\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[T2A]{fontenc}
\usepackage[ngerman,polish,greek,english]{babel}



\usepackage{float}








\begin{document}

\title{Resting HRV as a trait marker of rumination in healthy individuals? A
large cross-sectional analysis}



\author[1]{Zefeng Li}%
\author[2]{Matias Pulopulos}%
\author[1]{Jens Allaert}%
\author[3]{Stefanie De Smet}%
\author[1]{Linde De wandel}%
\author[1]{Mitchel Kappen}%
\author[1]{Louise Puttevils}%
\author[1]{Lais Razza}%
\author[1]{Emmanuelle Schoonjans}%
\author[1]{Gert Vanhollebeke}%
\author[4]{Chris Baeken}%
\author[5]{Rudi De Raedt}%
\author[5]{Marie-Anne Vanderhasselt}%
\affil[1]{Ghent University Faculty of Medicine and Health Sciences}%
\affil[2]{Ghent University Faculty of Psychology and Educational Sciences}%
\affil[3]{UZ Gent}%
\affil[4]{Ghent University}%
\affil[5]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
The tendency to ruminate (i.e., repetitive, self-referential, negative
thoughts) is a maladaptive form of emotional regulation and represents a
transdiagnostic vulnerability factor for stress-related psychopathology.
Vagally-mediated heart rate variability (HRV), reflecting
parasympathetic nervous system activity, is commonly used as a
physiological marker of stress regulation. Past research has suggested a
link between trait rumination and resting HRV at baseline; however,
inconsistent results exist in healthy individuals. In this study, we
investigated the association between the tendency to ruminate and
resting HRV measured at baseline in a healthy population using a large
cross-sectional dataset (N = 1189, 88\% women; mean age = 21.55, ranging
from 17 to 48 years old), which was obtained by combining samples of
healthy individuals from different studies from our laboratory. The
results showed no cross-sectional correlation between resting baseline
HRV and trait rumination (confirmed by Bayesian analyses), even after
controlling for important confounders such as gender, age, and
depressive symptoms. Also, a nonlinear relationship was rejected. In
summary, based on our results in a large sample of healthy individuals,
baseline resting HRV is not a trait marker of the tendency to ruminate.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Human consciousness flows continuously and spontaneously from one
thought to the next. Sometimes thinking evolves into an endless stream
of repetitive self-referential negative thoughts that are difficult to
let go of (Tseng \& Poppenk, 2020), defined as rumination. According to
the Response Styles Theory, rumination is the focused attention on the
symptoms of one's emotional distress and its possible causes and
consequences, as opposed to its solutions (Nolen-Hoeksema, 1991;
Nolen-Hoeksema et al., 2008). The habitual tendency to ruminate is
considered a maladaptive form of emotional regulation and has been
associated with a sustained physiological stress response that damages
the hormonal, cardiovascular, and nervous systems, ultimately leading to
observable physical and mental health problems (Aldao et al., 2010;
Brosschot et al., 2006; Kubzansky et al., 1997). Rumination is one of
the reasons responsible for incomplete cardiovascular recovery after
stress exposure and is therefore associated with cardiovascular indices
(Radstaak et al., 2011). Besides, even though the tendency to ruminate
is a well-known predictor of the onset and maintenance of depression
(McLaughlin et al., 2007; Nolen-Hoeksema et al., 2008), rumination is
considered a more general transdiagnostic factor of vulnerability and
outcome in stress-related psychopathology (e.g., alcohol abuse, eating
disorder, anxiety; Caselli et al., 2010; Dondzilo et al., 2016;
Wilkinson et al., 2013). Therefore, core neurophysiological mechanisms
underlying a tendency to ruminate are important to further understand
the relationship between rumination and the development of future
clinical symptoms and health problems.

The functional state of the Autonomic Nervous System (ANS), consisting
of the sympathetic and parasympathetic systems, has been reported as an
important neurophysiological mechanism associated with rumination
(Ottaviani et al., 2016; Vanderhasselt \& Ottaviani, 2021). More
specifically, both branches of the ANS act on the sino-atrial node of
the heart and result in a complex variation between consecutive
heartbeats over time, defined as heart rate variability (HRV; Goldberger
et al., 2001; Porges, 2001, 2007; Saul et al., 1990; Task Force, 1996).
In the face of stressors, the inhibitory control of the parasympathetic
(over the sympathetic) nervous system enables the regulation of
physiological and psychological states, an ability that is considered
key to behavioral adaptability to the environment (mainly via the vagus
nerve; Thayer et al., 2012). This parasympathetic dominance is
associated with higher HRV (Friedman, 2007; Grol \& De Raedt, 2020;
Nasso et al., 2019, 2020; Pulopulos et al., 2018). Higher HRV is
regarded as an indicator of better emotional regulation and mental
health (Brosschot et al., 2006; Perna et al., 2020; Reynard et al.,
2011; Thayer \& Lane, 2000). In addition, the heart and the brain are
reciprocally connected via the ANS pathways. Thayer's neurovisceral
integration model proposes a network of cortical and subcortical neural
structures, known as the central autonomic network (CAN), which receives
sensory input from the peripheral end organs such as the heart (Thayer
\& Lane, 2000). The neurovisceral integration model proposes that
vagally-mediated HRV is a read-out of the bi-directional interaction
between central and cardiovascular processes, with higher HRV denoting
more self-regulation and adaptability (Thayer et al., 2009).

Even though the association between rumination and vagally-mediated HRV
has been widely studied, research remains inconsistent across the
literature. Rumination can be separated into trait and state components,
with the habitual tendency to ruminate in daily life referring to trait
rumination, whereas state rumination is the act of ruminating elicited
by discrete stressors (Key et al., 2008; Nolen-Hoeksema \& Morrow,
1993). Most often, dynamic changes in HRV have been reported to examine
the link between state rumination and stress reactivity after a
laboratory induction (Ottaviani \& Shapiro, 2011). In these studies,
where individuals were put into a state of rumination in the laboratory
- such as a video clip triggering stress, coping with fear sources that
frighten the individual, or engaging in fearful or angry imagery or
recall - a reduction of HRV was observed (Beauchaine et al., 2007;
Castaneda \& Segerstrom, 2004; El-Sheikh et al., 2011). Furthermore, in
a meta-analysis reviewing 18 experimental studies, it was reported that
perseverative cognition, including rumination and worry that share a
commonality and are highly intercorrelated (Brosschot et al., 2006b;
Fresco et al., 2002), was associated with lower HRV in healthy
volunteers only on the state measures (Ottaviani et al., 2016). However,
in contrast to the inverse association between stress-induced HRV and
rumination response, questions remain regarding the association between
resting (baseline) HRV and the tendency to ruminate in daily life (trait
rumination). The habitual tendency to ruminate is known to be associated
with chronic stress (inability to recover) and has been associated with
a higher risk of cardiovascular disease (Busch et al., 2017; Schwartz et
al., 2003). Importantly, the meta-analysis mentioned above reported that
there was no association between HRV and the trait measure of
perseverative cognition in the experimental study; however, the number
of correlational studies (and the number of subjects) testing this
association was relatively low (Ottaviani et al., 2016). Therefore, the
relationship between resting HRV and trait rumination in healthy
individuals remains unclear, as many studies focus on clinical
populations or the HRV in response to induced rumination (i.e., a
momentary, reactive state). Even though resting HRV is assumed to be a
transdiagnostic and promising biomarker of mental health resilience
(Beauchaine \& Thayer, 2015; Perna et al., 2020), to the best of our
knowledge, there is no study describing the association between
resting-state HRV and trait rumination based on a large-scale and
well-powered dataset in healthy individuals.

