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OPEN

Pulse rate variability
and health‑related quality of life
assessment with the Short Form‑8
Japanese version in the general
Japanese population
Isao Saito 1*, Koutatsu Maruyama 2, Kanako Yamauchi 3, Yayoi Funakoshi 1, Tadahiro Kato 4,
Ryoichi Kawamura 5, Yasunori Takata 5 & Haruhiko Osawa 5
We aimed to investigate the association between pulse rate variability (PRV) and health-related
quality of life (HRQOL) in the general population. A cross-sectional study was conducted with 5908
Japanese men and women aged 30–79 years. PRV was assessed at rest using 5-min recordings ofpulse waves with a photoplethysmographic signal from a fingertip sensor, and the time and frequency
domains of PRV were determined. HRQOL was assessed with the Short Form-8 (SF-8) Japaneseversion, and poor HRQOL was defined as an SF-8 sub-scale score < 50. A test for nonlinear trends was
performed with the generalized additive model with a smoothing spline adjusted for confounders.
The lowest multivariable-adjusted odds ratios for poor physical component score were found in those
who had second or third quartile levels of standard deviation of normal-to-normal intervals (SDNN)
and root mean square of successive difference (RMSSD), and high-frequency (HF) power and trendedslightly upward in the higher levels. PRV-derived parameters were nonlinearly associated with poor
physical component scores. In conclusion, reduced PRV-derived SDNN, RMSSD and HF power wereassociated with poor HRQOL in the domain of physical function. Higher levels of these parameters did
not necessarily translate into better HRQOL.
Health-related quality of life (HRQOL) is measured as an outcome for chronic disease and medical evaluations in clinical settings and has been measured in the general p­ opulation1,2. HRQOL scores consist of physicaland mental components and are decreased in individuals with low physical a­ ctivity3, ­insomnia4, ­diabetes5, and­hypertension6. Furthermore, poor self-rated health, which is one of the domains of H
­ RQOL7, strongly predicts
mortality and major ­outcomes8–10.
Heart rate variability (HRV) parameters are indicators of autonomic nervous system f­ unction11. These indicators represent sympathetic and parasympathetic nervous system activity and balance, and cohort studies havereported that autonomic nervous system dysfunction increases the risk of m
­ ortality12 and is an independentrisk factor for incident d­ iabetes13,14 and cardiovascular d­ isease15,16. Alternatively, a systematic review found that
increases in levels of endocrine hormones, such as cortisol, and decreases in HRV were biomarkers of psychological stress, with various studies showing an association between workplace stressors and ­HRV17.
Pulse rate variability (PRV) has been used as a potential surrogate marker of HRV and is measured with photoplethysmography. A review of studies that compared photoplethysmography with electrocardiography foundthat PRV accurately reflects HRV conditions for healthy subjects when measured at ­rest18. However, some studies
indicated that PRV did not agree with the results from HRV under certain conditions, such as body posture andexercise, suggesting that PRV may be a useful biomarker distinct from ­HRV19. In line with this, a recent review

1

Department of Public Health and Epidemiology, Faculty of Medicine, Oita University, 1‑1 Idaigaoka,
Hasama‑machi, Yufu, Oita 879‑5593, Japan. 2Department of Bioscience, Graduate School of Agriculture, Ehime
University, Matsuyama, Ehime, Japan. 3Faculty of Education, Fukuyama City University, Fukuyama, Hiroshima,
Japan. 4Division of Life Span Development and Clinical Psychology, Graduate School of Education, Ehime
University, Matsuyama, Ehime, Japan. 5Department of Diabetes and Molecular Genetics, Ehime University
Graduate School of Medicine, Toon, Ehime, Japan. *email: saitoi@oita-u.ac.jp
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has suggested that it would be difficult to assume that PRV is a validated surrogate for HRV, rather that HRV
may reflect another aspect of cardiovascular ­dynamics20.
Poor HRQOL is usually explained by mental and physical conditions in individuals suffering from chronic
diseases or stressors as a w­ hole2. Consequently, poor HRQOL is associated with an increased risk of mortality
and other major ­outcomes10. However, the biological mechanisms by which poor HRQOL influences theseoutcomes have not been ­established21.
Some studies have demonstrated an association between HRV and HRQOL in patients with functional
somatic ­syndrome22, intellectual d­ isabilities23, and elderly individuals with cognitive i­mpairment24. Anotherstudy reported an association between HRV and HRQOL in 329 healthy volunteers aged 20–54 years and foundthat poor HRQOL in the physical domain was associated with a reduction in ­HRV25. Nonetheless, evidence fromthe general population is limited.
Although there is some debate as to whether the PRV-derived parameters reflect autonomic function or
another circulatory dynamics, previous studies that applied PRV in several settings suggested that PRV-derived
parameters represent some aspects of health s­ tatus20. Therefore, the purpose of this study was to examine therelationship between these indices and HRQOL in the general population.

