Autonomic Stress Responses in Oral Examination Simulations: Neuroscientific Insights from Comparing Peer-Led and Lecturer-Led Approaches

Autonomic Stress Responses in Oral Examination Simulations: Neuroscientific Insights from Comparing Peer-Led and Lecturer-Led Approaches | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Autonomic Stress Responses in Oral Examination Simulations: Neuroscientific Insights from Comparing Peer-Led and Lecturer-Led Approaches Morris Gellisch, Gabriela Morosan-Puopolo, Martin Bablok, Thorsten Schäfer, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5268524/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This mixed-methods study investigated the impact of simulated oral examinations on inducing neurocardiac stress in medical students, integrating physiological measurements, quantitative assessments, and qualitative feedback. Ninety-five students participated, with heart rate variability (HRV) markers such as RMSSD, pNN50, SDNN, and LF/HF ratios used to evaluate stress responses. Both lecturer-led and peer-led simulations significantly reduced HRV markers, indicating heightened sympathetic activation and reduced parasympathetic activity. In lecturer-led simulations, RMSSD showed significant reductions (t = 8.27, p < .001; t = 9.38, p < .001), paralleled in peer-led sessions (t = 4.47, p < .001; t = 4.97, p < .001). The LF/HF ratio significantly increased in lecturer-led exams (z=-2.69, p = 0.007), while peer-led simulations exhibited a more moderate response. Students' perceived competence and confidence significantly improved post-simulation (lecturer-led: t=-8.41, p < .001; student-led: t=-5.82, p < .001), and test anxiety significantly decreased. In the follow-up assessment conducted after the actual exams at the semester's end, 94.85% of students reported that the simulations were helpful in preparing for their final exams, aiding in reducing stress and enhancing performance. These findings highlight the potential of peer-led simulations as a resource-efficient alternative for fostering student resilience and coping under exam stress, though further exploration is needed to fully understand the nuanced autonomic responses in different settings. Biological sciences/Neuroscience/Stress and resilience Biological sciences/Neuroscience/Emotion/Amygdala Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Nearly everyone knows the pre-test jitters, and many have seen how even mild anxiety can throw off focus and performance. While not overwhelming, it’s often enough to turn confidence into second-guessing, leaving students struggling to showcase their true abilities. Test anxiety is characterized by a range of cognitive, emotional, and physiological responses triggered by the fear of potential negative outcomes in evaluative situations 1 . This pervasive and complex condition goes beyond ordinary stress and can severely impact cognitive performance and overall well-being 1 – 3 . Research shows that its effects are more widespread than often realized, with symptoms manifesting frequently in a large proportion of students 4 . Notably, a study revealed that over half of college students regularly experience test anxiety, with many enduring symptoms that significantly hinder their ability to perform under pressure 4 . This form of anxiety has been linked to poor cognitive outcomes, including diminished academic performance and psychological distress, underscoring its serious impact on both educational achievement and mental health 2 . As researchers have increasingly focused on test anxiety, it has become clear that the condition is not only common but also multi-dimensional, encompassing both cognitive (e.g., worry) and emotional (e.g., physiological arousal) responses, which can vary based on the type of exam 5 . Oral examinations, for example, tend to trigger different stress mechanisms compared to written tests, further complicating the ways in which test anxiety presents itself 6 . Medical students face test anxiety at particularly high rates, adding to the already intense pressures of their education 7 . With the rigorous demands and high stakes of medical exams, this group experiences anxiety more frequently than the general population, making test anxiety a prominent challenge throughout their training 8 – 11 . Many medical students are all too familiar with how these stressors can cloud focus and hinder performance, even when they are well-prepared 7 . Test anxiety in this context doesn’t just appear in one form; it can manifest both as a temporary, situation-based reaction (state anxiety) and as a more ingrained, personality-driven response (trait anxiety), as described in Spielberger's model of anxiety 12 . The combination of these acute and chronic pressures makes it especially difficult for medical students to manage the impact of anxiety on their performance 13 . When investigating test anxiety, relying solely on self-reported measures can often provide an incomplete or misleading understanding of the issue. A systematic review of 231 articles (29 eligible) found that while self-reports of test anxiety are sometimes associated with physiological arousal, the correlation is not consistently strong, highlighting the limitations of subjective reporting 14 . This suggests that a more comprehensive assessment should include objective physiological measures, such as heart rate variability or skin conductance, which offer a clearer view of the body's response to stress. Physiological activation, such as increased heart rate and other signs of heightened arousal, plays an important role in test anxiety 15 . High-pressure, socially evaluative tasks tend to elicit the strongest responses, with physiological symptoms lasting well beyond the test itself, further complicating the ability to perform under stress 16 . By incorporating physiological measurements into the study of test anxiety, researchers can gain a more accurate and complex understanding of how stress impacts student performance, moving beyond the limitations of self-reported data alone. Among these physiological measurements, Heart Rate Variability (HRV) stands out as a particularly valuable tool for examining the autonomic responses associated with stress in educational settings 17 – 19 . The brain’s regulation of cardiovascular function, particularly through the vagus nerve and its influence on heart rate, reflects the body’s ability to adapt to stressors. HRV provides a window into this regulatory mechanism by measuring fluctuations between heartbeats, making it an ideal marker for understanding physiological responses in stressful situations like test anxiety. Several specific HRV parameters provide deeper insight into autonomic function 20 : RMSSD (Root Mean Square of Successive Differences) is a key indicator of parasympathetic activity, reflecting short-term variability in heart rate. Higher RMSSD values are typically associated with greater vagal tone and a calmer physiological state. pNN50, which calculates the percentage of successive heartbeats that differ by more than 50 ms, is another marker of parasympathetic influence and provides a direct measure of heart rate irregularity under relaxed conditions. On the other hand, SDNN (Standard Deviation of Normal-to-Normal intervals) is a broader measure of overall HRV and is influenced by both parasympathetic and sympathetic inputs, capturing long-term autonomic balance. Finally, the LF/HF ratio—the ratio of low-frequency to high-frequency oscillations—offers an index of sympathetic to parasympathetic balance. Higher LF/HF ratios indicate sympathetic dominance, often linked to stress and heightened arousal, while lower ratios suggest parasympathetic predominance, reflective of a more relaxed state. The Yerkes-Dodson Law is a concept many are at least somewhat familiar with, highlighting how a certain amount of stress can be beneficial for performance 21 . According to this principle, moderate arousal helps enhance focus and memory consolidation, leading to improved performance. However, once stress levels surpass a critical point, this arousal turns into hyperarousal, which impairs cognitive functions, particularly memory retrieval 22 . In terms of memory, while a mild level of stress aids in consolidating information, hyperarousal disrupts the brain’s ability to retrieve stored knowledge 23 . This is especially true when stress interferes with the hippocampus, a brain region crucial for recalling information 24 . Under conditions of high stress, the pathways needed for effective memory retrieval become less efficient, making it harder to access previously learned material. Our project aimed to simulate an oral examination with the goal of inducing a stress response, providing students with an opportunity to train their ability to retrieve relevant information under pressure. The core research question was whether this simulated scenario could effectively evoke the necessary stress levels to mirror real exam conditions. To assess its success, we conducted a follow-up after the actual final oral examination, gathering both qualitative and quantitative data through a questionnaire. This approach allowed us to explore not only if students found the simulation helpful, but also why they felt it contributed to their preparation. To capture a comprehensive understanding of the effects, we employed a mixed-method design, integrating physiological measurements with qualitative feedback and quantitative analysis. By combining objective physiological data with subjective evaluations from students, we were able to assess both the measurable stress response and the perceived utility of the simulation in real exam contexts. This design ensured that we could evaluate the effectiveness of the simulated oral examination from multiple angles, offering deeper insights into the relationship between stress, memory retrieval, and exam performance. In addition to our primary research project, we also explored an important secondary question: whether near-peer tutors can be just as effective as lecturers in conducting exam simulations, particularly in terms of eliciting a similar stress response. Offering individual exam simulations by lecturers places significant demands on resources, making it challenging to implement on a larger scale. Given the established benefits of near-peer tutors in medical education 25 – 28 , such as improved accessibility and relatable guidance, we wanted to investigate whether these tutors could also trigger the necessary stress response in students during simulated oral exams. The research aimed to determine whether the stress response elicited by a near-peer tutor conducting the simulation would be comparable to that elicited by a lecturer. By examining physiological markers of stress, as well as student feedback, we sought to assess if near-peer-led simulations could serve as an effective alternative without compromising the desired outcomes of stress-induced training. This study allows us to explore a potentially more scalable solution for preparing students for oral examinations while maintaining the rigor and stress exposure necessary for effective learning. Materials and Methods This study employed a quasi-experimental design to investigate whether simulated oral examinations induce physiological stress in first-year medical students and to compare the effectiveness of simulations led by faculty lecturers versus senior students. The primary research question was to determine if the simulated oral examination exerts physiological stress, as measured by heart rate variability (HRV), and whether there are differences in outcomes when the examination is conducted by a lecturer compared to a student. This investigation is grounded in the neuroscientific understanding that recall is impaired under stress, highlighting the importance of accustoming students to perform under stressful conditions. Participants were randomly assigned to either the lecturer-led or student-led simulation group, with HRV, self-confidence, and exam anxiety measured before and after the simulation. Additionally, a follow-up questionnaire was administered after the real oral exam at the end of the semester to gain insights into the efficacy of the simulated oral examinations in preparing students for their actual exams. Hear Rate Variability measurements For heart rate variability (HRV) measurements, we utilized the Polar Vantage V2 in combination with the Polar H10 sensor (Polar Electro Oy, Kempele, Finland). The Polar H10 sensor has been validated against a 12-channel electrocardiogram (ECG) for HRV data, demonstrating nearly perfect correlations for linear parameters such as RR intervals and heart rate. Specifically, the Pearson correlation coefficient (r) was greater than 0.86, indicating a strong positive correlation, the concordance correlation coefficient (rc) was greater than 0.84, indicating substantial agreement, and the intraclass correlation coefficient (ICC3,1) was greater than 0.85, indicating excellent reliability (Schaffarczyk et al., 2022). The HRV recording procedure was carefully designed to ensure standardized conditions and minimize potential biases. The Polar Vantage V2 and Polar H10 sensor system were applied to each participant immediately before the start of the oral exam simulation. The simulation lasted for 10 minutes, followed by a 5-minute feedback session. After the feedback session, participants were directed to a designated resting room for baseline measurements. To further standardize conditions, each participant was placed in a separate resting room to ensure they were alone during the baseline measurement. This setup was crucial to avoid any potential bias in measuring the resting state, such as talking or laughing, which could affect the HRV data. Importantly, baseline measurements were taken after the oral examination to avoid bias from anticipatory stress, ensuring that the HRV data accurately reflected the participants' true physiological baseline without the influence of impending stress. The frequency domain measures of LF and HF, along with the time domain measures of RMSSD, pNN50 and SDNN, were computed following the guidelines of the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology 29 (1996), using a fast Fourier transform (FFT) algorithm to calculate the ratio of the main spectral components. Quantitative analyses For the quantitative analyses, we measured participants' confidence in passing the final exam along with their subjectively perceived performance assessment for both the lecturer-led and student-led simulation groups. This measurement was conducted twice: before the oral exam simulation and immediately afterward. Additionally, test anxiety was assessed at two points: before the oral examination simulation and within the follow-up questionnaire administered after the real oral examination. This adjustment in the second measurement time point allowed us to gain insights into the dynamics of test anxiety development between the simulation and the actual exam. Both confidence and test anxiety were measured using a Visual Analog Scale (VAS) (Hayes and Patterson, 1921; Yeung and Wong, 2019). Qualitative Analyses For the qualitative analyses, we included several open-ended questions in the follow-up questionnaire administered after the real exam. Open questions were chosen to allow for more comprehensive and precise feedback from the participants. We manually analyzed each response and categorized them into specific answer groups (e.g., not relevant, somewhat relevant, very relevant). The questions asked were: "Do you think the exam simulation was helpful in preparing you for the actual exam?", "Will you be more confident when taking the actual final exam after the exam simulation?", "How relevant do you find the lecturer's feedback after your exam simulation?", "Did the exam simulation influence your learning strategies for the final exam?", and "Do you think the experience gained from the exam simulation helped you during your final exam?". The first question aimed to understand the direct perceived impact of the simulation on exam preparedness, a critical measure of its effectiveness. Confidence is a key factor in performance, so we asked if participants would be more confident taking the final exam after the simulation. Evaluating the relevance of the lecturer's feedback was essential to gauge its effectiveness in enhancing learning. We also explored whether the simulation influenced students' learning strategies, providing insights into how it affected their study behaviors and methods. Finally, we included a retrospective question to assess the practical utility of the simulation experience during the actual exam. By incorporating these questions into the follow-up questionnaire, we ensured that participants could reflect on their experiences and provide detailed insights. Inclusion criteria Participants in this study were required to meet specific inclusion criteria to ensure a realistic and representative sample of the cohort. Eligible participants were healthy first-year medical students properly enrolled at Ruhr University Bochum. The age range for inclusion was set between 18 and 24 years to reflect the typical demographic of the student cohort. Importantly, we deliberately included students of all genders, ethnicities, and backgrounds to ensure diversity and generalizability of the findings across the entire student population. This inclusive approach aimed to provide comprehensive insights into the impact of simulated oral examinations on a varied group of medical students. Power analysis To ensure adequate statistical power for our study, we conducted a power analysis using G*Power. We specified a two-tailed test with an effect size (dz) of 0.6, an alpha level of 0.05, and a desired power of 0.95. The analysis yielded a noncentrality parameter (δ) of 3.75 and a critical t-value of 2.02, indicating that a total sample size of 39 participants per group would be necessary to detect a statistically significant effect with an actual power of approximately 0.95. To account for potential data loss due to measurement errors or participant attrition, we recruited additional participants, resulting in initial group sizes of n = 53 for the lecturer-led group and n = 42 