|
 
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| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 11
| Issue : 4 | Page : 90-96 |
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Influence of body shape index and heart rate variability in athletes as assessed during treadmill exercise
Snigdha Sharma1, Pradeep Bokariya1, Ruchi Kothari2, Sujay Srivastava1
1 Department of Anatomy, Mahatma Gandhi Institute of Medical Sciences, Wardha, Maharashtra, India
2 Department of Physiology, Mahatma Gandhi Institute of Medical Sciences, Wardha, Maharashtra, India
| Date of Submission | 29-Dec-2021 |
| Date of Decision | 20-Nov-2022 |
| Date of Acceptance | 23-Nov-2022 |
| Date of Web Publication | 21-Jan-2023 |
Correspondence Address:
MBBS Snigdha Sharma
MBBS, Mahatma Gandhi Institute of Medical Sciences, Sevagram, Wardha - 442 102, Maharashtra
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jcpc.jcpc_58_21
Background: Heart rate variability (HRV) is emanating as one of the most effectual techniques for probing the intricate and ever-evolving changes in the oscillations of a healthy heart, allowing us to guard against any abrupt physical threat to homeostasis, such as that which occurs when a person exercises. Aims and Objectives: To study the influence of body shape index with HRV indices in athletic population during treadmill exercise in athletic population. Materials and Methods: It was a cross-sectional study carried out on 120 healthy subjects and 30 athletes in the age range 17-35 years. Short-term HRV (5 min) was extracted from ECG recordings obtained using Power lab system, AD instruments and was analyzed by LabChart software in accordance to the standards set forth by the Task Force of the European Society of Cardiology and North American Society of Pacing and Electrophysiology. Results: Out of 30 athletes, there were a total of 17 (56%) male subjects and 13(44%) female subjects. Out of 120 controls, male subjects were 50 (41.66%) and females were 70 (58.33%). The mean age of males was 21.06±3.02 and that of females was 22.50 ± 6.25. A statistically significant positive (r=0.56) correlation between BSI and HRV on treadmill exercise was observed. Conclusion: Examining HR variations offers a window to evaluate the condition and integrity of ANS. In this study, the extent to which the ANS contributed to obesity was assessed and the relationship of measure of cardiovascular risk, a newly established and pragmatic metric Body Shape Index (ABSI) with the HRV indicators in the athletic population was explored.
Background: Heart rate variability (HRV) is emanating as one of the most effectual techniques for probing the intricate and ever-evolving changes in the oscillations of a healthy heart, allowing us to guard against any abrupt physical threat to homeostasis, such as that which occurs when a person exercises. Aims and Objectives: To study the influence of body shape index with HRV indices in athletic population during treadmill exercise in athletic population. Materials and Methods: It was a cross-sectional study carried out on 120 healthy subjects and 30 athletes in the age range 17-35 years. Short-term HRV (5 min) was extracted from ECG recordings obtained using Power lab system, AD instruments and was analyzed by LabChart software in accordance to the standards set forth by the Task Force of the European Society of Cardiology and North American Society of Pacing and Electrophysiology. Results: Out of 30 athletes, there were a total of 17 (56%) male subjects and 13(44%) female subjects. Out of 120 controls, male subjects were 50 (41.66%) and females were 70 (58.33%). The mean age of males was 21.06±3.02 and that of females was 22.50 ± 6.25. A statistically significant positive (r=0.56) correlation between BSI and HRV on treadmill exercise was observed. Conclusion: Examining HR variations offers a window to evaluate the condition and integrity of ANS. In this study, the extent to which the ANS contributed to obesity was assessed and the relationship of measure of cardiovascular risk, a newly established and pragmatic metric Body Shape Index (ABSI) with the HRV indicators in the athletic population was explored. Keywords: Athletes, body shape index, heart rate variability, treadmill
How to cite this article:
Sharma S, Bokariya P, Kothari R, Srivastava S. Influence of body shape index and heart rate variability in athletes as assessed during treadmill exercise. J Clin Prev Cardiol 2022;11:90-6 |
How to cite this URL:
Sharma S, Bokariya P, Kothari R, Srivastava S. Influence of body shape index and heart rate variability in athletes as assessed during treadmill exercise. J Clin Prev Cardiol [serial online] 2022 [cited 2023 Jun 8];11:90-6. Available from: https://www.jcpconline.org/text.asp?2022/11/4/90/368356 |
| Introduction | |  |