In this study, we use a large cross-sectional dataset of physiological
and self-report (trait rumination and depression) data from our
laboratory collected over a time span of 4 years (2017-2022) to
investigate the association between resting HRV (i.e., measured during a
baseline period) and the habitual tendency to ruminate in healthy
individuals. We hypothesize that baseline HRV at rest will be negatively
correlated with trait rumination as both high trait rumination and low
HRV are risk factors for stress-related psychopathology (Key et al.,
2008; Larsen \& Christenfeld, 2009). In addition, given that HRV is
sensitive to various demographic variables, we will investigate whether
HRV is associated with the tendency to ruminate after controlling for
gender, age, and the level of depressive symptoms that are known to
significantly influence HRV (Laborde et al., 2017). Moreover, a study
has indicated that parasympathetic regulation of the heart through the
vagus nerve might not have an unmitigated linear relationship with
emotion (Kogan et al., 2013), so we also explore whether there is a
non-linear relationship between HRV and emotion in our large
cross-sectional data.

\textbf{Methods}

Participants

A group of 1245 healthy participants (197M/1048F) with a mean age of
21.55 (\emph{SD} = 3.05, ranging from 17-48 years old) were included in
our dataset. All subjects participated in 14 experiments in our
laboratory, the specific experimental information is listed in the
supplementary material (Allaert et al., 2021, 2022; De Smet et al.,
2021; DeWandel et al., 2022; Pulopulos et al., 2020; Pulopulos et al.,
2020). Although there were differences in the inclusion criteria from
study to study, all studies excluded participants with cardiovascular
disease and psychiatric disorders. All the experiments involved
physiological data and questionnaires measurement. All studies were
approved by the medical ethical board of Ghent university or the Ethical
Committee from the Faculty of Psychology and Educational Sciences at
Ghent University and were performed in accordance with the Declaration
of Helsinki.

Questionnaires

Rumination. The Ruminative Response Scale (RRS) is administered to
measure the degree of ruminative thinking styles of individuals
(Nolen-Hoeksema \& Morrow, 1991; Dutch translation by Raes et al.,
2007). The RRS consists of 22 items describing how participants respond
to a depressed mood, related to focusing on the self, symptoms, and the
origin and consequences of the distress. This self-report questionnaire
asks participants to respond on a 4-point Likert scale rating the
frequency with which they generally think or do certain things when they
feel sad, down, or depressed (i.e., 1 = almost never, 2 = sometimes, 3 =
often, 4 = most of the time). The questionnaire also consists of two
subscales: brooding and reflection. Brooding means a passive comparison
of one's current situation with some unachieved work, and reflection
means a purposeful turning inward to engage in cognitive problem-solving
to alleviate one's depressive symptoms (Treynor et al., 2003). General
Cronbach's alphas of RRS were .92, .78, and .75 for the RRS total scale,
and the brooding and reflection subscales, respectively (Schoofs et al.,
2010). All reported Cronbach's alphas are cited from previously executed
validation studies and not from the current dataset, including the
depression questionnaires below. 1150 participants in the dataset had
RRS scores available.

Depression. Three different questionnaires were used to evaluate the
presence of depressive symptoms, namely the Beck Depression Inventory
(BDI)-II (Beck et al., 1996; Dutch version Van der Does, 2002), The Mood
and Anxiety Symptoms Questionnaire (MASQ) (Wardenaar et al., 2010), and
the depression subscale of the Depression, Anxiety, and Stress Scale
(DASS-21) (de Beurs et al., 2001; Henry \& Crawford, 2005). Given that
the studies used three different questionnaires to assess depression, a
standardized Z score was computed to increase the total sample size for
our dataset (Depression\textsubscript{Standardized}).

\emph{Beck Depression Inventory-II.} The BDI-II is administered to
evaluate depressive symptoms. The BDI-II is a widely used self-report
questionnaire consisting of 21 multiples-choice format items (4-point
scale), to assess the presence and severity of cognitive, motivational,
affective, and somatic symptoms of depression in the past two weeks.
Past reports demonstrated established reliability and validity in
clinical and non-clinical samples, with Cronbach's alpha of .92 and .93
in a psychiatric and student population, respectively (Alexandrowicz et
al., 2014). 288 participants in the dataset had BDI-II scores available.

\emph{Mood and Anxiety Symptoms Questionnaire.} The MASQ is one of the
common use self-reported questionnaires to assess symptoms of depression
and anxiety. The questionnaire is designed to measure a tripartite model
of anxiety and depression, which assumes that symptoms of depression and
anxiety can be described in three dimensions. ''General Distress''
includes general symptoms of psychological distress, ''Anhedonic
Depression'' describes a lack of positive emotions and loss of energy,
and ''Anxiety Arousal'' describes symptoms of excessive physical arousal
(Watson et al., 1995). The MASQ used in this study consists of 30 items
in the Dutch version with each item scored on a 5-point Likert scale
(i.e., 1 = not at all, 2 = a little, 3 = moderately, 4 = quite a bit, 5
= extremely) in the past week (including today). Cronbach's alpha of
Anhedonic Depression was from .93 to .95 (Wardenaar et al., 2010). 218
participants in the dataset had MASQ scores available.

\emph{Depression, Anxiety, and Stress Scale.} The DASS measures the
presence of depressive symptoms, anxiety, and stress in the past 7 days.
The DASS consists of 21 items rated on a 4-Point Likert scale (0 = Not
at all or never applicable, 1 = A little or sometimes applicable, 2 =
Proper or often applicable, 3 = Very sure or mostly applicable) and
widely used in clinical settings to assist in diagnosis and outcome
monitoring and in non-clinical settings as a mental health screening.
The scale has three subscales, depression, anxiety, and stress, each
containing seven items, The higher the score, the higher the level of
negative emotions. The Cronbach's alpha of the DASS-21 was .88 for the
depression scale and .93 for the total scale (Henry \& Crawford, 2005).
459 participants in the dataset had DASS scores available.

Physiological assessment of baseline HRV

Data were acquired using two different kinds of devices: BiopacMP150/160
(Biopac Systems Inc., California, USA), and Polar (Polar V800 H7 \& H10,
Kempele, Finland), at a sampling frequency of 1000 Hz for all devices.
The electrocardiogram (ECG) electrode placements were corresponding to
each device's instruction manual. There were two different baseline
conditions in the samples of this study: 1) Rest, sitting upright in a
chair with palm up and eyes open naturally (72.3 \% of the total
sample); 2) Vanilla, also sitting upright in a chair and reading
magazines (27.6 \% of the total sample). Data collection was performed
in a controlled environment. All data included baseline measurement for
5 or 10 minutes to calculate resting-state HRV. For the data which
lasted 5 minutes, we calculated the mean values of the time intervals
between R peaks over the 5 minutes. For the data which lasted 10
minutes, we used the last 5 minutes of cardio data because the last 5
minutes of heart rate were more stable and had fewer artifacts than
those of the first 5 minutes.

Data pre-processing

PhysioDataToolbox (Version 0.6.3), a MATLAB-based (MATLAB 2020b
Component Runtime (v9.9)) application, was used to pre-process data from
Biopac (Sjak-Shie, 2022). All data were formatted and imported into the
toolbox, and then the ECG signal analyzer and HRV analyzer in the
toolbox were used. The ECG signal analyzer treated the raw ECG data with
a 1 Hz high-pass filter, a 50 Hz low-pass filter, and 1x signal gain.
Then the ECG analyzer detected R-peak with the feature of 0.5 mV minimum
R-peak, 0.3 s minimum distance between R-peak, 0.3 s minimum interbeat
interval value, and 2 s maximum interbeat interval value. Baseline
epochs with 5 or 10 minutes were defined according to the procedures of
different experiments. After the R-peaks detection, the ECG data were
visually inspected and manually corrected to remove ectopic beats,
artifacts, and misidentified R-peaks singly. We extracted the root mean
square of continuous heartbeat interval difference (RMSSD) to assess
heart rate variability in time domains for the baseline epoch. Although
some frequency-domain metrics such as high-frequency power also reflect
parasympathetic activity, RMSSD is more correlated with vagal regulation
and is less affected by respiratory and motor artifacts (Penttil\selectlanguage{ngerman}ä et
al., 2001). The data from Polar, which only measures the intervals
between two R-peaks in a consecutive period, were different from the
data from Biopac which contains the whole heartbeat cycles and
intervals. As such, we analyzed these data via Artiifact and Kubios
3.0.2 (Kaufmann et al., 2011; Tarvainen et al., 2002, 2014). The
published polar data were adopted directly from three studies from our
lab (Pulopulos et al., 2020a; Pulopulos et al., 2020b).

