Facilitating ambulatory heart rate variability analysis using accelerometry-based classifications of body position and self-reported sleep

Abstract

OBJECTIVE: This study aimed to examine differences in heart rate variability (HRV) across accelerometer-derived position, self-reported sleep, and different summary measures (sleep, 24h-HRV) in free-living settings using open-source methodology.
APPROACH: HRV is a biomarker of autonomic activity. As it is strongly affected by factors such as physical behaviour, stress, and sleep, ambulatory HRV analysis is challenging. Beat-to-beat heart rate (HR) and accelerometry data were collected using single-lead electrocardiography and trunk- and thigh-worn accelerometers among 160 adults participating in the SCREENS trial. HR files were processed and analysed in the RHRV R package. Start time and duration spent in physical behaviours were extracted, and time and frequency analysis for each episode was executed. Differences in HRV estimates across activities were compared using linear mixed models adjusted for age and sex with subject ID as random effect. Next, repeated-measures Bland-Altman analysis was used to compare 24h RMSSD estimates to HRV during self-reported sleep. Sensitivity analyses evaluated the accuracy of the methodology, and the approach of employing accelerometer-determined episodes to examine activity-independent HRV was described.
MAIN RESULTS: HRV was estimated for 31,289 episodes in 160 individuals (53.1% female) at a mean age of 41.4 years. Significant differences in HR and most markers of HRV were found across positions [Mean differences RMSSD: Sitting (Reference) – Standing (-2.63 ms) or Lying (4.53 ms)]. Moreover, ambulatory HRV differed significantly across sleep status, and poor agreement between 24h estimates compared to sleep HRV was detected. Sensitivity analyses confirmed that removing the first and last 30 seconds of accelerometry-determined HR episodes was an accurate strategy to account for orthostatic effects.
SIGNIFICANCE: Ambulatory HRV differed significantly across accelerometry-assigned positions and sleep. The proposed approach for free-living HRV analysis may be an effective strategy to remove confounding by physical activity when the aim is to monitor general autonomic stress.

Full text

Physiological Measurement
ACCEPTED MANUSCRIPT • OPEN ACCESS
Facilitating ambulatory heart rate variability analysis using
accelerometry-based classifications of body position and self-reported
sleep
To cite this article before publication: Marlene Rietz
et al
2024
Physiol. Meas.
in press https://doi.org/10.1088/1361-6579/ad450d
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Facilitating ambulatory heart rate variability analysis using
accelerometry-based classifications of body position and self-
reported sleep
Marlene Rietz1,2, Jesper Schmidt-Persson1, Martin Gillies Banke Rasmussen 1,3, Sarah
Overgaard Sørensen 1, Sofie Rath Mortensen 1,4, Søren Brage 1,5, Peter Lund Kristensen1,
Anders Grøntved1† & Jan Christian Brønd1†
1 Center for Research in Childhood Health, Research Unit for Exercise Epidemiology,
Department of Sports Science and Clinical Biomechanics, University of Southern Denmark,
Odense M, Denmark
2 Division of Clinical Physiology, Department for Laboratory Medicine, Karolinska Institutet,
Stockholm, Sweden
3 Steno Diabetes Center Odense, Odense University Hospital, Odense, Denmark
4 The Research and Implementation Unit PROgrez, Department of Physiotherapy and
Occupational Therapy, Naestved-Slagelse-Ringsted Hospitals, Region Zealand, Denmark
5 MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
† These authors contributed equally to this work and share last authorship.
* Correspondence: Jan Christian Brønd, jbrond@health.sdu.dk
Keywords: Heart Rate Variability, Accelerometry, RHRV, Free-living, Behaviour
Word count: 5163
Figures/Tables: 2 Figures / 6 Tables / 2 Supplementary Figures / 1 Supplementary Table
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Abstract (300 Words)
Objective
This study aimed to examine differences in heart rate variability (HRV) across accelerometer-
derived position, self-reported sleep, and different summary measures (sleep, 24h-HRV) in
free-living settings using open-source methodology.
Approach
HRV is a biomarker of autonomic activity. As it is strongly affected by factors such as physical
behaviour, stress, and sleep, ambulatory HRV analysis is challenging. Beat-to-beat heart rate
(HR) and accelerometry data were collected using single-lead electrocardiography and trunk-
and thigh-worn accelerometers among 160 adults participating in the SCREENS trial. HR files
were processed and analysed in the RHRV R package. Start time and duration spent in
physical behaviours were extracted, and time and frequency analysis for each episode was
executed. Differences in HRV estimates across activities were compared using linear mixed
models adjusted for age and sex with subject ID as random effect. Next, repeated-measures
Bland-Altman analysis was used to compare 24h RMSSD estimates to HRV during self-
reported sleep. Sensitivity analyses evaluated the accuracy of the methodology, and the
approach of employing accelerometer-determined episodes to examine activity-independent
HRV was described.
Main Results
HRV was estimated for 31,289 episodes in 160 individuals (53.1% female) at a mean age of
41.4 years. Significant differences in HR and most markers of HRV were found across
positions [Mean differences RMSSD: Sitting (Reference) – Standing (-2.63 ms) or Lying (4.53
ms)]. Moreover, ambulatory HRV differed significantly across sleep status, and poor
agreement between 24h estimates compared to sleep HRV was detected. Sensitivity
analyses confirmed that removing the first and last 30 seconds of accelerometry-determined
HR episodes was an accurate strategy to account for orthostatic effects.
Significance
Ambulatory HRV differed significantly across accelerometry-assigned positions and sleep.
The proposed approach for free-living HRV analysis may be an effective strategy to remove
confounding by physical activity when the aim is to monitor general autonomic stress.
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1 Introduction
Heart rate variability (HRV) is a complex biomarker of the cardiac adaptability to internal and
external challenges such as stress, exercise, and disease (1). While HRV measurements are
a staple in commercial wearables commonly used in free-living research, there is a lack of
transparent and open-source methodologies to analyse continuous ambulatory HRV.
The cardiac system is innervated by the central nervous system, which adapts cardiac activity
through the autonomous nervous system (ANS) in response to several stimuli such as visual
and auditory afference, emotions, and physiological signalling from entities such as chemo-
and baroreceptors (2). When stimuli are processed, the ANS is innervated via pre- and post-
ganglionic neurons, and cardiac activity is adapted leading to a change in heart rate (HR)
represented as HRV and cardiac output in response (3). Therefore, HRV represents
fluctuations in the vagal and sympathetic innervation of the autonomous nervous system (1).
The activity of the parasympathetic nervous system results in quick, brief decreases in heart
rate, leading to high-frequency fluctuations in HRV. Conversely, the activity of the sympathetic
nervous system leads to slower and more prolonged changes in heart rate, resulting in low-
frequency fluctuations in HRV (4).
