I’ve been enjoying debunking my own cherished stories about my Daily Energy Curve and I went after a good one about the restorative power of the weekend. I always thought that I had a fresh start each Monday after a weekend of rest. I thought each Monday I was like this guy, ready to get going and make it happen.
I was curious about this because I would assign work to either Friday or Monday based on how stressful I thought it would be. Given my story, I would move the tough stuff to Monday thinking I was more rested.
Was I more rested, awake and ready to go on Mondays vs. Fridays?
What I Did
I captured how alert I was and how much stress I was feeling eight times a day for 25 consecutive days. I then compared Monday to Friday’s in terms of mental alertness and stress.
The DIY Tracker captures a score of 1 – 5 on my alertness and stress level. After 25 days I took the data generated and cut it by Monday and Friday, doing a TTest comparing the two days.
What I Learned
I was no more mentally alert on Monday than I was on Friday. The TTest on Mental Alertness showed that there was no significant difference between Monday and Friday.
Friday vs. Monday
Stress, however, was significantly higher on Monday than on Friday (p = .04). This made sense as I was moving tasks to Monday for no real reason other than my story about being rested. I have to conclude that I was overloading my Mondays as a result. What a lousy way to start my week.
Another story I had is that I was more alert in the morning than I was in the afternoon. I had even organized my day so that I would do my heavy lifting mental work in the mornings. Turns out that was wrong. Looking at the entire data set and comparing morning to afternoon, not a single measured dimension was significantly different.
Morning vs. Afternoon
So combining the two, I think I was generating my own Blue Mondays by believing these two stories. I would try and shift all my hard work to Monday mornings, and the result was not better output, just more stress.
I will look to spread out the work more evenly and compare that to this baseline. At the very least, I will look to try and make Mondays less hectic.
Earlier this summer, I was put on to a pre-workout strategy that I have since adopted: drinking coffee 30 minutes before a workout.
A friend had suggested I try it, knowing that I love coffee and play basketball when I can. He provided this Men’s Fitness link to kickstart my own research, and even this month, I later found a recent, more scientific and detailed article on BodyBuilding.com.
Heading into the test, I made drinking a cup of coffee 30 minutes before a workout a daily habit (or a daily habit on days I workout). I didn’t have any data yet, but I felt internally that it was giving me an extra boost. These last 10 days were the first time I decided to track it.
I’m an active exerciser and a daily coffee consumer already. I always have 1 cup of coffee with my breakfast or by lunch at the very latest. I usually have cup #2 between 3 and 4 in the afternoon and proceed to do a daily 20 minute stretching routine, followed by a 30 minute pilates routine at home (3-4 days/week). This was a great first week to track the caffeine’s effect because I was preparing for a 3-on-3 basketball tournament and played basketball 5 times in 7 days. In fact, the experiment began on Sunday, August 17th when I was inspired to go to the gym late that night and shoot around. I came straight from a movie and did not have a chance to drink coffee before going. Given the situation, and the general fact it was late at night, I felt extremely sluggish.
For the purposes of this test, I consumed the same two brands of coffee K-Cups (Starbucks Breakfast Blend & Dunkin Donuts Original Blend), each containing 150 mg of caffeine per cup.
I used both Apple Health & QuantXLaFont’s free DIY Tracker to record my caffeine intake and my observations of its effect. Both were conveniently on my smartphone and the DIY Tracker allowed me to customize my observations and essentially create my own rating system.
The graph and data already illustrate a few takeaways. One of the more general ones was that these were the first couple of weeks I upped my coffee intake to 3 cups/day. I have been steady at 2 cups/day for the past year-plus. I started to feel I needed a morning/afternoon/evening routine on days I played basketball at night.
That made it easy to visualize when I played basketball. I usually had that 3rd cup before playing just after 8pm at night. Over the weekend, I only had 1 cup of coffee each day. But one of those days was our 3-on-3 tournament that started at 10:30 in the morning.
I made sure to be diligent and record my observation after I finished my workout using the DIY Tracker. It was easy to tell if I still felt sluggish, had just enough boost to maintain a sufficient energy level, or best case scenario: a very high boost where I had an extra hop in my step and an extra level of mental awareness on the court.
