Heart rate variability reproducibility during exercise

McNarry, Melitta A.; Lewis, Michael

doi:10.1088/0967-3334/33/7/1123

Cited by 18 publications

(17 citation statements)

References 48 publications

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“…Supine measures show lower CV than standing and exercise measures (Sandercock et al, 2005). The CV for HRV during exercise has been shown to be large, with some values ranging from 120 to 190% (Winsley et al, 2003; Leicht and Allen, 2008; McNarry and Lewis, 2012). The CVs for HRex or HRR measures (3–35%, Table 1) are clearly lower than those for exercise HRV (Bosquet et al, 2008a; Al Haddad et al, 2011; Dupuy et al, 2012), and their CVs tend to decrease slightly when exercise intensity increases (Lamberts et al, 2004, 2011a).…”

Section: Interpreting Changes In Heart Rate Measures: “Statistics Arementioning

confidence: 99%

Monitoring training status with HR measures: do all roads lead to Rome?

2014

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Measures of resting, exercise, and recovery heart rate are receiving increasing interest for monitoring fatigue, fitness and endurance performance responses, which has direct implications for adjusting training load (1) daily during specific training blocks and (2) throughout the competitive season. However, these measures are still not widely implemented to monitor athletes' responses to training load, probably because of apparent contradictory findings in the literature. In this review I contend that most of the contradictory findings are related to methodological inconsistencies and/or misinterpretation of the data rather than to limitations of heart rate measures to accurately inform on training status. I also provide evidence that measures derived from 5-min (almost daily) recordings of resting (indices capturing beat-to-beat changes in heart rate, reflecting cardiac parasympathetic activity) and submaximal exercise (30- to 60-s average) heart rate are likely the most useful monitoring tools. For appropriate interpretation at the individual level, changes in a given measure should be interpreted by taking into account the error of measurement and the smallest important change of the measure, as well as the training context (training phase, load, and intensity distribution). The decision to use a given measure should be based upon the level of information that is required by the athlete, the marker's sensitivity to changes in training status and the practical constrains required for the measurements. However, measures of heart rate cannot inform on all aspects of wellness, fatigue, and performance, so their use in combination with daily training logs, psychometric questionnaires and non-invasive, cost-effective performance tests such as a countermovement jump may offer a complete solution to monitor training status in athletes participating in aerobic-oriented sports.

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Section: Interpreting Changes In Heart Rate Measures: “Statistics Arementioning

confidence: 99%

Monitoring training status with HR measures: do all roads lead to Rome?

2014

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“…HRV can be quantified reproducibly during rest and exercise (McNarry & Lewis, 2012) and our previous observations indicate that this is also generally true for beat-to-beat haemodynamic variables (D'Silva et al, 2014). Consequently single physiological assessments were considered appropriate at each defined stage of the assessment schedule.…”

Section: Physiological Measurementsmentioning

confidence: 99%

Changes in heart rate variability and QT variability during the first trimester of pregnancy