Ambulatory recordings of HRV are affected by an individual’s behaviour, including sleep (5),
activity status (6), and posture (7), as these are closely related to the ANS. Other non-
modifiable factors such as age (8), sex (9), and ethnicity (10) have also been reported to be
associated with cardiac adaptability. In detail, a change in posture or even trunk position
requires a rapid change in innervation of the cardiac muscle tissue to maintain stable blood
flow to the brain and internal organs (11,12). When standing up, the venous return drastically
decreases compared to when sitting or lying down. This results in a decrease in cardiac
output, mean arterial blood pressure, and leads to the activation of sympathetic neurons via
baroreceptors (11). Consequently, the sympathetic nervous system is activated which partly
results in an increase in HR and cardiac contractility. When this mechanism is insufficient,
individuals may experience orthostatic hypotension as their cardiac innervation does not lead
to sufficient blood supply to the brain (13). Moreover, HRV varies significantly across sleep
stages. During rapid eye movement (REM) sleep, sympathetic cardiac innervation may lead
to HRV measurements that are similar to wakefulness (14). Additionally, breathing during
sleep may be irregular (15) which may influence HRV assessments. In contrast, non-rapid
eye movement (NREM) sleep, such as slow-wave-sleep, is characterized by a decrease in
HR, blood pressure, and low sympathetic nerve activity (15). During the night, healthy
individuals cycle through the NREM (75%) and REM (25%) sleep stages which results in
substantial changes in breathing rate, HR, and HRV (16).
To appropriately record and analyse ambulatory HRV, standardized methodologies to adjust
for behaviours in a free-living setting are warranted, particularly, since it is recommended to
analyse HRV in a research environment where similar measurement conditions are ensured
for each subject (1). A common methodology to assess objective behaviour and physical
activity is accelerometry. Using accelerometry, one could potentially correct HRV for
environmental and lifestyle-related confounders in order to reach stable measurement
conditions such as previously reported in an animal model (17).
In field-based, experimental, and cohort studies following participants for longer periods of
time, methodologies to assess outcomes is required to be cost-efficient, specific, and
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available to be used in a larger sample size than in a laboratory setting. Therefore, small
tracking devices recording HR (18), accelerometry (19), and other physiological parameters
(18) are commonly used in ambulatory settings. HRV is an attractive endpoint to consider in
this context, as it may be employed to evaluate the effects of lifestyle interventions on an
individual’s psychological stress levels (20), health (21), and recovery from exercise (22).
Recently, several producers of commercial wearable devices have introduced an HRV-
estimate to their individual users and for the research community (23). While there is detailed
documentation over the recorded biomarkers and physiological implications of the
biofeedback (24–26), the algorithms estimating HRV and stress- or recovery-indexes are
commonly proprietary due to their commercial values (e.g. Patent IDs US9750415B2,
EP1545309B1, US10842429B2). This is a limiting factor on academic research as
investigators are required to have an insight into all data processing and handling to
accurately reproduce and report their scientific findings. Besides, as HRV results are heavily
affected by pre-processing and filtering of HR measurements, a large diversity in data
handling methodology has precedingly limited the comparability and reproducibility of current
scientific evidence (27). Consequently, automated, and transparent methods using freely
available software are favourable to the standardization of ambulatory HRV assessments in
academic research.
The aims of this study were twofold: Firstly, to describe and compare HRV estimates obtained
during various accelerometer-determined positions and sleep in a natural environment, and
secondly, to compare long-term recordings of HRV during sleep to the current 24-hour HRV
processing standard (1). By using an open-source R package to realise these aims, we
carefully present a transparent and automated methodology to analyse HRV data obtained in
free-living settings.
2 Materials and Methods
2.1 Study Population
HRV was recorded among 160 adults participating in the SCREENS randomised controlled
trial (RCT) for a total of six days. The SCREENS trial was originally designed to examine the
effect of a two-week screen time reduction on health and behaviour in families. It has
previously been described in detail (28). Briefly, inclusion criteria for the clinical trial were to
live in a family with children who were ≥4 years old, to consume >2.4 h of screen time per
day, and to be in full-time studies or employment. Regular night shifts, inability to be physically
active, diagnosed sleep disorders, neuropsychiatric disorders, developmental disorders, and
sick leave due to stress during the last three months were exclusion criteria as well. Data
obtained for this study was recorded in 68 parent pairs as well as 24 individuals where the
other partner did not participate in data collection (17 female, 7 male). The present study
utilised both baseline and follow-up data from the SCREENS trial as the comparisons of HRV
across episode classifications are likely unaffected by the intervention. Sample characteristics
including age, height, weight, and mean daily moderate-to-vigorous physical activity (MVPA)
were recorded at baseline.
2.2 Data Collection
HR and accelerometry data were collected using Firstbeat Bodyguard 2 (FB2, Finland) and
Axivity AX3 devices (United Kingdom) (28), respectively. Self-reported sleep was assessed
by sleep journals on the same nights as HR and accelerometry were recorded.
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Accelerometers were placed on the thigh and trunk. The FB2 and Axivity AX3 were initialised
on the same computer to ensure that real-time clocks in the devices were synchronised. With
a maximum of three consecutive recording days, it was expected that the clock drift is minimal
(29). Unique HR data files were generated for each measurement session when a device was
removed and reattached. In post-processing, all HR recordings of an individual were
combined into one data file for baseline and follow-up recordings, separately. After data
preparation, HR files containing interbeat intervals for each participant were loaded into an
HRVdata structure in the RHRV R package (30). In RHRV, data was processed by generating
HR, filtering for missing beats, ectopic beats, and arrhythmias, and interpolating the HR at a
frequency of 4 Hz. The filtering was done at a minimum HR of 25 beats per minute (bpm), a
maximum HR of (220 bpm – age) (31), and otherwise using default RHRV settings, twice,
consecutively.
Employing an algorithm, start time and duration (seconds) spent in the activities sitting,
standing, and lying were extracted for each participant in the form of episodes spent in an
activity. This activity type classification algorithm was developed by Skotte et al. (32) and has
previously been described in detail (33). While the algorithm also identified episodes of
walking (468 episodes), running (48 episodes), and biking (75 episodes), episode counts
were too small to be included in this study. Accelerometer-determined episodes of lying down
were differentiated into awake and asleep states using self-report sleep journals (34).
Moreover, 24h-HRV episodes were computed from the self-reported wake-up time. Total self-
reported sleep (bedtime – waketime) and normalized sleep (0:00 – 5:00) intervals were
additionally introduced as episodes for analysis.
All activity episodes extracted from accelerometry were added to the HRV data structure in
RHRV as time episodes. Each behaviour episode was assigned a unique HRV Tag. The HRV
Tags were generated using subject ID, activity type, and a unique list identifier for each
episode. Activity episodes recorded outside of HR measurements were discarded employing
a clock time variable corresponding to the recording. To qualify for time and frequency
analysis, a minimum episode duration of 360 seconds for each bout was required. The first
and last 30 seconds of each episode were removed to adjust for immediate changes in HRV
associated with hemodynamic changes rather than the position or sleep status itself, and this
ensures a minimum epoch duration of five minutes (300 seconds).
