People often ask me how I have the time to collect so much data on myself. Like many other things, good data collection is a matter of habit. For example, I got into the habit of tracking my weight training workouts and food intake back in high school. Although I lost those notebooks, and didn’t track actively during college, I returned to tracking in graduate school and have been doing it on and off ever since. Often I tracked while I was working towards a weight loss goal and stopped tracking when holidays came around or felt like I didn’t have time to pursue that goal anymore. Unfortunately, it took me many years to realize that it was worth it to continue the practice of recording my data, even during weight maintenance periods or stressful times where workouts were fewer and farther between and my eating habits were off base. The very act of tracking helps me feel connected to the path I want to be on, even if I’ve temporarily veered off that path.
I have been back to tracking consistently for the past 6 years or so and never skip tracking a workout or a day’s worth of food now. I track my food just before or after a meal, and I am currently using my Push Strength band/app to track rep-level data on all my workouts. If I am at home, I will usually write my exercises and sets down in my notebook as I move through the workout. If I’m elsewhere, I will transfer a summary of my set-level data into my notebook later. Before I had the Push band, if my notebook wasn’t in front of me, I used the Full Fitness iPhone app or a simple Notes session on my iPhone to track my data, then transferred it into my notebook later. It’s so much of a habit for me now that I think I would find it mentally more difficult to avoid tracking these things than to continue!
Similarly, my other tracking efforts don’t feel like a chore to me. My current morning routine includes taking an HRV measurement with my Polar H7 chest strap before I first get up, measuring my weight with my Withings wireless scale, then measuring my body fat percentage and muscle quality (MQ) with my Skulpt Aim. Daily, I also wear my Fitbit Charge HR to measure steps, sleep and resting heart rate. I use MyFitnessPal daily to record data on the quantity of the foods I eat.
Although I haven’t put all the pieces together just yet, I think about these various data points as feeding into a big predictive model, where the response I am trying to predict is maintenance of a specific body shape and an optimal health level. Mentally, I separate my various measures into inputs and outcomes like this:
Inputs – Activity, Sleep, Training Volume and Intensity, Diet
Outcomes – HRV, Weight, Body fat, MQ, and less frequently collected biomarker data (e.g., cholesterol, blood sugar)
Here’s a quick summary of how I think about and use the data I get from these various devices and apps:
- Heart Rate Variability/HRV (Polar H7)): Variability in the length of the beat to beat interval reflects the state of my sympathetic and parasympathetic nervous systems. My morning readiness reading taken with the Elite HRV app tells me something about how ready am I to train that day. If I am stressed, sick, injured, over-tired, over-trained, or my body is recovering from a recent workout, I can see a detectable impact on my HRV. I’ve been measuring HRV for several months after learning about it at QS15, and I can see clear correlations with all the factors I mentioned previously and my morning HRV reading. I now tend to choose a walk or a lower-intensity workout instead of a trip to the gym on the days where my morning readiness reading is poor.
- Weight (Withings Scale): I don’t think about my weight as my end-all-be-all success outcome like I did when I was in a fat loss phase. Rather, it’s as an indicator of various interacting changes. After so many years of training, I am likely not building any “new” muscle so my base lean body mass is fairly constant. Short-term changes in my weight largely reflect either changes in water retained in my muscles as a result of recent workouts, water retained in other tissues as a result of the sodium, carb, and food amounts I have recently consumed, and water retention changes due to monthly hormone fluctuations. It’s hard to tease out all these various factors, but they are cyclic within the week and month. Longer-term trends in my weight are the result of gradual but small scale changes in my body fat.
- Steps/Sleep/Resting HR (Fitbit): These measures give me a baseline for the day to determine approximately how active I was, whether I slept well the night before, and what was going on with my resting heart rate at that time.
- Velocity/Power and Volume (Push Strength): Even before I had the Push band, I tracked data on my workout volume, as I mentioned above and you can see from previous posts here and in my Fitness and Food series on the JMP blog. But I never had rep-level data on my workouts before, and adopting the Push band has changed the way I think about the weight I use. In the past, I might have used a lot of weight for certain exercises because I knew I was capable of lifting it, but I never paid much attention to how fast the weight was moving through space. I know this caused injury on at least a few occasions when I pushed beyond my limits. Now I’m a lot less likely to use more weight than I should. I am focusing on making sure my rep velocity stays within a certain range, and that when I am training heavier, I am seeing the patterns I expect indicating that I am taxing myself appropriately but not too much.
- Diet (MyFitnessPal): The number of calories I am eating is the single most important predictor of what is going on with my weight over time. If I am in a surplus, I will gain weight. If I am in a deficit, I’ll lose weight. I aim for maintenance calories to keep my weight stable, which for me would be about 1800 calories a day during the week and 2000 on the weekend (when I’m generally more active). I find I can “get away with” eating more than most women my age because I am on the high end for lean body mass for my height. (More on that topic below.)
- Body Fat (Skulpt Aim): I’ve been using my Aim almost daily for about two months. Each day, I do 3-5 repeats of the Aim’s Quick Test, which estimates an overall body fat percentage from a four-site measurement of my right biceps, triceps, abs and quad. Less frequently, I do additional measurements on my other body parts.
I’m not training for a weight lifting competition or a cardio event, and I don’t expect to be doing either of those things at any point in the future. So what good is collecting all these metrics to me anyway? I lift weights and track my food for four primary reasons: maintaining healthy biomarker levels, stress relief, the motivation of personal data collection and its usefulness for work purposes, and the reward of having an athletic looking body that I feel confident living in. In general, my biomarkers are in a much better place compared to where they were in the past. I’ve got a job that has cyclic stress changes so keeping a regular workout routine is very helpful. Collecting my data gives me information I can use for software testing and blogging.
