Abstract
This study investigated the effects of a novel, classroom-based emotion self-regulation program (TestEdge) on measures of test anxiety, socioemotional function, test performance, and heart rate variability (HRV) in high school students. The program teaches students how to self-generate a specific psychophysiological state—psychophysiological coherence—which has been shown to improve nervous system function, emotional stability, and cognitive performance. Implemented as part of a larger study investigating the population of tenth grade students in two California high schools (N = 980), the research reported here was conducted as a controlled pre- and post-intervention laboratory experiment, using electrophysiological measures, on a random stratified sample of students from the intervention and control schools (N = 136). The Stroop color-word conflict test was used as the experiment’s stimulus to simulate the stress of taking a high-stakes test, while continuous HRV recordings were gathered. The post-intervention electrophysiological results showed a pattern of improvement across all HRV measures, indicating that students who received the intervention program had learned how to better manage their emotions and to self-activate the psychophysiological coherence state under stressful conditions. Moreover, students with high test anxiety exhibited increased HRV and heart rhythm coherence even during a resting baseline condition (without conscious use of the program’s techniques), suggesting that they had internalized the benefits of the intervention. Consistent with these results, students exhibited reduced test anxiety and reduced negative affect after the intervention. Finally, there is suggestive evidence from a matched-pairs analysis that reduced test anxiety and increased psychophysiological coherence appear to be directly associated with improved test performance—a finding consistent with evidence from the larger study.
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Notes
Institutional Review Board (IRB) approval for this project was obtained through Claremont Graduate University, Claremont, California. Parental and student consent were obtained for all students participating in the study.
Since the time of this study, the Freeze–Framer system has been updated and renamed the emWave Stress Relief System.
The three items constituting Educational Plans were not included in the analysis that follows due to a low reliability of measurement coefficient (Cronbach’s alpha = 0.47).
The Test Anxiety Inventory (TAI), developed by Charles Spielberger, is the most commonly used validated self-report instrument for measuring test anxiety and has been utilized in the majority of more recent studies of student test anxiety. The TAI provides a global measure of test anxiety as well as a separate measurement of two theoretically relevant components defined as “worry” and “emotionality.” The “Worry” construct, which has been found to be most strongly correlated with depressed test performance in students with high test anxiety (Cizek and Burg 2006, p. 17), is essentially a measurement of the psychological aspects of test anxiety (i.e., thought processes and emotions relating to the fear of testing and dread regarding the potential for negative evaluation or failure). The “Emotionality” construct provides a measure of the physical symptoms of test anxiety (e.g., nervousness, sweating, fidgeting, etc.).
See Bradley et al. (2007) (http://www.heartmath.org/research/scientific-ebooks.html) for the SOS instrument (Appendix 3, pp. 329–335) and for the details of the analysis of construct reliability and convergent and discriminant validity (pp. 69–76).
While a behavioral screening of student color vision deficiency would have been more ideal, logistical considerations made this difficult to implement, along with all of the other elements of the protocol, in a manner that would minimize the experiment’s intrusion on student class time. However, perusal of the Stroop test results showed no instances of sufficiently poor student performance to suggest that any student participating in the experiment suffered this problem.
The pre–post results of a within-groups paired t-test analysis (not shown), performed separately on each of the four subgroups analyzed here (low vs. high test anxiety by intervention status), found that while an improvement in resting baseline HRV measures was observed in both subgroups of the experimental group—especially in the high test anxiety subgroup—these measures had declined in both the high and low test anxiety subgroups of the control group.
We matched students to within a range of 5 test score points to each other, as a closer matching was not possible given the frequency distribution of 9th grade ELA scores in the two groups.
VLF power was not analyzed in the short-term HRV recordings collected in this study.
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Acknowledgments
This study was funded by the U.S. Department of Education’s Fund for the Improvement of Education, grant number U215K040009. We would like to give special mention and acknowledgment to the administrators, teachers, and students at the primary study schools, including the site for the pretests and pilot study. Among these, we express particular appreciation to the following individuals from the intervention school: the Superintendent, Vice-Principal, Senior English Teacher and Project Coordinator, and Head of Technology. At the control school, we thank the Principal, Associate Principal, and key teachers who provided logistical and other support in making the study implementation a success. Also we are especially grateful to the team of highly dedicated and enthusiastic graduate students from Claremont Graduate University’s School of Educational Studies, who participated in the fieldwork and data collection in all phases and sub-studies of the TENDS research project. From the HeartMath Research Center, Jackie Waterman deserves special mention for her supportive role in the study. We also express our gratitude to the many teachers who participated in the HeartMath trainings and who made room for the TestEdge program in their classes. Ultimate appreciation goes to all the students who participated in this study. Finally, we thank the journal’s Editor-in-Chief for helpful comments on an earlier draft of this article.