2.3 Modification of RHRV
A complete script consisting of R loops and multiple functions was used to automate the
extraction of all HRV outcomes by episode. One function used to extract HRV time analysis
outcomes by episodes is currently not implemented and was thus obtained from an RHRV
admin in a forum (CreateTimeAnalysisByEpisodes) (35). Furthermore, the time-analysis and
frequency analysis functions were modified to only report HRV outcomes within episodes to
decrease the processing time. Next, the code was modified to recognize missing beats as
NA. We additionally provide R package code in a publicly available GitHub repository that
integrates the readily available RHRV R package with code as well as a tutorial to analyse
HRV using pre-defined episodes (Link: https://github.com/marleriee/RHRV_SDU).
2.3 HRV Outcomes
HRV outcomes were computed for each accelerometer-determined episode, individually. As
it is recommended to use three statistical and one geometric assessment of HRV for HRV
time analyses (1), time-analysis outcomes investigated were the SD of R-R intervals (SDNN,
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ms), the percentage of adjacent R-R-intervals differing from each other by > 50 ms (pNN50,
%), the root-mean square of the successive difference of R-R intervals (RMSSD, ms), and
the geometric HRV triangular index (HRVi). Time analyses were carried out using a segment
size of 300 seconds at a width of bins in R-R interval histograms of 7.8125 ms. Most time-
analysis outcomes are commonly sensitive to episode duration as the estimates are based
on the absolute R-R interval. The RMSSD however, is estimated based on the changes in
the R-R intervals (differences between each successive pair of intervals), and it is therefore
less sensitive to absolute changes in HR and thus episode duration as compared to SDNN
and HRVi. Accordingly, the RMSSD was used as main outcome for our analyses. In frequency
analysis, power band calculations were performed using Fourier transformation. Then,
powerbands were split by accelerometer-determined episodes. Power in very low frequency
(VLF, 0.0033 – 0.04 Hz), low frequency (LF, 0.04-0.15 Hz), and high frequency (HF, 0.15-0.4
Hz) were computed for intervals of 300 seconds with a frame shift of 60 seconds. Mean total
power (SD) of VLF, LF, and HF were extracted. Furthermore, normalized units of LF and HF
were computed using the equations LFnu = LF/(VLF + LF + HF) and HFnu = HF/(VLF + LF +
HF) (36). After HRV outcomes were estimated, extreme outliers, i.e., episodes with a RMSSD
> 3SD than the mean for corresponding activity types, were removed. This limit was in good
agreement with expected maximum RMSSD. Outlier removal resulted in a removal of 1746
out of 32526 (5.4%) episodes in the activities sitting, standing, and lying, and 217 out of 2411
(9.0%) episodes in 24h- and self-reported sleep-intervals. Variation analyses suggested that
outliers in HRV estimates may be due to incorrect attachment of the FB2 or motion artefacts.
2.4 Statistical Analysis
Descriptive information on sample characteristics were cross-tabulated. Distributions of HRV
outcomes were examined using histograms. HRV parameters that were not normally
distributed and did not include zeros were log-transformed. Moreover, summary statistics for
raw HRV as mean (SD) for normally distributed variables and median [inter-quartile range
(IQR)] for non-normally distributed variables in activities were presented for male and female
participants, separately. RMSSD in continuous accelerometer-determined episodes of
positions, sleep, and 24-h summaries were plotted for person-days with >50 episodes. The
plots presented in this publication were chosen based on complete sleep data, a shared
household, and diversity of recorded episodes. Mean differences between activities and
across sleep status were estimated using linear mixed models adjusted for age and sex (fixed
effects) with subject ID as random effect for outcomes with normal distribution before or after
log-transformation. As pNN50 describes a percentage and included zeros, this outcome was
unable to be log-transformed. Therefore, a generalised linear mixed model with binomial
probability-distribution function and identity link was employed to estimate mean differences
(37). Reliability of episode specific HRV outcomes was estimated from variance components
of the mixed model to calculate intraclass correlation coefficients for all models except pNN50
(ICC, [def]. proportion of the total variance in the HRV outcome that is due to differences
between subjects) (38). A high ICC was interpreted as no or minor within-subject variability
in the HRV measures across all positions and sleep states included in the models and that
the variability observed in the data is due to differences between subjects (39).
Furthermore, night-time RMSSD determined by sleeping schedule was compared to current
clinical practice 24h RMSSD measurements by evaluating mean differences computed using
linear mixed models as well as agreement examined in nested repeated-measures Bland-
Altman analyses for 24h intervals in contrast to total self-reported sleep and normalized sleep
(0:00 – 5:00). Limits of agreement (LoA) and concordance correlation coefficients (CCC) were
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computed. Statistical testing was carried out in R (Version 4.3.0) at a significance level of p <
0.05.
2.5 Sensitivity Analysis
The impact of removing the first and last 30 seconds of each episode was examined by
running a repeated Bland-Altman analysis comparing HRV estimates in episodes adjusted
for immediate changes in position with results for raw episodes at 95% LoA (40). To achieve
the computational power required to run an agreement analysis for <30,000 datapoints, the
analysis was carried out manually following methodology obtained from a forum by R Project
(41). At a significance level of 0.05, pair-wise differences and means were computed. We
calculated the mean difference and SD, performed a one-way analysis for pair-wise
differences based on subject ID to address interindividual effects, and generated a Bland-
Altman plot once we had computed the 95% LoA. Inter- and intra-individual variance was
described. Next, the effect of episode duration on differences between adjusted and raw
episode estimates was characterised using mixed linear models with difference between
methods as response variable, HRV duration of uncut episodes as predictor, and ID as
random effect. Finally, the variance in HRV outcomes within individuals was compared
between unique body positions, sleep, and 24h-assessments by computing ICC estimates for
linear mixed models ran on a subset of each type of episode classification.
3 Results
3.1 Sample Characteristics
During baseline and follow-up data collection within the SCREEN trial, HRV was estimated
for 31,289 episodes of accelerometer-determined activity (sitting, standing, lying) in 160
individuals (52.8% female) at a mean age (SD) of 41.44 (± 5.0) years. After data cleaning, an
average (SD) of 78.39 (± 24.65) hours of heart rate recordings in accelerometer-determined
activities were available for each subject. Moreover, 2207 episodes describing HRV in 24h-
intervals and sleep were analysed using self-reported sleep data. Sample characteristics
cross-tabulated by sex are presented in Table 1. Female and male participants reported a
mean BMI (SD) of 25.1 (± 4.0) kg/m2 and 26.6 (± 2.9) kg/m2, respectively, and accelerometer-
measured mean (SD) daily MVPA within the sample was 15.1 (± 11.9) min. Male participants
were significantly older, taller, heavier, had a larger BMI, and they reported marginally
reduced sleep duration compared to female participants. Additionally, participants reported a
mean (SD) of 7.6 (±1.1) hours of sleep per day. Mean (SD) for all HRV outcomes across sex
are presented for accelerometer-determined activities in Table 2. Summary statistics for HRV
during sleep and 24h intervals are shown in Supplementary Table 1.