for the peer-led group. Despite this precaution, some data were lost due to measurement errors, with 6 participants' data excluded from the lecturer-led group and 11 participants' data excluded from the peer-led group. This strategy ensured that our study maintained sufficient power even after accounting for data loss. Demographic information The study's participant demographics comprised a total of 95 first-semester medical students. The distribution included 51 females (53.7%) and 44 males (46.3%). The average age of participants was 19.3 years, with a standard deviation of 1.3 years. Female participants had a mean age of 19.3 years (SD = 1.4), while male participants had a mean age of 19.4 years (SD = 1.1). This age range and gender distribution reflect the typical demographic of first-year medical students, providing a realistic and diverse sample for the study. The deliberate inclusion of all genders and diverse backgrounds aimed to enhance the generalizability of the findings across the entire student population. The lecturer-led simulation group comprised a total of 53 participants. The distribution included 31 females (58.5%) and 22 males (41.5%). The average age in this group was 19.6 years, with a standard deviation of 1.4 years. Female participants had a mean age of 19.4 years (SD = 1.5), while male participants had a mean age of 19.9 years (SD = 1.2). The student-led simulation group comprised a total of 42 participants. The distribution included 20 females (47.6%) and 22 males (52.4%). The average age in this group was 19.0 years, with a standard deviation of 1.1 years. Female participants had a mean age of 19.2 years (SD = 1.5), while male participants had a mean age of 19.0 years (SD = 0.8). Ethical aspects This research was conducted in accordance with the principles outlined in the Declaration of Helsinki. Approval for the study was obtained from the Ethics Committee of the Professional School of Education at Ruhr University Bochum (Reference No. EPSE-2022-004, dated 12.09.2022). The study adhered to all ethical guidelines to ensure the safety, well-being, and confidentiality of all participants. Statistics Descriptive statistics were calculated including the median, mean, standard error of the mean, upper and lower 95% confidence intervals, mean absolute deviation, interquartile range, skewness, standard error of skewness, kurtosis, and standard error of kurtosis. These metrics provided a comprehensive summary of the central tendency and dispersion of the data. For inferential statistics, paired t-tests were conducted to assess the differences between pre- and post-simulation scores, with the significance level set at 0.05. Normality of the data was evaluated using the Shapiro-Wilk test. In cases where the normality assumption was not met, the Wilcoxon signed-rank test was employed as a non-parametric alternative. Results Descriptive statistics were calculated for selected heart rate variability (HRV) parameters during two 5-minute intervals at baseline and during the oral exam simulations. The metrics included RMSSD (Root Mean Square of Successive Differences between normal heartbeats), PNN50 (percentage of NN intervals that differ by more than 50 ms), SDNN (Standard Deviation of Normal-to-Normal interbeat intervals), and LF/HF (Ratio of Low Frequency to High Frequency). These measurements were conducted for both the lecturer-led and the student-led oral exam simulations to provide a comprehensive comparison of physiological stress responses. For RMSSD during the lecturer-led simulated oral examination, the median values at baseline were 32.11 ms and 32.20 ms for the first and second intervals, respectively, which decreased to 18.10 ms and 18.47 ms during the examination. The mean RMSSD values also decreased from 37.03 ms (first interval) and 36.30 ms (second interval) at baseline to 22.87 ms and 26.48 ms during the examination. PNN50 exhibited a similar trend, with median values at baseline of 9.8550 and 11.0300 decreasing to 2.7250 and 2.9300 during the examination. Mean PNN50 values decreased from 14.1774 and 15.3044 at baseline to 7.8298 and 8.9094 during the examination, reflecting reduced parasympathetic activity under stress. For SDNN, the median values were 44.44 ms and 41.04 ms at baseline, decreasing to 30.68 ms and 34.02 ms during the examination. The mean SDNN values showed a similar pattern, decreasing from 46.14 ms and 45.48 ms at baseline to 34.46 ms and 38.45 ms during the examination. The LF/HF ratio increased from baseline to the examination periods, with median values of 2.41 and 2.71 at baseline rising to 3.23 and 3.22 during the examination. Mean LF/HF ratios also increased from 3.36 and 3.33 at baseline to 4.11 and 4.42 during the examination, suggesting a shift towards sympathetic dominance (Table 1 ). Table 1 Heart Rate Variability (HRV) Characteristics During Lecturer-Led Oral Examination Simulation 1 RMSSDB 2 RMSSDB 1 RMSSDT 2 RMSSDT 1 PNN50B 2 PNN50B 1 PNN50T 2 PNN50T 1 SDNNB 2 SDNNB 1 SDNNT 2 SDNNT 1 LF/HFB 2 LF/HFB 1 LF/HFT 2 LF/HFT Median 32.1100 32.2000 18.1000 18.4700 9.8550 11.0300 2.7250 2.9300 44.4400 41.0400 30.6800 34.0200 2.4100 2.7100 3.2300 3.2200 Mean 37.0308 36.3036 22.8706 26.4792 14.1774 15.3044 7.8298 8.9094 46.1419 45.4751 34.4572 38.4483 3.3555 3.3342 4.1055 4.4217 Std. Error of Mean 3.8045 3.6367 2.8845 3.2357 2.1818 2.3053 1.7472 1.9782 2.8811 3.0456 2.8011 2.7749 0.5080 0.3973 0.4511 0.5947 95% CI Mean Upper 44.6651 43.6012 28.6588 32.9722 18.5535 19.9283 11.3342 12.8771 51.9232 51.5864 40.0780 44.0165 4.3748 4.1314 5.0106 5.6151 95% CI Mean Lower 29.3964 29.0059 17.0823 19.9863 9.8013 10.6806 4.3253 4.9417 40.3606 39.3637 28.8364 32.8801 2.3362 2.5369 3.2003 3.2283 Std. Deviation 27.6973 26.4758 20.9996 23.5565 16.0328 16.9405 12.8391 14.5365 20.9744 22.1720 20.3923 20.2015 3.6981 2.8925 3.2840 4.3297 MAD 12.7400 12.7200 8.6300 7.7000 7.7050 8.8600 2.5650 2.9300 14.2600 11.7800 11.7600 11.4700 1.0000 1.2200 1.2100 1.2200 IQR 23.8500 23.8400 16.4200 21.1700 15.5350 17.5375 6.8800 10.6625 29.2500 25.1800 23.0600 26.0300 2.0800 2.4800 2.4000 2.4000 Skewness 2.0708 2.1824 2.2822 2.4850 1.9639 1.8037 2.5675 2.8302 0.9624 1.3623 1.2016 0.9792 3.4770 3.1313 2.5685 4.5595 Std. Error of Skewness 0.3274 0.3274 0.3274 0.3246 0.3274 0.3246 0.3246 0.3246 0.3274 0.3274 0.3274 0.3274 0.3274 0.3274 0.3274 0.3274 Kurtosis 5.4810 6.2082 6.4309 7.8700 4.3606 3.3125 6.6209 8.5676 1.6814 3.3437 2.3567 1.2704 14.4925 13.9513 8.8309 26.4233 Std. Error of Kurtosis 0.6444 0.6444 0.6444 0.6444 0.6389 0.6389 0.6389 0.6389 0.6444 0.6444 0.6444 0.6444 0.6444 0.6444 0.6444 0.6444 Note. ‘B’ refers to baseline measurement, ‘T’ refers to test measurement, RMSSD refers to the Root Mean Square of Successive Differences, pNN50 stands for the percentage of NN intervals differing by more than 50 milliseconds, SDNN represents the Standard Deviation of Normal-to-Normal intervals, LF/HF is the Low-Frequency to High-Frequency ratio, MAD indicates the Mean Absolute Deviation, and IQR refers to the Interquartile Range. For the RMSSD metric during the student-led simulated oral examination, initial median values at baseline were 34.62 ms and 33.28 ms for the first and second intervals, respectively, reducing to 20.68 ms and 22.93 ms during the test. The mean RMSSD also showed a decrease from 48.91 ms and 48.85 ms at the start to 28.64 ms and 36.67 ms during the test, reflecting decreased parasympathetic activation due to stress. Similarly, PNN50 metrics started with median values of 13.43 and 11.04 at baseline and decreased to 2.96 and 4.12 during the test. The average values decreased from 17.48 and 16.83 to 5.85 and 11.55, further suggesting a reduction in parasympathetic response under stress. For SDNN, baseline median values were 42.90 ms and 43.94 ms, which decreased to 33.12 ms and 37.46 ms during the test. The corresponding mean values also reduced from 54.26 ms and 55.49 ms to 38.16 ms and 46.08 ms, indicating lowered heart rate variability under stress. The LF/HF ratio increased from initial median values of 2.23 and 2.76 to 3.12 and 3.15 during the examination. Mean values also slightly increased from 3.07 and 3.63 at baseline to 3.27 and 3.47 in the simulated oral examination (Table 2 ). Table 2 Heart Rate Variability (HRV) Characteristics During Student-Led Oral Examination Simulation 1 RMSSDB 2 RMSSDB 1 RMSSDT 2 RMSSDT 1 PNN50B 2 PNN50B 1 PNN50T 2 PNN50T 1 SDNNB 2 SDNNB 1 SDNNT 2 SDNNT 1 LF/HFB 2 LF/HFB 1 LF/HFT 2 LF/HFT Median 34.6150 33.2800 20.6750 22.9300 13.4300 11.0350 2.9550 4.1200 42.9000 43.9350 33.1150 37.4550 2.2250 2.7550 3.1150 3.1500 Mean 48.9138 48.8524 28.6369 36.6717 17.4764 16.8323 5.8507 11.5532 54.2560 55.4876 38.1571 46.0781 3.0676 3.6345 3.2700 3.4674 Std. Error of Mean 10.7027 12.4421 6.5421 10.8156 2.7078 2.7350 1.0978 2.7795 6.8425 7.9860 4.3561 7.2467 0.4870 0.5156 0.2892 0.3106 95% CI Mean Upper 70.5283 73.9797 41.8489 58.5143 22.9371 22.3480 8.0647 17.1585 68.0747 71.6157 46.9544 60.7132 4.0511 4.6757 3.8540 4.0946 95% CI Mean Lower 27.2994 23.7250 15.4249 14.8291 12.0156 11.3166 3.6367 5.9479 40.4372 39.3595 29.3599 31.4430 2.0842 2.5933 2.6860 2.8402 Std. Deviation 69.3611 80.6341 42.3975 70.0933 17.9614 18.1421 7.2822 18.4368 44.3447 51.7554 28.2304 46.9642 3.1559 3.3413 1.8741 2.0127 MAD 15.2900 10.1100 8.2100 8.8300 11.0850 8.4350 2.5600 3.7500 14.3300 9.3250 10.2700 9.7100 0.9850 0.8100 1.1550 1.3750 IQR 29.8300 19.1675 16.4950 20.6625 22.1475 17.6400 7.5100 15.3475 27.9000 21.2525 20.7750 22.3150 1.9275 1.7300 2.2900 2.6725 Skewness 5.4445 5.8934 5.7627 6.0666 1.2595 1.6685 1.4952 2.8538 4.4759 5.0715 4.4647 5.4035 2.3107 2.8059 0.6705 0.7343 Std. Error of Skewness 0.3654 0.3654 0.3654 0.3654 0.3575 0.3575 0.3575 0.3575 0.3654 0.3654 0.3654 0.3654 0.3654 0.3654 0.3654 0.3654 Kurtosis 32.6871 36.7204 35.6448 38.3031 0.8372 2.4825 1.1949 9.0419 24.7056 29.6132 24.7654 32.5446 6.0689 9.0811 0.2396 -0.1462 Std. Error of Kurtosis 0.7166 0.7166 0.7166 0.7166 0.7017 0.7017 0.7017 0.7017 0.7166 0.7166 0.7166 0.7166 0.7166 0.7166 0.7166 0.7166 Note. ‘B’ refers to baseline measurement, ‘T’ refers to test measurement, RMSSD refers to the Root Mean Square of Successive Differences, pNN50 stands for the percentage of NN intervals differing by more than 50 milliseconds, SDNN represents the Standard Deviation of Normal-to-Normal intervals, LF/HF is the Low-Frequency to High-Frequency ratio, MAD indicates the Mean Absolute Deviation, and IQR refers to the Interquartile Range. The inferential statistical analysis for the lecturer-led oral examination simulation revealed significant changes in heart rate variability (HRV) parameters, indicating substantial physiological stress responses among participants. Both paired t-tests and Wilcoxon signed-rank tests were utilized, depending on the normality of the data distribution as determined by the Shapiro-Wilk test. Significant reductions were observed in the Root Mean Square of Successive Differences (RMSSD) between normal heartbeats from the baseline to the test period. The first 5-minute interval showed a decrease, with the t-test yielding a statistic of 8.2650 (p < .001) and the Wilcoxon test producing a z-value of 6.3586 (p < .001). The total RMSSD also significantly declined, confirmed by a t-test statistic of 9.3824 (p < .001) and a Wilcoxon result of 6.3328 (p < .001) (Fig. 1 A). For the PNN50 metric, which measures the percentage of successive NN intervals that differ by more than 50 ms, there was also a notable decrease from baseline to examination. The first interval's changes were significant under both the t-test (t = 4.8572, p < .001) and the Wilcoxon test (z = 4.9211, p < .001), indicating a reduction in parasympathetic nervous system activity during the simulated examination (Fig. 1 B). Changes in the Standard Deviation of Normal-to-Normal (SDNN) intervals, which reflect overall HRV, showed a decrease, suggesting heightened physiological arousal. This was statistically significant across all measured intervals, with the first interval's t-test at 7.3313 (p < .001) and Wilcoxon test at 5.5751 (p < .001) (Fig. 1 C). The Low Frequency to High Frequency (LF/HF) ratio, indicative of the balance between sympathetic and parasympathetic nervous system activities, exhibited increases that reached statistical significance in non-parametric testing despite some t-tests not reaching conventional significance thresholds. Specifically, the Wilcoxon results for the first interval marked significant changes (z=-2.6907, p = 0.0072), contrasting with the t-test (t=-1.7919, p = 0.0789). The total LF/HF ratio changes were significant in both tests (t=-2.8664, p = 0.0059; z=-3.0179, p = 0.0026), suggesting a shift towards sympathetic dominance during the test (Fig. 1 D). In the student-led oral examination simulation, inferential statistics were computed using both paired t-tests and Wilcoxon signed-rank tests. This approach was chosen to accommodate the deviations from normality identified through Shapiro-Wilk tests, ensuring robust analysis of heart rate variability (HRV) parameters. Significant reductions in RMSSD were noted across the intervals. Specifically, the first interval's RMSSD decreased significantly, as evidenced by a Student t-test value of 4.4715 (p < .001) and a Wilcoxon z-value of 5.2828 (p < .001). This pattern was consistent for the total RMSSD, which also showed a significant decrease (Student t = 4.9695, p < .001; Wilcoxon z = 5.3578, p < .001) (Fig. 2 A). The PNN50 parameter similarly exhibited significant decreases. The first interval saw substantial changes (Student t = 5.8129, p < .001; Wilcoxon z = 5.1703, p < .001). While the second interval's decrease was statistically significant under the Wilcoxon test (z = 4.5678, p < .001), the Student t-test results were less pronounced (t = 2.0742, p = 0.0441) (Fig. 2 B). For SDNN (Standard Deviation of NN Intervals), there was a notable decrease in variability from baseline to during the test, indicating heightened physiological arousal. This increase was statistically significant across all intervals, confirmed by both the Student t-tests (1_SDNN: t = 5.2899, p < .001) and Wilcoxon tests (z = 5.1077, p < .001) (Fig. 2 C). The LF/HF ratio, reflecting the balance between sympathetic and parasympathetic nervous activities, showed no consistent significant changes. The first interval analysis with both Student t-test and Wilcoxon test did not reach significance (t=-0.4740, p = 0.6380; z=-1.4254, p = 0.1558), and this trend was observed across other intervals and the total LF/HF ratio (Fig. 2 D). To evaluate the impact of the lecturer-led simulated oral examination on students' perceived competence, a paired samples t-test was conducted. The analysis showed a statistically significant increase in perceived competence following the simulation. The test yielded a t-statistic of -8.4065 (df = 41, p < .001), indicating a substantial change in students' self-assessed competence levels. The effect size, measured by Cohen's d, was − 1.2971 with a standard error (SE) of 0.2642. This large effect size suggests that the lecturer-led simulation was highly effective in enhancing students' perceived competence (Fig. 3 A). The student-led simulated oral examination also had a significant impact on students' perceived competence. A paired samples t-test revealed a notable increase in self-assessed competence levels post-simulation. The results indicated a t-statistic of -5.8176 (df = 27, p < .001), demonstrating a statistically significant improvement. The effect size, represented by Cohen's d, was calculated at -1.0994 with a standard error (SE) of 0.2488. This substantial effect size suggests that the student-led simulation effectively enhanced students' perceived competence (Fig. 3 B). The lecturer-led simulated oral examination also had a significant effect on students' perceived confidence. A paired samples t-test was conducted to compare confidence levels before and after the simulation. The results revealed a t-statistic of -5.3550 (df = 41, p < .001), indicating a statistically significant increase in confidence post-simulation. The effect size, represented by Cohen's d, was − 0.8263 with a standard error (SE) of 0.1573. This moderate effect size suggests that the lecturer-led simulation was effective in boosting students' confidence, reflecting an enhancement in their self-assurance and belief in their ability to perform well in the actual examination (Fig. 3 C). The student-led simulated oral examination also demonstrated a notable impact on students' perceived confidence. Analyzing the data using a paired samples t-test revealed a significant increase in confidence levels following the simulation, with a t-statistic of -5.0054 (df = 27, p < .001). The effect size, measured by Cohen's d, was calculated to be -0.9459 with a standard error (SE) of 0.2846. This considerable effect size indicates that the student-led simulation significantly bolstered students' confidence, resulting in a substantial improvement in their self-assurance and preparedness for the actual examination (Fig. 3 D). Following the quantitative analysis, a qualitative analysis was undertaken to gain a deeper understanding of the subjective experiences and perceptions of the participants regarding the exam simulation. Participants responded to several open-ended questions designed to elicit detailed and unrestricted feedback. Their responses were systematically categorized into distinct answer groups, which were subsequently visualized using pie charts. Additionally, in certain instances, we conducted a more granular analysis by examining the frequencies of specific answer categories to provide a more holistic understanding of the data. To gauge the perceived effectiveness of the exam simulation in preparing students for their actual final exams, participants were asked to express their views on the helpfulness of the simulation. The responses were then categorized and analyzed. A significant majority, 92 out of 97 students (94.85%), indicated that the simulation was "very helpful" in their preparation process. A smaller portion, 4 students (4.12%), found the simulation to be "somewhat helpful." Notably, none of the respondents found the simulation unhelpful, and only 1 participant (1.03%) did not provide an answer (Fig. 4 A). As part of the