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We made it through the busy holiday season. Many of the entries in my athletes’ training logs contain common themes like working to get sleep schedules back on track after travel and late nights out with friends and family, as well as paying closer attention to metrics like resting heart rate, total sleep time, and readiness scores.
The holiday season is also a common time of year to gift or purchase items like wearables: GPS or smart watches with more and more high tech features, sleep and recovery trackers like Whoop bands or Oura rings, and chest and bicep strap heart rate monitors. Sure, we have officially moved into an era that provides us with unprecedented amounts of data, but it also has the potential to put us into analysis paralysis.
If a wearable tells us we aren’t fully recovered, but we feel decent, should we exercise? Should we rest? How accurate is wrist-based heart rate? What if our wearable beeps at us to move our legs and increase our daily step count, but a few hours prior, we completed a long run workout? How much stock should we put into overnight heart rate variability, and how do we apply these values to making training decisions? Is it possible to become too obsessed with the data?
These are questions I’m often asked by athletes I coach, and if you’re someone using any type of wearable, I’m certain you’ve wondered these things, too.
Recently, I sat down with Dr. Peter Tierney, Ph.D, senior researcher of innovation at lululemon, sports scientist and health and performance coach with extensive experience working within the English Football Association and Ireland’s national rugby and football teams. Dr. Tierney has gained traction in the sports science realm for digesting cutting edge research and translating it into more accessible visual graphics. We discussed how to sift through and maximize the data that wearables provide to their users.
Which Health Metrics Are of Value?
Dr. Tierney prefers to identify which metrics are actually of high importance and which of the metrics are measured well, as opposed to metrics that tech companies are marketing, but aren’t actually practical in their real-life application.
He recommends focusing on the metrics labeled in green (below), underscoring that these are important and useful metrics to track, and that wearable tech does a suitable job of measuring them accurately. He lists these as time (i.e. tracking duration of exercise), resting heart rate, steps, temperature, total sleep time, breathing rate, and overnight heart rate variability.
Moving through his chart, he identifies the metrics labeled in blue as somewhat important and useful metrics, but that wearable tech has limits in accurately collecting and processing. These include: sleep stages, recovery and readiness scores, calories, and latency (the “lag” in how quickly the device captures a metric such as heart rate).
The metrics labeled in orange are data sets that Dr. Tierney feels are pushed by wearable tech companies, but may not be completely useful to measure and have the potential to cause more anxiety than practical use. These include: daytime “stress,” standing hours, and blood oxygen (though in rare and specific instances, blood oxygen measuring can identify undiagnosed conditions such as sleep apnea).
Lastly, Dr. Tierney explains that the metrics labeled in red are an extremely important piece to the health puzzle, but wearable tech cannot measure them. Thus, these metrics are what athletes should utilize to help guide their decision-making process, often trumping isolated metrics provided by a wearable. He lists these as: energy, mood, soreness, and context.
Health Metrics Captured Overnight
Let’s take a deeper dive into the important and useful metrics, beginning with the ones that are collected overnight. These include resting heart rate, overnight heart rate variability, body temperature, breathing rate, and total sleep time.
What is overnight heart rate variability (HRV)? It’s the variation in time between heart beats, which can reflect an athlete’s adaptation to a stimulus and readiness to train or an athlete’s nervous system fatigue and possible overtraining—in general, a higher HRV is reflective of better adaptation to training and a lower HRV could signify overreaching.
Resting heart rate and overnight heart rate variability are best taken first thing in the morning. Watching the trends of these two and interpreting both in conjunction with each other can provide more insight than looking at them in isolation. It’s important to trend a metric such as heart rate variability without comparing it to other athletes, friends, or family members, as this metric is extremely individualized. Working with a coach or exercise physiologist who understands the relationship and context of resting heart rate and overnight heart rate variability is key in utilizing and interpreting them appropriately.
RELATED: The Connection Between Heart Rate Variability and Mental Health
Body temperature trends can be helpful for female athletes, too, particularly when it comes to menstrual tracking. We tend to see a gradual increase in basal body temperature in the days leading up to, and during, menstruation, due to hormonal fluctuations. Of course, it can also be helpful in identifying early signs of illness if fever is present.
Breathing rate can slow in instances of sleep apnea and other health conditions such as heartburn. Conversely, it can increase due to anxiety, excessive stress, or infection.
For total sleep time, he recommends evaluating trends versus focusing on this metric in isolation on a day-to-day basis, cautioning of the medical term “orthosomnia,” which refers to an unhealthy obsession with getting perfect sleep. A 2023 study out of the Journal of Nature and Science of Sleep refers to orthosomnia as a societal phenomenon, similar to “social jet lag,” and has even been linked to “nomophobia,” which is the fear of losing access to one’s cell phone.
They outline that individuals suffering from these intertwined phenomena experience insomnia-like symptoms: difficulty falling asleep, waking up throughout the night, waking up too early, not feeling well-rested after a night’s sleep, drowsiness during the day, anger, despair, or anxiety, as well a difficulty focusing with an increase in mistakes. This mental health concern was initially identified in patients with diabetes, with the roll-out of higher tech continuous glucose monitors, and the worry and psychological distress of having constant access to numbers.
Dr. Tierney underscores that wearable trackers are not perfect for measuring sleep and sleep stages, but peoples’ belief in these variables can be difficult to alter. He issues caution in using proprietary “scores” to infer readiness to train, as these have not been validated and likely have high levels of inter-individual variability.
