AJH
2005; 18:1218 –1225
BP Measurement
Factors Affecting Home-Measured Resting
Heart Rate in the General Population
The Ohasama Study
Junko Yamaguchi, Atsushi Hozawa, Takayoshi Ohkubo, Masahiro Kikuya,
Takashi Ugajin, Kaori Ohmori, Junichiro Hashimoto, Haruhisa Hoshi,
Hiroshi Satoh, Ichiro Tsuji, and Yutaka Imai
Background: We recently demonstrated that a homemeasured resting heart rate (HR) can predict cardiovascular disease mortality, and it is possible that the
determinants of home HR are different from casual HR.
Therefore, clarifying the determinants of home HR should
be useful.
Methods: Home HR was obtained using a self-monitored blood pressure (BP) measuring device. The impact
of factors including home-measured BP and lifestyle on
home HR was examined in 1275 members of the general
Japanese population aged ⱖ40 years.
Results: Multivariate linear regression analysis demonstrated that younger age (␤ ⫽ ⫺0.08, P ⱕ .01), current
smoking (␤ ⫽ 3.22, P ⱕ .01), female gender (␤ ⫽ 2.07, P
ⱕ .01), and sedentary lifestyle (walking for ⱕ1 h/day) (␤
⫽ 2.43, P ⱕ .01) were determinants of elevated morning
home HR. No significant association was observed between home HR and home systolic BP, whereas casual HR
was significantly and positively associated with casual
systolic BP. The difference between casual and home HR
was also significantly and positively associated with the
difference between casual and home systolic BP, suggesting that positive association between BP and HR obtained
in clinic settings would be a reflection of the so-called
white-coat effect.
Conclusions: We observed that, with the exception of
BP, most determinants of home HR were consistent with
the determinants observed in previous studies using casual
HR. These results suggest that reduction of home HR
through modification of smoking habit or sedentary lifestyle may have a potential to decrease cardiovascular risk
in addition to decreasing in these modifiable risk factor per
se. Am J Hypertens 2005;18:1218 –1225 © 2005 American Journal of Hypertension, Ltd.
Key Words: Heart rate, lifestyle, smoking, ambulatory
blood pressure monitoring.
eart rate (HR) varies widely depending on the
degree of psychological stress experienced.1 It
has also been demonstrated that both blood pressure (BP) and HR values often rise immediately when
measured during a physician’s visit.2 In contrast, selfmeasurement of BP at home (home BP) makes it possible
to obtain multiple measurements over a long period in
H
familiar, nonthreatening surroundings, thus avoiding the
so-called white-coat effect.3,4 As a result, home-measured
BP has been found to have better predictive power than
casual BP measurements taken by medical practitioners.5–7 Such advantages could also apply to home measurements of heart rate (home HR) as assessed by a device
used for home BP measurement. Accordingly, we first
Received December 14, 2004. First decision March 2, 2005. Accepted
April 3, 2005.
From the Departments of Clinical Pharmacology and Therapeutics
(JY, TU, YI), Public Health and Forensic Medicine (AH, KO, IT),
Planning for Drug Development and Clinical Evaluation (TO, MK, JH),
and Environmental Health Science (HS), Tohoku University Graduate
School of Pharmaceutical Science and Medicine, Sendai, Japan; Tohoku
University 21st Century COE Program “Comprehensive Research and
Education Center for Planning of Drug Development and Clinical Evaluation” (TO, JH, HS, YI), Sendai, Japan; and Ohasama Hospital (HH),
Iwate, Japan.
This work was supported by Grants for Scientific Research
(12877163, 13470085, 13671095, 14370217, 15790293, and
JSPS1410301) from the Ministry of Education, Culture, Sports, Science
and Technology; by Health Science Research Grants on Health Services
(13170201, 13072101, H12-Medical Care-002) and H15-17-Gan Yobou039 from the Ministry of Health, Labour and Welfare, Japan; and by
Research Grants from, Junkanki-byo Itaku Kenkyu 11C-5 (1999 and
2000), and the Japan Atherosclerosis Prevention Fund (2000 to 2003),
and Uehara Memorial Foundation (2002), and Grant from Japan Cardiovascular Research Foundation (2002), and the Takeda Medical Research
Foundation (2003).
