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ORIGINAL ARTICLE
Year : 2013  |  Volume : 27  |  Issue : 3  |  Page : 191-193

A study of heart rate variability among caregivers from a rural background in a busy urban infirmary


1 Department of Physiology, PES Institute of Medical Sciences and Research, Kuppam, Andhra Pradesh, India
2 Department of Physiology, Basaveshwara Medical College, Chitradurga, Karnataka, India
3 Department of Anesthesiology, Basaveshwara Medical College, Chitradurga, Karnataka, India

Date of Web Publication19-Feb-2014

Correspondence Address:
Amrith Pakkala
Department of Physiology, PES Institute of Medical Sciences and Research, Kuppam, Andhra Pradesh
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0972-4958.127391

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  Abstract 

Background: Urban life in modern times is completely laden with stressors. Caregivers of chronically sick patients are likely to suffer from mental and physical exhaustion leading to stress. This is important in view of the prevailing socioeconomic conditions as well as the healthcare system available in any developing country. Therefore, the present study was designed to measure the heart rate variability (HRV) in this special group of population who gives care to long-term bedridden patients in an urban setting. Materials and Methods: Fifteen female subjects were enrolled from among the attendants of patients admitted with immediate history of a cerebrovascular accident with locomotor deficits, who required constant care. They were free from any type of physical and mental health issues, nonsmokers, and nonaddicts. Each caregiver was subjected to HRV analysis on three occasions: the first record was performed in the 1st week of their arrival in the hospital as caregivers and the second observation was recorded after 4 months of caregiving. Two types of parameters were analyzed: time domain and frequency domain. Statistical analysis was done using paired t-test. Results: Both the HRV parameters - time and frequency domains - showed decreased values during the 3rd month and 6th month recording as compared to the 1st week recording. The decrease was much more during the 6th month recording as compared to the 3rd month recording. Statistically significant decrease was observed in the mean RR interval, heart rate, Very Low FrequencyVLF, and Low Frequency LF]. On comparing the 1st week recording with the 4th month recording significant decrease was found in mean RR interval and heart rate. Conclusion: Subjects involved in taking care of chronically ill bedridden patients are likely to undergo a lot of physical and mental stress, thus affecting their autonomic status. HRV analysis with short-term electrocardiogram (ECG) recording was used to detect changes consequent to this stress. It was found that almost all HRV parameters measuring heart rate complexity were decreased in the period of caregiving.

Keywords: Caregiver, Frequency domain, Heart rate variability, NN50, RMSSD, RR interval, Urban infirmary


How to cite this article:
Pakkala A, Ganashree CP, Raghavendra T. A study of heart rate variability among caregivers from a rural background in a busy urban infirmary. J Med Soc 2013;27:191-3

How to cite this URL:
Pakkala A, Ganashree CP, Raghavendra T. A study of heart rate variability among caregivers from a rural background in a busy urban infirmary. J Med Soc [serial online] 2013 [cited 2020 Oct 19];27:191-3. Available from: https://www.jmedsoc.org/text.asp?2013/27/3/191/127391


  Introduction Top


Modern day urban life is fully laden with stressors. The negative consequences of stress as a risk factor for cardiovascular disease and reduced human performances are well studied. [1] Stress is known to change the balance existing between the sympathetic and parasympathetic divisions of the autonomic nervous system. [2] Caregivers of chronically sick patients are likely to suffer from mental and physical exhaustion leading to stress. Heart rate variability (HRV) is a non-invasive study of variation over a period of time between consecutive heart beats and has been proved to be a reliable marker of autonomic nervous system (ANS) activity. [3] HRV analysis is one of the best parameters available today for evaluation of stress.

The level of HRV and the underlying stress is not well studied in developing countries. This is important in view of the prevailing socioeconomic conditions as well as the healthcare system available in a developing country like India. Therefore, the present study was designed to measure the HRV in this special group of population who gives care to long-term bedridden patients.

With life expectancy expected to increase, more such case scenarios can be expected in the future.


  Materials and Methods Top


The data were acquired from 15 female subjects in the age group of 21-25 years, who were free from any type of physical and mental health issues as determined by taking a detailed history and a thorough physical examination. All subjects were nonsmokers and free from any type of addiction or drug dependence.

These subjects were enrolled from among the attendants of patients admitted with a history of multiple old-age ailments with locomotor deficits, who required constant care. Informed consent was obtained from the subjects. Ethical clearance was obtained from the institutional ethics committee. Each caregiver was subjected to HRV analysis on two occasions. The first record was performed in the first week of their arrival in the hospital as caregivers. The second observation was recorded after 4 months of caregiving. All recordings were obtained under similar conditions of time and body position after an adaptation time of 30 min.

The anthropometric parameters of each subject were recorded. This was followed by a short-term 5-min three-lead electrocardiogram (ECG) recording using Biomed polygraph. From the data thus obtained, HRV analysis was done. The RR interval time series was extracted from the ECG records using Biomed HRV analysis software.