The heart rate (HR) changes beat by beat when there is a proper sinus rhythm. Heart rate variability (HRV) is a dynamic interaction of the several physiological systems that control the instantaneous HR. Examining HR variations offers a window to evaluate the condition and integrity of the autonomic nervous system (ANS) because short-term HR regulation is primarily controlled by sympathetic and parasympathetic neural activity.[1] The sympathetic and parasympathetic pathways of the ANS are primarily responsible for controlling the cardiovascular system. By reducing sympathetic activity and preventing weight gain by reducing parasympathetic activity, excessive energy storage may be encouraged.[2] The World Health Organization reports that overweight and obesity are becoming more common and are the fifth leading etiologies of death globally.[3],[4] The most practical method for determining an adult's level of excess weight is using their body mass index (BMI).[5],[6] Age, gender, stage of development, and ethnicity all matter. BMI does not discriminate between muscle and fat growth, and research shows that while having more muscle reduces risk of premature mortality, having more fat increases risk.[7],[8]
A body shape index (ABSI), a novel anthropometric measure that is independent of BMI, has been developed from waist circumference (WC). It is claimed that ABSI is a better index than utilizing either WC or BMI independently.[9]
It is acknowledged that risk is influenced by body shape for a given body size (BMI), notably as a measure of centripetal fat accumulation. WC is a risk factor that is utilized in addition to BMI, but it can be challenging to determine how useful WC is on its own. The ABSI was created as a risk indicator that takes into account the increased risk of WC while accounting for BMI and height. Hence, as shown by Sowmya et al., ABSI, which is independent of BMI, aids in determining whether abdominal obesity has predictive ability that cannot be explained by BMI alone.[9]
Due to ABSI's relative youth and the fact that studies are still in their early stages, few studies from western industrialized nations have focused on risk factors in relation to it. In comparison to BMI and WC, it has been verified in a study for its predictive potential for total and cause-specific mortality in a population of 2626 men and 3740 women from the Netherlands.[10] The ABSI demonstrated a larger correlation than the other weight measures, although its enhanced predictive power was only marginal. With a hip ratio (HR) of 1.15 for men and 1.10 for women for 1 standard deviation increase in ABSI, it also discovered a significant relationship with mortality. Obesity is a well-known risk factor for adult cardiovascular disease.[11] In comparison to BMI, WC, waist-to-height ratio (WHtR), and waist-hip ratio (WHR), ABSI has also demonstrated a stronger connection with overall, cardiovascular, and cancer mortality (WHR).[12]
Persistent athletes in particular likely to have better physiology than the ordinary individual because they frequently engage in regular exercise.[13] This study was created with the goal of examining the relationship between body shape index (BSI) and HRV indices in athletic populations and understanding the interaction of HRV with exercise as performed on a treadmill at various intensities. Neither studies in India nor elsewhere have examined BSI and HRV in relation to physical activity.[13] Insufficient research has been done on the association between obesity and HRV, and further research is required to determine WC cut points that may be used to determine cardiometabolic risk.
| Methodology | | |
It was a cross-sectional observational study with a 150-person sample size that included 30 cases and 120 age-and sex-matched controls. The investigation was conducted in the sports physiology lab of a rural medical college in central India. The research was conducted between May 1 and June 30, 2018. OpenEpi ver. 3.01 (Atlanta, GA, USA) estimated the sample size. Alpha was 5%, confidence level was 95%, and power was 80.
Before the study began, it gained approval from the institutional ethics committee (Ref. No. MGIMS/IEC/ANAT/129/2017, dated: December 15, 2017). All participants who supplied written informed permission and met the requirements outlined in the plan provided to them prior to the study's start.
Inclusion criteria
The subjects had to meet the inclusion criteria of participating in sports like swimming, basketball, badminton, football, handball, or other athletic activities like brisk walking, gym training, running, jogging, or cycling, while the controls were those who did not take part in any of the aforementioned sports or activities.
Exclusion criteria
People who were found to be at high risk for cardiovascular disease or who had a major co-morbidity, such as a history of cardiac, pulmonary, or psychiatric disorder, were excluded from the study. In addition, participants who used any medication (such as beta-blockers) known to impact HRV were omitted. The study did not include any subjects who were injured or inflamed in any way.
