The effect of BMI on resting heart rate, heart rate ... - MAFIADOC.COM

THE UNIVERSITY OF WINCHESTER

FACULTY OF BUSINESS, LAW AND SPORT

SPORTS SCIENCE DISSERTATION

The effect of BMI on resting heart rate, heart rate variability pre- and post- exercise, and on heart rate regulation post- exercise of 75%HRmax in males

Submitted in partial fulfilment of the requirements for the degree of BSc (Hons) Sports Science of The University of Winchester

Submission date: 4th April 2014

Andrew Mitchelmore (1132194)

The effect of BMI on heart rate variability pre- and post- exercise, and on heart rate regulation after exercise to 75%HRmax in males Abstract Obesity has reached epidemic proportions in western society. An increase in adipose tissue impairs a range of physical functions, including the cardiovascular and nervous systems. The autonomic nervous system controls both heart rate variability (HRV) and heart rate regulation (HRR) post-exercise. Previous research suggests that a lower HRV is indicative of bad cardiac health, but there has been little investigation of the effect of exercise on HRV in different body mass index (BMI) groups. This study investigated the effect of increased BMI on cardiovascular response to exercise. Twenty-four males (mean age = 21.5 σ = 1.84, mean height = 177.1cm; σ 5.4, normal mean weight: 70.12kg (n = 10), σ 7.6, overweight mean weight = 80.0kg (n = 7), σ 4.8, obese mean weight: 93.1kg (n = 7), σ 11.4) had resting heart rate (RHR) and HRV in standard deviation of normal to normal intervals (SDNN) recorded using a 3 lead ECG of 40 heart beats. Participants completed an upper body ergometer protocol to 75%HRmax. The time taken to reach this was noted. A second ECG recording of 40 beats was then taken. Time to HRR was recorded and a third ECG was taken. A one way ANOVA reported no significant difference between groups in RHR (p = .054) with a nonsignificant weak-moderate positive correlation found (rp = .313) between BMI and RHR after a Pearson’s rank correlation coefficient. There was no significant difference (p = .688) in time of exercise to 75% HRmax between groups. No significant difference was found between groups in resting HRV (p = .094), HRV immediately post-exercise (p = .235) or HRV postHRR (p = .276). A non-significant weak-moderate negative correlation was found between BMI and resting HRV (rp = -.383) and BMI and HRV after HRR (rp = -.255). A two way ANOVA showed an overall significant difference (p = .025) between HRV pre-exercise and immediately post-exercise and no other significant differences between time intervals. Means demonstrated that after HRR, HRV had only regulated to an average of 88% of its preexercise levels. Although no significant difference was found between groups in time to HRR (p = .690), a weak-moderate negative correlation (rp = -0.48) suggests there is a trend between BMI and time to HRR, implying possible suppression of the regulation of heart rate post-exercise in the overweight and obese BMI groups. The study concluded that a larger sample size may offer significant figures, as a trend seems to exist in many of the variables investigated. The findings may have use to reinforce the importance of a normal BMI in maintaining efficient and functional vagal modulation in both clinical and sporting settings. 1 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

Table of Contents Acknowledgements .................................................................................................................. 4 Key Abbreviations ................................................................................................................... 5 Introduction .............................................................................................................................. 5 Literature Review .................................................................................................................... 7 Research Into Obesity......................................................................................................... 7 Relationship between Cardiovascular and Nervous Systems ............................................. 9 Heart Rate Variability....................................................................................................... 10 Heart Rate Regulation ...................................................................................................... 12 Hypotheses ....................................................................................................................... 14 Research Design and Methodology ...................................................................................... 15 Participants ....................................................................................................................... 15 Research Design and Procedure ....................................................................................... 15 Data Analysis.................................................................................................................... 17 Results ..................................................................................................................................... 18 Resting Heart Rate ............................................................................................................ 18 Heart Rate Variability....................................................................................................... 18 Time to 75%HRmax ........................................................................................................... 20 Heart Rate Regulation ...................................................................................................... 21 Discussion................................................................................................................................ 21 Resting Heart Rate ............................................................................................................ 21 Heart Rate Variability at Rest Between Groups ............................................................... 21 Heart Rate Variability Immediately Post-Exercise Between Groups............................... 22 Heart Rate Variability After Heart Rate Regulation Between Groups............................. 23 Heart Rate Variability Between Time Intervals ............................................................... 23 2 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

Time to 75% HRmax .......................................................................................................... 24 Heart Rate Regulation ...................................................................................................... 25 General Observations of the Study ................................................................................... 26 Conclusion .............................................................................................................................. 27 References ............................................................................................................................... 30 Appendices .............................................................................................................................. 51 Appendix 1: Information Sheet ........................................................................................ 51 Appendix 2: Consent Form .............................................................................................. 54 Appendix 2: PAR-Q ......................................................................................................... 55

List of Figures and Tables Figure 1: Correlation Between BMI and Resting Heart Rate ................................................. 18 Table 1: Means and Standard Deviations of HRV in the Three Conditions ........................... 19 Figure 2: Correlation Between BMI and HRV Pre-Exercise ................................................. 19 Figure 3: Correlation Between BMI and HRV Immediately Post-Exercise ........................... 20 Figure 4: Correlation Between BMI and HRV Post-HRR ..................................................... 20

3 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

Acknowledgements I would like to thank my dissertation tutor, Helen Ryan, for her continued support, patience and guidance over the course of this dissertation process, and James Wright for originally sparking my interest in the area of heart rate variability. Thanks must also go to each of the participants who took part in the data collection process, as this dissertation could not have happened without them. I am grateful to my Mother and Father for the backing they have offered me through the undergraduate process and also appreciate the support of my close friends who have motivated me to keep strictly to my deadlines. I would finally like to thank Sophie for believing in me from the first to the last day of my undergraduate degree and consistently helping me to always see the good to come out of even the hardest days.


Key Abbreviations: ANOVA – Analysis of Variance BMI – Body Mass Index HRR – Heart Rate Regulation HRV – Heart Rate Variability RHR – Resting Heart Rate SDNN – Standard Deviation of Normal to Normal Intervals

Introduction Increased numbers of overweight and obese people in western society have been observed for a significant amount of time (Gonzalez et al., 2009; Dutton & McLaren, 2011; Garcia et al., 2013; Neovius et al., 2013; NHS, 2013). Obesity is defined simply as an excess of body fat (Villareal et al., 2005) and 26% of adults aged 16 or over in England were classified as obese in 2010 (BMI >30 kg·m2). Thirty-one percent of boys and 29% of girls aged 2-15 (NHS, 2012) are also classified as obese in the UK though some reports indicate a slowing of the trend (Flegal et al., 2012; Ogden et al., 2012; Voss et al.¸ 2013). Obesity is a medical condition which is considered to be implicated in a wide array of medical conditions which result in premature mortality and morbidity (Masters et al., 2013; Zheng et al., 2013). This includes an increased risk of cardiovascular disease (Hubert et al., 1983; Grundy, 2004; Kinalska et al., 2006; Van Gaal et al., 2006; Sallis et al., 2012) which is now the leading cause of death worldwide (Lin et al., 2013) as well as other conditions such as strokes, diabetes and sleep apnoea (Reilly & Kelly, 2011). This also places extra stress on health services with treatment of preventable diseases related to obesity, predicted to cost the USA between $44-66 billion per year and the UK an extra £1.9-2.2 billion per year by 2030 (Wang et al., 2011). The heart has been frequently identified as an organ which suffers from excess body fat due to increased left ventricular wall thickness. This results in concentric hypertrophy which, when paired with a non-increasing radius of the chamber itself, can lead to impairments to ventricular filling and diastolic function. This in turn increases the risk of heart failure (Lorell & Carabello, 2000). Obesity also causes an increase in low-density lipoproteins and triglycerides (Gruson et al., 2010). This increase is directly correlated with a higher risk of cardiovascular disease as atherosclerosis is likely due to hypercholesterolaemia (Kathiresan et 5 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