3.2 HRV across Positions
To examine differences in HRV across accelerometer-determined positions, linear mixed
models for HRV as response variable with the fixed effects sex and age and the random effect
subject ID were computed. For accelerometer-determined episodes, normal distribution was
confirmed for HR, SDNN, RMSSD, HRVi, LFnu, and HFnu (Supplementary Figure 1). The
variables raw power describing VLF, LF, and HF did not present zeros and were therefore
log-transformed to achieve Gaussian distribution (Supplementary Figure 2), and
exponentiated estimates and confidence intervals for log-transformed variables were
reported. For pNN50, describing a percentage, a generalised linear mixed-effects model with
binomial probability distribution function and identity link was employed.
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Significant differences across accelerometer-determined positions were observed for most
HRV outcomes (Table 3). Compared to seated position, mean differences (95% confidence
interval) in RMSSD in standing and lying position (awake) were -2.69 ms (-3.31, -2.06) and
2.73 ms (2.23, 3.23), respectively. At an ICC of 0.59, the RMSSD model presented the lowest
inter-individual variance and moderate reliability as compared to other models (42). While
ICCs for alternative HRV markers were lower, significant differences across activities were
observed for most outcomes. However, when comparing raw power in HRVi in sitting and
lying position, VLF in sitting, standing, and lying position, and LF in the sitting and standing
position, mean differences (95% CI) were not statistically significant at -0.12 (-0.27, 0.03),
1.01 (0.98, 1.04) and 1.02 (0.99, 1.04), and 1.01 (0.98, 1.04), respectively (Table 3). In the
linear mixed models, male sex was associated with significantly higher mean VLF and LF
power, as well as higher LFnu and lower HFnu. Moreover, age was a significant predictor of
pNN50, HRVi, and mean LF and HF power.
3.3 HRV across Sleep Status
By classifying episodes into sleep and awake status using self-report journals, mean
differences in lying HRV across sleep status were examined. Mean estimates (SD) for
RMSSD in individual episodes (6516 episodes) for female and male participants (Table 2)
were comparable to mean estimates (SD) for total and normalized sleep intervals
(Supplementary Table 1). There were significant differences in lying HRV across self-
reported sleep status presenting as increased time-analysis HRV estimates, raw VLF, LF, HF
power, and HFnu, and decreased HR and LFnu when individuals were asleep (ICC: 0.40 –
0.68) (Table 4). Male sex was significantly associated with higher SDNN, VLF and LF power,
LFnu, and lower HFnu in the linear mixed models for sleep status.
3.4 Current Practice 24h HRV summary compared to Sleep Intervals
When comparing HRV in sleep intervals to 24h current clinical practice estimates, significant
differences were determined for all variables. Mean estimates in linear mixed models for HR,
SDNN, HRVi, and LFnu were significantly lower in sleep intervals compared to 24h
assessments, and other HRV markers were significantly higher (Table 5). While estimates
for mean differences between 24h estimates and total sleep were commonly close to results
for normalized sleep (0:00 - 05:00), differences in SDNN varied between sleep intervals with
mean differences (95% CI) of -11.50 ms (-14.33, -8.68) for total sleep and -49.30 ms (-52.35,
-46.26) for normalized sleep. The largest ICC was determined for raw LF (0.8), and HF power
(0.79) followed by other frequency HRV estimates, pNN50 (0.76) and RMSSD (0.71) (Table
5). In agreement analysis, Bland-Altman analyses showed a mean bias (95% LoA) of 4.49
ms (-25.05, 16.07) and 4.42 ms (-26.88, 18.04) at a CCC (95% CI) of 0.78 (0.76, 0.80) and
0.72 (0.68, 0.75) for total and normalized sleep compared to 24h RMSSD, respectively. When
comparing total sleep to normalized sleep, agreement was superior at a CCC (95% CI) of
0.94 (0.93, 0.94). However, a mean bias (95% LoA) of -0.35 ms (-13.26, 12.55) was observed.
3.5 Continuous Assessment of HRV and Accelerometry
For person-days with an episode count > 50, dayplots presenting continuous RMSSD
estimates in accelerometry-determined episodes, 24-h and total sleep RMSSD estimates,
and self-reported waking and bedtime were graphed. The 24-h intervals included were
initiated at self-reported wake-up time to ensure similar conditions across participants. Upon
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visual inspection of continuous HRV assessments, trends in RMSSD across daytime and
activity were identified. There were some plots where 24-h summary measures and/or sleep
summaries visibly deviated from RMSSD during activity episodes. It was confirmed that this
was not due to measurement errors by manually checking all graphed person-days.
In Figure 1, an example of continuous HRV episodes by daytime, activity, and duration is
presented for a parent pair household participating in the study. Data was selected based on
complete sleep data, sufficient episode count, and shared wake-up time (i.e., 24-h summary
initiation time point) as well as household. The couple selected included a 39-year-old male
and a 37-year-old female, and data was recorded on measurement day 2 (SCREENS
baseline assessments). In comparison, the female individual presented an overall higher
RMSSD than the male. This is visible both in episodes and the summary measures. In both
participants, HRV was visibly increased during sleep compared to RMSSD recorded in wake
states, whereas the increase was stronger in the male participant. Total sleep and 24-h
summary RMSSD were determined at 41.07 ms and 29.75 ms for the male, and 50.74 ms
and 46.24 ms for the female, respectively. RMSSD was lowest when individuals were
standing, and it increased in sitting and lying position. Mean RMSSD in sitting, standing, and
lying episodes was 45.98, 39.64, and 53.03 ms in the female and 22.73, 21.34, and 38.89 ms
in the male participant, respectively. While some activity episodes present an RMSSD
estimate close to the 24-h summary measure, others deviate widely.
3.6 Sensitivity Analysis
The effect of removing the first and last 30 seconds of ECG data from each episode to account
for immediate changes in HRV due to changes in position was examined in 30730 episodes.
In a repeated-measures Bland-Altman analysis, RMSSD estimates for activity episodes with
adjusted duration were compared to raw episodes (+60 seconds) and a mean bias (95% LoA)
of -0.17 ms (-7.45, 7.11) was found (Figure 2). Within subject variation was 13.34 ms,
whereas the variance of subject and method interaction and variances of differences were
estimated to 0.45 ms and 13.79 ms, respectively. Furthermore, a mixed-effects model for
differences across cut and uncut episodes and episode duration with the random effect of
subject ID did not suggest statistically significant differences in method agreement across
durations of HRV episodes (p=0.124). Lastly, ICCs indicating intra-individual variance within
unique positions, sleep states, and measurement intervals are presented in Table 6. Briefly,
HRV recorded in sitting and lying position presented higher ICCs than HRV recorded in
standing position. Moreover, outcomes of frequency-analyses were more reliable than time-
analysis outcomes in 24h, total sleep, and normalized sleep intervals.
