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\begin{document}

\title{Smartphone-based heart rate variability biofeedback training improves
premenstrual and depressive symptoms as well as anxiety/stress symptoms
and attentional executive control: a pilot study~~~~}



\author[1]{Berenike Lisa Blaser}%
\author[1]{Mathias Weymar}%
\author[1]{Julia Wendt}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\par\null

\textbf{Berenike Lisa Blaser}\textsuperscript{\textbf{1*}}\textbf{,
Mathias Weymar}\textsuperscript{\textbf{1,2}}\textbf{, \& Julia
Wendt}\textsuperscript{\textbf{1}}

\textsuperscript{1} University of Potsdam, Department of Biological
Psychology and Affective Science, Faculty of Human Sciences,
Karl-Liebknecht Strasse 24/25, 14476 Potsdam, Germany

\textsuperscript{2}Faculty of Health Sciences Brandenburg, University of
Potsdam, Potsdam, Germany

~

\textbf{* Correspondence:}

Berenike Lisa Blaser

University of Potsdam

Karl-Liebknecht Strasse 24/25

14476 Potsdam, Germany

Email: {berenike.blaser@uni-potsdam.de}

~

Running head: Smartphone-based Heart Rate Variability Biofeedback for
PMS

~

\textbf{Keywords: smartphone photoplethysmography, mHealth, heart rate
variability, biofeedback, vagal tone, premenstrual syndrome,
premenstrual symptoms, menstrual cycle, stress, depression, attentional
control}

\textbf{Abstract}

\textbf{Introduction}

Heart rate variability biofeedback (HRVB) is a well-studied intervention
known for its positive effects on emotional, cognitive, and
physiological well-being, including relief from depressive symptoms.
However, its practical use is hampered by high costs and a lack of
trained professionals. Smartphone-based HRVB, which eliminates the need
for external devices, offers a promising alternative, albeit with
limited research. Additionally, premenstrual symptoms are highly
prevalent among menstruating individuals, and there is a need for
low-cost, accessible interventions with minimal side effects. With this
pilot study, we aim test, for the first time, the influence of
smartphone-based HRVB on depressive and premenstrual symptoms, as well
as anxiety/stress symptoms and attentional control.

\textbf{Methods}

Twenty-seven participants with above-average premenstrual or depressive
symptoms underwent a 4-week photoplethysmography smartphone-based HRVB
intervention using a waitlist-control design. Laboratory sessions were
conducted before and after the intervention, spaced exactly 4 weeks
apart. Assessments included resting vagally mediated heart rate
variability (vmHRV), attentional control via the revised attention
network test (ANT-R), depressive symptoms assessed with the BDI-II
questionnaire, and stress/anxiety symptoms measured using the DASS
questionnaire. Premenstrual symptomatology was recorded through the PAF
questionnaire if applicable. Data analysis employed linear mixed models.

\textbf{Results}

We observed improvements in premenstrual, depressive, and anxiety/stress
symptoms, as well as the Executive Functioning Score of the ANT-R during
the intervention period but not during the waitlist phase. However, we
did not find significant changes in vmHRV or the Orienting Score of the
ANT-R.

\textbf{Discussion}

These findings are promising, both in terms of the effectiveness of
smartphone-based HRVB and its potential to alleviate premenstrual
symptoms. Nevertheless, to provide a solid recommendation regarding the
use of HRVB for improving premenstrual symptoms, further research with a
larger sample size is needed to replicate these effects.