al., 2008; Chan et al., 2010). As a result of this wide range of research, it is now globally accepted that the cardiovascular system is severely damaged by abnormally large amounts of body fat in an individual. It has been suggested that obesity causes abnormalities in the sympathetic and parasympathetic nervous system (SNS and PNS) (Peterson et al., 1988; Dalila et al., 1999; Amano et al., 2001; Skrapari et al., 2007; Lazarova et al., 2009). More specifically, it has been reported that lower sympathetic activity due to the increased body fat leads to decreased energy expenditure, exacerbating the situation in terms of reducing body mass through exercise (Biaggioni, 2008). Autonomic imbalance between the SNS and the PNS has been linked to the risk of cardiac arrhythmia and sudden death (Rajalakshmi et al., 2012). Damage to the autonomic nervous system directly impacts the functional capacity of the cardiovascular system; effective vagal activity is essential to stimulate the sino-atrial (SA) node of the heart to initiate contraction of the muscle. The relationship between the nervous system and the cardiovascular system has been investigated, using heart rate regulation as the standard measure (Shetler et al., 2001; McLaughlin et al., 2003; Dimpka, 2009; Paradiso et al., 2013) and this is reported to be a reliable index of cardiovascular fitness (Dimpka, 2009). Heart rate regulation occurs immediately as exercise ceases as the medulla oblongata inhibits the firing rate of the SA node through the PNS, and through the weakening of the influence of the SNS on heart rate (Niewiadomski et al., 2007). It has been reported that a slow rate of heart rate regulation post-exercise is associated with obesity, increased cholesterol, and insulin resistance (Dimpka, 2009) due to the imbalance of the SNS and PNS in this population. In the overweight and obese population exaggerated physiological responses to exercise such as increased heart rate and altered blood pressure occur. These result in the chemoreceptors and baroreceptors monitoring blood chemistry and blood pressure to restrict the regulation of heart rate being unable to function as quickly as they must always ensure that enough blood and oxygen is being circulated to ensure the systems are maintained. The field of heart rate variability (HRV) is a significant focus for current research (Kamath et al., 2013). During normal sinus rhythm, the amount of time between heart beats varies (Tavernier & Jeanne, 2014) and the study of R-R intervals has been suggested to be able to predict levels of cardiac health (Thayer et al., 2010). It is generally accepted that a larger variation in time between beats is an indicator of good cardiac health (Greenberg et al., 6 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

2010), and that vagal activity is responsible for this variation (De Couck & Gidron, 2013). However, decreased vagal activity has been found to exist in obese individuals and has been linked to an increased risk of premature and sudden death (Dangardt et al., 2011. HRV has been widely examined in populations with a history of diagnosed heart disease resulting in surgery, or in other clinical conditions such as schizophrenia and Parkinson’s (Piotrowicz et al., 2009; Rachow et al., 2011; Uhlir et al., 2012; Currie et al., 2013; Rozen et al., 2013; Salamin et al., 2013). It has also been studied in healthy participants of a normal body mass but different age groups and training levels (Nicolini et al., 2012; Edmonds et al., 2013; Henriquez et al., 2013; Meeuse et al., 2013; Plews et al., 2013; Shen & Wen, 2013). There is, however, a distinct lack of research on HRV in younger obese people before any cardiovascular disease or other medical conditions have been recognised or treated. There has been sparse research incorporating both heart rate regulation and heart rate variability before and after exercise in overweight and obese individuals without a history of cardiac episodes, indicating a need for new studies. This study will therefore aim to investigate HRV before and after exercise in different BMI groups. It will also aim to examine heart rate regulation rates between the BMI groups in male participants who have not suffered a cardiac episode. It is possible that HRV may be linked directly to BMI, without being the result of pre-existing ill health, as may have been the case in previous work in the area. If results prove to be significant, this study could improve knowledge of identifying the mechanisms which indicate elevated risk factors of cardiovascular ill health. Literature Review Research into Obesity: Obesity occurs due to a large imbalance between energy intake and energy expenditure and presents as an increase in the level of adipose tissue (Uhbleck, 2009). Increased levels of body mass have been linked to a large spectrum of conditions including pulmonary compromise, diabetes, hypertension, coronary heart disease, venous thrombosis, degenerative joint disease and depression (Nejat et al., 2009). Adipose tissue stores energy in the body through holding lipids (Eroschenko & di Fiore, 2012) but an excess of these stores has strong negative implications for health. The prevalence of obesity means a full understanding of the mechanisms which are compromised by excess body fat is necessary, and knowledge of whether this damage is reversible in the current population is paramount. 7 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

Considerable research has been undertaken into the physiological consequences of increased adipose tissue and obesity, investigating body systems and populations – both healthy and diseased. This includes damage to the respiratory system (Behazin et al., 2008; Malli et al., 2010), digestive system (Johnson, 2007; Cheung et al., 2008), integumentary system (Chintamani, 2011), muscular system (Mayer et al., 2012; Lerner et al., 2013) and the immune system (de Heredia et al., 2012; Patel et al., 2013) whilst a malfunctioning endocrine system has been hypothesised to be a possible factor in the development of obesity itself (Newbold et al., 2008). Both the central nervous system (CNS) and PNS have also been investigated in previous literature (Skrapari et al., 2007; Bruce-Keller et al., 2009; Lazarova et al., 2009) with conclusions suggesting that increased levels of adipose tissue cause suppression of the nervous system. A huge amount of research has focused on the negative effects of obesity on the cardiovascular system and related conditions, (Kinalska et al., 2006; Hou & Luo, 2011; Northcott et al., 2012; Aballay et al., 2013; Miller, 2013) and heightened risk of associated clinical conditions. Only a few modern, innovative concepts, such as the hypothesis that central obesity protects patients with coronary heart disease from cardiovascular episodes, contradict general consensus (Bechlioulis et al., 2013). Most past research has been conducted using participants with a history of obesity-related diseases (Kelly et al., 2011; Sjoberg et al., 2011; Castello-Simoes et al., 2013; Currie et al., 2013; Salamin et al., 2013; Yilmaz et al., 2013). Far less investigation has been undertaken using obese individuals who have not yet experienced symptoms of clinical conditions as participants (Prado et al., 2010; Russoniello et al., 2010). This restricts the possibility for direct causality of symptoms to be attributed to the increased adipose tissue levels rather than an underlying medical condition. The quality of research into the effects of obesity on the human body is variable. Studies with smaller sample sizes (Figueroa et al., 2008; Haines & Kim., 2013; Mehta & Shortz, 2014) possess less generalisability than could have been attained with a larger participant group, and are less likely to find significant differences between groups,. Other research has recruited a unisex participant group (Skrapari et al., 2007; Jackson et al., 2010; Kawai et al., 2011; Shin et al., 2013), so results cannot be generalised, due to the immense physiological differences between the sexes, such as levels of muscle mass, levels of fat-free mass, voluntary activation patterns, left ventricular wall thickness and rates of thickening, as well as levels of fat oxidiation during intense exercise (Yasuda et al., 2006; Baechle & Earle, 2008; Billant & Bishop, 2009; Niemann et al., 2011). Despite the weaknesses of some studies, the 8 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

