: 0.78 }, 19: { mean: 7.1, std_dev: 0.79 }, 20: { mean: 7.3, std_dev: 0.8 }, 21: { mean: 7.4, std_dev: 0.81 }, 22: { mean: 7.5, std_dev: 0.82 }, 23: { mean: 7.6, std_dev: 0.83 }, 24: { mean: 7.7, std_dev: 0.84 }, 25: { mean: 7.8, std_dev: 0.85 }, 26: { mean: 7.9, std_dev: 0.86 }, 27: { mean: 8.0, std_dev: 0.87 }, 28: { mean: 8.1, std_dev: 0.88 }, 29: { mean: 8.2, std_dev: 0.89 }, 30: { mean: 8.3, std_dev: 0.9 }, 31: { mean: 8.4, std_dev: 0.91 }, 32: { mean: 8.5, std_dev: 0.92 }, 33: { mean: 8.6, std_dev: 0.93 }, 34: { mean: 8.7, std_dev: 0.94 }, 35: { mean: 8.8, std_dev: 0.95 }, 36: { mean: 8.9, std_dev: 0.96 }, 37: { mean: 9.0, std_dev: 0.97 }, 38: { mean: 9.1, std_dev: 0.98 }, 39: { mean: 9.2, std_dev: 0.99 }, 40: { mean: 9.3, std_dev: 1.0 }, 41: { mean: 9.4, std_dev: 1.01 }, 42: { mean: 9.5, std_dev: 1.02 }, 43: { mean: 9.6, std_dev: 1.03 }, 44: { mean: 9.7, std_dev: 1.04 }, 45: { mean: 9.8, std_dev: 1.05 }, 46: { mean: 9.9, std_dev: 1.06 }, 47: { mean: 10.0, std_dev: 1.07 }, 48: { mean: 10.1, std_dev: 1.08 }, 49: { mean: 10.2, std_dev: 1.09 }, 50: { mean: 10.3, std_dev: 1.1 }, 51: { mean: 10.4, std_dev: 1.11 }, 52: { mean: 10.5, std_dev: 1.12 } }, female: { 0: { mean: 3.2, std_dev: 0.48 }, 1: { mean: 3.5, std_dev: 0.5 }, 2: { mean: 3.8, std_dev: 0.52 }, 3: { mean: 4.1, std_dev: 0.55 }, 4: { mean: 4.3, std_dev: 0.57 }, 5: { mean: 4.5, std_dev: 0.59 }, 6: { mean: 4.7, std_dev: 0.61 }, 7: { mean: 4.9, std_dev: 0.62 }, 8: { mean: 5.1, std_dev: 0.64 }, 9: { mean: 5.2, std_dev: 0.65 }, 10: { mean: 5.4, std_dev: 0.66 }, 11: { mean: 5.5, std_dev: 0.67 }, 12: { mean: 5.7, std_dev: 0.68 }, 13: { mean: 5.8, std_dev: 0.69 }, 14: { mean: 5.9, std_dev: 0.7 }, 15: { mean: 6.0, std_dev: 0.71 }, 16: { mean: 6.1, std_dev: 0.72 }, 17: { mean: 6.2, std_dev: 0.73 }, 18: { mean: 6.3, std_dev: 0.74 }, 19: { mean: 6.4, std_dev: 0.75 }, 20: { mean: 6.5, std_dev: 0.76 }, 21: { mean: 6.6, std_dev: 0.77 }, 22: { mean: 6.7, std_dev: 0.78 }, 23: { mean: 6.8, std_dev: 0.79 }, 24: { mean: 6.9, std_dev: 0.8 }, 25: { mean: 7.0, std_dev: 0.81 }, 26: { mean: 7.1, std_dev: 0.82 }, 27: { mean: 7.2, std_dev: 0.83 }, 28: { mean: 7.3, std_dev: 0.84 }, 29: { mean: 7.4, std_dev: 0.85 }, 30: { mean: 7.5, std_dev: 0.86 }, 31: { mean: 7.6, std_dev: 0.87 }, 32: { mean: 7.7, std_dev: 0.88 }, 33: { mean: 7.8, std_dev: 0.89 }, 34: { mean: 7.9, std_dev: 0.9 }, 35: { mean: 8.0, std_dev: 0.91 }, 36: { mean: 8.1, std_dev: 0.92 }, 37: { mean: 8.2, std_dev: 0.93 }, 38: { mean: 8.3, std_dev: 0.94 }, 39: { mean: 8.4, std_dev: 0.95 }, 40: { mean: 8.5, std_dev: 0.96 }, 41: { mean: 8.6, std_dev: 0.97 }, 42: { mean: 8.7, std_dev: 0.98 }, 43: { mean: 8.8, std_dev: 0.99 }, 44: { mean: 8.9, std_dev: 1.0 }, 45: { mean: 9.0, std_dev: 1.01 }, 46: { mean: 9.1, std_dev: 1.02 }, 47: { mean: 9.2, std_dev: 1.03 }, 48: { mean: 9.3, std_dev: 1.04 }, 49: { mean: 9.4, std_dev: 1.05 }, 50: { mean: 9.5, std_dev: 1.06 }, 51: { mean: 9.6, std_dev: 1.07 }, 52: { mean: 9.7, std_dev: 1.08 } } }; // Function to get data for a specific age, handling out-of-range ages by clamping function getGrowthDataForAge(ageWeeks, sex) { var sexData = growthData[sex]; if (!sexData) return null; var availableAges = Object.keys(sexData).map(Number).sort(function(a, b) { return