I knew going into it that the sluggish workouts would be few and far between. The coffee at least gave me enough of a boost to start drinking it consistently heading into the test. After my first “Very High Boost” day, I was really curious how often coffee would give me this best case scenario.
Here are the results, recorded into a Google Spreadsheet in real-time via the DIY Tracker.
The results proved encouraging! 3 of the 4 times I drank coffee before playing basketball I experienced a very high boost. I noticed I had an extra spring in my step and was able to see the floor and make quicker decisions comapred to the 1 off-day I didn’t have a very high boost during this period.
(Beginner’s Tip: If you try this yourself before basketball, make sure to hydrate yourself even more than usual in between games. Your body needs to adjust to the caffeine, which naturally makes you more dehydrated. After a few days of this, you shouldn’t feel extra dehydrated but take it from me, I learned the hard way!)
Additionally, I did pilates after my mid-afternoon cup of coffee 5 times in these 10 days and experienced only 1 day where I still felt sluggish. This one off day helped me realize that coffee isn’t the end all, be all solution to having a quality workout. In other words, it was a pleasant reminder that you still have to get reasonable sleep and eat well to have a quality workout no matter what. I remember vividly not having those basic factors fulfilled on this particular day.
However, the results also gave me a number, albeit in a small sample size. 80% of the time I’ll feel a noticable boost in my workout (basketball, pilates, stretching) thanks to consuming a cup of coffee 30 minutes before. Again, I felt it was helping me internally all summer, but now I had a high success rate to keep me even more disciplined to have that pre-workout coffee.
From here, I intend to continue to gather data, track how much caffeine I consume each day, and add variables to arrive at even more concrete conclusions. For instance: how does the amount of caffeine in the pre-workout cup of coffee effect my workout. The one day I consumed 180 mg of caffeine was due to a large Dunkin Donuts coffee I bought on the run. I noticed a very high boost in playing basketball 30 minutes later.
What do you guys think? Is there anything more you would like to see added to my test? I personally believe in the science behind it (if you missed the links in the intro, I suggest you give those a read) but perhaps there’s an ingredient here that it’s all mental. You know, like when the TuneSquad drank “MJ’s Secret Stuff” at halftime.
I had a self-created story about butter. It was such a strong story that it was changing my behavior around what I ate. I had come to believe butter “gummed me up” and after eating it my body would react by feeling lousy.
Two months ago I transitioned to a low carb, medium protein, ketogenic diet. As I described at the time, the experience of weaning myself off carbohydrates involved feeling poorly for a number of the early days. As butter was a good source of fats I was eating a lot of it, so I started associated feeling lousy with butter. And I developed the story, “This butter is throwing me a beating.”
Each morning I would take butter or an alternative source of fat with my coffee. The alternative was usually flaxseed or coconut oil. I randomly determined which I would take each day. Four hours after my coffee I would capture how I was feeling in a Google Spreadsheet via a Google Form tool I call my DIY Tracker.
How I Did It
I used a Google Spreadsheet to generate a random “one” or “zero” for each day in the period. This gave me instructions on what to put in my coffee each morning, butter or the alternative. This was important because I needed to not choose what I took based on some bias, or more importantly, the story about butter. If I felt rough in the morning and arbitrarily chose the alternative fat source because of my belief butter would make me feel worse I would skew the data. So I stuck to my random schedule when I took my coffee after a workout at around 7am.
I captured how I felt each morning at 11am using my DIY Tracker. This provided my data for how I was reacting to butter and the alternative.
I threw out data that did not meet my control criteria, which is a fancy way to say if something out of the norm was happening I did not use that data. If I did not follow the randomly generated instruction or the amount of fat I consumed was not within a set range, I considered the reading invalid. I also only included readings on days I had exercised for 20 to 30 minutes prior to the coffee.
After thirty-seven days I had twenty-seven good data points. I separated the list into a “butter” and “other” list and ran a T-Test for butter and a value for feeling lousy that was embedded in the way the DIY Tracker poll solicited information.
What I Learned
Butter was redeemed. I compared it both to an average score that would indicate I was consistently feeling lousy and to the oil based source of fat. The way I gathered information in the DIY tracker a consistent score of “2” (Yawny, tired) would have shown I was feeling lousy.