overwhelming consensus from the wide array of populations and clinical states report the negative consequences of being overweight or obese. In the literature, studies which have taken place with overweight or obese participants used treadmills (Aslani & Kheirkhah, 2011; Castello et al., 2011; Ordonez et al., 2013) or cycle ergometers (Lafortuna et al., 2008; Whyte et al., 2010; Lafortuna et al., 2013). It is possible that treadmills or cycle ergometers increase safety risks to participants with an increased BMI during any data collection. In terms of practical considerations, the use of upper body ergometers seems to be a logical choice as these have been shown to elicit physiological changes (Bateni et al., 2013; Elmer et al., 2013) without exposing the overweight or obese population to unnecessary safety risks. Relationship between the Nervous and Cardiovascular Systems: Although a wide range of literature has investigated the relationship between obesity and cardiovascular damage, and between obesity and damage to the nervous system, limited research in recent times has investigated the link between the two systems. Vagal control of heart rate regulation and heart rate variability in participants with varying BMI has been investigated but some of the research still cited as fact in recent literature (Dimpka, 2009) has become dated and requires re-investigation (Cole et al., 1999; Cole et al., 2000; Nishime et al., 2000; Terziotti et al., 2001). It is now generally accepted that increased adipose tissue results in a suppression of both Parasympathetic nervous system and sympathetic nervous system, leading to an increased risk of lower renal and endothelial function (Rahmouni, 2007; Lazarova et al., 2009; Lambert et al., 2010). The finding that the suppression of SNS activation reduces energy expenditure (Biaggioni, 2008) asks questions as to whether this impaired SNS could be responsible for increased body-mass or vice-versa. During a literature review, no studies offering definitive conclusions were found answering this question. It could be argued that dysautonomias such as Guillain-Barré syndrome, Lyme disease or vasovagal syncope may be partially responsible for the reductions in energy expenditure which may lead to weight gain in humans. This is an area of research where current conclusions are blurred and further investigation is therefore merited.


Heart Rate Variability: Research into heart rate variability has become more extensive over the last 20 years. The concept of heart rate variability is not a new one by any means (Ernst, 2013) but has become a more significant area of investigation in recent times, with only 269 studies occurring between 1981 and 1990, 2574 between 1991 and 2000, and 5115 between 2001 and 2010 (Kamath et al., 2013). The research suggests that heart rate variability may be a factor directly indicative of the condition of an individual’s heart (Mikulski et al., 2013). Changes in HRV are reported to be caused by suppressed vagal activity and increased levels of serum high sensitivity C-reactive protein concentration (Araujo et al., 2006; Dangardt et al., 2011). Other research suggests that heart rate variability is inversely correlated with inflammatory markers in both healthy individuals and those who are cardiovascular compromised (Haensel, 2008; Lauer, 2009; Hart, 2013), with a standard deviation of normal to normal beats (SDNN) of 100ms may be an indicator of low risk of heart failure (Greenberg et al, 2010). Heart rate variation is a powerful predictor of cardiovascular mortality in adults (Nishime et al., 2000) and can be measured successfully using data representing SDNN (Szajzel, 2004). R-R variability has been used in a wide range of previous studies to determine functionality of the autonomic nervous system and its link with the cardiovascular system (Karavirta et al., 2008; Currie et al., 2009; Hu et al., 2009; Barrera-Ramirez et al., 2013; Henriquez et al., 2013). The measure quantifies fluctuations in atrio-ventricular conduction; a reduced HRV may imply a suppression of the autonomic nervous system. The increased frequency of respiration during exercise may affect HRV due to the reduction in HRV during inhalation as a part of respiratory sinus arrhythmia (Greenberg et al., 2010). A literature review determined the appropriate choice was R-R intervals to be used as the measure of HRV in this study to investigate the functionality of the autonomic nervous system and determine cardiac health. Previous research into HRV in populations in different BMI groups is variable in quality. Low sample sizes have limited the usefulness of research in some cases (Shibao et al., 2007; Figueroa et al., 2008; Nault et al., 2010). This is due to the difficulty in enrolling participants from higher BMI groups posing a challenge in itself. Research with larger sample sizes is more useful and generalisable. Araujo et al. (2006) and Uusitalo et al. (2007) both used over 400 participants, whereas Dietrich et al (2008) incorporated 1712 individuals into data collection. These studies integrated both males and females to allow for more comparisons to 10 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

be made and greater generalisations to be drawn from conclusions. Most studies found only investigated either male or female HRV, the majority relating to males only (Stejskal, 2007; Uusitalo et al., 2007; Mikulski et al., 2013) but some only recruited female participants (Earnest et al., 2008; Figueroa et al., 2008). This is a particularly relevant issue as recent research has reported differences in autonomic activity between the sexes. Both Lufti & Sukkar (2012) and Saleem et al. (2012) concluded that HRV is generally lower in females than males due to autonomic nervous system differences. Investigations into autonomic control in children are becoming more frequent (Gamelin et al., 2009; Pascoal et al., 2009) and have suggested that HRV is less likely to be improved in the long term through exercise than is the case in adults (Earnest et al., 2008; Routledge et al., 2010). There is little literature investigating the long-term effects of exercise on HRV in healthy populations, and the effect different types and intensities of exercise have on HRV. Sample groups in previous obesity research have been selected for a wide range of clinical conditions including diabetes, metabolic syndrome, myofibralgia, chronic obstructive pulmonary disease (COPD) and coronary heart disease (Figueroa et al., 2008; Frasure-Smith et al., 2009; Koskinen et al., 2009; Carvalho et al., 2011; Sjoberg et al., 2011). These studies are obviously relatable to others suffering from the same conditions, but not to the majority of the population. It could be argued that metabolic syndrome is an extremely frequent byproduct of obesity and is therefore more relevant to “healthy” obese populations than the others, but overall these studies lack widespread usefulness to the general population. Studies such as that conducted by Nault et al. (2010) investigating the HRV of morbidly obese patients pre- and post- surgery offer answers to a wider population, although the sample in this study was too small to be fully useful. As with the research investigating the general effects of obesity on the human body, the research suggesting elevated SDNN being a precursor of good cardiac health and decreased SDNN representing a suppressed nervous system were unanimous enough to compensate for the issues surrounding some research. In terms of protocol, the majority of research seemed to use electrocardiogram (ECG) technology to calculate SDNN (Uusitalo et al., 2007; Nault et al., 2010; Saleem et al., 2012), rather than Polar technology. Polar monitors were used in some research (Davy et al., 1997; Gutin et al., 2000; Gamelin et al., 2007), reducing the validity of the conclusions drawn as these provide inaccurate HRV data compared to directly analysing ECG traces, as ECG is the “gold standard” of HRV collection (Nunan et al., 2008; Porto & Junqueira, 2009; Engstrom et al., 2012). Differences in the data provided by ECG and Polar monitors have been noted, 11 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

