Skip to main content

Association between health-related quality of life and heart rate variability in elderly individuals with cognitive impairment in Korea: cross-sectional study



Cognitive impairment, a characteristic and prior stage of dementia, is a serious public health concern in Korea a country with rapidly aging population. In a neurovisceral integration model, cognitive ability is connected to emotional and autonomic regulation via an interconnection in the brain, which may be associated with health-related quality of life (HRQoL).


This study investigated the association between the HRQoL and the autonomic nervous system (ANS) via EuroQoL-5 Dimension (EQ-5D) and heart rate variability (HRV) among 417 patients who visited the Neurology Department in Veterans Health Service Medical Center, Seoul, South Korea.


The mean age of 275 patients in the cognitive impairment group (CIG) was higher than that of 142 patients in the normal cognition group (NCG) (74.85 years vs. 72.96 years, p < 0.001). In a generalized linear model with a beta coefficient (β), an increase in HRQoL was associated with higher HRV levels was observed only in CIG (CIG: the standard deviation of all NN intervals (SDNN) (ln, ms): β = 0.02, p = 0.007; Total power spectral density (TP) (ln, ms2): β = 0.01, p = 0.007; High frequency (HF) (ln, ms2): β = 0.01, p = 0.015; Low frequency (LF) (ln, ms2): β = 0.01, p = 0.003) (NCG: SDNN (ln, ms): β = 0.01, p = 0.214; TP (ln, ms2): β = 0.01, p = 0.144; HF (ln, ms2): β = 0.00, p = 0.249; LF (ln, ms2): β = 0.01, p = 0.294).


We found a significant association between HRQoL and HRV in Korean elders with cognitive impairment. However, this study is cross-sectional, so we cannot define direct causation. Further studies are needed to support our findings and to elucidate the biological mechanisms underlying these associations, especially in people cognitively impaired.

Peer Review reports


Cognitive impairment is an example of a neurodegenerative disease; thus, it is a serious public health problem in Korea, a country with a rapidly aging population [1,2,3]. Cognitive functions enable individuals to understand and interpret surrounding information, which is essential for everyday tasks and activities, and in complex situations [4]. Dysexecutive function, processing speed, working memory, sustained attention, behavioral inhibition, and general mental flexibility are cognitive functions associated with prefrontal cortex activity [5]. However, despite non-severe cognitive dysfunctions in individuals with cognitive impairment such as in dementia, health-related quality of life (HRQoL) may be negatively affected to some degree due to specific functional impairments. [6, 7].

Cognitive function often worsens under the dysfunction of the autonomic nervous system (ANS) [8]. The ANS generally consists of two major branches, the sympathetic system related to energy mobilization and the parasympathetic system related to vegetative and restorative functions [5]. The heart rate variability (HRV) analysis, which is a noninvasive examination that assesses ANS activity, includes variations between heartbeats of sympathetic and parasympathetic nerve system [9]. Higher HRV values represent better ANS function, whereas lower HRV values represent disease status and defective ANS function [9]. HRV can be used to predict total mortality, sudden death, cardiovascular disease risk, and other morbidities, and to measure physiological changes in psychiatric illnesses such as depression, anxiety disorder, panic disorder, and posttraumatic stress disorder (PTSD) [10, 11].

Numerous studies have emphasized that ANS activity has significantly impacted HRQoL patients with schizophrenia, end-stage renal disease, paroxysmal atrial fibrillation, and chronic obstructive pulmonary disease, since the altered ANS activity is a common physiological outcome of the diseases [12,13,14,15,16]. In the asymptomatic group consisting of those aged between 20 and 54 years, ANS activity has been associated with a decreased physical QoL because smoking, alcohol, and other cardiovascular risks may be related to physical condition [17]. Additionally, other studies suggest that sympathetic autonomic activity is associated with emotion regulation, such as controls of emotional stability and perseverative thinking, which are in turn associated with a dissatisfied life and lower QoL [18, 19].

Although previous studies have investigated a significant association between HRQoL and HRV depending on certain disease patients, there has been no study on people with cognitive impairment. In this study, we compared the association in Korean older adults with cognitive impairment with those with normal cognition.


Study population

This study recruited patients over 60 years old who received medical care at the Neurology Department in Veterans Health Service Medical Center, Seoul, South Korea in 2021–2022. The recruitment criteria were as follows: (1) patients who complained of cognitive decline; (2) patients capable of completing clinical tests and questionnaires by themselves; (3) patients who consented to participate in the study; (4) patients with no medical history of dementia (ICD-10: F00-F09, G30), brain infarction, cerebral hemorrhage, and Parkinson’s disease; and (5) patients with no medical history of fatal disease (e.g., cancer or mental illness). Criteria for inclusion were determined by expert neurological clinicians. The study protocols obtained approval from the Institutional Ethical Review Board of the Veterans Health Service Medical Center (IRB no. BOHUN 2021-02-024-001, BOHUN 2021-01-066-006).

A total of 575 patients volunteered for the study and underwent a health survey, comprising HRV, cognitive examinations, and questionnaires, at the Veterans Medical Research Institute of the Veterans Health Service. Among these, 66 patients were excluded from the study due to missing cognitive examinations (n = 17), HRV (n = 13), and quality of life (n = 6) data; and/or diagnosed dementia (n = 30). The final study population included 417 patients with no missing data for the following covariates: age, sex, education, income, marital status, body mass index, smoking, drinking, depression, hypertension, dyslipidemia, and diabetes (n = 95) (Fig. 1).

A three-lead wireless electrocardiogram (ECG) recording device (MINDD SCAN, Ybrain Inc., South Korea) was used for HRV analysis. The recording was conducted in a quiet room for 5 min where the patients were in a rested sitting position, and electrodes were placed on the wrists and right ankle in a standard three-lead position. The patients were instructed to breathe normally and sit motionless without sleeping or talking during the recording. The time and frequency domain HRV analysis was performed using the Ybrain software (MINDD SCAN, Ybrain Inc., South Korea).

Heart rate variability

The study used SDNN and percentage of pairs of adjacent intervals differing by more than 50 ms in the collection period (pnn50) among the time-domain indices. For the frequency-domain indices, LF (0.04–0.15 Hz, ms2), HF (0.15–0.40 Hz, ms2), total power spectral density (TP, ms2), and the LF/HF ratio were used. The pnn50 and the HF reflected parasympathetic activity, whereas LF represented both sympathetic and parasympathetic activity. The LF/HF power ratio reflected the sympathovagal balance, whereas SDNN and TP revealed the variability in the recording period which reflected global HRV [4, 9].

Quality of life

The patients completed the Korean version of the EuroQoL-5 Dimension (EQ-5D) and visual analogue scale (VAS). The EQ-5D-3 L was developed by the European QOL group for measuring HRQOL and is divided into five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. The patients evaluated each dimension as one of the following three levels: no problems, some or moderate problems, and extreme problems. Then, the total EQ-5D-3 L index was calculated by a Korean value set [20]. The VAS score ranged from 1 to 5 for self-health estimation. The EQ-5D-3 L and VAS provided a brief report on the quality of life of the participants as a single index of health status that can be applied for clinical and economic evaluation of health care and population health surveys.

Neuropsychological battery test

The brief version of Seoul Neuropsychological Screening Battery (SNSB), named SNSB-Core (SNSB-C), was conducted to evaluate the level of core cognitive ability in the five cognitive domains: attention, language and related functions, visuospatial functions, memory, and frontal/executive functions [21]. The SNSB-C comprises 14 sub-tests derived from the SNSB: Vigilance Test, Digit Span Test, Comprehension Test, Repetition Test, short form of the Korean-Boston Naming Test, Ideomotor Apraxia Test (IAT), Rey Complex Figure Test, Seoul Verbal Learning Test-Elderly’s version (SVLT-E), Contrasting Program, Go-No Go Test, short form of the Korean-Color Word Stroop Test, Controlled Oral Word Association Test (COWAT), Korean-Trail Making Test-Elderly’s version (K-TMT-E), and Digit Symbol Coding [21].

For screening patients with cognitive impairment, the composite SNSB-C is an indicator of overall cognitive function and a substitute for the Korean Mini-Mental State Examination, a global instrument used to assess cognitive abilities briefly [21]. The effects of age, education years, and sex on the composite SNSB-C score were adjusted using the z-score standardization [21]. Patients were divided into two groups, the normal cognition group (NCG) and the cognitive impairment group (CIG). The patients were categorized under CIG if at least one of the percentiles in the sub-tests of the SNSB-C was less than 16th.

Statistical analysis

To compare the statistical difference in participants’ characteristics between NCG and CIG, we performed the Chi-square test for categorical variables (i.e., sex, income, marital status, smoking, alcohol use, and history of disease) and the T-test for continuous variables (i.e., age, education year, and BMI). Since HRV variables are naturally non-normally distributed, the data were log-transformed to reduce skewness and reach normal distribution [22]. However, HRV data did not assume normality, so non-parametric methods were applied for the statistical analysis. Spearman rank correlation coefficients were used to examine the correlation between HRV and HRQoL, stratified by NCG and CIG. To assess the association between HRQoL and HRV by the presence of cognitive impairment, we conducted non-parametric regression by a multivariate generalized linear model. Each HRV variable was defined as an independent variable in the regression model, and HRQoL was described as a dependent variable. The model provided the beta coefficient (β) and 95% confidence interval (95% CI). The beta coefficient (β) is the regression coefficient and means the estimated effects or changes in the dependent variable (HRQoL) for a one-unit change in the independent variable (HRV). All regression models were adjusted for age, sex, education, monthly income, marital status, smoking, alcohol consumption, body mass index, depression, diabetes, and hypertension. Statistical analyses were performed using the Statistical Analysis System version 9.4 (SAS Institute, Cary, NC, United States), and the statistical significance level was set at p ≤ 0.05.


Participant characteristics

Table 1 shows the characteristics of the population (n = 417) and the comparison of characteristics between NCG (n = 142) and CIG (n = 275). The characteristics expressed as mean in all populations were 74.21 years (age), 10.6 years (education), and 24.85 kg/m2 (BMI). The rest of the characteristics were expressed as proportions, 55.4% were male; 82.25% were married; 5.28% were smokers; 33.33% consumed alcohol; 32.13% had depression; 27.82% had diabetes; and 67.15% had hypertension. The mean age (72.96 vs. 74.85 years) and the proportion of those with diabetes (37.32 vs. 22.91%) were significantly different between the NCG and CIG.

Table 1 Characteristics of subjects in the NCG and CIG

Table 2 shows the mean HRV and HRQoL in the study populations, NCG, and CIG. The mean HRV variables of the populations were 7.77 (SDNN, ln, ms), 0.11 (pnn50, %), 6.15 (TP, ln, ms2), 4.83 (HF, ln, ms2), 4.34 (LF, ln, ms2), and 0.97 (LF/HF, ln, ms2); the mean EQ-5D and VAS were 0.92 (EQ-5D) and 3.14 (VAS). The SDNN (7.79 vs. 7.75 ln, ms), TP (6.21 vs. 6.12 ln, ms2), HF (4.88 vs. 4.80 ln, ms2), LF (4.43 vs. 4.30 ln, ms2), LF/HF (– 0.45 vs. – 0.50 ln, ms2), and VAS (3.19 vs. 3.11) were higher in NCG than CIG. However, All the mean HRV and HRQoL were not significantly different between NCG and CIG.

Table 2 Mean HRV and QOL of subjects in the NCG and CIG

Table 3 shows the correlation matrix between HRV and HRQoL in the NCG and CIG. In the NCG, the correlation coefficients were not significant. In contrast, the EQ-5D of the CIG, was significantly correlated with pnn50 (r = 0.13; p = 0.04), HF (r = 0.15; p = 0.01), and LF (r = 0.15; p = 0.01). The VAS was significantly correlated with SDNN (r = 0.17; p < 0.001), pnn50 (r = 0.18; p < 0.001), TP (r = 0.17; p < 0.001), HF (r = 0.18; p < 0.001), and LF (r = 0.21; p < 0.001). The range of significant correlation in the CIG was 0.13–0.15 for the EQ-5D and 0.17–0.21 for VAS.

Table 3 Spearman correlation of HRV and QOL in the NCG and CIG

Table 4 shows the beta coefficient and 95% confidencs intervals of the general linear model of HRQoL by HRV in the NCG and CIG. In the NCG, all linear models were not significant regardless of the adjustment, indicating no significant association between HRV and HRQoL. In contrast, EQ-5D was positively associated with SDNN (β = 0.02; p = 0.007), TP (β = 0.01; p = 0.007), HF (β = 0.01; p = 0.015), and LF (β = 0.01; p = 0.003). VAS was positively associated with SDNN (β = 0.18; p = 0.003), pnn50 (β = 0.72; p < 0.001), TP (β = 0.09; p = 0.004), HF (β = 0.08; p = 0.001), and LF (β = 0.07; p = 0.003) after adjustment.

Table 4 Generalized linear model of quality of life predicted in the NCG and CIG

Figure 2 shows the regression lines and dot plotting of the association HRV and HRQoL in subjects with and without cognitive impairment.


We found that increased HRQoL was significantly associated with increased HRV variables in the CIG, whereas no association was found in the NCG. EQ-5D and VAS in the CIG, showed significant positive association with SDNN, TP, HF, and LF, and only VAS showed significant positive association with pNN50.

A recent nationwide population-based study in Taiwan investigated the relationship between HRQoL and cognitive impairment among 9,084 Taiwanese citizens aged over 65 years (normal cognition vs. MCI: 7035 vs. 2049) [7]. Those in the MCI showed having problem in all domains of HRQOL compared to normal individuals (beta coefficient (standard errors) in MCI with reference to normal group; mobility: β = 1.11 (0.09), p < 0.01; self-care: β = 1.58 (0.15), p < 0.01; usual activities: β = 1.35 (0.09), p < 0.01; pain/discomfort: β = 0.75 (0.06), p < 0.01; anxiety/depression: β = 0.76 (0.07), p < 0.01); overall HRQOL and VAS in MCI showed lower than normal (overall index of HRQoL: β = -0.16, p < 0.01; VAS: β = -6.68, p < 0.01) [7]. Another study in India suggested that the presence of low cognitive function reduced HRQoL by approximately 7% among older individuals as determined by the structural equation model approach [23]. We confirmed the relationship between HRQoL and cognitive impairment in older individuals through the previous studies.

Additionally, there have been studies investigating the association between HRV and cognition in older individuals. In 2018, the Geriatric Centre of the University of Campania Luigi Vanvitelli in Naples examined the HRV and cognitive ability of 117 older subjects (age > 65) for 24 h [10]. The study found an association between increased HRV and higher cognitive test scores from the Mini-Mental State Examination (MMSE; corrected for age and education) and the Montreal Cognitive Assessment (MoCA) test: the standard deviation of the average normal RR intervals over a 5-minute period (SDANN, ms) (β = 0.324, p < 0.05 for MMSE; β = 0.404, p < 0.001 for MoCA), SDNN (β = 0.390, p < 0.001 for MMSE; β = 0.481, p < 0.001 for MoCA), LF (β = 0.293, p < 0.001 for MMSE; β = 0.336, p < 0.001 for MoCA), and LF/HF (β = 0.293, p < 0.001 for MMSE; β = 0.326, p < 0.001 for MoCA) [10]. The result suggested an association between greater HRV parameters and better cognitive performance, especially parameters expressed sympathetic [10]. In contrast, another study targeted a cohort of women aged over 65 years and showed that the square root of the mean squared differences of successive normal RR intervals (RMSSD, ms), the number of interval differences of successive normal RR intervals greater than 50 ms (NN50, counts), HF power, and prevalent cognitive impairment according to MMSE [24]. Considering that the mechanism between autonomic dysfunction and cognitive impairment is unclear [4], these studies implied that both reduced sympathetic and parasympathetic functions may be associated with cognitive dysfunction.

Our results showed significant associations between HRQoL and HRV in patients with cognitive impairment. The prefrontal cortical areas, which include the orbitofrontal and medial prefrontal cortex, tonically inhibit the amygdala via GABAergic neurons in the amygdala [25, 26] Furthermore, prefrontal, cingulate, and insula cortices form an interconnected network with bidirectional communication with the amygdala [27]. The central nucleus of the amygdala (CeA) is the major efferent source to modulate the autonomic responses and, the disinhibition of CeA may lead to decreased HRV by lower activation of the prefrontal cortex which may lead to disinhibition of CeA. This in turn would disinhibit simultaneous sympathoexcitatory neurons in the rostral ventrolateral medulla (RVLM) and parasympathoexcitatory neurons in the nucleus of the solitary tract (NTS) [28].

The damage in the prefrontal cortex is also associated with the insula and cingulate due to communication. The insula is the main hub of the central autonomic network (CAN) and is implicated in both the regulation of ANS output and cognitive function via projections on preganglionic sympathetic and parasympathetic neurons [5]. Outputs of the CAN are under tonic inhibitory control via GABAergic neurons in the NTS [5, 8]. Emotional arousal is associated with concomitant decrease in brain activation in the right superior and dorsolateral prefrontal cortex and the left anterior cingulate cortex and a decrease in HRV [8]. The neurovisceral integration model considers the transmission of subcortical affective information to the cerebral cortex as required to experience emotion and that the top-down inhibitory influences related to the modulatory effect of subcortical shaped the nature of subjective experience [8]. Thus, prefrontal inhibitory control over brain activities and subcortical brain regions that are involved in emotional arousal and processing providing emotional dysregulation such as perseverative thoughts and emotional instability, which leads to life dissatisfaction and lower QoL [18]. Emotional dysregulation would be more serious among older individuals than the younger population because the prefrontal and amygdala activities are associated with age and cognition [29].

This study assumed that Korean olders with cognitive impairment might have ANS dysfunction and emotional dysregulation caused by problems in prefrontal inhibitory or brain activity. Therefore, this study suggested that impaired life satisfaction particularly related to health is associated with ANS among older individuals with cognitive impairment. To the best of our knowledge, the present study is the first study to report the association between HRQoL and HRV in a sample of older Korean individuals. The survey for EQ-5D, VAS, and HRV have benefits that include being non-invasive, relatively inexpensive, less time consuming, and does not have a complex procedure.

However, this study has a few limitations. First, since this study is based on a cross-sectional design, we cannot determine the causality for the observed association between HRV and HRQoL, especially in older adults who are cognitively impaired. Second, there are more powerful ways to monitor 24-hour Holter measurement than 5-min HRV measurement. However, short-term HRV remains stable and may be applicable for screening the variation in the heart rate. Third, cognitive functional status (herein, CIG vs. NCG) was assessed using a neurobehavioral test. Although the neurobehavioral examination is a clinical assessment of cognitive function, whether people are subject to cognitive impairment may be often misclassified when using brief cognitive assessments. Fourth, this study analyzed only specific HRV data (i.e., SDNN, TP, HF, and LF). Additional HRV data from Fourier transformation, approximate entry, and other measurements can be essential to clarify and assess the relationship between HRV and HRQoL. Finally, our study may be biased due to the limitation of unmeasured confounders and single-center study.


We found a significant association between HRQoL and HRV in Korean elders with cognitive impairment. However, this study is cross-sectional, so we cannot define direct causation. Further studies are needed to support our findings and to elucidate the biological mechanisms underlying these associations, especially in people cognitively impaired.

Fig. 1
figure 1

Selection of the study population

Fig. 2
figure 2

Plots of HRQoL and HRV in cognitive impairment group (a-f) and normal control group(g-l). The solid lines indicate regression lines

Data Availability

The datasets generated and analysed during the current study are not publicly available due to institutional restrictions but are available from the corresponding author on reasonable request.



autonomic nervous system


central autonomic network


central nucleus of the amygdala


cognitive impairment group


EuroQoL-5 Dimension


High frequency


health-related quality of life


heart rate variability


Low frequency


Mini-Mental State Examination


Montreal Cognitive Assessment


normal cognition group


the number of interval differences of successive normal RR intervals greater than 50 ms


nucleus of the solitary tract


percentage of pairs of adjacent intervals differing by more than 50 ms in the collection period


square root of the mean squared differences of successive normal RR intervals


rostral ventrolateral medulla


standard deviation of the average normal RR intervals over a 5-minute period


standard deviation of all NN intervals


Total power spectral density


  1. Bae JB, Kim YJ, Han JW, Kim TH, Park JH, Lee SB, Lee JJ, Jeong HG, Kim JL, Jhoo JH, et al. Incidence of and risk factors for Alzheimer’s Disease and mild cognitive impairment in Korean elderly. Dement Geriatr Cogn Disord. 2015;39(1–2):105–15.

    Article  PubMed  Google Scholar 

  2. Jang JW, Park JH, Kim S, Lee SH, Lee SH, Kim YJ. Prevalence and incidence of Dementia in South Korea: a Nationwide Analysis of the National Health Insurance Service Senior Cohort. J Clin Neurol. 2021;17(2):249–56.

    Article  PubMed  PubMed Central  Google Scholar 

  3. WHO Guidelines Approved by the Guidelines Review Committee. In: Risk Reduction of Cognitive Decline and Dementia: WHO Guidelines. Geneva: World Health Organization © World Health Organization 2019; 2019.

  4. Forte G, Favieri F, Casagrande M. Heart rate variability and cognitive function: a systematic review. Front Neurosci. 2019;13:710.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Thayer JF, Hansen AL, Saus-Rose E, Johnsen BH. Heart rate variability, prefrontal neural function, and cognitive performance: the neurovisceral integration perspective on self-regulation, adaptation, and health. Ann Behav Med. 2009;37(2):141–53.

    Article  PubMed  Google Scholar 

  6. Graham JE, Rockwood K, Beattie BL, Eastwood R, Gauthier S, Tuokko H, McDowell I. Prevalence and severity of cognitive impairment with and without Dementia in an elderly population. Lancet. 1997;349(9068):1793–6.

    Article  CAS  PubMed  Google Scholar 

  7. Liu HY, Tsai WC, Chiu MJ, Tang LY, Lee HJ, Shyu YL, Wang WS. Relationships between Cognitive Dysfunction and Health-Related Quality of Life among older persons in Taiwan: a Nationwide Population-based survey. Am J Alzheimers Dis Other Demen. 2019;34(1):41–8.

    Article  PubMed  Google Scholar 

  8. Thayer JF, Lane RD. Claude Bernard and the heart-brain connection: further elaboration of a model of neurovisceral integration. Neurosci Biobehav Rev. 2009;33(2):81–8.

    Article  PubMed  Google Scholar 

  9. 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. Circulation. 1996;93(5):1043–65.

    Google Scholar 

  10. Dalise AM, Prestano R, Fasano R, Gambardella A, Barbieri M, Rizzo MR. Autonomic nervous system and cognitive impairment in older patients: evidence from Long-Term Heart Rate Variability in Real-Life setting. Front Aging Neurosci. 2020;12:40.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Cohen H, Benjamin J, Geva AB, Matar MA, Kaplan Z, Kotler M. Autonomic dysregulation in panic disorder and in post-traumatic stress disorder: application of power spectrum analysis of heart rate variability at rest and in response to recollection of trauma or panic Attacks. Psychiatry Res. 2000;96(1):1–13.

    Article  CAS  PubMed  Google Scholar 

  12. Chang LR, Lin YH, Kuo TB, Wu Chang HC, Liu CM, Liu CC, Hwu HG, Yang CC. Autonomic modulation and health-related quality of life among Schizophrenic patients treated with non-intensive case management. PLoS ONE. 2011;6(11):e26378.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Fujii H, Koyama H, Fukuda S, Tokai H, Tajima S, Koizumi J, Yamaguti K, Kuratsune H, Watanabe Y, Hirayama Y, et al. Autonomic function is associated with health-related quality of life in patients with end-stage renal Disease: a case-control study. J Ren Nutr. 2013;23(5):340–7.

    Article  PubMed  Google Scholar 

  14. Hathaway DK, Wicks MN, Cashion AK, Cowan PA, Milstead EJ, Gaber AO. Heart rate variability and quality of life following kidney and pancreas-kidney transplantation. Transpl Proc. 1999;31(1–2):643–4.

    Article  CAS  Google Scholar 

  15. van den Berg MP, Hassink RJ, Tuinenburg AE, van Sonderen EF, Lefrandt JD, de Kam PJ, van Gelder IC, Smit AJ, Sanderman R, Crijns HJ. Quality of life in patients with paroxysmal atrial fibrillation and its predictors: importance of the autonomic nervous system. Eur Heart J. 2001;22(3):247–53.

    Article  PubMed  Google Scholar 

  16. Van Gestel AJ, Kohler M, Steier J, Teschler S, Russi EW, Teschler H. Cardiac autonomic dysfunction and health-related quality of life in patients with Chronic Obstructive Pulmonary Disease. Respirology. 2011;16(6):939–46.

    Article  PubMed  Google Scholar 

  17. Lu WC, Tzeng NS, Kao YC, Yeh CB, Kuo TB, Chang CC, Chang HA. Correlation between health-related quality of life in the physical domain and heart rate variability in asymptomatic adults. Health Qual Life Outcomes. 2016;14(1):149.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Steinmetz J-P, Voegele C, Theisen-Flies C, Federspiel C, Sütterlin S. The relationship between emotion regulation capacity, heart rate variability, and quality of life in individuals with alcohol-related brain damage. Psychol Res Behav Manage. 2016;9:219.

    Article  Google Scholar 

  19. Critchley HD. Psychophysiology of neural, cognitive and affective integration: fMRI and autonomic indicants. Int J Psychophysiol. 2009;73(2):88–94.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Lee YK, Nam HS, Chuang LH, Kim KY, Yang HK, Kwon IS, Kind P, Kweon SS, Kim YT. South Korean time trade-off values for EQ-5D health states: modeling with observed values for 101 health states. Value Health. 2009;12(8):1187–93.

    Article  PubMed  Google Scholar 

  21. Jahng S, Na DL, Kang Y. Constructing a composite score for the Seoul neuropsychological screening battery-core. Dement Neurocognitive Disorders. 2015;14(4):137–42.

    Article  Google Scholar 

  22. Godijk NG, Vos AG, Jongen VW, Moraba R, Tempelman H, Grobbee DE, Coutinho RA, Devillé W, Klipstein-Grobusch K. Heart Rate Variability, HIV and the risk of Cardiovascular Diseases in Rural South Africa. Glob Heart. 2020;15(1):17.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Singh P, Govil D, Kumar V, Kumar J. Cognitive impairment and quality of life among elderly in India. Appl Res Qual Life. 2017;12(4):963–79.

    Article  Google Scholar 

  24. Kim DH, Lipsitz LA, Ferrucci L, Varadhan R, Guralnik JM, Carlson MC, Fleisher LA, Fried LP, Chaves PH. Association between reduced heart rate variability and cognitive impairment in older disabled women in the community: women’s Health and Aging Study I. J Am Geriatr Soc. 2006;54(11):1751–7.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Shekhar A, Sajdyk TJ, Gehlert DR, Rainnie DG. The amygdala, panic disorder, and cardiovascular responses. Ann N Y Acad Sci. 2003;985:308–25.

    Article  CAS  PubMed  Google Scholar 

  26. Barbas H, Saha S, Rempel-Clower N, Ghashghaei T. Serial pathways from primate prefrontal cortex to autonomic areas may influence emotional expression. BMC Neurosci. 2003;4:25.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Gianaros P. Brain-body pathways to cardiovascular disease risk. In: Herbert Weiner Early Career Award Lecture, 66th Annual Meeting of the American Psychosomatic Society, Baltimore, MD, March: 2008.

  28. Saha S. Role of the central nucleus of the amygdala in the control of blood pressure: descending pathways to medullary cardiovascular nuclei. Clin Exp Pharmacol Physiol. 2005;32(5–6):450–6.

    Article  CAS  PubMed  Google Scholar 

  29. Nashiro K, Sakaki M, Mather M. Age differences in brain activity during emotion processing: reflections of age-related decline or increased emotion regulation. Gerontology. 2012;58(2):156–63.

    Article  PubMed  Google Scholar 

Download references


Not applicable.


This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (grant number 2022R1A2C2010463).

Author information

Authors and Affiliations



JY Min and KB Min supervised the study; Donghoon Kim, JY Min, and KB Min designed the study and D Kim, JY Choi, J Lee, and HJ Lee collected the data. D Kim and KB Min analyzed and interpreted of data; D Kim drafted and revised the manuscript; All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jin-Young Min or Kyoung-Bok Min.

Ethics declarations

Ethics approval and consent to participate

All methods were carried out in accordance with relevant guidelines and regulations including Declaration of Helsinki. Study protocols were approved by the Institutional Ethics Review Board of the Veterans Health Service Medical Center (IRB No. BOHUN 2021–02–024, BOHUN 2022–05–006). All the participants have given their informed consent to participate in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, D., Lee, J., Choi, JY. et al. Association between health-related quality of life and heart rate variability in elderly individuals with cognitive impairment in Korea: cross-sectional study. BMC Geriatr 23, 847 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: