Reference ranges for ambulatory heart rate measurements in a middle-aged population

831

PerssonAP, etal. Heart 2024;110:831–837. doi:10.1136/heartjnl-2023-323681

Original research

Reference ranges for ambulatory heart rate

measurements in a middle- agedpopulation

Anders Paul Persson ,

1,2

Alexandra Måneheim,

1,2

Johan Economou Lundeberg,

1,3

Artur Fedorowski ,

4,5

Jeff S Healey,

6,7

Johan Sundström ,

8,9

Gunnar Engström ,

Linda S B Johnson

1,6

Arrhythmias and sudden death

To cite: PerssonAP,

MåneheimA, Economou

LundebergJ, etal. Heart

2024;110:831–837.

► Additional supplemental

material is published online

only. To view, please visit the

journal online (https:// doi.

org/ 10. 1136/ heartjnl- 2023-

323681).

Department of Clinical

Sciences, Lund University,

Malmö, Sweden

Department of Clinical

Physiology, Skånes

universitetssjukhus Malmö,

Malmö, Sweden

Department of Clinical

Physiology, Skånes

universitetssjukhus Lund, Lund,

Sweden

Department of Clinical

Sciences, Lund University Faculty

of Medicine, Malmö, Sweden

Department of Medicine,

Karolinska Institute, Solna,

Sweden

Population Health Research

Institute, Hamilton, Ontario,

Canada

Department of Medicine,

McMaster University, Hamilton,

Ontario, Canada

Department of Medical

Sciences, Uppsala University,

Uppsala, Sweden

The George Institute for Global

Health, Newtown, New South

Wales, Australia

Correspondence to

Dr Anders Paul Persson,

Department of Clinical Sciences,

Lund University, Malmö 205 02,

Sweden;

anders_ p. persson@ med. lu. se

Received 9 November 2023

Accepted 10 March 2024

Published Online First

5April2024

employer(s)) 2024. Re- use

permitted under CC BY.

Published by BMJ.

ABSTRACT

Background Elevated heart rate (HR) predicts

cardiovascular disease and mortality, but there are no

established normal limits for ambulatory HR. We used

data from the Swedish CArdioPulmonary Imaging

Study to determine reference ranges for ambulatory HR

in a middle- aged population. We also studied clinical

correlates of ambulatory HR.

Methods A 24- hour ECG was registered in 5809 atrial

ﬁbrillation- free individuals, aged 50–65 years. A healthy

subset (n=3942) was used to establish reference values

(excluding persons with beta- blockers, cardiovascular

disease, hypertension, heart failure, anaemia, diabetes,

sleep apnoea or chronic obstructive pulmonary disease).

Minimum HR was deﬁned as the lowest 1- minute HR.

Reference ranges are reported as means±SDs and 2.5th–

97.5th percentiles. Clinical correlates of ambulatory HR

were analysed with multivariable linear regression.

Results The average mean and minimum HRs were

73±9 and 48±7 beats per minute (bpm) in men and

76±8 and 51±7 bpm in women; the reference range

for mean ambulatory HR was 57–90 bpm in men and

61–92 bpm in women. Average daytime and night- time

HRs are also reported. Clinical correlates, including age,

sex, height, body mass index, physical activity, smoking,

alcohol intake, diabetes, hypertension, haemoglobin level,

use of beta- blockers, estimated glomerular ﬁltration rate,

per cent of predicted forced expiratory volume in 1 s and

coronary artery calcium score, explained <15% of the

interindividual differences in HR.

Conclusion Ambulatory HR varies widely in healthy

middle- aged individuals, a ﬁnding with relevance for

the management of patients with a perception of

tachycardia. Differences in ambulatory HR between

individuals are largely independent of common clinical

correlates.

INTRODUCTION

High resting heart rates have been linked to cardio-

vascular, cancer and all- cause death.

The finding

has been consistent in population- based studies,

2–4

as well as in studies of individuals with hyperten-

sion,

diabetes,

chronic obstructive pulmonary

disease,

cardiovascular disease

and heart failure.

Low heart rate, on the other hand, is associated with

atrial fibrillation.

10–12

Regardless of whether these

associations imply a causal relation between heart

rate and outcomes or not, heart rate measurements

can be useful for disease prediction. Ambulatory

heart rates can be inexpensively, reliably and non-

invasively measured, and ambulatory ECG moni-

toring is frequently performed in clinical practice.

Furthermore, patients with post- COVID- 19 condi-

tion frequently report inappropriately elevated

resting heart rates,

13 14

a symptom which cannot be

put in context without knowledge of normal limits.

Despite this, studies that report reference ranges

and or predictors of heart rate at 24- hour ECG

(24hECG) are lacking.

We aimed to describe reference ranges for, and

predictors of ambulatory heart rate measured with

24hECG in a middle- aged general population

sample.

METHODS

Study sample

The population- based Swedish CArdioPulmonary

Imaging Study (SCAPIS) cohort included 30 154

participants aged 50–65 years old recruited from

the general population, in six municipal centres

containing university hospitals in Sweden (Gothen-

burg, Linköping, Malmö, Stockholm, Uppsala and

Umeå). Examination with 24hECG was part of the

protocol at the Malmö (6235 participants) and

Uppsala (5038 participants) sites. All participants

examined in Malmö between September 2016 and

2018 as well as all participants in Uppsala were

asked to contribute a 24hECG registration, of

which 1288 (Malmö) and 5004 (Uppsala) accepted.

WHAT IS ALREADY KNOWN ON THIS TOPIC

⇒ Ambulatory heart rate varies widely and is

only explained by clinical correlates to a minor

degree.

WHAT THIS STUDY ADDS

⇒ This study presents reference ranges of

ambulatory heart rate in a middle- aged

population.

HOW THIS STUDY MIGHT AFFECT RESEARCH,

PRACTICE OR POLICY

⇒ These normal limits for heart rate can be used

for the interpretation of ambulatory ECGs, for

instance, to determine whether patients with

subjective perception of tachycardia have

abnormally elevated heart rates.

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Arrhythmias and sudden death

After excluding individuals with a previous diagnosis of atrial

fibrillation (n=112), atrial fibrillation during the ambula-

tory ECG recording (n=35), registration duration <16 hours

(n=77) or insufficient 24hECG registration quality (n=258),

and one participant with an implausible mean heart rate (155

beats per minute (bpm)), 5809 participants constituted the final

study population (figure 1). Reference ranges for heart rate

were reported in a healthy population subset after exclusion

Figure 1 Derivation of study population. 24hECG, 24- hour ECG; SCAPIS, Swedish CArdioPulmonary Imaging Study.

Table 1 Sample characteristics

All (N=5809) Women Men Healthy reference sample (n=3942)

Age, mean (range) 58 (50–65) 58 (50–65) 58 (50–65) 57 (50–65)

Women, % 53 – – 54

Height, cm (SD) 172 (9.7) 166 (6.5) 179 (7.0) 172 (9.7)

BMI, %

<25 35 42 27 40

25–30 44 38 51 44

>30 21 20 22 16

Smoking, %

Never 56 53 59 58

Former 34 37 30 32

Current 11 11 11 10

Alcohol intake units/week, median (IQR) 2.6 (1.1–4.1) 1.1 (0.4–3.8) 2.6 (1.1–6.0) 2.6 (1.1–4.1)

Physical activity, %

Low 57 60 54 54

High 43 40 46 46

Diabetes, % 25 7 11 –

Hypertension, % 21 20 22 –

Use of oral beta- blockers, % 5,8 6.1 5.5 –

FEV

%predicted (SD) 109 (15) 109 (15) 108 (14) 110 (14)

Coronary artery calcium score, %

0 61 74 46 67

1–99 27 21 35 25

≥100 11 5 19 8

eGFR, mL/min/1.73 m

(IQR) 88 (79–96) 88 (78–96) 89 (80–96) 88 (79–96)

Haemoglobin, g/L (SD) 142 (12) 135 (9.1) 149 (9.4) 142 (11)

Sleep apnoea, % 3.7 2.6 4.9 –

Coronary artery disease, % 2.0 1.1 3.0 –

Heart failure, % 0.3 0.3 0.4 –

BMI, body mass index; eGFR, estimated glomerular ﬁltration rate; FEV

%predicted, per cent of predicted forced expiratory volume during 1 s.

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Arrhythmias and sudden death

of participants with known prevalent cardiovascular diseases,

hypertension, heart failure, anaemia, diabetes, obstructive sleep

apnoea, chronic obstructive pulmonary disease, and individuals

using beta- blockers, resulting in a healthy reference cohort of

3942 subjects. Neither the study participants nor the public

were involved in the design, conduct, reporting or dissemination

plans of this study.

Data collection

24hECGs were recorded using CardioSpy equipment (Labtech,

Debrecen, Hungary), with X, Y, Z coupling and sampling

frequency of 256 Hz, and measures of heart rate were derived

from the CardioSpy ECG analysis software. Cases of atrial fibril-

lation during 24hECG were detected using a Food and Drug

Association and CE- approved artificial intelligence algorithm

(MEDICALgorithmics, Warsaw, Poland). The measures of heart

rate included in this study were mean heart rate during the

entire recording, minimum heart rate during 1 min, and mean

day (06:00–22:00) and night- time (22:00–06:00) heart rates.

Nightly dip in heart rate was calculated as daytime heart rate

minus night- time heart rate. Maximum heart rate during 1 min

was not studied in detail due to its dependence on maximum

exertion but percentiles are presented in online supplemental

table 1.

Each participant completed an extensive health and lifestyle

questionnaire from which data concerning physical activity,

smoking and alcohol use were obtained, as was the prevalence

of known prevalent diseases and drug treatment. A capillary

glucose sample was collected, and if elevated (≥7 mmol/L), a

repeat measurement was performed on another day to diagnose

diabetes.

Height was measured to the nearest centimetre and weight

was measured on a digital scale in light indoor clothing without

shoes. Body mass index (BMI) was calculated as kg/m

and

stratified at 25 kg/m

, 25–30 kg/m

and >30 kg/m

. Dynamic

spirometry (Jaeger MasterScreen PFT; Carefusion, Hoech-

berg, Germany) was performed 15 min after bronchodilation

using 400 µg salbutamol with subjects in the sitting position

and wearing a nose clip, and forced expiratory volume during

1 s (FEV

) was measured according to the American Thoracic

Society and European Respiratory Society standards.

15 16

The

Hedenström formula was used to calculate per cent of predicted

FEV

(FEV

%predicted).

17 18

Smoking status was categorised as

current, former or never smoker. Reported leisure time physical

activity was categorised as low (mostly sedentary or some light

physical activity) or high (physical activity with moderate to

strenuous intensity at least 2 hours per week). Alcohol intake in

units per week was calculated by multiplying average times per

week of drinking during the last year with the typical intake on

a day of drinking. Alcohol intake was then stratified into two

groups (above or below the median intake of 2.6 units per week).

A fasting venous blood sample was retrieved and haemoglobin

and creatinine concentrations were measured using standard

laboratory procedures at the Uppsala and Malmö University

Hospitals. Estimated glomerular filtration rate (eGFR) was

calculated using the creatinine- based Chronic Kidney Disease

Epidemiology Collaboration formula.

CT was performed in all participants using Siemens Defini-

tion Flash 2×128 slice, stellar detector, 4D‐Care dose, Care‐kV

and sinogram‐affirmed iterative reconstruction (Forchheim,

Germany). Coronary artery calcium score was calculated using

the Agatston score

and stratified into three groups: 0, 1–99

and ≥100.

Statistical analyses

Reference ranges for heart rates are reported as means (±SD

and percentiles) and presented in histograms and sex- specific

cumulative distribution function plots. The reported percen-

tiles were taken directly from the observed distribution and not

derived from the SD. The sex- specific cumulative distribution

function plots for average day and night- time heart rate are

presented in online supplemental figure 1. Multivariable linear

regression models were used to analyse the association between

heart rate and a prespecified set of predictors including age,

sex, height, BMI, physical activity, smoking, alcohol intake,

diabetes, hypertension, haemoglobin level, use of beta- blockers,

eGFR, FEV

%predicted and coronary artery calcium score. All

continuous parameters were assessed visually in histograms for

normality. Collinearity was ruled out using the variance inflation

factor, using a cut- off of 2.5. Model diagnostics were performed

using plots of residuals and fitted values and histograms of

the residuals. No violations of the assumptions were observed

(online supplemental figures 2–5). Linear regression robust to

heteroskedasticity was also performed for mean and minimum

heart rate with virtually unchanged results (online supplemental

table 3).

All statistical analyses were performed using Stata V.15.1

(StataCorp, College Station, Texas, USA).