~

\section*{1~~~~~~~~~~~~~~ Introduction}\label{introduction}

Heart rate variability biofeedback (HRVB) is a well-researched
intervention that has demonstrated effectiveness in a wide range of
areas (Lehrer et al., 2020), including relieving anxiety and stress
(Goessl et al., 2017), ameliorating depression (Pizzoli et al., 2021),
improving sleep (Stein \& Pu, 2012), alleviating asthma symptoms (Lehrer
et al., 2004), and even enhancing sports performance (Jim\selectlanguage{ngerman}énez Morgan \&
Molina Mora, 2017). However, despite its potential, this user-friendly
method has seen limited practical implementation. This can be
attributed, in part, to the high costs associated with necessary
stationary and mobile electrocardiography (ECG) devices, as well as the
required training and expertise of staff members entrusted with its
administration, which further strains healthcare systems. Encouragingly,
smartphone apps capable of assessing heart rate through the device's
camera, without additional equipment, are promising to yield similar
results (Yuda et al., 2020). Nevertheless, empirical validation of
smartphone-based HRVB applications remains limited. This study aims to
validate the effectiveness of an HRVB intervention applied through
smartphones, specifically targeting the alleviation of depressive
symptoms, a well-documented outcome of conventional HRVB. Additionally,
we explore a novel application of HRVB for premenstrual symptoms.

HRVB is a method in which vagally mediated heart rate variability
(vmHRV), an indicator of parasympathetic activity (Laborde et al., 2023;
Penttilä et al., 2001), is systematically increased through slow,
controlled breathing and visual feedback of heart rate oscillations. The
primary driving mechanism involves slow-paced breathing at 0.1 Hz or an
individual resonance frequency (Laborde, Allen, Borges, Iskra, et al.,
2022). It is believed to exert its various beneficial effects through
bottom-up modulation of a neural network described by Thayer and Lane
(2000) in their neurovisceral integration model.

This model delineates a network of interconnected structures known as
the central autonomic network (CAN), responsible for integrating
information and regulating appropriate responses. At the core of this
regulatory network, Thayer and Lane (2009) propose an inhibitory
connectivity between the medial prefrontal cortex (mPFC) and the
amygdala. The stronger this connectivity, the greater an individual's
capacity to downregulate a presumed default stress response and deliver
a precise and personalized reaction to internal and environmental
demands. VmHRV is considered both a peripheral index for this capacity
and a reciprocal element within this network (Thayer et al., 2009). This
theory is grounded in a substantial body of evidence linking low vmHRV
to psychopathology (Heiss et al., 2021) and reduced performance in
cognitive self-control tasks (Holzman \& Bridgett, 2017; Zahn et al.,
2016).

When practiced over several weeks, HRVB enhances the capacity of the CAN
through coherence phenomena involving the synchronization of breathing
rate, blood pressure, and heart rate oscillations (Sevoz-Couche \&
Laborde, 2022). These phenomena contribute to several bottom-up routes.
The most crucial of these routes involve input into the CAN through
baroreceptors via the nucleus of the solitary tract, stretch receptors
in the lungs, and a vagal afferent pathway (Lehrer \& Gevirtz, 2014;
Noble \& Hochman, 2019; Sevoz-Couche \& Laborde, 2022).

HRVB interventions have demonstrated the potential to improve various
affective and cognitive outcomes associated with CAN capacity, including
depression (Pizzoli et al., 2021) and anxiety (Goessl et al., 2017). Our
study aims to expand these effects in the context of a smartphone-based
intervention. While vmHRV is reliably associated with cognitive
outcomes, particularly executive functions, the impact of HRVB on these
variables is less clear (Tinello et al., 2022). In a systematic review,
Tinello et al. (2022) found that existing effects are primarily observed
in the domain of attentional control and are often found in patient
populations or individuals experiencing high levels of stress. Given
that attention is strongly linked to vmHRV, we also investigate the
effect of HRVB on attentional control using the revised Attention
Network Test (ANT-R, Blaser et al., 2023a; Fan et al., 2009).

Expanding on these replications, we further investigate HRVBs impact on
premenstrual syndrome (PMS), a highly prevalent condition characterized
by a diverse collection of psychological and physiological symptoms.
These symptoms typically manifest in individuals with active menstrual
cycles during the week leading up to menstruation and tend to subside
shortly after. As many as 90\% of menstruating individuals regularly
encounter at least one symptom of PMS (Tschudin et al., 2010). Commonly
reported symptoms encompass heightened stress reactivity, anxiety,
depressive mood, breast tenderness, and abdominal pain (Allen et al.,
1991).

