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Using Heart Rate Variability to Maximize Performance

Four case studies explore the individual variability of HRV.

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Two men using heart rate variability

Here’s a pop quiz: What do Ironman® triathlon training, college reunion drinking, COVID-19, HIIT, the altitude of your city, family illness, food sensitivities, pregnancy, aging and excessive holiday consumption have in common? Answer: They affect the body’s neuromuscular and endocrine systems similarly, based on heart rate variability (HRV) data.

People in each of these examples have used HRV as a metric to guide their workout plans, but the dramatic lifestyle responses were unexpected and revealing. Can fitness professionals use HRV data to help clients fine-tune their workouts and their all-important recovery? Research and real-life case studies shed some light.

What Is Heart Rate Variability?

First, it’s important to understand the basics of heart rate variability, which measures the variance in the time between heartbeats. When someone’s heart rate is 60 beats per minute, the logical expectation is that each beat comes every second. In reality, the timing is less regular, with beats coming at 0.8 of a second, 1.2 seconds, 1 second, etc. These time measurements reflect how the brain and body react to daily physical, emotional and mental challenges.

The nervous system re-sponds to imposed challenges via two subsystems: the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). HRV measurements reflect which nervous system is most active. The SNS is dominant when the beats come at more regular intervals. More variable intervals indicate PNS dominance.

The SNS is the action/reaction system that helps us go about our day, perform workouts and face mental and emotional challenges. It releases chemicals such as cortisol and adrenaline to increase the heartbeat, blood pressure, strength and stamina, and it sharpens the senses for increased focus (Gedam & Paul 2021).

The PNS is associated with a passive physical response, helping us to rest and recover from challenges following an SNS reaction. It activates chemicals from an anti-inflammatory pathway to promote digestion and rest, lower heart rate, and reduce SNS-mediated inflammatory chemicals (Owens 2020).

See also: Wearable Heart Rate Trackers: Which Works Best?

How HRV, PNS and SNS interact

When the neuromuscular system is handling many unusual challenges (SNS dominant), HRV trends downward to help the body through the crisis, while HRV trends upward when the system is dealing with minimal challenges (PNS dominant), where a more regular rate is less crucial.

Monitoring HRV can help you make exercise and nutrition programming more effective. This case study review, with relevant concurrent research, provides further insight.

Case Study One: Overindulgence and Suboptimal Nutrition

Group of people drinking to show shifts in heart rate variability

Excessive consumption of pro-inflammatory food and drink produces an HRV response.

In 1 month, an elite 45-year-old male triathlete had two interesting 20% HRV sympathetic drops. The first came after a full Ironman, and the second after a weekend college reunion.

Most HRV exercise science has focused on endurance training and found HRV to be an accurate measurement of physical challenges.

A literature review found more flexible HRV-guided training to be more effective than rigid preplanned periodization training (Nuuttila et al. 2017). Since HRV can detect inflammatory changes in the body, endurance athletes should adjust their training when HRV drops.

Excessive consumption of pro-inflammatory food and drink produces a similar HRV response (Young & Benton 2018; Strüven et al. 2021).

The two similar reactions linked an ultra-endurance event to suboptimal food consumption, highlighting this athlete’s need to improve nutritional choices overall.

Case Study Two: COVID-19 and Shock

A 52-year-old real estate agent and fitness instructor saw a 20%–30% drop in HRV after being exposed to the coronavirus, despite testing negative. Symptoms followed a few days later. Fast-forward 1 month, and a similar decline occurred for several days upon receiving news of a close relative’s cancer diagnosis. After clarification and acceptance of the relative’s prognosis, HRV rose.

Shock is a detrimental stressor, and HRV can drop precipitously from mental and emotional shock or due to physiological stress from an invading virus.

Some research has recently called for HRV as a predictive tool for coronavirus and other pathogens. In this case study, HRV data reflect similar physiological and psychological shock from two disparate occurrences (Parin, Polevaia & Polevaia 2017; Drury et al. 2021).

Case Study Three: Age and Intensity

A 69-year-old fitness instructor’s HRV dropped 10%–20% during a period of increased workout intensity leading up to the holidays. More significant drops occurred after prime holiday gatherings, culminating in a positive coronavirus test between Christmas and New Year’s Day.

The HRV decrease was expected, but there are unknowns about aging and high-intensity exercise that inhibited data interpretation. Most research on high-intensity exercise after age 60 is scarce and has yet to catch up with research on high-performance activities (Hoolihan 2019).