Total sleep time data comparison across three different wearables captured over 28 nights by Dr. Tierney
For resting heart rate, overnight heart rate variability, body temperature, and breathing rate, Dr. Tierney recommends looking for deviations outside of the normal range and assessing these deviations in conjunction with how you feel.
But what is the “normal range”? Typically, there is a broad range for these metrics outlined for all humans. As you use your wearable more consistently, the device gathers more and more data from you in order to establish what your normal range is for these metrics. It’s important to trend these metrics using a single device over time, in order to have the most accurate set of data to assess.
Dr. Tierney shared this graph, based on his own experience of comparing resting heart rate across three different wearables. The datasets differ across devices, making it important to trend via one wearable and then paying attention to deviations outside of your normal range.
Metrics Captured During Exercise
Most athletes are familiar with these metrics, which include heart rate, pace, distance, and time. In deciding which of these metrics to focus on, it largely depends on the intention of the specific workout, your goals in training, and the training philosophies of you or your coach. For most athletes, a combination of all of them is used within training.
There are many factors that can affect these exercise-specific metrics. As trail runners, it’s important to understand the role that elevation gain, variety of terrain, and altitude play in heart rate. Many wearables solely use heart rate captured during a run to estimate fitness (and projected race times), so these predictors will be skewed if your run contains a lot of elevation gain and slower paces (but with a higher heart rate) compared to flat terrain, as the wearable is not taking terrain into account. For this reason, I don’t recommend putting a lot of stock into the wearable VO2max or race predictors if you are logging the bulk of your miles on trails or very hilly terrain.
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It’s important to note that, in general, chest or bicep and proximal arm heart rate monitors tend to be more accurate than wrist-based heart rate. A study from 2017 assessed 60 participants and found greater device error in accurately capturing wrist-based heart rate in males, greater body mass index, darker skin tone, and in walking and running versus cycling.
Additionally, in cold conditions, the blood circulation to the skin may become too weak, which disrupts the device’s ability to get an accurate reading. If you are someone who trains by heart rate zones, it’s helpful to work with a coach who’s familiar with how to re-test throughout training and adjust these zones for you as you gain fitness.
Health Metrics Captured During the Day
Dr. Tierney doesn’t recommend closely monitoring the metrics that wearables provide during the day, but he states that it can be helpful to keep an eye on steps, movement every hour, and “mindful” minutes. We agreed that these metrics can be useful in reminding athletes to take breaks, especially for those working on a job that requires a lot of sitting.
Utilizing this metric can be helpful in building in walk breaks and breathwork, but he cautions that wearables aren’t great at measuring mindfulness. For those in healthcare or jobs that require a lot of time on foot, tracking steps can be helpful in understanding the physiological stress that the job is placing on the athlete’s body, and training can be adjusted in order to preserve energy levels.
For athletes in the peak of training, I do not recommend tracking step count or movement every hour and in most instances, it’s helpful to turn these notifications off. This specifically applies to the annoying beep on your watch to “Move!” when you are intentionally trying to rest following a long run.
Application of Health Metrics in Real Life
OK, so we’ve identified the metrics that are important to trend. Now, how do we apply this in making decisions in real life?
Dr. Tierney created the following perception versus data matrix to help bridge the gap between what your wearable is telling you and how you feel. Here is our insight on how to navigate the two, leading with how you wake up feeling:
- If you wake up feeling poorly and the data is below or worse than your normal range, our recommendation is to pull back and make training adjustments, with the plan to shift back towards your routine in the evening. Focus on high quality nutrition and hydration, as well as an earlier bedtime if possible, in order to maximize your recovery heading into the following day.
- If you wake up feeling poorly but your data is within normal range, our recommendation is to proceed forward with caution depending on how severe you feel, keeping a close eye on this and how your data looks the following day. We recommend modifying your training to an easy effort, removing intensity from any work-outs, and being mindful of the mental impacts of trying to force a work-out when you aren’t feeling your best.
- If you wake up feeling poorly but your data looks better than normal, this could mean that your body is trying to address something. It is common for this to happen in athletes during early stages of infection or illness, particularly in regards to heart rate variability. Our recommendation is to monitor if this trend continues and if feeling very poorly, modify training for the day and consider removing intensity from your work-out.
- If you wake up feeling normal to very good but your data shows something worse than normal, no need to be alarmed. This could be due to malfunction with your wearable or gaps in data overnight that can skew averages. We do not recommend making training adjustments, but it’s important to check in with the data and how you’re feeling the next day
The remaining scenarios include waking up feeling normal to very good in the presence of normal or very good data. In these instances, we recommend proceeding forward with training.
It’s helpful to also note your nutrition and recovery methods on these days, the time you went to bed and woke up, as well as overall life stress, so that you can have a better idea of what is contributing to you feeling your best and what is positively impacting your objective health metrics.
Final Thoughts
The data supplied by wearable technology can be extremely useful in providing insight to one’s health, readiness to train hard, and how the athlete is adapting to training. But maximizing the use of wearable technology requires consistency both in using the wearable and assessing the data from the same time each day, as well as trending the data over time.
For some, this can help catch early signs of illness or overtraining, which can be very beneficial in righting the ship. For others, the overload of data may cause additional stress or the desire to achieve high “scores,” leading to fixation on and detriment to sleep quality and the overattachment to the wearable and cellular phone as is observed in both orthosomnia and nomophobia.
The most important takeaway here is to be led by how you’re feeling and to prioritize the metrics that wearable technology cannot measure (energy, mood, soreness, and context). The data from your wearable can then function as additional and supportive evidence in guiding your training and decision-making.
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