As a component of the gender data gap, premenstrual syndrome (PMS)
remains significantly under-researched (Zehravi et al., 2023). Even
today, treatment options remain limited, primarily centered on
addressing specific psychological or physiological symptoms through
hormonal cycle suppression or antidepressant medication in both clinical
practice and research (Ryu \& Kim, 2015). Both of these approaches are
associated with substantial adverse side effects (Price et al., 2009;
Robinson et al., 2004; Skovlund et al., 2016).

Premenstrual symptoms have been linked to cyclic fluctuations in vagally
mediated heart rate variability (vmHRV) (Schmalenberger et al., 2019).
Individuals who experience more severe symptoms tend to exhibit a
pronounced reduction in vmHRV during the luteal phase of their menstrual
cycle, coinciding with the experience of these symptoms (Matsumoto et
al., 2007). Matsumoto et al. (2007) have suggested a potential causal
relationship in this regard.

One possible explanation for this phenomenon lies in a metabolite of
progesterone, one of the main fluctuating gonadal steroids during the
menstrual cycle. Sundström-Poromaa et al. (2003) have identified this
metabolite, namely Allopregnanolone (ALLO), an allosteric
Gamma-Aminobutyric Acid (GABA) receptor modulator as a likely cause of
the experience of premenstrual symptoms (Hantsoo \& Epperson, 2020). As
ALLO operates on the GABAergic system, the proposed CAN in the
neurovisceral integration theory (Thayer \& Lane, 2000, 2009) might also
be affected. In this theory, successful adaptation relies on inhibitory
connectivity between the mPFC and the amygdala. The strength of these
connections, which are part of the central nervous system's inhibitory
GABAergic network, are influenced by GABA levels in the mPFC (Delli
Pizzi et al., 2017). Compromised inhibition in this circuit due to ALLO
withdrawal and/or maladaptive ALLO responses may lead to a compromised
self-regulatory capacity of the organism on both affective and
physiological levels, as observed in PMS.

Following this line of reasoning, HRVB is a promising candidate to
counteract some of these effects through two mechanisms. Firstly, the
most pronounced effects of HRVB are observed in stress management
(Goessl et al., 2017). If stress throughout the cycle causes
irregularities in the ALLO system during the premenstrual phase,
reducing stress throughout the cycle may prevent some of the symptom
development. Existing evidence already suggests that various relaxation
techniques can positively impact PMS (Jose et al., 2022). Secondly, HRVB
is assumed to increase the inhibitory capacity of the mPFC over the
amygdala and, as a result, enhance the inhibition of the default stress
response (Schumann et al., 2021). Although GABAergic transmission may be
compromised during the premenstrual phase, boosting the baseline
inhibitory strength between these two brain structures could raise
inhibition levels. This might make it less likely for a sudden drop to
cross the threshold to trigger symptoms that cause significant distress.

Initial studies have already provided evidence of the effectiveness of
HRVB for mental health outcomes when administered through smartphones.
Previous studies that utilized smartphone-based HRVB interventions to
improve outcomes like depressive or anxiety symptoms, however, have
typically relied on external devices connected to the smartphone via
Bluetooth. These devices include wearable ECG-measuring breast straps
(Chung et al., 2021; Herhaus et al., 2022; Lin, 2018; Schumann et al.,
2022; Schumann et al., 2021) or earlobe-clip pulse measuring devices
(Economides et al., 2020; Minen et al., 2021; Schuman et al., 2023).
Acquiring a wearable device presents a significant obstacle for
potential HRVB users.

