Quantifying Stress and Relaxation: A New Measure of Heart Rate Variability as a Reliable Biomarker
<b>Background/Objectives:</b> For the rapid, objective characterization of the physiological stress response, there is currently no generally recognized standard. The stress measurement methods used in practice (e.g., for psychological measures of stress) are often subjective, or in the...
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2025-01-01
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| author | Emese Rudics András Buzás Antónia Pálfi Zoltán Szabó Ádám Nagy Emőke Adrienn Hompoth József Dombi Vilmos Bilicki István Szendi András Dér |
| author_facet | Emese Rudics András Buzás Antónia Pálfi Zoltán Szabó Ádám Nagy Emőke Adrienn Hompoth József Dombi Vilmos Bilicki István Szendi András Dér |
| author_sort | Emese Rudics |
| collection | DOAJ |
| description | <b>Background/Objectives:</b> For the rapid, objective characterization of the physiological stress response, there is currently no generally recognized standard. The stress measurement methods used in practice (e.g., for psychological measures of stress) are often subjective, or in the case of biological markers (e.g., cortisol, amylase), they usually require a blood test. For this reason, the use of heart rate variability (HRV) to characterize stress has recently come to the fore. HRV is the variability in the length of heartbeat intervals, which indicates the ability of the heart to respond to various physiological and environmental stimuli. However, the conventional HRV metrics are not corrected for heart rate dependence; hence, they fail to fully account for the complex physiology of stress and relaxation. In order to remedy this problem, here we introduce a novel HRV parameter, the normalized variability derived from an RMSSD “Master Curve”, and we compare it with the conventional metrics. <b>Methods:</b> In Study 1, the relaxation state was induced either by heart rate variability biofeedback training (N = 21) or by habitual relaxation (N = 21), while in Study 2 (N = 9), the Socially Evaluated Cold Pressor Test and the Socially Evaluated Stroop Test were used to induce stress in the subject. For a statistical evaluation of the data, the Kolmogorov–Smirnov test was used to compare the distributions of mean HR, log(RMSSD), log(SDNN), and normalized variability before, during, and after relaxation and stress. <b>Results:</b> The results of this study indicate that while log(RMSSD) and log(SDNN) did not change significantly, the normalized variability did undergo a significant change both in relaxation states and in stress states induced by the Socially Evaluated Cold Pressor Test. <b>Conclusions:</b> Overall, we suggest this novel type of normalized variability ought to be used as a sensitive stress indicator, and in general, for the characterization of the complex processes of the vegetative nervous system. |
| format | Article |
| id | doaj-art-8656b21daae8406f91c01bd89c4caa82 |
| institution | Kabale University |
| issn | 2227-9059 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Biomedicines |
| spelling | doaj-art-8656b21daae8406f91c01bd89c4caa822025-01-24T13:23:57ZengMDPI AGBiomedicines2227-90592025-01-011318110.3390/biomedicines13010081Quantifying Stress and Relaxation: A New Measure of Heart Rate Variability as a Reliable BiomarkerEmese Rudics0András Buzás1Antónia Pálfi2Zoltán Szabó3Ádám Nagy4Emőke Adrienn Hompoth5József Dombi6Vilmos Bilicki7István Szendi8András Dér9Doctoral School of Interdisciplinary Medicine, University of Szeged, H-6720 Szeged, HungaryInstitute of Biophysics, HUN-REN Biological Research Centre, H-6701 Szeged, HungaryDepartment of Software Engineering, University of Szeged, H-6720 Szeged, HungaryDepartment of Software Engineering, University of Szeged, H-6720 Szeged, HungaryDepartment of Software Engineering, University of Szeged, H-6720 Szeged, HungaryDepartment of Software Engineering, University of Szeged, H-6720 Szeged, HungaryHUN-REN-SZTE Research Group on Artificial Intelligence, Institute of Informatics, University of Szeged, H-6701 Szeged, HungaryDepartment of Software Engineering, University of Szeged, H-6720 Szeged, HungaryDepartment of Psychiatry, Kiskunhalas Semmelweis University Teaching Hospital, H-6400 Kiskunhalas, HungaryInstitute of Biophysics, HUN-REN Biological Research Centre, H-6701 Szeged, Hungary<b>Background/Objectives:</b> For the rapid, objective characterization of the physiological stress response, there is currently no generally recognized standard. The stress measurement methods used in practice (e.g., for psychological measures of stress) are often subjective, or in the case of biological markers (e.g., cortisol, amylase), they usually require a blood test. For this reason, the use of heart rate variability (HRV) to characterize stress has recently come to the fore. HRV is the variability in the length of heartbeat intervals, which indicates the ability of the heart to respond to various physiological and environmental stimuli. However, the conventional HRV metrics are not corrected for heart rate dependence; hence, they fail to fully account for the complex physiology of stress and relaxation. In order to remedy this problem, here we introduce a novel HRV parameter, the normalized variability derived from an RMSSD “Master Curve”, and we compare it with the conventional metrics. <b>Methods:</b> In Study 1, the relaxation state was induced either by heart rate variability biofeedback training (N = 21) or by habitual relaxation (N = 21), while in Study 2 (N = 9), the Socially Evaluated Cold Pressor Test and the Socially Evaluated Stroop Test were used to induce stress in the subject. For a statistical evaluation of the data, the Kolmogorov–Smirnov test was used to compare the distributions of mean HR, log(RMSSD), log(SDNN), and normalized variability before, during, and after relaxation and stress. <b>Results:</b> The results of this study indicate that while log(RMSSD) and log(SDNN) did not change significantly, the normalized variability did undergo a significant change both in relaxation states and in stress states induced by the Socially Evaluated Cold Pressor Test. <b>Conclusions:</b> Overall, we suggest this novel type of normalized variability ought to be used as a sensitive stress indicator, and in general, for the characterization of the complex processes of the vegetative nervous system.https://www.mdpi.com/2227-9059/13/1/81heart rate variabilitybiofeedback trainingstressnormalized variabilitymaster curverelaxation |
| spellingShingle | Emese Rudics András Buzás Antónia Pálfi Zoltán Szabó Ádám Nagy Emőke Adrienn Hompoth József Dombi Vilmos Bilicki István Szendi András Dér Quantifying Stress and Relaxation: A New Measure of Heart Rate Variability as a Reliable Biomarker Biomedicines heart rate variability biofeedback training stress normalized variability master curve relaxation |
| title | Quantifying Stress and Relaxation: A New Measure of Heart Rate Variability as a Reliable Biomarker |
| title_full | Quantifying Stress and Relaxation: A New Measure of Heart Rate Variability as a Reliable Biomarker |
| title_fullStr | Quantifying Stress and Relaxation: A New Measure of Heart Rate Variability as a Reliable Biomarker |
| title_full_unstemmed | Quantifying Stress and Relaxation: A New Measure of Heart Rate Variability as a Reliable Biomarker |
| title_short | Quantifying Stress and Relaxation: A New Measure of Heart Rate Variability as a Reliable Biomarker |
| title_sort | quantifying stress and relaxation a new measure of heart rate variability as a reliable biomarker |
| topic | heart rate variability biofeedback training stress normalized variability master curve relaxation |
| url | https://www.mdpi.com/2227-9059/13/1/81 |
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