Blog / Category

Health & Pediatrics

Pediatric growth charts, LMS Box-Cox methodology, CDC and WHO standards, and integration patterns for digital health applications covering children and adolescents.

19 articles  ·  ← All categories

healthhipaa

How to Build a HIPAA-Compliant Patient Body Assessment Feature

Add body dimension predictions to a healthcare application without HIPAA liability — stateless API architecture, no PHI transmission, and no BAA required.

body-compositionhealth

Estimating Body Fat Percentage from Circumference Measurements

Validated regression formulas for body fat estimation from circumference measurements — how they work, their limits, and a Python implementation.

healthtechhipaa

How to Build a HIPAA-Ready Fitness Onboarding Flow Without Storing a Photo

HIPAA-ready body measurement collection for fitness apps — stateless API design, onboarding patterns, and what 'HIPAA-ready by architecture' means in practice.

wearablesapi

Integrating Wearable Health Data with Body Measurement APIs

Integrating Apple Health, Google Fit, Garmin, and Oura data with body dimension prediction APIs — normalization, data freshness, and architecture patterns.

digital-twinarchitecture

Body Digital Twins: Managing Measurement Profiles Over Time

Managing a user's body digital twin lifecycle — when to update measurement profiles, how body dimensions change over time, and handling profile staleness.

healthtechpediatrics

Pediatric Body Measurements in Digital Health: What CDC/WHO Standards Mean for Developers

Pediatric body prediction for digital health apps — LMS Box-Cox growth charts, CDC vs. WHO standards, and integration patterns for children's health apps.

anthropometrystandards

Waist Circumference Standards: Why Measurement Landmark Matters for API Consistency

Waist circumference measurement standards — why landmark choice matters, what WHO, NIH, and ISO say, and how to handle it correctly as an API input or output.

pediatricsgrowth

Predicting Adult Height: Methods for Pediatric Health Applications

Bayley-Pinneau, Khamis-Roche, mid-parental height — three adult height prediction methods explained for developers building pediatric health applications.

comparisonsizing

Photo-Based vs. Statistical Body Measurement: Which Approach Is Right for Your App?

Photo-based body measurement vs. statistical prediction from height and weight — accuracy, privacy, user friction, and which use cases each approach handles.

pediatricsgrowth

The Measurement Switch at Age 2: Handling the Length-to-Height Transition in Pediatric Apps

The recumbent length to standing height transition at age 2 introduces a 0.7-1.0 cm discrepancy that WHO/CDC growth chart software must handle. Here's how.

architectureprivacy

Stateless vs. Stateful Biometric Architecture: The Decision That Defines Your Stack

Stateless vs. stateful biometric architecture — compliance, security, and product implications that compound over time, and how to decide before you build.

anthropometrybody-science

How the Human Body Changes With Age: An Anthropometric Timeline

How body dimensions change from infancy through aging — documented anthropometric patterns over a lifetime and their implications for product design.

fitnesswearables

How to Add Body Proportions to a Fitness App Onboarding Flow

Enrich fitness app onboarding with body proportions — stride length, reach, shoulder width — from height and weight alone, with no extra questions asked.

bmianthropometry

What Is BMI Actually Measuring — and Where It Falls Short

BMI is widely used in medicine — and widely misunderstood. What it actually measures, where it fails, and what the scientific literature says about its limits.

ergonomicsworkplace

Building an Ergonomic Workstation Calculator from Body Measurement Data

Ergonomic workstation recommendations from body dimensions — desk height, monitor, chair settings, ISO standards, and a complete Python implementation.

pediatricsstatistics

LMS Box-Cox Growth Charts Explained: The Math Behind Pediatric Anthropometry

The LMS Box-Cox method explained — how it turns skewed pediatric growth data into z-scores and percentiles behind CDC and WHO growth charts.

statisticspercentile

Implementing Body Measurement Percentiles: Where Does a User Rank in Their Population?

Computing population percentiles for body dimensions — the normal distribution assumption, scipy.stats implementation, and caveats about population specificity.

pediatricsgdpr

How to Build a Children's Sizing Feature Without Storing Children's Data

Children's size calculator using the pediatric API — age, height, weight to clothing size — with stateless architecture for GDPR and COPPA compliance.

sizingpediatric

Building a Children's Size Calculator: Age, Height, and Growth Accommodation

Age-based children's sizing — the age_category API parameter, growth accommodation strategies, and a complete implementation from infant through pre-teen.