Occasional articles about what's coming in the next 12-24 months in health care IT.
Friday, May 13, 2016
Patients as Data Scientists: A Study about Blood Pressure
Sensors, health and wellness apps, and data analysis tools are now maturing to the point where clinicians, researchers, and even healthcare consumers can analyze and correlate interesting measurements. As I described in last week’s post I recently used some data science techniques to analyze factors that impact my blood pressure (BP).
I am not a clinician. I’m looking for patterns in data without much information about the underlying physiology.
I’m not a statistician, either. I’m not trying to prove that any of the observed results are statistically significant.
I’m not completely comfortable with publicly sharing my health information. I have removed specific values from the charts and graphs, so that I’m not publishing my actual health data, just the general trends.
Here are some of the results.
Overall average blood pressure
I’ll start with a snapshot of my average blood pressure readings. The following graph represents the average systolic blood pressure measured in 333 readings from December 25, 2015 through April 15, 2016.
Reading the graph
The horizontal axis represents systolic blood pressure, with high values on the right and low values on the left. The vertical axis represents how frequently each measurement appears in the data. The more frequently a specific reading is observed, the higher it will peak. A low point on the curve represents a relatively uncommon reading.
In this graph, there are separate lines for readings before 9:00 a.m. (red), after 6:00 p.m. (blue), and between 9:00 a.m. - 6:00 p.m. (green). The morning measurements more commonly have lower readings. Midday readings are generally higher (further to the right) and more variable (lower peaks). Evening readings are, on average, lower than midday but not as low as the morning.
The Withings blood pressure cuff records systolic, diastolic and heart rate data. I’m simplifying it by showing just one reading.
The BP cuff captures the date and time of each reading. By converting that into a day of the week in Excel (and cross-referencing my calendar), I can tell which days were workdays and which were weekends, holidays or vacation days. It’s no big surprise that being at work shifts my blood pressure slightly higher (3.5 points on average).
The benefits of cardiovascular exercise are apparent from a comparison of days that included running at least 5 km: 4.5 points lower on average.
Compared to cardio, it appears that resistance training offered no particular benefit. Days that included weight lifting had a mean and median BP reading that was nearly identical to days without.
The stress of traveling seems to increase BP by a couple of points.
Cardio: -4.5 BP systolic points vs. not running
Beet juice: -2 points vs. non beet juice days
Resistance training: no effect
Workday: +3.5 BP points vs. non-workday
Travel: +4.5 BP points vs. not traveling
These findings are consistent with expectations, but the observed effects are quite small and might be smaller than the margin of error. Even so, as a patient I find it interesting to see these correlations, and it reinforces the need for exercise. From my doctor’s point of view, the daily BP readings provide more value from the analysis, because they show a more complete picture than the measurements he takes in his office once a year.
Another result of this exercise is that I was able to demonstrate it’s possible to collect, combine and analyze data using low-cost equipment and tools. In the next installment, I will dive into the details of how I carried out the analysis and discuss the tools and techniques I used. I’ll demonstrate how it’s now possible to conduct this type of study without a science lab.
There is still a long way to go to make this a simple, automatic process.