4 Discussion
4.1 Interpretation of Results
In this study, we combined accelerometry data with HR measurements obtained from single-
lead ECG recordings and described significant differences in HRV across position and sleep
in 160 adults. While a previous study in animals proposed to adjust models examining HRV
for accelerometry-derived energy-expenditure (17), we employed an accelerometry-based
algorithm to categorise episodes of HRV measurements into positions (sitting, standing,
lying).
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Our results demonstrate that most time-domain and frequency-domain HRV measurements
significantly differ across various physical positions, including sitting, standing, and lying
(awake) as well as sleep. These observations may be explained by a variety of physiological
mechanisms which differ across positions and sleep status. Briefly, hemodynamic changes
affect signalling to cardiac baroreceptors resulting in a change in heart rate and consequently
cardiac output (11). Moreover, breathing patterns are affected by posture-dependent
breathing styles and respiratory movements, including abdominal and rib cage movements,
which differ in supine and upright positions (43). Differences in HRV across sleep status are
likely due to high vagal activity with relatively low sympathetic tone (44), as well as reduced
movement and arousal in combination with a more regular respiratory sinus arrhythmia (45).
RMSSD was the most reliable marker of HRV across the included behaviours. Briefly,
RMSSD was suppressed when participants were standing compared to sitting and lying
positions. Moreover, RMSSD was elevated in lying position when individuals were asleep
compared to awake. This highlights that sleep recording is a crucial data source for HRV
analysis in free-living settings to account for the reported differences.
In contrast, VLF and LF power did not significantly differ across sitting and standing positions,
and HRVi and VLF power were not significantly different across sitting and lying positions,
respectively. Briefly, the prevailing effects of vagal activity and respiration on frequency-
related outcomes of HRV and HRVi may overshadow postural effects (46). This may also
explain why differences in VLF, LF, and HF could be observed across sleep status as
respiratory sinus arrhythmia is stabilised during sleep (45). Furthermore, while VLF and LF
power are appropriate to analyse in 5-minute episodes, VLF has been suggested to be best
monitored in 24-hour recordings (46). Finally, thermoregulation, the renin-angiotensin system,
endothelial effects, or baroreceptor activity may additionally play a role when discussing
short-term VLF, LF, and HRVi recordings(46).
Furthermore, differences in HRV between standard practice 24h- and sleep-summary
measures were examined, and results suggest notable differences and poor agreement of
total sleep HRV with 24h-estimates. However, there was a small mean difference and high
agreement in HRV measures between total sleep and normalised sleep (0:00-5:00). This may
imply that episodes of night-time HR recordings >5h may be comparable to HRV assessed
during the total sleep duration. This may facilitate data analysis for participants with missing
data due to the detachment of electrodes, which often occurs during sleep.
In assessing the reliability of HRV measurements within individuals, the ICC values reported
highlight a poor to good reliability ranging from 0.31 to 0.88. Some of the ICC estimates
suggest that while individual-specific factors contribute significantly to the variability in HRV,
there is also a notable stability within individuals over time, particularly for RMSSD, indicating
a strong individual physiological signature in HRV. Nevertheless, the variability indicated by
the lower ICC values for certain indices in episodes of position or sleep, such as SDNN, HRVi,
and VLF, underscores the dynamic nature of autonomic regulation and its sensitivity to
situational factors. Interestingly, the reliability of RMSSD across episodes recorded in sitting
position (0.63) and lying position (0.67) is similar to the ICC computed for RMSSD recorded
24h-measurements of HRV (0.70). For frequency outcomes, the highest reliability was
computed in longer intervals (24h, total sleep, normalized sleep).
While summary measures of HRV (24h, sleep) are current standard practice in free-living
research and clinical assessments (46), continuous plots of HRV episodes showed some
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deviation of episode-assessed HRV from summary measures across positions. Especially
when examining the effect of an intervention on HRV, we hypothesize that summary
measures may therefore dilute intervention effects on HRV during specific behaviours, such
as periods of relaxation (sedentary).
4.2 Methodological Considerations
Removing the first and last 30 seconds of HRV recordings within episodes was determined
to be a successful strategy to account for immediate changes in HRV due to posture changes.
Importantly, adjustments for immediate changes in position may be required, because
orthostatic effects on the autonomic nervous system may reduce the accuracy of short-term
HRV estimations in different positions (12). In a linear mixed model, differences across cut
and uncut episodes were also not shown to be associated with episode duration. Therefore,
we suggest that our strategy is equally effective at accounting for orthostatic effects in
episodes ~5 minute duration and hour-long periods of unchanged position or behaviour.
Overall, the approach of adding accelerometry-determined episodes of position to estimate
HRV was highlighted to be a cost-effective and transparent methodology for ambulatory
research. Our approach, which allows for precise adjustments based on specific behaviours
may provide an enhanced general measure of the autonomic nervous system compared to
standard 24-hour HRV assessments. This could be particularly valuable in experimental
studies that include ambulatory activities, such as exercise. Furthermore, standardised
methodologies for heart rate signal processing, filtering, and data handling are warranted to
promote reproducibility and transparency in the HRV research field. When commercial
software is used, proprietary algorithms for data cleaning may limit reproducibility using other
methodologies (47). In our study, we employed the freely available RHRV package to
interpolate, filter, and analyse heart rate data (48). While some functions needed to be added
to the official package and some small modifications were made to increase the speed of data
analysis, available code, a tutorial, and open-source software allow other researchers to use
our approach in a standardised format (Link: https://github.com/marleriee/RHRV_SDU).
4.3 Limitations
Some limitations of this study should be discussed. Although the sample size of 160
participants provided large amounts of data, the rather narrow age range of 31-58 years
reduces generalisability to young people and older adults. As the SCREENS trial required
high screen media use in addition to living in a family with children at baseline, HRV within
the sample may be affected by inclusion criteria. However, the intervention is unlikely to affect
differences in HRV between positions. While data processing followed standard
recommendations (1,48), protocols may differ from commonly employed commercial
processing applications, such as Kubios (49). When examining differences in HRV across
position and sleep, mixed-effects models were adjusted for age and sex. However, in free-
living environments, these analyses may also be at risk of confounding by other factors, such
as psychosocial factors, smoking, and the consumption of alcohol and caffeinated beverages
(50). Yet, having repeated assessment of HRV across numerous episode classifications
within subjects tends to increase the robustness of findings against potential confounding. As
sleep was self-reported, it may also be affected by social desirability bias and recall bias.
Therefore, some episodes which are classified as “sleeping” may describe “awake”
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behaviours that were misreported to conceal adverse sleep habits. However, a recent
publication (Zorko et al., 2020) has developed a methodology for simple sleep detection using
markers of respiratory sinus arrhythmia in heart rate recordings, and similar approaches may
be introduced to analysis protocols for real-time sleep detection during ambulatory HRV
assessments in the future. Furthermore, HRV was estimated for episodes of different
durations as accelerometry measurements directed the analyses. While this may be seen as
a limitation when comparing results to findings from studies employing standard 5 minute
short-term HRV estimates, we suggest that ensuring the longest possible stable
measurement of HRV promotes more accurate results.
