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The potential to track changes in training status and fitness levels of especially team sport participants by making use of more time efficient and accessible methods such as heart rate variability (HRV) and heart rate recovery (HRR) cannot be overlooked and needs to be considered. However, studies that have investigated this aspect in team sport participants are scarce. It is against this background that the main objectives of this study were firstly, to determine the relationships between HRV and HRR as well as the fitness levels of a cohort of university-level rugby players. The second objective was to determine the validity of the BioForce Heart Rate Variability System to determine the HRV of a cohort of university-level rugby players. Twenty-four university-level rugby players (age: 20.1 ± 0.41 years; body stature: 182.7 ± 6.2 cm; body mass: 89.7 ± 12.7 kg) of a South African university’s Rugby Institute participated in the first part of the study. During the test day players’ fasting baseline HRV (baseline HRV) values were taken. This was followed by the measurement of the post-breakfast HRV (Pre-Yo-Yo IR1 HRV). Players were then required to perform the Yo-Yo Intermittent Recovery Test Level 1 (Yo-Yo IR1) while they were fitted with a portable Cosmed K4b2 gas analyser apparatus and a Fix Polar Heart Rate Transmitter Belt. After completion of the test, HRR was taken on 1 and 3 minutes and followed by the measurement of HRV (Post-Yo-Yo IR1 HRV). For the second part of the study a group of twenty u/21 university-level rugby players (age: 20.06 ± 0.40 years; body stature: 181.8 ± 5.5 cm; body mass: 91.1 ± 10.7 kg) of a South African university’s Rugby Institute were recruited to participate in this study. HRV was measured simultaneously by the Actiheart monitor system as well as the BioForce Heart Rate Variability System over three times periods: during the morning in a fasting state just after players had woken up (baseline); in the morning just after the players ate breakfast (pre-anaerobic); after completion of a high-intensity anaerobic training session (post-anaerobic) and after completion of a 20 min recovery session (post-recovery). Significant correlations (p ≤ 0.05) were found between Pre-Yo-Yo IR1 HRV and heart rate (HR) at the respiratory compensation point (RCP-HR (bpm)) (r = -0.468) as well as oxygen uptake at the RCP (RCP- 2max VO (% of 2max VO )) (r = 0.476), respectively. A forward stepwise regression analysis showed that HR at ventilatory threshold 1 (VT1-HR (bpm)) contributed significantly (p ≤ 0.05) to the post-Yo-Yo IR1 HRV with a variance of 39.8%. Final Yo-Yo IR1 level also contributed significantly (p ≤ 0.05) to 3 minute post-Yo-Yo IR1 HRR with a variance of 16.5%. For the second part of the study the majority of significant relationships (p < 0.05) between the Actiheart and Bioforce obtained HRV results were observed for the post-recovery period (Mean RR, SDNN, RMSSD and Peak LF power), followed by the pre-anaerobic period (Mean R-R and SDNN) and the baseline period (LF:HF ratio). No significant relationships were observed between the HRV results of the two apparatuses during the post-anaerobic period. In conclusion, HRV and HRR may have the potential to act as affordable and easy measurement tools of team sport participants’ fitness levels. However, the study results suggested that the BioForce Heart Rate Variability System that is used to obtain team sport participants’ HRV is especially valid to determine HRV after recovery periods that follow hard training sessions. The results do however cast a shadow of doubt over the accuracy of this apparatus when used directly after hard training sessions. / MSc (Sport Science), North-West University, Potchefstroom Campus, 2014