Address correspondence and reprint requests to Dr. Yutaka Imai,
Department of Clinical Pharmacology and Therapeutics, Tohoku University Graduated School of Pharmaceutical Science and Medicine, 1-1
Seiryomachi, Aobaku, Sendai, 980-8574, Japan; e-mail: [email protected]
0895-7061/05/$30.00
doi:10.1016/j.amjhyper.2005.04.009
© 2005 by the American Journal of Hypertension, Ltd.
Published by Elsevier Inc.
AJH–September 2005–VOL. 18, NO. 9
demonstrated that home HR is a strong predictor of the
risk of cardiovascular disease (CVD) mortality, independent of BP values and other possible confounding factors.8
Thus, it would be worthwhile to clarify modifiable factors
that affect home HR for better prevention of CVD.
Although a few studies showed higher HR values in
casual settings compared with HR measured at home,9 no
detailed information on the factors that affect home HR
values has been published. Because our previous study
demonstrated that modifiable lifestyle-related factors such
as smoking habit, independently associated with the whitecoat effect (defined based on the difference between casual
BP and home BP),10 HR might also be affected by differential factors between home and casual measurement.
However, there have been no studies to clarify the determinants of home HR including lifestyle-related factors.
Thus we conducted the present analysis to identify factors
that might affect home HR values, including home BP and
lifestyle-related factors, in the general Japanese population.
Methods
Study Population
This study was performed as a part of the Ohasama study,
a community-based BP measurement project.11,12
Ohasama is a town in Iwate prefecture, Japan. In February
1998, the total population of three of the four regions of
the town numbered 4208. Of these individuals, 2769 were
ⱖ40 years of age. Among this subgroup, 621 worked out
of town and were considered ineligible for the study. This
exclusion was necessary because our project also included
ambulatory BP measurements; in order for us to attach the
ambulatory BP-monitoring devices to the study subjects,
they had to be in town on working days. Of the 2148
subjects remaining, those who were hospitalized (n ⫽
124), mentally ill or bedridden (n ⫽ 40) were not invited
to participate. Thus a total of 1984 subjects were eligible
for the study. Of these, 1662 (84%) gave informed consent
and participated in the BP-measuring program. We have
previously confirmed that these subjects were representative of the total population.13 In the present study, we
measured HR on the basis of cuff-oscillation of the brachial pulse; therefore, the value could be called a pulse
rate. However, because of previous studies that called such
values HR values8,9 we also used the term HR in this
study. Home measurements of morning HR and BP were
obtained from 1570 subjects who collected their own data
on at least three occasions (3 days) during the 4-week
study period. This inclusion criterion was based on our
previous observation that the average HR and BP for the
first three measurement occasions did not differ significantly from the mean for the entire study period.8,11 We
also excluded subjects with a history of chronic heart
failure, ischemic heart disease, and significant arrhythmias, such as atrial fibrillation, sick sinus syndrome, and
permanent pacemaker implantation (n ⫽ 107), and those
FACTORS AFFECTING HOME HEART RATE
1219
who did not answer the questions about lifestyle and
health (n ⫽ 188). Therefore, the study population consisted of 1275 individuals (77% of 1662 representative
participants13). Age and home systolic blood pressure
(SBP) were significantly lower in the 1275 study subjects
compared with the 387 excluded subjects (age: 1275 subjects, 61.7⫾11.8; 387 subjects, 66.0⫾12.4, P ⬍ .001,
SBP: 1275 subjects, 123.3⫾15.0 mm Hg; 387 subjects,
127.6⫾16.5 mm Hg, P ⬍ .001). Gender distribution was
not significantly different (1275 subjects, 41% men and
59% women; 387 subjects, 47% men and 53% women, P
⫽ .06). Casual HR and BP measurements were obtained
from 890 (70%) of these 1275 subjects.
Measurement of BP and HR
The procedures used for the casual and home HR and
BP measurements and the measuring device have been
described in detail in previous reports.8,11,14,15 Briefly,
physicians and public health nurses conducted health
education classes to inform the participants about the
home HR and BP recording method, to teach them how
to measure their own HR and BP, and to validate their
ability to perform these tasks on a consistent basis. The
subjects were then asked to measure their HR and BP
once every morning and evening and to record the
results for 4 weeks. Measurements of morning HR and
BP were made within 1 h of waking, before breakfast or
taking any drugs, with the subject seated and having
rested for at least 2 min. Measurements of evening HR
and BP were obtained in a homologous way just before
going to bed. Home HR and BP were measured using a
HEM701C automatic device (Omron Healthcare Co.