The participants in the study were found through word of mouth. The subjects were made sure not to ingest caffeine within 8 h before the test and to abstain from rigorous activity (such as jogging, swimming, or playing sports for 2 to 3 h) the day before the test. Before testing, the subjects were required to complete a pro forma. After that, the individual was instructed on the use of the equipment and the proper blood pressure protocols.
The participant was instructed to wear a Polar transmitter SPO180 (with a chest strap), a wireless HR kit, and an Equivital EQ-02-B3 wireless electrocardiogram (ECG) sensor belt. The probability of artifacts was significantly lower with the ECG vest and polar transmitter being wireless than with corded devices. A motorized treadmill model number AF101 was used for symptom-limited exercise testing in accordance with the Modified Bruce protocol.
The anthropometric data for the parameters in the cases and controls were as follows: Standing, with age indicated in years and months Using a portable stadiometer, height was measured to the nearest 1 cm without shoes. With the bare minimum of clothing, weight (in kg) was measured using a digital scale.
A conventional, nonstretchable tape was used to measure WC in cm while the subject was standing over either bare skin or thin underwear. The narrowest part between the iliac crest and the final floating rib was measured horizontally. To avoid diurnal variation, all measures were made between the hours of 1 and 4 pm, and they were all performed by the same individual to reduce the possibility of bias-related mistake.
BMI was calculated as per standard formula:
BMI = weight in kilogram/(height in meters) 2
BSI was calculated according to the standard formula:
BSI = WC/(BMI)2/3× (Height)½
The HRV Module (Windows) in Lab Chart software was used to evaluate the ECG data after it had been obtained using the Power lab Model PL3508 Power Lab 8/35 data recording equipment (AD Instruments, Bella Vista, Australia). The HRV module evaluates RR interval fluctuation as well as beat-to-beat interval variation in ECG records. To produce RR Interval data, it uses a threshold detector to identify the R component in each raw ECG waveform. The software automatically separates beats into three categories: Normal, ectopic, and artifact.
A 5-min RR interval sequence from the workout recording was selected, and it was then analyzed for HRV using Labchart software after the artifacts and ectopic were removed. Raw ECG signals and RR intervals were captured utilizing data acquisitions at a rate of 1000 samples per second, and the data were then uploaded to a Windows laptop running HRV analysis software. The recording speed was set at 256 Hz, while the filter frequency was 50 Hz. According to the guidelines established by the Task Force of the European Society of Cardiology and North American Society of Pacing and Electrophysiology in 1996, short-term HRV (5 min) recordings were extracted.[14] Accordingly, time-domain and frequency-domain analyses of HRV data were both taken into consideration. Using RR-interval based on ECG, the spectral and temporal domain indices of HRV were calculated. N-N intervals were calculated after each QRS was extracted. Mean R-R interval (pulse interval), Standard deviation of N-N interval (SDNN), and Square Root of Mean Squared Difference of N-N Intervals were time-domain indices that were measured Root mean square of the successive differences (RMSSD). The intervals between typical R-peaks are represented by the root mean square of successive differences between normal heartbeats. The NN50 is the adjacent NN intervals that differ from each other by more than 50 ms and pNN50 is the percentage of NN50. It is clearly evident in the Analysis report from LabChart software [Figure 1].
With the exception of RMS-SD, which is linked to rapid fluctuation, these measures are primarily associated with the overall variability of HR over the course of recording. The parasympathetically mediated variations in HRV are estimated using RMSSD and pNN50%. By affecting the RR interval, parasympathetic and sympathetic activity regulate HR. The low frequency/high frequency (LF/HF) ratio is based on the assumptions that HF power, LF power, and LF/HF ratio respectively stand for parasympathetic activity, sympathetic activity, and sympathovagal balance. When the LF/HF ratio is low, parasympathetic dominance is present.[14],[15]
This is evident when we practice tend-and-befriend actions and preserve energy. A high LF/HF ratio, on the other hand, denotes sympathetic dominance, which happens when we engage in fight-or-flight or parasympathetic withdrawal activities.[15] The RR tachograms have a HF component that is thought to be caused by resting parasympathetic activity and a LF component that is thought to be primarily caused by resting sympathetic activity. The entire HRV seen in the recording is given by the total power. HF power, HF normalized units (HFnu), Standard deviation of the NN (R-R) interval (SDNN), RMSSD, and the number of pairs of successive NN (R-R) intervals that differ by more than 50 ms are all primarily influenced by resting parasympathetic activity. LF component is primarily caused by resting sympathetic activity, with some effect from resting parasympathetic activity, whereas LF nu indicates sympathetic activity. Short-term HRV recordings cannot be used to evaluate the physiological basis for the VLF component; hence, it is not further examined.