The data showed I wasn’t consistently feeling lousy. There was a statistically significant difference between the feeling score after eating butter (avg of 3.6) and the expected average score of feeling lousy (avg of 2.5). The T-Test returned a value of .004. Within the data I only felt lousy on two of the 27 days.
When I consumed the oil based sources of fat my average feeling score was higher than with butter, but the results were not statistically significant when I compared oil to butter (T-Test returned .17). So butter remains in the morning coffee mixture rotation.
Behold the redeemed!
Try Your Own Test
Do you have a food story? A cherished belief that you can’t eat a certain food because it makes you feel bloated, or bad? N of 1 Testing is quite easy to set up when you have a specific question you are asking based on a story you have. Get free instructions on how to create you own DIY Tracker or we can help you out at QuantXLaFont.
I set out to the Bay to attend the annual Quantifed Self Conference and had the intention to stay out there and work remotely for as long as I could be away from home. I also set a daily intention to explore new neighborhoods, tourist attractions, and the many outdoor activities the Bay has to offer, especially being it was my first visit to the area.
Naturally, I knew I would be much more active walking around San Francisco than I am currently — commuting by car or simply not leaving home to work. I wanted to see how much more active I would be in a new environment and establish a new daily routine going forward.
I was also hoping that as a result from this trip, I would be able to test an upper limit of what I can physically endure in a day. To set my ceiling, I went on two hikes and compared the data below between those days and my “normal”, primarily pedestrian work day. I’ll explain more as we go…
First, here is a line graph that charts my total amount of steps across the 3 weeks, using the Moves app. As one might assume, my 2 highest step counts were the 2 days I went on hikes.
The 2 hikes I went on were very different, yet equally exhausting. The first was through Muir Woods, up, then back down a mountain that lasted for over 4 hours and accumulated 9.5 miles.
The next weekend, I walked an even 9 miles mainly exploring the Marin Headlands and walking from there to the Bonita Point Lighthouse. This was not nearly as steep compared to the more traditional hike I had 6 days before, but my friends and I were able to explore more land this time during a relatively equal 4+ hour period.
For me, these hikes, and this trip in general, were my first experience at really tracking and analyzing my physical activity. I knew that to improve my daily goals I had to test my limits. In the couple of months since, the record of June 21st still stands as my ceiling. I know I can pass it one day, but this number still acts as an inspiration that I can handle and reach that Fitbit magic number of 10,000 steps a day during a normal work week.
The Non-Hike Days
Using my data above, I calculated the average amount of steps I took per day, excluding the two days I went on hikes.
Over 18 non-hike days, I averaged 7,625 steps/day.
It fell short of my 10K/day goal, but I still have my head held high.
One of the biggest factors that skewed my average down was a 4-day extended recovery from not just the hike, but the start of my trip June 17-20 that included active days at the QS conference. I stayed dormant, worked from my friend’s place, and relied on BART or my buddy’s car to get around the city when otherwise during the trip I would walk. I enjoyed unwinding, but just fell comfortable into it for 2 days too long.
Furthermore, when I’m home, I’m more able to properly excercise. As you all know when you travel, one of the hurdles sometimes is finding a gym, treadmill, or even simply the time to go out for a run, bike ride, what have you. My main form of cardio is even harder to accomplish on the road: playing basketball.
Throughout the 3 weeks, I only found one day to play basketball: July 4th, pictured below, and that was not the full court pickup games I usually play in 2-3 times a week. My friend and I just shot around in the 80 degree heat for about an hour. I immediately noticed I didn’t have the same wind or leg strength after 2 weeks of not touching a basketball.
Once I got settled in San Francisco, I figured a couple of things would happen for the last couple weeks of my trip that ultimately did not:
1. I would find more time to play basketball and not drop off my cardio too much.
2. The amount of walking would compensate for my normal cardio.
I dissected the latter takeaway and concluded that overall, I wasn’t as active as I thought I was. My active days inflated my perception, but I was brought back down to earth when I felt so out of shape after just 2 weeks of not playing basketball.
In fact, one other quantified result from the trip that proved this was a 5 pound weight gain. I felt I had been more active in San Francisco, and looking at the high step counts on my most active days contributed to that sense of security. But I didn’t realize it until it was too late: I just didn’t make enough time for cardio. I figured that with all the walking I did, plus Pilates 2-3 days a week from home, it would be enough to maintain. It wasn’t, but I didn’t feel I dug myself into an insurmountable hole either.