Smartphone cameras can now measure heart rate when the user places a
finger on the camera. An application activates the camera flash and
analyzes the red-to-green ratio in the image at high frequency,
generating pulse curves. This process is known as photoplethysmography
(PPG) and closely resembles the process behind the optical sensors that
emit infrared or green light in commonly used pulse measurement devices.
Yuda et al. (2020) suggest that the heart rate variability indicator
used in smartphone apps, which they term `pulse rate variability' as
measured through PPG, may contain distinct information compared to its
ECG-measured counterpart. Nevertheless, recent research has demonstrated
very high correlations between HRV parameters measured through ECG and
PPG of over .9 (van Dijk et al., 2023), even though the reliability is
somewhat dependent on sampling rate of the device (Guede-Fernández et
al., 2020). Moreover, the associations with mental health outcomes are
also evident when assessing vmHRV via PPG using the smartphone camera
(Liu et al., 2020). This supports the use of PPG as a foundation for
HRVB.

In this study, we investigated the novel application of a 4-week
smartphone-based HRVB intervention using PPG via smartphone camera
instead of an external device for alleviating depressive and
premenstrual symptoms. Our sample comprised young adults who either
exhibited above-average PMS or depressive symptoms. Additionally, we
examined the impact of the intervention on various other outcomes,
including anxiety and stress symptoms, attentional control, and vmHRV.

\par\null

\section*{2~~~~~~~~~~~~~~ Methods}\label{methods}

\subsection*{2.1~~~~~~~~ Participants}\label{participants}

A G*Power analysis revealed that a sample size of 40 was necessary to
detect an effect size of .4, based on a meta-analytic effect of HRVBFB
on depressive symptoms reported by Pizzoli et al. (2021), with a power
of .8 and a one-tailed alpha error probability of .05. However, due to
recruitment difficulties and resourcing issues by the company providing
the app during the extended recruitment period, we were unable to reach
our target of 40 participants.

Twenty-nine participants were recruited from the student population of
the University of Potsdam for this study. Recruitment was carried out
via the online recruiting platform for study participants of the
cognitive sciences (Sona Systems, https://www.sona-systems.com) of
University Potsdam as well as via flyers on campus and advertisement in
university mailing lists. Inclusion criteria required participants to
have either above-average premenstrual symptomatology (short version
Premenstrual Assessment Form, PAF20 \selectlanguage{english}[?] 50), depressive symptoms that
indicate at least minimal depression (Beck's Depression Inventory,
BDI-II \selectlanguage{english}[?] 9), or both. Participants who exceeded a BDI-II score of 14
received a consultation with a clinical psychologist to discuss possible
necessary treatment prior to study participation.

Exclusion criteria included factors proposed by Laborde et al. (2017)
such as pregnancy, heart rate-altering chronic diseases or medication.
We additionally excluded competitive athletes to avoid ceiling effects,
since this population has systematically increased vmHRV (Da Silva et
al., 2015). Participants currently in any treatment or planning
significant lifestyle changes during the period of study participation
were also excluded. In addition, participants were required to be at
least 18 years of age.

All participants provided informed consent prior to their inclusion for
a study protocol approved by the ethics committee of the University of
Potsdam (No. 30/2022). Participants who met the inclusion criteria were
eligible for study participation and received either course credits or
monetary compensation.

\par\null

\subsection*{2.2~~~~~~~~ Procedure}\label{procedure}

The study protocol was preregistered on Open Science Framework
(osf.io/68fzq). The study procedure began with an online screening
questionnaire to determine eligibility based on inclusion and exclusion
criteria, as well as to assess sociodemographic factors such as age,
gender, study program, and BMI. Participants were also required to
provide information about their menstrual cycle to ensure that the
appropriate questionnaires were administered. Additionally, participants
were asked to provide their email address for communication throughout
the study.

All eligible participants took part in a 4-week biofeedback intervention
during which they practiced smartphone-based HRVB for at least 5 minutes
every day. After the first and second week, participants additionally
received an online coaching session to improve their technique and
address any technical or other difficulty they encountered.