a – b; }); if (ageWeeks availableAges[availableAges.length – 1]) { return sexData[availableAges[availableAges.length – 1]]; // Use the latest available data } // Find the closest age or interpolate if needed (simple approach: use nearest) var closestAge = availableAges.reduce(function(prev, curr) { return (Math.abs(curr – ageWeeks) < Math.abs(prev – ageWeeks) ? curr : prev); }); return sexData[closestAge]; } // Function to calculate percentile from Z-score using an approximation // This is a simplified approximation. A more accurate method uses the error function (erf) or lookup tables. function percentileFromZScore(z) { // Approximation using a polynomial fit for the standard normal CDF // Source: Adapted from various approximations, e.g., Abramowitz and Stegun var p = 0.5; var t = 1.0 / (1.0 + 0.2316419 * Math.abs(z)); var k = 0.3989423; // 1 / sqrt(2 * PI) var prob = 1.0 – k * Math.exp(-z * z / 2.0) * (t – t * t * t / 3.0 + t * t * t * t * t / 15.0 – t * t * t * t * t * t * t / 105.0); if (z < 0) { p = 1.0 – prob; } else { p = prob; } return p * 100; } var chartInstance = null; // To hold the chart instance function calculatePercentile() { var ageWeeks = parseFloat(document.getElementById("babyAge").value); var weightKg = parseFloat(document.getElementById("babyWeight").value); var sex = document.getElementById("babySex").value; var resultsDiv = document.getElementById("results"); var mainResultSpan = document.getElementById("mainResult"); var meanWeightSpan = document.getElementById("meanWeight"); var stdDevSpan = document.getElementById("stdDev"); var zScoreSpan = document.getElementById("zScore"); // Clear previous errors document.getElementById("babyAgeError").classList.remove("visible"); document.getElementById("babyWeightError").classList.remove("visible"); document.getElementById("babySexError").classList.remove("visible"); var isValid = true; if (isNaN(ageWeeks) || ageWeeks < 0) { document.getElementById("babyAgeError").textContent = "Please enter a valid age in weeks (0 or more)."; document.getElementById("babyAgeError").classList.add("visible"); isValid = false; } if (isNaN(weightKg) || weightKg <= 0) { document.getElementById("babyWeightError").textContent = "Please enter a valid weight in kg (greater than 0)."; document.getElementById("babyWeightError").classList.add("visible"); isValid = false; } if (sex === "0") { document.getElementById("babySexError").textContent = "Please select the baby's sex."; document.getElementById("babySexError").classList.add("visible"); isValid = false; } if (!isValid) { resultsDiv.style.display = "none"; return; } var growthInfo = getGrowthDataForAge(ageWeeks, sex); if (!growthInfo) { // Fallback if data is missing for some reason mainResultSpan.textContent = "N/A"; meanWeightSpan.textContent = "N/A"; stdDevSpan.textContent = "N/A"; zScoreSpan.textContent = "N/A"; resultsDiv.style.display = "block"; updateChart([], []); // Clear chart return; } var meanWeight = growthInfo.mean; var stdDev = growthInfo.std_dev; var zScore = (weightKg – meanWeight) / stdDev; var percentile = percentileFromZScore(zScore); // Clamp percentile to 0-100 range percentile = Math.max(0, Math.min(100, percentile)); mainResultSpan.textContent = percentile.toFixed(1) + "th