Methods

Study subjects

We enrolled 6013 men and women, 30–79 years of age, who lived in Ozu and Toon Cities in Ehime prefecture,
Japan, from 2009 to 2012 and 2014–2018 (Toon City only). Both cities are in rural areas of Shikoku Island Japan.
We excluded subjects who did not have a PRV examination (n = 33) and who had atrial fibrillation on an electrocardiogram (ECG) (n = 37). After the exclusion of those who did not respond to the HRQOL questionnaire(n = 36), 5908 individuals remained and were included in the analysis.
This study was conducted in accordance with the Declaration of Helsinki. Written informed consent was
obtained from all participants. The study protocol was approved by the Human Ethics Review Committee of theFaculty of Medicine, Oita University (approval number, 2114).

Measurements

Blood pressure was measured twice with an automatic sphygmomanometer (BP-103iII; OMRON Colin Co.,Tokyo, Japan) with the subject in the sitting position after a rest of at least 5 min. We used the mean of the two
measurements for the analysis. The use of antihypertensive drugs was ascertained by questionnaire. Diabetes was
defined as hemoglobin A1c (HbA1c) ≥ 6.5% or current use of anti-diabetes agents. HbA1c was measured withhigh-performance liquid chromatography (Ozu City) and the immunoagglutination inhibition method (ToonCity). Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared.
HRQOL was assessed with the Short Form-8 (SF-8) Japanese version ­questionnaire1, which consists of eightsubscales—general health (GH), physical functioning (PF), role physical (RP), bodily pain (BP), vitality (VT),social functioning (SF), mental health (MH), and role emotional (RE)—and two summary scores—the physicalcomponent score (PCS), which consists of GH, PF, RP, and BP and the mental component score (MCS), which
consists of VT, SF, MH, and RE. All subscales and summary scores were standardized in the general Japanese
population (mean = 50 and standard deviation = 10). In the present study, poor HRQOL was defined as a scoreof less than 50 for each subscale and summary score.
A self-administered questionnaire was used to assess medical history (heart disease, stroke, and kidney disease), smoking habits (≥ 20 cigarettes/day, 1–19 cigarettes/day, past smoker, and never smoker), regular alcohol
drinking, exercise habits, and sleep duration. Current smokers were defined as individuals who had smoked
100 cigarettes in their lifetime and who currently smoked cigarettes. Exercise was defined as doing continuous
sports or physical exercise ≥ 2 times/week during the year. Unhealthy sleep duration was defined as < 6-h or ≥ 9-hof sleep per d­ ay26.

Assessment of autonomic function

Analysis of PRV was performed with the TAS9 device (YKC Co. Ltd, Japan) and its software to assess cardiac
autonomic control. The pulse rate was recorded for 5 min with a fingertip pulse wave sensor using a photoplethysmographic signal. The sensor was attached to the index finger, with the subject in the sitting position after
a rest of at least 5 min, with the software filtering out arrhythmias and artifacts. Filtering was based on the Butterworth bandpass filter method to detect the normal-to-normal intervals a­ ccurately27. The five-minute PRV
measurement procedure, including PRV indices and standardization, was based on the recommendation of theEuropean Society of Cardiology and the North American Society of Pacing and E­ lectrophysiology28. The Task
Force recommends appropriate HRV parameters in the time and frequency domains and states that five-minuterecordings with a stationary system are preferable. The sampling frequency for all recordings was 1000 Hz. PRV
obtained from the fingertip pulse wave sensor is comparable with that of an ECG ­signal29.
All PRV assessments were done between 10:00 am and 12:00 pm to control for daily variation. We maintained
a comfortable room temperature with air conditioners. Participants were required to have at least a five-minuterest before the measurement. The TAS9 software-provided resting heart rate (RHR) was expressed as beats per
minute (bpm), and the following time-domain measures of PRV were then determined: standard deviation of
normal-to-normal intervals (SDNN), root mean square of successive difference (RMSSD), and percentage differences between normal NN intervals > 50 ms (pNN50). PRV in the frequency domain was calculated using thefast Fourier Transform with a sample rate of 3 Hz. The area under the curve fitted by the power spectral density
was measured for the frequency bands of low-frequency (LF) power (0.04–0.15 Hz) and high-frequency (HF)
power (0.15–0.40 Hz).
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Physical component score
Number of subjects