qualitative analysis, participants were asked if the exam simulation increased their confidence in taking the actual final exam. The responses were categorized and analyzed, revealing that 58 out of 97 participants (59.79%) felt that the simulation would indeed make them more confident. Additionally, 32 participants (32.99%) reported feeling "a little" more confident, while 5 participants (5.15%) indicated that the simulation did not enhance their confidence. There were 2 participants (2.06%) who did not provide an answer. Participants were asked to evaluate the relevance of the lecturer's feedback following the exam simulation (Fig. 4 B). The majority, 89 out of 97 participants (91.75%), considered the feedback to be "very relevant." An additional 4 participants (4.12%) found the feedback "somewhat relevant," while 2 participants (2.06%) rated it as "not relevant." Another 2 participants (2.06%) did not provide a response (Fig. 4 C). Participants were asked whether the exam simulation influenced their learning strategies for the final exam, with 74 out of 89 respondents (83%) indicating a positive impact. A smaller number, 4 participants (4%), felt the simulation had "a little" influence, while 5 participants (6%) reported no influence, and 6 participants (7%) did not respond (Fig. 4 D). Among those who answered "yes," the qualitative analysis revealed several key ways in which the simulation helped adjust their learning strategies. Fourteen participants (19%) mentioned that the simulation aided in content familiarization and revision, allowing them to identify areas they had already covered and focus on targeted revision. The most significant insight, noted by 36 participants (49%), was the understanding gained about the exam format and the depth of questions, which enabled them to tailor their study approaches more effectively. Additionally, 4 participants (5%) appreciated the feedback on their current knowledge level, which helped them identify specific areas needing improvement. A notable portion, 20 participants (27%), mentioned that the simulation experience reduced their stress and nervousness, enhancing their self-confidence. This reduction in anxiety allowed them to approach their studies with a calmer and more focused mindset, which was deemed crucial for their preparation. Additionally, test anxiety was measured quantitatively both before and after the exam simulation, and the results indicated a significant decrease in anxiety levels, with a p-value less than 0.05 (Fig. 4 F). Participants were further asked whether the experience gained from the exam simulation helped them during the actual final exam. The majority, 74 out of 89 respondents (83%), affirmed that the simulation experience was helpful. A smaller group, 5 participants (6%), felt that it helped "a little," while 4 participants (4%) indicated that the simulation did not help them. Additionally, 6 participants (7%) did not provide an answer (Fig. 4 E). Discussion Our study successfully demonstrated that even in a simulated oral examination setting, significant neurocardiac stress can be induced, as evidenced by substantial physiological responses across several HRV markers. This was a crucial finding, validating our first major research goal: to create a stress-inducing, exam-like environment through simulation. Key indicators such as RMSSD, pNN50, and SDNN all showed marked decreases, reflecting a shift towards heightened sympathetic activation and diminished parasympathetic regulation during the simulated exams. Furthermore, we were able to show that the peer-led simulations also triggered a comparable physiological stress response, addressing our second major research goal. This finding suggests that near-peer tutors can effectively induce the necessary stress levels for meaningful performance training, akin to those observed in lecturer-led simulations. However, a noteworthy difference in the data was the lack of significant increase in the LF/HF ratio in the peer-led simulations, indicating that the sympathetic dominance typically reflected in this marker was not as pronounced. Despite this, the other HRV parameters demonstrated clear signs of stress, suggesting that peer-led simulations are still effective in evoking physiological arousal, though perhaps with slightly different autonomic balance compared to lecturer-led scenarios. Our quantitative data revealed a significant increase in perceived competence and confidence following both lecturer-led and peer-led simulations. Paired t-tests showed that students felt notably more confident and capable of handling the actual oral exam after completing the simulated sessions, regardless of whether a lecturer or peer conducted the simulation. Additionally, a marked reduction in test anxiety was observed after the simulations, indicating the effectiveness of these sessions in alleviating some of the anticipatory stress that students typically experience before oral exams. The qualitative data, as illustrated in Fig. 4 , further supported these findings. The vast majority of students found the simulation very helpful in preparing for their final exam, and most reported feeling more confident about their performance in the real exam as a result. The feedback provided by the lecturer during the simulation was also rated as very relevant by most participants. Importantly, a significant number of students indicated that the simulation influenced their learning strategies, allowing them to focus their studies more effectively. Finally, when asked whether the experience helped them during the actual exam, a substantial majority of students affirmed that the simulation contributed positively to their performance. Physiological measurements in educational contexts represent a small but growing field of research, with heart rate variability (HRV) emerging as a particularly valuable tool for assessing stress and engagement in learning environments. A systematic review has highlighted the sensitivity and utility of HRV as a marker of neurocardiac stress, illustrating its capacity to capture real-time physiological responses that are often overlooked in purely self-reported measures 19 . Moreover, higher levels of physiological arousal in learning environments have been linked to enhanced engagement with both the learning material and the environment itself 17 . Research suggests that this heightened arousal can correlate with increased enjoyment, better concentration, and greater involvement in academic tasks 11 , 17 . However, while these elevated arousal levels may boost engagement, they are also associated with higher rates of anxiety 17 . This exact anxiety is the focal point of this study, as we deliberately designed a scenario that would evoke test anxiety to simulate the pressures students face in real exam conditions. Our aim was to train students in navigating this heightened emotional and physiological state, preparing them for the challenges of oral examinations. Anxiety, as shown in numerous studies, is associated with physiological changes such as elevated blood pressure, increased heart rate (HR), increased muscle tension, as well as enhanced respiratory rate and sweating 30 – 34 . These responses reflect the body’s activation of the autonomic nervous system during stress 35 . Through our analysis of heart rate variability (HRV), we were able to capture these stress-induced bodily changes, providing a window into the neurocardiac stress experienced by students during simulated oral examinations. By measuring fluctuations in HRV markers, we detected how students’ autonomic systems responded to the anxiety-inducing simulation, allowing us to assess the physiological impact of test anxiety in real-time. When stress levels rise, parasympathetic activity, which promotes relaxation and recovery, decreases, allowing sympathetic activation to take the lead 30 , 35 , 36 . This shift in balance is a hallmark of the body's physiological response to stress, and it is clearly reflected in reduced HRV measurements. Such findings are well-supported in the literature, where decreased parasympathetic activity, indicated by reduced HRV, has been consistently associated with elevated stress levels in various high-stakes settings, especially in tasks that require significant mental engagement, such as exams and performance-based assessments 37 – 39 . In our study, we made a novel contribution by demonstrating that even in a simulated oral examination, significant reductions in parasympathetic activity can be observed, indicating a robust stress response not only in lecturer-led simulations, but importantly, in student-led simulations as well. This adds a new layer of insight to the existing body of literature by confirming that near-peer tutoring scenarios can induce similar physiological stress responses, making them an effective and scalable alternative for exam preparation. However, it’s essential to approach these findings with a dash of caution. Interestingly, the LF/HF ratios in the student-led simulations did not exhibit the notable elevation seen in the lecturer-led oral examination. This subtle shift adds a layer of complexity to how we interpret the overall stress response. The LF/HF ratio reflects the tug-of-war between the low-frequency (LF) band (0.04 to 0.15 Hz) and the high-frequency (HF) band (0.15 to 0.40 Hz) in HRV. LF captures both sympathetic and parasympathetic activity, while HF is mainly linked to parasympathetic regulation, often signaling calm through vagal tone. Typically, a higher LF/HF ratio means more sympathetic dominance, which usually correlates with increased stress and arousal 40 . In our student-led simulations, the lack of a noticeable spike in the LF/HF ratio suggests that while other HRV markers confirmed stress, the autonomic balance between sympathetic and parasympathetic activity didn’t shift as dramatically as it did in the lecturer-led exams. This could imply that the stress response in peer-led simulations was more moderate, or that the sympathetic nervous system was less engaged. The more moderate stress response observed in the peer-led simulations can likely be attributed to the differences in perceived uncontrollability and social-evaluative threat, both of which are well-known triggers of psychobiological stress responses 16 , 41 . Uncontrollability refers to a situation where individuals feel they have little control over the outcome, while social-evaluative threat arises when individuals believe they are being judged or evaluated by others. These factors are key drivers in inducing significant physiological stress responses 16 , 42 , including heightened sympathetic activation indicated by increased stress markers like certain HRV parameters 43 . In peer-led simulations, these elements may not be as prominent as they are in lecturer-led environments, where the power dynamics and stakes of being evaluated by an authoritative figure may intensify the sense of threat and lack of control. As a result, the sympathetic nervous system might be less activated, leading to the more balanced autonomic response we observed in the peer-led group. Through both the lecturer-led and peer-led oral examination simulations, we were able to significantly enhance perceived competence and perceived confidence among participants. These factors are especially crucial in educational contexts, even from a young age, as perceived academic competence has been shown to mediate the relationship between peer relationships and both life satisfaction and academic achievement 44 . Additionally, well-being and perceived competence in various subjects are closely linked, which plays a key role in fostering resilience in academic environments 45 , 46 . Our findings align with the existing body of research underscoring the importance of perceived competence and confidence in educational success. Students who view themselves as capable are more likely to persist in their efforts, pursue mastery, and set performance-based goals, which can lead to reduced anxiety and better academic outcomes 47 . Importantly, perceived competence also plays a significant role in how students handle negative feedback. Studies using fMRI have shown that students who believe in their abilities and are interested in the task are more adaptive in processing negative feedback, engaging cognitive control networks that help regulate emotions and facilitate learning 48 . Our study also revealed a notable decrease in test anxiety among participants after the simulated oral examinations. This outcome is significant, given the well-known adverse effects of test anxiety on students' academic performance and mental health 49 . Elevated test anxiety is often associated with impaired cognitive function, including difficulties in memory retrieval and lower overall academic achievement 50 , 51 . By effectively reducing test anxiety, our simulations appear to mitigate these negative consequences, thereby enhancing students' ability to perform more effectively under pressure. Our findings demonstrate that simulated oral exams can boost perceived competence and confidence while simultaneously easing test anxiety, creating a more resilient mental framework that equips students to handle the pressures of academic assessments more effectively. Participation in our simulated oral examinations allowed students to effectively adjust their learning strategies, optimizing their preparation for this specific assessment format. Literature emphasizes the importance of adapting different learning strategies to align with the demands of different academic settings, as this alignment is critical for achieving optimal performance 52 – 54 . Our findings highlight the significance of this adaptability and echo research suggesting that choosing the appropriate learning strategies is often a challenge for students, particularly in rigorous fields like medical education 55 . Moreover, this aligns with the idea that medical educators should promote deep and self-directed learning strategies rather than surface approaches to enhance learning outcomes 55 . Such guidance is essential, as students frequently struggle to develop strategies that support meaningful engagement with the learning material​. Conclusion Our study underscores the potential of simulated oral examinations as an effective tool for inducing neurocardiac stress, as demonstrated through significant physiological responses measured by heart rate variability (HRV) markers. Notably, we showed that both lecturer-led and peer-led simulations could successfully evoke a stress response, with decreases in RMSSD, pNN50, and SDNN indicating heightened sympathetic activation and reduced parasympathetic activity. This is a crucial insight, suggesting that these simulated environments can mirror the pressures of real exam conditions, offering a valuable training ground for students. Importantly, the success of peer-led simulations in inducing similar stress responses highlights their potential as a resource-efficient alternative to lecturer-led sessions. This is particularly relevant for educational institutions where resources are limited, and the demand for stress-inducing, performance-based training is high. However, we must also acknowledge the nuance in our findings: the lack of a significant increase in the LF/HF ratio in the peer-led simulations suggests a more moderate stress response compared to the lecturer-led versions. This could imply that the elements of uncontrollability and social-evaluative threat are less pronounced in peer-led environments, resulting in a less intense sympathetic activation. Despite this subtle difference, our study demonstrates that simulated oral exams, whether led by lecturers or peers, can significantly enhance perceived competence and confidence while effectively reducing test anxiety. This is a highly relevant finding, as it suggests that such simulations can equip students with a more resilient mental framework to handle academic assessments. Additionally, our results show that students were able to adjust their learning strategies more effectively after participating in these simulations, aligning with literature emphasizing the importance of adaptive learning strategies for academic success. In conclusion, our research not only validates the utility of simulated oral examinations in promoting resilience and enhancing performance but also advocates for the inclusion of peer-led simulations as a feasible and resource-efficient approach in medical education. However, the nuanced differences in autonomic response between lecturer-led and peer-led settings invite further exploration to fully understand their implications for student learning and performance under stress. Declarations Acknowledgements We extend our gratitude to the student participants for their active involvement and dedication throughout this study. Your enthusiasm and commitment were vital to the success of this initiative. We also wish to thank the Institute of Anatomy for their continuous support and resources, which were crucial in ensuring the smooth execution of this research project. Your support made a significant impact, and we are truly appreciative. Additionally, we acknowledge that large language models were used solely under the supervision and control of the authors, with the aim of enhancing the readability and clarity of this manuscript. All scientific content, analyses, and interpretations presented in this work are the original contributions of the authors. Competing interests: The authors declare no competing interests. References M. Zeidner. Test Anxiety: The State of the Art . (Plenum, New York, 1998). Zeidner, M. 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J Educ Health Promot 13, 198 (2024). Fatima, M., Khan, A., Naz, R. & Noori, M. Y. Impact of Teaching Methods on Clinical Reasoning in Forensic Medicine: A Quasi-Experimental Study. J Coll Physicians Surg Pak 34, 1096–1100 (2024). Madan, C. R. Using Evidence-Based Learning Strategies to Improve Medical Education. Med Sci Educ 33, 773–776 (2023). Chiu, Y.-C. et al. To examine the associations between medical students’ conceptions of learning, strategies to learning, and learning outcome in a medical humanities course. BMC Med Educ 19, 410 (2019). Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5268524","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":369697470,"identity":"396bbfc5-a235-40fe-803b-2e2478969751","order_by":0,"name":"Morris Gellisch","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-1378-0280","institution":"Ruhr University Bochum","correspondingAuthor":true,"prefix":"","firstName":"Morris","middleName":"","lastName":"Gellisch","suffix":""},{"id":369697471,"identity":"f5f374d8-c7f2-40c5-abda-d305c9888185","order_by":1,"name":"Gabriela Morosan-Puopolo","email":"","orcid":"","institution":"Ruhr University Bochum","correspondingAuthor":false,"prefix":"","firstName":"Gabriela","middleName":"","lastName":"Morosan-Puopolo","suffix":""},{"id":369697472,"identity":"8f1ec42b-2e2a-4151-9b11-80418ed01ad8","order_by":2,"name":"Martin Bablok","email":"","orcid":"","institution":"Ruhr University Bochum","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Bablok","suffix":""},{"id":369697473,"identity":"fed703a5-45ec-46a6-aebb-44041ba79580","order_by":3,"name":"Thorsten Schäfer","email":"","orcid":"","institution":"Ruhr University Bochum","correspondingAuthor":false,"prefix":"","firstName":"Thorsten","middleName":"","lastName":"Schäfer","suffix":""},{"id":369697474,"identity":"0fc9e435-be15-4fbc-8a81-4cd167ac99c6","order_by":4,"name":"Beate Brand-Saberi","email":"","orcid":"","institution":"Ruhr University Bochum","correspondingAuthor":false,"prefix":"","firstName":"Beate","middleName":"","lastName":"Brand-Saberi","suffix":""}],"badges":[],"createdAt":"2024-10-15 12:00:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5268524/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5268524/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67952518,"identity":"09fda13b-b520-48dd-8c9c-602240f2eac9","added_by":"auto","created_at":"2024-10-31 15:39:41","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":449610,"visible":true,"origin":"","legend":"\u003cp\u003eBar charts with jitter representing the changes in HRV parameters across different intervals during the lecturer-led simulated examination. (A) RMSSD, (B) pNN50, (C) SDNN, and (D) LF/HF are shown for four time points: Baseline 1 (first five-minute interval of baseline measurement), Baseline 2 (second five-minute interval of baseline measurement), Test 1 (first five-minute interval of the simulated exam), and Test 2 (second five-minute interval of the simulated exam). Each figure illustrates the shift in autonomic regulation from the baseline to the test conditions.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5268524/v1/351d2273f249165648b73c6d.jpeg"},{"id":67952534,"identity":"65c93179-8057-44ce-b4ce-f9f65f94401c","added_by":"auto","created_at":"2024-10-31 15:39:45","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":431465,"visible":true,"origin":"","legend":"\u003cp\u003eBar charts with jitter representing the changes in HRV parameters across different intervals during the student-led simulated examination. (A) RMSSD, (B) pNN50, (C) SDNN, and (D) LF/HF are shown for four time points: Baseline 1 (first five-minute interval of baseline measurement), Baseline 2 (second five-minute interval of baseline measurement), Test 1 (first five-minute interval of the simulated exam), and Test 2 (second five-minute interval of the simulated exam). Each figure illustrates the shift in autonomic regulation from the baseline to the test conditions.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5268524/v1/300dd8899808315f4bdcfd6c.jpeg"},{"id":67952509,"identity":"d4609d2f-1042-4014-81c0-0ca8291dae28","added_by":"auto","created_at":"2024-10-31 15:39:40","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":291638,"visible":true,"origin":"","legend":"\u003cp\u003ePre- and post-simulation perceived competence and confidence in passing the actual examination in lecturer-led and student-led oral exam simulations. (A) Perceived competence before and after lecturer-led simulations, showing a significant increase in perceived competence post-simulation (***p \u0026lt; .001). (B) Perceived competence in student-led simulations, also demonstrating a significant rise post-simulation (***p \u0026lt; .001). (C) Perceived confidence in lecturer-led simulations, with a marked increase after the simulation (***p \u0026lt; .001). (D) Perceived confidence in student-led simulations, indicating a similar significant increase (***p \u0026lt; .001). Each plot includes individual data points connected by lines representing changes in participants’ self-assessments, accompanied by box plots and distribution plots illustrating the data spread and density\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5268524/v1/102c533bbd914f64294b1748.jpeg"},{"id":67952520,"identity":"91a04a42-aea8-4604-b177-6bab206fdeb9","added_by":"auto","created_at":"2024-10-31 15:39:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":221701,"visible":true,"origin":"","legend":"\u003cp\u003eFollow-up\u003cstrong\u003e \u003c/strong\u003esurvey results after the actual oral examination and test anxiety before and after the exam simulation. (A) Responses to the question, \"Do you think the exam simulation was helpful in preparing you for the actual final exam?\", with the majority of participants finding it \"very helpful.\" (B) Responses to whether participants felt more confident when taking the actual final exam after the simulation, with most indicating increased confidence. (C) Relevance of the lecturer's feedback after the exam simulation, where most participants rated it as \"very relevant.\" (D) Influence of the simulation on participants' learning strategies, with the majority responding \"yes\" or \"a little.\" (E) Perceived usefulness of the simulation during the final exam, with most students reporting it as helpful. (F) Bar chart showing a significant reduction in test anxiety levels (p \u0026lt; 0.05) from before the exam simulation to after, with individual data points and connected lines showing the change for each participant.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5268524/v1/d11ed206186d5acb4e45c3ae.png"},{"id":70190512,"identity":"a2126bf9-af11-417b-b1dc-4e5a0981b4fd","added_by":"auto","created_at":"2024-11-29 10:13:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2041411,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5268524/v1/b034e37d-93ce-4d86-9232-b30871cb1d25.pdf"}],"financialInterests":"\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e","formattedTitle":"Autonomic Stress Responses in Oral Examination Simulations: Neuroscientific Insights from Comparing Peer-Led and Lecturer-Led Approaches","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNearly everyone knows the pre-test jitters, and many have seen how even mild anxiety can throw off focus and performance. While not overwhelming, it\u0026rsquo;s often enough to turn confidence into second-guessing, leaving students struggling to showcase their true abilities.\u003c/p\u003e \u003cp\u003eTest anxiety is characterized by a range of cognitive, emotional, and physiological responses triggered by the fear of potential negative outcomes in evaluative situations\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. This pervasive and complex condition goes beyond ordinary stress and can severely impact cognitive performance and overall well-being\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Research shows that its effects are more widespread than often realized, with symptoms manifesting frequently in a large proportion of students\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Notably, a study revealed that over half of college students regularly experience test anxiety, with many enduring symptoms that significantly hinder their ability to perform under pressure\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This form of anxiety has been linked to poor cognitive outcomes, including diminished academic performance and psychological distress, underscoring its serious impact on both educational achievement and mental health\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. As researchers have increasingly focused on test anxiety, it has become clear that the condition is not only common but also multi-dimensional, encompassing both cognitive (e.g., worry) and emotional (e.g., physiological arousal) responses, which can vary based on the type of exam\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Oral examinations, for example, tend to trigger different stress mechanisms compared to written tests, further complicating the ways in which test anxiety presents itself\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMedical students face test anxiety at particularly high rates, adding to the already intense pressures of their education\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. With the rigorous demands and high stakes of medical exams, this group experiences anxiety more frequently than the general population, making test anxiety a prominent challenge throughout their training\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Many medical students are all too familiar with how these stressors can cloud focus and hinder performance, even when they are well-prepared\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Test anxiety in this context doesn\u0026rsquo;t just appear in one form; it can manifest both as a temporary, situation-based reaction (state anxiety) and as a more ingrained, personality-driven response (trait anxiety), as described in Spielberger's model of anxiety\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The combination of these acute and chronic pressures makes it especially difficult for medical students to manage the impact of anxiety on their performance\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhen investigating test anxiety, relying solely on self-reported measures can often provide an incomplete or misleading understanding of the issue. A systematic review of 231 articles (29 eligible) found that while self-reports of test anxiety are sometimes associated with physiological arousal, the correlation is not consistently strong, highlighting the limitations of subjective reporting\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This suggests that a more comprehensive assessment should include objective physiological measures, such as heart rate variability or skin conductance, which offer a clearer view of the body's response to stress. Physiological activation, such as increased heart rate and other signs of heightened arousal, plays an important role in test anxiety\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. High-pressure, socially evaluative tasks tend to elicit the strongest responses, with physiological symptoms lasting well beyond the test itself, further complicating the ability to perform under stress\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. By incorporating physiological measurements into the study of test anxiety, researchers can gain a more accurate and complex understanding of how stress impacts student performance, moving beyond the limitations of self-reported data alone.\u003c/p\u003e \u003cp\u003eAmong these physiological measurements, Heart Rate Variability (HRV) stands out as a particularly valuable tool for examining the autonomic responses associated with stress in educational settings\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The brain\u0026rsquo;s regulation of cardiovascular function, particularly through the vagus nerve and its influence on heart rate, reflects the body\u0026rsquo;s ability to adapt to stressors. HRV provides a window into this regulatory mechanism by measuring fluctuations between heartbeats, making it an ideal marker for understanding physiological responses in stressful situations like test anxiety. Several specific HRV parameters provide deeper insight into autonomic function\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e: RMSSD (Root Mean Square of Successive Differences) is a key indicator of parasympathetic activity, reflecting short-term variability in heart rate. Higher RMSSD values are typically associated with greater vagal tone and a calmer physiological state. pNN50, which calculates the percentage of successive heartbeats that differ by more than 50 ms, is another marker of parasympathetic influence and provides a direct measure of heart rate irregularity under relaxed conditions. On the other hand, SDNN (Standard Deviation of Normal-to-Normal intervals) is a broader measure of overall HRV and is influenced by both parasympathetic and sympathetic inputs, capturing long-term autonomic balance. Finally, the LF/HF ratio\u0026mdash;the ratio of low-frequency to high-frequency oscillations\u0026mdash;offers an index of sympathetic to parasympathetic balance. Higher LF/HF ratios indicate sympathetic dominance, often linked to stress and heightened arousal, while lower ratios suggest parasympathetic predominance, reflective of a more relaxed state.\u003c/p\u003e \u003cp\u003eThe Yerkes-Dodson Law is a concept many are at least somewhat familiar with, highlighting how a certain amount of stress can be beneficial for performance\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. According to this principle, moderate arousal helps enhance focus and memory consolidation, leading to improved performance. However, once stress levels surpass a critical point, this arousal turns into hyperarousal, which impairs cognitive functions, particularly memory retrieval\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In terms of memory, while a mild level of stress aids in consolidating information, hyperarousal disrupts the brain\u0026rsquo;s ability to retrieve stored knowledge\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This is especially true when stress interferes with the hippocampus, a brain region crucial for recalling information\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Under conditions of high stress, the pathways needed for effective memory retrieval become less efficient, making it harder to access previously learned material.\u003c/p\u003e \u003cp\u003e Our project aimed to simulate an oral examination with the goal of inducing a stress response, providing students with an opportunity to train their ability to retrieve relevant information under pressure. The core research question was whether this simulated scenario could effectively evoke the necessary stress levels to mirror real exam conditions. To assess its success, we conducted a follow-up after the actual final oral examination, gathering both qualitative and quantitative data through a questionnaire. This approach allowed us to explore not only if students found the simulation helpful, but also why they felt it contributed to their preparation. To capture a comprehensive understanding of the effects, we employed a mixed-method design, integrating physiological measurements with qualitative feedback and quantitative analysis. By combining objective physiological data with subjective evaluations from students, we were able to assess both the measurable stress response and the perceived utility of the simulation in real exam contexts. This design ensured that we could evaluate the effectiveness of the simulated oral examination from multiple angles, offering deeper insights into the relationship between stress, memory retrieval, and exam performance.\u003c/p\u003e \u003cp\u003eIn addition to our primary research project, we also explored an important secondary question: whether near-peer tutors can be just as effective as lecturers in conducting exam simulations, particularly in terms of eliciting a similar stress response. Offering individual exam simulations by lecturers places significant demands on resources, making it challenging to implement on a larger scale. Given the established benefits of near-peer tutors in medical education\u003csup\u003e\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, such as improved accessibility and relatable guidance, we wanted to investigate whether these tutors could also trigger the necessary stress response in students during simulated oral exams. The research aimed to determine whether the stress response elicited by a near-peer tutor conducting the simulation would be comparable to that elicited by a lecturer. By examining physiological markers of stress, as well as student feedback, we sought to assess if near-peer-led simulations could serve as an effective alternative without compromising the desired outcomes of stress-induced training. This study allows us to explore a potentially more scalable solution for preparing students for oral examinations while maintaining the rigor and stress exposure necessary for effective learning.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis study employed a quasi-experimental design to investigate whether simulated oral examinations induce physiological stress in first-year medical students and to compare the effectiveness of simulations led by faculty lecturers versus senior students. The primary research question was to determine if the simulated oral examination exerts physiological stress, as measured by heart rate variability (HRV), and whether there are differences in outcomes when the examination is conducted by a lecturer compared to a student. This investigation is grounded in the neuroscientific understanding that recall is impaired under stress, highlighting the importance of accustoming students to perform under stressful conditions. Participants were randomly assigned to either the lecturer-led or student-led simulation group, with HRV, self-confidence, and exam anxiety measured before and after the simulation. Additionally, a follow-up questionnaire was administered after the real oral exam at the end of the semester to gain insights into the efficacy of the simulated oral examinations in preparing students for their actual exams.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHear Rate Variability measurements\u003c/h2\u003e \u003cp\u003eFor heart rate variability (HRV) measurements, we utilized the Polar Vantage V2 in combination with the Polar H10 sensor (Polar Electro Oy, Kempele, Finland). The Polar H10 sensor has been validated against a 12-channel electrocardiogram (ECG) for HRV data, demonstrating nearly perfect correlations for linear parameters such as RR intervals and heart rate. Specifically, the Pearson correlation coefficient (r) was greater than 0.86, indicating a strong positive correlation, the concordance correlation coefficient (rc) was greater than 0.84, indicating substantial agreement, and the intraclass correlation coefficient (ICC3,1) was greater than 0.85, indicating excellent reliability (Schaffarczyk et al., 2022). The HRV recording procedure was carefully designed to ensure standardized conditions and minimize potential biases. The Polar Vantage V2 and Polar H10 sensor system were applied to each participant immediately before the start of the oral exam simulation. The simulation lasted for 10 minutes, followed by a 5-minute feedback session. After the feedback session, participants were directed to a designated resting room for baseline measurements. To further standardize conditions, each participant was placed in a separate resting room to ensure they were alone during the baseline measurement. This setup was crucial to avoid any potential bias in measuring the resting state, such as talking or laughing, which could affect the HRV data. Importantly, baseline measurements were taken after the oral examination to avoid bias from anticipatory stress, ensuring that the HRV data accurately reflected the participants' true physiological baseline without the influence of impending stress. The frequency domain measures of LF and HF, along with the time domain measures of RMSSD, pNN50 and SDNN, were computed following the guidelines of the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e (1996), using a fast Fourier transform (FFT) algorithm to calculate the ratio of the main spectral components.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuantitative analyses\u003c/h3\u003e\n\u003cp\u003eFor the quantitative analyses, we measured participants' confidence in passing the final exam along with their subjectively perceived performance assessment for both the lecturer-led and student-led simulation groups. This measurement was conducted twice: before the oral exam simulation and immediately afterward. Additionally, test anxiety was assessed at two points: before the oral examination simulation and within the follow-up questionnaire administered after the real oral examination. This adjustment in the second measurement time point allowed us to gain insights into the dynamics of test anxiety development between the simulation and the actual exam. Both confidence and test anxiety were measured using a Visual Analog Scale (VAS) (Hayes and Patterson, 1921; Yeung and Wong, 2019).\u003c/p\u003e\n\u003ch3\u003eQualitative Analyses\u003c/h3\u003e\n\u003cp\u003eFor the qualitative analyses, we included several open-ended questions in the follow-up questionnaire administered after the real exam. Open questions were chosen to allow for more comprehensive and precise feedback from the participants. We manually analyzed each response and categorized them into specific answer groups (e.g., not relevant, somewhat relevant, very relevant). The questions asked were: \"Do you think the exam simulation was helpful in preparing you for the actual exam?\", \"Will you be more confident when taking the actual final exam after the exam simulation?\", \"How relevant do you find the lecturer's feedback after your exam simulation?\", \"Did the exam simulation influence your learning strategies for the final exam?\", and \"Do you think the experience gained from the exam simulation helped you during your final exam?\". The first question aimed to understand the direct perceived impact of the simulation on exam preparedness, a critical measure of its effectiveness. Confidence is a key factor in performance, so we asked if participants would be more confident taking the final exam after the simulation. Evaluating the relevance of the lecturer's feedback was essential to gauge its effectiveness in enhancing learning. We also explored whether the simulation influenced students' learning strategies, providing insights into how it affected their study behaviors and methods. Finally, we included a retrospective question to assess the practical utility of the simulation experience during the actual exam. By incorporating these questions into the follow-up questionnaire, we ensured that participants could reflect on their experiences and provide detailed insights.\u003c/p\u003e\n\u003ch3\u003eInclusion criteria\u003c/h3\u003e\n\u003cp\u003eParticipants in this study were required to meet specific inclusion criteria to ensure a realistic and representative sample of the cohort. Eligible participants were healthy first-year medical students properly enrolled at Ruhr University Bochum. The age range for inclusion was set between 18 and 24 years to reflect the typical demographic of the student cohort. Importantly, we deliberately included students of all genders, ethnicities, and backgrounds to ensure diversity and generalizability of the findings across the entire student population. This inclusive approach aimed to provide comprehensive insights into the impact of simulated oral examinations on a varied group of medical students.\u003c/p\u003e\n\u003ch3\u003ePower analysis\u003c/h3\u003e\n\u003cp\u003eTo ensure adequate statistical power for our study, we conducted a power analysis using G*Power. We specified a two-tailed test with an effect size (dz) of 0.6, an alpha level of 0.05, and a desired power of 0.95. The analysis yielded a noncentrality parameter (δ) of 3.75 and a critical t-value of 2.02, indicating that a total sample size of 39 participants per group would be necessary to detect a statistically significant effect with an actual power of approximately 0.95. To account for potential data loss due to measurement errors or participant attrition, we recruited additional participants, resulting in initial group sizes of n\u0026thinsp;=\u0026thinsp;53 for the lecturer-led group and n\u0026thinsp;=\u0026thinsp;42 for the peer-led group. Despite this precaution, some data were lost due to measurement errors, with 6 participants' data excluded from the lecturer-led group and 11 participants' data excluded from the peer-led group. This strategy ensured that our study maintained sufficient power even after accounting for data loss.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDemographic information\u003c/h2\u003e \u003cp\u003eThe study's participant demographics comprised a total of 95 first-semester medical students. The distribution included 51 females (53.7%) and 44 males (46.3%). The average age of participants was 19.3 years, with a standard deviation of 1.3 years. Female participants had a mean age of 19.3 years (SD\u0026thinsp;=\u0026thinsp;1.4), while male participants had a mean age of 19.4 years (SD\u0026thinsp;=\u0026thinsp;1.1). This age range and gender distribution reflect the typical demographic of first-year medical students, providing a realistic and diverse sample for the study. The deliberate inclusion of all genders and diverse backgrounds aimed to enhance the generalizability of the findings across the entire student population. The lecturer-led simulation group comprised a total of 53 participants. The distribution included 31 females (58.5%) and 22 males (41.5%). The average age in this group was 19.6 years, with a standard deviation of 1.4 years. Female participants had a mean age of 19.4 years (SD\u0026thinsp;=\u0026thinsp;1.5), while male participants had a mean age of 19.9 years (SD\u0026thinsp;=\u0026thinsp;1.2). The student-led simulation group comprised a total of 42 participants. The distribution included 20 females (47.6%) and 22 males (52.4%). The average age in this group was 19.0 years, with a standard deviation of 1.1 years. Female participants had a mean age of 19.2 years (SD\u0026thinsp;=\u0026thinsp;1.5), while male participants had a mean age of 19.0 years (SD\u0026thinsp;=\u0026thinsp;0.8).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical aspects\u003c/h3\u003e\n\u003cp\u003e This research was conducted in accordance with the principles outlined in the Declaration of Helsinki. Approval for the study was obtained from the Ethics Committee of the Professional School of Education at Ruhr University Bochum (Reference No. EPSE-2022-004, dated 12.09.2022). The study adhered to all ethical guidelines to ensure the safety, well-being, and confidentiality of all participants.\u003c/p\u003e\n\u003ch3\u003eStatistics\u003c/h3\u003e\n\u003cp\u003eDescriptive statistics were calculated including the median, mean, standard error of the mean, upper and lower 95% confidence intervals, mean absolute deviation, interquartile range, skewness, standard error of skewness, kurtosis, and standard error of kurtosis. These metrics provided a comprehensive summary of the central tendency and dispersion of the data. For inferential statistics, paired t-tests were conducted to assess the differences between pre- and post-simulation scores, with the significance level set at 0.05. Normality of the data was evaluated using the Shapiro-Wilk test. In cases where the normality assumption was not met, the Wilcoxon signed-rank test was employed as a non-parametric alternative.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive statistics were calculated for selected heart rate variability (HRV) parameters during two 5-minute intervals at baseline and during the oral exam simulations. The metrics included RMSSD (Root Mean Square of Successive Differences between normal heartbeats), PNN50 (percentage of NN intervals that differ by more than 50 ms), SDNN (Standard Deviation of Normal-to-Normal interbeat intervals), and LF/HF (Ratio of Low Frequency to High Frequency). These measurements were conducted for both the lecturer-led and the student-led oral exam simulations to provide a comprehensive comparison of physiological stress responses. For RMSSD during the lecturer-led simulated oral examination, the median values at baseline were 32.11 ms and 32.20 ms for the first and second intervals, respectively, which decreased to 18.10 ms and 18.47 ms during the examination. The mean RMSSD values also decreased from 37.03 ms (first interval) and 36.30 ms (second interval) at baseline to 22.87 ms and 26.48 ms during the examination. PNN50 exhibited a similar trend, with median values at baseline of 9.8550 and 11.0300 decreasing to 2.7250 and 2.9300 during the examination. Mean PNN50 values decreased from 14.1774 and 15.3044 at baseline to 7.8298 and 8.9094 during the examination, reflecting reduced parasympathetic activity under stress. For SDNN, the median values were 44.44 ms and 41.04 ms at baseline, decreasing to 30.68 ms and 34.02 ms during the examination. The mean SDNN values showed a similar pattern, decreasing from 46.14 ms and 45.48 ms at baseline to 34.46 ms and 38.45 ms during the examination. The LF/HF ratio increased from baseline to the examination periods, with median values of 2.41 and 2.71 at baseline rising to 3.23 and 3.22 during the examination. Mean LF/HF ratios also increased from 3.36 and 3.33 at baseline to 4.11 and 4.42 during the examination, suggesting a shift towards sympathetic dominance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeart Rate Variability (HRV) Characteristics During Lecturer-Led Oral Examination Simulation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"34\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c23\" colnum=\"23\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c24\" colnum=\"24\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c25\" colnum=\"25\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c26\" colnum=\"26\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c27\" colnum=\"27\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c28\" colnum=\"28\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c29\" colnum=\"29\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c30\" colnum=\"30\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c31\" colnum=\"31\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c32\" colnum=\"32\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c33\" colnum=\"33\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c34\" colnum=\"34\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1 RMSSDB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2 RMSSDB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1 RMSSDT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e2 RMSSDT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e1 PNN50B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e2 PNN50B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e1 PNN50T\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e2 PNN50T\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e1 SDNNB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e2 SDNNB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e1 SDNNT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e2 SDNNT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c28\" namest=\"c27\"\u003e \u003cp\u003e1 LF/HFB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c30\" namest=\"c29\"\u003e \u003cp\u003e2 LF/HFB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c32\" namest=\"c31\"\u003e \u003cp\u003e1 LF/HFT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c34\" namest=\"c33\"\u003e \u003cp\u003e2 LF/HFT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.4700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9.8550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e11.0300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e2.7250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e2.9300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e44.4400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e41.0400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e30.6800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e34.0200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e2.4100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e2.7100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e3.2300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e3.2200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.0308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.3036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.8706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e26.4792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e14.1774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e15.3044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e7.8298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e8.9094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e46.1419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e45.4751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e34.4572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e38.4483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e3.3555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e3.3342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e4.1055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e4.4217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Error of Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.8045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.6367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.8845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.2357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.1818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.3053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1.7472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1.9782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e2.8811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e3.0456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e2.8011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e2.7749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e0.5080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e0.3973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e0.4511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e0.5947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95% CI Mean Upper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.6651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.6012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.6588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e32.9722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e18.5535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e19.9283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e11.3342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e12.8771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e51.9232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e51.5864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e40.0780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e44.0165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e4.3748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e4.1314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e5.0106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e5.6151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95% CI Mean Lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.3964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.0059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.0823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e19.9863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9.8013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e10.6806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e4.3253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e4.9417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e40.3606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e39.3637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e28.8364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e32.8801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e2.3362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e2.5369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e3.2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e3.2283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.6973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.4758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.9996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23.5565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e16.0328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e16.9405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e12.8391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e14.5365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e20.9744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e22.1720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e20.3923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e20.2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e3.6981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e2.8925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e3.2840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e4.3297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.7200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.6300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.7000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7.7050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e8.8600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e2.5650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e2.9300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e14.2600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e11.7800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e11.7600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e11.4700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e1.2200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e1.2100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e1.2200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.8500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.8400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.4200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e21.1700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e15.5350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e17.5375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e6.8800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e10.6625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e29.2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e25.1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e23.0600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e26.0300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e2.0800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e2.4800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e2.4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e2.4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.1824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.2822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.4850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.9639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.8037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e2.5675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e2.8302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e0.9624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e1.3623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e1.2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e0.9792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e3.4770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e3.1313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e2.5685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e4.5595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Error of Skewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.3246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.3274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.3246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.3246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.3246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e0.3274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e0.3274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e0.3274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e0.3274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e0.3274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e0.3274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e0.3274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e0.3274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.4810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.2082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.4309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.8700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.3606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e3.3125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e6.6209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e8.5676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e1.6814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e3.3437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e2.3567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e1.2704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e14.4925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e13.9513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e8.8309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e26.4233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Error of Kurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.6444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.6389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.6389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.6389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.6389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e0.6444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e0.6444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e0.6444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e0.6444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e0.6444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e0.6444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e0.6444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e0.6444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"34\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u0026lsquo;B\u0026rsquo; refers to baseline measurement, \u0026lsquo;T\u0026rsquo; refers to test measurement, RMSSD refers to the Root Mean Square of Successive Differences, pNN50 stands for the percentage of NN intervals differing by more than 50 milliseconds, SDNN represents the Standard Deviation of Normal-to-Normal intervals, LF/HF is the Low-Frequency to High-Frequency ratio, MAD indicates the Mean Absolute Deviation, and IQR refers to the Interquartile Range.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor the RMSSD metric during the student-led simulated oral examination, initial median values at baseline were 34.62 ms and 33.28 ms for the first and second intervals, respectively, reducing to 20.68 ms and 22.93 ms during the test. The mean RMSSD also showed a decrease from 48.91 ms and 48.85 ms at the start to 28.64 ms and 36.67 ms during the test, reflecting decreased parasympathetic activation due to stress. Similarly, PNN50 metrics started with median values of 13.43 and 11.04 at baseline and decreased to 2.96 and 4.12 during the test. The average values decreased from 17.48 and 16.83 to 5.85 and 11.55, further suggesting a reduction in parasympathetic response under stress. For SDNN, baseline median values were 42.90 ms and 43.94 ms, which decreased to 33.12 ms and 37.46 ms during the test. The corresponding mean values also reduced from 54.26 ms and 55.49 ms to 38.16 ms and 46.08 ms, indicating lowered heart rate variability under stress. The LF/HF ratio increased from initial median values of 2.23 and 2.76 to 3.12 and 3.15 during the examination. Mean values also slightly increased from 3.07 and 3.63 at baseline to 3.27 and 3.47 in the simulated oral examination (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeart Rate Variability (HRV) Characteristics During Student-Led Oral Examination Simulation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"34\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c23\" colnum=\"23\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c24\" colnum=\"24\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c25\" colnum=\"25\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c26\" colnum=\"26\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c27\" colnum=\"27\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c28\" colnum=\"28\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c29\" colnum=\"29\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c30\" colnum=\"30\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c31\" colnum=\"31\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c32\" colnum=\"32\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c33\" colnum=\"33\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c34\" colnum=\"34\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1 RMSSDB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2 RMSSDB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1 RMSSDT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e2 RMSSDT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e1 PNN50B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e2 PNN50B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e1 PNN50T\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e2 PNN50T\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e1 SDNNB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e2 SDNNB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e1 SDNNT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e2 SDNNT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c28\" namest=\"c27\"\u003e \u003cp\u003e1 LF/HFB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c30\" namest=\"c29\"\u003e \u003cp\u003e2 LF/HFB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c32\" namest=\"c31\"\u003e \u003cp\u003e1 LF/HFT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c34\" namest=\"c33\"\u003e \u003cp\u003e2 LF/HFT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.6150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.2800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.6750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e22.9300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e13.4300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e11.0350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e2.9550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e4.1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e42.9000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e43.9350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e33.1150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e37.4550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e2.2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e2.7550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e3.1150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e3.1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.9138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.8524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.6369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e36.6717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e17.4764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e16.8323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e5.8507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e11.5532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e54.2560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e55.4876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e38.1571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e46.0781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e3.0676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e3.6345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e3.2700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e3.4674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Error of Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.7027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.4421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.5421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.8156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.7078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.7350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1.0978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e2.7795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e6.8425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e7.9860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e4.3561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e7.2467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e0.4870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e0.5156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e0.2892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e0.3106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95% CI Mean Upper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.5283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.9797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41.8489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e58.5143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e22.9371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e22.3480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e8.0647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e17.1585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e68.0747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e71.6157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e46.9544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e60.7132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e4.0511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e4.6757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e3.8540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e4.0946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95% CI Mean Lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.2994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.7250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.4249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.8291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e12.0156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e11.3166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e3.6367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e5.9479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e40.4372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e39.3595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e29.3599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e31.4430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e2.0842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e2.5933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e2.6860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e2.8402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.3611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.6341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.3975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e70.0933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e17.9614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e18.1421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e7.2822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e18.4368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e44.3447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e51.7554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e28.2304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e46.9642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e3.1559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e3.3413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e1.8741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e2.0127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.2900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.2100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.8300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11.0850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e8.4350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e2.5600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e3.7500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e14.3300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e9.3250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e10.2700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e9.7100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e0.9850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e0.8100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e1.1550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e1.3750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.8300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.1675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.4950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20.6625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e22.1475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e17.6400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e7.5100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e15.3475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e27.9000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e21.2525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e20.7750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e22.3150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e1.9275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e1.7300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e2.2900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e2.6725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.4445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.8934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.7627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.0666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.2595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.6685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1.4952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e2.8538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e4.4759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e5.0715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e4.4647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e5.4035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e2.3107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e2.8059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e0.6705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e0.7343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Error of Skewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.3654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.3575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.3575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.3575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.3575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e0.3654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e0.3654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e0.3654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e0.3654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e0.3654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e0.3654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e0.3654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e0.3654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.6871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.7204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35.6448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e38.3031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.8372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.4825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1.1949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e9.0419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e24.7056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e29.6132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e24.7654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e32.5446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e6.0689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e9.0811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e0.2396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e-0.1462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Error of Kurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.7166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.7017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.7017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.7017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.7017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e0.7166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e0.7166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e \u003cp\u003e0.7166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e \u003cp\u003e0.7166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e \u003cp\u003e0.7166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e \u003cp\u003e0.7166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c30\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e \u003cp\u003e0.7166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c33\"\u003e \u003cp\u003e0.7166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c34\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"34\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u0026lsquo;B\u0026rsquo; refers to baseline measurement, \u0026lsquo;T\u0026rsquo; refers to test measurement, RMSSD refers to the Root Mean Square of Successive Differences, pNN50 stands for the percentage of NN intervals differing by more than 50 milliseconds, SDNN represents the Standard Deviation of Normal-to-Normal intervals, LF/HF is the Low-Frequency to High-Frequency ratio, MAD indicates the Mean Absolute Deviation, and IQR refers to the Interquartile Range.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe inferential statistical analysis for the lecturer-led oral examination simulation revealed significant changes in heart rate variability (HRV) parameters, indicating substantial physiological stress responses among participants. Both paired t-tests and Wilcoxon signed-rank tests were utilized, depending on the normality of the data distribution as determined by the Shapiro-Wilk test. Significant reductions were observed in the Root Mean Square of Successive Differences (RMSSD) between normal heartbeats from the baseline to the test period. The first 5-minute interval showed a decrease, with the t-test yielding a statistic of 8.2650 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and the Wilcoxon test producing a z-value of 6.3586 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The total RMSSD also significantly declined, confirmed by a t-test statistic of 9.3824 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and a Wilcoxon result of 6.3328 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). For the PNN50 metric, which measures the percentage of successive NN intervals that differ by more than 50 ms, there was also a notable decrease from baseline to examination. The first interval's changes were significant under both the t-test (t\u0026thinsp;=\u0026thinsp;4.8572, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and the Wilcoxon test (z\u0026thinsp;=\u0026thinsp;4.9211, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating a reduction in parasympathetic nervous system activity during the simulated examination (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Changes in the Standard Deviation of Normal-to-Normal (SDNN) intervals, which reflect overall HRV, showed a decrease, suggesting heightened physiological arousal. This was statistically significant across all measured intervals, with the first interval's t-test at 7.3313 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and Wilcoxon test at 5.5751 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The Low Frequency to High Frequency (LF/HF) ratio, indicative of the balance between sympathetic and parasympathetic nervous system activities, exhibited increases that reached statistical significance in non-parametric testing despite some t-tests not reaching conventional significance thresholds. Specifically, the Wilcoxon results for the first interval marked significant changes (z=-2.6907, p\u0026thinsp;=\u0026thinsp;0.0072), contrasting with the t-test (t=-1.7919, p\u0026thinsp;=\u0026thinsp;0.0789). The total LF/HF ratio changes were significant in both tests (t=-2.8664, p\u0026thinsp;=\u0026thinsp;0.0059; z=-3.0179, p\u0026thinsp;=\u0026thinsp;0.0026), suggesting a shift towards sympathetic dominance during the test (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the student-led oral examination simulation, inferential statistics were computed using both paired t-tests and Wilcoxon signed-rank tests. This approach was chosen to accommodate the deviations from normality identified through Shapiro-Wilk tests, ensuring robust analysis of heart rate variability (HRV) parameters. Significant reductions in RMSSD were noted across the intervals. Specifically, the first interval's RMSSD decreased significantly, as evidenced by a Student t-test value of 4.4715 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and a Wilcoxon z-value of 5.2828 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). This pattern was consistent for the total RMSSD, which also showed a significant decrease (Student t\u0026thinsp;=\u0026thinsp;4.9695, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; Wilcoxon z\u0026thinsp;=\u0026thinsp;5.3578, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The PNN50 parameter similarly exhibited significant decreases. The first interval saw substantial changes (Student t\u0026thinsp;=\u0026thinsp;5.8129, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; Wilcoxon z\u0026thinsp;=\u0026thinsp;5.1703, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). While the second interval's decrease was statistically significant under the Wilcoxon test (z\u0026thinsp;=\u0026thinsp;4.5678, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), the Student t-test results were less pronounced (t\u0026thinsp;=\u0026thinsp;2.0742, p\u0026thinsp;=\u0026thinsp;0.0441) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). For SDNN (Standard Deviation of NN Intervals), there was a notable decrease in variability from baseline to during the test, indicating heightened physiological arousal. This increase was statistically significant across all intervals, confirmed by both the Student t-tests (1_SDNN: t\u0026thinsp;=\u0026thinsp;5.2899, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and Wilcoxon tests (z\u0026thinsp;=\u0026thinsp;5.1077, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The LF/HF ratio, reflecting the balance between sympathetic and parasympathetic nervous activities, showed no consistent significant changes. The first interval analysis with both Student t-test and Wilcoxon test did not reach significance (t=-0.4740, p\u0026thinsp;=\u0026thinsp;0.6380; z=-1.4254, p\u0026thinsp;=\u0026thinsp;0.1558), and this trend was observed across other intervals and the total LF/HF ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e To evaluate the impact of the lecturer-led simulated oral examination on students' perceived competence, a paired samples t-test was conducted. The analysis showed a statistically significant increase in perceived competence following the simulation. The test yielded a t-statistic of -8.4065 (df\u0026thinsp;=\u0026thinsp;41, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating a substantial change in students' self-assessed competence levels. The effect size, measured by Cohen's d, was \u0026minus;\u0026thinsp;1.2971 with a standard error (SE) of 0.2642. This large effect size suggests that the lecturer-led simulation was highly effective in enhancing students' perceived competence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e The student-led simulated oral examination also had a significant impact on students' perceived competence. A paired samples t-test revealed a notable increase in self-assessed competence levels post-simulation. The results indicated a t-statistic of -5.8176 (df\u0026thinsp;=\u0026thinsp;27, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), demonstrating a statistically significant improvement. The effect size, represented by Cohen's d, was calculated at -1.0994 with a standard error (SE) of 0.2488. This substantial effect size suggests that the student-led simulation effectively enhanced students' perceived competence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e The lecturer-led simulated oral examination also had a significant effect on students' perceived confidence. A paired samples t-test was conducted to compare confidence levels before and after the simulation. The results revealed a t-statistic of -5.3550 (df\u0026thinsp;=\u0026thinsp;41, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating a statistically significant increase in confidence post-simulation. The effect size, represented by Cohen's d, was \u0026minus;\u0026thinsp;0.8263 with a standard error (SE) of 0.1573. This moderate effect size suggests that the lecturer-led simulation was effective in boosting students' confidence, reflecting an enhancement in their self-assurance and belief in their ability to perform well in the actual examination (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e The student-led simulated oral examination also demonstrated a notable impact on students' perceived confidence. Analyzing the data using a paired samples t-test revealed a significant increase in confidence levels following the simulation, with a t-statistic of -5.0054 (df\u0026thinsp;=\u0026thinsp;27, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The effect size, measured by Cohen's d, was calculated to be -0.9459 with a standard error (SE) of 0.2846. This considerable effect size indicates that the student-led simulation significantly bolstered students' confidence, resulting in a substantial improvement in their self-assurance and preparedness for the actual examination (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFollowing the quantitative analysis, a qualitative analysis was undertaken to gain a deeper understanding of the subjective experiences and perceptions of the participants regarding the exam simulation. Participants responded to several open-ended questions designed to elicit detailed and unrestricted feedback. Their responses were systematically categorized into distinct answer groups, which were subsequently visualized using pie charts. Additionally, in certain instances, we conducted a more granular analysis by examining the frequencies of specific answer categories to provide a more holistic understanding of the data. To gauge the perceived effectiveness of the exam simulation in preparing students for their actual final exams, participants were asked to express their views on the helpfulness of the simulation. The responses were then categorized and analyzed. A significant majority, 92 out of 97 students (94.85%), indicated that the simulation was \"very helpful\" in their preparation process. A smaller portion, 4 students (4.12%), found the simulation to be \"somewhat helpful.\" Notably, none of the respondents found the simulation unhelpful, and only 1 participant (1.03%) did not provide an answer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). As part of the qualitative analysis, participants were asked if the exam simulation increased their confidence in taking the actual final exam. The responses were categorized and analyzed, revealing that 58 out of 97 participants (59.79%) felt that the simulation would indeed make them more confident. Additionally, 32 participants (32.99%) reported feeling \"a little\" more confident, while 5 participants (5.15%) indicated that the simulation did not enhance their confidence. There were 2 participants (2.06%) who did not provide an answer. Participants were asked to evaluate the relevance of the lecturer's feedback following the exam simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The majority, 89 out of 97 participants (91.75%), considered the feedback to be \"very relevant.\" An additional 4 participants (4.12%) found the feedback \"somewhat relevant,\" while 2 participants (2.06%) rated it as \"not relevant.\" Another 2 participants (2.06%) did not provide a response (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Participants were asked whether the exam simulation influenced their learning strategies for the final exam, with 74 out of 89 respondents (83%) indicating a positive impact. A smaller number, 4 participants (4%), felt the simulation had \"a little\" influence, while 5 participants (6%) reported no influence, and 6 participants (7%) did not respond (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Among those who answered \"yes,\" the qualitative analysis revealed several key ways in which the simulation helped adjust their learning strategies. Fourteen participants (19%) mentioned that the simulation aided in content familiarization and revision, allowing them to identify areas they had already covered and focus on targeted revision. The most significant insight, noted by 36 participants (49%), was the understanding gained about the exam format and the depth of questions, which enabled them to tailor their study approaches more effectively. Additionally, 4 participants (5%) appreciated the feedback on their current knowledge level, which helped them identify specific areas needing improvement. A notable portion, 20 participants (27%), mentioned that the simulation experience reduced their stress and nervousness, enhancing their self-confidence. This reduction in anxiety allowed them to approach their studies with a calmer and more focused mindset, which was deemed crucial for their preparation. Additionally, test anxiety was measured quantitatively both before and after the exam simulation, and the results indicated a significant decrease in anxiety levels, with a p-value less than 0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Participants were further asked whether the experience gained from the exam simulation helped them during the actual final exam. The majority, 74 out of 89 respondents (83%), affirmed that the simulation experience was helpful. A smaller group, 5 participants (6%), felt that it helped \"a little,\" while 4 participants (4%) indicated that the simulation did not help them. Additionally, 6 participants (7%) did not provide an answer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e Our study successfully demonstrated that even in a simulated oral examination setting, significant neurocardiac stress can be induced, as evidenced by substantial physiological responses across several HRV markers. This was a crucial finding, validating our first major research goal: to create a stress-inducing, exam-like environment through simulation. Key indicators such as RMSSD, pNN50, and SDNN all showed marked decreases, reflecting a shift towards heightened sympathetic activation and diminished parasympathetic regulation during the simulated exams. Furthermore, we were able to show that the peer-led simulations also triggered a comparable physiological stress response, addressing our second major research goal. This finding suggests that near-peer tutors can effectively induce the necessary stress levels for meaningful performance training, akin to those observed in lecturer-led simulations. However, a noteworthy difference in the data was the lack of significant increase in the LF/HF ratio in the peer-led simulations, indicating that the sympathetic dominance typically reflected in this marker was not as pronounced. Despite this, the other HRV parameters demonstrated clear signs of stress, suggesting that peer-led simulations are still effective in evoking physiological arousal, though perhaps with slightly different autonomic balance compared to lecturer-led scenarios. Our quantitative data revealed a significant increase in perceived competence and confidence following both lecturer-led and peer-led simulations. Paired t-tests showed that students felt notably more confident and capable of handling the actual oral exam after completing the simulated sessions, regardless of whether a lecturer or peer conducted the simulation. Additionally, a marked reduction in test anxiety was observed after the simulations, indicating the effectiveness of these sessions in alleviating some of the anticipatory stress that students typically experience before oral exams. The qualitative data, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, further supported these findings. The vast majority of students found the simulation very helpful in preparing for their final exam, and most reported feeling more confident about their performance in the real exam as a result. The feedback provided by the lecturer during the simulation was also rated as very relevant by most participants. Importantly, a significant number of students indicated that the simulation influenced their learning strategies, allowing them to focus their studies more effectively. Finally, when asked whether the experience helped them during the actual exam, a substantial majority of students affirmed that the simulation contributed positively to their performance.