The relationship of heart rate variability measures with trait emotional intelligence in the presence of acute mental stress

The relationship of heart rate variability measures with trait emotional intelligence in the presence of acute mental stress | 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 Short Report The relationship of heart rate variability measures with trait emotional intelligence in the presence of acute mental stress SHAGUFTA GHORI, MEHA FATIMA AFTAB, BEDANTA ROY, NASRIN HABIB, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4442638/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 Objective: Emotional intelligence (EI) is essential for effective stress management and may influence cardiac responses. This study seeks to investigate the relationship between EI and heart rate variability (HRV) due to limited physiological data, contributing valuable insights into this unexplored connection and its potential impact on overall well-being. Results: In a study of 55 participants, mainly undergraduate students (45.5%) and single (70.9%), females constituted 56.4% of the sample. The highest well-being score was 5.187 (Cronbach’s alpha = 0.76). One-way repeated measures ANOVA showed significant HRV differences across time points. Median HF at stress induction was 0.193 (IQR: 0.160–0.217), significantly decreasing from baseline (Z = -4.926, p < 0.001). LF/HF ratio increased at T2 (M = 0.579, p < 0.001), with SD2/SD1 ratio rising to 2.171. Pairwise comparisons indicated differences between baseline and stress induction (M = -0.018, p < 0.001), and stress induction and post-induction periods (M = 0.174, p = 0.032). SDNN correlated positively at T1 (r = 0.321, p = 0.020) and T3 (r = 0.280, p = 0.045). pNN50% correlated positively at T1 (r = 0.332, p = 0.020), while stress index showed negative correlations at T1 (r = -0.318, p = 0.023) and T3 (r = -0.337, p = 0.012). Sociability negatively correlated with HRV measures (r = -0.407, p = 0.002), indicating autonomic nervous system activity links. INTRODUCTION Emotional intelligence (EI) is a predictor of various success markers, and recent research has examined the relationship between emotional intelligence and the capacity to cope with stress (Khassawneh et al., 2022). Stress is the body’s physical, chemical, and mental response and is significantly associated with EI (Yamani et al., 2014). High EI is linked to stress and better adaptation ( 1 ). Previous research has strengthened the study of neural and psychological correlates of EI by referencing high cortisol release during stressful encounters in individuals with lower EI and lesions in the area of emotional processing ( 1 – 3 ). Psychophysiological stress manipulations have also revealed an intense relationship between EI and autonomic stress modulation ( 4 , 5 ). Emotionally intelligent people show more adaptive responses to stressful situations with less physiological arousal. Trait emotional intelligence (TEI) can be explained through its factors and facets, including well-being, self-control, emotionality, and sociability. Previous studies have addressed facets of EI through self-report surveys and ability-based tests; however, there is a lack of physiological data regarding emotional regulation ( 6 – 8 ). Since EI is related to effective stress management and coping abilities, it can be studied through physiological regulators of stress mechanisms like the autonomic nervous system ( 9 , 10 ). The activity of the autonomic nervous system can be well studied through HRV regarding mental stress ( 11 – 13 ). However, these studies have assessed relationships between physical stress and heart rate variability to the best of our knowledge; no sufficient data exists to establish a relationship between heart rate variability and TEI with its four factors. Therefore, the current study aimed to elucidate the relationship between heart rate variability and TEI. This study will provide recommendations to aid in the psychological diagnosis of stress and depression through heart rate variability in clinical settings, which are currently diagnosed based on a questionnaire. It cannot predict health outcomes or the prognosis of psychological therapies. METHODS Study setting: The study included 55 healthy participants between 18 and 35 who could attempt the mental arithmetic task. The study was conducted from March 2022 to December 2023, following a lab-based experimental approach within the facilities of DUHS. Monetary incentives were provided upon completion of data collection. Sampling procedures and sample size calculation: The sample size was calculated using the effect size of a previous study ( 14 ) for correlation by using the formula for the correlation coefficient and after adjusting the 10% respondent rate. The non-probability convenience sampling technique is used for this study. Data Collection Procedure: Participants were instructed to sit quietly in a calm, relaxing room for 5 minutes. They were provided with a detailed explanation of the procedure and requested to provide their informed consent. Demographic data was collected as part of the initial phase. Before the HRV recording, participants were administered the TEIQue-30 questionnaire. HRV recordings were acquired across three distinct phases: baseline (T1), stress induction period (T2), and recovery (T3). During the short-term baseline phase (T1), participants were seated for 5 minutes, and HRV data were collected. Subsequently, participants were asked to engage in a brief mental arithmetic task, specifically the serial subtraction method. The task presented is an integral component of the Trier Social Stressor Test (TSST), typically employed to induce physiological responses without specifically assessing performance or cognitive aptitude ( 15 ). The laboratory-induced stress task is deliberately crafted with diminished personal emotional salience, indicating its intentional design to engage cognitive abilities without evoking intense emotional responses ( 16 ). A research investigation comparing various stress-inducing tasks to stimulate sympathetic reactivity confirmed that the serial subtraction task effectively triggers a notable cortisol stress response and activates an ample number of brain regions, resulting in increased neural activity ( 17 ). The task consisted of repeatedly subtracting single or double-digit numbers from a 4-digit number within a 5-second interval. Participants were required to make multiple attempts, or a minimum of 10 attempts per block, as the time taken to complete the stress task varied among individuals. The experiment consisted of four blocks. In the first and third blocks, participants performed subtractions using a single-digit number (e.g., subtracting 7 from the starting number 9095 in the first difficulty block and 8185 in the third difficulty block). Conversely, participants subtracted a two-digit number in the second and fourth blocks (e.g., subtracting 13 from the 4-digit number 6233 in the second difficulty block and 5245 in the final difficulty block). The duration of the arithmetic task was kept at 5 minutes by repeating the subtraction method if it ended earlier to meet each protocol phase for the calculated HRV measures. Immediate feedback was given to participants regarding accuracy and response time, focusing on promoting concentration. Concurrently with the mental arithmetic task (T2), HRV measurements were taken. Following the completion of the stress induction phase, a 5-minute HRV recording was conducted to capture the recovery period (T3). Data collection tools: Assessment of Trait EI Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF) was constructed from its original form, i.e., a TEIQue 153 item with a 7-point Likert scale that yields a score on 15 facets belonging to four factors: wellbeing (happiness, optimism, self-esteem), self-control (emotion regulation, impulse control, stress management), emotionality (empathy, emotion perception, emotion expression, relationship), and sociability (emotion management, assertiveness, social awareness), where adaptability and self-motivation are considered as independent facets. Factors mentioned in the TEIQue questionnaire represent a level of measurement that is broader than that of the facets, whereas the facets descriptions are detailed and focused. The factors level provides a useful level of intermediate measurement and description. The TEIQue assesses all of the above-mentioned facets through 15 subscales and provides scores on four factors of broader relevance. The short and long forms of TEIQue questionnaires have the same psychometric properties and construct criterion validity. The assessment of TEI is a reversed score for some item numbers and then a sum-up for all the responses. Still, in the current study, the total score for four factors is obtained from the provided online scoring engine, “London Psychometric Lab," which provides the mean of each factor obtained individually from the participant. The questionnaire was translated into several languages and administered in English with a validated version of TEIQue sf 30 Urdu among the Pakistani population ( 18 ). Scoring and Reliability Analysis for Emotional Intelligence Questionnaire The scoring process for the questionnaire involves several steps. Initially, specific item scores are recorded based on specified rules. For instance, scores are adjusted according to predetermined mappings, such as 7 becoming 1, 6 becoming 2, and so forth. The emotional intelligence (EI) score is computed by summing all individual item scores and dividing by the total number of items in the questionnaire, which is 30. Additionally, factor scores are calculated to assess specific aspects of emotional intelligence. These factors include well-being, self-control, emotionality, and sociability. Each factor's score is determined by summing the relevant item scores and dividing by the total number of items associated with that factor. Subsequently, a reliability analysis is performed to evaluate the internal consistency of items within each factor, typically using measures like Cronbach's alpha. This analysis provides insights into the reliability and consistency of the questionnaire's assessment of emotional intelligence traits. Computational Parameters for Heart Rate Variability Assessment For heart rate variability, Kubios software provides individual bands of powers for frequency measures. Specifically for the frequency domain, we use Fast Fourier Transforms, an efficient algorithm to compute. It transforms a function of time or space into a function of frequency, revealing the frequency components present in the original signal. The standard frequency selected from the FFT spectrum is VLF 0.00–0.04 Hz, LF: 0.04–0.15 Hz, and HF: 0.15–0.40 Hz. Default values from Kubios are selected as an embedding dimension of sample entropy (m = 2) and a common selection (r = 0.2 SDNN), which is tolerance that strongly affects ApEn. Similarly, the default values set for the embedding dimension and the tolerance parameter in Kubios HRV