Poor

Good

3237

2671

Mental component score
P value

Poor

Good

2626

3282

P value

Male sex, n (%)

1204 (37.2)

1094 (41.0)

0.003 924 (35.2)

1374 (41.9)< 0.001

Age, years

62.2 ± 10.4

60.3 ± 10.7

< 0.001 59.7 ± 11.2

62.6 ± 9.9< 0.001< 0.001

Body mass index, kg/m2

23.3 ± 3.4

22.8 ± 3.1< 0.001 22.9 ± 3.3

23.2 ± 3.3

Resting heart rate, bpm

68.9 ± 10.3

68.4 ± 9.7

0.025 69.2 ± 9.9

68.3 ± 10.1

0.001

Systolic blood pressure, mmHg

129.7 ± 19.4

128.2 ± 19.8

0.005 127.5 ± 19.7

130.2 ± 19.4

< 0.001< 0.001

Hypertension, n (%)

1535 (47.4)

1039 (38.9)< 0.001 1066 (40.6)

1508 (46.0)

Diabetes, n (%)

272 (8.4)

161 (6.0)< 0.001 186 (7.1)

247 (7.5)

0.52

Anti-hypertensive drug use, n (%)

967 (29.9)

544 (20.4)< 0.001 635 (24.2)

876 (26.7)

0.028

0.26

1894 (72.1)

2270 (69.2)

0.070

461 (17.6)

658 (20.1)

Smoking status, n (%)
Never smoker

2311 (71.4)

1853 (69.4)

Past smoker

588 (18.2)

531 (19.9)

1–19 cigarettes/day

126 (3.9)

117 (4.4)

103 (3.9)

140 (4.3)≥ 20 cigarettes/day

212 (6.6)

170 (6.4)

168 (6.4)

214 (6.5)

Regular alcohol drinker, n (%)

1519 (46.9)

1357 (50.8)

0.003 1259 (47.9)

1617 (49.3)

0.31

Regular exercise, n (%)

1441 (44.5)

1345 (50.4)< 0.001 1157 (44.1)

1629 (49.6)< 0.001

Unhealthy sleep duration, n (%)

504 (15.6)

313 (11.7)< 0.001 416 (15.8)

401 (12.2)< 0.001

Medical histories, n (%)
Heart disease

303 (9.4)

139 (5.2)< 0.001 202 (7.7)

240 (7.3)

0.58

Stroke137 (4.2)

77 (2.9)0.006 86 (3.3)

128 (3.9)

0.20

Kidney disease

9 (0.3)8 (0.3)0.88

7 (0.2)0.23

10 (0.4)

PRV parameters
lnSDNN, ms

3.61 ± 0.52

3.65 ± 0.48

0.004 3.63 ± 0.50

3.63 ± 0.50

0.77

lnRMSSD, ms

3.38 ± 0.67

3.39 ± 0.63

0.34

3.37 ± 0.64

3.39 ± 0.65

0.161

pNN50, %47.8 ± 18.9

50.0 ± 18.1

< 0.001 49.3 ± 18.9

48.5 ± 18.3

0.115

lnLF power, ­ms2

4.99 ± 1.33

5.12 ± 1.23

< 0.001 5.06 ± 1.27

5.05 ± 1.30

0.82

lnHF power, ­ms2

4.63 ± 1.35

4.70 ± 1.24

0.033 4.67 ± 1.28

4.65 ± 1.31

0.55

Table 1.  Population characteristics according to physical and mental component scores. PRV pulse ratevariability, SDNN standard deviation of the normal-to-normal intervals, RMSSD root mean square of thesuccessive differences of NN intervals, pNN50 percentage differences between normal NN intervals > 50 ms, LF
low-frequency, and HF high-frequency.

In general, SDNN reflects the total modulation of autonomic function, while RMSSD and pNN50 reflect vagal
control of the heart. LF power reflects both sympathetic and parasympathetic stimulation of the heart, and HF
power reflects vagal a­ ctivity28.
Although PRV-derived parameters were determined based on five-minute recordings, we confirmed that they
were significantly correlated with those provided by 24-h Holter ECG ­assessment30.

