My question throughout these months of study has been around unnecessary distraction from an enjoyable life due to stressing out about things that did not merit the stress. My very first results were around surprising ways I was stressing myself out that on examination disappeared. Most of this work was self reported upsets.
Once I started down the road to finding technology that could help me know when I was upset things became very complicated very fast. There are a large number of devices that claim to measure stress but my experience was that they were not consistently reading stress events. I highlight Heartmath in a lot of my work and it does provide a good baseline for relaxed coherence, but the technology is virtually unusable in all but quiet meditative session. For example, if you turn your head the readings will drop out to zero which is not very useful if you are meeting someone in a coffee shop.
I have spent a lot of time with apps and Bluetooth heart rate belts. I built my knowledge reading academic papers and with off the shelf apps got to the point I have a good set of readings on stress events. The most complicated manual structure I devised was this:
- take a reading with a Polar H7 heart rate sensor,
- capture the output in SweetbeatLife app,
- email the results to myself from the app,
- download that associate file
- import the data in that file to Kubios,
- in Kubios cut the data into 30 second increments which meant three keystrokes repeated up to 120 times,
- generate a pdf report that shows 11 different heart rate variability features,
- enter the data from those features back into a spreadsheet (typically 660 entries),
- manually tag whether I was clear headed or upset in a new column on the spreadsheet,
- save that spreadsheet to CSV format,
- copy a text version of that CSV format into the WEKA arff format,
- use that arff formatted data to run an Artifical Neural Network in WEKA,
- reword that WEKA data back into a prose form that said “this is how much I was upset or not”
I got pretty good results that were consistent with some academic papers I read and I had the satisfaction of learning a lot about HRV, and the process was not sustainable. I want to take readings while shooting shotguns and getting a foot massage, not spend all my time crunching data.
To cut through that complexity and make the measuring and reading of data a small part of the experience I am settling on rMSSD as a single metric on which I am going to focus. Both the SweetBeatLIfe app and HeartRateLogger give a real time rMSSD reading and have that as an exportable feature. I can use Kubios to extract it from all the Heartmath work I have done. And I was pleased to read in an academic paper “SDNN/RMSSD as a Surrogate for LF/HF: A Revised Investigation” in Section 4.1 titled “The Epoch Aspect” that the rMSSD was a stable measurement when used in 10, 20, 30, 60, 120, 300 and 600 second readings. That means there has been some work done to support the fact that an rMSSD on 30 second increments can be reliable as long as I compare it to other 30 second increments. The study also indicates that rMSSD is a good proxy for the frequency based measurements that many of the apps use to indicate stress.
So with my process of data collection vastly simplified I can work on refining and interpreting data given the various contexts I will be operating in. Administrative burden relieved I am off to the foot masseuse.