Table 2 Reference ranges for measures of ambulatory heart rate

Mean heart rate (beats/min) Minimum heart rate* (beats/min) Average daytime heart rate (beats/min) Average night- time heart rate (beats/min)

Men Women Men Women Men Women Men Women

Healthy individuals (1825 men and 2117 women)†

Mean (SD) 73 (9) 76 (8) 48 (7) 51 (7) 78 (9) 81 (8) 63 (9) 65 (8)

Percentiles

2.5 57 61 36 39 60 65 48 51

5 59 63 38 40 63 67 50 53

25 67 71 44 46 71 75 57 60

50 73 76 48 51 78 81 63 65

75 79 81 53 55 84 86 68 71

95 87 89 60 62 93 94 79 80

97.5 90 92 63 65 96 97 83 83

*Lowest average heart rate during 1 min.

†Based on 3942 (2117 women) healthy individuals, excluding individuals with new or known diabetes (490) or known prevalent hypertension (n=995), sleep apnoea (n=111), coronary

artery disease (n=39), chronic obstructive pulmonary disease (n=32), heart failure (n=6) or anaemia (haemoglobin <120 g/L in women and <130 g/L in men) (n=126, missing 15) and

individuals using beta- blockers (54).

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Arrhythmias and sudden death

RESULTS

The age of study participants was evenly distributed between

50 and 65 years of age and 52% were women. While 22% had

hypertension, heart failure was rare (0.3%). Study sample char-

acteristics are presented in more detail in table 1.

Reference ranges for heart rate

A healthy population subset was used to determine reference

ranges for heart rate measures. The mean heart rate was 73

(±9) bpm on average for men and 76 (±8) bpm on average

for women, and the minimum heart rate was 48 (±7) bpm on

average for men and 51 (±7) bpm on average for women.

The range of heart rate was wide; the 2.5th–97.5th percentile

of mean heart was 57–90 bpm in men and 61–92 bpm in women

(table 2 and figure 2). Reference heart rates (only including

healthy individuals) did not differ substantially from heart rates

in the entire study population (online supplemental table 2). The

average nightly dip in heart rate was 15 bpm in men and 16 bpm

in women. Heart rate parameters are reported in detail for both

men and women in table 2.

Clinical predictors of heart rate

Clinical predictors of heart rate are calculated in the full study

sample and presented in table 3. We included 14 clinical variables

in the multivariable linear regression, and 11 (sex, height, BMI,

smoking, high physical activity, alcohol intake, diabetes, using

oral beta- blockers, FEV

%predicted, eGFR and haemoglobin)

were statistically significantly associated with mean heart rate

(table 3). The associations were overall rather weak, however.

For example, the mean heart rate was on average only 4.5 bpm

lower in beta- blocker users and 2.7 bpm higher in smokers,

after multivariable adjustment (table 3). In keeping with this,

the variation in heart rate was explained by the clinical vari-

ables included in the multivariable model only to a small degree;

adjusted R

and unadjusted R

in the multivariable model were

<15% and <16%, respectively, for all the studied measures of

heart rate. In sex- stratified multivariable models, the adjusted R

for mean heart rate was 16% in men and 10% in women. Within

the age range of this study, heart rate was virtually unchanged

with age.

DISCUSSION

This is the largest population- based study of heart rate at ambu-

latory ECG to date, and the first to provide reference ranges for

measures of heart rate at 24hECG in a middle- aged population.

We found the interindividual differences in heart rate to be large

and largely independent of clinical risk factors for cardiovascular

disease.

Previous studies have shown that there is prognostic value in

ambulatory heart rate measurements,

but since reference ranges

have been lacking, it has not been clear how ambulatory heart

rate measurements could be used in clinical practice. This matter

has become even more relevant in the context of the COVID- 19

pandemic and the emergence of post- COVID- 19 syndromes,

where a feeling of increased heart rate is often reported, but

objective guidelines as to what constitutes elevated heart rate and

pre- COVID- 19 references for ambulatory heart rate have been

lacking. For instance, although inappropriate sinus tachycardia is

typically diagnosed when average heart rate on 24hECG moni-

toring exceeds 90 bpm,

population- based studies supporting

this diagnostic threshold are limited, and definitions in current

literature are inconsistent.

Data in this study were collected

before the pandemic, ensuring that the study population is free

from patients suffering from post- COVID- 19 condition.

We found less than one- sixth of the differences in ambula-

tory heart rate measurements to be explained by risk factors for

cardiovascular disease, so most of the interindividual differences

in heart rate remain unexplained. Even so, markers for poor

health (such as inactivity, smoking, higher BMI and lower lung

function) were associated with a higher heart rate. Somewhat

surprisingly, worse kidney function (ie, lower eGFR) was associ-

ated with a lower mean and minimum heart rate. One previous

study has shown that lower eGFR is associated with chrono-

tropic incompetence in patients with heart failure with preserved

ejection fraction.

We also found lower haemoglobin levels to be associated

with lower heart rate, which might seem contraintuitive, since

lower haemoglobin physiologically should lead to a compensa-

tory increase in heart rate. This association could perhaps be

explained by unmeasured confounding (for example, by dehy-

dration, which can cause both an increase in heart rate and a

higher level of haemoglobin), an indication that there could be

other unknown determinants of heart rate that our relatively

extensive model does not include.

Causal pathways between heart rate and cardiovascular

disease have been suggested,

25–27

but in most studies, heart rate

is associated with a similar risk increase for all- cause mortality,

cancer mortality and cardiovascular mortality,

which could

Figure 2 Heart rates in the healthy reference sample, histogram and sex- speciﬁc cumulative distribution curves.

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Arrhythmias and sudden death

indicate that higher heart rates are not causally related to cardio-

vascular disease, but rather markers of poor health. On the other

hand, a genome- wide association study has identified heart rate-

associated genes which explain 2.5% of the differences in resting

heart rate and shown these genes to be associated with increased

all- cause mortality,

implying that a causal component to the

association between heart rate and mortality may exist. Heart

rate only slightly increases the risk of stroke,

which is likely

due to the fact that the association between low heart rate and

atrial fibrillation is in the opposite direction from the association

between heart rates and cardiovascular disease

—two major

stroke risk factors with different mechanisms.

Limitations

We have used a large population- based study with extensive infor-

mation on comorbidities and lifestyle to describe reference ranges

and predictors for ambulatory measures of heart rate in the general

population. Some limitations exist that need to be considered. The

cross- sectional nature of this study does not allow any estimation

Table 3 Multivariable linear regression models for ambulatory heart rate measures

N=5021

Mean heart rate, beats/min

(95% CI)

Minimum heart rate,

beats/min (95% CI)

Average daytime heart

rate, beats/min (95% CI)

Average night- time heart

rate, beats/min (95% CI)

Beta (95% CI) t ratio* Beta (95% CI) t ratio* Beta (95% CI) t ratio* Beta (95% CI) t ratio*

Age (per 1 year) −0.0

(−0.1, 0.0)

−1.4 0.1

(0.0, 0.1)

3.3 −0.0

(−0.1, 0.0)

−1.4 −0.0

(−0.1, 0.0)

−0.6

Men (vs women) −2.8

(−3.5, −2.0)

−7.3 −2.2

(−2.9, −1.6)

−7.1 −3.2

(−4.0, −2.4)

−7.7 −2.0

(−2.8, −1.3)

−5.2

Height (per 10 cm) −0.8

(−1.2, −0.5)

−4.9 −0.8

(−1.0, −0.5)

−5.2 −0.8

(−1.2, −0.5)

−4.4 −0.9

(−1.2, −0.6)

−5.1

Body mass index, kg/m

<25 Ref Ref Ref Ref Ref Ref Ref Ref

25–30 0.9

(0.4, 1.4)

3.5 0.8

(0.4, 1.3)

3.8 0.6

(0.1, 1.2)

2.3 1.4

(0.9, 2.0)

5.4

>30 1.9

(1.2, 2.6)

5.6 1.4

(0.9, 2.0)

5.1 1.4

(0.7, 2.1)

3.9 2.9

(2.3, 3.6)

8.5

Smoking

Never Ref Ref Ref Ref Ref Ref Ref Ref

Former 1.2

(0.7, 1.7)

4.9 0.9

(0.5, 1.3)

4.3 1.1

(0.6, 1.7)

4.2 1.3

(0.8, 1.9)

5.2

Current 2.7

(1.9, 3.4)

6.7 2.2

(1.6, 2.9)

6.8 2.2

(1.3, 3.0)

5.0 3.7

(2.9, 4.5)

9.2

High physical activity (vs low) −3.6

(−4.1, −3.1)

−15.1 −2.7

(−3.1, −2.3)

−13.5 −3.7

(−4.2, −3.2)

−14.4 −3.3

(−3.8, −2.9)

−13.7

Alcohol intake, above median (vs below median) 0.7

(0.2, 1.1)

3.0 0.8

(0.4, 1.2)

4.1 0.5

(0.0, 1.0)

2.1 1.0

(0.6, 1.5)

4.3

Diabetes (yes vs no) 2.0

(1.1, 2.8)

4.5 2.1

(1.4, 2.8)

5.8 1.7

(0.8, 2.6)

3.6 2.5

(1.6, 3.4)

5.5

Hypertension (yes vs no) 0.4

(−0.1, 1.0)

1.5 0.1

(−0.4, 0.6)

0.4 0.5

(−0.1, 1.2)

1.6 0.3

(−0.3, 0.9)

0.8

Using oral beta- blockers (yes vs no) −4.5

(−5.5, −3.4)

−8.5 −0.9

(−1.8, −0.1)

−2.2 −5.7

(−6.8, −4.5)

−9.9 −1.8

(−2.8, −0.7)

−3.3

FEV

%predicted, per 10% increase −0.3

(−0.4, −0.1)

−3.6 −0.3

(−0.5, −0.2)

−4.8 −0.2

(−0.4, 0.0)

−2.4 −0.4

(−0.6, −0.3)

−5.3

Coronary artery calcium score

0 Ref Ref Ref Ref Ref Ref Ref Ref

1–99 0.4

(−0.1, 1.0)

1.6 0.4

(−0.1, 0.8)

1.6 0.4

(−0.2, 1.0)

1.3 0.5

(0.0, 1.1)

1.8

≥100 0.6

(−0.2, 1.4)

1.5 0.5

(−0.1, 1.2)

1.6 0.5

(−0.4, 1.3)

1.1 0.8

(0.0, 1.6)

2.0

eGFR (per 10 mL/min/1.73 m

) 0.6

(0.4, 0.8)

6.1 0.4

(0.3, 0.6)

5.0 0.7

(0.5, 0.9)

6.2 0.5

(0.3, 0.7)

5.1

Haemoglobin (per 10 g/L increase) 0.7

(0.5, 1.0)

5.7 0.3

(0.1, 0.5)

2.5 0.8

(0.6, 1.1)

6.1 0.4

(0.2, 0.7)

3.5

Constant† 76.1

(75.4, 76.8)

49.9

(49.3, 50.5)

81.5

(80.7, 82.3)

64.4

(63.7, 65.2)

Unadjusted R

0.149 0.143 0.133 0.151

Adjusted R

0.146 0.140 0.130 0.148

Bold numbers indicate statistical signiﬁcance (p<0.05).

*Larger t ratios (both positive and negative) imply larger impacts on heart rate.

†The constant is what is expected for an individual with values that have been set as reference as stated in the table and age 50 years old, the mean of the continuous variables

height (172 cm), eGFR (89 mL/min/1.73 m

), haemoglobin (142 g/L) and FEV

%predicted (109%).

eGFR, estimated glomerular ﬁltration rate; FEV

%predicted, per cent of predicted forced expiratory volume during 1 s.

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Arrhythmias and sudden death

of the predictive value of different ambulatory heart rate ranges,

and though we found large interindividual differences in heart rates

even among otherwise healthy individuals, we are not able to study

whether ambulatory heart rates predict or cause incident disease.

There are also some potential sources of measurement error. We

adjusted for habitual leisure time physical activity, but mean heart rate

is reasonably also affected by activity during the registration, which

we did not adjust for. The mean monitor duration was 24.2 hours

with an SD of 1.3 hours. Since the device was both attached and

removed when awake, individuals with longer recording duration

than 24 hours would have slightly lower sleep/awake time quotas,

resulting in somewhat higher mean heart rates. Daytime and night-

time hearts rates are reported at fixed times of day and not equiv-

alent to awake/sleeping heart rates. Assuming that subjects did not

sleep the full duration of time between 22:00 and 06:00, sleeping

heart rates may be somewhat lower than the reported night- time

heart rates and the awake heart rate perhaps somewhat higher than

the reported daytime heart rate. We lacked data on thyroid hormone

levels. This may have reduced the degree to which the differences of

the heart rates are explained by the model somewhat. Inclusion of

individuals with abnormal thyroid hormone levels could also possibly

have resulted in slightly wider reference ranges. The women in the

study are mostly post- menopausal, and thus the results may not be

generalisable to a pre- menopausal population where the menstrual

cycle may also have an impact on ambulatory heart rates. Finally, the

most significant limitation of this study is that we have only been able

to include individuals between 50 and 65 years of age.

