Workplace experiment: How does your sleep affect your productivity?

Editor’s Note: This is a guest post by Ben Wisbey, Managing Director of FitSense Australia. I came across his recent post about the relationship between his sleep and productivity, and thought it was really great example of how to do a meaningful personal analytics experiment. You can follow Ben on Twitter at @benwisbey.

As a health professional, I have an obvious interest in the relationship between lifestyle habits and their impact on health. As I specialise in the delivery of workplace health programs, this interest extends to the link between lifestyle, health and work productivity.

As I am someone who likes to track and monitor everything, I decided to use some of the data I capture on myself to conduct a basic case study. The goal was to see if there was any relationship between my sleep habits, activity throughout the day and work productivity.

Methods

Sleep - I used the FitBit Ultra to track my nightly sleep. It does this by measuring movement. While not completely accurate, it provides a good indication and as the same method was used for the period of my analysis it offered standardisation.

Activity - I also used the FitBit to monitor my steps and activity throughout the day. However, this analysis focussed on my activity during work hours and did not include my morning run. The reason being is that my run is daily and I was more interested in the incidental activity I undertake during the day.

Productivity – Being an office based worker, productivity is traditionally difficult to measure. However I have used RescueTime for quite some time to monitor what I am doing on my computer and rating my level of productivity. I used the daily efficiency rating in RescueTime as well as looking at both morning and afternoon efficiency independently.

I collected all this data over a two month period and analysed only work. I then analysed the data using SPSS to determine statistical relationships.

So what did I find?

The key findings that were statistically significant (at a 0.05 level) included:

An inverse correlation between how many times I awoke during the night and my productivity. This means that the more interrupted my sleep was, the less productive I was during the day. This relationship was also true for the number of hours worked, so the more times I woke during the night, the less hours of work during the day.

A correlation existed between the amount of sleep and productivity. I was more productive at work following a longer sleep.

Not surprisingly there was also a link between the length of my work days and my productivity. So the longer I worked, the less efficient I was. This is of concern for those longer work days (9 hours + in my case).

The relationship between sleep and morning and afternoon productivity was similar. This indicates that poor sleep impacted my whole day, not just the afternoon when the tiredness may have been exaggerated.

My productivity in the morning and afternoon was closely related. This indicates that you generally have good or bad days, as opposed to just an unproductive afternoon.

Interestingly, there was no strong relationships between daily activity and productivity. However, I am probably not the best subject in this case as I have minimal variation in my daily activity levels.

What does it mean?

While this was only a short term case study with one subject, it did highlight the importance of health and lifestyle factors on your work productivity. It is also worth noting that I am of good general health and fitness, so the impacts on productivity would likely be more dramatic for people of poorer health.

It was interesting that the number of times I woke up during the night had a greater impact on my productivity than the amount of sleep I had. However, this is to be expected given that the benefit of sleep is largely associated with those deeper sleep stages, and regular interruptions limit your ability to spend time in these stages, regardless of your sleep volume.

My average time spent sleeping each night was 7 hours and 14 minutes. However, I appeared to be most productive when I obtained around 7.5 hours, with a noticeable decline when I slept for less than 6 hours and 45 minutes. My least productive days were associated with only 6.5 hours of sleep.

Thanks to RescueTime and the FitBit, this small case study was quick and easy to conduct. It provided me with some individual benchmarks I want to achieve in order to maximise my productivity by focussing on good quality sleep.

Would you like to start trying your own workplace experiments? Sign up for a RescueTime account today.