4.4 Future Outlook
By establishing a protocol for HRV analysis in free-living settings based on 24-h data, we
hope to offer potential enhancements in the accuracy of HRV measurements, particularly
relevant when assessing the impact of interventions (in experimental studies) or exposures
(in observational studies) that include ambulatory activities, such as exercise. In detail, we
introduced a simple, transparent, and open-source protocol for the analysis of ambulatory
HRV across episode classifications by processing accelerometry and HR files in R and
employing RHRV for automated time- and frequency-analysis of HRV. Briefly, accelerometry
and HR data are obtained simultaneously, accelerometry data is categorised into episodes
of physical behaviours using an algorithm (32), and HR is analysed by these episodes in
RHRV to estimate markers of HRV. While several of the functions used for the analysis are
not yet included in the RHRV package, we supply a modified version of RHRV. Guided by
detailed recommendations for the analysis of HRV in RHRV (48), we hereby aim to facilitate
HRV analysis in free-living settings. One potential application for our proposed methodology
is research aiming to quantify autonomic stress in non-stationary conditions. As power
spectral analysis requires stable condition across the measurement period (51), extracting
episodes in unique behaviours would potentially account for changes in hemodynamic and
task-related physiological requirements. As accelerometry and ECG or
photoplethysmography are widely accessible, this methodology may reduce the need for
more advanced and costly commercial wearables in open-field research
4.5 Conclusion
Ambulatory HRV in healthy adults was shown to significantly differ across position and sleep
status. Moreover, HRV across the total sleep duration was in poor agreement with 24h-
summary measures. These findings have notable implications for the assessment of general
autonomic activity in free-living situations using 24-hour HRV. It emphasises the necessity to
consider the impact of different physical behaviours and sleep status when interpreting 24-
hour HRV data, particularly in the context of a study examining the effects of engagement in
activities in free-living. Lastly, this analysis was concluded by proposing a simple, transparent
approach for the analysis of HRV in accelerometer-defined episodes employing open-source
methodology in RHRV.
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5 Figures
Figure 1 – Example of continuous HRV episodes by daytime, activity, and duration. Example of a continuous HRV
assessment in a parent couple on the same recording day using accelerometer-determined classifications of the positions sitting
(purple), standing (turquoise), and lying (yellow). Dots represent HRV assessments > 5 minute and corresponding horizontal lines
represent episode duration. Self-reported sleep is marked in grey, and summary RMSSD for sleep (dotted grey line) and 24h
assessment (dashed black line) was computed according to current clinical practice. 24-h intervals were initiated at the self-
reported wake-up time, which was shared in the two individuals. Two self-reported wake-up times and one bedtime are shown
on the x-axis representing continuous time. The RMSSD (ms) is one of the most robust HRV biomarkers. Differences in HRV
across position and summary measures are visible. | Abbreviations: RMSSD - root mean square of successive differences; ms -
milliseconds; N – night
20
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07:45:00 23:50:00 07:00:00
Daytime
RMSSD (ms)
Female, 37 years
24−h RMSSD: 46.24 | 24h−Start: 7:45 AM
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07:45:00 23:20:00 07:35:00
Daytime
RMSSD (ms)
Male, 39 years
24−h RMSSD: 29.75 | 24h−Start: 7:45 AM
AID Sitting Standing Lying
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Figure 2 – Bland-Altman plot comparing HRV estimates for episodes adjusted for immediate change in posture to raw
estimates. Bland-Altman scatterplot for mean and difference between adjusted episodes and raw episodes (+60 seconds) for
each datapoint included in the agreement analysis (n=30730) including 95% LoA (±1.96 SD). | Abbreviations: RMSSD - root
mean square of successive differences; ms – milliseconds; LoA – limits of agreement.
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6 Tables
Table 1 – Sample characteristics
Female (n=85)
Male (n=75)
p value
Episodes (n)
16692
14088
0.38
Age (years)
40.4 ± 4.4
42.6 ± 5.4
<0.05
Height (cm)
169.5 ± 6.3
182.4 ± 6.8
<0.01
Weight (kg)
72.0 ± 11.3
88.5 ± 10.7
<0.01
BMI (kg/m2)
25.1 ± 4
26.6 ± 2.9
<0.01
Mean MVPA/day (min/day)
16.6 ± 13.3
13.3 ± 9.8
0.17
Mean Total Sleep Duration (h/day)
7.8 ± 0.6
7.5 ± 0.7
<0.05
Continuous variables are presented as mean ± standard deviation (SD). | Abbreviations: n – number of; BMI – body-mass-
index; MVPA – moderate-to-vigorous physical activity
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Table 2 – Time- and frequency results of heart rate variability across accelerometer-determined activities
Heart rate variability (HRV) by sex and accelerometer-determined activity is presented as mean [standard deviation (SD)] for
normally distributed variables and median [IQR (Q3-Q1] for non-normally distributed variables (*). Accelerometer-determined
states of supine position (lying) are divided into awake and asleep states determined by self-reported sleep. | Abbreviations: n –
number of episodes; HR – heart rate (beats/minute); NN – normal to normal interval; SDNN – standard deviation of NN-intervals;