Data analysis

The SPSS software for Windows (IBM SPSS Statistics 27) was used for
descriptive, correlation, and regression analysis. First, HRV values
that differed from the mean value by more than three standard deviations
were removed. Then, we performed an overall descriptive analysis of the
HRV, questionnaire scores, and demographic information (age, gender).

Pearson correlation was used to calculate the correlation coefficient
between HRV, questionnaire scores, and age. Independent sample t-tests
were used to examine gender differences in HRV and questionnaire scores
and the effects of baseline type (i.e., resting and vanilla) on HRV.
Observed power was estimated by retrospective power analysis to evaluate
the statistical reliability whereas higher power indicates less
probability of type II error. Then, two linear regression analyses were
conducted with HRV as the dependent variable. In model 1, we included
RRS\textsubscript{total} as the independent variable and age, gender,
and Depression\textsubscript{standardized} as control variables. In
model 2, we included Brooding and Reflection together with the control
variables (age, gender, and Depression\textsubscript{standardized}). To
explore whether there is a non-linear effect between HRV and rumination
or depression as reported by previous studies, we conducted curve
estimations under regression analyses in SPSS with HRV as the dependent
variable; RRS\textsubscript{total}, Brooding, Reflection, the scores
from BDI-II, MASQ\textsubscript{depression},
DASS\textsubscript{depression}, and
Depression\textsubscript{standardized} as independent variables. One
independent variable was included for each estimation.

\textbf{Results}

Sample characteristics

Table 1 reports descriptive statistics for the HRV index, the
subdimensions of the RRS, the three depression questionnaires, and
Depression\textsubscript{standardized}. After removing the samples that
had physiological data with poor quality or with values higher than
three standard deviations above the mean, a total of 1189 participants
were finally included. The sample sizes vary somewhat per variable due
to the different types of questionnaires collected in different
experiments.

\emph{Table 1 places here, please.}

\emph{Correlation analyses}

As shown in Table 2, HRV was not significantly correlated with
RRS\textsubscript{total}, Brooding, Reflection, BDI-II,
MASQ\textsubscript{depression}, or DASS\textsubscript{depression}, nor
with Depression\textsubscript{standardized}. However,
RRS\textsubscript{total}, Brooding, and Reflection were significantly
correlated with BDI-II, MASQ\textsubscript{depression}, and
DASS\textsubscript{depression}, and
Depression\textsubscript{standardized}, indicating that the tendency to
ruminate was associated with higher levels of depression symptoms. There
was a significantly negative correlation between age and
RRS\textsubscript{total}, Brooding, and Reflection, which means that the
tendency to ruminate gradually decreased with the increase of age.
Similarly, age was negatively correlated with BDI-II and
Depression\textsubscript{standardized}, respectively. The analyses with
gender as the grouping variable found that the HRV was lower in women
than in men (\emph{t} (233.162) = 2.399, \emph{p} = .017), and the
RRS\textsubscript{total} (\emph{t} (1148) = -3.618, \emph{p} \textless{}
.001), Brooding (\emph{t} (1148) = -3.649, \emph{p} \textless{} .001)
and Reflection (\emph{t} (1148) = -2.895, \emph{p} \textless{} .001)
were higher in women than in men, but there were no differences in
BDI-II (\emph{t} (342) = -1.006, \emph{p} = .315),
DASS\textsubscript{depression} (\emph{t} (457) = .415, \emph{p} = .678)
and Depression\textsubscript{standardized} (\emph{t} (1019) =
-.403,\emph{p} = .687). Sex differences in depression could not be
investigated using the MASQ\textsubscript{depression} because all the
participants were female. In addition, there was a significant
difference in HRV between the types of physiological baseline
measurement (\emph{t} (1187) = -2.060, \emph{p =} .040), showing lower
HRV during resting baseline than during Vanilla baseline.

\emph{Table 2 places here, please.}

Regression analysis

None of the linear models with RRS\textsubscript{total}, brooding, and
reflection with and without covariates (age, gender, and
Depression\textsubscript{standardized}) were statistically significant
(see Table 3).

\emph{Table 3 places here, please.}

Based on the frequentists approach showing non-significant regard to
HRV, rumination, and depression, which thereby failed to reject the null
hypothesis, Bayesian factor analyses (using JASP 0.16.3) were conducted
to assess the likelihood of a correct null hypothesis (Quintana \&
Williams, 2018). Bayesian factor analysis is a development and
alternative to testing the null hypotheses significance test. The
Bayesian framework allows for the probabilistic description of
parameters and hypotheses and can quantify the degree to which the data
favors the null hypothesis (H0) or the alternative hypothesis (H1)
(Gelman et al., 2014; McElreath, 2020). The H1 is that HRV is negatively
correlated to rumination. The H0 rejects these correlations. In this
study, we use Bayes factor 10 (BF\textsubscript{10}) which measures the
degree to which H1 is supported by data compared with the H0. When the
BF is higher than 1, the evidence is in favor of H1. The larger the
factor, the higher the probability of the evidence in favor of H1. On
the contrary, when the BF is less than 1, the evidence is in favor of
H0. The smaller the factor, the higher the probability of the evidence
in favor of H0.

Bayesian analysis showed that there was strong evidence supporting no
correlations between HRV and
RRS\textsubscript{total}(BF\textsubscript{10} = 0.152), Brooding
(BF\textsubscript{10} = 0.061), and Reflection (BF\textsubscript{10} =
0.41), because the BF\textsubscript{10} were less than 1.

\emph{Table 4 places here, please.}

For non-linear effects, we created a linear term and a quadratic term
for each variable in the equation to do a curve estimate. Significant
linear and quadratic effects of Reflection and a significant quadratic
effect of MASQ\textsubscript{depression} on HRV were observed. No other
quadratic effect was found between HRV with rumination and depression
(see Table 4).

\textbf{Discussion}

In this study, we aimed to explore whether baseline vagally-mediated HRV
(measured via RMSSD) is a marker of the habitual tendency to ruminate,
which is a maladaptive thought process that makes individuals vulnerable
to mental disorders (measured by the RRS), in a large sample of healthy
individuals. Based on our data from over 1100 volunteers, no evidence is
found for the relationship between baseline vagally-mediated HRV at rest
and rumination.

Reduced resting HRV has been reported in various forms of
psychopathology (Chalmers et al., 2014; Faurholt-Jepsen et al., 2017;
Tak et al., 2009), and the association between reduced HRV with trait
rumination has been reported in clinical and experimental studies
(Johnson et al., 2014; McLaughlin \& Nolen-Hoeksema, 2011). For example,
a correlational study consisting of 117 women (55 recovered from
depression and 56 healthy controls) found a significant relation between
brooding, a maladaptive form of rumination, and HRV, such that women
reporting higher levels of brooding exhibited lower levels of HRV (Woody
et al., 2014). Another study monitored HRV in 52 healthy volunteers
throughout the day and found that the tendency to worry (another
perseverative cognition, highly correlated to rumination) was associated
with lower HRV (Brosschot et al., 2007; Fresco et al., 2002). In line
with these results, the seminal neurovisceral integration model proposes
that in healthy individuals, the prefrontal cortex is in control over
subcortical areas, resulting in tonic inhibitory control of the
parasympathetic branch of the ANS (i.e., higher HRV; Thayer \& Lane,
2009). As a result, these neural and autonomic regulations result in
less viscous cycles of ruminative thoughts and turn higher resting HRV
(Song et al., 2022). However, in the current study, this association
between the tendency to ruminate and baseline vagally-mediated HRV
measured at rest cannot be observed in healthy individuals, even after
controlling for gender, age, and the level of depressive symptoms.
Moreover, Bayesian analyses showed a higher likelihood of no association
between HRV. Finally, our results exclude a non-linear relationship
between HRV with rumination and the level of depressive symptoms, which
is in line with a prior study that cannot find a linear or non-linear
effect of baseline resting HRV on positive emotion and depression
(Silvia et al., 2014).

