Matthew and I started measuring because we didn’t know what else to do.
We didn’t know we were measuring his recovery, because his health hadn’t started to improve yet.
Research and evaluation are part of my trade, and Matthew has a background in Continuous Quality Improvement, so when we’re in doubt: we measure!
And we were in a lot of doubt.
Why Measure Health?
Measurement can seem superfluous when you are really close to your experience.
Especially if the numbers don’t change from day to day.
Or if they swing around wildly for no obvious reason.
But if you stick with it, measurement can be incredibly useful as a way to discern trends and track progress over time. In fact, measurement can be a way to begin the process of recovery.
In December 2013, we started measuring two things: Matthew’s autoimmune symptoms and his mysterious nausea.
That was also the month we committed to the Autoimmune Protocol (AIP).
And the month Matthew went on disability leave from work.
It was a crisis point.
At that time Matthew was taking hydromorphone painkillers daily for joint pain from psoriatic arthritis and had been experiencing debilitating nausea for two months.
Our life was in chaos, and measuring Matthew’s symptoms seemed to be one of the only things we could do to try to increase the knowability of our situation.
We started tracking his joint pain and psoriasis, which we bundled together under the heading ‘autoimmune symptoms’. We tracked the nausea separately.
He assessed his condition and assigned a number between 1-10 for both of those problematic areas.
At first he measured both daily, but that didn’t prove to be useful, because the numbers didn’t change from day to day. So he switched to measuring weekly, then monthly, and as his autoimmune symptoms started to improve on the AIP, quarterly.
Here is the result.
Each point is the number he gave to his symptoms at 3-month intervals during the 30 months from December 2013 to March 2016:
This graph shows:
- An overall decline in autoimmune symptoms (psoriasis and joint pain) from a 10 in December 2013 to 2.5 in September 2014. Wow, right?!
- A seasonal low in autoimmune symptoms in September of each year, with a moderate increase in symptoms beginning in late fall and continuing until the following summer; and
- A more erratic pattern for the nausea.
But it doesn’t tell the whole story.
For example, Matthew was well on his way to having the lowest-yet measure of nausea and autoimmune symptoms on March 1 of 2016, after a 3-month yeast busting protocol last fall, but an extremely stressful event in the second half of February this year caused a massive flare and elevated both of those numbers.
So obviously the graph doesn’t describe context.
Context (the back story)
You can never tell the whole story with numbers.
Because you can’t measure everything. Some things resist quantification.
And even if you could measure everything, no one has time!
So, we need to decide what to measure.
In retrospect, I wish we’d measured two additional things.
Additional Thing One: medication
The graph above doesn’t explain that in the fall of 2013, before Matthew started the AIP, he was taking Methotrexate by injection weekly, as well as 6-8 hydromorphone painkillers a day to manage his psoriatic arthritis.
He hasn’t taken Methotrexate since December 2013. And he now takes only a few over-the-counter Tylenol Arthritis a week.
It would be exciting to have that data in our graph for comparison.
I’ve reconstructed the reduction in his painkiller use, based on my memory and his prescription records:
Additional Thing Two: Brain Fog
Likewise, I wish we had tracked the alleviation in his brain fog during the past 30 months.
Because that’s been dramatic.
I reconstructed that, from memory, as well:
Now we can start to see a definite pattern of improvement.
The final step of personalized (n=1) experimentation is reporting.
So we can learn from each other.
In addition to reporting the results of Matthew’s recovery, in this post I’m also reporting on the value of measurement for his healing process.
Why Measure Recovery?
Tracking this data in the past 30 months has:
- Enabled us to discern trends that wouldn’t have been obvious otherwise (that’s the analysis stage of the n=1 process);
- Provoked us to ask new questions;
- Provided direction for further research, which enabled us to generate new hypotheses and conduct fresh n=1 experiments; and
- Helped us tell the story of his recovery, to ourselves and to his health care team.
Measurement has both contributed to Matthew’s recovery and has helped us to understand it.
That’s a powerful impact for a simple practice!
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