HRV naturally declines with age, but research does not quantify what factors influence this fall in an active population (Jandackova et al. 2016; Garavaglia et al. 2021). In this case, riding out the declines in HRV after intensive exercise periods and holiday feasting was a poor choice. Nutritional modification will be next year’s priority over increased exercise, and more attention will be given to downward HRV trends.

Case Study Four: Long-Term Versus Short-Term Analysis

An elite competitive Olympic-style weightlifter has been using HRV as a training guide in national competitions since 2014 and adjusts his training schedule every week if a low reading occurs during a recovery day following a heavy lift day. Small changes in the next recovery workout, like cutting out a set or two, help him see how his body responds.

This mirrors the recommendations of most HRV strength and power research, suggesting HRV data as an analysis of more specific workouts rather than long-term training (Chen et al. 2011; Iellamo et al. 2019).

See also: Unlocking the Potential of Heart Rate Monitors

Other Cases and Causes of Heart Rate Variability Drops

Other case studies showed significant changes in HRV as a result of a variety of other factors:

  • A mountain resident’s HRV rose when they spent time at sea level.
  • An individual on an elimination diet experienced an HRV drop when gluten was introduced.
  • A 46-year-old pregnant woman used HRV to modulate her exercise intensity for a safer pregnancy.

Recent research has confirmed that altitude and, as mentioned earlier, inflammatory foods affect HRV through SNS pathways (Javaloyes et al. 2021; Young and Benton 2018; Strüven et al. 2021). And a research review on pregnancy and HRV found interesting trends indicating moderate physical activity improved the HRV of both the mother and the fetus (Dietz et al. 2016).

Heart Rate Variability Practices

Man using hrv for running

Research indicates wearables are practical measuring tools for HR and HRV, but there is wide variance with wrist sensor HR measurements.

HRV can be useful, but complete scientific validation is qualified. The increase of phone apps and other activity-measuring devices has produced many options, further complicating consistent reliability. As more apps and devices become available, more caution is needed when assessing readings. Research indicates wearables are practical measuring tools for HR and HRV, but there is wide variance with wrist sensor HR measurements (Nelson et al. 2020). HRV can be open to discrepancy as well. Here are some best practices that can help improve HRV measurement and monitoring.

Take consistent measurements. The optimal way to take HRV readings is while calm and in a consistent setting, immediately upon waking. It’s important to remain supine and to breathe in a controlled, regular manner, as movement or random breathing patterns may alter the data. Results can become an accurate baseline for assessing progress and physiological need. Some devices measure HRV throughout the day but, at present, research regarding accuracy is scant. Combine active HRV results with a morning resting reading to provide proper context to trends.

Combine HRV with other biofeedback. Since HRV is affected by numerous variables, combining it with a few other metrics to provide a more accurate physiological profile will help determine exercise program design. A low resting HR and increased HRV are indicators of a system in balance—using trends in both can guide adjustments. For example, endurance athletes can perform a weekly 30-minute, moderate-intensity workout and monitor working heart rate and rating of perceived exertion (RPE). If HR or RPE is higher than usual, this indicates increased stress. Weekly baseline workouts provide additional confirmation.

It has recently become an essential practice in the athletic training world to measure repetition speed as a recovery assessment (Mann 2016). Personal trainers can use similar protocols to design programs for strength athletes and adjust lifting sets using RPE and repetition speed.

Use trends to interpret data. HRV trends are more important than daily readings. Numerous mental, physical, emotional and environmental factors affect HRV and may be temporary or quickly resolved. Downward weekly or biweekly trends indicate adjustments need to be made in training if no scheduled recovery period is coming soon.

Of note: For most people, there are no “bad” HRV results. The SNS and PNS are elegantly designed to work together. An SNS downward trend may be necessary to help the endocrine and nervous systems prepare for significant challenges. Large PNS upswings are also needed for better recovery to reduce chronic SNS demands (Kim et al. 2018).

Use data for simple changes. HRV downward trends are opportunities for self-reflection and lifestyle changes. Improving nutrition, sleep behavior, reactions to challenges, and exercise recovery are all good things and can change the HRV trajectory.

Evidence, Experience, Efficacy

As heart rate variability research continues with different populations, methods and hypotheses, fitness professionals will be better able to truly customize and predict program outcomes and help clients track and meet their goals. Technological advances will also increase HRV’s reliability as a physiological assessment tool. Increased research and case studies will expand our knowledge of interpreting HRV data and how, exactly, to use it to guide health and exercise programming.