A meta-analysis reviewing the relationship between physiological
concomitants with perseverative cognition found that maladaptive thought
processes (including rumination and worry) were associated with
decreased HRV in 8 correlational studies, but the literature was
insufficient to conclude whether this association existed specifically
for both traits (i.e., habitual tendency) and state (i.e., momentary)
rumination (Ottaviani et al., 2016). Two studies focused on the
relationship between the tendency to ruminate and HRV recovery in
children aged 7-14 and reported inconsistent results (Sample size: 100
and 103; Borelli et al., 2014; Gentzler et al., 2013). One study found a
negative correlation between resting HRV and emotion regulation, as
measured by the difficulties in emotion regulation scale, in 198
undergraduate students (Rankin et al., 2014). Only two studies described
in the previous paragraph specifically focused on trait rumination,
worry, and resting HRV and reported significant negative associations
(Brosschot et al., 2007; Woody et al., 2014). The remaining three
studies (of the 8 studies included in the meta-analysis) were all
related to instantaneous changes in HRV or state rumination and worry
showing significant results but in the context of laboratory-based
operation cannot generalize to the effects of chronic stress in everyday
life (Cash et al., 2013; Gazelle \& Druhen, 2009; Mezulis et al., 2014).
Thus, existing studies either have focused on all-female samples and
include depression risk rather than completely healthy controls,
conclusions were drawn from young school children, or the sample size
was relatively low. In addition, although an overlap, there is still
distinguishing characteristic of rumination and worry (Nolen-Hoeksema et
al., 2008). Therefore, the finding of this study, based on a large
sample size and a young healthy population with a wider age range,
suggests that the tendency to ruminate is not associated with lower HRV
in healthy individuals.

Some characteristics of our study can, at least in part, explain the
null association between HRV and rumination. Although the participants
in the current dataset report a wide range of habitual tendencies
towards rumination, they are healthy young individuals without mental
diseases. Thus, it is possible that the association between resting HRV
and rumination is more robust in individuals who have impaired
parasympathetic control due to long-term stress exposure or who suffer
from stress-related mental disorders. As a systematic review suggests,
individuals who reported childhood adversity (retrospect of the past, a
ruminative thought) have no significant overall association with resting
HRV, whereas the clinical sample, as significant moderators, may have
small significant association under specific circumstances (Wesarg et
al., 2022). These might provide an explanation for the absent
association between trait rumination and resting HRV in the current
large sample of healthy individuals. Also, HRV was measured during a
resting period, and no stress or ruminative processes were induced.
Thus, another explanation could be that the negative correlation is more
related to state (i.e., at that moment) rumination and acute changes in
HRV under experimental induction paradigms rather than in a resting
phase (Ottaviani et al., 2016). In sum, based on our findings, resting
vagally-mediated HRV cannot be regarded as an index of the tendency to
ruminate, and thus a vulnerability factor in trait rumination, in
healthy individuals.

Overall, despite an absent correlation between baseline HRV measured
during rest, associations between other variables are in line with the
literature. The results of our large-scale cross-sectional dataset show
that the resting HRV of males was slightly higher than that of females
(Hamidovic et al., 2020). Furthermore, sex differences were found in the
rumination scores (females scoring higher as compared to males), but not
in the questionnaires regarding depressive symptoms. In addition, our
results also show that, based on a limited age range, there was a
significant negative correlation between age and rumination scores, as
well as age and BDI-II scores, indicating that with the increase in age,
people experience fewer symptoms of rumination and depression. This
observation is in line with prior studies reporting that older people
show a decrease in processing negative stimuli and thus show a positive
effect on emotion (Nashiro et al., 2012). Rumination and depression were
also highly correlated, as previous studies suggested that rumination is
a potential risk factor for depression and can prolong and intensify
depressive symptoms (Nolen-Hoeksema et al., 1997; Spasojevi\selectlanguage{polish}ć \selectlanguage{english}\& Alloy,
2001). Overall, these findings indicate that the association between
rumination and related psychological constructs is consistent with those
reported in the previous literature, showing the validity of our data.

Accurate baseline measurements are critical to measuring the impact of
cognitive tasks or group designations. At the resting baseline phase,
participants usually sit with their knees at a 90\selectlanguage{ngerman}° angle, both feet flat
on the floor, hands on thighs, and no psychological tasks. This protocol
involves inevitable mind wandering, disruptive thoughts, or difficulty
sitting still for some participants. A popular alternative to this
forced relaxation could be vanilla baseline, where participants must
perform a task that requires sustained attention but a minimal cognitive
load (such as reading a magazine or coloring). Importantly, our results
show that different baseline types influenced resting HRV, denoting that
the HRV values during a vanilla baseline were higher as compared to a
baseline in rest. Further studies should consider baseline type
depending on specific experimental tasks and purpose, while there is no
simple answer to defining an appropriate baseline measurement.

Some limitations of our study should be considered. It is important to
mention that the sex ratio in this study is not balanced and that sex
itself affects HRV, which may impede the broad applicability of our
results. Body mass index is also associated with HRV, but it was not
measured in some of our experiments. In addition, the measurement of
trait rumination and resting HRV was inconsistent in time and space. The
trait rumination questionnaire was generally completed online (before
the experiment), while HRV was measured in a laboratory environment, and
generally not on the same day. Moreover, as all the participants were
recruited in the same lab, we cannot rule out the possibility of
subjects that participated in multiple experiments. All the above
factors might contribute to the observed results.

To conclude, this is the first study using a large cross-sectional
dataset to examine the relationship between baseline resting HRV and
trait rumination in healthy individuals. The results indicate that there
is no association between HRV under a specific condition, namely
baseline resting, and the tendency to ruminate in daily life, based on a
sample of more than thousands of adults. Thus, vagally-mediated HRV at
rest could not be regarded as a marker of trait rumination in healthy
individuals.

\textbf{Acknowledgments}

We thank all our participants who have participated in our studies and
the research assistants who have made this work possible. Zefeng Li is
funded by the China Scholarship Council. Stefanie De Smet is funded by
the FWO-Flanders Ph.D. fellowship (Grant Number: 11J7521N). Laís Boralli
Razza is funded by the FWO-Flanders Ph.D. fellowship (Grant Number:
G0F4619N). Rudi De Raedt received funding from the Research Foundations
FWO and F.R.S.-FNRS under the Excellence of Science (EOS) program (EOS
40007528). Marie-Anne Vanderhasselt received funding from the special
research funds (BOF), grant number BOF17/STA/030.

\textbf{Reference}

Aldao, A., Nolen-Hoeksema, S., \& Schweizer, S. (2010).
Emotion-regulation strategies across psychopathology: A meta-analytic
review. \emph{Clinical Psychology Review} , \emph{30} (2), 217--237.

Alexandrowicz, R. W., Fritzsche, S., \& Keller, F. (2014). Die
Anwendbarkeit des BDI-II in klinischen und nichtklinischen Populationen
aus psychometrischer Sicht. Eine vergleichende Analyse mit dem
Rasch-Modell. \emph{Neuropsychiatrie} , \emph{28} (2), 63--73.

Allaert, J., De Raedt, R., Sanchez-Lopez, A., Baeken, C., \&
Vanderhasselt, M.-A. (2022). Mind the social feedback: Effects of tDCS
applied to the left DLPFC on psychophysiological responses during the
anticipation and reception of social evaluations. \emph{Social Cognitive
and Affective Neuroscience} , \emph{17} (1), 131--141.

Allaert, J., De Raedt, R., van Der Veen, F. M., Baeken, C., \&
Vanderhasselt, M.-A. (2021). Prefrontal tDCS attenuates counterfactual
thinking in female individuals prone to self-critical
rumination.\emph{Scientific Reports} , \emph{11} (1), 1--14.

Beauchaine, T. P., Gatzke-Kopp, L., \& Mead, H. K. (2007). Polyvagal
theory and developmental psychopathology: Emotion dysregulation and
conduct problems from preschool to adolescence. \emph{Biological
Psychology} , \emph{74} (2), 174--184.

Beauchaine, T. P., \& Thayer, J. F. (2015). Heart rate variability as a
transdiagnostic biomarker of psychopathology. \emph{International
Journal of Psychophysiology} , \emph{98} (2), 338--350.

