I love the word balance. It implies that you have enough of everything. You’re not wanting for anything, or drowning in anything. When we talk about work/life balance, it means you’re getting enough work done, but you’re also spending enough time resting, relaxing, and attending to your family, hobbies, and interests outside work.
For those of us whose work tends to blend into our lives it’s even more important to find this balance. For my co-founder Josh and I, we find ourselves working in some form or another every single day. Which means if we’re not working we tend to feel a nagging sensation that we should be, because it’s become our default state.
Not to mention the ever-growing mountain of side projects and volunteer activities we want to take on, and new skills we want to learn.
I’ve always been keen to fill up every day with learning and practising new skills, but I’ve never been great at making sure I get enough exercise. Maybe you have a particular area of work or life that gets neglected. My ongoing imbalance was the impetus for me to start tracking my activity and other areas of my life.
I started out with a simple activity tracker on my phone, and graduated to wearing a Fitbit all day, every day. I use apps like RescueTime to track what I do each day, and put as much of this data into Exist as I can.
Exist is designed to help you find meaning in the data you track. There are three big reasons it’s helpful for finding that balance between work and “life” activities: it uncovers hidden correlations and trends, it has built-in mood tracking, and it creates personalised goals based on your data.
Tracking data about my own activities causes me to ask myself questions like “Am I improving?” and “Have I been doing x more or less this month?”. Exist helps me answer these questions by surfacing insights into my data. For example, I recently had this insight on my dashboard:
Walking less this week
8,545 average steps, 1% decrease
Walking less isn’t something I want to make a habit of, but thankfully I only dropped by 1% in the past week. And knowing that my overall average steps is around 8,000 per day, I’m pretty happy with that average from last week.
I also noticed these sleep-related insights recently:
For some people, going to bed later and getting less sleep would be a bad thing but those sleep numbers are pretty good for me. I have a tendency to oversleep some days, just because I don’t have a set time I have to start work, and it tends to set my day up badly. Knowing this, I’m putting in a conscious effort to not stay in bed too long in the mornings, and these insights show that it’s working.
Seeing what my average is for each type of data can be illuminating, too. Exist breaks down averages by day of the week, as well as showing my overall average for each data point.
(Note: I used a Jawbone UP between my Fitbit Force breaking and the Fitbit Charge being released, which doesn’t track floors. I haven’t been wearing my Fitbit Charge long enough to increase my floors average yet)
It’s good to see, for instance, that my average mood is 4/5. It’s also pretty obvious, looking at this chart, that I tend to rate my mood higher on weekends.
I can also see that I tend to walk more on Fridays, and that my average steps is just over 8,000 per day.
My productivity tends to dip on weekends, and jumps up most on Tuesdays and Wednesdays. This makes sense, since Monday is our catch up day at Hello Code, so Tuesday is when I start to really get stuck into my work for the week.
I like knowing these averages, because it helps me calibrate my own goals. If you’ve ever used a fitness tracker or a pedometer app on your phone, you’ve probably been confronted with a suggested (or enforced) 10,000 steps per day goal. Although this might be suggested as a healthy amount of exercise for adults, it’s ridiculous to expect someone who walks 3,000 steps per day on average to suddenly jump up to 10,000.
8,000 steps per day has been my average for the past six months or so. I know this is the amount of exercise I get without trying too hard, so if I want to increase my activity levels I’ll know to start by aiming for around 8,500 steps.
Seeing the correlations between different data points is one of the most surprising and useful parts of Exist. Although correlation doesn’t imply causation (i.e. just because two things are related doesn’t mean one causes the other), correlations can still give us useful clues into our existing behaviour and how different things affect us.
I’m especially interested in what affects my productivity (tracked with RescueTime) – both negatively and positively. I’d like to learn from my correlations so I can set myself up for the best chance of being productive each day.