but not satisfactorily explained (Engstrom et al., 2012). An ECG offers the opportunity for a clinician or researcher to visually observe HRV artefacts such as missed beats and extra beats, whilst a Polar monitor only displays a numerical output. The visual aspect of the ECG allows a researcher to note when errors may have occurred so a repeat measurement can occur, whereas Polar monitors do not allow this. Although some research had investigated the long term effects of exercise on HRV, there were no studies found investigating the immediate consequences of exercise. This means that the subject of this dissertation offers new insight into this area of physiology and possible important implications for the overweight and obese populations who have not yet suffered from clinical conditions. Heart Rate Regulation: Increased levels of body fat have been found to impede HRR (Campos et al., 2012) due to suppression of the autonomic nervous system. There is supporting evidence suggesting that HRR is related to a lower resting heart rate; something directly correlated with lower amounts of adipose tissue in the body (Cooney et al., 2010; Huang & Lee, 2012). Weight loss has been suggested to improve HRR through the increasing of vagal tone in the human body (Brinkworth et al., 2006). This finding is compatible with other data suggesting that training programmes for both healthy and cardiovascular compromised participants can improve HRR, even making it superior to sedentary, un-diseased populations (Henriquez, 2009; Piotrowicz et al., 2009; Esco & Williford, 2011). Heart rate regulation post-exercise has been described as a powerful indicator of cardiovascular and all-cause mortality (Cole et al., 2000; Piotrowicz et al., 2009; Kelly et al., 2011). However, most previous research has observed the rate of regulation (1 min, 2 min, 3 min measurements) rather than the final time when homeostasis has been reached postexercise (Barack et al., 2011; Kelly et al., 2011; Currie et al., 2013; Henriquez et al., 2013), which would also be a useful measurement in assessing cardiac health. There are criticisms of recent work which should be highlighted when considering the validity and usefulness of conclusions drawn from these studies. The standard of sampling has varied greatly from study to study. This primary literature review noted unisex sampling to be prominent with either male (Sugawara et al., 2001; Brinkworth et al., 2006; Kelly et al., 2011; Huang & Lee, 2012; Henriquez, 2013) or female (Du et al., 2005; Caroll et al., 2012) 12 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

dominance. These papers lack generalisability, as females are reported to have higher resting heart rates than males (Lufti & Sukkar, 2012), and therefore differing rates of heart rate regulation (Huang & Lee, 2012). Research investigating HRR has tended to focus on clinical populations before and after a health-based intervention. Predominant conditions have included coronary heart disease, spinal cord injuries and psychological issues (Piotrowicz et al., 2009; Myers et al., 2010; Kelly et al., 2011; Gordon et al., 2012; Huang & Lee., 2012; Currie et al., 2013; Yilmaz et al., 2013). Whilst most of these studies have only required sub-maximal effort from participants, Kelly et al. (2011) required volitional exhaustion to be reached by participants with coronary heart disease. Whether or not this is a viable and safe request is debatable; relatively healthier obese participants have usually been asked to perform aerobically between 50% and 85% of HRmax (Vogelsang et al., 2008; Chaudhary et al., 2010; Habibzadeh, 2010; McNeilly et al., 2012). When healthy participants have been used (Mellis et al., 2014), comparisons between weight groups have not been made. Research protocols of a sub-maximal nature investigating HRR have been inconsistent at best. In terms of calculating the heart rate at which to begin measuring HRR, knowledge of HRmax is required. Differing calculations of this have been suggested, including 220 – age (McHugh et al., 2012) and 202 – (0.55 x age) (Whyte et al., 2008). Papers using 220 – age such as that by McHugh et al. (2012) may overestimate HRmax (Gulati et al., 2010), reducing the validity of conclusions drawn in studies using this measure. Some research has not specified how HRmax or % HRmax was calculated (Barack et al., 2011), leaving unanswered questions in terms of replicability. A majority of studies seem to have calculated HRmax purely on the basis of The Borg Scale of Perceived Exertion (RPE) feedback (Carroll et al., 2012; Figoni et al., 2012) which is a purely subjective form of feedback and may differ from participant to participant. The use of the Borg scale has been reported to be a reliable scale (Psycharakis, 2011; Scott et al., 2013) but the fact that studies have used this as well as the variety of HRmax calculations suggests a lack of standardisation in the literature. It is worth noting that psychological conditions such as depression and stress may affect HRR as well as physiological issues (Gordon et al., 2011; Gordon et al., 2012). These studies incorporated large, mixed sex sample groups (>150 people) and offered conclusions as a result. The findings in these papers suggests it may be difficult to draw direct causality in this dissertation as no psychological assessment was undertaken on participants. 13 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

In summary, the overall review of literature focusing on HRR reflected general agreement in previous research that heart rate regulation is suppressed due to decreased vagal activity in populations with diagnosed diseases both related and unrelated to increased levels of adipose tissue. An absence of research was apparent in terms of direct comparisons between healthy participants with varying BMI. Hypotheses: H(1) = There will be a significant difference in heart rate variability pre-exercise between the three examined BMI groups. H(0) = There will be no significant difference in heart rate variability pre-exercise between the three examined BMI groups. H(2) = There will be a significant difference in heart rate variability immediately post-exercise between the three examined BMI groups. H(0) = There will be no significant difference in heart rate variability immediately postexercise between the three examined BMI groups. H(3) = There will be a significant difference in heart rate variability after HRR between the three examined BMI groups. H(0) =There will be no significant difference in heart rate variability after HRR between the three examined BMI groups. H(4) = There will be a significant difference in heart rate regulation rates between the three examined BMI groups. H(0) = There will be no significant difference in heart rate regulation rates between the three examined BMI groups.


Research Design and Methodology Participants: 24 males (mean age = 21.5 σ = 1.84, mean height = 177.1cm; σ 5.4, normal mean weight: 70.12kg, σ 7.6, overweight mean weight = 80.0kg, σ 4.8, obese mean weight: 93.1kg, σ 11.4) were selected using a criterion sampling method. Each of the participants reported that they lived at least semi-active lifestyle, but none played professional sport. The number of participants selected allowed for an even distribution between three BMI groups and was a sample size deemed achievable with the time constraints in place during the creation of the study. The criterion sampling method made it easier to find participants and ensure the experimental groups all contained an equal number of participants, thus allowing for more reliable data analysis. Participant age was also matched to improve the likelihood that the altered BMI was causative of any differences found in the functionality of the cardiovascular system rather than age. All participants were over the age of 18 to avoid complexities of BMI calculation for younger participants due to their varying speeds of development in terms of growth and different stages of physical maturity. These methods are designed to remove as many possible confounding factors and obtain the best results possible from a small sample. Research Design and Procedure: The data collection was designed to investigate heart rate regulation and HRV pre- and postexercise in participants with varying BMI scores. BMI was chosen as the categorising factor in the participants undertaking the study. It was used as it is the most readily available method of weight classification (Reilly 2010) and allows for direct comparisons to previous research which has used the measure. A wide array of previous research has used BMI as the indicator of increased adipose tissue, categorising participants as normal, overweight or obese. (Lazzer et al., 2009; Chaudhary et al., 2010; Habibzadeh, 2010; Briethaupt et al., 2012; Felix & West, 2013; Ordonez et al., 2013; Baynard et al., 2014). BMI was calculated using the formula: BMI = mass in kg/height in m2. A BMI of 20-25 represented the normal group (10 participants), 25-30 the overweight group (7 participants), and >30 the obese group (7 participants).