Beck, A. T., Steer, R. A., \& Brown, G. K. (1996). \emph{Beck depression
inventory (BDI-II)} (Vol. 10, Issue 3). Pearson London, UK.

Borelli, J. L., Hilt, L. M., West, J. L., Weekes, N. Y., \& Gonzalez, M.
C. (2014). School-aged children's depressive rumination is associated
with their reactivity to sadness but not fear. \emph{Journal of Clinical
Child \& Adolescent Psychology} , \emph{43} (5), 799--812.

Brosschot, J. F., Gerin, W., \& Thayer, J. F. (2006a). The perseverative
cognition hypothesis: A review of worry, prolonged stress-related
physiological activation, and health. \emph{Journal of Psychosomatic
Research} , \emph{60} (2), 113--124.

Brosschot, J. F., Gerin, W., \& Thayer, J. F. (2006b). The perseverative
cognition hypothesis: A review of worry, prolonged stress-related
physiological activation, and health. \emph{Journal of Psychosomatic
Research} , \emph{60} (2), 113--124.

Brosschot, J. F., Van Dijk, E., \& Thayer, J. F. (2007). Daily worry is
related to low heart rate variability during waking and the subsequent
nocturnal sleep period. \emph{International Journal of Psychophysiology}
, \emph{63} (1), 39--47.

Busch, L. Y., Pössel, P., \& Valentine, J. C. (2017). Meta-analyses of
cardiovascular reactivity to rumination: A possible mechanism linking
depression and hostility to cardiovascular disease. \emph{Psychological
Bulletin} , \emph{143} (12), 1378.

Caselli, G., Ferretti, C., Leoni, M., Rebecchi, D., Rovetto, F., \&
Spada, M. M. (2010). Rumination as a predictor of drinking behaviour in
alcohol abusers: A prospective study. \emph{Addiction} , \emph{105} (6),
1041--1048.

Cash, C. S., Patel, A. I., Williams, D. P., \& Thayer, J. F. (2013).
Greater heart rate variability during a negative emotion induction is
associated with lower trait rumination and
anxiety.\emph{Psychophysiology} , \emph{50} , S37.

Castaneda, J. O., \& Segerstrom, S. C. (2004). Effect of stimulus type
and worry on physiological response to fear. \emph{Journal of Anxiety
Disorders} , \emph{18} (6), 809--823.

Chalmers, J. A., Quintana, D. S., Abbott, M. J.-A., \& Kemp, A. H.
(2014). Anxiety disorders are associated with reduced heart rate
variability: A meta-analysis. \emph{Frontiers in Psychiatry} , \emph{5}
, 80.

de Beurs, E., Van Dyck, R., Marquenie, L. A., Lange, A., \& Blonk, R. W.
(2001). De DASS: een vragenlijst voor het meten van depressie, angst en
stress. \emph{Gedragstherapie} , \emph{34} (1), 35--54.

De Smet, S., Baeken, C., De Raedt, R., Pulopulos, M. M., Razza, L. B.,
Van Damme, S., De Witte, S., Brunoni, A. R., \& Vanderhasselt, M.-A.
(2021). Effects of combined theta burst stimulation and transcranial
direct current stimulation of the dorsolateral prefrontal cortex on
stress. \emph{Clinical Neurophysiology} , \emph{132} (5), 1116--1125.

DeWandel, L., DeSmet, S., M Pulopulos, M., Lemmens, G., Hidalgo, V.,
Salvador, A., Vanderhasselt, M.-A., Pruessner, J., \& Baeken, C. (2022).
THE EFFECTS OF TRANSCRANIAL DIRECT CURRENT STIMULATION ON EPISODIC
FUTURE THINKING FOLLOWING ACUTE PSYCHOSOCIAL STRESS. \emph{Psychiatria
Danubina} , \emph{34} (suppl 3), 42--42.

Dondzilo, L., Rieger, E., Palermo, R., Byrne, S., \& Bell, J. (2016).
Association between rumination factors and eating disorder behaviours in
young women. \emph{Advances in Eating Disorders} , \emph{4} (1), 84--98.

El-Sheikh, M., Hinnant, J. B., \& Erath, S. (2011). Developmental
trajectories of delinquency symptoms in childhood: The role of marital
conflict and autonomic nervous system activity. \emph{Journal of
Abnormal Psychology} , \emph{120} (1), 16.

Faurholt-Jepsen, M., Kessing, L. V., \& Munkholm, K. (2017). Heart rate
variability in bipolar disorder: A systematic review and
meta-analysis.\emph{Neuroscience \& Biobehavioral Reviews} , \emph{73} ,
68--80.

Fresco, D. M., Frankel, A. N., Mennin, D. S., Turk, C. L., \& Heimberg,
R. G. (2002). Distinct and overlapping features of rumination and worry:
The relationship of cognitive production to negative affective
states.\emph{Cognitive Therapy and Research} , \emph{26} (2), 179--188.

Friedman, B. H. (2007). An autonomic flexibility--neurovisceral
integration model of anxiety and cardiac vagal tone. \emph{Biological
Psychology} , \emph{74} (2), 185--199.

Gazelle, H., \& Druhen, M. J. (2009). Anxious solitude and peer
exclusion predict social helplessness, upset affect, and vagal
regulation in response to behavioral rejection by a
friend.\emph{Developmental Psychology} , \emph{45} (4), 1077.

Gelman, A., Hwang, J., \& Vehtari, A. (2014). Understanding predictive
information criteria for Bayesian models. \emph{Statistics and
Computing} , \emph{24} (6), 997--1016.

Gentzler, A. L., Wheat, A. L., Palmer, C. A., \& Burwell, R. A. (2013).
Children's responses to cognitive challenge and links to self-reported
rumination. \emph{Cognition \& Emotion} , \emph{27} (2), 305--317.

Goldberger, J. J., Challapalli, S., Tung, R., Parker, M. A., \& Kadish,
A. H. (2001). Relationship of heart rate variability to parasympathetic
effect. \emph{Circulation} , \emph{103} (15), 1977--1983.

Grol, M., \& De Raedt, R. (2020). The link between resting heart rate
variability and affective flexibility. \emph{Cognitive, Affective, \&
Behavioral Neuroscience} , \emph{20} (4), 746--756.

Hamidovic, A., Van Hedger, K., Choi, S. H., Flowers, S., Wardle, M., \&
Childs, E. (2020). Quantitative meta-analysis of heart rate variability
finds reduced parasympathetic cardiac tone in women compared to men
during laboratory-based social stress. \emph{Neuroscience \&
Biobehavioral Reviews} , \emph{114} , 194--200.

Henry, J. D., \& Crawford, J. R. (2005). The short\selectlanguage{english}-form version of the
Depression Anxiety Stress Scales (DASS\selectlanguage{english}-21): Construct validity and
normative data in a large non\selectlanguage{english}-clinical sample. \emph{British Journal of
Clinical Psychology} , \emph{44} (2), 227--239.

Johnson, J. A., Key, B. L., Routledge, F. S., Gerin, W., \& Campbell, T.
S. (2014). High trait rumination is associated with blunted nighttime
diastolic blood pressure dipping. \emph{Annals of Behavioral Medicine}
,\emph{48} (3), 384--391.

Kaufmann, T., Sutterlin, S., Schulz, S. M., \& Vogele, C. (2011).
ARTiiFACT: a tool for heart rate artifact processing and heart rate
variability analysis. \emph{Behavior Research Methods} , \emph{43} (4),
1161--1170.

Key, B. L., Campbell, T. S., Bacon, S. L., \& Gerin, W. (2008). The
influence of trait and state rumination on cardiovascular recovery from
a negative emotional stressor. \emph{Journal of Behavioral Medicine}
,\emph{31} (3), 237--248.

Kogan, A., Gruber, J., Shallcross, A. J., Ford, B. Q., \& Mauss, I. B.
(2013). Too much of a good thing? Cardiac vagal tone's nonlinear
relationship with well-being. \emph{Emotion} , \emph{13} (4), 599.

Kubzansky, L. D., Kawachi, I., Spiro III, A., Weiss, S. T., Vokonas, P.
S., \& Sparrow, D. (1997). Is worrying bad for your heart? A prospective
study of worry and coronary heart disease in the Normative Aging
Study.\emph{Circulation} , \emph{95} (4), 818--824.

Laborde, S., Mosley, E., \& Thayer, J. F. (2017). Heart rate variability
and cardiac vagal tone in psychophysiological research--recommendations
for experiment planning, data analysis, and data
reporting.\emph{Frontiers in Psychology} , \emph{8} , 213.

Larsen, B. A., \& Christenfeld, N. J. (2009). Cardiovascular disease and
psychiatric comorbidity: The potential role of perseverative
cognition.\emph{Cardiovascular Psychiatry and Neurology} , \emph{2009} .

McElreath, R. (2020). \emph{Statistical rethinking: A Bayesian course
with examples in R and Stan} . Chapman and Hall/CRC.

McLaughlin, K. A., Borkovec, T. D., \& Sibrava, N. J. (2007). The
effects of worry and rumination on affect states and cognitive
activity.\emph{Behavior Therapy} , \emph{38} (1), 23--38.

McLaughlin, K. A., \& Nolen-Hoeksema, S. (2011). Rumination as a
transdiagnostic factor in depression and anxiety. \emph{Behaviour
Research and Therapy} , \emph{49} (3), 186--193.

Mezulis, A., Crystal, S., \& Ahles, J. (2014). Resting RSA predicts
stress-reactive rumination following induced stress among young
adolescents. \emph{Psychophysiology} , \emph{51} , S53--S53.

Nashiro, K., Sakaki, M., \& Mather, M. (2012). Age differences in brain
activity during emotion processing: Reflections of age-related decline
or increased emotion regulation. \emph{Gerontology} , \emph{58} (2),
156--163.

Nasso, S., Vanderhasselt, M.-A., \& De Raedt, R. (2020). Testing the
neurocognitive framework for regulation expectation: The relationship
between actual/ideal self-esteem and proactive/reactive autonomic stress
regulation. \emph{Journal of Behavior Therapy and Experimental
Psychiatry} , \emph{69} , 101598.

Nasso, S., Vanderhasselt, M.-A., Demeyer, I., \& De Raedt, R. (2019).
Autonomic regulation in response to stress: The influence of
anticipatory emotion regulation strategies and trait
rumination.\emph{Emotion} , \emph{19} (3), 443.

Nolen-Hoeksema, S. (1991). Responses to depression and their effects on
the duration of depressive episodes. \emph{Journal of Abnormal
Psychology} , \emph{100} (4), 569.

Nolen-Hoeksema, S., McBride, A., \& Larson, J. (1997). Rumination and
psychological distress among bereaved partners. \emph{Journal of
Personality and Social Psychology} , \emph{72} (4), 855.

Nolen-Hoeksema, S., \& Morrow, J. (1991). A prospective study of
depression and posttraumatic stress symptoms after a natural disaster:
The 1989 Loma Prieta Earthquake. \emph{Journal of Personality and Social
Psychology} , \emph{61} (1), 115.

Nolen-Hoeksema, S., \& Morrow, J. (1993). Effects of rumination and
distraction on naturally occurring depressed mood. \emph{Cognition \&
Emotion} , \emph{7} (6), 561--570.

Nolen-Hoeksema, S., Wisco, B. E., \& Lyubomirsky, S. (2008). Rethinking
rumination. \emph{Perspectives on Psychological Science} , \emph{3} (5),
400--424.

Ottaviani, C., \& Shapiro, D. (2011). Do we need a stressor to be
stressed? Insights from cardiac regulation 1. \emph{Japanese
Psychological Research} , \emph{53} (2), 155--162.

Ottaviani, C., Thayer, J. F., Verkuil, B., Lonigro, A., Medea, B.,
Couyoumdjian, A., \& Brosschot, J. F. (2016). Physiological concomitants
of perseverative cognition: A systematic review and
meta-analysis.\emph{Psychological Bulletin} , \emph{142} (3), 231.

Ottaviani, C., Watson, D. R., Meeten, F., Makovac, E., Garfinkel, S. N.,
\& Critchley, H. D. (2016). Neurobiological substrates of cognitive
rigidity and autonomic inflexibility in generalized anxiety
disorder.\emph{Biological Psychology} , \emph{119} , 31--41.

Penttila, J., Helminen, A., Jartti, T., Kuusela, T., Huikuri, H. V.,
Tulppo, M. P., Coffeng, R., \& Scheinin, H. (2001). Time domain,
geometrical and frequency domain analysis of cardiac vagal outflow:
Effects of various respiratory patterns. \emph{Clinical Physiology}
,\emph{21} (3), 365--376.

Perna, G., Riva, A., Defillo, A., Sangiorgio, E., Nobile, M., \&
Caldirola, D. (2020). Heart rate variability: Can it serve as a marker
of mental health resilience?: Special Section on ``Translational and
Neuroscience Studies in Affective Disorders'' Section Editor, Maria
Nobile MD, PhD. \emph{Journal of Affective Disorders} , \emph{263} ,
754--761.

Porges, S. W. (2001). The polyvagal theory: Phylogenetic substrates of a
social nervous system. \emph{International Journal of Psychophysiology}
,\emph{42} (2), 123--146.

Porges, S. W. (2007). The polyvagal perspective. \emph{Biological
Psychology} , \emph{74} (2), 116--143.

Pulopulos, M. M., Baeken, C., \& De Raedt, R. (2020). Cortisol response
to stress: The role of expectancy and anticipatory stress
regulation.\emph{Hormones and Behavior} , \emph{117} , 104587.

Pulopulos, M. M., Schmausser, M., De Smet, S., Vanderhasselt, M.-A.,
Baliyan, S., Venero, C., Baeken, C., \& De Raedt, R. (2020). The effect
of HF-rTMS over the left DLPFC on stress regulation as measured by
cortisol and heart rate variability. \emph{Hormones and Behavior}
,\emph{124} , 104803.

Pulopulos, M. M., Vanderhasselt, M.-A., \& De Raedt, R. (2018).
Association between changes in heart rate variability during the
anticipation of a stressful situation and the stress-induced cortisol
response. \emph{Psychoneuroendocrinology} , \emph{94} , 63--71.

Quintana, D. S., \& Williams, D. R. (2018). Bayesian alternatives for
common null-hypothesis significance tests in psychiatry: A non-technical
guide using JASP. \emph{BMC Psychiatry} , \emph{18} (1), 1--8.

Radstaak, M., Geurts, S. A., Brosschot, J. F., Cillessen, A. H., \&
Kompier, M. A. (2011). The role of affect and rumination in
cardiovascular recovery from stress. \emph{International Journal of
Psychophysiology} , \emph{81} (3), 237--244.

Raes, F., Hermans, D., Williams, J. M. G., \& Eelen, P. (2007). A
sentence completion procedure as an alternative to the Autobiographical
Memory Test for assessing overgeneral memory in non-clinical
populations. \emph{Memory} , \emph{15} (5), 495--507.

Rankin, C., Bernardi, A., Upchurch, B., Cash, C., Koenig, J., Williams,
D. P., \& Thayer, J. F. (2014). The relationship between heart rate
variability and perceptions of emotional control.\emph{Psychophysiology}
, \emph{51} , S57.

Reynard, A., Gevirtz, R., Berlow, R., Brown, M., \& Boutelle, K. (2011).
Heart rate variability as a marker of self-regulation. \emph{Applied
Psychophysiology and Biofeedback} , \emph{36} (3), 209--215.

Saul, J. P., Rea, R. F., Eckberg, D. L., Berger, R. D., \& Cohen, R. J.
(1990). Heart rate and muscle sympathetic nerve variability during
reflex changes of autonomic activity. \emph{American Journal of
Physiology-Heart and Circulatory Physiology} , \emph{258} (3),
H713--H721.

Schoofs, H., Hermans, D., \& Raes, F. (2010). Brooding and reflection as
subtypes of rumination: Evidence from confirmatory factor analysis in
nonclinical samples using the Dutch Ruminative Response
Scale.\emph{Journal of Psychopathology and Behavioral Assessment}
,\emph{32} (4), 609--617.