Statistical analysis

Because of their skewed distribution, SDNN, RMSSD, LF power, and HF power were log-transformed before
analysis. An analysis was also carried out to measure differences in the means of age, BMI, RHR, systolic bloodpressure (SBP), and the proportions of men and women, subjects with hypertension, diabetes, smoking, regularalcohol drinking, regular exercise, unhealthy sleep duration, and medical history, grouped by PCS and MCS(poor versus good conditions). The differences between groups were tested with a t-test or chi-square test.
While stratified by community, we created a sex and age-adjusted model, and a multivariable model including
covariates, i.e., BMI (continuous), hypertension (Yes or No), diabetes (Yes or No), smoking (≥ 20 cigarettes, 1–19
cigarettes, past smoker, and never smoker), regular alcohol drinking (Yes or No), regular exercise (Yes or No),
unhealthy sleep duration (Yes or No), and medical history (Yes or No) and to estimate odds ratios (ORs) and
95% confidence intervals (Cis) for poor HRQOL. Trend tests were performed in the two models with linear or
quadratic equations for a linear or nonlinear trend test, respectively. To illustrate the nonlinear trends of lnSDNN, lnRMSSD, lnLF power, and lnHF power for poor HRQOL, the generalized additive model (GAM) with asmoothing spline [degree of freedom = 3] was adopted in the logistic regression m­ odel31, adjusting for covariates
in the multivariable-adjusted model. Statistical significance was assumed at P < 0.05. All statistical analyses were
performed with SAS software, version 9.4 (SAS Institute, Inc., Cary, NC, USA).

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Subscale of SF-8 (Multivariable-adjusted models)Parameter

RHR

SDNN

RMSSD

pNN50

LF power

HF power

General health

Physical function

Role physicalBodily pain

Category

OR

OR

OR

OR

< 60 bpm

1.00

95% CI

95% CI

1.00

95% CI

1.00

95% CI

1.00

60–69

1.15

0.95–1.38

1.16

1.00–1.36

1.07

0.92–1.25

1.02

0.88–1.18

70–79

1.21

0.99–1.47

1.08

0.91–1.27

1.07

0.91–1.26

0.99

0.84–1.16

80 +

1.27

1.00–1.60

1.25

1.02–1.52

1.19

0.98–1.44

1.04

0.86–1.26

Linear P

0.017

0.035

0.036

0.95

Nonlinear P

0.33

0.092

0.147

0.92

Q1 (lowest)

1.00

1.00

1.00

1.00

Q2

0.82

0.69–0.98

0.82

0.70–0.95

0.79

0.68–0.92

0.92

0.80–1.07

Q3

0.84

0.71–1.01

0.93

0.80–1.09

0.94

0.81–1.10

0.95

0.82–1.11

Q4 (highest)

0.86

0.72–1.03

0.84

0.72–0.98

0.78

0.67–0.90

0.96

0.83–1.12

Linear P

0.90

0.57

0.076

0.94

Nonlinear P< 0.001< 0.001< 0.001

0.96

Q1 (lowest)

1.00

1.00

1.00

1.00

Q2

0.87

0.73–1.03

0.87

0.73–1.03

0.89

0.77–1.03

0.93

0.80–1.07

Q3

0.89

0.75–1.06

0.89

0.75–1.06

0.91

0.78–1.05

1.03

0.89–1.19

Q4 (highest)

0.82

0.69–0.98

0.82

0.69–0.98

0.91

0.79–1.06

0.96

0.83–1.11

Linear P

0.39

0.92

0.30

0.61

Nonlinear P

0.004< 0.001< 0.001

0.72

Q1 (lowest)

1.00

1.00

1.00

1.00

Q2

0.96

0.80–1.14

0.84

0.73–0.98

0.85

0.73–0.98

1.10

0.95–1.27

Q3

0.93

0.78–1.11

0.88

0.76–1.03

0.87

0.75–1.01

0.99

0.86–1.15

Q4 (highest)

0.76

0.64–0.92

0.86

0.73–1.00

0.87

0.74–1.01

1.01

0.87–1.18

Linear P

0.002

0.018

0.023

0.61

Nonlinear P

0.35

0.006

0.001

0.59

Q1 (lowest)

1.00

1.00

1.00

1.00

Q2

0.77

0.64–0.92

0.78

0.67–0.90

0.86

0.75–1.00

0.92

0.80–1.07

Q3

0.79

0.66–0.95

0.85

0.73–0.99

0.90

0.77–1.04

0.87

0.75–1.01

Q4 (highest)