The potential to track changes in training status and fitness levels of especially team sport participants by making use of more time efficient and accessible methods such as heart rate variability (HRV) and heart rate recovery (HRR) cannot be overlooked and needs to be considered. However, studies that have investigated this aspect in team sport participants are scarce. It is against this background that the main objectives of this study were firstly, to determine the relationships between HRV and HRR as well as the fitness levels of a cohort of university-level rugby players. The second objective was to determine the validity of the BioForce Heart Rate Variability System to determine the HRV of a cohort of university-level rugby players. Twenty-four university-level rugby players (age: 20.1 ± 0.41 years; body stature: 182.7 ± 6.2 cm; body mass: 89.7 ± 12.7 kg) of a South African university’s Rugby Institute participated in the first part of the study. During the test day players’ fasting baseline HRV (baseline HRV) values were taken. This was followed by the measurement of the post-breakfast HRV (Pre-Yo-Yo IR1 HRV). Players were then required to perform the Yo-Yo Intermittent Recovery Test Level 1 (Yo-Yo IR1) while they were fitted with a portable Cosmed K4b2 gas analyser apparatus and a Fix Polar Heart Rate Transmitter Belt. After completion of the test, HRR was taken on 1 and 3 minutes and followed by the measurement of HRV (Post-Yo-Yo IR1 HRV). For the second part of the study a group of twenty u/21 university-level rugby players (age: 20.06 ± 0.40 years; body stature: 181.8 ± 5.5 cm; body mass: 91.1 ± 10.7 kg) of a South African university’s Rugby Institute were recruited to participate in this study. HRV was measured simultaneously by the Actiheart monitor system as well as the BioForce Heart Rate Variability System over three times periods: during the morning in a fasting state just after players had woken up (baseline); in the morning just after the players ate breakfast (pre-anaerobic); after completion of a high-intensity anaerobic training session (post-anaerobic) and after completion of a 20 min recovery session (post-recovery). Significant correlations (p ≤ 0.05) were found between Pre-Yo-Yo IR1 HRV and heart rate (HR) at the respiratory compensation point (RCP-HR (bpm)) (r = -0.468) as well as oxygen uptake at the RCP (RCP- 2max VO (% of 2max VO )) (r = 0.476), respectively. A forward stepwise regression analysis showed that HR at ventilatory threshold 1 (VT1-HR (bpm)) contributed significantly (p ≤ 0.05) to the post-Yo-Yo IR1 HRV with a variance of 39.8%. Final Yo-Yo IR1 level also contributed significantly (p ≤ 0.05) to 3 minute post-Yo-Yo IR1 HRR with a variance of 16.5%. For the second part of the study the majority of significant relationships (p < 0.05) between the Actiheart and Bioforce obtained HRV results were observed for the post-recovery period (Mean RR, SDNN, RMSSD and Peak LF power), followed by the pre-anaerobic period (Mean R-R and SDNN) and the baseline period (LF:HF ratio). No significant relationships were observed between the HRV results of the two apparatuses during the post-anaerobic period. In conclusion, HRV and HRR may have the potential to act as affordable and easy measurement tools of team sport participants’ fitness levels. However, the study results suggested that the BioForce Heart Rate Variability System that is used to obtain team sport participants’ HRV is especially valid to determine HRV after recovery periods that follow hard training sessions. The results do however cast a shadow of doubt over the accuracy of this apparatus when used directly after hard training sessions. / MSc (Sport Science), North-West University, Potchefstroom Campus, 2014

Physical health can be measured in several ways both based on subjective experiences and with objective tools. However, mental health can only be measured through subjective experiences and sensations, which can be biased. Therefore, researchers adopted the notion of an objective tool to assess well-being as a complement to existing self-reported scales and suggested that heart rate variability (HRV) might be an indicator of well-being. Hence, this thesis investigates the relationship between subjective well-being (SWB) and HRV, particularly high frequency-HRV (HF-HRV). Three hypotheses, which included different forms of well-being, were developed to test the relationship. And the hypotheses were: Cognitive well-being correlates positively with HF-HRV. Positive affect correlates positively with HF-HRV, and negative affect correlates negatively with HF-HRV. A total of 19 healthy Swedish females aged from 20-35 participated and answered questionnaires measuring SWB. After they completed the SWB-scales, their heart rate was measured and then converted into HF-HRV data. The findings revealed no correlations between the cognitive SWB and HF-HRV, neither to affective SWB.

Heart rate variability (HRV) reflects dynamic variation in sympathetic and parasympathetic nervous system (SNS and PNS) activity. The parasympathetic vagus nerve is responsible for HRV between 0.12 and 0.4 Hz, which is thought to index the capacity for effective coping, and is linked to physical and emotional well-being. Yogic breathing to increase vagal activity is often paced below 0.12 Hz (< 7.2 breaths per minute (BrPM)), where its impact HRV can be due to both sympathetic and parasympathetic mechanisms. Five healthy volunteers completed three pharmacological blockade sessions (placebo, sympathetic blockade with Esmolol, and parasympathetic blockade with Glycopyrrolate) about 48 hours apart, and during each session completed 11 Sudarshan Kriya Yogic breathing exercises at 4-9 BrPM. HRV was the lowest under Glycopyrrolate (p < 0.001), and there was no significant difference between placebo and sympathetic blockade with Esmolol. In addition, the spectral power of specific HRV frequencies was greatest at similar frequencies of breathing, a pattern prevented only by Glycopyrrolate. These findings suggest that heart rate is vagally influenced at all breathing rates, and that the SNS is not the mechanism by which slow breathing increases HRV.