Two types of parameters were analyzed. They were as follows.

Time domain parameters like mean RR interval; RMSSD (ms): Square root of the mean of the sum of the squares of differences between adjacent NN intervals (this gives information regarding HRV in short time interval); NN50: Number of pairs of adjacent NN intervals differing by more than 50 ms in the entire recording; and pNN50 (%): NN50 count divided by the total number of all NN intervals.

Frequency domain parameters like VLF (m s²): Power in very low frequency range (<0.04 Hz); LF (m s²): Power in low frequency range (0.04-0.15 Hz); HF (m s²): Power in high frequency range (0.15-0.4 Hz); and LF/HF ratio.

The fraction of total RR intervals labeled as normal to normal (NN) intervals was computed as NN/RR ratio. This ratio has been used as a measure of data reliability with the purpose to exclude records with a ratio less than 90% of the threshold. [4],[5],[6]

Statistical analysis

HRV features were depicted as mean ± standard deviation during the three set of recordings. Nonlinear properties of HRV were analyzed by the following methods: Time domain parameters and frequency domain parameters were compared using paired t-test.


  Results Top


Fifteen females were enrolled in the study with a mean age of 24.40 ± 2.52 years, height 161.08 ± 0.09 cm, weight 59.48 ± 1.45 kg, and body mass index (BMI) 22.61 ± 0.02 Kg/m². Both the HRV parameters - time and frequency domains - showed decreased values during the 4th month and 7th month recording as compared to the 1st week recording. The decrease was much more during the 7th month recording as compared to the 4th month recording. Statistically significant decrease was observed in the mean RR interval, heart rate, VLF, and LF only when the 1st week recording was compared with the 4th month recording, but when the 1st week recording was compared with the 7th month recording, significant decrease was found in mean RR interval and heart rate [Table 1] and [Table 2].
Table 1: Comparison of HRV time domain parameters

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Table 2: Comparison of HRV frequency domain parameters

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  Discussion Top


In the present study, various HRV parameters were computed among caregivers of chronically sick patients. Recordings were made within 1 week of the patient being admitted to the hospital and the arrival of the attendant as caregiver. Subsequently, repeat recordings were obtained 4 months after the first recording with the subject continuing as the primary caregiver of the concerned patient.

The mental stress associated with taking care of these patients is likely to produce a cardio-sympathetic excitation. This is reflected in a significant decrease in the mean RR interval. The mean RR interval is an indicator of the ratio of the cardiac sympathovagal balance. The results suggest an overall sympathetic dominance subsequent to taking up of the caregiving among the subjects. The significant decrease in mean RR interval and increase in mean heart rate after 4 months reflects a decreased total HRV in the presence of mental stress.


  Conclusion Top


Subjects involved in taking care of chronically ill sick patients are likely to undergo a lot of physical and mental stress, thus affecting their autonomic status. HRV analysis using short-term ECG recording was used to detect changes consequent to this stress.

It was found that almost all HRV parameters measuring heart rate complexity were decreased in the period of caregiving. The mean value of the HF power was of lower magnitude after 4 months of caregiving, but the decrease was not statistically significant except in the case of VLF and LF power. However, the combined decrease in spectral power of all three bands contributes to a significant decrease in the total power during the caregiving period. Decreased HRV indicates diminished responsiveness of the cardiac autonomic system to normal physiological stimuli. The HRV was unchanged during this period probably because the level of stress induced in these subjects for 4 months might have started adaptive mechanisms counteracting the effect of stress.

 
  References Top

1.Hubert C, Lambertz M, Nelesen RA, Bardwell W, Choi JB, Dimsdale JE. Effects of stress on heart rate complexity-A comparison between short term and chronic stress. Biol Psychol 2009;80:325-32.  Back to cited text no. 1
    
2.Kim D, Seo Y, Cho J, Cho CH. Detection of subjects with higher self reporting stress scores using heart rate variability patterns during the day. Conf Proc IEEE Eng Med Biol Soc 2008;2008:682-5.  Back to cited text no. 2
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3.Malik M, Bigger JT, Camm AJ, Kleiger RE, Malliani A, Moss AJ, et al. Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Eur Heart J 1996;17:354-81.  Back to cited text no. 3
    
4.Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 2000;101:E215-20.  Back to cited text no. 4
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5.Zong W, Moody GB, Jiang D. A robust open source algorithm to detect onset and duration of QRS complexes. Comput Cardiol 2003;2003:737-40.  Back to cited text no. 5
    
6.Melillo P, Fusco R, Sansone M, Bracale M, Pecchia L. Discrimination power of long term heart rate variability measures for chronic heart failure detection. Med Biol Eng Comput 2011;49:67-74.  Back to cited text no. 6
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    Tables

  [Table 1], [Table 2]



 

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Introduction
Materials and Me...
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