Before and after the 4-week intervention, participants completed
laboratory sessions that were scheduled at the same time, exactly 4
weeks apart (T1 and T5). During these sessions, various measures were
collected, including vagally mediated heart rate variability by ECG,
attentional control using the reaction time paradigm ANT-R (Fan et al.,
2009), and self-reported symptoms of depression, premenstrual syndrome
(PMS), and anxiety/stress via questionnaires.

To ensure balanced allocation of participants to the waitlist group,
half of the participants within each group of inclusion criteria
(depression, PMS, or both) were pseudo-randomly assigned to the waitlist
group. The waitlist group additionally completed a laboratory session
four weeks prior to study inclusion, during which the same parameters
were assessed (W1).

Throughout the study, participants completed short versions of the
depressive and premenstrual symptom questionnaires and underwent a
photoplethysmography based HRV measurement at home using the biofeedback
app, each week on the same day and at the same time that they chose
(W2-W4 and T2-T4). Participants received automated email reminders and a
link to the respective questionnaire to ensure compliance. The results
of these measurements are not analysed and reported in this report.
Figure 1 provides an overview of the study procedure.

All participants received an introduction to smartphone-based HRVB
during T1. The waitlist group received a tutorial on conducting vmHRV
measurements at home with the app during W1, while the intervention-only
group received this tutorial during T1.

Since PMS occurs only once during each menstrual cycle, and cycle
lengths can vary significantly both between and within individuals, we
included a follow-up measurement of the online questionnaire 4 weeks
after T5. If a participant reported no new menstruation onset during the
last two weeks of the intervention, indicating no new premenstrual
phase, we used the PMS values reported in the follow-up measurement as
the post-intervention values, describing the next premenstrual phase
after completing the intervention.

\textbf{Figure 1.} Study procedure\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/fig1-smartBFB-studyplan1/fig1-smartBFB-studyplan1}
\caption{{~
{\label{692637}}%
}}
\end{center}
\end{figure}

Note. SocDem -- sociodemographic information, PAF10 -- premenstrual
assessment form 10 item version; PAF20 -- premenstrual assessment form
20 item version; BDI-II -- Beck's Depression Inventory; BDI-FS -- Beck's
Depression Inventory short version; ANT-R -- attention network
test-revised; DASS -- Depression Anxiety Stress Scale; HRV ECG -- heart
rate variability measurement in lab (electrocardiography); HRV basic --
measurement at rest at home on phone (photoplethysmography); W -- week.

~

\subsection*{2.3~~~~~~~~ Smartphone-based heart rate variability
biofeedback}\label{smartphone-based-heart-rate-variability-biofeedback}

The HRVB intervention used in this study was app-based and built with
the software development kit (SDK) provided by Kenkou GmbH. The app
measured HRV via photoplethysmography (PPG), whereby participants placed
their index finger on the camera lens and a flash was used to illuminate
the tissue. The camera measured the intensity of blood flow, and a peak
detection algorithm was used to detect heartbeats.

Before each biofeedback session, participants underwent a 1-minute
baseline measurement to assess their current state, which allowed the
feedback to be adjusted accordingly. During the biofeedback session,
participants saw a growing and shrinking circle indicating the breathing
rhythm at 0.1 Hz. A fixed sinus-like wave was also displayed at the
frequency of the paced breathing, and the pulse rate over time was
mapped on top of this. Participants were instructed that the two waves
would converge more as they relaxed. After the HRVB exercise, the user
was given feedback on HRV improvement during the exercise to increase
motivation. Screenshots of the app can be seen in Figure 2.

~

\textbf{Figure 2.} Screenshots of the HRVB application\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.84\columnwidth]{figures/fig2-Screenshot-Kenkou/fig2-Screenshot-Kenkou}

\end{center}
\end{figure}

Note. For the biofeedback, the current respiratory sinus arrhythmia,
measured through photoplethysmography using the device's integrated
camera, was represented as a blue line. A dynamic expanding and
contracting circle visually represented the paced breathing rhythm at a
frequency of 0.1 Hz. This frequency was further depicted by gray
sinusoidal waves in the background, behind the measured heart rate
oscillations.