This study compared psychological and physiological differences between children diagnosed with migraine and their healthy peers. Physiological measures were obtained at baseline, after discussing an emotionally relevant stressor, and after recovery in 21 children with pediatric migraine and 32 healthy peers. Comparisons were also made on psychological measures investigating sleep problems, anxiety, and family stress. It was hypothesized that children with migraine compared to their peers 1) would report more sleep disturbances, anxiety, and family stress 2) would exhibit greater sympathetic activation at rest, in response to an emotional stressor, and after a recovery period and 3) that autonomic functioning would mediate the relation between the presence of pediatric migraine and sleep disturbances. Results indicated that the migraine group reported significantly greater anxiety compared to peers but there were no significant differences in sleep disturbances or family stress. Within the migraine group, migraine severity was significantly associated with total sleep disturbance and greater incidence of parasomnias, while migraine duration was significantly associated with greater night time awakenings. Migraine children also exhibited a significantly higher pulse rate compared to their peers at rest and a significantly higher diastolic blood pressure and marginally significant higher LF/HF ratio at recovery from an emotional stressor. These findings suggest that sleep disturbance and pediatric migraine are significantly related but the relation is unclear and warrants additional research. Results also indicate that children with migraine may experience more anxiety than peers. Of most interest, results suggest that children with migraine may experience a disinhibition of the autonomic nervous system characterized by a dominance of the sympathetic nervous system resulting in a longer recovery period following an emotional stressor.

The change in R-R intervals between adjacent heartbeats is referred to as Heart Rate Variability (HRV). HRV data provides information regarding an individual’s Autonomic Nervous System (ANS), specifically the ANS’s two branches, the Sympathetic Nervous System (SNS) and the Parasympathetic Nervous System (PNS). The HRV of a healthy, well-conditioned heart is large at rest, while low HRV is associated with adverse health outcomes such diabetes, heart disease and early mortality. There has been a substantial amount of HRV research conducted with typically developing individuals. One group who is greatly underrepresented in research is individuals with intellectual disabilities. Currently, no studies have been undertaken with Special Olympics athletes. Therefore, the purpose of this study was to create HRV profiles at rest, during submaximal exercise, and at recovery of adult Special Olympic athletes. The study also sought to examine the impact that Down syndrome, age, sex, and medication on HRV profiles. The current study found that although heart rate responded appropriately during the three testing conditions (rest, exercise, recovery) the athletes were sympathetically dominated across all three conditions, indicating an imbalance between the SNS and the PNS. In addition, male and female athletes were significantly different with regards to low frequency and high frequency power. It is possible that anxiety or excitement about the testing influenced some athletes, and future research should examine how additional protocol familiarization could impact the HRV profiles within this population. Additionally, more research with larger sample sizes is needed to more fully understand the impact that age, etiology of intellectual disability, and medication use may be having on HRV profiles. / Graduate

Writing about personal traumatic experiences is associated with beneficial effects on physical and psychological symptoms compared with writing about emotionally neutral events. However, not everyone benefits from trauma writing to the same extent. The present study hypothesized that the effectiveness of trauma writing may be moderated by emotion regulation, as indexed by respiratory sinus arrhythmia (RSA). Research also shows that greater physiological reactivity is predictive of better trauma writing outcomes. Given the importance of physiological output in emotional processing, response training was developed and found to increase appropriate physiological reactivity. Because higher RSA is thought to indicate a more flexible response style including processing both emotional and physiological cues, it was hypothesized that trauma writers with higher resting RSA who received response training (as opposed to stimulus or no training) would have the best outcomes. It was also predicted that higher resting RSA would be related to lower baseline levels of depression, posttraumatic stress disorder (PTSD), physical illness symptoms, heart rate, and skin conductance. In the current study, participants wrote for 20 minutes on three occasions about a personal traumatic event (n = 113) or a neutral topic (n = 133) and received response training (n = 79), stimulus training (n = 84) or no training (n = 83). Heart rate and skin conductance were recorded in sessions one and three during a 10-minute baseline, 20-minute writing, and 10-minute recovery period. Self-reported trauma symptoms were assessed in each session. At baseline and one month after completing the sessions, participants filled out assessments of depression, PTSD, and physical illness symptoms. As predicted, participants with higher resting RSA who wrote about a trauma had greater reductions in symptoms of PTSD at post-writing session three. Higher resting RSA was also related to lower levels of resting heart rate and skin conductance. No relation was found between RSA and baseline symptoms of depression, PTSD, and physical illness. This study also found no effect of resting RSA as a moderator of response training outcomes.