A pilot test was run before full data collection to ensure the protocol ran fluidly and collected the type of data required, to confirm full competence and comfort using the equipment. After the approved consent form and PAR-Q forms were signed, the pilot study was successfully run on one male aged 22 with a body mass of 79kg and a height of 1.80m. Before any data collection occurred, participants received an information sheet and completed a university-endorsed informed consent form and PAR-Q to ensure that they had been fully briefed and no pre-existing health conditions presented a risk to either the participant or researcher during data collection (Appendices 1, 2 and 3) Height and mass were recorded (Leicester Height Measure, Invicta Plastics Limited, Germany; Quadra 808, Seca, Germany). After a 5 minute seated period to ensure the participant was at resting levels, a baseline heart rate was recorded (RS800CX, Polar, Finand). Blood pressure was also recorded (MX2 Basic, Omron, United Kingdom) to ensure participants began the data collection with a normal blood pressure. From the age provided by each participant, maximum heart rate was calculated using the formula 202- (0.55 x age) as suggested for males by Whyte et al. (2008). This formula differs from the commonly used calculation of 220 – age but it has been suggested that this latter formula overestimates HRmax (Gulati et al., 2010). A 3 lead ECG was used to measure an initial baseline trace using the Lab Tutor software (PowerLab, AD Instruments, United Kingdom). An ECG trace was chosen to measure HRV for best reliability of results compared to alternate methods. Polar heart rate monitors have the capability of recording HRV but have been suggested to offer unreliable results compared to ECG traces (Nunan et al., 2008), both underestimating and overestimating different indices of HRV (Porto & Junqueira, 2009). As a result, ECG traces were used to ascertain R-R values. After these baseline measurements were recorded, participants began the exercise using an upper body ergometer (Rehab Trainer 881E, Monark, Sweden). The resistance was set at 50 W – a level suggested to result in significant physiological responses during training (Kang et al., 1999) although there is a lack of recent research to confirm this. Participants continued to exercise on the ergometer until their heart rate reached 75% of predicted maximum. This value is suggested to be safe for obese individuals to reach whilst being high enough for regulation values to be noticeable, as demonstrated by previous studies 16 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

using heart rates between 50-85% of projected max with obese participants, to ensure that the health of the participants in this study was not at risk through over-exertion (Vogelsang et al., 2008; Adamo et al., 2010; Chaudhary et al.¸ 2010; Habibzadeh, 2010; Habibzadeh & Daneshmandi, 2010; McNeilly et al., 2012). Once the target heart rate was reached, the time taken to reach it was noted, the timer was reset to measure time-to-homeostasis and a second ECG reading was immediately taken. The participant remained in a seated position whilst their body recovered. Once heart rate had regulated to pre-exercise levels, the time taken for this to happen was recorded and a third and final ECG trace was immediately taken. The participant was not offered water for rehydration until this time as hydration before this point has been reported to enhance heart rate variability regulation and might affect the data collected (de Oliveira et al., 2011). The participants were then shown their initial results once they had been calculated. The standardisation of protocol ensured the results drawn from the data were as reliable as possible. The investigation of more than one variable throughout data collection allowed for data triangulation to take place during data analysis. This improved the validity of the conclusions drawn from the research experiment (Olsen, 2004). All participants had the right to withdraw at any time. The full protocol received ethical clearance from the University of Winchester and was in accordance with BASES guidelines (BASES, 2006). Anonymity of the participants was preserved throughout the data collection process, and verbal communication was maintained during testing to ensure the good health of each participant was monitored. This communication also ensured that participants were relaxed throughout, as it has been suggested that feelings of time pressure, emotional strain and elevated state anxiety can alter HRV (Nickel & Nachreiner, 2003; Jonsson, 2007), therefore reducing the reliability of any data recorded. Data Analysis: R-R intervals were calculated using the Lab Tutor software and the SDNN were entered into SPSS (SPSS Statistics for Windows, version 21.0. Armonk, N.Y.: IMB Corp) along with baseline heart rates, time to 75%max and HRR times. Tests for normality were run on the data before a one way ANOVA, a two way ANOVA and a Pearson product-moment correlation test were run to test for significance between the groups and time intervals. The significance value was set for p as .05. 17 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

The raw data was also examined to determine whether HRV had returned to its pre-exercise levels by the time heart rate had regulated to homeostatic levels, allowing for conclusions to be drawn as to whether HRV regulates to initial levels at the same rate as heart rate itself. Effect sizes were also calculated using the equation: √Sum of squares/Sum of errors. Results Resting Heart Rate: All data was found to be normally distributed. There was no significant difference (F

(2)

=

3.368, p = .054) in resting HR between groups but an effect size of 0.49 was discovered. There was a non-significant weak-moderate positive correlation (rp = .313, p = .146) between BMI and resting heart rate. Data is displayed in Figure 1.

Figure 1: Correlation between BMI and Resting Heart Rate

Heart Rate Variability: A one way ANOVA was run and there was no significant difference (F (2) = 2.653, p = .094) in resting HRV between groups, with an effect size of 0.45. A non-significant weak-moderate negative correlation (rp = -.383, p = .065) was found between BMI and resting HRV. No significant difference was found (F (2) = 1.551, p = .235) in HRV immediately post-exercise between groups, with an effect size of 0.36, and there was no correlation (rp = -.143, p = .504) 18 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

between BMI and HRV at this time-point. There was no significant difference (F (2) = 1.371, p = .276 in HRV between groups after HRR had occurred with an effect size of 0.34. There was a non-significant weak-moderate negative correlation (rp = -.255, p = .229) between BMI and HRV after HRR had occurred. A two way ANOVA reported a significant difference (F

(2, 3)

= 14.389, p = .047) in HRV in

all groups between time intervals (pre-exercise, post-exercise and post-HRR). Post-hoc tests revealed a significant difference (p = .025) between HRV pre-exercise and immediately postexercise. There was no significant difference in HRV between pre-exercise and post-HRR (p = .154) or post-exercise and post-HRR HRV (p = .127). Means and standard deviations are displayed in Table 1 and data is displayed in chart form in Figures 2, 3 and 4.

Table 1: Means and Standard Deviations of HRV (SDNN) in the Three Conditions Mean HRV PreExercise (ms)

Mean HRV PostExercise (ms)

Mean HRV PostHRR (ms)

Normal BMI

78.1, σ 32.3

52.5, σ 35.7

62, σ 30.2

Overweight BMI

57.3, σ 24.4

42, σ 29.9

55, σ 32.5

Obese BMI

50.4, σ 14.3

27.3, σ 12.2

39.9, σ 13.4

Figure 2: Correlation between BMI and HRV (SDNN) Pre-Exercise


Figure 3: Correlation between BMI and HRV (SDNN) Immediately Post-Exercise

Figure 4: Correlation between BMI and HRV (SDNN) Post-HRR

Time to 75% HRmax: There was no significant difference (F (2) = .381, p = .688) in time of exercise to 75% HRmax between groups. There was an effect size of 0.19. No correlation was found (rp = -.048, p = .824) between BMI and the amount of time taken for HRmax to be reached during exercise.