software are the same as those for the ApEn computation. For short-term detrended fluctuation analysis, slope α1 is obtained within the 4 ≤ n ≤ 12 range. In accordance, the slope obtained by default from the range 13 ≤ n ≤ 64 characterises long-term fluctuations. Data Instruments, equipment and software E-wave 8.0.8 was used with a sampling rate of 1000 Hz. An active electrode was placed on the right radial, a reference electrode on the left radial, and the ground electrode on the pronator teres muscle. The software template “baseline ECG” is used to record the simple ECG with time (T/D 50 ms) and voltage (V/D 100µV) for better visualisation of recorded signals (RR signals) on the Muse monitor. The high and low power frequencies were kept in default settings, i.e., HPF 200 mHz and LPF 30 Hz. The output of the recorded data is received as interbeat intervals, or RRI, in text form and is measured on KUBIOS. Low threshold beat correction is performed to remove artefacts if required. Statistical analysis: The data was analysed using IBM SPSS version 21, and p < 0.05 was considered statistically significant. Descriptive statistics employed frequency and percentage for qualitative variables, while the quantitative variable "age" was described using mean and SD. Valid percentages were taken out of the TEIQue-SF-30 score by reverse scoring, where the mean and Cronbach alpha for four factors were obtained by the London Psychometric Lab scoring engine. After assessing the continuous variables of HRV indices, it was established that their distribution deviates significantly from normality, as evidenced by a Kolmogorov-Smirnov test yielding a P-value less than 0.05. Consequently, traditional statistical tests predicated on the assumption of normality were deemed inappropriate for further analysis. Moreover, outliers were identified through box plot visualisation and subsequently removed from the dataset to mitigate potential distortions in the analysis. Subsequently, a one-way repeated measures ANOVA was employed to explore possible differences among normally distributed variables, while the Friedman test was utilised for non-normally distributed variables. Furthermore, to ascertain significant relationships between the factors of TEI and HRV measures, Pearson and Spearman correlations were employed based on the normal status of the data. Results Table 1 Demographics of study participants Variable N Percent Gender Male 24 43.6% Female 31 56.4% Education Undergraduate 25 45.5% Graduate 18 32.7% Postgraduate 12 21.8% Marital status Single 39 70.9% Married 16 29.1% Occupation Employed 29 52.7% Unemployed 26 47.3% Socioeconomic Low 1 1.8% Middle 34 61.8% High 20 36.4% Age Mean St. deviation 26.163 6.88 Descriptive values for TEIQUE SF 30 Wellbeing 5.187 1.082 Self-control 4.296 0.932 Emotionality 4.586 0.987 Sociability 4.363 0.954 Total TEI 4.647 0.789 Table 1 shows that most participants were females (56%) and undergraduates (45.5%). A majority were single (70.9%). Most participants were employed (52.7%) and belonged to the middle class (61.8%). TEIQue SF 30 respondents were the highest who marked item number 20, specifically 41.8%. Conversely, the lowest number of respondents who marked items is number 10, specifically 3.6%. Similarly, the mean and standard deviation of TEIQue SF 30 are reported in Table 1 . The Cronbach's alpha coefficients for the TEI factors are found to be well-being (α = 0.76), self-control (α = 0.45), emotionality (α = 0.59), and sociability (α = 0.52). The overall TEI score, encompassing all factors, demonstrates high internal consistency with a Cronbach's alpha value of 0.86. Table 2 a: One-way repeated measures of ANOVA for the time domain, frequency domain, and nonlinear HRV measures. Time domain F- value P-value \(\eta p2\) SDNN 4.297 0.023* 0.081 Frequency domain F- value P-value Partial Eta Squared LF 10.044 0.000* 0.159 LF/HF ratio 9.980 0.000* 0.196 Frequency domain Chi-squared statistics P-value Asymp. sig HF 19.614 × 0.000* Non-linear F-value P-value \(\eta p2\) SD2 7.855 0.002* 0.136 SD2SD1ratio 7.847 0.001* 0.129 ApEn 56.840 0.000* 0.517 Dfaα1 21.637 0.000* 0.286 Dfaα2 24.397 0.000* 0.319 × represents the chi-squared value in the above table *p < 0.05 Table 2 a represents one-way repeated measures of ANOVA for the time domain, frequency domain, and nonlinear HRV measures. A statistically significant difference in mean SDNN values across the three-time points (F = 4.297, df = 1.646, p = 0.023) was observed. The descriptive statistics for SDNN indicate an elevated value at stress induction time T2 (37.27 ± 13.04) compared to baseline T1 (33.78 ± 11.91). The pairwise comparisons also indicated a significant difference in SDNN from baseline to the induction phase − 3.49(CI: -6.67 to -0.305, p = 0.032) However, no significant difference was observed between SDNN values during recovery and at baseline 0.196 (CI: -1.889 to 2.281, p = 0.851). For HF, (the Friedman test) reveals a significant difference between baseline and stress-inducing time points for the recorded value of HRV, χ2 ( 2 ) = 19.614, p = 0.000. Post hoc analysis with a Wilcoxinoxon signed-rank test was conducted with a Bonferroni correction (p < 0.05). A significant difference was observed between the mental stress-inducing task and baseline ( Z = -4.926, p = 0.000) and during the recovery and stress induction time points (Z = -3.538, p = 0.000). The analysis of variance for LF shows a significant statistical difference (F = 10.44, df = 1.991, p = 0.000). Significant mean differences at different time points were observed between baseline and stress induction period − 0.018(CI: -0.027 to -0.008, p = 0.000), where the recovery phase also exhibits significant differences with stress induction time 0.019 (CI: 0.009 to 0.028, p = 0.000). A statistically significant LF/HF ratio difference across different time points ( F = 9.980, df = 1.821, p = 0.000) was found. Post hoc analysis revealed a significant LF/HF ratio increase during the stress induction phase compared to baseline, induced by the mental arithmetic task 0.579 (CI: -0.844 to -0.315, p = 0.000). Furthermore, a significant difference was observed between baseline and the recovery period, with a substantial mean decrease − 0.437(CI: -0.675 to -0.198, p = 0.001). A significant difference between baseline and stress induction time − 6.173(CI: -10.24 to -2.105, p = 0.004) and stress induction time to recovery time 5.241(CI: 1.783 to 8.699, p = 0.004) was observed. A significant difference for SD2 SD1 ratio (F = 1.226, df = 1.709, p = 0.001) was found. The pairwise comparison shows a significant difference between the baseline and stress induction phase − 0.275(CI: -0.427 to -0.124, p = 0.001). A significant difference is also observed between stress induction time and post-induction period 0.174(CI: 0.016 to 0.332, p = 0.032). However, no significant difference between baseline and post-induction time points is observed − 0101(CI: − 0.210 to 0.007, p = 0.067). Post hoc pairwise comparisons indicated significant differences between time points T1 and T2 0.162(CI: 0.117 to 0.206, p = 0.000). An important difference in mean values was observed from the stress induction phase to the recovery period − 0.170(CI: -0.208 to -0.132, p = 0.000). Table 2 b shows significant correlations between the TEI four factors and HRV measures at different time points. VARIABLES T1 T2 T3 r p r p r p Well being Sdnn 0.321 0.020 x x 0.280 0.045 Pnn50% 0.332 0.020 x x 0.297 0.036 Stress index -0.318 0.023 x x -0.337 0.012 Hf x x 0.348 0.012 x x LFHF ratio x x -0.310 0.027 x x Sd2 0.328 0.016 x x x x Sd2sd1ratio x x x x 0.279 0.039 Self control Lf 0.321 0.017 x x x x Emotionality Sdnn 0.306 0.028 x x x x Stress index x x x x -0.297 0.028 Sd2 0.300 0.028 x x x x Dfaα2 x x x x -0.319 0.019 Sociobility HF3 x x x x -0.407 0.002 Table 2 . b shows significant correlations between the TEI four factors and HRV measures at different time points. SDNN exhibited moderate positive correlations at T1 (r = 0.321, p = 0.020) and T3 (r = 0.280, p = 0.045); pNN50% displayed a significant positive correlation at T1 (r = 0.332, p = 0.020), and stress index demonstrated negative correlations at T1 (r = -0.318, p = 0.023) and T3 (r = -0.337, p = 0.012). Positive correlations between high frequency (HF) power and well-being at T2 (r = 0.348, p = 0.012), as well as negative correlations between the LF/HF ratio and well-being at T2 (r = -0.310, p = 0.027) were observed. From the time domain, SDNN exhibited a moderate positive correlation at T1 (r = 0.306, p = 0.028), whereas the stress index shows a significant negative correlation at T3 (r = -0.297, p = 0.028). The Poincaré plot standard deviation perpendicular to the line of identity (SD2) showed a moderately positive correlation at T1 (r = 0.300, p = 0.028), indicating the potential involvement of nonlinear dynamics in heart rate variability in emotional experiences. The Detrended Fluctuation Analysis exponent alpha 2 (DFAα2) displayed a positive correlation at T3 (r = 0.319, p = 0.019) The stress index demonstrated a negative correlation at T3 (r = -0.297, p = 0.028). There are marked correlations between the HRV measures and self-control factor. LF demonstrated a moderate positive correlation (r = 0.321, p = 0.017), and HRV measures significantly correlated with sociability factor. A strong negative correlation was observed (r = -0.407, p = 0.002), indicating a potential link between autonomic nervous system activity and sociability. DISCUSSION Influence of mental arithmetic tasks on autonomic activity The current study revealed that emotion regulation is intricately connected with HRV parameters at baseline, stress induction, and recovery time. The changes that occurred at stress induction time point T2 are of great significance to discuss, as HRV parameters have been known to predict and validate the presence of mental stress through autonomic modulation of cardiovascular activity ( 19 ) . Time domain measures (Table 2 .a), showed an elevated SDNN at T2, which indicates higher sympathetic dominance, contrary to the expected pattern since SDNN is generally higher during rest and recovery periods, reflecting the standard deviation of normal-to-normal beats ( 20 ). However, previous research has reported similar findings and associated them with speech tasks. Previous researchers reported an increased SDNN during the stress condition, which is potentially attributed to the influence of speech-related respiratory patterns on HRV measures ( 21 , 22 ).