DIY Growth Charts

Today I’ll discuss do-it-yourself charts I use to track growth for my daughters.

First, a brief summary for new visitors. A few years ago I started tracking key aspects of my family’s day-to-day well-being.[1] You can browse the full list of variables I am tracking here.  I use this data to conduct casual exploration and N-of-1 experimentation to address issues and opportunities affecting my family’s growth, health and happiness.

I use Microsoft’s excellent PowerBI data visualization tool to mine through the data and create visualizations that make sense to me and my family.

For example, see these growth charts comparing height and weight for my three daughters to CDC growth standards (see figure 1 and 2.)

figure 1: comparing daughter’s weight to CDC Growth Chart averages

Visual Design

I’ve added a few elements to make these charts easier to understand. First, I needed to plot the CDC Growth Chart averages as a reference point. These datasets contain the percentile averages for height, weight and other growth variables, for each month of growth, across a diverse sample of the population. In his data visualization book, Now You See It, Stephen Few talks about the role of pre-attentive attributes in preparing the user’s focus during the visualization. For these growth charts, I used color and shape (dotted lines) to highlight the range of percentile groupings and gently ‘push’ them to the background. In plotting the actual values for my daughters’ height and weight, I emphasized this line (solid red), allowing it to ‘call’ for our attention while also comparing values to the CDC averages. PowerBI supports the features to configure pre-attentive attributes this way. In addition, it allows me to easily create, publish and access these charts from the web, on my mobile phone and in a secure way.

figure 2: comparing daughters height to CDC Growth Chart averages

And this is quite helpful when providing context in general discussions with our pediatrician.

[1] My self-tracking projects are powered by ostlog – an open-source Personal Well-being Library

Imagining the Possibilities

Imagine starting your morning with your smart home assistant gently informing you to take it a little easier over the coming days. It suggests this because it detected an emerging acute cough and reminds you that during this same period of seasonal change over the past five years, you have tended towards multiple days with coughing and or bronchitis. Imagine how in the busyness of everyday, this small nugget of timely information helps you adjust and avert a more serious bronchitis, for example.

Looking ahead a few years, imagine smart sensors spread throughout the home, maybe embedded in the walls. These sensors casually record observations regarding your family’s growth, health and happpiness. They observe coughs and colds, stress or excitement, and other aspects you control. You do this because the data collected by these sensors feeds analytical processes to deliver highly personalized and timely well-being insights.

Recent advances in cloud computing, machine learning and the emerging discipline of Data Science are enabling these unprecedented opportunities, and allowing us to rethink how we nurture, encourage and care for the members of our family.