Compare and Despair

While healthy competition among clients in your small-group training is great, encourage people to avoid comparing their HRV with others. Individual differences make it impossible to compare one’s daily readings with those of others. Chest or waist circumference, body temperature or harmless genetic heartbeat anomalies often affect results. For example, in the first case study in this article (page 75), the athlete’s HRV was about 10%–15% lower than that of his training group, despite his elite competition status. Data comparisons can also be misinterpreted when participants are using applications and programs that have different collection methodologies. An average HRV “score” on one app may be considered high on another.

Age, Exercise and HRV

Long-term HRV users will be able to use data to modify exercise and training programs as needed to avoid unnecessary illness and injury caused by overreaching after age 60. Additionally, since HRV has been proven to be an accurate measurement of health and vitality, it may detect potential problems in advance of noticeable symptoms. A rapid change in HRV trends without any concurrent training or lifestyle change can indicate something’s amiss in the cardiovascular system (Shaffer & Ginsberg 2017).


Chen, J-L., et al. 2011. Parasympathetic nervous activity mirrors recovery status in weightlifting performance after training. The Journal of Strength and Conditioning Research, 25 6, 1546–52.

Dietz, P., et al. 2016. The influence of physical activity during pregnancy on maternal, fetal or infant heart rate variability: A systematic review. BMC Pregnancy and Childbirth, 16 (1), 326.

Drury, R.L., et al. 2021. Wireless heart rate variability in assessing community COVID-19. Frontiers in Neuroscience, 15 (564159).

Garavaglia, L., et al. 2021. The effect of age on the heart rate variability of healthy subjects. PLOS ONE, 16 (10).

Gedam, S., & Paul, S. 2021. A review on mental stress detection using wearable sensors and machine learning techniques. IEEE Access, 9, 84045–66.

Hoolihan, C. 2019. Training techniques for high-performance masters athletes. IDEA Fitness Journal. Accessed Mar. 31, 2022: ideafit.com/personal-training/training-techniques-for-high-performance-older-athletes/.

Iellamo, F., et al. 2019. Autonomic nervous system responses to strength training in top-level weight lifters. Physiological Reports, 7 (20), e14233.

Jandackova, V.K., et al. 2016. Are changes in heart rate variability in middle-aged and older people normative or caused by pathological conditions? Findings from a large population-based longitudinal cohort study. Journal of the American Heart Association, 5 (2).

Javaloyes, A., et al. 2021. The use of a smartphone application in monitoring HRV during an altitude training camp in professional female cyclists: A preliminary study. Sensors, 21 (16), 5497.

Kim, H-G., et al. 2018. Stress and heart rate variability: A meta-analysis and review of the literature. Psychiatry Investigation, 15 (3), 235–45.

Mann, B. 2016. Developing Explosive Athletes: Use of Velocity Based Training in Training Athletes. Scotts Valley, CA: CreateSpace.

Nelson, B.W., et al. 2020. Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research. npj Digital Medicine, 3 (90).

Nuuttila, O-P., et al. 2017. Effects of HRV-guided vs. predetermined block training on performance, HRV and serum hormones. International Journal of Sports Medicine, 38 (12), 909–20.

Owens, A.P. 2020. The role of heart rate variability in the future of remote digital biomarkers. Frontiers in Neuroscience, 14 (58245).

Parin, S., Polevaia, S., & Polevaia, A. 2017. A neurochemical framework to stress and the role of the endogenous opioid system in the control of heart rate variability for cognitive load. Presented at COGNITIVE 2017: The Ninth International Conference on Advanced Cognitive Technologies and Applications.

Shaffer, F., & Ginsberg, J.P. 2017. An overview of heart rate variability metrics and norms. Frontiers in Public Health, 5 (258).

Strüven, A., et al. 2021. Obesity, nutrition and heart rate variability. International Journal of Molecular Sciences, 22 (8), 4215.

Young, H., & Benton, D. 2018. Heart-rate variability: A biomarker to study the influence of nutrition on physiological and psychological health? Behavioural Pharmacology, 29 (2 & 3), 140–51.


Charlie Hoolihan

Director of personal training for the Pelican Athletic Club in Mandeville, Louisiana. He is a member of the IDEA personal trainer membership committee, a fitness writer and presenter. Certifications: NASM, NSCA

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