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How to use HRV daily

This is an excerpt from Heart Rate Variability by Sylvain Laborde,Marco Altini,Emma Mosley,Dan Plews.

Developing an HRV routine starts with understanding when and how to measure your HRV for the most meaningful insights. For many athletes, the best time to measure HRV is right after waking up, before the body has been influenced by any stressor such as food, caffeine, or physical activity. This HRV baseline reading can provide a reliable indicator of the state of your autonomic nervous system, specifically cardiac vagal activity, in response to acute and chronic stressors. However, your HRV routine can offer even more insight. Incorporating pre- and postexercise measurements (following the model of the three Rs; see chapter 1) can provide additional information on your training. Using techniques such as cold water immersion (Laborde et al., 2023) or slow-paced breathing (Laborde, Lentes, et al., 2019) might help quicken the recovery of the autonomic nervous system to baseline levels. Furthermore, newly developed methods such as HRV analysis during training using detrended fluctuation analysis alpha 1 could provide guidance in terms of exercise intensity. In addition, it is important to be mindful of lifestyle factors that can influence HRV, such as sleep quality, nutrition, and hydration (see chapter 3). Keeping a journal or using an app to log these variables alongside your HRV readings can help you identify patterns and make more informed decisions about your training and recovery.

A typical day might start with a morning HRV measurement, followed by a subjective questionnaire and strategic checks before key training sessions, coupled with a session of slow-paced breathing to shift toward cardiac parasympathetic dominance just before sleep. Let us look at these aspects in more detail.

The Morning Routine

For the morning routine, we want to emphasize the importance of consistency in timing. We recommend that HRV be measured immediately after waking up, ideally in a seated body position (or alternatively using the orthostatic test, from supine to standing; see chapter 4). This timing minimizes the impact of external factors such as movement and other stressors, ensuring that the readings accurately reflect your baseline cardiac vagal activity. This level of clarity is often not achievable with night measurements because the body is still in a sleep state and the activity of the autonomic nervous system differs from that in a wakeful state. This could potentially lead to less straightforward insights or obvious changes given that the body has not been given enough time to bounce back from evening stressors. Measuring on awakening in the morning allows us to truly capture the body’s response because morning is well after the previous day’s stressors and after the restorative effect of sleep (and before today’s stressors). So keep in mind that timing is key.

Morning HRV measurements are crucial because they provide a stable reference point for evaluating your body’s readiness to handle stress, whether that stress comes from physical training, work, or life in general. By following a similar routine each morning to measure HRV, you can monitor trends over time and make informed decisions about your training intensity, recovery needs, and overall health management. This is the assessment phase.

Remember, though, that it is important to focus on tracking trends and comparing HRV measurements to your own normal range. If a low value still unsettles you, see the sidebar called When a Low Morning HRV Reading Worries You for help in reframing these situations.

To get the most accurate insights, it is essential to establish a routine of regular recordings. Ideally, you should record HRV every day. If for any reason this is not feasible, you should aim for a minimum of three recordings per week to ensure meaningful trend data (Plews et al., 2014). That being said, we strongly recommend daily monitoring: Making it a habit to record HRV is the most effective way of ensuring reliable and valuable data over time.

Night Data

Nighttime HRV data offer a unique glimpse into the body’s autonomic nervous system during extended periods of rest, which can provide insights into recovery and reveal how the body copes with stress through baseline HRV. In particular, under certain circumstances—for example, if there are no late-evening stressors (no alcohol intake, no evening exercise, no late psychological stressors, no late eating, etc.)—nighttime HRV can be rather similar to morning measurements taken while lying down. The main advantage of measuring nighttime HRV is that it is a passive form of data collection, which might be more practical for some of us.

However, there are some downsides to measuring HRV at night as well. Nighttime HRV can be influenced by the natural fluctuations of sleep stages, which might introduce variability that is not necessarily related to recovery or stress. As we discussed in chapter 3, different stages of sleep—such as deep sleep or rapid eye movement sleep—can cause variations in HRV that might not directly correlate with the body’s overall stress response or recovery state. Analyzing HRV from short segments can make it challenging to draw clear conclusions. The interpretation of nighttime HRV data, which span several hours, is particularly complex because it involves understanding data collected over a prolonged period rather than a single point in time. For instance, should we focus on the early part of the night when deep sleep is more prevalent? This might result in data that are closely tied to the previous day’s stress rather than reflecting current conditions. Alternatively, using data from the latter part of the night could be problematic if you are partially awake, which could potentially affect the accuracy of the measurements. Each approach has its own considerations, and it is important to choose the method that best aligns with your goals for HRV monitoring (Bellenger et al., 2021).

In addition, disturbances during the night, such as waking up or simply moving, can affect HRV readings given the low-quality data captured by optical sensors (e.g., sensors worn at the wrist or on the finger) when any physical movement is present. Unfortunately, under these circumstances, HRV cannot be measured correctly. These factors can skew the data and make it harder to interpret nighttime HRV with the same clarity as morning readings, which are taken at a consistent time under controlled conditions.

Moreover, arrhythmias, which are experienced by up to 75% of the population (Ahn, 2013), might also make night data unreliable, an issue that can often be easily avoided (or noticed) when taking a morning measurement and looking at the data. This actually happened to Marco when he had a more frequent arrhythmia a few years ago. Data from wearables showed high HRV every night for weeks (with the associated “advice” to go and smash it), despite the fact that his HRV was in fact low, but highly artifacted, something that was obvious in morning measurements. The lack of transparency in wearables, which do not report signal quality but always pretend the quality is optimal, makes it impossible to tell whether there are issues in the data when we finally wake up and look at our HRV in the associated app, because we were asleep during data collection. These are important issues to consider when deciding whether to use a wearable to track our HRV.

Finally, night data can only be collected while lying down. This means that we lose the opportunity to measure HRV while exploiting the orthostatic stressor (i.e., sitting up or standing, to increase the sensitivity of the measurement to stress). We recommend leveraging the orthostatic stressor for morning measurements, especially for endurance athletes with relatively low resting heart rates (e.g., 50 beats per minute or lower). Although night data can serve as a useful complement to morning HRV measurements and might help us identify strong stressors (such as sickness or late-day or evening factors, such as alcohol intake), they may not be as reliable for making specific, short-term training decisions (Nuuttila et al., 2022).

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