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

Buchheit M - Front Physiol (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Sport Science Department, Myorobie Association Montvalezan, France.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Effect of an ectopic beat on traditional heart rate (HR) variability (HRV) indices. R-R intervals recorded in supine position for 5 min with data either non-edited (upper left panel) or with beat removal and linear interpolation with adjacent values (upper right panel). Middle panels show the associated power spectral density (PSD) distribution on a spectrogram (Kubio HRV, 2.1, Biosignal Analysis and Medical Imaging Group, Kuopio, Finland) and lower panels, the common HRV indices derived from those two R-R series. SDNN, standard deviation of normal R-R intervals; rMSSD, square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals; SD1, standard deviation of instantaneous beat-to-beat R-R interval variability measured from Poincaré plots; SD2, standard deviation of long-term beat-to-beat R-R interval variability measured from Poincaré plots; LF, low-frequency oscillations, HF, high-frequency oscillations.
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Figure 3: Effect of an ectopic beat on traditional heart rate (HR) variability (HRV) indices. R-R intervals recorded in supine position for 5 min with data either non-edited (upper left panel) or with beat removal and linear interpolation with adjacent values (upper right panel). Middle panels show the associated power spectral density (PSD) distribution on a spectrogram (Kubio HRV, 2.1, Biosignal Analysis and Medical Imaging Group, Kuopio, Finland) and lower panels, the common HRV indices derived from those two R-R series. SDNN, standard deviation of normal R-R intervals; rMSSD, square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals; SD1, standard deviation of instantaneous beat-to-beat R-R interval variability measured from Poincaré plots; SD2, standard deviation of long-term beat-to-beat R-R interval variability measured from Poincaré plots; LF, low-frequency oscillations, HF, high-frequency oscillations.

Mentions: The importance of R-R series editing prior to analyse is often overlooked (Task Force, 1996). As shown in Figure 3, the presence of a single ectopic beat (or a skipped beat) over a 5-min recording can modify common HRV indices up to 50%. Since these differences might not reflect real changes in the ANS status, a proper editing of R-R series before analysis is crucial. While the examination of the complete ECG trace is required to draw definitive conclusions on the nature of “abnormal” heart beats (i.e., missed vs. ectopic beats), doing so is unrealistic in practice when dealing with a large number of files daily. R-R series are often automatically edited within manufacturers' software (beat removal and linear extrapolation from adjacent beats) (Nunan et al., 2009). While a few errors may still occur using these automatic filters (e.g., removal of ANS-generated change in HR), this is not a major issue for athlete monitoring when their use is consistent prior to each analysis. The impact of ectopic beats on HRR are likely similar than for HRV when fitting a monoexponential model, since those beats can distort the overall fit and in turn, the time constant of the exponential. Ectopic beats are likely less problematic for HRR60 s, since the beats used for calculation are generally averaged over a few seconds (Buchheit et al., 2007).


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

Buchheit M - Front Physiol (2014)

Effect of an ectopic beat on traditional heart rate (HR) variability (HRV) indices. R-R intervals recorded in supine position for 5 min with data either non-edited (upper left panel) or with beat removal and linear interpolation with adjacent values (upper right panel). Middle panels show the associated power spectral density (PSD) distribution on a spectrogram (Kubio HRV, 2.1, Biosignal Analysis and Medical Imaging Group, Kuopio, Finland) and lower panels, the common HRV indices derived from those two R-R series. SDNN, standard deviation of normal R-R intervals; rMSSD, square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals; SD1, standard deviation of instantaneous beat-to-beat R-R interval variability measured from Poincaré plots; SD2, standard deviation of long-term beat-to-beat R-R interval variability measured from Poincaré plots; LF, low-frequency oscillations, HF, high-frequency oscillations.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3936188&req=5

Figure 3: Effect of an ectopic beat on traditional heart rate (HR) variability (HRV) indices. R-R intervals recorded in supine position for 5 min with data either non-edited (upper left panel) or with beat removal and linear interpolation with adjacent values (upper right panel). Middle panels show the associated power spectral density (PSD) distribution on a spectrogram (Kubio HRV, 2.1, Biosignal Analysis and Medical Imaging Group, Kuopio, Finland) and lower panels, the common HRV indices derived from those two R-R series. SDNN, standard deviation of normal R-R intervals; rMSSD, square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals; SD1, standard deviation of instantaneous beat-to-beat R-R interval variability measured from Poincaré plots; SD2, standard deviation of long-term beat-to-beat R-R interval variability measured from Poincaré plots; LF, low-frequency oscillations, HF, high-frequency oscillations.
Mentions: The importance of R-R series editing prior to analyse is often overlooked (Task Force, 1996). As shown in Figure 3, the presence of a single ectopic beat (or a skipped beat) over a 5-min recording can modify common HRV indices up to 50%. Since these differences might not reflect real changes in the ANS status, a proper editing of R-R series before analysis is crucial. While the examination of the complete ECG trace is required to draw definitive conclusions on the nature of “abnormal” heart beats (i.e., missed vs. ectopic beats), doing so is unrealistic in practice when dealing with a large number of files daily. R-R series are often automatically edited within manufacturers' software (beat removal and linear extrapolation from adjacent beats) (Nunan et al., 2009). While a few errors may still occur using these automatic filters (e.g., removal of ANS-generated change in HR), this is not a major issue for athlete monitoring when their use is consistent prior to each analysis. The impact of ectopic beats on HRR are likely similar than for HRV when fitting a monoexponential model, since those beats can distort the overall fit and in turn, the time constant of the exponential. Ectopic beats are likely less problematic for HRR60 s, since the beats used for calculation are generally averaged over a few seconds (Buchheit et al., 2007).

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Sport Science Department, Myorobie Association Montvalezan, France.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus