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 Table of Contents  
ORIGINAL ARTICLE
Year : 2014  |  Volume : 17  |  Issue : 1  |  Page : 7-9

Evaluation of heart rate variability among caregivers of geriatric patients undergoing surgery


1 Department of Physiology, Peoples Education Society 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 Publication7-Apr-2014

Correspondence Address:
Amrith Pakkala
40, SM Road 1st cross, T. Dasarahalli, Bangalore 560 057
India
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DOI: 10.4103/1119-0388.130174

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  Abstract 

Background: Caregivers of geriatric patients undergoing surgery are likely to suffer from mental and physical exhaustion leading to stress. This is important in view of the prevailing socioeconomic and the healthcare system available in a developing country like India. Therefore, the present study is designed to measure heart rate variability (HRV) among this special group of population who give care to geriatric patients undergoing surgery. Materials and Methods: In all, 15 female subjects were enrolled from among attendants of patients admitted with an immediate indication of surgery, 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 on the first day of their arrival in the hospital as caregivers. The second observation was recorded after 3 days of caregiving. The final HRV analysis was done after 1 month 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 domain, showed decreased values during the third day and 1 month recording as compared with the first day recording. The decrease is much more during the 1 month recording as compared with the third day recording. Statistically significant decrease is observed in mean RR interval, heart rate, very low frequency (VLF), and low frequency (LF) only when the first day recording is compared with the third day recording, but when the first day recording was compared with the 1 month recording, significant decrease was found in mean RR interval and heart rate. Conclusion: Subjects involved in taking care of geriatric patients are likely to undergo a lot of physical and mental stress, thus affecting their autonomic status. HRV analysis using short term electrocardiography (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: Care giver, frequency domain, geriatric surgery, heart rate variability, normal to normal 50, root mean square successive difference, RR interval


How to cite this article:
Pakkala A, Ganashree CP, Raghavendra T. Evaluation of heart rate variability among caregivers of geriatric patients undergoing surgery. Trop J Med Res 2014;17:7-9

How to cite this URL:
Pakkala A, Ganashree CP, Raghavendra T. Evaluation of heart rate variability among caregivers of geriatric patients undergoing surgery. Trop J Med Res [serial online] 2014 [cited 2019 Sep 23];17:7-9. Available from: http://www.tjmrjournal.org/text.asp?2014/17/1/7/130174


  Introduction Top


The adverse 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 (ANS). [2] Caregivers of geriatric patients are likely to suffer from mental and physical exhaustion leading to stress. Heart rate variability (HRV) is a noninvasive 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 parameter available today for evaluation of stress.

The level of HRV and the underlying stress is not well studied in India. This is important in view of the prevailing socioeconomic and the healthcare system available in a developing country like India. Therefore, the present study is designed to measure HRV among this special group of population who give care to geriatric patients undergoing surgery.


  Materials and Methods Top


The data were acquired from 15 female subjects in the age group of 20-25 years 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 attendants of geriatric patients admitted for undergoing surgery who required constant care. Informed consent was obtained from the subjects. Ethical clearance was obtained from the institutional ethical committee. Each caregiver was subjected to HRV analysis on three occasions: The first record was performed on the first day of their arrival in the hospital as caregivers. The second observation was recorded after 3 days of caregiving. The final HRV analysis was done after one month 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 recording (ECG) using Biomed polygraph. From the data thus obtained HRV analysis was done. The RR interval time series were extracted from ECG records using Biomed HRV analysis software. Two types of parameters were analyzed. Time domain parameters like mean RR interval, RMSSD (ms): Square root of the mean of the sum of the squares of differences between adjacent normal to normal (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, pNN50 (%): NN50 count divided by the total number of all NN intervals. And frequency domain parameters like very low frequency (VLF) (ms2): Power in VLF range (<0.04 Hz), low frequency (LF) (ms2): Power in low frequency range (0.04-0.15 Hz), high frequency (HF) (ms2): Power in high frequency range (0.15-0.4 Hz), and LF/HF ratio.

The fraction of total RR intervals labeled as 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 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 mean age of 24.40 ± 2.52 years, height 162.01 ± 0.09 cm, weight 58.43 ± 1.45 kg, and body mass index (BMI) 21.60 ± 0.02. It is clear from [Table 1] and [Table 2] that the HRV parameters-time and frequency domain-showed decreased values during the third day and first month recording as compared with the first day recording. The decrease is much more during the first month recording as compared with the third day recording. Statistically significant decrease is observed in mean RR interval, heart rate, VLF, and LF only when the first day recording is compared with the third day recording, but when the first day recording was compared with the first month recording, significant decrease was found in mean RR interval and heart rate.
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 geriatric patients undergoing surgery. Recordings were made on the first day of the patient being admitted to the hospital and the arrival of the attendant as caregiver. Subsequently, repeat recordings were obtained after 3 days and 1 month 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 3 days reflects a decreased total HRV in the presence of mental stress. The mean value of the HF power is of lower magnitude after 3 days and 1 month of caregiving, but the decrease is 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 the 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 frequency and time domain parameters show no change between 3 days and 1 month. The HRV was unchanged during this period probably because the level of stress induced in these subjects for 1 month might have started adaptive mechanisms counteracting the effect of stress.


  Conclusion Top


Subjects involved in taking care of geriatric patients undergoing surgery 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.

 
  References Top

1.Schubert 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, Jaegeol C, Chul-Ho C. Detection of subjects with higher self reporting stress scores using heart rate variability patterns during the day. Engineering in Medicine and Biology Society (EMBS). 30 th Annual International Conference of the IEEE; 20-25 Aug, 2008. p. 682-5.  Back to cited text no. 2
    
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. Task force of the european society of cardiology and the north american society of pacing and electrophysiology. 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
    
5.Zong W, Moody GB, Jiang D. A robust open source algorithm to detect onset and duration of QRS complexes. Comput Cardiol 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
    



 
 
    Tables

  [Table 1], [Table 2]



 

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