CONCLUSIONS

Normal limits of ambulatory heart rate have a wide range in a

healthy middle- aged population sample, and measures of ambu-

latory heart rate are only explained by clinical correlates to a

relatively small degree. The reference ranges presented here can

be used in the interpretation of ambulatory ECGs, in particular

to determine whether patients with a perception of tachycardia

have abnormally elevated mean heart rates.

X Artur Fedorowski @ArturFedorowski and Linda S B Johnson @lsjMD

Contributors LSBJ conceived of the study. LSBJ, GE and APP designed the

methodology. APP performed the formal analysis under supervision of LSBJ. GE and

JS acquired and curated the data. APP drafted the manuscript. AF, JEL, AM, GE, JS,

LSBJ and JSH all provided critical revisions of the manuscript and approved the ﬁnal

version. APP is responsible for the overall content of the study as the guarantor.

Funding The main funding body of SCAPIS (Swedish CArdioPulmonary Imaging

Study) is the Swedish Heart and Lung Foundation. SCAPIS was also supported

by grants from the Knut and Alice Wallenberg Foundation, the Swedish Research

Council and Verket för innovationssystem (Sweden’s innovation agency). LSBJ and GE

are supported by the Swedish Heart and Lung Foundation. LSBJ is supported by the

Swedish Research Council and the Swedish Society for Medical Research.

Competing interests AF has received speaker fees from Bristol- Myers Squibb,

Finapres Medical Systems and Medtronic, and is a consultant to Argenx and

Medtronic in the ﬁeld of syncope, cardiovascular autonomic dysfunction and

postural orthostatic tachycardia syndrome. LSBJ receives consulting fees from

MEDICALgorithmics. JS is a shareholder of Symptoms Europe and Anagram

kommunikation. JSH has research grants and speaker fees from BMS/Pﬁzer,

Boehringer- Ingelheim, Boston Scientiﬁc, Novartis, Medtronic and Servier.

Patient and public involvement Patients and/or the public were not involved in

the design, or conduct, or reporting, or dissemination plans of this research.

Patient consent for publication Not applicable.

Ethics approval This study involves human participants and was approved by

Regionala etikprövningsnämnden i Umeå (Dnr 2016- 150- 31M) and Regionala

etikprövningsnämnden i Lund (Dnr 2018- 935). All participants gave written informed

consent. The study has been carried out in accordance with the Declaration of

Helsinki.

Provenance and peer review Not commissioned; externally peer reviewed.

Data availability statement Data are available upon reasonable request. Any

request for data will need to be approved by the study authors as well as the SCAPIS

leadership.

Supplemental material This content has been supplied by the author(s). It

has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have

been peer- reviewed. Any opinions or recommendations discussed are solely those

of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and

responsibility arising from any reliance placed on the content. Where the content

includes any translated material, BMJ does not warrant the accuracy and reliability

of the translations (including but not limited to local regulations, clinical guidelines,

terminology, drug names and drug dosages), and is not responsible for any error

and/or omissions arising from translation and adaptation or otherwise.

Open access This is an open access article distributed in accordance with the

Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits

others to copy, redistribute, remix, transform and build upon this work for any

purpose, provided the original work is properly cited, a link to the licence is given,

and indication of whether changes were made. See:https://creativecommons.org/

licenses/by/4.0/.

ORCID iDs

Anders PaulPersson http://orcid.org/0000-0001-7506-3899

ArturFedorowski http://orcid.org/0000-0002-5352-6327

JohanSundström http://orcid.org/0000-0003-2247-8454

GunnarEngström http://orcid.org/0000-0002-8618-9152

Linda S BJohnson http://orcid.org/0000-0002-2249-8220

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