Is always interesting to me that the last item on my list-looks-is underrated and often derided as a motivation for exercise. As in-you shouldn’t exercise just to look better! You should do it to be faster, or stronger, or better at some event. For some reason, there sometimes seems to be a stigma attached to admitting looking better can be a primary motivator for training. But if people were just being honest, they’d admit this always factors in. Fortunately I’ve learned a ton from my Venus Factor resources over the past few years about the connection between looking good and more “scientific” factors like the amount of muscle you carry, its location, overlying body fat patterns, and health biomarkers. For the most part, looking good and having a healthy body go hand in hand. For that reason, I’m at the point now where I can tie a look in the mirror that I like to a more quantitative assessment of my body fat and muscle quality. Although Venus is based on an ideal measurement system that uses a tape measure for this purpose, I now prefer using a tool that takes into account body composition-the Skulpt Aim.
I’ve stated elsewhere that I don’t have access to a DXA scanner to do direct comparisons with the Aim to assess its accuracy. But I have had a DXA done in the past and as a result, I believe the data I’m getting from the Aim is in the right ballpark of my actual body fat percentage. I am happy with the device’s within-day precision and between-day consistency, which ultimately is more important to me than perfect accuracy. My DXA was a single data point, taken on a single morning. I haven’t used any other gold standard tests like underwater weighing or BodPod, so I can’t assess the accuracy nor the precision of my DXA number either, other than assuming that the machine was well calibrated and the numbers I got were reproducible.
As part of the Venus online fitness community, I know quite a few women who have had DXAs. I have seen their data and know generally how their measurements and body builds correlate to their lean body mass and fat statistics. This information convinces me the numbers I got from my own DXA were reasonable given my training history and body type. In any case, does it really matter what my exact body fat percentage was on the morning of my DXA or even now as measured with the Aim? I would argue (like John and Brad do in Venus) that the exact numbers aren’t really that important. My body fat percentage on the day of my DXA was certainly not representative of my normal state. I was dieted down and fasted that morning at 127.5 lbs, I did not even maintain that dieted down weight for long and I haven’t been back that low since. What the DXA did for me was to give me a sense of my own base lean body mass (LBM) so I could understand where I fit in the distribution that includes all women of my height. On the day of my DXA, my LBM numbers put me in the top 4%-clearly I am an outlier and that’s helpful to know.
For me, it’s much more useful to have a precise daily measurement that seems relatively accurate based on my prior knowledge than to use an infrequent gold standard measure. But precision alone isn’t enough-the measure should needs to be reproducible within day. I haven’t been happy with my BIA scale because while the numbers it gives me are precise at any a given moment, they are so highly water-distribution dependent. If I take a measurement when I first get up, it can vary by 5-8 points from one taken only hours later (even if I don’t eat or drink in between). It is well-known that the distribution of water within a body is very different upon first waking than it is several hours later, which impacts the accuracy of BIA when used first thing in the morning. In this case, the measure might be precise, but not accurate given my usage pattern.
As far as the Aim, it’s taken me a while to get my measurement technique to a place where I am happy with it. I don’t really trust my first month’s worth of data as a result of needing to resolve various issues I’ve detailed in earlier blogs. But I feel good about my technique now, and am continuing to take measures daily. This has been very valuable because I am getting a better sense of a few things.
1) How often do I need to train to maintain where I am? I am considering changing my workout approach based on this information. For example, if doing 3 hour-long full body workouts a week (at a significant 1 hour time commitment for each session) makes little difference to my MQ, BF and look vs. doing 2 shorter sessions each of upper/lower work at 25-30 min each, and I find it easier to maintain eating less while doing the shorter sessions, why do more than I need to and increase my risk of injury? I am a lot less worried now about taking a day or two off when I feel I need it too, which is a good thing for my stress levels.
2) Lower body fat/higher MQ doesn’t always correlate with changes in my look the way I’d expect. I have observed interesting weekend water retention effects where my weight trends up, my BF goes down and MQ goes up, but I don’t like the changes in my look, which is puffier and bloated at the higher weight despite the BF and MQ being “better.” You can see below that the relationships between water retention and MQ/BF are extremely clear. As I re-carb and retain water on the weekend, the measures track exactly as you’d expect, given that muscle tissue is mostly water. As my muscles store more glycogen, they also retain more water, and my MQ goes up and BF % goes down. Below is a graph that shows these trends for 5 weeks of daily data.
Another look at the data that combines across weeks. There are sodium and training effects going on here too, which is why there is more variability when you look at all weeks together than in the week to week data above:
Between my Aim data and my HRV data, I am feeling much more confident about my ability to assess how, when, and how much to train, and it’s a very good transition for me to make at this point in my life. I have two kids and sticking to the kind of highly scheduled routine that I used to isn’t an option anymore. I used to stick to a routine schedule because I was deathly afraid if I didn’t, I’d fall into old bad habits-stop my workouts, gain weight again, and be depressed about it. But I am now confident that’s just not going to happen so I can be more relaxed about my training schedule and experiment with it. OK, so weekday morning workouts don’t work that well anymore. I can still use my home gym on the weekends, and work out at my work gym or in my office during the week at lunchtime or whenever I can fit it in. It’s nice to have more metrics that help support my goals and let me tie numbers to the look I like!