Heart Rate Regulation: There was no significant difference (F (2) = .377, p = .690) in time to HRR between groups with an effect size of 0.19. A non-significant, weak-moderate correlation (rp = -0.48, p = .824) was found between BMI and the amount of time taken for HRR to complete. Discussion Resting Heart Rate: Whilst no significant difference in resting heart rate between groups was observed a p value of .54 was close to demonstrating significance. An effect size of 0.49 demonstrated a borderline-medium effect of BMI on RHR (Cohen, 1988). This, combined with a weakmoderate negative correlation, suggests that a larger sample might have offered a statistically significant result which would be in agreement with previous research. The current consensus is that resting heart rate is partly influenced by differing levels of adipose tissue in the body (Mathew et al., 2008; Bemelmans et al., 2012) and is a direct indicator of cardiovascular disease (Jensen et al., 2012). The results of this study are consistent with past research which also rarely reached significance (Cooney et al., 2010). A possible explanation for the lack of statistical significance in this study is the similar training status (physically active but not professional athlete) of all 24 participants, when lower resting heart rate is connected to increased activity levels as well as elevated levels of adipose tissue (Boyett et al., 2013). With a range of BMI scores in each BMI grouping which were compared in the ANOVAs, the correlation was run to examine any relationship between precise BMI and RHR. The effect size reported implies that BMI does have an influence on resting heart rate, which concurs with previous literature on the subject, even though statistical significance was not reached in the ANOVA or correlation. The reasons for elevated RHR being related to cardiovascular events are still not known (Whelton et al., 2013) and further research is needed in this area. The study results also suggest that athletic performance may affect RHR more than levels of adipose tissue, contributing towards a larger picture of understanding the cardiovascular system at rest. Heart Rate Variability at Rest Between Groups: Although no significant difference was found between BMI groups in resting HRV, an effect size of 0.45 almost reaches a medium effect according to Cohen’s (1988) effect size categories. A weak-moderate negative correlation between the variables suggests a trend 21 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

which could be significant with the existence of a larger sample. The mean values displayed in Table 1 support the existence of a relationship between BMI and resting HRV levels, as the normal BMI group SDNN of 78.1ms is noticeably higher than both the overweight (57.3ms) and obese (50.4ms) groups. Figures for what is considered a “healthy” SDNN are inconsistent. Hart (2013) reported a mean SDNN of 62.2ms in participants belonging to the normal BMI category, only marginally above the 60ms figure suggested by Greenberg et al. (2010) to be an indicator of increased risk of cardiac events. Shuchun et al. (2010), however, reported that amateur male footballers recorded a SDNN of only 39.4ms at rest, placing them in Greenberg et al. (2010)’s danger zone suggesting a compromised heart. A control group of a healthy population with a SDNN of 71ms compared to the 60ms found in a cardiac inhibited sample (Alter et al., 2009) compares well with the results found in this study. The 78.1ms SDNN indicates a high level of cardiac health in the normal BMI group, as this is higher than most figures reported in the literature. The recorded scores from the overweight and obese groups suggest that these populations suffer from a supressed autonomic nervous system in terms of HRV, although the SDNN recorded are higher than those found by Sjoberg et al. (2011) who reported 35ms at rest in the obese group. One possible explanation for these lower scores is the degree to which participants were on the borderline of BMI categories. For example, a BMI of 24.9 is technically a normal BMI score but only slightly lower than 25.1 (technically an overweight BMI). In this study, the mean BMI of the obese group was 31.2, whereas research investigating morbid obesity may include heavier participants. The suggestion that lower BMI is associated with higher HRV and consequent improved cardiac health at rest supports similar results in previous research (Dietrich et al., 2008; Alter et al., 2009; Sjoberg et al., 2011; Hart, 2013). Heart Rate Variability Immediately Post-Exercise Between Groups: The lack of significant difference between SDNN post-exercise between groups demonstrated that once medium-high intensity exercise has taken place, HRV levels are statistically similar irrespective of BMI category. An effect size of 0.36 suggests that BMI has an effect on HRV, and an observation of the means clearly shows that a difference exists between normal (52.5ms), overweight (42ms) and obese (27.3ms) groups after exercise has taken place. Previous research has reported a “healthy male group” to present with a SDNN of 96ms after “mild” exercise at 65%HRmax (Raczak et al., 2005), 43ms greater than the figure found in this study. Other literature investigating healthy participants suggested a SDNN of between 35 and 43ms to be expected after cycling protocol up to 80%HRmax (Barak et al., 2010). The lack 22 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

of research for other BMI groups made any comparisons difficult. Results from this data collection process seem to fit somewhere in the middle of what has been previously established for HRV immediately post-exercise. Reasons for the variation in results may include the variance in %HRmax reached (65%, 75%, 80% in different studies) and the type of activity undertaken (treadmill/cycling/ergometer). This concurs with dated research from Brewer et al. (1993) who reported an exponential decrease in HRV as exercise intensity increased. The results of this study are therefore useful to support previous research, and offer baseline data for two higher BMI categories for future research to compare to. Heart Rate Variability After Heart Rate Regulation Between Groups: Whilst no significant difference was found between groups in HRV after HRR had occurred, a non-significant weak-moderate correlation was found between BMI and HRV after HRR, suggesting that a trend may well exist between the two. Rates of HRV regulation have also been observed from the means, with the SDNN of the normal BMI group increasing by 9.5ms between the cessation of exercise and HRR occurring, the overweight group by 13ms and the obese group by 12.6ms. This finding suggests that whilst the higher BMI groups present with lower HRV after exercise, it does regulate at a slightly greater speed than the normal BMI group, whose HRV does not reach such a low point post-HRR. Heart Rate Variability Between Time Intervals: A two way ANOVA reported an overall significant difference in HRV between pre-exercise and immediately post-exercise levels, and no other significant differences between any time intervals. There is conflicting evidence from previous research about what should be expected from populations who do not already have a compromised cardiovascular system. The majority of previous research (Barak et al., 2010; Shuchum et al., 2010; Grant & van Rensburg, 2013; Leutheuser & Eskofier, 2013) has reported that there is a decrease in HRV due to exercise, with one paper suggesting exercise has no effect on HRV (Cornelissen et al., 2010) and a few studies arguing that an increase occurs (Shuchun et al., 2010; James et al., 2010). Shuchun et al. (2010) concluded a significant decrease in HRV occurs in a younger healthy population (~35-40 years), but a significant increase was reported in older healthy participants (56 years +). The other research found to suggest an increase in HRV postexercise (James et al., 2010) was the only study from this group to use female participants. If future research finds similar results with female and older populations, this could suggest that physiological factors have more influence than BMI on the levels of vagal modulation before 23 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

and after exercise. The importance of systolic blood pressure especially in minimising exercise-induced changes in HRV was noted by Grant & van Rensburg (2013) alongside contributions from VO2max (athletic status), BMI and gender (ranging between 12.8329.82%). These factors were reported to be a less important determinant than baseline autonomic function, but influential nonetheless. This finding supports the need for individuals to improve fitness to maintain optimum blood pressure and BMI. The maintenance of a healthy lifestyle seems to minimise the effects that some physiological factors have on HRV during and post- exercise. James et al.’s study included male participants only, ranging from 18-22 years old. Anomalous findings such as those reported by Shuchun et al. (2010) and James et al. (2013) require follow-up research to validate or discredit the conclusions drawn, and further investigation into the causes of HRV variation during and after exercise is advised. This may clarify the relative importance of BMI, aerobic fitness, blood pressure, sex and age in cardiovascular response to exercise. A point worth noting from the results is the comparative speed of HRV regulation compared with HRR. By the time HRR had fully occurred across the three groups, HRV was only at an average of 88% of its original level. Five of the 24 participants had a higher HRV post-HRR than pre-exercise but the remaining 19 still had lower HRV scores once heart rate had reached homeostatic levels. This suggests that even once an individual’s heart rate has returned to its pre-exercise levels, full homeostasis of the cardiovascular system has not occurred. Limited research was found investigating the rates of HRV regulation, although Barak et al. (2010) reported that HRV had not reached pre-exercise levels 15 minutes postexercise. No research found during a literature review has compared the rates of different measures of the cardiovascular system to reach homeostasis, and this is an area which should be investigated and the conclusions taken into account when setting intensities for sporting training programmes, as well as in the clinical rehabilitation environment. Time to 75% HRmax: No significant difference was found in how long it took for participants to reach 75% HRmax between groups. There was a low effect size and no correlation found. This suggests that heart rate increases at a similar rate between BMI groups once moderately intense exercise has taken place.