Looking at it positively though, I found I really enjoyed hiking and just walking more in general throughout the work week. I would take breaks and seek out a new destination for work, meetings, a tourist spot, food, etc. throughout San Francisco. Those days in the city were ones that increased the 7.5K average.
Overall, my trip helped me determine that this is the routine I would like to establish, given my current, remote work situation. The goals are now to hit my 7.5K average from just walking during the work hours, and then reach 10K through nighttime exercise. On the few nights a week I play basketball, 12.5K steps total is a more challenging goal. Unfortunately, there’s nowhere to really hike around flatland Chicago (my current home) but that’s where the basketball steps in.
Up next: Streamlining my data tracking and creating even better graphs to visualize my activity. I had to manually input data and create the graph above, when I could have been doing this automatically with this all-new DIY Tracker on QuantXLaFont.com. It’s a free download, with optional personalized coaching, to start to track your own version of either my experiment above or something like weight tracking or measuring your blood glucose levels.
If you enjoyed my story here, you can use this tool and start your own story too. Check it out and let us know what you think: @QuantSelfLafont.
There was an article in Bloomberg this week about UK companies measuring employee’s heart rate and other biomarkers to determine if traders were working in the zone. It suggested in the future we will see corporate workers measured like professional athletes.
The concept makes sense on the face of it. If you can improve an athlete’s performance by measuring output, a similar measuring of performance should be applicable in business. The problem in the business setting is the lack of clarity around what the improved outcome might be. A 100m dash sprinter has one number they are improving, the finish time. All measurements and interventions revolve around that number. In more complex sports there are multiple metrics, all are clear and measurable.
What is the Worker’s Goal Line?
For the worker in business, what is the equivalent? Take the case of the construction worker. Would we look at strength, hand-eye coordination or compliance with instructions? Or the retail clerk. Would they be measured in pleasant demeanor, speed of button strokes at the cash point or sales presentation? When we move to knowledge worker the potential measurements become much more elusive. Is the programmer’s ability to create a complex solution driven by cognitive ability, knowledge or access to good tools?
In each of these cases taking heart rate and heart rate variability (HRV) seems very far away from finding the relevant connection to the drivers of performance. I have been working on this for some time, having writing 15 blog posts about different interaction performance tests I have done at work. When I started with Quantified Self, my hypothesis was to see if I could improve negotiation outcomes using a physiological training plan similar to how a runner would prepare for a marathon. To test the idea I measured HRV in 86 different meetings of various types. Very few of my interventions had any impact on outcomes.
An Example of the Challenge
Here is a specific example of how difficult it would be to have access to biometric data from an employee and make sense of it. This is real data. Say you were my boss and our company’s biosensing device gave you these two graphs. In the first, I was meeting with you and several of your peers. The blue lines are that period of the meeting where I was in Vapor Lock and not at my mental best.
The second graph shows my physiology ten minutes later as I was brainstorming with a colleague. Very few blue lines.
How would you counsel me to improve? What do these graphs even say? Was I not “in the zone” with the you and your colleagues and “in the zone” with my colleague? In a world where performance feedback is hard enough to conduct already, you can see how there will need to be a very significant new framework to make sense of data like this.
So the London traders can keep measuring heart rates and perhaps achieve some improved performance. For the construction worker, the retail clerk and the programmer we have a long way to go. In my study from which I pulled the graphs I did draw a conclusion about how to prepare for meetings like that with the Big Bosses.
A couple of weeks ago, I attended one of the biggest summer music festivals in the country: Lollapalooza. Everyone from Paul McCartney to The Weeknd and Sam Smith to even Metallica were performing across the 3-day festival at Grant Park in Chicago.
I turned the festival experience into a fun Quantified Self experiment for the weekend and all I needed was the Moves app. The results provide a peek into the amount of exercise you actually get at a music festival, which you can compare to your most strenuous or inactive days. Plus, there’s always the added inspiration to run more QS experiments like this in your daily life going forward.
So without further ado…
To first give you some background, the festival is laid out across the entirety of Grant Park, with the two main stages being on opposite sides of the 3.4 square mile area.
Here’s the visual, which shows my 24 minute walk from main stage to main stage amidst heavy foot traffic from the 100,000+ festival goers per day.