Ltd, Kyoto, Japan), which uses the cuff-oscillometric
method to generate a digital display of HR and systolic/
diastolic BP values. Pulse interval was calculated by
pulse wave, which was differentiated by a microprocessor incorporated in the equipment. Pulse interval obtained between SBP and DBP were averaged and HR
was calculated as follows: HR (beats/min) ⫽ 60 (sec) /
average pulse interval (sec). The home HR and BP of an
individual were defined as the mean of all measurements obtained for that person. The casual HR and BP
were measured at screening settings using an USM700F device (UEDA Electronic Works Co. Ltd, Tokyo,
Japan), a fully automatic device that uses the Korotkoff
sound technique (a microphone method). After the subject had been resting in a seated position for at least 2
min, two consecutive measurements of HR and BP were
taken by a nurse or technician. The casual HR and BP
were defined as the averages of the two readings. The
devices used for the casual and home measurements
were previously validated14,15 and satisfied the criteria
of the Association for the Advancement of Medical
Instrumentation.16
1220
FACTORS AFFECTING HOME HEART RATE
AJH–September 2005–VOL. 18, NO. 9
Table 1. Characteristics of the study subjects
Characteristic
Age (y)*
Gender (men %)
Home HR (beats/min)*
Home SBP (mm Hg)*
Antihypertensive medication (%)
BMI (kg/m2)*
Time spent walking (ⱖ1 h/day %)
Smoking status (current smoker %)
Alcohol-drinking status (current drinker %)
Coffee intake (ⱖ1 cup/week %)
History (%)
Stroke
Diabetes mellitus
Hypercholesterolemia
Morning
(n ⴝ 1275)
Evening
(n ⴝ 1133)
61.7 ⫾
41
66.1 ⫾
123.3 ⫾
29
23.6 ⫾
79
20
40
76
62.1 ⫾
38
68.7 ⫾
120.9 ⫾
29
23.5 ⫾
80
18
38
75
3
12
13
11.8
7.9
15.0
3.1
11.7
7.8
14.6
3.1
3
12
14
BMI ⫽ body mass index; HR ⫽ heart rate; SBP ⫽ systolic blood pressure.
* Mean ⫾ standard deviation.
Questionnaire Survey
Information on age, antihypertensive medication, smoking
and alcohol drinking status, body mass index (BMI), activity levels (time spent walking per day), coffee intake,
and any history of stroke, diabetes mellitus, or hypercholesterolemia was obtained from a questionnaire sent to
each subject at the time of the first home HR and BP
measurement. The information on antihypertensive medication was confirmed from medical records kept at
Ohasama Hospital.
Statistical Analysis
All data are expressed as mean ⫾ SD. Variables were
compared using the Pearson regression analysis, Student t
test, ␹2 test, multiple linear regression analysis, or analysis
of variance (ANOVA) as appropriate. The threshold level
for statistical significance was set at P ⬍ .05. All statistical
analyses were performed using SAS software, version 8.2
(SAS Institute, Cary, NC).
Results
Characteristics of the Study Subjects
Of the 1275 study subjects, 1133 (89%) measured their
home HR and BP in both the morning and evening. Table
1 shows the characteristics of the study subjects who
measured HR in the morning (n ⫽ 1275) and in the
evening (n ⫽ 1133). The mean number of home HR
measurements was 22.6 ⫾ 6.5 for the morning and 22.8 ⫾
6.5 for the evening.
Of the 1275 subjects who measured HR in the morning,
365 (29%) were receiving antihypertensive medication. Of
these, 232 (64%) were taking calcium (Ca) antagonists, 63
(17%) angiotensin-converting enzyme (ACE) inhibitors,
45 (12%) ␤-blockers, 34 (9%) ␣1-blockers, 20 (5%) diuretics, 7 (2%) ␣␤-blockers, and 5 (1%) other drugs,
respectively. The most common combinations of antihypertensive agents were Ca antagonists ⫹ ACE inhibitors
(n ⫽ 46, 13%) and Ca antagonists ⫹ ␤-blockers (n ⫽ 40,
11%).
