In an earlier post, I talked about devices, wearables, and the Internet of Things as inevitable developments in connected healthcare. Today, Apple’s Health app typifies the state of the art for consumer devices data collection. It aggregates and displays data collected from various sensors and apps, but simple chronological displays of readings are rarely helpful when studying causes and effects. And furthermore, not every kind of data can be aggregated within the app.
Can consumer-grade devices be used to learn about health and wellness causes and effects? I decided to run an experiment to find out. In this entry, I’ll explore the purpose of the experiment and procedure I used. Next week, I will reveal the results and conclusions, followed by a review of the tools and techniques.
A Blood Pressure (BP) reading is one of the fundamental measures of a patient’s health, yet it varies constantly in response to numerous factors – including the subject’s presence in a clinical setting, which may artificially increase BP readings.
This is extremely inconvenient because the doctor’s office is one place where an accurate BP reading is essential for gauging the patient’s health. If a patient’s only BP readings are gathered at his annual physical exam, the data will be too sparse and too skewed to be useful.
That’s why I decided to invest in a home BP monitor and measure my blood pressure three times a day. The Withings BP monitor automatically measures blood pressure and transmits the result via Bluetooth to an app running on an iOS device. My experiment was to collect about three months’ worth of readings and combine them with data I was already collecting for other purposes.
Here are the data streams I was able to aggregate:
- Travel dates and destinations. This is manually tracked in an Excel spreadsheet.
- Cardio and resistance exercises, which are tracked in three places: automatically by an app called MapMyRun, in the Apple Health app, and on paper. The Apple Health app also tracks my daily steps.
- Alcoholic beverage intake, which is recorded for reasons that are too complex to explore in this blog post. This is tracked in an Apple Numbers spreadsheet on my phone.
Plus, there are several other data points that are available “automatically” as a result of the data collection process:
- Time of day
- Day of week
- Workday vs. non-workday
Finally, I read a report that beet juice can lower blood pressure, so I decided to manually track beet juice intake to see if it actually had any effect.
For the most part I didn’t make any attempt to control the variables; I simply went about my normal activities (with a little extra record keeping). This was not a carefully controlled experiment. It was an analysis of real world data.
My plan was to aggregate the data in Excel, and my hope was that most of the different data sources could be easily imported into a single spreadsheet. Since some of the data was already in Excel, those sources were no problem. The Withings app made it fairly straightforward to export BP data.
MapMyRun, however, only allows data exports if you’ve paid for the Pro version. I tried working around this by exporting “walking + running” data from the Apple Health app, but the output from Apple Health was in an inconvenient XML format that I didn’t care to parse. In the end I hand-entered the fitness data into the spreadsheet, which was a drag.
Bottom line: before embarking on an experiment like this, know your data sources and how to access them. It’s worth doing a practice run on a small set of data, just to make sure everything is going to come together.
In the next installment, I’ll cover the tools I used to analyze the data and the findings.
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