Recurrence quantification analysis of heart rate variability is a COVID-safe alternative to gas analysis in the detection of metabolic thresholds

  1. G. Zimatore
  2. L. Falcioni
  3. M.C. Gallotta
  4. V. Bonavolontà
  5. M. Campanella
  6. M. De Spirito
  7. L. Guidetti
  8. C. Baldari

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Abstract

The first aim of the study was to verify if in individuals with different physical fitness levels the Recurrence Quantification Analysis (RQA) of Heart Rate Variability (HRV) time series could be an alternative to Gas Exchange (GE) analysis in the determination of metabolic thresholds. The second aim was to investigate the validity of the RQA method compared to the GE method in thresholds detection. The two metabolic thresholds were estimated in thirty young individuals during Cardiopulmonary Exercise Testing on a cycle-ergometer and HR, VO 2 and Workload were measured by the two different methods (RQA and GE methods). RM ANOVA was used to assess main effects of methods and methods-by-groups interaction effects for HR, VO 2 and Workload at the aerobic (AerT) and the anaerobic (AnT) thresholds. Validity of the RQA at both thresholds was assessed for HR, VO 2 and Workload by Ordinary Least Products (OLP) regression analysis, Typical Percentage Errors (TE), Intraclass Correlation Coefficients (ICC) and the Bland Altman plots. No methods-by-groups interaction effects were detected for HR, VO 2 and Workload at the AerT and the AnT. The OLP regression analysis showed that at both thresholds RQA and GE methods had very strong correlations ( r >0.8) in all variables (HR, VO 2 and Workload). Slope and intercept values always included the 1 and the 0, respectively. At the AerT the TE ranged from 4.02% to 10.47% (HR and Workload, respectively) and in all variables ICC values were excellent (≥0.85). At the AnT the TE ranged from 2.61% to 6.64% (HR and Workload, respectively) and in all variables ICC values were excellent (≥0.89). Our results suggest that the RQA of HRV time series is a COVID-safe approach for the determination of metabolic thresholds in individuals with different physical fitness levels, therefore, it can be used as a valid method for threshold detection alternative to gas analysis.

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  1. ScreenIT

    SciScore for 10.1101/2021.03.22.436405: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All statistical analysis was performed by SPSS version 24.0 software (SPSS Inc., Chicago, IL).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The following limitations are acknowledged. First, the number of participants in the study was relatively small. Second, the population was not heterogeneous (composed by 28 males and only 2 females). Thus, considering that metabolic thresholds are influenced by phenotypical sex differences [44], further research should investigate the RQA of HRV time series separately in males and females. In addition, future physiological exercise studies should assess if the findings of the present work could be adopted in particular field tests which do not necessarily require the use of a cycle-ergometer.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2021.03.22.436405v1 on bioRxiv