Schwartz, A. R., Gerin, W., Davidson, K. W., Pickering, T. G.,
Brosschot, J. F., Thayer, J. F., Christenfeld, N., \& Linden, W. (2003).
Toward a causal model of cardiovascular responses to stress and the
development of cardiovascular disease. \emph{Psychosomatic Medicine}
,\emph{65} (1), 22--35.

Silvia, P. J., Jackson, B. A., \& Sopko, R. S. (2014). Does baseline
heart rate variability reflect stable positive
emotionality?\emph{Personality and Individual Differences} , \emph{70} ,
183--187.

Sjak-Shie, E. E. (2022). \emph{PhysioData Toolbox (Version 0.6.3)
{[}Computer software{]}} . Retrieved from
https://PhysioDataToolbox.leidenuniv.nl.

Song, X., Long, J., Wang, C., Zhang, R., \& Lee, T. M. (2022). The
inter-relationships of the neural basis of rumination and inhibitory
control: Neuroimaging-based meta-analyses. \emph{Psychoradiology}
,\emph{2} (1), 11--22.

Spasojevi\selectlanguage{polish}ć, \selectlanguage{english}J., \& Alloy, L. B. (2001). Rumination as a common mechanism
relating depressive risk factors to depression. \emph{Emotion} ,\emph{1}
(1), 25.

Tak, L. M., Riese, H., de Bock, G. H., Manoharan, A., Kok, I. C., \&
Rosmalen, J. G. (2009). As good as it gets? A meta-analysis and
systematic review of methodological quality of heart rate variability
studies in functional somatic disorders. \emph{Biological Psychology}
,\emph{82} (2), 101--110.

Tarvainen, M. P., Niskanen, J.-P., Lipponen, J. A., Ranta-Aho, P. O., \&
Karjalainen, P. A. (2014). Kubios HRV--heart rate variability analysis
software. \emph{Computer Methods and Programs in Biomedicine}
,\emph{113} (1), 210--220.

Tarvainen, M. P., Ranta-Aho, P. O., \& Karjalainen, P. A. (2002). An
advanced detrending method with application to HRV analysis. \emph{IEEE
Transactions on Biomedical Engineering} , \emph{49} (2), 172--175.

Task Force of the European Society of Cardiology the North American
Society of Pacing. (1996). Heart rate variability: Standards of
measurement, physiological interpretation, and clinical
use.\emph{Circulation} , \emph{93} (5), 1043--1065.

Thayer, J. F., \selectlanguage{ngerman}Åhs, F., Fredrikson, M., Sollers III, J. J., \& Wager, T.
D. (2012). A meta-analysis of heart rate variability and neuroimaging
studies: Implications for heart rate variability as a marker of stress
and health. \emph{Neuroscience \& Biobehavioral Reviews} , \emph{36}
(2), 747--756.

Thayer, J. F., Hansen, A. L., Saus-Rose, E., \& Johnsen, B. H. (2009).
Heart rate variability, prefrontal neural function, and cognitive
performance: The neurovisceral integration perspective on
self-regulation, adaptation, and health. \emph{Annals of Behavioral
Medicine} , \emph{37} (2), 141--153.

Thayer, J. F., \& Lane, R. D. (2000). A model of neurovisceral
integration in emotion regulation and dysregulation. \emph{Journal of
Affective Disorders} , \emph{61} (3), 201--216.

Thayer, J. F., \& Lane, R. D. (2009). Claude Bernard and the
heart--brain connection: Further elaboration of a model of neurovisceral
integration. \emph{Neuroscience \& Biobehavioral Reviews} , \emph{33}
(2), 81--88.

Treynor, W., Gonzalez, R., \& Nolen-Hoeksema, S. (2003). Rumination
reconsidered: A psychometric analysis. \emph{Cognitive Therapy and
Research} , \emph{27} (3), 247--259.

Tseng, J., \& Poppenk, J. (2020). Brain meta-state transitions demarcate
thoughts across task contexts exposing the mental noise of trait
neuroticism. \emph{Nature Communications} , \emph{11} (1), 1--12.

Van der Does, A. J. W. (2002). Manual of the Dutch version of the Beck
Depression Inventory (BDI-II-NL). \emph{Amsterdam, NL: Harcourt} .

Vanderhasselt, M.-A., \& Ottaviani, C. (2021). Combining top-down and
bottom-up interventions targeting the vagus nerve to increase
resilience. \emph{Neuroscience \& Biobehavioral Reviews} .

Wardenaar, K. J., van Veen, T., Giltay, E. J., de Beurs, E., Penninx, B.
W., \& Zitman, F. G. (2010). Development and validation of a 30-item
short adaptation of the Mood and Anxiety Symptoms Questionnaire
(MASQ).\emph{Psychiatry Research} , \emph{179} (1), 101--106.

Watson, D., Weber, K., Assenheimer, J. S., Clark, L. A., Strauss, M. E.,
\& McCormick, R. A. (1995). Testing a tripartite model: I. Evaluating
the convergent and discriminant validity of anxiety and depression
symptom scales. \emph{Journal of Abnormal Psychology} , \emph{104} (1),
3.

Wesarg, C., Van den Akker, A. L., Oei, N. Y., Wiers, R. W., Staaks, J.,
Thayer, J. F., Williams, D. P., \& Hoeve, M. (2022). Childhood adversity
and vagal regulation: A systematic review and
meta-analysis.\emph{Neuroscience \& Biobehavioral Reviews} , 104920.

Wilkinson, P. O., Croudace, T. J., \& Goodyer, I. M. (2013). Rumination,
anxiety, depressive symptoms and subsequent depression in adolescents at
risk for psychopathology: A longitudinal cohort study. \emph{BMC
Psychiatry} , \emph{13} (1), 1--9.

Woody, M. L., McGeary, J. E., \& Gibb, B. E. (2014). Brooding rumination
and heart rate variability in women at high and low risk for depression:
Group differences and moderation by COMT genotype. \emph{Journal of
Abnormal Psychology} , \emph{123} (1), 61.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& \textbf{N} & \textbf{Min} & \textbf{Max} & \textbf{Mean} &
\textbf{SD}\tabularnewline
\midrule
\endhead
\textbf{Sample (age)} & & & & &\tabularnewline
Men & 197 & 18 & 44 & 22.19 & 3.71\tabularnewline
Women & 1048 & 17 & 48 & 21.43 & 2.89\tabularnewline
\textbf{HRV} & & & & &\tabularnewline
RMSSD & 1189 & 4.22 & 165.72 & 40.85 & 24.03\tabularnewline
Man & 184 & 6.89 & 150.79 & 45.23 & 27.55\tabularnewline
Women & 1005 & 4.22 & 165.72 & 40.05 & 23.26\tabularnewline
Baseline & & & & &\tabularnewline
Rest & 883 & 4.22 & 150.79 & 40.01 & 23.27\tabularnewline
Vanilla & 306 & 5.50 & 165.72 & 43.29 & 26.00\tabularnewline
\textbf{Questionnaires} & & & & &\tabularnewline
RRS\textsubscript{total} & 1150 & 22 & 83 & 41.66 & 12.60\tabularnewline
Man & 197 & 22 & 76 & 38.81 & 12.04\tabularnewline
Women & 953 & 22 & 83 & 42.25 & 12.63\tabularnewline
Brooding & 1150 & 5 & 20 & 9.84 & 3.4\tabularnewline
Man & 197 & 5 & 20 & 9.07 & 3.24\tabularnewline
Women & 953 & 5 & 20 & 10.00 & 3.45\tabularnewline
Reflection & 1150 & 5 & 20 & 9.17 & 3.28\tabularnewline
Man & 197 & 5 & 18 & 8.59 & 3.08\tabularnewline
Women & 953 & 5 & 20 & 9.29 & 3.31\tabularnewline
BDI-II & 344 & 0 & 26 & 5.38 & 5.24\tabularnewline
Man & 67 & 0 & 25 & 4.81 & 5.00\tabularnewline
Women & 277 & 0 & 26 & 5.52 & 5.30\tabularnewline
MASQ\textsubscript{depression} & 218 & 11 & 49 & 27.63 &
8.38\tabularnewline
Man & 0 & & & &\tabularnewline
Women & 218 & 11 & 49 & 27.63 & 8.38\tabularnewline
DASS\textsubscript{depression} & 479 & 0 & 19 & 4.08 &
3.80\tabularnewline
Man & 64 & 0 & 17 & 4.27 & 3.96\tabularnewline
Women & 395 & 0 & 19 & 4.05 & 3.77\tabularnewline
Depression\textsubscript{standardized} & 1021 & -1.99 & 3.94 & 0 &
1\tabularnewline
\bottomrule
\end{longtable}

Table 1. Descriptive Statistics for the demographic information,
physiological variables, and questionnaire scores.