~

The experimenter provided approximately 15 minutes of instructions to
participants on how to use the app and how the biofeedback worked.
Participants were coached on how to engage in relaxed slow-paced
breathing. They were instructed to practice at least 5 minutes daily for
the next 4 weeks, with the option to practice for longer periods of time
if desired. Participants were also informed that more practice would
likely lead to greater benefits.

Three participants encountered technical difficulties while running the
application on their devices. To ensure their participation in the
intervention, they were provided with an alternative mobile HRVB system
('Qiu' by Biosign\selectlanguage{ngerman}®, D-85570, Ottenhofen, Germany).

\par\null

\subsection*{2.4~~~~~~~~ Outcome measures}\label{outcome-measures}

\subsubsection*{2.4.1~~ Premenstrual Assessment Form (short
form)}\label{premenstrual-assessment-form-short-form}

The short form of the Premenstrual Assessment Form (PAF20) is a
retrospective instrument that assesses PMS symptoms during the last
premenstrual phase (Allen et al., 1991). It was derived from the 20 most
endorsed items of the long form PAF, which includes almost 100 items
(Halbreich et al., 1982). Each item represents one premenstrual symptom,
for which the participant must indicate how strongly they experienced it
during the last cycle on a 6-point Likert scale from 1 (not at all/no
change) to 6 (extreme change). The German translation of the PAF-20
shows good internal consistency and reliability and loads on two
factors, indicating a psychological and physiological subscale (Blaser
et al., 2023b).

The 10-item version (PAF-10) was constructed using the items with the
highest factor loadings and shows a very high correlation with the
PAF-20 (Blaser et al., 2023b). To assess the fluctuations of symptoms
throughout the cycle and approximate a prospective assessment, the
participants filled out the PAF-10 once a week with altered
instructions, asking for a report of the 10 symptoms during the last
week.

\subsubsection*{2.4.2~~ Becks Depression
Inventory}\label{becks-depression-inventory}

The Beck Depression Inventory II (BDI-II) is a widely used questionnaire
that assesses the severity of depressive symptoms. It consists of 21
items, each containing four statements about depressive symptoms ranging
from 0 (normal) to 3 (most severe). The total maximum score is 63. The
BDI-II has good psychometric properties, including high internal
consistency, test-retest reliability, and concurrent and discriminant
validity. Additionally, the questionnaire has been translated into
multiple languages and is widely used in clinical and research settings
to assess depression severity, monitor treatment progress, and evaluate
outcomes. Previous studies have also shown that the BDI-II has good
discrimination between patients with varying degrees of depression and
accurately reflects changes in depression intensity over time (Beck et
al., 1988; Richter et al., 1998).

The Fast Screen Version of the Becks Depression Inventory (BDI-FS) was
developed as a short form to allow for parsimonious screenings, e.g., in
research settings. It includes seven items and is based on the DSM-5
criteria for depression, clinical importance, and factor loadings (Beck
et al., 2000).

\subsubsection*{2.4.3~~ Depression Anxiety Stress
Scale}\label{depression-anxiety-stress-scale}

The German version of the Depression Anxiety and Stress Scale (DASS),
developed by Henry and Crawford (2005) and based on the original version
by Lovibond and Lovibond (1995), was employed for data collection. The
DASS-21, a shortened version of the scale, consists of 21 statements
that assess three distinct subscales: depression, anxiety, and stress.

Participants were asked to rate the extent to which each statement
applied to them during the designated period using Likert scales ranging
from 0 to 3. Higher scores on the DASS-21 indicate elevated levels of
depressive symptoms, anxiety, and stress.

The internal consistency of the DASS-21 was found to be satisfactory,
with a Cronbach's \selectlanguage{greek}α \selectlanguage{english}coefficient of 0.89 (Bibi et al., 2020). The DASS-21
was selected as an outcome measure in this study based on its consistent
effects in biofeedback interventions, as demonstrated in prior research
(Goessl et al., 2017).