Here are some of my current productivity correlations:
This is a fairly obvious one. The more I’m exercising, the more time I’m spending away from my desk. If I had a treadmill desk I might be able to turn this correlation around…
I’m pleased to see that I have a better day when I’m productive. I’d be in a tricky position if being productive put me in a bad mood!
Although I don’t work at night, a warm overnight temperature usually means less sleep (or lower quality sleep), which doesn’t bode well for a productive day. It also means it’s likely that the next day will be warm, which makes me uncomfortable and less likely to get work done.
I can also see from my correlations what affects my mood, and when I’m more likely to exercise:
Lots of floors climbed could either be walking up and down hills (yuck) or staying home all day where I go up and down stairs a lot.
I don’t purposely go out walking in the rain, but I guess it just happens to catch me often.
Exist has built-in mood tracking that works via a simple email. Every night at 9pm you get an email you can reply to including a rating for your day from 1-5 (1 being terrible, 5 being perfect) and a note about what happened.
Mood tracking is a really simple way to make sure you reflect on what happens each day and how you feel. We’re adding mood tracking to our mobile apps (currently in beta testing) to make it even easier: each night at 9pm you’ll get a notification that will take you to a simple form with five numbered buttons and a box to type your note into.
Although I tend to dread the effort of thinking back over my day and choosing a rating for it, I’ve found mood tracking to be so useful that I’ve kept it up for over a year now. As I go about my day, I tend to be more mindful of how things affect me because I always have in mind that I’ll be rating my day later and making a note about what happened.
My favourite part of mood tracking is that in the nightly emails we’ve added a feature called “Looking back” that shows you the mood entry you made on this day one year ago, or a random old entry if you don’t have one from exactly a year ago. It’s fun to open the email wondering how I felt and what I was doing this time last year, and to reflect on the notes I left to myself.
This reflective feature also makes me more mindful each night of what I enter as my note. Knowing that I’m essentially leaving a note to my future self each day helps me think about what was most important about my day, and what I’d want to know about it on this day in the future.
I also love comparing my old mood notes with my partner Josh to see what he wrote on the same day. We’ll often find we both mentioned something fun we did together, or the weather or some big news that was happening at the time.
Using averages as goals
We dropped goals from Exist a few months ago. One of the problems we’ve always had personally when tracking our behaviour, especially exercise, is working to hit a particular goal every day and losing motivation to do so after a while.
These days we use averages as goals. It works like this: if today is Monday, we create your steps goal for today by finding the average of your steps for every Monday in the past 90 days. We do this for productivity goals, too. So if you’ve been working late on Friday nights in the past few weeks, your RescueTime data will reflect that and your productivity goal will be higher on Fridays.
And here’s why it’s awesome:
I don’t need to waste any time setting goals. Exist does it for me, and each goal is personalised to me.
This also means I’m competing against myself. Every goal is created from averages of my own data, so I’m only ever competing against “past me”, rather than aiming for a goal set by someone else.
And lastly, it’s always up-to-date. When I moved house recently my average steps per day dropped as my situation changed, and after a few weeks my averages started to reflect that. Because we only use averages based on the last 90 days of your data, your goals will always reflect what your activity has been like recently.
This affects each daily goal, as well. If you play in a sports team on Wednesday nights and get lots of steps those days, your Wednesday average will be higher than other days. Exist will create a goal for you, then, that will be higher on Wednesdays than it will on other days. This makes sure your goal is always as appropriate as it’s based on your existing behaviour.
I tend to get number fatigue really easily, so aiming for a set goal every day didn’t motivate me for long at all. One thing I really enjoy about having a new goal created for me each day is that I need to check Exist to see what my goal is. The simple act of checking my goal is a good reminder to be more active or productive.
With just RescueTime, mood tracking, and an activity tracking device or app, you can get a lot of useful data. Exist connects to other service like Twitter and last.fm as well, but just a few data points are enough to start seeing insights and correlations that will help you improve your work/life balance.
You can try it yourself with a 14-day free trial (note: we start you off with a set goal and switch to averages as goals once we’ve collected enough data).
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.