As a health professional, I have an obvious interest in the relationship between lifestyle habits and their impact on health. As we specialise in the delivery of workplace health programs, this interest extends to the link between lifestyle, health and productivity.
Being 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, the same method was used for the period of my analysis.
- Activity - I also used the FitBit Ultra 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.
- Productivity - This is traditionally difficult to measure however I use a computer tracking system called Rescue Time which monitors what I am doing on my computer and uses a grading system to determine how productive each activity is and then provides a daily efficiency rating out of 100.
I collected all this data over 5 weeks and analysed only work days for obvious reasons. I then analysed the data using SPSS to determine statistical relationships.
So what did I find?
The key findings that were statistically significant:
- 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 that day.
- Correlation between the amount of sleep and productivity. I was more productive at work during the day following a longer sleep.
- An inverse correlation between amount of sleep and sedentary time. So the more sleep I had at night, the less I sat around during the day. This was important for me as while my daily exercise levels are fine, I often sit at work for extended periods.
- 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.
What does it mean?
While this was only a short term case study, 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 8 minutes . However, I appeared to operate best when I obtained around 7.5 hours, with a noticeable decline in productivity when I slept for less than 6 hours and 45 minutes.
This small case study was quick and easy to conduct and also provided me with some individual benchmarks I want to achieve in order to maximise my productivity and reduce sedentary behaviour. I would be interested if anybody has done a similar analysis.
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