\subsubsection*{2.4.4~~ Vagally mediated heart rate
variability}\label{vagally-mediated-heart-rate-variability}

Resting vmHRV was determined using the BioSign software and hardware
(``HRV-Scanner''; Biosign\selectlanguage{ngerman}®, D-85570, Ottenhofen, Germany). Participants
had been sitting down for at least 15 minutes before the measurement.
The measurement was taken in a sitting position. Participants were
instructed to sit comfortably, place their feet side by side on the
floor, close their eyes, and were told that they didn't have to pay
attention to anything in particular. Following the recommendations by
Laborde et al. (2017), the measurement had a duration of 5 minutes.

HRV was measured by a one-lead electrocardiogram (ECG) through two
surface sensors attached to the right and left wrists of the
participant. The device worked with a sampling rate of 500 Hz and a
16-bit resolution. Artifacts and abnormal beats were filtered in a
two-step process following the software documentation (BioSign GmbH,
2023). First, the HRV Scanner software automatically marked areas of the
heart rate curve that included implausible changes in heart rate
(through the division of the heart rate curve into small segments and a
subsequent scan of each segment). This process was based on an algorithm
patented by the BioSign company that identifies outliers in a Poincaré
plot, where each RR interval is plotted against the previous RR
interval.

Working with these recognized areas of possible disturbances, in the
second step, the R-spike recognition was manually assessed and
corrected, and artifacts (e.g., due to movement) were removed. After the
two-step process, the data quality was excellent, with less than 0.1\%
artifacts per measurement on average.

Participants additionally conducted short resting vmHRV measurements
through the app using PPG, as described above, once a week. These
measurements lasted one minute, and participants were instructed to take
these measurements each week on the same day, at the same time, and in
the same place, ensuring they would not be disturbed. They were also
instructed to sit comfortably and close their eyes during the
measurement, similar to the way they were during the ECG measurement in
the laboratory.

We used the root mean square of successive differences (RMSSD) as a
measure for vagally mediated heart rate variability. This choice was due
to its indication of parasympathetic output and robustness to influences
of breathing rate (Chapleau \& Sabharwal, 2011).

\subsubsection*{2.4.5~~ Attentional network test
revised}\label{attentional-network-test-revised}

We employed the ANT-R, developed by Fan et al. (2009), as a measure of
attentional control. This task is reaction time task and was designed as
a combination of the Eriksen flanker task (Eriksen \& Eriksen, 1974) and
the Posner cueing task (Posner, 1980).

During the ANT-R, participants were presented with a grey background and
a black horizontal arrow. Their task was to indicate the direction of
the arrow by pressing the corresponding button with their left or right
index finger.

The ANT-R task consists of a total of 288 trials, divided into two
identical runs of 144 trials each. The duration of the entire test is
approximately 30 minutes. Previous studies have demonstrated good
split-half reliability in the Executive (r = .74) and Orienting network
scores (r = .70) (Greene et al., 2008). To reduce participant burden,
only one run was completed per session.

The task was administered using the Presentation® software
(Neurobehavioral Systems, Inc.) on a 24-inch screen positioned 80 cm
away from the participants. Before the main task, participants completed
6 practice trials with feedback and 32 practice trials without feedback.
Written and visual instructions were provided prior to the practice
trials.

During the main task, participants were required to achieve a minimum
accuracy of 80\%. On average, participants reached an accuracy rate of
95\% in the main task block.

\par\null

\subsection*{2.5~~~~~~~~ Statistical
analysis}\label{statistical-analysis}

All analyses were conducted using R (version 4.2.2). A linear mixed
model was calculated for each target variable, with data points
clustered per participant by introducing participant intercepts as
random effects. When applicable, items were also included as random
effects. The fixed effects included in the model were TIME point,
TREATMENT, the TREATMENT * TIME interaction, and control variables (AGE,
GENDER, BMI, RMSSD), along with exploratory three-way interactions
involving potential mediators of the main TREATMENT * TIME effect.