Day 1 — Friday
Lollapalooza is also known for being an all-day event that starts at 11am and goes until 10pm. This isn’t my first go around at Lolla so I arrived around 7pm. On this night, I only wanted to catch the headlining acts, with an after-party to attend later and 2 more days at the fest to come.
Ultimately, the 3 hours I spent at the festival and the 1.5 mile walk to the after-party concert afterwards was the least amount of distance I traveled for the 3 days.
I, somewhat surprisingly, hit the standard FitBit goal of 10,000 steps. I wasn’t bouncing around to too many stages on Friday night and acknowledged when I went to sleep that I had another level of energy still in reserve. I also immediately realized I would need that for the increased ground I knew I was going to cover in the next 2 days.
Day 2 — Saturday
Saturday proved to be my most active day before and after the festival.
I started by walking to brunch near my place. Later in the afternoon, I stopped by a lounge party across the street from the festival’s main entrance. And after the festival, my friends and I hopped from one hotel bar to another, taking in the many brand sponsored parties that come to town for the festivities.
All of these spots were walking distance from one another but we were going back and forth across the Chicago River in an inefficient manner, only adding to my steps. My night ended at 3am, and afterwards, my body was aching everywhere.
The first variable that skewed the data can be seen above. “Moves off 2:03 h” is the Moves app’s way of saying my phone died.
Pro Tip: If you go to a festival, your phone will be searching for a signal the entire time and your battery will drain faster than normal. Bring one of those small battery pack chargers so you can be plugged in amidst the crowd.
However, I can accurately estimate how many total steps I ended up with on Saturday. The biggest factor: I made the same 35 minute walk on Friday, to the same hotel Saturday night. This walk amounted to 2,369 steps.
I would then add another 0.7 miles of walking from hotel to hotel while my phone was off. That is about half of the distance of the 1.3 mile, 35 minute walk the night before.
The resulting, approximated total for Saturday: 16,800 steps. This is the highest amount of steps for my 3 days.
Day 3 — Sunday
The last day of the festival turned out to be my most active day actually within the festival. I was there for 6 hours and was stage-hopping most of the time too.
My data highlights a few interesting observations. The hidden one is that my night ended much earlier than the previous two: roughly at 10:35pm.
The more obvious one: I set my month record of 13,640 steps (though I just calculated above my Saturday was a few thousand more steps) AND I ran… for 932 steps, a.k.a. 0.6 miles.
I was humorously running toward Union Station to catch the last train after the festival had ended. The train was set to take off at 10:35 and as you’ll note above the map, my running ended at 10:34. I just made it!
That experience was made more memorable by the fact that I had to carry around a 40lb. backpack with my laptop, media equipment, and change of clothes that entire time. All the while, I was weaving past attendees who were walking with no knowledge of the train schedule. Unfortunately, Moves can’t quantify the added weight to my exercise but I’m comfortable knowing it was a great, and unexpected cardio workout. (I was sitting on the train in a full sweat and catching my breath for 15 minutes.)
Festivals are tiring.
OK obviously. But more useful to you, I found out that festivals are actually an unexpected, suitable workout substitute, especially if you’re worried you’ll fall behind your regular workout schedule.
Heading into the weekend, I was worried about just that. I had turned the corner in recent months recovering from an injury and following a strict routine that mixes full-court basketball, treadmill sprint workouts, pilates, and weight lifting.
On that train ride home Sunday night, and the couple of days after, I felt more sore than after any of those other workouts. The festival, for 3 straight days, flat out takes a heavy toll on your body.
Here’s how the weekend’s steps compared to my more inactive days:
A quick glance at my monthly data shows pretty clearly when I attended Lollapalooza. Relative to my work days, the weekend contains a lot more steps. (The variables to note here: I work from home and I don’t have a FitBit or wearable to track when I play basketball or run on the treadmill, etc.)
The latter point is actually another one of my takeaways from this experience. I really want to see how attending a festival compares to my normal day playing pickup basketball and the days I’m actually active.
I did track my all-time ceiling: in June, I made a 4 mile hike up a mountain in California that amounted to 30,000+ steps. Now I know that a busy day at a festival is about half of that for me. So where do my other activities lie on this spectrum?
