28 December 2010 3 Comments

100 Days of Sleep – Part 2: Simplifying the Data

Yesterday, I posted averages for my sleep phases, namely Time to Z, Total Z, Time in Light, Time in REM, Time in Deep, Time in Wake, Awakenings, and the ZQ score. Next I started looking into the data provided by my sleep journal.

The MyZeo web application lets you track a number of things in your sleep journal, the most relevant to this analysis are the Morning and Day Feel scores. These are qualitative scores from 1-5. The Morning Feel is an indicator of how well you feel you slept (Poorly to Very Well), while the 3 Day Feel indicators track how you feld during the day in regard to: 1) Irritable vs Easygoing, 2) Unfocused vs Focused, and 3) Tired vs Energetic.

Since there were so many different factors to consider in both the sleep phase data and the sleep journal information, I performed a principal components analysis (PCA) to see I could reduce the number of variables that could represent the data to a few key elements.

Again, I used JMP v.8 to do the PCA. Without going into the details of interpreting the results, the graph below shows the following:

ZQ, Total Z, Time in Light, and Time in REM are all correlated [blue arrow]. In other words, I don’t need to look at all 4 variables to understand my sleep. This suggests that I could use the single variable “Total Z” and keep most of the prediction power, with the added advantage that it is easy to measure and understand.

Another possibility for simplification is around the “Day Feel” indicators [purple arrow], which all track together, suggesting that, e.g., if I’m going to be tired, I’m also going to be unfocused and irritable. (Note: Anecdotal evidence from my family suggests the data may be accurate!)

It’s also worth noting the analysis indicates “Time in Wake” correlate to “Awakenings,” which seems to make perfect sense. In addition, “Time to Z” (how long it took me to fall asleep) is not at all correlated to the other variables, and has a very weak effect, as noted by the short length of the arrow.

Summarizing today’s insights:

  • The data suggests that the number of variables used to represent my night’s sleep can be simplified to a few key elements.
  • The same is true for the output of my sleep, in other words, how I feed the following day.

Tomorrow, I’ll explore whether it’s possible to build a predictive model that can tell me exactly what type of night’s sleep I’d need to maximize how I feel the next day.

3 Responses to “100 Days of Sleep – Part 2: Simplifying the Data”

  1. Derek@Zeo 28 December 2010 at 2:10 pm #

    Hi Walter –

    I’m Derek at Zeo, and am really enjoying this analysis. Looking forward to the next posts and seeing how you avoid tired, unfocused & irritable.

    On your graph, I see deep sleep correlating lightly with your day feel too. Interesting. Maybe a sign of things to come?

    One other thought. I’ve been tracking my energy levels throughout the day and have noticed how much they vary within a given day. Were you able to record your Day Feel data at a consistent time?

    Great stuff!

  2. Walter Reade 28 December 2010 at 2:30 pm #

    Spot on about deep sleep and day feel. This will be shown in detail in tomorrow’s post.

    I’ve thought about recording a number of variables through the day, e.g., day feel, caffeine intake, etc. I’ll explain what I’m after with that in my 4th post on Thursday. But to your question, I typically assign the day feel to how I felt mid-to-late afternoon. Interestingly, I don’t have a lot of variation through the day. It’s typically mostly good or mostly bad.

  3. Ben Rubin 28 December 2010 at 2:46 pm #

    Fascinating analysis. I don’t see a vector for Time in Deep – would be interested to see that in the analysis as well…

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