et al. 2015

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The risk of new-onset arrhythmia during pregnancy is high, presumably relating to changes in both haemodynamic and cardiac autonomic function. The ability to non-invasively assess an individual's risk of developing arrhythmia during pregnancy would therefore be clinically significant. We aimed to quantify electrocardiographic temporal characteristics during the first trimester of pregnancy and to compare these with non-pregnant controls. Ninety-nine pregnant women and sixty-three non-pregnant women underwent non-invasive cardiovascular and haemodynamic assessment during a protocol consisting of various physiological states (postural manoeurvres, light exercise and metronomic breathing). Variables measured included stroke volume, cardiac output, heart rate, heart rate variability, QT and QT variability and QTVI (a measure of the variability of QT relative to that of RR). Heart rate (p < 0.0005, p < 0.0005, p < 0.0005) and cardiac output (p = 0.043, p < 0.0005, p < 0.0005) were greater in pregnant women in all physiological states (respectively for the supine position, light exercise and metronomic breathing state), whilst stroke volume was lower in pregnancy only during the supine position (p < 0.0005). QTe (Q wave onset to T wave end) and QTa (T wave apex) were significantly shortened (p < 0.05) and QTeVI and QTaVI were increased in pregnancy in all physiological states (p < 0.0005). QT variability (p < 0.002) was greater in pregnant women during the supine position, whilst heart rate variability was reduced in pregnancy in all states (p < 0.0005). Early pregnancy is associated with substantial changes in heart rate variability, reflecting a reduction in parasympathetic tone and an increase in sympathetic activity. QTVI shifted to a less favourable value, reflecting a greater than normal amount of QT variability. QTVI appears to be a useful method for quantifying changes in QT variability relative to RR (or heart rate) variability, being sensitive not only to physiological state but also to gestational age. We support the use of non-invasive markers of cardiac electrical variability to evaluate the risk of arrhythmic events in pregnancy, and we recommend the use of multiple physiological states during the assessment protocol.

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“…A similar relationship between vmHRV and aerobic exercise has been observed between individuals of differing levels of self-reported daily physical activity and sedentary controls (De Meersman, 1993;Rennie et al, 2003). While changes in vmHRV during exercise have been examined in recreationally active individuals and elite athletes (McNarry & Lewis, 2012;Sarmiento et al, 2013), further research is still needed to understand the impact that BMI may have on PNS reactivity and recovery under exercising conditions. The present study expands on previous research by attempting to examine individual patterns of vmHRV reactivity as a function of BMI throughout baseline (seated rest), a graded ergometer task, and recovery (seated rest) conditions.…”

Section: Introductionmentioning

confidence: 64%

“…A similar relationship between vmHRV and aerobic exercise has been observed between individuals of differing levels of self‐reported daily physical activity and sedentary controls (De Meersman, ; Rennie et al, ). While changes in vmHRV during exercise have been examined in recreationally active individuals and elite athletes (McNarry & Lewis, ; Sarmiento et al, ), further research is still needed to understand the impact that BMI may have on PNS reactivity and recovery under exercising conditions.…”

Section: Introductionmentioning

confidence: 99%

Body mass index and parasympathetic nervous system reactivity and recovery following graded exercise

Gerardo

Williams

Kessler

et al. 2018

American J Hum Biol

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Objectives The present study sought to expand upon prior investigations examining patterns of vagally mediated heart rate variability (vmHRV) and perceived exertion as a function of body mass index (BMI) in response to and recovery from exercise. Methods Participants underwent a resting (baseline) period, followed by a graded exercise protocol on an ergometer with ascending difficulty stages, and finally another resting (recovery) period. Individuals were stratified into three BMI groups: low, moderate, and high. Results Individuals in the high BMI group exhibited a significantly greater decrease in vmHRV from baseline to graded exercise in comparison to the moderate BMI group. Individuals in the high BMI group also showed significantly lower vmHRV at recovery compared with baseline than individuals with moderate BMI; indicating that the high BMI group's vmHRV did not recover to the degree of those in the moderate BMI group. No significant results regarding vmHRV were found in the low BMI group. Of note, BMI and perceived exertion during the recovery period were positively associated. Results also showed a significant negative association between vmHRV and perceived exertion at each grade of exercise. There was no significant association between vmHRV and perceived exertion during baseline or recovery. Conclusions This report extends prior research studying BMI and patterns of vmHRV reactivity in the domain of physical exercise. Our data contribute to previous reports suggesting that high BMI can lead to maladaptive patterns of vmHRV reactivity to and recovery from physical exercise.

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Heart rate variability reproducibility during exercise

Cited by 18 publications

References 48 publications

Monitoring training status with HR measures: do all roads lead to Rome?

Monitoring training status with HR measures: do all roads lead to Rome?

Changes in heart rate variability and QT variability during the first trimester of pregnancy

Body mass index and parasympathetic nervous system reactivity and recovery following graded exercise

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