pNN50 - adjacent NNs that differ from each other by > 50 ms (%); root-mean square of successive difference of NN; HRVi –
triangular heart rate variability index; VLF – very low frequency (0.0033 – 0.04 Hz); LF – low frequency (0.04-0.15 Hz); HF – high
frequency (0.15-0.4 Hz); nu – normalized unit
Activity
Sitting
(14887 Episodes)
Standing
(3155 Episodes)
Lying (all)
(12738 Episodes)
Lying (awake)
(5623 Episodes)
Lying (asleep)
(6516 Episodes)
Sex
F
M
F
M
F
M
F
M
F
M
HR(beats/min)
71.25
(11.86)
68.79
(13.48)
74.31
(14.55)
71.49
(15.87)
66.13
(13.00)
63.41
(13.37)
67.71
(14.27)
65.89
(14.87)
64.41
(11.63)
60.84
(11.28)
Time
Analysis
SDNN(ms)
66.92
(29.20)
73.29
(35.09)
65.89
(30.71)
67.18
(32.23)
73.89
(32.67)
82.63
(39.22)
70.11
(32.46)
74.84
(36.78)
77.23
(32.56)
91.26
(39.92)
pNN50*(%)
9.52
(22.93)
7.58
(17.54)
7.16
(18.50)
5.57
(12.73)
12.88
(28.80)
11.88
(23.80)
11.43
(27.33)
9.47
(21.31)
15.07
(29.94)
14.79
(25.10)
RMSSD(ms)
39.48
(23.77)
37.27
(23.24)
38.67
(25.22)
35.19
(23.51)
43.19
(25.05)
42.32
(24.43)
41.98
(24.93)
38.99
(23.24)
44.30
(24.82)
45.98
(25.19)
HRVi
14.96
(5.64)
15.56
(6.28)
14.04
(5.25)
13.77
(5.36)
15.78
(6.41)
16.54
(6.95)
15.00
(6.11)
15.09
(6.23)
16.47
(6.57)
18.08
(7.30)
Frequency
Analysis
VLF*(power)
100.12
(118.92)
119.27
(161.00)
106.09
(147.84)
113.67
(160.9)
118.63
(161.61)
153.72
(217.43)
99.44
(140.17)
117.93
(169.10)
136.52
(180.12)
196.44
(236.82)
LF*(power)
255.82
(361.46)
331.07
(443.05)
299.44
(419.40)
349.56
(515.49)
228.78
(343.56)
336.63
(438.59)
221.43
(324.48)
288.82
(372.47)
234.30
(360.45)
396.77
(507.1)
HF*(power)
91.76
(163.79)
73.82
(114.24)
77.55
(151.45)
58.32
(91.21)
112.15
(189.35)
94.37
(158.2)
102.70
(184.14)
82.17
(132.71)
121.37
(191.06)
108.10
(175.17)
LF(nu)
70.97
(15.62)
79.90
(11.75)
74.41
(15.90)
82.09
(12.71)
66.63
(15.84)
76.33
(12.57)
67.38
(15.99)
76.57
(13.14)
65.93
(15.57)
76.12
(12.06)
HF(nu)
29.03
(15.62)
20.10
(11.75)
25.59
(15.90)
17.91
(12.71)
33.37
(15.84)
23.67
(12.57)
32.62
(15.99)
23.43
(13.14)
34.07
(15.57)
23.88
(12.06)
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Table 3 – Linear mixed model for daily habitual physical behaviours
Sitting
Standing
Lying (awake)
Estimate (95% CI)
p(sitting-standing)
Estimate (95% CI)
p(sitting-lying)
ICCOverall
HR(beats/min)
Reference (0.00)
2.81 (2.42, 3.21)
<0.01
-4.02 (-4.34, -3.70)
<0.01
0.48
Time
Analysis
SDNN(ms)
Reference (0.00)
-4.10 (-5.09, -3.11)
<0.01
3.17 (2.38, 3.97)
<0.01
0.42
pNN50(%)
Reference (0.00)
-2.36 (-3.60, -1.13)
<0.01
1.72 (0.26, 3.19)
0.02
-
RMSSD(ms)
Reference (0.00)
-2.69 (-3.31, -2.06)
<0.01
2.73 (2.23, 3.23)
<0.01
0.59
HRVi
Reference (0.00)
-1.44 (-1.63, -1.26)
<0.01
-0.12 (-0.27, 0.03)
0.11
0.39
Frequency
Analysis
VLF(power)*
Reference (0.00)
0.97 (0.94, 1.00)
0.06
1.02 (0.99, 1.04)
0.23
0.35
LF(power)*
Reference (0.00)
1.01 (0.98, 1.04)
0.58
0.89 (0.87, 0.92)
<0.01
0.45
HF(power)*
Reference (0.00)
0.82 (0.79, 0.85)
<0.01
1.12 (1.09, 1.15)
<0.01
0.56
LF(nu)
Reference (0.00)
2.68(2.18, 3.18)
<0.01
-3.93 (-4.31, -3.54)
<0.01
0.35
HF(nu)
Reference (0.00)
-2.68 (-3.18, -2.18)
<0.01
3.93 (3.54, 4.31)
<0.01
0.35
Differences in HRV across the activities sitting, standing, and lying were investigated using simple or generalised linear mixed-
effects modelling adjusted for age and sex (fixed effects) with subject ID as random effect. Sitting was used as reference.
Intraclass correlation coefficients (ICC) are reported to estimate overall reliability across the model. ICC for pNN50 could not be
computed. *Outcomes were log transformed and coefficients exponentiated to give ratios of geometric means and expressed in
percentage. (i.e. the HF (power) was 18% lower and 12% higher for standing and lying (awake) as compared to sitting,
respectively).| Abbreviations: HR – heart rate (beats/minute); NN – normal to normal interval; SDNN – standard deviation of NN-
intervals; pNN50 - adjacent NNs that differ from each other by > 50 ms (%); root-mean square of successive difference of NN;
HRVi – triangular heart rate variability index; VLF – very low frequency (0.0033 – 0.04 Hz); LF – low frequency (0.04-0.15 Hz);
HF – high frequency (0.15-0.4 Hz); nu – normalized unit; p – p-value; ICC – intraclass correlation coefficient; 95% CI – 95%
confidence interval
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Table 4 – Linear mixed model for lying physical behaviour during awake and asleep status
Lying (awake)
Lying(asleep)
Estimate (95% CI)
p(awake-asleep)
ICCOverall
HR(beats/min)
Reference (0.00)
-3.22 (-3.57, -2.88)
<0.01
0.54
Time Analysis
SDNN(ms)
Reference (0.00)
10.23 (9.25, 11.20)
<0.01
0.48
pNN50(%)
Reference (0.00)
3.10 (1.52, 4.67)
<0.01
-
RMSSD(ms)
Reference (0.00)
3.59 (3.04, 4.14)
<0.01
0.68
HRVi
Reference (0.00)
2.22 (2.04, 2.41)
<0.01
0.43
Frequency
Analysis
VLF(power)*
Reference (0.00)
1.39 (1.35, 1.43)
<0.01
0.40
LF(power)*
Reference (0.00)
1.16 (1.13, 1.19)
<0.01
0.53
HF(power)*
Reference (0.00)
1.21 (1.18, 1.24)
<0.01
0.62
LF(nu)
Reference (0.00)
-0.60 (-1.03, -0.16)
0.01
0.41
HF(nu)
Reference (0.00)
0.60 (0.16, 1.03)
0.01
0.41
Differences in lying HRV across sleep status were investigated using simple or generalised linear mixed-effects modelling
adjusted for age and sex (fixed effects) with subject ID as random effect. Awake status was used as reference. Intraclass
correlation coefficients (ICC) are reported to estimate overall reliability across the model. ICC for pNN50 not be computed.
*Outcomes were log transformed and coefficients exponentiated to give ratios of geometric means and expressed in percentage.