0.81

0.68–0.97

0.86

0.73–1.00

0.83

0.72–0.97

0.93

0.80–1.08

Linear P

0.18

0.20

0.050

0.20

Nonlinear P

0.014< 0.001

0.079

0.81

Q1 (lowest)

1.00

1.00

1.00

1.00

Q2

0.78

0.65–0.93

0.79

0.68–0.91

0.80

0.69–0.93

0.87

0.75–1.01

Q3

0.79

0.66–0.94

0.86

0.74–1.00

0.82

0.71–0.96

0.85

0.73–0.99

0.69–0.98

0.91

0.78–1.06

0.88

0.75–1.02

0.90

0.78–1.05

Q4 (highest)

0.82

Linear P

0.40

0.98

0.21

0.22

Nonlinear P

0.001< 0.001< 0.001

0.76

Table 2.  Multivariable-adjusted ORs and 95% CIs according to RHR levels and quartile of PRV parameters
for poor general health, physical functioning, role physical, and bodily pain (n = 5908). ORs were adjusted forsex, age, BMI, hypertension, diabetes, smoking, regular alcohol drinking, regular exercise, unhealthy sleep
duration, and medical histories stratified by community. Ranges: SDNN (ms), Q1: < 3.30 (log), Q2: 3.30–3.58,
Q3: 3.59–3.91, Q4: 3.92+; RMSSD (ms), Q1: < 3.00 (log), Q2: 3.00–3.32, Q3: 3.33–3.73, Q4: 3.74+; pNN50 (%),
Q1: < 37, Q2: 37–50, Q3: 51–62, Q4: 63+; LF (­ ms2), Q1: < 4.23 (log), Q2: 4.23–4.94, Q3: 4.95–5.80, Q4: 5.81+;
and HF (­ ms2), Q1: < 3.83 (log), Q2: 3.83–4.50, Q3: 4.51–5.30, Q4: 5.31+ OR odds ratio, CI confidence interval,
RHR resting heart rate, PRV pulse rate variability, SDNN standard deviation of the normal-to-normal intervals,
RMSSD root mean square of the successive differences of NN intervals; pNN50, percentage differences
between normal NN intervals > 50 ms; LF low-frequency, and HF high-frequency.

Results

Table 1 shows the comparison of variables for cardiovascular risk factors and lifestyle between groups with poorand good PCS and MCS. The mean age was higher in the poor PCS group and lower in the poor MCS group.
Individuals in the poor PCS group had higher means or percentages of BMI, SBP, diabetes, anti-hypertensive
drug use, and medical history. PRV-derived parameters were significantly lower in the poor PCS group except
for RMSSD. On the other hand, the poor MCS group had lower levels of BMI and SBP, and a significant difference in PRV-derived parameters between groups was not seen. The percentage of subjects with regular exercise
habits was significantly lower in both the poor PCS and MCS groups compared with the good PCS and MCS

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Subscale of SF-8 (Multivariable-adjusted models)Vitality
Parameter

RHR

SDNN

RMSSD

pNN50

LF power

HF power

CategoryOR< 60 bpm

1.00

95% CI

Social functioning

Mental healthRole emotional

OR

OR

OR

95% CI

1.00

95% CI

1.00

95% CI

1.00

60–69

1.18

1.01–1.38

1.03

0.88–1.21

1.21

1.02–1.43

1.17

1.01–1.36

70–79

1.32

1.12–1.56

1.11

0.94–1.32

1.24

1.04–1.48

1.28

1.09–1.51

1.28–1.90

1.09

0.89–1.34

1.23

1.00–1.52

1.12

0.92–1.36

80+

1.56

Linear P< 0.001

0.172

0.077

0.131

Nonlinear P

0.80

0.68

0.38

0.096

Q1 (lowest)

1.00

1.00

1.00

Q2

0.83

0.72–0.96

0.89

0.76–1.04

0.92

0.79–1.08

0.90

0.78–1.04

Q3

0.85

0.73–0.99

0.97

0.83–1.14

0.88

0.75–1.03

0.93

0.80–1.08

0.72–0.97

0.94

0.81–1.10

0.86

0.73–1.01

0.88

0.75–1.02

1.00

Q4 (highest)