( 23 ) The results also presented alterations in the frequency domain of HRV parameters that are considered most reliable in validating the irregular HRV fluctuations in the presence of mental stress due to the activation of SNS. Significant group differences across different time points, indicating elevated sympathetic activity markers, such as LF and LH/HF ratio, were observed at the time of mental arithmetic stress induction T2, affirming sympathetic activation in response to the acute stressor. This is a consistent finding that goes parallel in affirmation with previous literature, as the LF/HF ratio is supposed to show elevated values in the presence of sympathetic activation( 24 ). Concurrently, the parasympathetic activity marker, HF, exhibited a decreased value at T2, suggesting parasympathetic suppression during stress. The present study's findings are consistent with previous literature, as HF suggests vagal modulation in cardiovascular variability. This finding represents suppression of parasympathetic activity through stress induction ( 25 ). The spectral analysis measures support the findings, suggesting alterations in HRV patterns, reflecting autonomic activity changes, as predictors of mental stress and potentially linked to stress regulation abilities ( 19 ). Notably, no significant differences were found between baseline and recovery, indicating consistent autonomic regulation without irregular fluctuations. Significant variations are observed in nonlinear measures of HRV. SD2 mainly represents sympathetic and parasympathetic contributions to the heart ( 26 ). Increased SD2 at T2 represents a shift towards SNS and increased long-term variability in HRV associated with PNS withdrawal. This finding is consistent with the result obtained by increasing SD2 during the simulated evaluation ( 27 ). Similarly, the increased SD2SD1 ratio from baseline to stress induction time explains an increase in sympathetic activity, which is also consistent with the findings from the published literature. This represents the suppression of parasympathetic activity, and at the same time, it postulates that nonlinear HRV indices change significantly from baseline to stress induction task, thus suggesting a successful stress induction and increased sympathetic activity( 28 , 29 ). ApEn, a measure of predictability and signal similarity, shows more regularity at T2, suggesting a decrease in values compared to T1 and T3. This suggests there is more predictability and less complexity in HRV signals in the presence of mental stress. This finding is consistent with other studies that suggest an influence of mental stress on the nonlinear domain of HRV ( 30 ). The nonlinear measures DFAα1 found increased at T2, where DFAα2 decreased compared to baselines. The published studies do support both aspects. The results for DFAα1 show a greater mean at T2 with a significant mean difference from the baseline. This indicates that at stress, DFAα1 increases, which is consistent and has also been found in other studies that show that in mental stress aloud, decreased HF power is associated with the increase of DFAα1 ( 31 ). Another study with a higher DFAα1 value can validate this finding, indicating more persistent and less variable HRV patterns ( 30 , 32 ). Meanwhile, the values for DFAα2 decrease at T2 compared to baseline in consistency with previous studies claiming DFAα2 has previously been shown to correlate with acute psychological stress in subjects, decreasing amplitude with increasing stress ( 33 ). This indicates disruptions in heart rate complexity over more extended periods, which makes a valid point that psychological stress relates to more predictability and a high correlation in HRV signals. Correlation of TEI with time domain measure of HRV Higher trait EI correlated with favourable HRV profiles, including SDNN, indicative of improved autonomic nervous system regulation. Additionally, TEI exhibited a significant correlation with the percentage of successive NN intervals differing by pNN50%, highlighting its influence on parasympathetic modulation during resting states and potential cardiovascular resilience ( 34 ). Moreover, a negative correlation was observed between TEI and the HRV stress index, indicating its role in modulating the autonomic nervous system response to stressors. This suggests that individuals with greater TEI may demonstrate a more adaptive physiological stress response characterized by heightened parasympathetic activity and reduced sympathetic dominance. For the emotionality factor, the results again represent HRV as a demonstrating tool for trait emotional intelligence. These findings are consistent with previous results from relevant studies on understanding emotion regulation ( 35 ). Like the well-being factor, the emotionality factor shows a positive correlation with SDNN at baseline, suggesting that individuals with a wide range of emotion-related skills may exhibit enhanced overall autonomic function during resting conditions ( 36 ). The significant inverse correlation observed between the stress index and the emotionality factor at recovery highlights the potential role of emotional competence in shaping the autonomic response to stressors. This finding suggests that individuals with greater emotional competence, characterized by their ability to perceive and express emotions effectively, may exhibit a more adaptive physiological response to stress ( 3 ). Correlation of TEI with Frequency Domain Measure of HRV The significant associations between the HF component and LF/HF ratio from spectral analysis and the well-being factor are notable findings. The time point coinciding with the induction of mental stress in participants provides valuable insights into the interplay between emotional intelligence and autonomic nervous system regulation under stress. The significant association suggests that greater TEI may demonstrate heightened parasympathetic activity even in acute mental stress. The findings indicate the potential role of TEI in buffering against the physiological effects of stress, particularly by promoting adaptive autonomic responses characterized by increased parasympathetic tone ( 37 ). It implies that higher trait EI may exhibit greater resilience to stress-induced autonomic dysregulation, potentially mitigating the detrimental impact of stress on health and well-being ( 5 ). The inverse correlation between the well-being factor and LF/HF ratio during stress induction suggests that higher emotional well-being is associated with a shift towards parasympathetic dominance over sympathetic activity in response to stress. This implies that greater emotional well-being exhibits a more adaptive physiological response to stress, characterized by increased parasympathetic tone and decreased sympathetic arousal ( 38 ). The self-control factor of TEI shows a significant relationship with LF at baseline. The self-control factor of TEI represents emotion regulation and stress management ( 39 ). This finding is noteworthy, implying that higher emotional and stress regulation can be linked with higher TEI. This finding is also inconsistent with previous work that suggests a negative association of LF with self-control factor( 14 ) The sociability factor shows a highly negative correlation at recovery with HF, representing parasympathetic activity. This finding is inconsistent with the one that indicates that assertiveness and emotion management are linked with parasympathetic modulation and that sociability predicts post-task cardiac vagal activity ( 40 ). However, our findings suggest that a higher parasympathetic tone is associated with lower sociability or individuals with higher sociability may exhibit delayed recovery from a given stressor. Its important to note that findings for the sociability and self-control factors remain inconclusive. Correlation of TEI with Nonlinear Measure of HRV The significant positive correlation observed between the well-being factor of TEI and SD2 at baseline suggests a relationship between emotional well-being and the complexity of heart rate dynamics during resting conditions ( 41 ). Higher levels of emotional well-being tend to exhibit greater complexity in heart rate variability at baseline. This suggests that higher emotional well-being is associated with more intricate and dynamic patterns in heart rate fluctuations during rest conditions ( 42 ). This finding implies that emotional well-being may be linked to enhanced autonomic nervous system regulation, characterized by increased flexibility and adaptability in response to internal and external stimuli ( 43 ). The SD2SD1 ratio, indicating the balance between sympathetic and parasympathetic influences on heart rate variability during recovery, correlates positively with higher emotional well-being. This suggests that individuals with greater emotional well-being exhibit a more adaptive autonomic response to stress, characterized by balanced sympathetic and parasympathetic nervous system activity regulation. This balanced response may improve cardiovascular health and resilience following stressful events ( 44 ).The results again represent HRV as a demonstrating tool in relationship with TEI for the emotionality factor. These findings are consistent with previous results from relevant studies on emotion regulation ( 45 ). Like the well-being factor, the emotionality factor positively correlates with SDNN at baseline, providing insight into the physiological process towards a stressor. This suggests that high HRV and emotionality exhibit a relationship, whereas the antagonistic relationship of the stress index at recovery shows the emotionality factor is associated with less stress ( 46 ). The nonlinear measure of HRV shows a significant positive correlation for SD2 at baseline and an inverse correlation with detrended fluctuations (α2) at recovery. The observed correlation between SD2 and emotionality across baseline and recovery phases suggests that this association remains consistent throughout emotional processing and recovery. It implies that individuals with a greater capacity for autonomic regulation, as indicated by higher HRV, also demonstrate heightened emotional responsiveness and expressiveness ( 47 ). The DFAα2 negatively correlated at recovery, which indicates a decrease in long-range correlations, is associated with higher levels of emotionality. The reduction in fractal-like properties during recovery departs from the typical self-similar and organized patterns seen in healthy physiological systems. This shift may reflect a less adaptive and more disordered physiological state, potentially indicating a diminished ability to respond flexibly to internal and external demands. This finding is consistent with published literature, which states that lower emotional reactivity and higher emotional regulation capacity, as indicated by heart rate variability, are associated with higher levels of TEI. CONCLUSION The findings concluded a well-established relationship between trait emotional intelligence and heart rate variability, shedding light on the physiological interplay between emotion regulation and mental stress resilience. The results suggest that HRV may be a physiological indicator of emotional intelligence in acute mental stress. This implies that individuals with higher emotional intelligence may exhibit greater autonomic flexibility and adaptive responses to stress, as reflected in their HRV patterns. These findings contribute to our understanding of the physiological correlates of emotional intelligence and underscore the importance of HRV as a potential marker for assessing emotional regulation and resilience in the face of acute mental challenges. LIMITATION We want to conduct a baseline study to show the possible relationship between heart rate variability measures and TEI and identify HRV as a physiological correlate of emotional intelligence. The study is limited by a small sample size (n = 55), potentially limiting generalizability to the larger population. Due to the age restriction, the participants' young mean age may not accurately represent older age groups. The study's urban area data collection may introduce bias, limiting its applicability to rural or suburban populations. The inclusion of only educated candidates also restricts generalizability to a broader population. HRV data collection experienced occasional noise artefacts, influencing data quality. Response delays during the arithmetic task posed a potential confounding factor, necessitating exclusion. Logistical constraints arose from participants needing to visit the campus for data collection, preventing data collection outside the research setting. Declarations DATA AVAILABILITY The data set generated and analyzed during the current study is available in the psycharchive repository, https://doi.org/10.23668/psycharchives.14622 . ETHICS APPROVAL The study received approval from the Institutional Review Board (IRB) at Dow University of Health Sciences, in accordance with the guidelines set forth by the National Bioethics Committee of Pakistan (Approval No. IRB-2460/DUHS/Approval/2022/822), as well as the Joint Research Ethics Committee (JREC) at Quest International University, Malaysia. CONSENT TO PARTICIPATE Prior to the study, written informed consent was obtained from all participants. A brief discussion of the study was provided on the informed consent form, outlining the non-harmful benefits of participation. Participants were informed that their involvement in the study was voluntary and that they could withdraw at any time without any consequences. CONSENT FOR PUBLICATION Consent for publication was included in the research information and informed consent form, where it was also stated that participant anonymity would be prioritized throughout the publication process. COMPETING INTEREST The authors declare that there are no competing interests regarding this research. FUNDING This research was supported by funding from Quest International University under Fund ID QIUP-FIN-F01. ACKNOWLEDGEMENTS I would like to thank my former supervisors, Dr. Ebrahim NKC and Dr. Satish Kumar, for their initial guidance and support. They introduced me to the concept of Heart Rate Variability in relation to studying mental stress and provided essential support to keep this study ongoing. References Bar-On R, Tranel D, Denburg NL, Bechara A. Exploring the neurological substrate of emotional and social intelligence. Brain. 2003;126(8):1790–800. Craig A, Tran Y, Hermens G, Williams L, Kemp A, Morris C, et al. Psychological and neural correlates of emotional intelligence in a large sample of adult males and females. Pers Indiv Differ. 2009;46(2):111–5. Salovey P, Stroud LR, Woolery A, Epel ES. Perceived emotional intelligence, stress reactivity, and symptom reports: Further explorations using the trait meta-mood scale. Psychol health. 2002;17(5):611–27. Prajapati V, Routray A, Guha R. Cardiac autonomic flexibility is associated with higher emotional intelligence. Cogent Psychol. 2020;7(1):1870809. Schneider TR, Lyons JB, Khazon S. Emotional intelligence and resilience. Pers Indiv Differ. 2013;55(8):909–14. Gohm CL, Corser GC, Dalsky DJ. Emotional intelligence under stress: Useful, unnecessary, or irrelevant? Pers Indiv Differ. 2005;39(6):1017–28. Hunt N, Evans D. Predicting traumatic stress using emotional intelligence. Behav Res Ther. 2004;42(7):791–8. Ramesar S, Koortzen P, Oosthuizen RM. The relationship between emotional intelligence and stress management. SA J Industrial Psychol. 2009;35(1):39–48. Houghton JD, Wu J, Godwin JL, Neck CP, Manz CC. Effective stress management: A model of emotional intelligence, self-leadership, and student stress coping. J Manage Educ. 2012;36(2):220–38. Ziegler MG. Psychological stress and the autonomic nervous system. Primer on the autonomic nervous system. Elsevier; 2012. pp. 291–3. Dal N, Doğan E. Heart Rate Variability, Flow and Emotional Intelligence as Predictors of Novice Archers’ Shooting Accuracy. JESSM. 2019;1(2):65–73. Duong HTH, Tadesse GA, Nhat PTH, Van Hao N, Prince J, Duong TD, et al. Heart rate variability as an indicator of autonomic nervous system disturbance in tetanus. Am J Trop Med Hyg. 2020;102(2):403. Vanuk JR, Alkozei A, Raikes AC, Allen JJ, Killgore WD. Ability-based emotional intelligence is associated with greater cardiac vagal control and reactivity. Front Hum Neurosci. 2019;13:181. Laborde S, Brüll A, Weber J, Anders LS. Trait emotional intelligence in sports: A protective role against stress through heart rate variability? Pers Indiv Differ. 2011;51(1):23–7. Ritter FE, Kase SE, Klein LC, Bennett J, Schoelles M. In. Fitting a model to behavior tells us what changes cognitively when under stress and with caffeine. 2009. Bourassa KJ, Sbarra DA. Cardiovascular reactivity, stress, and personal emotional salience: Choose your tasks carefully. Psychophysiology. 2022;59(8):e14037. Dedovic K, Duchesne A, Andrews J, Engert V, Pruessner JC. The brain and the stress axis: the neural correlates of cortisol regulation in response to stress. NeuroImage. 2009;47(3):864–71. Shahzad S, Riaz Z, Begum N, Khanum SJ. Urdu translation and psychometric properties of trait emotional intelligence questionnaire short form (TEIQue-SF). Asian J Manage Sci Educ. 2014;3(1):130–40. Immanuel S, Teferra MN, Baumert M, Bidargaddi N. Heart Rate Variability for Evaluating Psychological Stress Changes in Healthy Adults: A Scoping Review. Neuropsychobiology. 2023;1–16. Buccelletti F, Gilardi E, Scaini E, Galiuto L, Persiani R, Biondi A et al. Heart rate variability and myocardial infarction: systematic literature review and metanalysis. Eur Rev Med Pharmacol Sci. 2009;13(4). Rodrigues S, Paiva JS, Dias D, Aleixo M, Filipe RM, Cunha JPS. Cognitive impact and psychophysiological effects of stress using a biomonitoring platform. Int J Environ Res Public Health. 2018;15(6):1080. Schubert C, Lambertz M, Nelesen R, Bardwell W, Choi JB, Dimsdale J. Effects of stress on heart rate complexity—a comparison between short-term and chronic stress. Biol Psychol. 2009;80(3):325–32. von Rosenberg W, Chanwimalueang T, Adjei T, Jaffer U, Mandic DP. Resolving Ambiguities in the LF/HF Ratio: LF-HF Scatter Plots for the Categorization of Mental and Physical Stress from HRV. Front Physiol. Seong H, Lee J, Shin T, Kim W, Yoon Y. The analysis of mental stress using time-frequency distribution of heart rate variability signal. In IEEE; 2004. pp. 283–5. Pagani M, Montano N, Porta A, Malliani A, Abboud FM, Birkett C, et al. Relationship Between Spectral Components of Cardiovascular Variabilities and Direct Measures of Muscle Sympathetic Nerve Activity in Humans. Circulation. 1997;95(6):1441–8. Dong J. The role of heart rate variability in sports physiology. Experimental therapeutic Med. 2016;11(5):1531–6. Beltrán-Velasco AI, Bellido-Esteban A, Ruisoto-Palomera P, Clemente-Suárez VJ. Use of portable digital devices to analyze autonomic stress response in psychology objective structured clinical examination. J Med Syst. 2018;42:1–6. Dimitriev DA, Saperova EV, Dimitriev AD. State anxiety and nonlinear dynamics of heart rate variability in students. PLoS ONE. 2016;11(1):e0146131. Rahman F, Pechnik S, Gross D, Sewell L, Goldstein DS. Low frequency power of heart rate variability reflects baroreflex function, not cardiac sympathetic innervation. Clin Auton Res. 2011;21:133–41. Castaldo R, Montesinos L, Pecchia L. Ultra-short entropy for mental Stress detection. In Springer; 2018. pp. 287–91. Vuksanović V, Gal V. Heart rate variability in mental stress aloud. Med Eng Phys. 2007;29(3):344–9. Stein PK, Le Q, Domitrovich PP, Cast Investigators. Development of more erratic heart rate patterns is associated with mortality post–myocardial infarction. J Electrocardiol. 2008;41(2):110–5. Harris BR, Beesley SJ, Hopkins RO, Hirshberg EL, Wilson E, Butler J, et al. Heart rate variability and subsequent psychological distress among family members of intensive care unit patients. J Int Med Res. 2021;49(11):03000605211057829. Brown RL, Chen MA, Paoletti J, Dicker EE, Wu-Chung EL, LeRoy AS, et al. Emotion regulation, parasympathetic function, and psychological well-being. Front Psychol. 2022;13:879166. Lee SP, Sung IK, Kim JH, Lee SY, Park HS, Shim CS. The effect of emotional stress and depression on the prevalence of digestive diseases. J Neurogastroenterol Motil. 2015;21(2):273. Silvia PJ, Jackson BA, Sopko RS. Does baseline heart rate variability reflect stable positive emotionality? Pers Indiv Differ. 2014;70:183–7. Mikolajczak M, Roy E, Luminet O, Fillée C, De Timary P. The moderating impact of emotional intelligence on free cortisol responses to stress. Psychoneuroendocrinology. 2007;32(8–10):1000–12. Shiga K, Izumi K, Minato K, Sugio T, Yoshimura M, Kitazawa M, et al. Subjective well-being and month-long LF/HF ratio among deskworkers. PLoS ONE. 2021;16(9):e0257062. Ahmed A, Ahmed M, Mahmoud W, Kamal W, Elnaggar R. Effect of high intensity interval training on heart rate variability and aerobic capacity in obese adults with type 2 Diabetes Mellitus. Bioscience Res. 2019;16:2450–8. Mosley E, Laborde S, Kavanagh E. The contribution of coping-related variables and cardiac vagal activity on prone rifle shooting performance under pressure. J Psychophysiol. 2018. Boman K. Heart rate variability: A possible measure of subjective wellbeing? 2018. Kogan A, Gruber J, Shallcross AJ, Ford BQ, Mauss IB. Too much of a good thing? Cardiac vagal tone’s nonlinear relationship with well-being. Emotion. 2013;13(4):599. Casties JF, Mottet D, Le Gallais D. Non-linear analyses of heart rate variability during heavy exercise and recovery in cyclists. Int J Sports Med. 2006;780–5. McCraty R, Zayas MA. Cardiac coherence, self-regulation, autonomic stability, and psychosocial well-being. Front Psychol. 2014;5:104218. Mather M, Thayer JF. How heart rate variability affects emotion regulation brain networks. Curr Opin Behav Sci. 2018;19:98–104. Zhu J, Ji L, Liu C. Heart rate variability monitoring for emotion and disorders of emotion. Physiol Meas. 2019;40(6):064004. Appelhans BM, Luecken LJ. Heart rate variability as an index of regulated emotional responding. Rev Gen Psychol. 2006;10(3):229–40. Additional Declarations No competing interests reported. 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Stress is the body\u0026rsquo;s physical, chemical, and mental response and is significantly associated with EI (Yamani et al., 2014). High EI is linked to stress and better adaptation (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Previous research has strengthened the study of neural and psychological correlates of EI by referencing high cortisol release during stressful encounters in individuals with lower EI and lesions in the area of emotional processing (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Psychophysiological stress manipulations have also revealed an intense relationship between EI and autonomic stress modulation (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Emotionally intelligent people show more adaptive responses to stressful situations with less physiological arousal. Trait emotional intelligence (TEI) can be explained through its factors and facets, including well-being, self-control, emotionality, and sociability. Previous studies have addressed facets of EI through self-report surveys and ability-based tests; however, there is a lack of physiological data regarding emotional regulation (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Since EI is related to effective stress management and coping abilities, it can be studied through physiological regulators of stress mechanisms like the autonomic nervous system (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The activity of the autonomic nervous system can be well studied through HRV regarding mental stress (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, these studies have assessed relationships between physical stress and heart rate variability to the best of our knowledge; no sufficient data exists to establish a relationship between heart rate variability and TEI with its four factors. Therefore, the current study aimed to elucidate the relationship between heart rate variability and TEI. This study will provide recommendations to aid in the psychological diagnosis of stress and depression through heart rate variability in clinical settings, which are currently diagnosed based on a questionnaire. It cannot predict health outcomes or the prognosis of psychological therapies.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting:\u003c/h2\u003e \u003cp\u003eThe study included 55 healthy participants between 18 and 35 who could attempt the mental arithmetic task. The study was conducted from March 2022 to December 2023, following a lab-based experimental approach within the facilities of DUHS. Monetary incentives were provided upon completion of data collection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSampling procedures and sample size calculation:\u003c/h2\u003e \u003cp\u003eThe sample size was calculated using the effect size of a previous study (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) for correlation by using the formula for the correlation coefficient and after adjusting the 10% respondent rate. The non-probability convenience sampling technique is used for this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Collection Procedure:\u003c/h2\u003e \u003cp\u003e Participants were instructed to sit quietly in a calm, relaxing room for 5 minutes. They were provided with a detailed explanation of the procedure and requested to provide their informed consent. Demographic data was collected as part of the initial phase. Before the HRV recording, participants were administered the TEIQue-30 questionnaire. HRV recordings were acquired across three distinct phases: baseline (T1), stress induction period (T2), and recovery (T3). During the short-term baseline phase (T1), participants were seated for 5 minutes, and HRV data were collected. Subsequently, participants were asked to engage in a brief mental arithmetic task, specifically the serial subtraction method. The task presented is an integral component of the Trier Social Stressor Test (TSST), typically employed to induce physiological responses without specifically assessing performance or cognitive aptitude (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The laboratory-induced stress task is deliberately crafted with diminished personal emotional salience, indicating its intentional design to engage cognitive abilities without evoking intense emotional responses (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). A research investigation comparing various stress-inducing tasks to stimulate sympathetic reactivity confirmed that the serial subtraction task effectively triggers a notable cortisol stress response and activates an ample number of brain regions, resulting in increased neural activity (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The task consisted of repeatedly subtracting single or double-digit numbers from a 4-digit number within a 5-second interval. Participants were required to make multiple attempts, or a minimum of 10 attempts per block, as the time taken to complete the stress task varied among individuals. The experiment consisted of four blocks. In the first and third blocks, participants performed subtractions using a single-digit number (e.g., subtracting 7 from the starting number 9095 in the first difficulty block and 8185 in the third difficulty block). Conversely, participants subtracted a two-digit number in the second and fourth blocks (e.g., subtracting 13 from the 4-digit number 6233 in the second difficulty block and 5245 in the final difficulty block). The duration of the arithmetic task was kept at 5 minutes by repeating the subtraction method if it ended earlier to meet each protocol phase for the calculated HRV measures. Immediate feedback was given to participants regarding accuracy and response time, focusing on promoting concentration. Concurrently with the mental arithmetic task (T2), HRV measurements were taken. Following the completion of the stress induction phase, a 5-minute HRV recording was conducted to capture the recovery period (T3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData collection tools: Assessment of Trait EI\u003c/h2\u003e \u003cp\u003eTrait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF) was constructed from its original form, i.e., a TEIQue 153 item with a 7-point Likert scale that yields a score on 15 facets belonging to four factors: wellbeing (happiness, optimism, self-esteem), self-control (emotion regulation, impulse control, stress management), emotionality (empathy, emotion perception, emotion expression, relationship), and sociability (emotion management, assertiveness, social awareness), where adaptability and self-motivation are considered as independent facets. Factors mentioned in the TEIQue questionnaire represent a level of measurement that is broader than that of the facets, whereas the facets descriptions are detailed and focused. The factors level provides a useful level of intermediate measurement and description. The TEIQue assesses all of the above-mentioned facets through 15 subscales and provides scores on four factors of broader relevance. The short and long forms of TEIQue questionnaires have the same psychometric properties and construct criterion validity. The assessment of TEI is a reversed score for some item numbers and then a sum-up for all the responses. Still, in the current study, the total score for four factors is obtained from the provided online scoring engine, \u0026ldquo;London Psychometric Lab,\" which provides the mean of each factor obtained individually from the participant. The questionnaire was translated into several languages and administered in English with a validated version of TEIQue sf 30 Urdu among the Pakistani population (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eScoring and Reliability Analysis for Emotional Intelligence Questionnaire\u003c/h2\u003e \u003cp\u003eThe scoring process for the questionnaire involves several steps. Initially, specific item scores are recorded based on specified rules. For instance, scores are adjusted according to predetermined mappings, such as 7 becoming 1, 6 becoming 2, and so forth. The emotional intelligence (EI) score is computed by summing all individual item scores and dividing by the total number of items in the questionnaire, which is 30. Additionally, factor scores are calculated to assess specific aspects of emotional intelligence. These factors include well-being, self-control, emotionality, and sociability. Each factor's score is determined by summing the relevant item scores and dividing by the total number of items associated with that factor. Subsequently, a reliability analysis is performed to evaluate the internal consistency of items within each factor, typically using measures like Cronbach's alpha. This analysis provides insights into the reliability and consistency of the questionnaire's assessment of emotional intelligence traits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eComputational Parameters for Heart Rate Variability Assessment\u003c/h2\u003e \u003cp\u003eFor heart rate variability, Kubios software provides individual bands of powers for frequency measures. Specifically for the frequency domain, we use Fast Fourier Transforms, an efficient algorithm to compute. It transforms a function of time or space into a function of frequency, revealing the frequency components present in the original signal. The standard frequency selected from the FFT spectrum is VLF 0.00\u0026ndash;0.04 Hz, LF: 0.04\u0026ndash;0.15 Hz, and HF: 0.15\u0026ndash;0.40 Hz. Default values from Kubios are selected as an embedding dimension of sample entropy (m\u0026thinsp;=\u0026thinsp;2) and a common selection (r\u0026thinsp;=\u0026thinsp;0.2 SDNN), which is tolerance that strongly affects ApEn. Similarly, the default values set for the embedding dimension and the tolerance parameter in Kubios HRV software are the same as those for the ApEn computation. For short-term detrended fluctuation analysis, slope α1 is obtained within the 4\u0026thinsp;\u0026le;\u0026thinsp;n\u0026thinsp;\u0026le;\u0026thinsp;12 range. In accordance, the slope\u0026nbsp;obtained by default from the range\u0026nbsp;13\u0026thinsp;\u0026le;\u0026thinsp;n\u0026thinsp;\u0026le;\u0026thinsp;64\u0026nbsp;characterises long-term fluctuations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData Instruments, equipment and software\u003c/h2\u003e \u003cp\u003eE-wave 8.0.8 was used with a sampling rate of 1000 Hz. An active electrode was placed on the right radial, a reference electrode on the left radial, and the ground electrode on the pronator teres muscle. The software template \u0026ldquo;baseline ECG\u0026rdquo; is used to record the simple ECG with time (T/D 50 ms) and voltage (V/D 100\u0026micro;V) for better visualisation of recorded signals (RR signals) on the Muse monitor. The high and low power frequencies were kept in default settings, i.e., HPF 200 mHz and LPF 30 Hz. The output of the recorded data is received as interbeat intervals, or RRI, in text form and is measured on KUBIOS. Low threshold beat correction is performed to remove artefacts if required.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis:\u003c/h2\u003e \u003cp\u003eThe data was analysed using IBM SPSS version 21, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Descriptive statistics employed frequency and percentage for qualitative variables, while the quantitative variable \"age\" was described using mean and SD. Valid percentages were taken out of the TEIQue-SF-30 score by reverse scoring, where the mean and Cronbach alpha for four factors were obtained by the London Psychometric Lab scoring engine.