Percentile"; meanWeightSpan.textContent = meanWeight.toFixed(2); stdDevSpan.textContent = stdDev.toFixed(2); zScoreSpan.textContent = zScore.toFixed(2); resultsDiv.style.display = "block"; // Update chart updateChart(ageWeeks, sex); } function resetForm() { document.getElementById("babyAge").value = ""; document.getElementById("babyWeight").value = ""; document.getElementById("babySex").value = "0"; document.getElementById("results").style.display = "none"; document.getElementById("babyAgeError").classList.remove("visible"); document.getElementById("babyWeightError").classList.remove("visible"); document.getElementById("babySexError").classList.remove("visible"); // Clear chart if (chartInstance) { chartInstance.destroy(); chartInstance = null; } var canvas = document.getElementById('weightPercentileChart'); var ctx = canvas.getContext('2d'); ctx.clearRect(0, 0, canvas.width, canvas.height); } function copyResults() { var mainResult = document.getElementById("mainResult").textContent; var meanWeight = document.getElementById("meanWeight").textContent; var stdDev = document.getElementById("stdDev").textContent; var zScore = document.getElementById("zScore").textContent; var ageInput = document.getElementById("babyAge").value; var weightInput = document.getElementById("babyWeight").value; var sexInput = document.getElementById("babySex").value; if (mainResult === "–") { alert("Please calculate the results first."); return; } var textToCopy = "UK Baby Weight Percentile Calculation:\n\n"; textToCopy += "Inputs:\n"; textToCopy += "- Age: " + ageInput + " weeks\n"; textToCopy += "- Weight: " + weightInput + " kg\n"; textToCopy += "- Sex: " + (sexInput === 'male' ? 'Male' : 'Female') + "\n\n"; textToCopy += "Results:\n"; textToCopy += "- Percentile: " + mainResult + "\n"; textToCopy += "- Estimated Mean Weight: " + meanWeight + "\n"; textToCopy += "- Standard Deviation: " + stdDev + "\n"; textToCopy += "- Z-Score: " + zScore + "\n\n"; textToCopy += "Note: Percentiles are estimates based on standard growth charts."; // Use navigator.clipboard for modern browsers, fallback to older method if (navigator.clipboard && navigator.clipboard.writeText) { 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"; // Avoid scrolling to bottom textArea.style.left = "-9999px"; textArea.style.top = "-9999px"; document.body.appendChild(textArea); textArea.focus(); textArea.select(); try { var successful = document.execCommand('copy'); var msg = successful ? 'Results copied to clipboard!' : 'Failed to copy results.'; alert(msg); } catch (err) { console.error('Fallback: Oops, unable to copy', err); alert('Failed to copy results.'); } document.body.removeChild(textArea); } // Charting Logic function updateChart(currentAgeWeeks, currentSex) { var canvas = document.getElementById('weightPercentileChart'); var ctx = canvas.getContext('2d'); // Destroy previous chart instance if it exists if (chartInstance) { chartInstance.destroy(); } // Prepare data for chart var chartLabels = []; var p3Data = []; var p50Data = []; var p97Data = []; var babyWeightData = []; var sexData = growthData[currentSex]; if (sexData) { var agesToChart = [0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52]; // Sample ages