0.84

Linear P

0.080

Nonlinear P
Q1 (lowest)
Q2

0.82

0.71–0.95

0.92

0.79–1.07

0.83

0.71–0.97

0.89

0.76–1.02

Q3

0.89

0.77–1.03

0.89

0.76–1.04

0.81

0.70–0.95

0.83

0.71–0.96

0.69–0.93

0.92

0.79–1.08

0.83

0.71–0.97

0.83

0.72–0.96

0.47

0.121

0.123

0.23

< 0.001

0.001

0.027

1.00

1.00

1.00

1.00

Q4 (highest)

0.81

Linear P

0.054

Nonlinear P
Q1 (lowest)
Q2

0.92

0.79–1.06

0.84

0.72–0.98

0.91

0.78–1.07

0.94

0.81–1.09

Q3

0.86

0.74–1.00

0.88

0.76–1.03

0.86

0.73–1.01

0.87

0.75–1.01

0.70–0.95

0.84

0.72–0.99

0.88

0.75–1.03

0.87

0.74–1.01

0.41

0.042

0.031

0.294

< 0.001

0.143

0.022

1.00

1.00

1.00

1.00

Q4 (highest)

0.81

Linear P< 0.001

0.033

0.065

0.053

Nonlinear P

0.26

0.033

0.51

0.63

Q1 (lowest)

1.00

1.00

1.00

Q2

0.88

0.75–1.02

0.88

0.75–1.03

0.86

0.74–1.01

0.83

0.72–0.97

Q3

0.90

0.77–1.05

0.89

0.76–1.04

0.83

0.71–0.97

0.82

0.71–0.96

0.75–1.03

0.81

0.69–0.95

0.85

0.72–1.00

0.84

0.72–0.98

1.00

Q4 (highest)

0.88

Linear P

0.043

0.038

0.029

0.041

Nonlinear P

0.011

0.149

0.42

0.36

Q1 (lowest)

1.00

1.00

1.00

Q2

0.77

0.66–0.89

0.89

0.76–1.04

0.88

0.75–1.03

0.89

0.77–1.04

Q3

0.83

0.71–0.97

0.92

0.79–1.08

0.83

0.71–0.97

0.89

0.77–1.03

0.69–0.93

0.94

0.80–1.10

0.92

0.79–1.08

0.90

0.78–1.05

1.00

Q4 (highest)

0.80

Linear P

0.027

0.38

0.144

0.160

Nonlinear P

0.011

0.062

0.38

0.35

Table 3.  Multivariable-adjusted ORs and 95% CIs according to RHR levels and quartile of PRV parameters
for poor vitality, social functioning, mental health, and role emotional (n = 5908). Table footnote is the same as
Table 2.

groups, and that of subjects with unhealthy sleep duration was significantly higher in both the poor PCS andMCS groups compared with the good PCS and MCS groups.
Sex- and age-adjusted ORs and 95% CIs for poor HRQOL subscales according to quartile of PRV parameter(as referenced to the lowest quartile) and results of linear and nonlinear tests are presented (S1 and S2 Tables).
ORs of RHR for subjects with the poor GH, PF, RP, and VT increased linearly, and the OR of RHR for 80 + bpm
versus < 60 bpm in individuals with poor VT increased to 1.56 (95% CI 1.29–1.89). The association between
SDNN and poor GH, PF, RP, VT, SF, and RE showed a significant nonlinear trend rather than a linear trend.
Similarly, for RMSSD, LF power, and HF power, there were significant nonlinear trends toward the poor SF-8
subscale groups.
Tables 2 and 3 show multivariable-adjusted ORs and 95% CIs and results of linear and nonlinear trend tests
after multivariable adjustment. The linear association between RHR; and poor GH, PF, RP, and VT remained
significant after adjustment for covariates. Nonlinear trends in SDNN, RMSSD, and HF power were found forpoor GH, PF, and RP. The nonlinear trends in SDNN, RMSSD, LF power, and HF power for poor VT weakened
consistently; however, the ORs of RMSSD and HF power and the nonlinear trends remained significant.
Regarding the summary component of PCS and MCS, SDNN, RMSSD, and HF parameters were associatedwith poor PCS in nonlinear manners (Table 4). On the other hand, the associations were not clear for poor MCS.
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Subscale of SF-8 (Multivariable-adjusted
models)Poor physicalcomponent score
Parameter

RHR

SDNN

RMSSD

pNN50

LF power

HF power

CategoryOR< 60 bpm

1.00

95% CI

Poor mental
component score
OR

95% CI

1.00

60–69

1.09

0.93–1.27

1.12

0.96–1.30

70–79

1.00

0.85–1.18

1.25

1.06–1.47

80+

1.28

1.05–1.55

1.18

0.97–1.44

Linear P

0.057

0.116

Nonlinear P

0.143

0.185

Q1 (lowest)

1.00

1.00

Q2

0.81

0.70–0.94

0.91

0.79–1.06

Q3

0.93

0.80–1.08

0.89

0.77–1.04

Q4 (highest)

0.83

0.71–0.97

0.91

0.78–1.06

Linear P

0.148

0.182

Nonlinear P< 0.001

0.26

Q1 (lowest)

1.00

1.00

Q2

0.82

0.71–0.96

0.87

0.75–1.00

Q3

0.96

0.83–1.12

0.84

0.72–0.97

Q4 (highest)

0.90

0.78–1.04

0.82

0.71–0.95

Linear P

0.46

0.041

Nonlinear P

0.047

0.29

Q1 (lowest)

1.00

1.00

Q2

0.89

0.76–1.03

0.85

0.73–0.99

Q3

0.85

0.73–0.99

0.85

0.74–0.99

Q4 (highest)

0.85

0.73–0.99

0.88

0.75–1.02

Linear P

0.018

0.139

Nonlinear P

0.056

0.103

Q1 (lowest)

1.00

1.00

Q2

0.82

0.70–0.95

0.91

0.79–1.06

Q3

0.88

0.76–1.03

0.89

0.77–1.04

Q4 (highest)

0.83

0.72–0.97

0.87

0.75–1.02

Linear P

0.029

0.091

Nonlinear P

0.017

0.85

Q1 (lowest)

1.00

1.00

Q2

0.81

0.70–0.95

0.90

0.77–1.04

Q3

0.81

0.70–0.94

0.91

0.78–1.06

0.74–1.00

0.96

0.82–1.11

Q4 (highest)

0.86

Linear P

0.18

0.37

Nonlinear P

0.007

0.56

Table 4.  Multivariable-adjusted ORs and 95% CIs according to RHR levels and quartile of PRV parameters forpoor physical component score and mental component score (n = 5908). Table footnote is the same as Table 2.

The associations between SDNN, RMSSD, and HF power; and the summary components of SF-8 were illustrated using GAM for nonlinear trends (Fig. 1). The spline lines for poor PCS increased in those who had lower
SDNN, RMSSD, and HF power conditions and trended slightly upward in the higher levels. The nonlinear trends
were all significant (P for nonlinear trend: 0.040 for SDNN, 0.036 for RMSSD, and 0.019 for HF power). On thecontrary, the effect on poor MCS was almost even across the levels of PRV-derived parameters.

Discussion

We found that PRV-derived parameters, such as SDNN, RMSSD, and HF power were associated with poor
HRQOL on the GH, PF, RP, VT, and SF subscales of the SF-8. Factors related to the PCS were more strongly
associated compared with those related to the MCS. The association was robust after adjustment for several
confounders. RHR was positively correlated with a decrease in HRQOL, mainly represented by GH, PF, and VT.
GAM suggested that the relationship between PRV-derived parameters and poor PCS was nonlinear.
Following the interpretation of HRV parameters, SDNN represents overall autonomic function, and RMSSD
and HF power reflect parasympathetic regulation, but caution should be used in the interpretation of PRVderived parameters. We measured resting PRV in participants from the general population. The accuracy of PRV
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Figure 1.  Associations between SDNN, RMSSD, and HF power; and the summary components of SF-8 using
the generalized additive model with a smoothing spline [degree of freedom = 3]. Covariates included variablesof sex, age, community, BMI, hypertension, diabetes, smoking, regular alcohol drinking, regular exercise,unhealthy sleep duration, and medical history. PCS physical component score, MCS mental component score,
SDNN standard deviation of the normal-to-normal intervals, RMSSD root mean square of the successive
differences of NN intervals, and HF high-frequency.