\u003c/p\u003e \u003cp\u003eAfter assessing the continuous variables of HRV indices, it was established that their distribution deviates significantly from normality, as evidenced by a Kolmogorov-Smirnov test yielding a P-value less than 0.05. Consequently, traditional statistical tests predicated on the assumption of normality were deemed inappropriate for further analysis. Moreover, outliers were identified through box plot visualisation and subsequently removed from the dataset to mitigate potential distortions in the analysis. Subsequently, a one-way repeated measures ANOVA was employed to explore possible differences among normally distributed variables, while the Friedman test was utilised for non-normally distributed variables. Furthermore, to ascertain significant relationships between the factors of TEI and HRV measures, Pearson and Spearman correlations were employed based on the normal status of the data.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\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\u003eDemographics of study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndergraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocioeconomic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSt. deviation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDescriptive values for TEIQUE SF 30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWellbeing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotionality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal TEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that most participants were females (56%) and undergraduates (45.5%). A majority were single (70.9%). Most participants were employed (52.7%) and belonged to the middle class (61.8%). TEIQue SF 30 respondents were the highest who marked item number 20, specifically 41.8%. Conversely, the lowest number of respondents who marked items is number 10, specifically 3.6%. Similarly, the mean and standard deviation of TEIQue SF 30 are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The Cronbach's alpha coefficients for the TEI factors are found to be well-being (α\u0026thinsp;=\u0026thinsp;0.76), self-control (α\u0026thinsp;=\u0026thinsp;0.45), emotionality (α\u0026thinsp;=\u0026thinsp;0.59), and sociability (α\u0026thinsp;=\u0026thinsp;0.52). The overall TEI score, encompassing all factors, demonstrates high internal consistency with a Cronbach's alpha value of 0.86.\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\u003ea: One-way repeated measures of ANOVA for the time domain, frequency domain, and nonlinear HRV measures.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF- value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\eta p2\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrequency domain\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eF- value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePartial Eta Squared\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLF/HF ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrequency domain\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChi-squared statistics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAsymp. sig\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.614 \u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-linear\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eF-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\eta p2\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD2SD1ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApEn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDfaα1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDfaα2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026times; represents the chi-squared value in the above table\u003c/p\u003e \u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea represents one-way repeated measures of ANOVA for the time domain, frequency domain, and nonlinear HRV measures.\u003c/p\u003e \u003cp\u003eA statistically significant difference in mean SDNN values across the three-time points (F\u0026thinsp;=\u0026thinsp;4.297, df\u0026thinsp;=\u0026thinsp;1.646, p\u0026thinsp;=\u0026thinsp;0.023) was observed. The descriptive statistics for SDNN indicate an elevated value at stress induction time T2 (37.27\u0026thinsp;\u0026plusmn;\u0026thinsp;13.04) compared to baseline T1 (33.78\u0026thinsp;\u0026plusmn;\u0026thinsp;11.91). The pairwise comparisons also indicated a significant difference in SDNN from baseline to the induction phase \u0026minus;\u0026thinsp;3.49(CI: -6.67 to -0.305, p\u0026thinsp;=\u0026thinsp;0.032) However, no significant difference was observed between SDNN values during recovery and at baseline 0.196 (CI: -1.889 to 2.281, p\u0026thinsp;=\u0026thinsp;0.851).\u003c/p\u003e \u003cp\u003eFor HF, (the Friedman test) reveals a significant difference between baseline and stress-inducing time points for the recorded value of HRV, χ2 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;19.614, p\u0026thinsp;=\u0026thinsp;0.000. Post hoc analysis with a Wilcoxinoxon signed-rank test was conducted with a Bonferroni correction (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A significant difference was observed between the mental stress-inducing task and baseline (\u003cem\u003eZ\u003c/em\u003e = -4.926, p\u0026thinsp;=\u0026thinsp;0.000) and during the recovery and stress induction time points (Z = -3.538, p\u0026thinsp;=\u0026thinsp;0.000). The analysis of variance for LF shows a significant statistical difference (F\u0026thinsp;=\u0026thinsp;10.44, df\u0026thinsp;=\u0026thinsp;1.991, p\u0026thinsp;=\u0026thinsp;0.000). Significant mean differences at different time points were observed between baseline and stress induction period \u0026minus;\u0026thinsp;0.018(CI: -0.027 to -0.008, p\u0026thinsp;=\u0026thinsp;0.000), where the recovery phase also exhibits significant differences with stress induction time 0.019 (CI: 0.009 to 0.028, p\u0026thinsp;=\u0026thinsp;0.000). A statistically significant LF/HF ratio difference across different time points (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.980, df\u0026thinsp;=\u0026thinsp;1.821, p\u0026thinsp;=\u0026thinsp;0.000) was found. Post hoc analysis revealed a significant LF/HF ratio increase during the stress induction phase compared to baseline, induced by the mental arithmetic task 0.579 (CI: -0.844 to -0.315, p\u0026thinsp;=\u0026thinsp;0.000). Furthermore, a significant difference was observed between baseline and the recovery period, with a substantial mean decrease \u0026minus;\u0026thinsp;0.437(CI: -0.675 to -0.198, p\u0026thinsp;=\u0026thinsp;0.001). A significant difference between baseline and stress induction time \u0026minus;\u0026thinsp;6.173(CI: -10.24 to -2.105, p\u0026thinsp;=\u0026thinsp;0.004) and stress induction time to recovery time 5.241(CI: 1.783 to 8.699, p\u0026thinsp;=\u0026thinsp;0.004) was observed. A significant difference for SD2 SD1 ratio (F\u0026thinsp;=\u0026thinsp;1.226, df\u0026thinsp;=\u0026thinsp;1.709, p\u0026thinsp;=\u0026thinsp;0.001) was found. The pairwise comparison shows a significant difference between the baseline and stress induction phase \u0026minus;\u0026thinsp;0.275(CI: -0.427 to -0.124, p\u0026thinsp;=\u0026thinsp;0.001). A significant difference is also observed between stress induction time and post-induction period 0.174(CI: 0.016 to 0.332, p\u0026thinsp;=\u0026thinsp;0.032). However, no significant difference between baseline and post-induction time points is observed \u0026minus;\u0026thinsp;0101(CI: \u0026minus;\u0026thinsp;0.210 to 0.007, p\u0026thinsp;=\u0026thinsp;0.067). Post hoc pairwise comparisons indicated significant differences between time points T1 and T2 0.162(CI: 0.117 to 0.206, p\u0026thinsp;=\u0026thinsp;0.000). An important difference in mean values was observed from the stress induction phase to the recovery period \u0026minus;\u0026thinsp;0.170(CI: -0.208 to -0.132, p\u0026thinsp;=\u0026thinsp;0.000).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb shows significant correlations between the TEI four factors and HRV measures at different time points.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" 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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eWell being\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSdnn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePnn50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFHF ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSd2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSd2sd1ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf control\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmotionality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSdnn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSd2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDfaα2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSociobility\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cb\u003eb\u003c/b\u003e shows significant correlations between the TEI four factors and HRV measures at different time points. SDNN exhibited moderate positive correlations at T1 (r\u0026thinsp;=\u0026thinsp;0.321, p\u0026thinsp;=\u0026thinsp;0.020) and T3 (r\u0026thinsp;=\u0026thinsp;0.280, p\u0026thinsp;=\u0026thinsp;0.045); pNN50% displayed a significant positive correlation at T1 (r\u0026thinsp;=\u0026thinsp;0.332, p\u0026thinsp;=\u0026thinsp;0.020), and stress index demonstrated negative correlations at T1 (r = -0.318, p\u0026thinsp;=\u0026thinsp;0.023) and T3 (r = -0.337, p\u0026thinsp;=\u0026thinsp;0.012). Positive correlations between high frequency (HF) power and well-being at T2 (r\u0026thinsp;=\u0026thinsp;0.348, p\u0026thinsp;=\u0026thinsp;0.012), as well as negative correlations between the LF/HF ratio and well-being at T2 (r = -0.310, p\u0026thinsp;=\u0026thinsp;0.027) were observed.\u003c/p\u003e \u003cp\u003eFrom the time domain, SDNN exhibited a moderate positive correlation at T1 (r\u0026thinsp;=\u0026thinsp;0.306, p\u0026thinsp;=\u0026thinsp;0.028), whereas the stress index shows a significant negative correlation at T3 (r = -0.297, p\u0026thinsp;=\u0026thinsp;0.028). The Poincar\u0026eacute; plot standard deviation perpendicular to the line of identity (SD2) showed a moderately positive correlation at T1 (r\u0026thinsp;=\u0026thinsp;0.300, p\u0026thinsp;=\u0026thinsp;0.028), indicating the potential involvement of nonlinear dynamics in heart rate variability in emotional experiences. The Detrended Fluctuation Analysis exponent alpha 2 (DFAα2) displayed a positive correlation at T3 (r\u0026thinsp;=\u0026thinsp;0.319, p\u0026thinsp;=\u0026thinsp;0.019) The stress index demonstrated a negative correlation at T3 (r = -0.297, p\u0026thinsp;=\u0026thinsp;0.028). There are marked correlations between the HRV measures and self-control factor.