I’m a graduate student studying and researching psychology so any time I want to understand something better I turn to data. As long as you collect it honestly and in a methodologically sound manner, data don’t lie. Good science is built on good data and one of the most important experiments I’m involved with isn’t funded by any grants and doesn’t have a team of scientists working on it — it’s the ongoing study of the way I work and live.
Every year I try to take a look at the data that best describes my work habits over the past 12 months to better understand whether I’m doing what I’ve set out to do. I’m trying to find inefficiencies, misguided attention, and other gaps so I can make sure I’m doing my best work as much as possible. I happen to work for myself while going to school full-time, but regardless of the details of your work situation you probably want to be operating at peak capacity as much as possible. Conducting an End of Year Review is a great way to recalibrate as you move into the new year.
The first step of any End of Year Review is deciding what questions you want to answer. This is partially dependent on the data you have available but some possible examples include:
- How am I using my time?
- What do my actions say about my priorities?
- Have I left important but non-urgent projects by the wayside?
- How much time do I spend doing email?
- What have I done in the past year that I want to make sure I never/always do again?
The answers to these (and I’m sure countless other) questions can provide very benficial information for how you’ll try to conduct yourself in 2013. The next step is to look at the data that will help you answer these questions accurately. While many people do End of Year Reviews that are nothing more than pure mental reflection on the past 365 days, I’m always skeptical of my ability to remember things accurately. One thing being a psychology student has taught me is to be intensely skeptical of my memory. We aren’t nearly as good at remembering things as we like to think. Go with the hard data whenever possible.
Sources of Data
Obviously, RescueTime is a great source of data if you’re interested in knowing how you spent time at your computer. This is the first place I start with any sort of review on my work habits. There’s nothing quite like the shock of seeing you spent over 24 hours on time wasting activities over the course of several weeks to serve as a serious wakeup call.
Other than RescueTime, other great sources of data include; your calendar, daily journal or log, digital pictures, financial information, saved text messages, archived information from task management software, personal writing of any kind, etc. All of these sources help you see where you spent time, attention, and energy.
As you look through your calendar you may remember the awesome conference you went to last February which reminds you to follow up with that promising business lead. Looking through a year of photos will make you realize you’ve accidentally distanced yourself from some people important in your life (work, personal, or both). You may look at a year’s worth of saved work files and realize the big project you told yourself you’d work on last year is still sitting forlornly in the “unfinished” file.
Using The Data
Once you have all this data and have done any analyzing you need to do to draw some conclusions (more time on work that matters, less time on Facebook, call Steve, more writing in the morning, less computer on the weekend, etc.) how do you move forward?
First, let me point out that a potentially great first step is to decide to spend a little bit more time and effort recording more data on yourself in 2013. The better the data you have, the more you can learn about what does or doesn’t work for you.
Assuming you’re happy with the data you collect, my favorite way to make changes is to focus on one major change for 30 days. For example, when I did my most recent End of Year Review I realized I was spending way too much time on mind-numbing websites. I decided that I’d severely limit the amount of time I allow myself to mindlessly surf for 30 days. At the end of that period I’ll re-assess how the past 30 days went and whether I want to a.) continue with the experiment, b.) modify the experiment, or c.) go back to the way I was before.
Obviously, you can make changes in your life without collecting data on yourself first. You could also “do science” without collecting data — but nobody would take you very seriously. Why not apply the same standards that ensure good science to the way you make changes in your own life?
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.
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.