I have discovered some unexpected correlations around various health metrics and my body’s response to different stimuli. It’s a fascinating discovery at the intersection of being hyper aware of my body and digging into the slew of various metrics I track across multiple axis. Some things aren’t surprising. I knew that I had COVID before I demonstrated any symptoms, on both occasions. Seeing my overnight respiratory rate skyrocket is pretty uncommon. It may elevate with stress or allergies, but those increases are nothing compared to being infected with a respiratory illness. What is more fascinating is seeing connections between my mental and emotional well being as translated to various health metrics.1 I recently have had several experiences where I could physically see my stress levels in the data.
Viewing Data Trends Across Longer Timelines
I’m a big data nerd, and find so much valuable information in viewing the trend lines of various health metrics. Training adaptation is often a slow process, and overanalyzing on the smaller scale doesn’t suit me well. While I have primarily looked at data trends from a training management perspective, I recently was confronted with some other scenarios where this proved useful. Health metrics tell stories far beyond response to training.
May was an incredibly stressful month for a variety of reasons. There was a week or so where my resting heart rate was notably elevated. It wasn’t a challenging conclusion to reach that I was stressed. I also wasn’t sleeping well. Instead I was fascinated by viewing this data across a trend line. Viewing it across a longer timeline indicated how large of an impact this had on baseline metrics including my heart rate. It literally charted my stress levels.
This is still a bold and aggressive example that may border on being reductive. However, it kicked off greater awareness around examining outliers of metrics. Heart rate has continued to be the most discernible indicator for me. It’s relevant in more circumstances and comparing against similar activities across time. Heart rate variability (HRV) is another measure which can add additional details around the body’s readiness for training load, but that typically is compared at a daily basis. It’s a helpful metric to monitor, but I use it more for general training review and adaptations. It is a helpful metric, but not the one I want to focus on in this post.
Heart Rate Deviations and Subjective Introspection
A common means of viewing heart rate data as an athlete is awareness and tracking of how my body is responding to a particular level of effort. Fatigue, heat, dehydration, long illness recovery, and so much more can result in either a heightened or reduced heart rate from baseline expectations. A consistently lower heart rate than baseline can also indicate increased fitness levels and the need to recalibrate my exercise zones. On the converse side, a higher heart rate than normal for a given effort can indicate that my body isn’t responding to a typical work load as expected. This may warrant decreasing my training effort in duration and/or intensity, as well as reviewing other environmental factors. All of this data allows on the fly adjustments of a training plan, and a much needed guardrail for me doing too much of a good thing and increasing the risk of injury.
My subjective introspection about heart rate deviations has historically been viewed through the lens of adaptation to exercise. However, with the stress example above, I started to pay more attention to my heart rate before and after yoga sessions. Subjectively, this seems to provide a decent indicator of my mental and/or emotional state. Yoga serves multiple purposes for me specifically:
- It is a fantastic means of stretching and helping with muscle recovery from hard efforts.
- It strengthens muscle groups that may be neglected from my typical exercise forms2.
- Core strengthening directly impacts performance on the bike. A stronger core enables maintaining a more aggressive position, which can dramatically reduce aerodynamic drag.
- Yoga remains the only reliable means of engaging in meditative behavior for my neurodivergent brain.
Since I am constantly monitoring data, I have a general idea of where my resting heart rate is during the day. This includes accounting for increases due to poor sleep, completing a hard workout earlier in the day, etc. Recently, I was processing through multiple difficult and large life events. I had gone for a ride which tends to help me regulate my mood, but I was still not in a great mental space. I reached for yoga as the next option to hopefully find some larger emotional self-regulation. I completed the session, but was still feeling pretty activated. A quick look at some of the data, including my heart rate confirmed that I was notably higher than my typical baseline even after factoring in some training earlier in the day.
Yes these examples seem obvious at face value and in retrospect. It’s hard to capture notable examples without that being the case. The interesting end result which isn’t obvious is in the more mundane day to day. It happens when I’m exhausted at the middle of the day, especially if that happens multiple times in a short period of time. It has shifted how I look at various metrics on a day to day basis, larger trend lines, and trying to understand why I may feel off. Not everything will have an answer, but it’s helpful to have some more clues to try and see what my body or brain needs. Navigating life across multiple points of intersectional marginalization can be exhausting—sometimes confronting outright antagonism. Balancing that, typical adult responsibilities, and being an athlete is a constant dynamic tension to keep things in balance as best I can. I’ll take all of the tools and introspection mechanisms that I can find.
This also acknowledges that health metrics from wearable technology are not the most accurate measurements. Some devices are better than others for different metrics. Most are absolutely horrible at calculating caloric burn. The main benefit in my experience is observing the changes of metrics according to a baseline of similarly recorded data. ↩︎
This is particularly important as muscle imbalances can create their own form of overuse injuries. In 2024, I experienced a strain in my TFL, which is a smaller muscle within the larger hip muscle group. The root cause ended up being a large strength imbalance due to cycling not activating this muscle. Thankfully I caught it early and it was solved quickly with some focused PT. I still do focused hip muscle exercises to try and avoid that imbalance—especially if I start noticing warning signs. ↩︎