Hopefully this experience is as inspiring to you as it was for me to track more of your everyday movements. If so, let us know @quantselflafont!
I have reported in earlier posts that I am mapping my Daily Energy Curve so I can make changes in diet, exercise and mental frameworks that will maximize my physically feeling good. In the world of self quantifying we tend to maximize for a desired weight, blood glucose level, steps we take in a day or distance we can run. It gives us an organizing principle for our activity and measurements. My current work is to maximize just like this guy does:
I needed a simple way to capture how I was feeling at different times. I like Taplog but it is only for Android and I had just switched to iPhone. I could not find an iPhone app that I liked. So I created a Google Form that would be easily accessible on the iPhone and I could capture my data often. The survey on the phone looks like this:
I started gathering reports along three dimensions, six times a day. The dimensions were how awake I felt, how mentally sharp I felt and how stressed I felt.
I am very early days so I can’t describe what I have learned yet. I can show you what the early data looks like and how much data you can gather with a DIY tool like the one I created. This represents four days of data and 37 data points.
Here is the early data on how Awake I felt over the course of the day:
I was surprised to see how much the curve held up in the back end of the day. Here is mental alertness:
Too early to be definitive but the drop the afternoon seems to be interesting. The curve for how stressed I felt has a different and flatter curve:
I can now start running randomized experiments on the day parts. I am pleased to have created this tracker because I have a lot of control over what I can capture and I already have expansion ideas. And it keeps my tracking top of mind.
I encourage you to make your own DIY tracker. To help you out I have created a set of step-by-step instructions in a PDF and you can download it for free on QuantXLaFont.
After my last Booze test I wanted to find out the limit of how much alcohol I could drink and have it not impact my Heart Rate Variability (HRV), Muse % Calm score and Glucose level. In a scientific study I had seen that 2 drinks is a limit of what men can drink and have it not effect their HRV. I thought I would give it a whirl by conducting a randomized test.
If I have three alcoholic drinks in an evening does it significantly change my morning HRV and mental calm readings?
What I Did
For twenty days I would either drink three drinks in an evening, or none. To ensure this was a randomized test I used a randomly generated instruction for which days I would drink and which I would not. It made for some funny Tuesday evenings and some less than social Fridays, but science must be served.
How I Did It
I used a Google Spreadsheet to generate a random list of instructions for the twenty day period with an output of “zero” or “three.” Each evening I would either have the drinks or not.
The following morning I would measure my mental calm using Muse EEG headset and my HRV using a Polar H7 heart rate belt that sent data to an app called the Heart Rate Variability Logger. All data went into the same Google spreadsheet.
At the end of the twenty days, I separated the lists into two arrays based on the amount of alcohol and ran a T-Test using the Google Spreadsheet. For days where there was an unusual circumstance (odd food consumption, travel, drinking neither 3 nor 0) I threw out those measurements.
What I Learned
Drinking three drinks in the evening does not significantly affect my HRV, % Calm or Glucose levels the following morning. After separating the data and running the T Test, here were the resulting p values:
For any of the measures to have been significantly impacted the p value would have needed to be .05. So I found a level at which I could have a social drink and not impact my physiology significantly. Anecdotally, on the mornings after 3 drinks mornings I felt fine so my experience matched the results.
I have had enough people ask me how to start doing these studies that I have created a site specifically to help people do these types of N=1 studies. You can get the basic instructions absolutely free and can pay for coaching if you would like. The mission is to support people in taking their own data and testing the dimensions of their own unique physiology.
Last month’s Quantified Self conference concluded with the premiere of a new documentary on the 2012 U.S. Women’s Cycling Team — Personal Gold: An Underdog Story.
The premise: in the wake of Lance Armstrong’s doping scandal, the women’s team were the only U.S. cycling representatives. They had minimal support for their training leading up to the London games, especially when compared to the multi-million dollar budgets of the U.K. or Australia. So the four women, under the guidance of former Olympic cyclist Sky Christopherson, adopted a ‘Data Not Doping’ mentality to understand each of their individual bodies and personalize their training to cut even the slightest amount of seconds off their time.
It was a fitting close to the 3-day conference because Christopherson led the big data focused efforts by incorporating many Quantified Self studies and applications into tracking the athletes’ response to trainings, sleep habits, and even to the detail of the sleeping room temperature.