\u003c/p\u003e \u003cp\u003eLF demonstrated a moderate positive correlation (r\u0026thinsp;=\u0026thinsp;0.321, p\u0026thinsp;=\u0026thinsp;0.017), and HRV measures significantly correlated with sociability factor. A strong negative correlation was observed (r = -0.407, p\u0026thinsp;=\u0026thinsp;0.002), indicating a potential link between autonomic nervous system activity and sociability.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInfluence of mental arithmetic tasks on autonomic activity\u003c/h2\u003e \u003cp\u003eThe current study revealed that emotion regulation is intricately connected with HRV parameters at baseline, stress induction, and recovery time. The changes that occurred at stress induction time point T2 are of great significance to discuss, as HRV parameters have been known to predict and validate the presence of mental stress through autonomic modulation of cardiovascular activity (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003eTime domain measures (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.a), showed an elevated SDNN at T2, which indicates higher sympathetic dominance, contrary to the expected pattern since SDNN is generally higher during rest and recovery periods, reflecting the standard deviation of normal-to-normal beats (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, previous research has reported similar findings and associated them with speech tasks. Previous researchers reported an increased SDNN during the stress condition, which is potentially attributed to the influence of speech-related respiratory patterns on HRV measures (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe results also presented alterations in the frequency domain of HRV parameters that are considered most reliable in validating the irregular HRV fluctuations in the presence of mental stress due to the activation of SNS. Significant group differences across different time points, indicating elevated sympathetic activity markers, such as LF and LH/HF ratio, were observed at the time of mental arithmetic stress induction T2, affirming sympathetic activation in response to the acute stressor. This is a consistent finding that goes parallel in affirmation with previous literature, as the LF/HF ratio is supposed to show elevated values in the presence of sympathetic activation(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Concurrently, the parasympathetic activity marker, HF, exhibited a decreased value at T2, suggesting parasympathetic suppression during stress. The present study's findings are consistent with previous literature, as HF suggests vagal modulation in cardiovascular variability. This finding represents suppression of parasympathetic activity through stress induction (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The spectral analysis measures support the findings, suggesting alterations in HRV patterns, reflecting autonomic activity changes, as predictors of mental stress and potentially linked to stress regulation abilities (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Notably, no significant differences were found between baseline and recovery, indicating consistent autonomic regulation without irregular fluctuations.\u003c/p\u003e \u003cp\u003eSignificant variations are observed in nonlinear measures of HRV. SD2 mainly represents sympathetic and parasympathetic contributions to the heart (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Increased SD2 at T2 represents a shift towards SNS and increased long-term variability in HRV associated with PNS withdrawal. This finding is consistent with the result obtained by increasing SD2 during the simulated evaluation (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Similarly, the increased SD2SD1 ratio from baseline to stress induction time explains an increase in sympathetic activity, which is also consistent with the findings from the published literature. This represents the suppression of parasympathetic activity, and at the same time, it postulates that nonlinear HRV indices change significantly from baseline to stress induction task, thus suggesting a successful stress induction and increased sympathetic activity(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). ApEn, a measure of predictability and signal similarity, shows more regularity at T2, suggesting a decrease in values compared to T1 and T3. This suggests there is more predictability and less complexity in HRV signals in the presence of mental stress. This finding is consistent with other studies that suggest an influence of mental stress on the nonlinear domain of HRV (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The nonlinear measures DFAα1 found increased at T2, where DFAα2 decreased compared to baselines. The published studies do support both aspects. The results for DFAα1 show a greater mean at T2 with a significant mean difference from the baseline. This indicates that at stress, DFAα1 increases, which is consistent and has also been found in other studies that show that in mental stress aloud, decreased HF power is associated with the increase of DFAα1 (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Another study with a higher DFAα1 value can validate this finding, indicating more persistent and less variable HRV patterns (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Meanwhile, the values for DFAα2 decrease at T2 compared to baseline in consistency with previous studies claiming DFAα2 has previously been shown to correlate with acute psychological stress in subjects, decreasing amplitude with increasing stress (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). This indicates disruptions in heart rate complexity over more extended periods, which makes a valid point that psychological stress relates to more predictability and a high correlation in HRV signals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of TEI with time domain measure of HRV\u003c/h2\u003e \u003cp\u003eHigher trait EI correlated with favourable HRV profiles, including SDNN, indicative of improved autonomic nervous system regulation. Additionally, TEI exhibited a significant correlation with the percentage of successive NN intervals differing by pNN50%, highlighting its influence on parasympathetic modulation during resting states and potential cardiovascular resilience (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Moreover, a negative correlation was observed between TEI and the HRV stress index, indicating its role in modulating the autonomic nervous system response to stressors. This suggests that individuals with greater TEI may demonstrate a more adaptive physiological stress response characterized by heightened parasympathetic activity and reduced sympathetic dominance. For the emotionality factor, the results again represent HRV as a demonstrating tool for trait emotional intelligence. These findings are consistent with previous results from relevant studies on understanding emotion regulation (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Like the well-being factor, the emotionality factor shows a positive correlation with SDNN at baseline, suggesting that individuals with a wide range of emotion-related skills may exhibit enhanced overall autonomic function during resting conditions (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe significant inverse correlation observed between the stress index and the emotionality factor at recovery highlights the potential role of emotional competence in shaping the autonomic response to stressors. This finding suggests that individuals with greater emotional competence, characterized by their ability to perceive and express emotions effectively, may exhibit a more adaptive physiological response to stress (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of TEI with Frequency Domain Measure of HRV\u003c/h2\u003e \u003cp\u003eThe significant associations between the HF component and LF/HF ratio from spectral analysis and the well-being factor are notable findings. The time point coinciding with the induction of mental stress in participants provides valuable insights into the interplay between emotional intelligence and autonomic nervous system regulation under stress.\u003c/p\u003e \u003cp\u003eThe significant association suggests that greater TEI may demonstrate heightened parasympathetic activity even in acute mental stress. The findings indicate the potential role of TEI in buffering against the physiological effects of stress, particularly by promoting adaptive autonomic responses characterized by increased parasympathetic tone (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). It implies that higher trait EI may exhibit greater resilience to stress-induced autonomic dysregulation, potentially mitigating the detrimental impact of stress on health and well-being (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe inverse correlation between the well-being factor and LF/HF ratio during stress induction suggests that higher emotional well-being is associated with a shift towards parasympathetic dominance over sympathetic activity in response to stress. This implies that greater emotional well-being exhibits a more adaptive physiological response to stress, characterized by increased parasympathetic tone and decreased sympathetic arousal (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe self-control factor of TEI shows a significant relationship with LF at baseline. The self-control factor of TEI represents emotion regulation and stress management (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). This finding is noteworthy, implying that higher emotional and stress regulation can be linked with higher TEI. This finding is also inconsistent with previous work that suggests a negative association of LF with self-control factor(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe sociability factor shows a highly negative correlation at recovery with HF, representing parasympathetic activity. This finding is inconsistent with the one that indicates that assertiveness and emotion management are linked with parasympathetic modulation and that sociability predicts post-task cardiac vagal activity (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). However, our findings suggest that a higher parasympathetic tone is associated with lower sociability or individuals with higher sociability may exhibit delayed recovery from a given stressor. Its important to note that findings for the sociability and self-control factors remain inconclusive.