The demands and pressures of everyday life sometimes get the best of us. Whether reacting to new assignments, unplanned issues at work, or juggling after school activities for the kids, sometimes it feels these demands accumulate to a point that exceeds our ability to handle them. If we begin to worry at this stage, stress sets in, making it more difficult to regain the focus to move forward in the best way possible.
In every waking hour we are being triggered by demands, in the form of people, events, and circumstances that have the potential to change us. Sometimes the demands accumulate towards a tipping point, other times it takes just one to set the ‘worry’ chain in action. Although we cannot control the influx of these demands in our lives, we can improve how to respond to them.
Over the past three years, I have been tracking the occasional periods of stress in my life. I am learning more and more about how they are triggered and how best to cope with them.
My goal is to completely eliminate these occasional bouts of daily stress in my life. This ongoing n-of-1 experiment will test the effectiveness of my coping mechanisms in achieving this goal.
Check back soon as I continue to update the results of this experiment.
 Work Without Stress: Building a Resilient Mindset (link)
 Triggers: Sparking Positive Change and Making it Last (link)
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. 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.
For example, see these growth charts comparing height and weight for my three daughters to CDC growth standards (see figure 1 and 2.)
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.
And this is quite helpful when providing context in general discussions with our pediatrician.
 My self-tracking projects are powered by ostlog – an open-source Personal Well-being Library
A few years ago I started tracking key aspects of my family’s day-to-day well-being. 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.
The variables I collect represent the individual members of my family. For example, how many workouts did I do to the past month, or what is the average sleep duration for my daughter during Winter months. When reporting through dashboards and indicators, the variables are aggregated in time and space but the unit of reporting remains the individual.
Recently I began experimenting with household-level reporting — the family as a single unit of reporting. For example, how many times did family attend Sunday Mass in the current year, or how many sick days did we experience last winter.
Aggregating by family this way made me realize a few things. First, there is a whole world of household well-being indicators waiting to be explored. Here I am not only referring to household well-being as an aggregation of the individual level. Rather, well-being indicators that only make sense at the household level. Second, reporting on well-being this way raises interesting new questions regarding the role of family in helping improve the well-being of its members.
It’s been a while since my last post. Here are some new things I’ve been doing and reading during this time. There’s a lot to talk about!
On the data collection and analysis side of things, I started tracking new variables including mood, energy levels, sleep/meal duration and quality, continuous-learning activities, ongoing projects, and Church attendance. This data joins twenty-three other aspects of my family’s well-being that I’ve been tracking since 2013. Together the data is giving me a holistic view of our growth, health and happiness across the six dimensions of well-being I care about — Spiritual, Social, Intellectual, Physical, Financial and Environment.
For example, I now have indicators tracking my family’s sleep duration goals over the course of the year (see figure 1.)
A separate indicator allows me to track time we spend at the dinner table. The indicator was inspired by OECD and their chart showing the time spent eating & drinking each day across countries (see figure 2.) It also supports my belief in quality time together at the dinner table.
The indicator tracks the percentage of dinners lasting at least 40 minutes, with a goal of at least 80% each year.
These are just a few examples. I am tracking fifteen more indicators and dozens of time-series charts across the six dimensions of well-being I mentioned earlier. I am also working on a new experiment in the area of stress, anxiety and resilience. I hope to cover this and other aspects of family-tracking in the coming months – stay tuned!
Regarding general news and trends in the area of personal health tracking, digital well-being, personal informatics or, more generally the quantified self, it definitely feels that we are in the midst of a bubble of great promise and hype. Three general observations I have during this period are:
The digital divide is real. The market for digital well-being / personal health tracking will continue to be constrained by inadequate data skills in citizen scientists.
Collection fatigue is also real. This applies to the commercial wearables industry. In my opinion, these companies are only stressing out their customers; weighing them down with low-value data collection tasks. Without a clear path to transform raw data into truly useful and actionable insights, customers will continue to be underwhelmed and dissapointed.
This creates a big opportunity to provide users withactionable well-being insights without overburdening them with data collection, or the need to acquire sophisticated data science skills to make sense of the data.
 My self-tracking projects are powered by ostlog – an open-source Personal Well-being Library.
A few months ago I wrote about the possibilities of integrating my family’s self-tracking data with smart home assistants, like the Amazon Alexa powered Echo Dot. One of my near-term self-tracking goals is to give members of my family the possibility of also deriving value from this data, and the Echo Dot represents one way to achieve this.
First, a brief overview for those of you new to this blog. A few years ago I started tracking key aspects of my family’s day-to-day well-being including symptoms, medicines taken, doctor’s visits, activity, vitals, finances and more. I use this data to conduct casual exploration and N-of-1 experimentation to address issues and opportunities affecting my family’s well-being—a process I refer to as Family Data Science.
My self-tracking projects are powered by ostlog – an open-source Personal Well-being Library. ostlog works great for my needs, but not so much for my wife, who prefers simpler access to the information. Today I rely on Microsoft PowerBI, generic SQL tools and other software for collecting and managing this data. The Amazon Echo Dot opens the possibility of providing natural language interface to query the same data.
We own a single Echo Dot that sits on our piano in the living room. The kids use it to ask Alexa to play their favorite songs from their favorite films (e.g. “Alexa, play Trolls on Spotify”.). My wife uses it to stream her favorite radio station from Argentina (i.e. “Alexa, play radio maria”.) As for me, the first step was to create a custom Alexa skill that responds to requests for well-being insights, queried directly from my self-tracking database.
Thanks to Amazon and Microsoft’s cloud serverless services (i.e. Lambda, Azure Functions), accomplishing this turned out to be a piece of cake. This was the first Alexa request implemented:
Alexa, start ostlog
Alexa, how much do we need to pay the baby sitter this week?
With this new Alexa skill, my wife now has a hands-free way to access the self-tracking data, no special software required. And with this personal finances related request, we no longer have to fumble through devices and software applications in order to retrieve this data (while the sitter waits patiently at the end of a long day and week!)
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.