Statistical Software SPSS Inc. Version 18.0 performed the statistical analysis using the unpaired t-test and Pearson's correlation coefficient. SPSS Inc., Chicago (USA). Statistical significance was defined as a P value of 0.05 or lower.
| Results | | |
Males and females between the ages of 17 and 35 made up the study's population. There were a total of 17 (56%) male participants and 13 (44%) female subjects among the 30 athletes. Male athletes had a mean age of 20.94 ± 2.75, while female athletes had a mean age of 22.17 ± 3.30. Male individuals made up 68 (56.66%) and female subjects made up 52 (43.33%) of the 120 controls. Males had a mean age of 21.06 ± 3.02 while females had a mean age of 22.50 ± 6.25. [Table 1] lists various demographic information about the study participants.
As shown in [Table 1], there was no discernible difference between the two research groups' mean body surface area, height, weight, BMI, or age. However, it was discovered that compared to controls, sporty groups' resting HRs were lower. It was observed that BSI in athletic males was 48.91 ± 8.44, more than that of athletic females which was found to be 44.73 ± 8.82. An opposite trend was observed in the nonathletic population where males had a BSI of 40.85 ± 16.92 which was less than that of females which was 43.23 ± 10.12. Data of Time domain indices and Frequency domain indices of Males and Females of both the study groups as measured on Treadmill are expressed as mean ± standard deviation and given in [Table 2]. In both the athletic and nonathletic male populations, the sympathovagal ratio (LF/HF) was larger than that of the female population, indicating a dominating sympathetic response on the treadmill [Figure 2], as opposed to the female population, which had a strong parasympathetic response. The nonathletic ladies displayed a greater sympathovagal ratio, indicating a predominately sympathetic response during exercise, in contrast to the athletic females. The correlation analysis of body shape index with heart rate variability parameters as obtained on treadmill for both males and females in both the groups have been depicted in [Table 3]. As it is evident from the graphs, [Figure 3] and [Figure 4], there was a positive correlation between BSI and HRV obtained on treadmill exercise in athletic males (r = 0.56, P = 0.01) which was statistically significant and in athletic females it was (r = 0.02). | Figure 2: Graphical representation of comparison of LF/HF ratio in study group. LF = Low frequency, HF = High frequency
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 | Table 2: Heart rate variability indices in males and females measured on treadmill
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 | Table 3: Correlation of body shape index with heart rate variability parameter obtained on treadmill
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 | Figure 3: Scatterogram of correlation of BSI with LF/HF ratio in females. LF = Low frequency, HF = High frequency, BSI = Body shape index
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 | Figure 4: Scatterogram of correlation of BSI with LF/HF ratio in males. LF = Low frequency, HF = High frequency, BSI = Body shape index
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| Discussion | | |
A useful indicator of cardiac regulation by sympathetic and vagal ANS components is HRV. According to estimates, one-third of Indians have chronic energy deficiency, which raises their risk of acquiring chronic illnesses. Visceral fat measurements are challenging, time-consuming, and demand sophisticated equipment.[16] These methods could aid us in providing reliable measurements, but their viability and use at the population level are debatable. For a very long time, simple measures like WC have been utilized as a substitute for central adiposity. HRV is a useful marker for the ANS's sympathetic and vagal components' control of the heart (ANS). Chronic energy deficiency, which raises the chance of acquiring chronic diseases, is reported to affect one third of the population in India. Visceral fat measurements are challenging, laborious, and call for expensive equipment.[16] These methods might aid in providing precise measurements, however it is debatable whether or not they can be applied at the population level. Long used as a substitute for central adiposity are straightforward indicators like WC. An increased central concentration of body volume is correlated with high ABSI.[17],[18] When used as a predictor variable along with BMI, ABSI dissociates the effect of body size from that of the body shape component evaluated by WC. When frequency domain HRV characteristics and HR were compared across the two study groups, it was discovered that in obese people, LF, HF, and HFnu were much lower while Lfnu and LF/HF were significantly greater.[19] When BMI was correlated with frequency domain variables like the LF/HF ratio, normalized LF and HF units showed a markedly inverse relationship to Hfnu. When BMI and the LF/HF ratio were correlated, normalized LF and HF units showed a markedly inverse relationship to Hfnu. It was shown that the male athletes and the nonathletic ladies had greater BSIs when compared to the findings of our study.