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of TEI with Nonlinear Measure of HRV\u003c/h2\u003e \u003cp\u003eThe significant positive correlation observed between the well-being factor of TEI and SD2 at baseline suggests a relationship between emotional well-being and the complexity of heart rate dynamics during resting conditions (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Higher levels of emotional well-being tend to exhibit greater complexity in heart rate variability at baseline. This suggests that higher emotional well-being is associated with more intricate and dynamic patterns in heart rate fluctuations during rest conditions (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). This finding implies that emotional well-being may be linked to enhanced autonomic nervous system regulation, characterized by increased flexibility and adaptability in response to internal and external stimuli (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The SD2SD1 ratio, indicating the balance between sympathetic and parasympathetic influences on heart rate variability during recovery, correlates positively with higher emotional well-being. This suggests that individuals with greater emotional well-being exhibit a more adaptive autonomic response to stress, characterized by balanced sympathetic and parasympathetic nervous system activity regulation. This balanced response may improve cardiovascular health and resilience following stressful events (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).The results again represent HRV as a demonstrating tool in relationship with TEI for the emotionality factor. These findings are consistent with previous results from relevant studies on emotion regulation (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Like the well-being factor, the emotionality factor positively correlates with SDNN at baseline, providing insight into the physiological process towards a stressor. This suggests that high HRV and emotionality exhibit a relationship, whereas the antagonistic relationship of the stress index at recovery shows the emotionality factor is associated with less stress (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). The nonlinear measure of HRV shows a significant positive correlation for SD2 at baseline and an inverse correlation with detrended fluctuations (α2) at recovery. The observed correlation between SD2 and emotionality across baseline and recovery phases suggests that this association remains consistent throughout emotional processing and recovery. It implies that individuals with a greater capacity for autonomic regulation, as indicated by higher HRV, also demonstrate heightened emotional responsiveness and expressiveness (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). The DFAα2 negatively correlated at recovery, which indicates a decrease in long-range correlations, is associated with higher levels of emotionality. The reduction in fractal-like properties during recovery departs from the typical self-similar and organized patterns seen in healthy physiological systems. This shift may reflect a less adaptive and more disordered physiological state, potentially indicating a diminished ability to respond flexibly to internal and external demands. This finding is consistent with published literature, which states that lower emotional reactivity and higher emotional regulation capacity, as indicated by heart rate variability, are associated with higher levels of TEI.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe findings concluded a well-established relationship between trait emotional intelligence and heart rate variability, shedding light on the physiological interplay between emotion regulation and mental stress resilience. The results suggest that HRV may be a physiological indicator of emotional intelligence in acute mental stress. This implies that individuals with higher emotional intelligence may exhibit greater autonomic flexibility and adaptive responses to stress, as reflected in their HRV patterns. These findings contribute to our understanding of the physiological correlates of emotional intelligence and underscore the importance of HRV as a potential marker for assessing emotional regulation and resilience in the face of acute mental challenges.\u003c/p\u003e "},{"header":"LIMITATION","content":"\u003cp\u003eWe want to conduct a baseline study to show the possible relationship between heart rate variability measures and TEI and identify HRV as a physiological correlate of emotional intelligence. The study is limited by a small sample size (n = 55), potentially limiting generalizability to the larger population. Due to the age restriction, the participants' young mean age may not accurately represent older age groups. The study's urban area data collection may introduce bias, limiting its applicability to rural or suburban populations. The inclusion of only educated candidates also restricts generalizability to a broader population. HRV data collection experienced occasional noise artefacts, influencing data quality. Response delays during the arithmetic task posed a potential confounding factor, necessitating exclusion. Logistical constraints arose from participants needing to visit the campus for data collection, preventing data collection outside the research setting.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data set generated and analyzed during the current study is available in the psycharchive repository,\u003c/p\u003e\n\u003cp\u003e\u003cu\u003ehttps://doi.org/10.23668/psycharchives.14622\u003c/u\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received approval from the Institutional Review Board (IRB) at Dow University of Health Sciences, in accordance with the guidelines set forth by the National Bioethics Committee of Pakistan (Approval No. IRB-2460/DUHS/Approval/2022/822), as well as the Joint Research Ethics Committee (JREC) at Quest International University, Malaysia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT TO PARTICIPATE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to the study, written informed consent was obtained from all participants. A brief discussion of the study was provided on the informed consent form, outlining the non-harmful benefits of participation. Participants were informed that their involvement in the study was voluntary and that they could withdraw at any time without any consequences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent for publication was included in the research information and informed consent form, where it was also stated that participant anonymity would be prioritized throughout the publication process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests regarding this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by funding from Quest International University under Fund ID QIUP-FIN-F01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI would like to thank my former supervisors, Dr. Ebrahim NKC and Dr. Satish Kumar, for their initial guidance and support. They introduced me to the concept of Heart Rate Variability in relation to studying mental stress and provided essential support to keep this study ongoing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBar-On R, Tranel D, Denburg NL, Bechara A. Exploring the neurological substrate of emotional and social intelligence. Brain. 2003;126(8):1790\u0026ndash;800.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCraig A, Tran Y, Hermens G, Williams L, Kemp A, Morris C, et al. Psychological and neural correlates of emotional intelligence in a large sample of adult males and females. Pers Indiv Differ. 2009;46(2):111\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalovey P, Stroud LR, Woolery A, Epel ES. Perceived emotional intelligence, stress reactivity, and symptom reports: Further explorations using the trait meta-mood scale. 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Emotion. 2013;13(4):599.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasties JF, Mottet D, Le Gallais D. Non-linear analyses of heart rate variability during heavy exercise and recovery in cyclists. Int J Sports Med. 2006;780\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCraty R, Zayas MA. Cardiac coherence, self-regulation, autonomic stability, and psychosocial well-being. Front Psychol. 2014;5:104218.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMather M, Thayer JF. How heart rate variability affects emotion regulation brain networks. Curr Opin Behav Sci. 2018;19:98\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu J, Ji L, Liu C. Heart rate variability monitoring for emotion and disorders of emotion. Physiol Meas. 2019;40(6):064004.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAppelhans BM, Luecken LJ. Heart rate variability as an index of regulated emotional responding. Rev Gen Psychol. 2006;10(3):229\u0026ndash;40.\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-4442638/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4442638/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective:\u003c/h2\u003e \u003cp\u003eEmotional intelligence (EI) is essential for effective stress management and may influence cardiac responses. This study seeks to investigate the relationship between EI and heart rate variability (HRV) due to limited physiological data, contributing valuable insights into this unexplored connection and its potential impact on overall well-being.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eIn a study of 55 participants, mainly undergraduate students (45.5%) and single (70.9%), females constituted 56.4% of the sample. The highest well-being score was 5.187 (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.76). One-way repeated measures ANOVA showed significant HRV differences across time points. Median HF at stress induction was 0.193 (IQR: 0.160\u0026ndash;0.217), significantly decreasing from baseline (Z = -4.926, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). LF/HF ratio increased at T2 (M\u0026thinsp;=\u0026thinsp;0.579, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with SD2/SD1 ratio rising to 2.171. Pairwise comparisons indicated differences between baseline and stress induction (M = -0.018, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and stress induction and post-induction periods (M\u0026thinsp;=\u0026thinsp;0.174, p\u0026thinsp;=\u0026thinsp;0.032). SDNN correlated positively at T1 (r\u0026thinsp;=\u0026thinsp;0.321, p\u0026thinsp;=\u0026thinsp;0.020) and T3 (r\u0026thinsp;=\u0026thinsp;0.280, p\u0026thinsp;=\u0026thinsp;0.045). pNN50% correlated positively at T1 (r\u0026thinsp;=\u0026thinsp;0.332, p\u0026thinsp;=\u0026thinsp;0.020), while stress index showed negative correlations at T1 (r = -0.318, p\u0026thinsp;=\u0026thinsp;0.023) and T3 (r = -0.337, p\u0026thinsp;=\u0026thinsp;0.012). Sociability negatively correlated with HRV measures (r = -0.407, p\u0026thinsp;=\u0026thinsp;0.002), indicating autonomic nervous system activity links.\u003c/p\u003e","manuscriptTitle":"The relationship of heart rate variability measures with trait emotional intelligence in the presence of acute mental stress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-17 13:26:11","doi":"10.21203/rs.3.rs-4442638/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":"5d89b484-8437-430e-9fe1-73574f74ac5b","owner":[],"postedDate":"June 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-10T10:54:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-17 13:26:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4442638","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4442638","identity":"rs-4442638","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}