for (var i = 0; i < agesToChart.length; i++) { var age = agesToChart[i]; var dataPoint = getGrowthDataForAge(age, currentSex); if (dataPoint) { chartLabels.push(age + "w"); p50Data.push(dataPoint.mean); // Approximate 3rd and 97th percentiles using Z-scores -1.88 and +1.88 var p3 = dataPoint.mean – 1.88 * dataPoint.std_dev; var p97 = dataPoint.mean + 1.88 * dataPoint.std_dev; p3Data.push(Math.max(0, p3)); // Ensure non-negative p97Data.push(p97); if (currentAgeWeeks === age) { babyWeightData.push(parseFloat(document.getElementById("babyWeight").value)); } else { babyWeightData.push(null); // Plot null for other ages } } } } // Create new chart instance chartInstance = new Chart(ctx, { type: 'line', data: { labels: chartLabels, datasets: [ { label: '3rd Percentile', data: p3Data, borderColor: 'rgba(255, 99, 132, 1)', // Red backgroundColor: 'rgba(255, 99, 132, 0.2)', fill: false, tension: 0.1, pointRadius: 0 }, { label: '50th Percentile (Median)', data: p50Data, borderColor: 'rgba(54, 162, 235, 1)', // Blue backgroundColor: 'rgba(54, 162, 235, 0.2)', fill: false, tension: 0.1, pointRadius: 0 }, { label: '97th Percentile', data: p97Data, borderColor: 'rgba(75, 192, 192, 1)', // Green backgroundColor: 'rgba(75, 192, 192, 0.2)', fill: false, tension: 0.1, pointRadius: 0 }, { label: 'Your Baby\'s Weight', data: babyWeightData, borderColor: 'rgba(255, 159, 64, 1)', // Orange backgroundColor: 'rgba(255, 159, 64, 0.5)', fill: false, tension: 0, // Straight line for the single point pointRadius: 6, pointHoverRadius: 8 } ] }, options: { responsive: true, maintainAspectRatio: false, scales: { x: { title: { display: true, text: 'Age (Weeks)' } }, y: { title: { display: true, text: 'Weight (kg)' }, beginAtZero: true } }, plugins: { legend: { position: 'top', }, tooltip: { callbacks: { label: function(context) { var label = context.dataset.label || ''; if (label) { label += ': '; } if (context.parsed.y !== null) { label += context.parsed.y.toFixed(2) + ' kg'; } return label; } } } } } }); } // Initial chart setup (optional, can be empty or show default) // document.addEventListener('DOMContentLoaded', function() { // updateChart(); // Call once on load if you want an initial empty chart // }); // Add Chart.js library – NOTE: In a real production scenario, you'd include this via a CDN script tag in the or manage dependencies. // For this self-contained HTML, we'll simulate its presence. // If running this code, ensure Chart.js is loaded in the environment. // Example CDN: // Since we cannot include external scripts, this code assumes Chart.js is available globally. // If you save this as an HTML file, you MUST add the Chart.js CDN link in the . // For this example, we'll proceed assuming Chart.js is available. // If Chart.js is not available, the `new Chart(…)` call will fail. // Dummy Chart object for simulation if Chart.js is not present if (typeof Chart === 'undefined') { console.warn("Chart.js library not found. Chart functionality will be disabled."); window.Chart = function() { this.destroy = function() { console.log("Dummy destroy called"); }; }; window.Chart.prototype = { destroy: function() { console.log("Dummy destroy called"); } }; }

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