The characteristics of the subjects who measured HR in
the evening were similar to those of the individuals who
took measurements in the morning (Table 1).
Bivariate Analysis of Factors Affecting
Home HR
An initial bivariate analysis was performed to determine
which factors influenced home HR values. The factors
investigated were age, gender, SBP, the use of antihypertensive medication, BMI, time spent walking, smoking,
and alcohol-drinking status, coffee intake, and any history
of stroke, diabetes mellitus or hypercholesterolemia (Table
2). Morning home HR levels showed significant negative
correlation with age (r ⫽ ⫺0.11, P ⬍ .001) and morning
home SBP (r ⫽ ⫺0.06, P ⫽ .02). There was also significant negative correlation between single home HR and
home SBP in the morning obtained on the first day of the
home measurement (r ⫽ ⫺0.07 P ⫽ .014). There were
significant differences in home HR values between subjects who were and were not taking antihypertensive medication (with antihypertensive medication, 65.3 ⫾ 8.5
beats/min; without antihypertensive medication, 66.5 ⫾
7.8 beats/min, P ⫽ .01), between those who spent ⱖ1
h/day walking (ⱖ1 h/day) and those who spent ⬍h/day
walking (⬍1 h/day) (ⱖ1 h/day, 65.7 ⫾ 7.9 beats/min; ⬍1
h/day, 67.8 ⫾ 7.8 beats/min, P ⬍ .001), and between
current smokers and former or never smokers (current
smokers, 67.8 ⫾ 8.3 beats/min; former or never smokers,
65.8 ⫾ 7.9 beats/min, P ⬍ .001). Similarly, evening home
HR levels were associated with age, use of antihypertensive medication, time spent walking, and smoking. In
addition, evening home HR levels were significantly as-
AJH–September 2005–VOL. 18, NO. 9
FACTORS AFFECTING HOME HEART RATE
1221
Table 2. Bivariate analysis of factors affecting home heart rate value
Morning
Continuous
variable
Evening
Correlation
coefficient
P
value
Correlation
coefficient
P
value
⫺0.11
⫺0.06
⫺0.02
⬍.001
.02
.39
⫺0.15
⫺0.01
0.05
⬍.001
.75
.07
Age
Home SBP
BMI
Morning
Categorical variable*
Gender
Antihypertensive medication
Time spent walking
Smoking
Alcohol-drinking
Coffee-drinking
Stroke
Diabetes mellitus
Hypercholesterolemia
Men
Women
Present
Absent
ⱖ 1 h/day
⬍ 1 h/day
Current smoker
Former or never smoker
Current drinker
Former or never drinker
ⱖ 1 cup/week
⬍ 1 cup/week
Present
Absent
Present
Absent
Present
Absent
Evening
Mean value
(beats/min)
P
value
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
.20
65.8
66.4
65.3
66.5
65.7
67.8
67.8
65.8
66.4
66.0
66.5
65.8
64.9
66.3
66.7
66.1
66.4
66.3
8.6
7.5
8.5
7.8
7.9
7.8
8.3
7.9
8.1
8.0
8.0
7.9
10.2
7.9
8.7
7.7
8.4
7.9
.01
⬍.001
⬍.001
.31
.19
.39
.43
.93
Mean value
(beats/min)
69.1
68.3
67.2
69.2
68.4
70.0
71.0
68.1
69.6
68.1
69.1
68.3
67.2
68.8
69.2
68.6
68.8
68.7
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
8.5
7.4
8.2
7.6
7.9
7.5
8.1
7.7
7.8
7.8
7.8
8.1
9.6
7.7
8.3
7.5
7.8
7.7
P
value
.07
⬍.001
.01
⬍.001
.001
.14
.33
.44
.86
Abbreviations as in Table 1.
Continuous variables were tested by Pearsons regression analysis. Categorical variables were tested by Student t test.
* Mean ⫾ standard deviation.
sociated with alcohol drinking status (current drinkers,
69.6 ⫾ 7.8 beats/min; former or never drinkers, 68.1 ⫾ 7.8
beats/min, P ⫽ .001). The other variables were not significantly associated with home HR levels.