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Technical Appendix: Derivation of the HRV Measures from the Electrophysiological Data
Technical Appendix: Derivation of the HRV Measures from the Electrophysiological Data
Continuous pulse plethysmograph recordings (at a sample rate of 250 Hz) were digitized using a model MP30 data acquisition hardware system (Biopac Systems) onto a Dell Latitude laptop computer. These data were then transferred to a PC workstation for RR interval calculation and artifact editing, where all abnormal intervals were eliminated, first, by automated algorithm, followed by manual inspection and correction by an experienced technician. Next, a regularly-spaced HRV time series was derived from the RR intervals by linear interpolation. Gaps in the time series resulting from noise or ectopic beats were filled in with linear splines. The RR interval power spectrum was computed over 3 min of the recording interval for the resting baseline and stress preparation phases of the experiment, beginning 30 s from the initiation of each phase.
Frequency domain measures were calculated by, first, linear de-trending, which is accomplished by subtracting a straight line (standard least-squares method) from the RR interval segment. Then a Hanning window was applied, and the power spectral density (PSD) was computed. The frequency domain measures of RR variability were computed by integration over their frequency intervals. We calculated the power to within two frequency bands of the RR interval power spectrum: (1) low frequency (LF) power (0.04 to <0.15 Hz); and (2) high frequency (HF) power (0.15 to <0.4 Hz). In addition, we calculated total power (power in the band <0.4 Hz) and the coherence ratio. The coherence ratio was calculated as follows: peak power/(total power − peak power), where peak power is a 0.03-Hz-wide area under the largest peak in the 0.04–0.26 Hz region of the HRV power spectrum (Tiller et al. 1996; McCraty et al. 2006).
The time domain HRV measures employed in this study were: the mean heart rate (HR); the mean RR interval; the standard deviation of all normal RR intervals; and the standard deviation of all normal intervals for each segment in the recording. To correct for the skewed distribution of frequency domain and coherence ratio measures, the statistical analysis was performed on the natural log transform values; absolute values are also reported.
Interpreting the HRV Measures
The mathematical translation of HRV into power spectral density measures is accomplished by a Fourier transform function, and is used to discriminate and quantify sympathetic and parasympathetic activity as well as overall autonomic nervous system activity. Power spectral analysis deconstructs the heart rhythm pattern into its constituent frequency components and quantifies the relative power of these components. In a typical analysis, the HRV power spectrum is divided into three main ranges, and each range is associated with an underlying physiological mechanism that gives rise to the oscillations in that range.
The very low frequency (VLF) range (0.0033–0.04 Hz) is primarily an index of sympathetic activity,Footnote 10 while power in the high frequency (HF) range (0.15–0.4 Hz), reflects more rapidly occurring changes in the beat-to-beat heart rate, which are primarily due to modulation of the efferent parasympathetic activity associated with changes in respiration. The frequency range encompassing the 0.1 Hz region is called the low frequency (LF) range (0.04–0.15 Hz), and it reflects activity in the feedback loops between the heart and brain that control short-term blood pressure changes and other regulatory processes. The physiological factors contributing to activity in the LF range are complex, reflecting a mixture of sympathetic and parasympathetic efferent and afferent activity as well as vascular system resonance.
Heart rhythm coherence is reflected in the HRV power spectrum as a large increase in power in the low frequency (LF) band (typically around 0.1 Hz) and a decrease in the power in the VLF and HF bands. A coherent heart rhythm can therefore be defined as a relatively harmonic (sine wave-like) signal with a very narrow, high-amplitude peak in the LF region of the HRV power spectrum and no major peaks in the other regions.
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Bradley, R.T., McCraty, R., Atkinson, M. et al. Emotion Self-Regulation, Psychophysiological Coherence, and Test Anxiety: Results from an Experiment Using Electrophysiological Measures. Appl Psychophysiol Biofeedback 35, 261–283 (2010). https://doi.org/10.1007/s10484-010-9134-x
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DOI: https://doi.org/10.1007/s10484-010-9134-x