Heart Rate Regulation: Results demonstrated no significant difference in time to HRR between groups, with a low effect size of 0.19. There was a non-significant weak-moderate negative correlation (rp = 0.48) found between BMI and HRR which is close to being categorised as a moderate-strong negative correlation (rp = > 0.5). The lack of significance in this correlation may be due to the large standard deviations found in this category, including a few anomalous results. The numerical extent of a “normal” HRR is not yet understood (Vicente-Campos et al., 2014), yet other research states the importance of an “abnormal” HRR as a predictor of increased mortality without defining any parameters (Allison et al., 2013). As a result, any research contributing to this area is extremely valuable. However, any comparisons with previous research are difficult to make. This is due to the different methods used to compile statistics. For example, Vicente-Campos et al. (2014) examined HRR at 1 and 3 minutes post-exercise to formulate a statistical rate of regulation, Aggarwal et al. (2013) used only measurements 1 minute post-exercise to compare between groups, and Savonen et al. (2011) based their conclusions on 2 minute post-exercise HRR. The lack of standardisation as well as the minimal previous research investigating the total time to complete regulation means that the results from this study are difficult to directly compare to previous results. When comparing the mean times to regulation between groups, a trend is apparent, as would be suggested by the correlation results. The normal BMI category (227.9s) demonstrated HRR noticeably faster than both the overweight category (257.6s) and the obese category (258.3s). These means and the correlation results suggest that once an individual’s BMI is above what is considered healthy, cardiovascular regulation quickly loses efficiency. This has implications for anyone losing weight, even once they have narrowly crossed the borderline into the normal BMI category. It also suggests that increased levels of adipose tissue do have a noticeable effect on the autonomic aspect of the cardiovascular system. This is interesting when compared with the apparently small effect of BMI as a standalone factor in HRV, discussed above, and more research would be valuable here. High standard deviations were noted during data analysis, and may be one of the reasons for few significant differences being found while some correlations seem to exist. The relatively small sample size may have resulted in one or two anomalous results increasing the standard deviations to a point where statistical significance could not be reached. Larger sample groups in studies with fewer time and practical constraints using a similar protocol should be able to reach more reliable conclusions. 25 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

General Observations of the Study: The relatively simple protocol of this study means that it is easily replicable for possible future investigations, focusing on different athletic statuses, sexes and ages to find the importance of these factors compared to BMI in autonomic control of HRV pre- and postexercise, as well as post-HRR. Although time constraints limited the sample size of the study, there was a discovery of trends and correlations in some areas of investigation. These suggested a link between BMI and resting heart rate, BMI and resting HRV, BMI and HRV post- HRR and BMI, and time to HRR post-exercise. The overall significant difference between HRV pre- and immediately post- exercise is a conclusion which should be followed up in future research in wider sample groups to observe whether this physiological reaction is predominantly down to BMI suppressing the autonomic nervous system, or due to other physiological aspects of the human body. The use of ECG in this study allowed for the conclusions to be more satisfactorily drawn compared to studies which have used Polar monitors in the past, as discussed above. However, the use of a short data collection time (40 heart beats) using a 3 lead ECG could be argued to reduce the reliability of the results. Twenty-four hour ECG recording, as used by Saleem et al. (2012), is a more accurate predictor of future morbidity and mortality levels than any other HRV data collection method (Faber et al., 1996), but far lower time periods have been used successfully in recent research. Uusitalo et al. (2007) incorporated 5 minutes of ECG into their protocol, whereas Nussinovitch et al. (2011) suggest that any measurements as low as 10 seconds in duration can offer at least “crude” estimation of HRV when used with non-diseased participants, as this study did. Although the use of a 12 lead ECG is advocated where possible (Arena et al., 2010; Myers et al., 2010), 3 and 5 lead data collection has been used in recent studies (Lu et al., 2009; Weipert et al., 2010; Kelly et al., 2011) and is considered a satisfactory method of data collection for this paper. The existence of confounding variables influencing the results must also be considered. HRV has been suggested to be influenced by factors impeding autonomic cardiovascular regulation such as sleep deprivation (Dettoni et al., 2012; Wehrens et al., 2012; Mikulski et al., 2013), depression (Gordon et al., 2012; Meerwijk et al., 2014) and breathing patterns pre- and postexercise (Reis et al., 2010; Hallman et al., 2011). No psychological assessment or questions were asked before exercise took place, meaning that with the relatively small sample size, one or two participants with sleeping or psychological issues may produce noticeably 26 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

anomalous results due to physiological changes in the body, because of the lack of sleep or any antidepressants being prescribed (Meerwijk et al., 2014). It is also worth considering other factors on BMI such as nutritional habits and smoking. A “normal” BMI is not directly indicative of a healthy lifestyle and future research should incorporate more detailed background checks of lifestyle in participants grouped into BMI categories. Hopkins (2002) has suggested that a view of correlations can offer more information than purely a ‘p’ value when looking for differences between data. For example, in this study, a non-significant ‘p’ value of .054 was discovered between groups in RHR, and a value of .094 between groups in resting HRV. Both of these facets, however, demonstrated a weakmoderate correlation when a Pearson’s product-moment correlation coefficient was run. It is worth considering how different a non-significant ‘p’ value of .054 (as was found in RHR between groups) is from a significant ‘p’ value of .047 (as was found between pre- and postexercise HRV). Although a line in the sand has to be drawn somewhere to differentiate between significance and non-significance, the use of correlations can be argued to be more useful, especially in studies with low participant numbers where individual outliers can influence significance values. Although the results were not statistically significant, research seems to indicate that BMI may play a role in autonomic control of the cardiovascular system through vagal modulation in terms of resting heart rate and heart rate regulation, as well as possibly HRV pre- and postexercise. The extent to which BMI is a key factor compared to other suggested factors such as age, sex, blood pressure and life stresses is not yet fully known, and should be investigated in future studies – ideally with larger sample sizes and wider sample demographics. The results of this study also suggest that as well as BMI being a possibly unreliable measure due to it not taking into account muscle mass (Ferreira, 2005), the broad categories may not be reliable guides due to the spectrum of physiological differences even within groups. Research into the differences between the low and high end of each BMI category in terms of cardiovascular health is advised to fully understand the usefulness of the BMI categories. Conclusion This study investigated the influence of BMI on parameters which reveal autonomic cardiovascular maintenance. Although time constraints limited the sample size in this study and restricted it to a unisex group, low level correlations were found between BMI and RHR, BMI and resting HRV, BMI and HRV post-HRR, and BMI and time to HRR. A significant 27 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