measurements has been r­ eviewed18, and PRV was found to be sufficiently accurate as a measure of HRV whenmeasured at rest in healthy subjects. A five-minute measurement of PRV (LF power, HF power, LF/HF ratio)in the present study was moderately correlated with the measurement given from 24-h recordings of H
­ RV30.
Nevertheless, it is possible that PRV represents the different cardiovascular dynamics than HRV, and further
research is needed, including research in relation to autonomic ­function20. Whereas the SF-8 could evaluate
HRQOL related to both physical and mental health. HRQOL often decreases due to physical or mental ­stresses5,32
and socioeconomic s­ tatus33.
Although we analyzed our data with a multivariable-adjusted model, including BMI, hypertension, diabetes,smoking, regular alcohol drinking, regular exercise, unhealthy sleep duration, and medical history, these factors
did not attenuate the association. This implied that the association was not explained only by the confounders
which strongly affect both PRV-derived parameters and HRQOL.
The SF-8 Japanese version was developed as a short version of the SF-362 and the two versions are­comparable34. Although the two summary scores are classified into the physical and mental aspects, GH and VT
were interpreted to be involved with both physical and mental ­conditions2. SF, MH, and RE, which mainly comprise the MCS, also showed a trend toward lower ORs with some higher PRV-derived parameters. Thus, we could
not deny the association of poor HRQOL with psychological causes. Vital exhaustion, a psychological stressor,
has been associated with decreased SDNN and LF p
­ ower35. PRV-derived parameters might be influenced bypsychological factors, probably leading to a decline in self-rated health as represented in the GH or VT subscales.
Of note, for some PRV-derived parameters, higher values were not preferable for HRQOL for reasons thatare not well understood. Higher HRV was associated with abnormal heart rate patterns in the elderly which
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increased the mortality r­ isk36. In the present study, individuals with atrial fibrillation on ECG were excluded;thus, this effect was not considered. When values of PRV-derived parameters are very high, physicians should
detect some of the causes for lower self-rated health.
Given the well-established link between socioeconomic inequalities and health, the association between PRV
and HRQOL may be confounded or mediated by the socioeconomic status or underlying chronic diseases, which
we could not access. Socioeconomic status affected psychological stress assessed by depressed HRV ­conditions37,
though it will also be required to examine the association using PRV-derived parameters.
Although we analyzed a large population to determine an association between PRV and HRQOL in Japan,
several limitations should be mentioned. First, we did not have information on socioeconomic status, such as
work, income, and education. Indeed, socioeconomic status affects HRV parameters as a stressor and affects­HRQOL33. Second, we could not identify the various kinds of illnesses the participants may have had or the
medications they were taking, and that possibly influenced PRV. Third, a causal relationship between PRV-derived
parameters and HRQOL scores could not be determined, because a possible bi-directional impact was consideredin this cross-sectional study. A longitudinal study is needed to investigate this relationship. Finally, a variety ofreasons may explain the difference from HRV, including the effect of pulse wave noise, the measurement position, and the location of the measurement ­sensor20. In particular, the pre-processing techniques, including themethod of removing noise in the pulse wave signal, are dependent on the measurement device, and the lack ofstandardization is a major issue.
In conclusion, reduced PRV-derived SDNN, RMSSD and HF power were associated with poor HRQOL in thedomain of physical function. Higher levels of these parameters did not necessarily translate into better HRQOL.
Data suggest that maintaining PRV-derived parameters in the range indicated as the second or third quartile of
PRV-derived parameters is important for good HRQOL. PRV measurement is a useful and non-invasive tool toassess an individual’s perceived health.

Data availability

All data generated or analyzed during this study are included in this published article (and its Supplementary
Information files).
Received: 16 September 2023; Accepted: 15 February 2024

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Acknowledgements

We wish to thank the staff and participants of the Toon Health Study and Ozu Cohort II, and the municipal
authorities, officers, and health professionals of both cities for their valuable contributions.

Author contributions

I.S.: Conceptualization, Formal Analysis, Funding Acquisition (equal), Investigation (equal), Methodology,
Resources (equal), Validation, and Writing—Original Draft Preparation; K.M.: Data Curation, Investigation(equal), Funding Acquisition (equal), and Writing—Review and Editing (equal); K.Y., Y.F., Y.T. and R.K.: Investigation (equal) and Writing—Review and Editing (equal); T.K. (Ozu Cohort II) and H.O. (Toon Health Study):
Project Administration.

Funding

This study was supported, in part, by Grants-in-Aid for Scientific Research from the Ministry of Education,
Culture, Sports, Science, and Technology of Japan (Grants-in-Aid for Research B: no. 21H03202 in 2021–2024)
and AMED under Grant Number JP23rea522104.

Competing interests

The authors declare no competing interests.

Additional information

Supplementary Information The online version contains supplementary material available at https://​doi.​org/​
10.​1038/​s41598-​024-​54748-9.
Correspondence and requests for materials should be addressed to I.S.
Reprints and permissions information is available at www.nature.com/reprints.
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