(i.e. the HF (power) was 18% lower and 12% higher for standing and lying (awake) as compared to sitting, respectively).|
Abbreviations: HR – heart rate (beats/minute); NN – normal to normal interval; SDNN – standard deviation of NN-intervals; pNN50
- adjacent NNs that differ from each other by > 50 ms (%); root-mean square of successive difference of NN; HRVi – triangular
heart rate variability index; VLF – very low frequency (0.0033 – 0.04 Hz); LF – low frequency (0.04-0.15 Hz); HF – high frequency
(0.15-0.4 Hz); nu – normalized unit; p – p-value; ICC – intraclass correlation coefficient; 95% CI – 95% confidence interval
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Table 5 – Linear mixed model for full-day assessments compared to selected sleep intervals
24h Interval
Total Sleep
Normalized Sleep (5h)*
Estimate (95% CI)
pTS
Estimate (95% CI)
pNS
HR(beats/min)
Reference (0.00)
-9.48 (-10.19, -8.76)
<0.01
-11.36 (-12.13, -10.59)
<0.01
Time
Analysis
SDNN(ms)
Reference (0.00)
-11.50 (-14.33, -8.68)
<0.01
-49.30 (-52.35, -46.26)
<0.01
pNN50(%)
Reference (0.00)
4.92 (4.05, 5.79)
<0.01
6.11 (4.78, 7.43)
<0.01
RMSSD(ms)
Reference (0.00)
4.77 (3.84, 5.71)
<0.01
4.50 (3.49, 5.51)
<0.01
HRVi
Reference (0.00)
-11.40 (-12.15, -10.64)
<0.01
-17.00 (-17.81, -16.19)
<0.01
Frequency
Analysis
VLF(power)*
Reference (0.00)
1.22 (1.16, 1.28)
<0.01
1.20 (1.14, 1.26)
<0.01
LF(power)*
Reference (0.00)
0.97 (0.92, 1.02)
<0.01
0.92 (0.87, 0.97)
<0.01
HF(power)*
Reference (0.00)
1.15 (1.07, 1.23)
<0.01
1.13 (1.05, 1.21)
<0.01
LF(nu)
Reference (0.00)
-3.42 (-4.36, -2.48)
<0.01
-4.39 (-5.34, -3.43)
<0.01
HF(nu)
Reference (0.00)
3.42 (2.48, 4.36)
<0.01
4.39 (3.43, 5.34)
<0.01
Differences in HRV across the 24h-assessments and self-reported were investigated using investigated using simple or
generalised linear mixed-effects modelling adjusted for age and sex (fixed effects) with subject ID as random effect. Sleep was
normalized by only analysing 5h-intervals between 0:00-5:00 to adjust for different sleep routines and sleep stages. Intraclass
correlation coefficients (ICC) are reported to estimate overall reliability across the model. ICC for pNN50 could not be computed.
*Outcomes were log transformed and coefficients exponentiated to give ratios of geometric means and expressed in percentage.
(i.e. the HF (power) was 18% lower and 12% higher for standing and lying (awake) as compared to sitting, respectively). |
Abbreviations: Ref. – Reference; 24 – 24h Episode; TS – Total Sleep ; NS – Normalized Sleep; HR – heart rate (beats/minute);
NN – normal to normal interval; SDNN – standard deviation of NN-intervals; pNN50 - adjacent NNs that differ from each other by
> 50 ms (%); root-mean square of successive difference of NN; HRVi – triangular heart rate variability index; VLF – very low
frequency (0.0033 – 0.04 Hz); LF – low frequency (0.04-0.15 Hz); HF – high frequency (0.15-0.4 Hz); nu – normalized unit; p –
p-value; ICC – intraclass correlation coefficient; 95% CI – 95% confidence interval
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Table 6 – Intraclass correlation coefficients across activities and intervals
Position
Sleep Status (Lying)
Intervals
ICCSitting
ICCStanding
ICCLying
ICCAwake
ICCAsleep
ICC24h
ICCTotal Sleep
ICCNormalized Sleep
HR(beats/min)
0.53
0.42
0.51
0.51
0.58
0.45
0.52
0.51
Time Analysis
SDNN(ms)
0.44
0.37
0.44
0.44
0.51
0.54
0.63
0.59
RMSSD(ms)
0.63
0.50
0.67
0.67
0.70
0.70
0.76
0.76
HRVi
0.41
0.38
0.39
0.39
0.49
0.47
0.61
0.58
Frequency Analysis
VLF(power)
0.37
0.34
0.38
0.38
0.45
0.84
0.79
0.76
LF(power)
0.46
0.45
0.49
0.49
0.58
0.88
0.84
0.80
HF(power)
0.58
0.51
0.61
0.61
0.66
0.86
0.81
0.76
LF(nu)
0.37
0.31
0.43
0.43
0.46
0.63
0.79
0.78
HF(nu)
0.37
0.31
0.43
0.43
0.46
0.63
0.79
0.78
Interclass correlation coefficients (ICC) for each unique position, sleep state, or measurement interval were calculated based on
the estimated within-subject and between-subject variance in the respective HRV measure obtained from the linear mixed models
including all available respective episodes across subjects. Higher ICC values suggest greater consistency of HRV measures
within individuals in the respective body position, state, or interval, while lower ICC values indicate more episodic variability and
thus less individual consistency.| Abbreviations: HR – heart rate (beats/minute); NN – normal to normal interval; SDNN – standard
deviation of NN-intervals; pNN50 - adjacent NNs that differ from each other by > 50 ms (%); root-mean square of successive
difference of NN; HRVi – triangular heart rate variability index; VLF – very low frequency (0.0033 – 0.04 Hz); LF – low frequency
(0.04-0.15 Hz); HF – high frequency (0.15-0.4 Hz); nu – normalized unit; p – p-value; ICC – intraclass correlation coefficient; 95%
CI – 95% confidence interval
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Resource Identification Initiative
Software: RStudio (RRID:SCR_000432)
Software: R Project for Statistical Computing (RRID:SCR_001905)
Package Archive Network: CRAN (RRID:SCR_003005)
R package: RHRV (RRID:SCR_023329)
R package: tidyr (RRID:SCR_017102)
R Package: dplyr (RRID:SCR_016708)
R package: lme4 (RRID:SCR_015654)
R package: reshape2 (RRID:SCR_022679)
R package: openxlsx (RRID:SCR_019185)
R package: tidyverse (RRID:SCR_019186)
R package: ggpubr (RRID:SCR_021139)
R package: ggplot2 (RRID:SCR_014601)
R package: RColorBrewer (RRID:SCR_016697)
R package: viridis (RRID:SCR_016696)
R package: lubridate (RRID:SCR_024571)
7 Acknowledgments
The SCREENS project was funded by the European Research Council (grant number
716657).We’d like to thank and acknowledge all individuals which participated in the
SCREENS RCT. Moreover, thank you to research assistant Jasmin Helledie, who assisted
during data collection, and to the researcher service organization Open Patient Data
Explorative Network, Odense, Denmark. Finally, we’d like to thank Line G. Olesen for her
past engagement in the project.
8 Ethical Statement
This study was performed in accordance with the Declaration of Helsinki. This study was
performed in accordance with the Nurenberg Code. This human study was approved by
Scientific Committee of Southern Denmark - approval: S-20170213 CSF. All adult participants
provided written informed consent to participate in this study.
9 Data availability statement
Data can be made available upon request for research purposes after a data handling
agreement is made in accordance with the General Data Protection Regulation and the
Danish Data Protection Act.
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