difference was found overall between HRV pre-exercise and immediately post-exercise. The unisex sample means results of this research are only generalisable to young males, as contrasting results have previously been found in females (James et al., 2010) and older populations (Shuchun et al., 2010). The relative simplicity of the protocol used in this study means that a future investigation, using a similar protocol, with other population groups may be useful in furthering knowledge of this area of physiology, allowing direct comparisons to be made. The possibility of a correlation between BMI and RHR is in accordance with the conclusions drawn by previous literature (Mathew et al., 2008; Bemelmans et al., 2012), whilst the findings that a correlation may exist between BMI and resting HRV are also consistent with previous reports (Dietrich et al., 2008; Alter et al., 2009; Sjoberg et al., 2011; Hart, 2013). The possible correlation found between BMI and time to HRR suggests the suppression of the autonomic nervous system in higher BMI groups and indicates that longer is taken to reach homeostasis in higher BMI groups than the normal BMI group. The finding that after HRR had occurred, HRV had only recovered to an average of 88% of its pre-exercise levels is an interesting note to be taken from this paper. This suggests that heart rate alone is not an adequate indicator of homeostasis being reached during postexercise recovery and this should be considered in fitness programmes and rehabilitation situations. Statistical significance was found between pre-exercise HRV and immediately post-exercise HRV, concurring with previous research with young, male populations (Grant & van Rensburg, 2013; Leutheuser & Eskofier, 2013). Although this agrees with previous findings, further research with other populations is needed for wide-ranging conclusions to be drawn. In terms of relating these findings to the real world, the trends found in these results would suggest that a high priority for any males with any BMI above normal should be to reduce this to improve autonomic function. This, in turn, may reduce the risk of cardiovascular complications. Whilst obesity is heavily linked with cardiovascular disease, strokes, diabetes and a suppressed autonomic nervous system, the means found in this study relating to time to HRR post-exercise suggest that once an individual’s BMI has reached an overweight level, some suppression of the autonomic nervous system may have occurred before that person is technically obese. This further stresses the importance of maintaining a normal BMI. 28 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

As a result of the data analysis, the four following null hypotheses were accepted: H(0) = There was no significant difference in heart rate variability pre-exercise between the three examined BMI groups. H(0) = There was no significant difference in heart rate variability immediately post-exercise between the three examined BMI groups. H(0) =There was no significant difference in heart rate variability after HRR between the three examined BMI groups. H(0) = There was no significant difference in heart rate regulation rates between the three examined BMI groups. Whilst the null hypotheses were accepted, an examination of the means and possible correlations suggested extremely interesting findings with the potential to reinforce the importance of maintaining a healthy body weight to ensure function of the autonomic nervous system is not compromised. Future research on a larger sample group would give a more reliable data set for each variable. Future research should also ensure that larger samples drawn from more population categories (e.g. age, sex, history of disease, hypertensive) are also investigated using similar protocols to permit valid comparisons between groups. Word count: 9596 words.


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Appendices Appendix 1: Information Sheet

Participants’ Information Sheet, Consent Form & PAR-Q

Title of the Study: The effect of BMI on resting heart rate, heart rate variability pre- and postexercise, and on heart rate regulation post-exercise of 75%HRmax in males Thank you for expressing an interest in this project. Please read the following information sheet carefully before deciding whether or not to participate in the project. If you choose to participate in the project, we thank you. As a participant, prior to taking part in any testing, you will be required to: 1. Carefully read this Information Sheet which will outline the procedures and the potential risks to yourself; 2. Complete and sign a Consent Form and; 3. Complete and sign a Physical Activity Readiness Questionnaire (PAR-Q)

The Consent Form and the PAR-Q can be found at the end of this document. If you do not decide to participate in the project there will be no disadvantage to you of any kind and we thank you for considering our request. 1. What are the aims of the project? The aim of the present project is to investigate the effects of BMI on how the heart recovers from exercise 2. What type of participants does the project require? The present project hopes to recruit an approximate total of twenty four participants with eight from each BMI category. 3. What will the participants be asked to do? 51 Andrew Mitchelmore (1132194) SP3301A – Dissertation 2013-2014

Individuals who volunteer to participate in the present project will be initially asked for height and weight so body mass index can be calculated. They will then be asked to use an upper body ergometer until heart rate reaches 75% of what is considered normal for their age. After this marker is reached, regulation of heart rate and heart rate variability will be measured. The data for each BMI group will be compared to see if any significant differences are found. 4. What are the potential risks and discomforts of the project? Because the project involves exercise, feelings of fatigue are to be expected. The exercise isn’t particularly strenuous but could be slightly uncomfortable. 5. Other general health and safety considerations If the participant feels worried about how they are feeling during exercise, the researcher will be present at all times to speak with. After data collection is completed post-exercise, water bottles will be provided. 6. Can participants change their mind and withdraw from the project? Individuals may withdraw from participation in the project at any time and without any disadvantage of any kind. 7. What information will be collected, and how will it be used? Data from the testing procedures described in Section 3 will be collected and used to determine whether BMI affects regulation of heart rate and heart rate variability. This data will be stored securely in a lockable filing cabinet in the Department of Sports Studies. Only the Project Supervisor and Investigator will have access to the data. All data will be anonymous and destroyed after a period of five years. The results of this project may be published, but the information will not be linked to any specific person. A copy of all your personal information, including results, supplement type and dosage will be given to you after completion of testing. 8. What if participants have any questions? If you have any questions about the project please feel free to contact either:


The Investigator

The Project Supervisor

Andrew Mitchelmore

Helen Ryan

Student

Lecturer

Email: [email protected]

Email: [email protected]

Contact No. 07768016708

Contact No. 01962 827112


Appendix 2: Consent Form

Participant’s Consent: I __________________________________consent to take in part in the research study titled:

The effect of BMI on resting heart rate, heart rate variability pre- and postexercise, and on heart rate regulation post- exercise of 75%HRmax in males The investigator has explained the full details and parameters of all tests and procedures to me, and I have read the Information Sheet. I confirm that I have understood what participation will involve, and confirm that I have been made aware of all the potential benefits and risks of participation. I declare that I have completed and signed the accompanying Physical Activity Readiness Questionnaire truthfully to the best of my knowledge, and that I have never been advised to abstain from any form of exercise by a medical practitioner. I know of no reason why participation in these testing procedures might present a risk to my safety. I understand that any medical information that I have submitted will be treated as highly confidential. I would like to be provided with a copy of the following for my personal records (please tick): Information Sheet Consent Form PAR-Q

Signature __________________________

Date

Witness ____________________________

Date


Appendix 3: PAR-Q

Physical Activity Readiness Questionnaire (PAR-Q) Date of Birth

Blood Pressure (mmHg)

Height (m)

Body Mass (kg)

Please tick either ‘Yes’ or ‘No’ for all of the following questions. If you are unsure about any question, please ask the investigator. Yes

No

Yes

No

Yes

No

Are you used to vigorous exercise? Has your medical doctor said that you must not undertake vigorous activity? Do you have:

Yes

No

heart disease?

any blood disorder?

frequent chest pains?

diabetes mellitus?

raised blood pressure?

thyroid disease?

episodes where you become breathless very easily?

arthritis that is made worse by exercise?

a persistent cough?

back pain that is made worse by exercise?

asthma?

hiatus (chest) hernia or heartburn?

a recent chest infection?

inguinal (groin) hernia?

Do you lose your balance because of dizziness? Do you have episodes where you regularly lose consciousness? To the best of your knowledge, are you pregnant? Do you know of any reason why you must not exercise? Please give details below: If you have any other concerns or questions with regard to completing this form or are unsure as to your general state of health please contact the supervising technician in person or by email: [email protected] For Official Use only Details of any further discussions with research subject regarding health indications stated above: Signed: _________________ ____(Laboratory Supervisor)

Date:_______ ___

__