PROBLEM STATEMENT: Growing evidence indicates that psychological stress contributes to cardiovascular diseases through complex neuroendocrine mechanisms. Psychological stress leads to several physiological responses including increased heart rate (HR) and blood pressure (BP) as well as decreased heart rate variability (HRV) through alterations in the autonomic nervous system (ANS), specifically increased sympathetic nervous system (SNS) activity and decreased parasympathetic nervous system (PNS) activity. Meditation is thought to induce an innate relaxation response leading to reduced psychological stress. Findings from past studies have provided inconclusive evidence regarding the direction and strength of relationships among stress, BP, HRV, and meditation practice. PROCEDURES: A cross-sectional descriptive-correlational design was used to examine relationships among perceived stress, BP, HRV and meditation practice in meditators. A convenience sample of 71 meditators at two meditation centers in the southeast United States was used. Sample size was based on a power analysis. Each participant was asked to complete meditation, perceived stress, and demographic questionnaires. Participants' BP was measured before meditation and HRV was recorded during a 30 minute meditation session. Finally, BP was recorded after meditation. RESULTS: Participants were predominantly female (55%), Caucasian/white (94%), and Buddhist (76%), with 93% having at least college graduate. Most participants practiced soto zen or vipassana meditation (45% and 30%, respectively). The average length of total meditation practice was 103.66 months. Participants practiced meditation an average of once a day for 4 days a week with mean session duration of 34 minutes. Most participants had a low level of perceived stress and normal HRV. There was a statistically significant decrease in mean systolic BP after meditation (t = 5.31, p CONCLUSIONS: The results suggested meditators had low levels of perceived stress and that meditation had an effect on systolic BP and perceived current stress. Future research needs to include longitudinal studies to elucidate the cumulative effects of consistent meditation practice on psychological and physiological outcomes.

Over the last 20 years, much work has focused on computer-aided clinical decision support systems due to a rapid increase in the need for management and processing of medical knowledge. Among all fields of medicine, trauma care has the highest need for proper information management due to the high prevalence of complex, life-threatening injuries. In particular, hemorrhage, which is encountered in most traumatic injuries, is a dominant factor in determining survival in both civilian and military settings. This complication can be better managed using a more in-depth analysis of patient information. Trauma physicians must make precise and rapid decisions, while considering a large number of patient variables and dealing with stressful environments. The ability of a computer-aided decision making system to rapidly analyze a patient’s condition can enable physicians to make more accurate decisions and thereby significantly improve the quality of care provided to patients. The first part of this study is focused on classification of highly complex databases using a hierarchical method which combines two complementary techniques: logistic regression and machine learning. This method, hereafter referred to as Classification Using Significant Features (CUSF), includes a statistical process to select the most significant variables from the correlated database. Then a machine learning algorithm is used to identify the data into classes using only the significant variables. As the main application addressed by CUSF, a set of computer-assisted rule-based trauma decision making system are designed. Computer aided decision-making system not only provides vital assistance for physicians in making fast and accurate decisions, proposed decisions are supported by transparent reasoning, but also can confirm a physicians’ current knowledge, enabling them to detect complex patterns and information which may reveal new knowledge not easily visible to the human eyes. The second part of this study proposes an algorithm based on a set of novel wavelet features to analyze physiological signals, such as Electrocardiograms (ECGs) that can provide invaluable information typically invisible to human eyes. These wavelet-based method, hereafter referred to as Signal Analysis Based on Wavelet-Extracted Features (SABWEF), extracts information that can be used to detect and analyze complex patterns that other methods such as Fourier cannot deal with. For instance, SABWEF can evaluate the severity of hemorrhagic shock (HS) from ECG, while the traditional technique of applying power spectrum density (PSD) and fractal dimension (FD) cannot distinguish between the ECG patterns of patients with HS (i.e. blood loss), and those of subjects undergoing physical activity. In this study, as the main application of SABWEF, ECG is analyzed to distinguish between HS and physical activity, and show that SABWEF can be used in both civilian and military settings to detect HS and its extent. This is the first reported use of an ECG analysis method to classify blood volume loss. SABWEF has the capability to rapidly determine the degree of volume loss from hemorrhage, providing the chance for more rapid remote triage and decision making.