A Week of Jet Lag

This week I was traveling to the USA on a personal vacation and no one can imagine the amount of Data available for collecting but at the same time I was trying to get some quality time visiting my son at his University. To me this trip was a 12-hour timezone difference and jet lag was creeping especially at night when my body is used to be fully awake.

I started logging my wake up hours and estimated my sleeping time to a proximity of +/-10 minutes. The data gathered was simple time of sleep, time of waking and the depth of the sleep. The thicker the line the deeper the sleep or the more alert my waking. The data was collected Saturday to Friday from 7pm to 7am.

The Legend

Here is my sleeping / waking-up data visualisation chart during a week of jet lag.

After looking at the chart, I was trying to make sense of it or finding a pattern but it was really hard. I was not getting any day time sleeping as we were out and about. By 7pm, I started the sleep struggle cycle. Tried to push my sleeping time at different time blogs like the second day still I was not able to sleep through the night. My deep sleep average time is about 2-3 hours only and not always in a continuous stretch.

Going to the science, a “typical sleep cycle” includes five stages of sleep with stages 1-2 as light sleep, 3-4 as deep sleep, and the fifth stage as REM (rapid eye movement) sleep according to sleepcycle.com.


Comparing my chart to the typical sleep cycle that I might have had the same deep sleep number of hours but I have not been able to maintain the cyclic approach especially when I get wide awake in between the sleep periods which made it harder for me to get back to sleep and continue the cycle.

Most of my hours are either at light sleeping or not fully awake which is a sign of being in a jet lag and also getting distracted with phone / messages at home country time zones.

Of course, there are many environmental factors affecting sleep like the bed, pillows, noise level and temperature which I have experienced during my hotel stay. Also, if I compare my non jet lag sleeping patterns; I’m usually a light sleeper, waking up at night more frequently and also age affects sleeping patterns especially being a women in 40ies. According to Psychology Today: “about 31 percent of women say they have trouble staying asleep at least four nights a week and wake in the morning feeling tired, rather than rested”.

How can I take this to a governing with data angle? I just finished reading Anagnostopoulos & Jacobsen 2013 reading and I would like to reflect the sentence: “Rather than empowering the people, the data may constrain what people know and how they think about their schools” in reference to how information that test-based infrastructures make available can only provide partial views of schools, teachers, and students. The challenge is that by setting an ideal performance / testing standards that could be used for building educational policies / regulations, we are looking at numerical ratings that are only saying: “little about the causes or failures” and how can measure qualitative teaching and learning data like: “creativity, and commitment, social capabilities” and etc. We keep going back to “one size fits all” – one standard sleeping cycle – governing by data approach. The question how can we design and construct information infrastructures that measures contextual learning and teaching data to provide true value and influence inclusive policy development.


  1. Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. 2013. Introduction: Mapping the Information Infrastructure of Accountability. In, Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds.) The Infrastructure of Accountability: Data use and the transformation of American education.
  2. The sleeping cycle website and app – https://www.sleepcycle.com/how-sleep-cycle-works/
  3. Psychology Today website – https://www.psychologytoday.com/us/blog/sleep-newzzz/201910/what-your-sleep-is-in-your-40s-and-50s

2 thoughts on “A Week of Jet Lag

  1. I wonder if there is anything to say about ‘sleepy data’, that is, data that aren’t collected, or are only partially collected, because either the data collection system is ‘asleep’ or only partially ‘awake’? We are often led to believe that data are collected continuously, but the Anagnostopoulos et al reading is primarily about test data that are collected at temporal intervals. In that sense, the data collection process is asleep most of the time, only woken for critical moments of activity. Maybe there is also something a bit ‘dreamy’ about data? It’s a lot of material in disorganized form that has to be (psycho-)analysed for patterns, sense-making and meaning.

    • Interesting perspective about sleepy and what are the impact of the times of no data collection on the data to be collected after that dormant period.

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