Multivariate Linear Regression Analysis
of Factors Affecting Home HR
Because many of the previously mentioned factors might
be interrelated, we performed a multivariate linear regression analysis including gender and the other factors that
were significantly associated with home HR levels in the
bivariate analysis. The results of this analysis are summarized in Table 3. When all subjects were included, the
multivariate linear regression analysis revealed significant
negative relation between morning home HR and age, time
spent walking, male gender, and former smoking or never
smoking status. Evening home HR was significantly associated with similar variables as the morning HR other than
gender. No significant associations were observed between
home HR levels and the other variables including home
SBP. The associations between home HR and age and
smoking status were greater in men than in women. Subjects who were not taking antihypertensive medication
(untreated subjects) had similar morning and evening results with the overall study population.
Effects of Antihypertensive Medication on
Home HR
Multiple linear regression analysis adjusted for age, time
spent walking, gender, and smoking status showed a significant inverse association between the use of ␤-blocker
and morning home HR (Model 1, Table 4). Similarly, the
use of Ca antagonist and ␤-blocker were significantly and
inversely associated with evening home HR, whereas diuretic use was positively associated with the evening home
HR (Model 1, Table 4). Because these antihypertensive
drugs were simultaneously used in some cases, we included all of these drugs simultaneously in a multivariate
model (Model 2, Table 4). The model showed that
␤-blocker inversely and independently associated with
both morning and evening home HR, whereas diuretic use
was positively and independently associated with evening
home HR values. In addition, even when we used each
type of antihypertensive medication as a covariate, results
that smoking and sedentary lifestyle effects home HR
value were unchanged (data not shown).
1222
Morning
Variable
Age (per 1 year)
Smoking (current ⫽ 1)
Time spent walking (ⱖ1 h/day ⫽ 1)
Gender (men ⫽ 1)
Antihypertensive medication
(present ⫽ 1)
Home SBP (per 1 mm Hg)
R2
Evening
All subjects
(n ⴝ 1275)
Coefficient
⫺0.08
3.22
⫺2.43
⫺2.07
⫺0.32
⫺0.005
0.052
Men
(n ⴝ 523)
Women
(n ⴝ 752)
Untreated subjects
(n ⴝ 910)
P
value
Coefficient
P
value
Coefficient
P
value
⬍0.001
⬍0.001
⬍0.001
⬍0.001
⫺0.15
3.01
⫺2.21
NA
⬍.001
⬍.001
.01
NA
⫺0.02
2.63
⫺2.41
NA
.48
.15
⬍.001
NA
⫺0.09
3.08
⫺2.06
⫺1.90
⬍.001
⬍.001
.001
.002
⫺0.48
0.04
0.095
0.59
0.22
⫺0.26
⫺0.04
0.025
0.72
0.09
NA
⫺0.003
0.050
NA
0.88
0.57
0.80
All subjects
(n ⴝ 1133)
Men
(n ⴝ 432)
Women
(n ⴝ 701)
Coefficient
P
value
Untreated subjects
(n ⴝ 809)
Coefficient
P
value
Coefficient
P
value
Coefficient
P
value
Coefficient
P
value
Age (per 1 year)
Smoking (current ⫽ 1)
Time spent walking (ⱖ1 h/day ⫽ 1)
Gender (men ⫽ 1)
Antihypertensive medication
(present ⫽ 1)
Alcohol-drinking (current ⫽ 1)
R2
⫺0.07
3.09
⫺1.93
⫺0.91
.002
⬍.001
⬍.001
.14
⫺0.18
2.77
⫺1.56
NA
⬍.001
⬍.001
.09
NA
0.003
0.96
⫺1.65
NA
.91
.63
.02
NA
⫺0.08
2.86
⫺1.44
⫺0.18
.001
⬍.001
.03
.80
⫺1.24
0.61
.07
.42
NA ⫽ not analyzed; SBP ⫽ systolic blood pressure.
.06
.10
⫺1.23
1.48
0.140
.17
.09
0.013
NA
0.61
0.051
NA
.34
AJH–September 2005–VOL. 18, NO. 9
Variable
⫺1.04
0.93
0.055
FACTORS AFFECTING HOME HEART RATE
Table 3. Multivariate regression analysis of factors affecting home heart rate value
FACTORS AFFECTING HOME HEART RATE
1223
.07
.40
.04
.17
.03
Effects of Casual HR and Casual BP on
Home HR
ACE ⫽ angiotensin converting enzyme; Ca ⫽ calcium.
Model 1: adjusted for age, smoking status, time spent walking, gender. Model 2: Model 1 ⫹ each antihypertensive drug.
* Including ␣␤-blockers.
⫺1.26
0.97
⫺2.60
⫺2.18
4.07
.01
.71
.01
.07
.04
⫺1.60
0.41
⫺3.23
⫺2.74
3.69
Ca antagonist (present ⫽ 1)
ACE inhibitors (present ⫽ 1)
␤-Blocker* (present ⫽ 1)
␣-Blocker (present ⫽ 1)
Diuretics (present ⫽ 1)
⫺0.34
1.47
⫺2.50
⫺2.18
2.67
.57
.15
.02
.11
.13
0.05
1.58
⫺2.64
⫺2.19
2.51
.94
.15
.03
.12
.17
Coefficient
P
value
Coefficient
P
value
Coefficient
P
value
Coefficient
Model 2
Model 1
Model 2
Model 1
Morning HR (N ⴝ 1275)
Table 4. Effect of each antihypertensive drugs on home heart rate (HR) value
Evening HR (N ⴝ 1133)
P
value
AJH–September 2005–VOL. 18, NO. 9
Among the 890 individuals who underwent casual HR and
BP measurements, casual HR and morning home HR
showed significant correlation with casual SBP and home
morning SBP, respectively (casual, r ⫽ 0.08 P ⫽ .02;
home, r ⫽ ⫺0.09, P ⫽ .01). This association persisted
after adjustment for major confounding factors in casual
measurements (P ⫽ .003), although it disappeared in
home measurements (P ⫽ .3). The difference between
casual and morning home HR (casual HR value ⫺ morning home HR value) was significantly and positively associated with the difference between casual and morning
home SBP (r ⫽ 0.11, P ⫽ .001). Similar results were
obtained even when the correlations were calculated by
using data from single home measurement obtained on the
first day. Correlation between casual and morning home
HR was statistically significant (r ⫽ 0.40, P ⱕ .0001)
although it was weaker than the correlation between morning home and casual BP values (SBP: r ⫽ 0.52, P ⱕ
.0001; DBP: r ⫽ 0.43, P ⱕ .0001). Similar associations
were observed for evening home HR (data not shown).
Discussion
We recently clarified the strong predictive value of resting
HR for CVD mortality using home HR obtained by a
self-monitored blood pressure measuring device,8 which
makes it possible to obtain reliable HR values at rest
through multiple measurements under stable conditions.
Knowledge of the factors that affect home HR values may
be useful in the reduction of risk for CVD. However, no
information on the determinants of elevated home HR
values has been published. Therefore, we conducted a
cross-sectional community survey to identify factors that
might affect home HR.
In the present study, we first identified the factors that
affect home-measured resting HR in the general population. Elevated home HR was found to be associated with
younger age, current smoking, walking for ⬍1 h/day, and
female gender. Among these factors influencing home HR
values, smoking status, and time spent walking were modifiable. Joint effects of modification of these two were
correspond to approximately 5 beats/min, suggesting that
these modification may lead to a 17% decrease in CVD
mortality risk.8 However, possibility of selection bias
needs to be considered to generalize the present findings,
as there were differences in age or systolic BP between the
study subjects and those excluded.
The gender difference in HR (higher HR in women than
in men) has already been reported in several studies.17–21
A number of studies have also reported a negative assocation between HR and age,18,20,22 whereas others have
shown no association between alcohol consumption and
HR.17,19,22 A negative association between HR and physical activity has also been established by some au-
1224
FACTORS AFFECTING HOME HEART RATE
thors.17,19 –22 Our results are in agreement with those
previous studies. Although younger age was the determinant of home HR, younger age is also established to be
associated with lower CVD risks. The discrepancy may be
explained by the clustering effect of other risk factors.
Heart rate is known to increase immediately after
smoking a cigarette.23 However, Gidding et al reported
that HR level was higher in habitual smokers than in
nonsmokers, even though in their study smokers were
asked not to smoke at least for 2 h before examination.24
They suggest that the effect is associated with the increased myocardial oxygen consumption at rest.24 Some
population-based studies have also found a positive association between smoking and HR.19,22 These findings were
consistent with ours. When we analyzed this association in
men and women separately, home HR did not relate significantly to smoking in women. The lack of a significant
association between home HR and smoking status in
women might reflect the smaller number of women who
were current smokers compared with men.
Most previous studies have demonstrated a strong positive correlation between casual HR and casual SBP values.17,19 –22 On the other hand, one study reported that HR
was related to BP when measured in the clinic, but not
when mean 24-h ambulatory HR and BP readings were
compared.21 This finding regarding 24-h ambulatory measurements is comparable to the results of our present
study, in which no association between home HR and
home SBP was observed after adjustment for other variables. Nevertheless, among the 890 subjects who underwent casual HR and BP measurements, casual HR showed
significant positive correlation with casual SBP, whereas
home HR was not positively correlated with home SBP.
The difference between casual and home HR values was
also significantly and positively correlated with the difference between casual and home SBP values. Similar results
were obtained even when the correlations were calculated
by using data from single home measurement obtained on
the first day. These results suggest that the positive association HR and BP reported in previous studies was
mainly attributable to the white coat effect that reflected an
alarm reaction to the medical settings,2 rather than some
pressor effects associated with the first measurement.17,21
In conclusion, we observed most of determinants, except for BP, of home HR is consistent with the determinants observed in previous studies using casual HR. These
results suggest that reduction of home HR through modifying smoking habit or sedentary lifestyle may have a
potential to decrease cardiovascular risk in addition to
decreasing in these modifiable risk factors per se.
AJH–September 2005–VOL. 18, NO. 9
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
References
15.
1.
2.
Palatini P: Office versus ambulatory heart rate in the prediction of
the cardiovascular risk. Blood Press Monit 1998;3:153–156.
Mancia G, Bertinieri G, Grassi G, Parati G, Pomidossi G, Ferrari A,
Gregorini L, Zanchetti A: Effects of blood-pressure measurement by
the doctor on patient’s blood pressure and heart rate. Lancet
1983;2:695– 697.
O’Brien E, Asmar R, Beilin L, Imai Y, Mallion JM, Mancia G,
Mengden T, Myers M, Padfield P, Palatini P, Parati G, Pickering T,
Redon J, Staessen J, Stergiou G, Verdecchia P, European Society of
Hypertension Working Group on Blood Pressure Monitoring: European Society of Hypertension recommendations for conventional,
ambulatory and home blood pressure measurement. J Hypertens
2003;21:821– 848.
Imai Y, Otsuka K, Kawano Y, Shimada K, Hayashi H, Tochikubo
O, Miyakawa M, Fukiyama K, for the Japanese Society of Hypertension: Japanese Society of Hypertension (JSH) Guidelines for
Self-monitoring of Blood Pressure at Home. Hypertens Res 2003;
26:771–782.
Ohkubo T, Imai Y, Tsuji I, Nagai K, Kato J, Kikuchi N, Nishiyama
A, Aihara A, Sekino M, Kikuya M, Ito S, Satoh H, Hisamichi S:
Home blood pressure measurement has a stronger predictive power
for mortality than does screening blood pressure measurement: a
population-based observation in Ohasama, Japan. J Hypertens 1998;
16:971–975.
Bobrie G, Chatellier G, Genes N, Clerson P, Vaur L, Vaisse B, Menard
J, Mallion JM: Cardiovascular prognosis of “masked hypertension”
detected by blood pressure self-measurement in elderly treated hypertensive patients. J Am Med Assoc 2004;291:1342–1349.
Ohkubo T, Asayama K, Kikuya M, Metoki H, Hoshi H, Hashimoto
J, Totsune K, Satoh H, Imai Y: How many times should blood
pressure be measured at home for better prediction of stroke risk?
Ten-year follow-up results from the Ohasama study. J Hypertens
2004;22:1099 –1104.
Hozawa A, Ohkubo T, Kikuya M, Ugajin T, Yamaguchi J, Asayama
K, Metoki H, Ohmori K, Hoshi H, Hashimoto J, Satoh H, Tsuji I,
Imai Y: Prognostic value of home heart rate for cardiovascular
mortality in the general population. The Ohasama study. Am J
Hypertens 2004;17:1005–1010.
Ohkubo T, Hozawa A, Nagatomi R, Fujita K, Sauvaget C, Watanabe Y, Anzai Y, Tamagawa A, Tsuji I, Imai Y, Ohmori H,
Hisamichi S: Effects of exercise training on home blood pressure
values in older adults: a randomized controlled trial. J Hypertens
2001;19:1045–1052.
Hozawa A, Ohkubo T, Nagai K, Kikuya M, Matsubara M, Tsuji I,
Ito S, Satoh H, Hisamichi S, Imai Y: Factors affecting the difference
between screening and home blood pressure measurements: the
Ohasama study. J Hypertens 2001;19:13–19.
Imai Y, Satoh H, Nagai K, Sakuma M, Sakuma H, Minami N,
Munakata M, Hashimoto J, Yamagishi T, Watanabe N, Yabe T,
Nishiyama A, Nakatsuka H, Koyama H, Abe K: Characteristics of
a community-based distribution of home blood pressure in Ohasama
in northern Japan. J Hypertens 1993;11:1441–1449.
Tsuji I, Imai Y, Nagai K, Ohkubo T, Watanabe N, Minami N, Itoh
O, Bando T, Sakuma M, Fukao A, Satoh H, Hisamichi S, Abe K:
Proposal of reference values for home blood pressure measurement:
prognostic criteria based on a prospective observation of the general
population in Ohasama, Japan. Am J Hypertens 1997;10:409 – 418.
Hozawa A, Ohkubo T, Tsuji I, Kikuya M, Matsubara M, Suzuki T,
Nagai K, Kitaoka H, Arai Y, Hosokawa T, Satoh H, Hisamichi S,
Imai Y: Relationship between personality and self-measured blood
pressure value at home: the Ohasama study. Clin Exp Hypertens
2002;24:115–123.
Imai Y, Abe K, Sasaki S, Minami N, Munakata M, Sakuma H,
Hashimoto J, Sekino H, Imai K, Yoshinaga K: Clinical evaluation of
semiautomatic and automatic devices for home blood pressure measurement: comparison between cuff-oscillometric and microphone
methods. J Hypertens 1989;7:983–990.
Imai Y, Nishiyama A, Sekino M, Aihara A, Kikuya M, Ohkubo T,
Matsubara M, Hozawa A, Tsuji I, Ito S, Satoh H, Nagai K, Hisamichi S: Characteristics of blood pressure measured at home in the
morning and in the evening: the Ohasama study. J Hypertens 1999;
17:889 – 898.
AJH–September 2005–VOL. 18, NO. 9
16. Association for the Advancement of Medical Instrumentation:
American National Standards for Electronic or Automated Sphygmomanometers. Washington, DC: Association for the Advancement
of Medical Instrumentation, 1987.
17. Palatini P, Casiglia E, Pauletto P, Staessen J, Kaciroti N, Julius S:
Relationship of tachycardia with high blood pressure and metabolic
abnormalities: a study with mixture analysis in three populations.
Hypertension 1997;30:1267–1273.
18. Jaquet F, Goldstein IB, Shapiro D: Effects of age and gender on
ambulatory blood pressure and heart rate. J Hum Hypertens 1998;
12:253–257.
19. Ferrieres J, Ruidavets JB: Association between resting heart rate and
hypertension treatment in a general population. Am J Hypertens
1999;12:628 – 631.
FACTORS AFFECTING HOME HEART RATE
1225
20. Morcet JF, Safar M, Thomas F, Guize L, Benetos A: Associations
between heart rate and other risk factors in a large French population. J Hypertens 1999;17:1671–1676.
21. Palatini P, Julius S: Heart rate and the cardiovascular risk. J Hypertens 1997;15:3–17.
22. Wannamethee G, Shaper AG: The association between heart rate
and blood pressure, blood lipids and other cardiovascular risk factors. J Cardiovasc Risk 1994;1:223–230.
23. Trap-Jensen J: Effects of smoking on the heart and peripheral
circulation. Am Heart J 1988:115:263–267.
24. Gidding SS, Xie X, Liu K, Manolio T, Flack JM, Gardin JM:
Cardiac function in smokers and nonsmokers: the CARDIA study.
The Coronary Artery Risk Development in Young Adults Study.
J Am Coll Cardiol 1995;26:211–226.