\u003c/p\u003e \u003cp\u003ePhysiological measurements in educational contexts represent a small but growing field of research, with heart rate variability (HRV) emerging as a particularly valuable tool for assessing stress and engagement in learning environments. A systematic review has highlighted the sensitivity and utility of HRV as a marker of neurocardiac stress, illustrating its capacity to capture real-time physiological responses that are often overlooked in purely self-reported measures\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Moreover, higher levels of physiological arousal in learning environments have been linked to enhanced engagement with both the learning material and the environment itself\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Research suggests that this heightened arousal can correlate with increased enjoyment, better concentration, and greater involvement in academic tasks\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, while these elevated arousal levels may boost engagement, they are also associated with higher rates of anxiety\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. This exact anxiety is the focal point of this study, as we deliberately designed a scenario that would evoke test anxiety to simulate the pressures students face in real exam conditions. Our aim was to train students in navigating this heightened emotional and physiological state, preparing them for the challenges of oral examinations.\u003c/p\u003e \u003cp\u003eAnxiety, as shown in numerous studies, is associated with physiological changes such as elevated blood pressure, increased heart rate (HR), increased muscle tension, as well as enhanced respiratory rate and sweating\u003csup\u003e\u003cspan additionalcitationids=\"CR31 CR32 CR33\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. These responses reflect the body\u0026rsquo;s activation of the autonomic nervous system during stress\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Through our analysis of heart rate variability (HRV), we were able to capture these stress-induced bodily changes, providing a window into the neurocardiac stress experienced by students during simulated oral examinations. By measuring fluctuations in HRV markers, we detected how students\u0026rsquo; autonomic systems responded to the anxiety-inducing simulation, allowing us to assess the physiological impact of test anxiety in real-time. When stress levels rise, parasympathetic activity, which promotes relaxation and recovery, decreases, allowing sympathetic activation to take the lead\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. This shift in balance is a hallmark of the body's physiological response to stress, and it is clearly reflected in reduced HRV measurements. Such findings are well-supported in the literature, where decreased parasympathetic activity, indicated by reduced HRV, has been consistently associated with elevated stress levels in various high-stakes settings, especially in tasks that require significant mental engagement, such as exams and performance-based assessments\u003csup\u003e\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e In our study, we made a novel contribution by demonstrating that even in a simulated oral examination, significant reductions in parasympathetic activity can be observed, indicating a robust stress response not only in lecturer-led simulations, but importantly, in student-led simulations as well. This adds a new layer of insight to the existing body of literature by confirming that near-peer tutoring scenarios can induce similar physiological stress responses, making them an effective and scalable alternative for exam preparation. However, it\u0026rsquo;s essential to approach these findings with a dash of caution. Interestingly, the LF/HF ratios in the student-led simulations did not exhibit the notable elevation seen in the lecturer-led oral examination. This subtle shift adds a layer of complexity to how we interpret the overall stress response. The LF/HF ratio reflects the tug-of-war between the low-frequency (LF) band (0.04 to 0.15 Hz) and the high-frequency (HF) band (0.15 to 0.40 Hz) in HRV. LF captures both sympathetic and parasympathetic activity, while HF is mainly linked to parasympathetic regulation, often signaling calm through vagal tone. Typically, a higher LF/HF ratio means more sympathetic dominance, which usually correlates with increased stress and arousal\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In our student-led simulations, the lack of a noticeable spike in the LF/HF ratio suggests that while other HRV markers confirmed stress, the autonomic balance between sympathetic and parasympathetic activity didn\u0026rsquo;t shift as dramatically as it did in the lecturer-led exams. This could imply that the stress response in peer-led simulations was more moderate, or that the sympathetic nervous system was less engaged. The more moderate stress response observed in the peer-led simulations can likely be attributed to the differences in perceived uncontrollability and social-evaluative threat, both of which are well-known triggers of psychobiological stress responses\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Uncontrollability refers to a situation where individuals feel they have little control over the outcome, while social-evaluative threat arises when individuals believe they are being judged or evaluated by others. These factors are key drivers in inducing significant physiological stress responses\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, including heightened sympathetic activation indicated by increased stress markers like certain HRV parameters\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. In peer-led simulations, these elements may not be as prominent as they are in lecturer-led environments, where the power dynamics and stakes of being evaluated by an authoritative figure may intensify the sense of threat and lack of control. As a result, the sympathetic nervous system might be less activated, leading to the more balanced autonomic response we observed in the peer-led group.\u003c/p\u003e \u003cp\u003e Through both the lecturer-led and peer-led oral examination simulations, we were able to significantly enhance perceived competence and perceived confidence among participants. These factors are especially crucial in educational contexts, even from a young age, as perceived academic competence has been shown to mediate the relationship between peer relationships and both life satisfaction and academic achievement\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Additionally, well-being and perceived competence in various subjects are closely linked, which plays a key role in fostering resilience in academic environments\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Our findings align with the existing body of research underscoring the importance of perceived competence and confidence in educational success. Students who view themselves as capable are more likely to persist in their efforts, pursue mastery, and set performance-based goals, which can lead to reduced anxiety and better academic outcomes\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Importantly, perceived competence also plays a significant role in how students handle negative feedback. Studies using fMRI have shown that students who believe in their abilities and are interested in the task are more adaptive in processing negative feedback, engaging cognitive control networks that help regulate emotions and facilitate learning\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Our study also revealed a notable decrease in test anxiety among participants after the simulated oral examinations. This outcome is significant, given the well-known adverse effects of test anxiety on students' academic performance and mental health\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Elevated test anxiety is often associated with impaired cognitive function, including difficulties in memory retrieval and lower overall academic achievement\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. By effectively reducing test anxiety, our simulations appear to mitigate these negative consequences, thereby enhancing students' ability to perform more effectively under pressure. Our findings demonstrate that simulated oral exams can boost perceived competence and confidence while simultaneously easing test anxiety, creating a more resilient mental framework that equips students to handle the pressures of academic assessments more effectively.\u003c/p\u003e \u003cp\u003eParticipation in our simulated oral examinations allowed students to effectively adjust their learning strategies, optimizing their preparation for this specific assessment format. Literature emphasizes the importance of adapting different learning strategies to align with the demands of different academic settings, as this alignment is critical for achieving optimal performance\u003csup\u003e\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Our findings highlight the significance of this adaptability and echo research suggesting that choosing the appropriate learning strategies is often a challenge for students, particularly in rigorous fields like medical education\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Moreover, this aligns with the idea that medical educators should promote deep and self-directed learning strategies rather than surface approaches to enhance learning outcomes\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Such guidance is essential, as students frequently struggle to develop strategies that support meaningful engagement with the learning material​.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e Our study underscores the potential of simulated oral examinations as an effective tool for inducing neurocardiac stress, as demonstrated through significant physiological responses measured by heart rate variability (HRV) markers. Notably, we showed that both lecturer-led and peer-led simulations could successfully evoke a stress response, with decreases in RMSSD, pNN50, and SDNN indicating heightened sympathetic activation and reduced parasympathetic activity. This is a crucial insight, suggesting that these simulated environments can mirror the pressures of real exam conditions, offering a valuable training ground for students. Importantly, the success of peer-led simulations in inducing similar stress responses highlights their potential as a resource-efficient alternative to lecturer-led sessions. This is particularly relevant for educational institutions where resources are limited, and the demand for stress-inducing, performance-based training is high. However, we must also acknowledge the nuance in our findings: the lack of a significant increase in the LF/HF ratio in the peer-led simulations suggests a more moderate stress response compared to the lecturer-led versions. This could imply that the elements of uncontrollability and social-evaluative threat are less pronounced in peer-led environments, resulting in a less intense sympathetic activation. Despite this subtle difference, our study demonstrates that simulated oral exams, whether led by lecturers or peers, can significantly enhance perceived competence and confidence while effectively reducing test anxiety. This is a highly relevant finding, as it suggests that such simulations can equip students with a more resilient mental framework to handle academic assessments. Additionally, our results show that students were able to adjust their learning strategies more effectively after participating in these simulations, aligning with literature emphasizing the importance of adaptive learning strategies for academic success. In conclusion, our research not only validates the utility of simulated oral examinations in promoting resilience and enhancing performance but also advocates for the inclusion of peer-led simulations as a feasible and resource-efficient approach in medical education. However, the nuanced differences in autonomic response between lecturer-led and peer-led settings invite further exploration to fully understand their implications for student learning and performance under stress.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe extend our gratitude to the student participants for their active involvement and dedication throughout this study. Your enthusiasm and commitment were vital to the success of this initiative. We also wish to thank the Institute of Anatomy for their continuous support and resources, which were crucial in ensuring the smooth execution of this research project. Your support made a significant impact, and we are truly appreciative. Additionally, we acknowledge that large language models were used solely under the supervision and control of the authors, with the aim of enhancing the readability and clarity of this manuscript. All scientific content, analyses, and interpretations presented in this work are the original contributions of the authors.\u003c/p\u003e\u003cp\u003eCompeting interests: The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eM. Zeidner. \u003cem\u003eTest Anxiety: The State of the Art\u003c/em\u003e. (Plenum, New York, 1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeidner, M. Test Anxiety in Educational Contexts. in \u003cem\u003eEmotion in Education\u003c/em\u003e 165\u0026ndash;184 (Elsevier, 2007). doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/B978-012372545-5/50011-3\u003c/span\u003e\u003cspan address=\"10.1016/B978-012372545-5/50011-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErgene, T. 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J Coll Physicians Surg Pak 34, 1096\u0026ndash;1100 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadan, C. R. Using Evidence-Based Learning Strategies to Improve Medical Education. Med Sci Educ 33, 773\u0026ndash;776 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiu, Y.-C. \u003cem\u003eet al.\u003c/em\u003e To examine the associations between medical students\u0026rsquo; conceptions of learning, strategies to learning, and learning outcome in a medical humanities course. BMC Med Educ 19, 410 (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5268524/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5268524/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e This mixed-methods study investigated the impact of simulated oral examinations on inducing neurocardiac stress in medical students, integrating physiological measurements, quantitative assessments, and qualitative feedback. Ninety-five students participated, with heart rate variability (HRV) markers such as RMSSD, pNN50, SDNN, and LF/HF ratios used to evaluate stress responses. Both lecturer-led and peer-led simulations significantly reduced HRV markers, indicating heightened sympathetic activation and reduced parasympathetic activity. In lecturer-led simulations, RMSSD showed significant reductions (t\u0026thinsp;=\u0026thinsp;8.27, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; t\u0026thinsp;=\u0026thinsp;9.38, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), paralleled in peer-led sessions (t\u0026thinsp;=\u0026thinsp;4.47, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; t\u0026thinsp;=\u0026thinsp;4.97, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The LF/HF ratio significantly increased in lecturer-led exams (z=-2.69, p\u0026thinsp;=\u0026thinsp;0.007), while peer-led simulations exhibited a more moderate response. Students' perceived competence and confidence significantly improved post-simulation (lecturer-led: t=-8.41, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; student-led: t=-5.82, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and test anxiety significantly decreased. In the follow-up assessment conducted after the actual exams at the semester's end, 94.85% of students reported that the simulations were helpful in preparing for their final exams, aiding in reducing stress and enhancing performance. These findings highlight the potential of peer-led simulations as a resource-efficient alternative for fostering student resilience and coping under exam stress, though further exploration is needed to fully understand the nuanced autonomic responses in different settings.\u003c/p\u003e","manuscriptTitle":"Autonomic Stress Responses in Oral Examination Simulations: Neuroscientific Insights from Comparing Peer-Led and Lecturer-Led Approaches","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-31 15:39:24","doi":"10.21203/rs.3.rs-5268524/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8bc6aa8-4649-4d53-89a1-811bf83eb4c3","owner":[],"postedDate":"October 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39344662,"name":"Biological sciences/Neuroscience/Stress and resilience"},{"id":39344663,"name":"Biological sciences/Neuroscience/Emotion/Amygdala"}],"tags":[],"updatedAt":"2024-11-29T10:05:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-31 15:39:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5268524","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5268524","identity":"rs-5268524","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}