On the treadmill, the male population of the athletic population displayed a greater LF/HF ratio than the female population of the same group. While the central command of the body raises HR and cardiac output through vagal withdrawal during intense exercise, the ANS initially increases primarily due to the muscle chemoreflex. As a result, HRV's overall power and output in its two main frequency bands—LF and HF—decrease Parasympathetic withdrawal and increased sympathetic activity are the initial effects of this reaction.[20]
While many authors link metabolic acidosis to increased sympathetic activity,[21],[22] some scientists contend that sympathetic activity remains constant up to 100% of the ventilatory threshold and increases abruptly at 110%[23] and that total parasympathetic withdrawal does not take place even during high-intensity exercise.[24] The HRV of 65 students in the 13–20 years age group was assessed by time domain methods during rest and after exercise.[25] However, the HRV increased soon after exercise in all groups, and there was no difference in HRV based on gender or BMI. According to the study's findings for the resting condition, parasympathetic activity is higher in females than in males and increases with ageing in both cases. Strong effects of age and gender were therefore found in this investigation. The current study's findings are in agreement with those of this study.
The people who created the ABSI measure stressed the significance of identifying central adiposity in estimating the risk because they discovered that it was more accurate in predicting mortality risk than the previously accepted measures of abdominal obesity, including WC, WHR, and waist-height ratio (WHtR). These findings of the combined obesity measure's (ABSI) higher predictability compared to the individual measures of obesity, BMI and WC, are supported by an Australian study.[26] With the exception of WHR in females, ABSI was likewise discovered to be the best predictor of all-cause death in an Iranian population.[27] Even though studies have been done while subjects are at rest, there is little information available about how BSI affects a person's HRV.
There are few studies on HRV indices, and much less is known about the initial conditions of HRV during physical activity. There is a paucity of research on the HRV of healthy people or athletes during activity, despite a few studies being done to establish the HRV indices before and postexercise.
The study's novelty comes in identifying the relationship between BSI and HRV and how that relationship affects physical activity, both of which have not before been investigated, at least in Indian populations, according to the literature that could be found. In this study, an effort has been made to assess and forecast potential autonomic activity under exercise-induced stress by linking HRV with BSI during treadmill exercise. Given that BSI has a strong correlation with cardio-metabolic risk and that HRV is a measure of neuro-cardiac function that reflects heart-brain interaction, ANS dynamics, and quantitatively estimates cardiac autonomic anomalies, their correlation would predict cardiovascular morbidity and mortality much earlier.
The baseline data on HRV parameters in athletes and nonathletes discovered in this study can be used to assess how the heart responds to stress during exertion in order to identify abnormal variations in HR. By evaluating HRV and ABSI, this study will aid in the early detection of cardiovascular disorders and assist reduce cardiac morbidity and mortality. If at all possible, one should concentrate on lowering their WC because this will lower their ABSI score and hence lower their risk of dying. A major strength of our study is its use of wireless ECG vest to obtain the readings instead of using wired leads which enabled our subjects to be assessed during exercise and we were able to study the effect of exertion on their autonomic response.
Limitations
Due to the brief duration of the research (a short-term studentship) (ICMR-STS), the sample size was limited. According to the reachable target population of athletes and controls, the age range has to be limited.
| Conclusion | | |
BSI allows for the diagnosis of even the smallest metabolic changes seen in otherwise healthy persons, and it may be a useful tool for athletes with BMIs within the normal range and central obesity to carefully analyze their cardiovascular risk factors. The development of control strategies, as well as the prevention and prognosis of cardiac illnesses, may benefit more from the application of ABSI.
All things considered, this study investigated the extent to which the ANS contributed to obesity as well as the relationship between the BSI and HRV indices in the athletic population. In order to compare the HRV of a healthy sedentary person with an athlete, baseline values for HRV spectral and temporal domain analyses in athletes and nonathletes of this region have also been created.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3]
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