\emph{Note.} Abbreviation: RMSSD = root mean square of successive
differences; RRS\textsubscript{total} = total score of Ruminative
Response Scale; BDI = Beck Depression Inventory;
MASQ\textsubscript{depression} = Anhedonic Depression score of the Mood
and Anxiety Symptoms Questionnaire; DASS\textsubscript{depression} =
depression subscale score of the Depression, Anxiety, and Stress Scale;
Depression\textsubscript{standardized} = Z-scores from BDI-II, MASQ, and
DASS.~

Table 2. Correlation matrix between rumination and the level of
depressive symptoms\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllllll@{}}
\toprule
& \textbf{RMSSD} & \textbf{RMSSD} & \textbf{N} & \textbf{Power} &
\textbf{RRS\textsubscript{total}} & \textbf{N} & \textbf{Power} &
\textbf{Brooding} & \textbf{N} & \textbf{Power} & \textbf{Reflection} &
\textbf{N} & \textbf{Power} & \textbf{Age}\tabularnewline
\midrule
\endhead
\textbf{RMSSD} & \textbf{RMSSD} & 1 & & & & & & & & & & & &
.032\tabularnewline
\textbf{RRS\textsubscript{total}} & \textbf{RRS\textsubscript{total}} &
-.038 & 1094 & .242 & 1 & & & & & & & & &
-.111\textsuperscript{**}\tabularnewline
\textbf{Brooding} & \textbf{Brooding} & -.016 & 1094 & .083 &
.841\textsuperscript{**} & 1150 & 1 & 1 & & & & & &
-.101\textsuperscript{**}\tabularnewline
\textbf{Reflection} & \textbf{Reflection} & -.003 & 1094 & .051 &
.771\textsuperscript{**} & 1150 & 1 & .521\textsuperscript{**} & 1150 &
1 & 1 & & & -.067\textsuperscript{*}\tabularnewline
\textbf{BDI-II} & \textbf{BDI-II} & -.004 & 335 & .051 &
.341\textsuperscript{**} & 342 & .999 & .296\textsuperscript{**} & 342 &
.999 & .152\textsuperscript{**} & 342 & .997 &
-.200\textsuperscript{**}\tabularnewline
\textbf{MASQ\textsubscript{depression}} &
\textbf{MASQ\textsubscript{depression}} & .133 & 215 & .993 &
.363\textsuperscript{**} & 218 & .999 & .256\textsuperscript{**} & 218 &
.973 & .133 & 218 & .505 & -.068\tabularnewline
\textbf{DASS\textsubscript{depression}} &
\textbf{DASS\textsubscript{depression}} & .022 & 440 & .075 &
.591\textsuperscript{**} & 365 & 1 & .548\textsuperscript{**} & 365 & 1
& .377\textsuperscript{**} & 365 & 1 & -.037\tabularnewline
\textbf{Depression\textsubscript{standardized}} &
\textbf{Depression\textsubscript{standardized}} & .034 & 990 & .019 &
.436\textsuperscript{**} & 925 & 1 & .380\textsuperscript{**} & 925 & 1
& .235\textsuperscript{**} & 925 & 1 &
-.097\textsuperscript{**}\tabularnewline
\bottomrule
\end{longtable}

\emph{Note.} The correlation coefficients between HRV with the dimension
of RRS, three depression questionnaires, and
Depression\textsubscript{standardized} are reported above. N is the
sample size for the analyses for that outcome. Power = 1 - \selectlanguage{greek}β.
\selectlanguage{english}*p~\textless{}~0.05; **p~\textless{}~0.001.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\textbf{HRV} & \textbf{B} & \textbf{SE} & \textbf{t} & \textbf{p} &
\textbf{95\%CI} & \textbf{95\%CI} &
\textbf{R\textsuperscript{2}}\tabularnewline
\midrule
\endhead
\emph{Model 1} & & & & & & &\tabularnewline
\textbf{RRS\textsubscript{total}} & -.022 & .068 & -.317 & .751 &
{[}-.155, .112{]} & {[}-.155, .112{]} & .008\tabularnewline
\textbf{Age} & -.142 & .281 & -.503 & .615 & {[}-.693, .410{]} &
{[}-.693, .410{]} &\tabularnewline
\textbf{Gender} & -5.585 & 2.261 & -2.470 & .014 & {[}-10.022, -1.147{]}
& {[}-10.022, -1.147{]} &\tabularnewline
\textbf{Depression\textsubscript{standardized}} & .866 & .869 & .997 &
.319 & {[}-.839, 2.571{]} & {[}-.839, 2.571{]} &\tabularnewline
\emph{Model 2} & & & & & & &\tabularnewline
\textbf{Brooding} & -.244 & .278 & -.880 & .379 & {[}-.789, .301{]} &
{[}-.789, .301{]} & .011\tabularnewline
\textbf{Reflection} & .400 & .271 & 1.477 & .140 & {[}-.132, .932{]} &
{[}-.132, .932{]} &\tabularnewline
\textbf{Age} & -.152 & .281 & -.542 & .588 & {[}-.704, .400{]} &
{[}-.704, .400{]} &\tabularnewline
\textbf{Gender} & -5.737 & 2.260 & -.085 & .011 & {[}-10.171, -1.302{]}
& {[}-10.171, -1.302{]} &\tabularnewline
\textbf{Depression\textsubscript{standardized}} & .758 & .849 & .893 &
.372 & {[}-.909, 2.424{]} & {[}-.909, 2.424{]} &\tabularnewline
\bottomrule
\end{longtable}

Table 3. Linear regressions with HRV as the dependent variable.

Table 4. Curve estimation of quadratic effect for the HRV and
rumination, depression.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
& \textbf{Linear term} & \textbf{Linear term} & \textbf{Linear term} &
\textbf{Linear term} & \textbf{Quadratic term} & \textbf{Quadratic term}
& \textbf{Quadratic term} & \textbf{Quadratic term} &\tabularnewline
\textbf{HRV} & B & SE & t & p & B & SE & t & p &
R\textsuperscript{2}\tabularnewline
\textbf{RRS\textsubscript{total}} & -.643 & .349 & -1.842 & .066 & .006
& .004 & 1.654 & .098 & .004\tabularnewline
\textbf{Brooding} & -1.331 & 1.209 & -1.101 & .271 & .056 & .054 & 1.024
& .306 & .001\tabularnewline
\textbf{Reflection} & -2.621 & 1.171 & -2.239 & .025 & .125 & .055 &
2.261 & .024 & .005\tabularnewline
\textbf{BDI-II} & -.060 & .610 & -.098 & .922 & .002 & .032 & .073 &
.942 & .000\tabularnewline
\textbf{MASQ\textsubscript{depression}} & -1.244 & 1.028 & -1.210 & .228
& .027 & .017 & 1.557 & .121 & .029\tabularnewline
\textbf{DASS\textsubscript{depression}} & .182 & .849 & .214 & .830 &
-.003 & .061 & -.054 & .957 & .000\tabularnewline
\textbf{Depression\textsubscript{standardized}} & .692 & .941 & .735 &
.462 & .097 & .536 & .181 & .856 & .001\tabularnewline
\bottomrule
\end{longtable}

\textbf{Hosted file}

\verb`Table.docx`  available at \url{https://authorea.com/users/568090/articles/614100-resting-hrv-as-a-trait-marker-of-rumination-in-healthy-individuals-a-large-cross-sectional-analysis}

\selectlanguage{english}
\FloatBarrier
\end{document}