The 90-minute documentary focused on the trials and tribulations of the team’s grueling training in Europe before the thrilling conclusion at the 2012 London games. In addition to highlighting the quantified self processes, we got an inside glimpse at the discipline the athletes needed to compete at the highest level. It was inspiring to see the team’s sacrifice (even the women’s husbands were working full-time to help with the training) and their resourcefulness. Christopherson consulted with numerous big data leaders to learn about the technology and apply it towards the athletes in trial by error fashion. Seeing the errors amidst a heavy time crunch before London only made the result that much more gratifying.
Needless to say, Personal Gold connected with everyone in the QS audience that day. We saw the world’s best athletes use quantified self experiments to improve their optimal self and achieve their goals. I have no doubt that, no matter if you’re a cycling or quantified self enthusiast, the underdog story of the 2012 U.S. Women’s Cycling Team will appeal to you too.
To find out when Personal Gold will be screened near you, visit personal-gold.com and follow the film’s Twitter account @Personal_Gold. Without further ado, watch the trailer below to drive home everything above.
One critical task that I solve for is ensuring that I maximize my energy during daily negotiations. I want to be present and balanced when I engage with others and not have my physiology defaulting me to Vapor Lock because the night before I had a bad batch of cannoli. So I look to maximize my Daily Energy Curve.
To measure my state of balance I use Heart Rate Variability (HRV). I know that my balance and energy goes down over the course of a day as I shared in a past post. If that was the case, was a hard workout in the morning depleting my energy before I went into negotiations?
Logic said that using energy early in the day would leave less for the remainder of the day. So I had to test it.
Was exercising early in the day lowering the remaining energy I had for the remainder of the day when I would be in negotiations with others?
The Resulting Potential Action
If exercise first thing in the morning had no effect on my energy levels I would continue to exercise in the morning. If it did have an impact on energy levels on the days when I was engaged in important negotiations I would either skip the workout or workout later in the day after the negotiations.
What I Did
I created a random list of Workout/No Workout days to ensure that the results were not skewed by some personal bias. On days I was to workout, I exercised for 30 minutes on an elliptical machine either at my house or in a hotel on the road.
I took a measure of my HRV at least three times a day, one on waking, one in the afternoon and one on going to bed in the evening. The combination of these three measures I put into a Google spreadsheet and calculated the slope of the three measures. To measure my HRV I used a Polar H7 heart rate belt, an iPhone6 and the Heart Rate Variability app.
I took 25 readings over the course of a month. 19 of the readings were from randomly generated instruction, 6 were due to life events (elliptical not available, had an opportunity to workout).
What I Learned
The difference in my HRV slope on days I exercised had no statistically significant difference than on days that I did not exercise. There was no correlation between exercise and my energy levels.
The idea of the HRV slope reflecting my Daily Energy Curve which would steadily drop over time means that we would expect the slope to be nearly always negative. Supposedly HRV starts high and ends low. With that assumption, the workout would drop the early day energy and the negativity of the slope would increase. The Daily Energy Curve in the Exercise or Not Exercise would look something like this:
In the actual readings, there were 9 days that the slope of the curve was positive, with close to an equal number of those positive days being on both Exercise and No Exercise days. You can see the positive days in the scattergram of the readings over time:
And a scattergram of the Exercise (1) vs. No Exercise (0) showing the distribution during the study:
Running a few statistical tests on the data it came back that there was no difference between the HRV slope on days of exercise and a random sample of HRV slope readings. On both a T Test and Pearson Correlation the difference was not significant.
So in a (semi) randomized test of exercise effect on the Daily Energy Curve I dispelled a cherished personal myth. In the past, when I would wake up on the day of an important negotiation I would say “I should save my energy for the discussion” and blow off the workout. A study and a bit of math now tell me that I’m not really saving up energy if I skip the workout. I’m just blowing off the workout.
I have to give full credit for the randomization method to Cara Mae Cirignano of Whatify. I did use Whatify for a portion of this study but because I did not get the entire study done with them they are off the hook for methodological irregularities. I highly recommend you check out their service.
So going forward my workout decisions are independent of my pending negotiations. And I have to take more of a look at the HRV Slope. If sometimes it is positive, what is driving that? For a future study.