A model selection process was applied to each analysis, with predictors
being consecutively added to the model. Likelihood Ratio Testing
compared the goodness of fit of each model to the next simpler one, and
predictors were retained if they improved the model's fit.

The main hypotheses were tested with TREATMENT * TIME interactions in
the respective model. The hypothesis was considered accepted if the
interaction term was included in the final model, a significant
predictor, and the effect aligned with the expected direction. Post-hoc
comparisons and plots of the interaction effects were used to verify the
expected direction of the effects.

As this study was a randomized controlled trial (RCT) with a
waiting-list control group, the analyses included post-treatment data
from the control group. Therefore, the post-treatment data points of the
control group were classified into the post-treatment intervention
group.

The same procedure was applied for reaction time data, involving
three-way interactions instead of two-way interactions. These three-way
interactions included TREATMENT, TIME, and FLANKER or CUE condition for
the Executive and Orienting Network performance, respectively.

\textbf{~}

\section*{3~~~~~~~~~~~~~~ Results}\label{results}

\subsection*{3.1~~~~~~~~ Descriptive
Statistics}\label{descriptive-statistics}

Out of the 29 participants initially included in the study, 27 attended
at least the first session and were included in the analyses. However,
an additional 3 participants dropped out after the first and before the
last session, resulting in 24 participants who completed the study
entirely. Table 1 provides a description of the groups that underwent
only the intervention and those who completed both the waitlist and
intervention protocols.

\subsection*{3.2~~~~~~~~ Premenstrual
Symptoms}\label{premenstrual-symptoms}

Out of 21 PAF20 post values recorded for the T5 measurement, 9 were
replaced with the follow-up measurements. This occurred because there
was either no premenstrual phase during the intervention period or the
premenstrual phase occurred during the first two weeks of the
intervention phase.

The final model for predicting premenstrual symptoms incorporated the
TREATMENT and TIME variables along with their interaction. Furthermore,
it included the SCALE of the PAF20 questionnaire to which each symptom
belonged (psychological vs. physiological symptoms) and its interaction
with the TREATMENT~*~TIME interaction (see Table 2). The final model
was:

Value \textasciitilde{} treatment*time + scale + treatment:time:scale +
(1\textbar{}vpn) + (1\textbar{}item).

Post-hoc Tukey testing of the two-way interaction TREATMENT~*~TIME
revealed a significant improvement in the intervention group,
\emph{d}~=~-0.30, \emph{t}\textsubscript{\emph{ratio}}(1258)~=~-5.89,
\emph{p}~\textless{}~.001, whereas there was no significant pre-post
difference in the waitlist group, \emph{d}~=~0.10,
\emph{t}\textsubscript{\emph{ratio}}(1252)~=~1.35, \emph{p}~=~.18 (see
Figure 3). When SCALE was included in the interaction, it showed that
the improvement in the intervention group was larger for psychological
scale items (\emph{d}\textsubscript{\emph{psych}} = -0.42) than for
physiological scale items
(\emph{d}\textsubscript{\emph{physio}}~=~-0.19), with both improvements
being significant. Detailed post-hoc testing results for the TIME *
TREATMENT * SCALE interaction can be found in Table 3.

~

~

\textbf{Figure 3.} Course of premenstrual symptoms\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.42\columnwidth]{figures/fig3-paf-small/fig3-paf-small}

\end{center}
\end{figure}

Note. Colored lines indicate individual participants. Black lines
indicate the predicted interaction effect based on the mixed effect
model depicted in Table 2. The waitlist period occurred between sessions
W1 and T1, while the heart rate variability biofeedback intervention
period took place between T1 and T5. PAF -- premenstrual assessment
form; ***~--~p\textless{}.001; ns -- not significant.

\subsection*{3.3~~~~~~~~ Depressive Symptoms}\label{depressive-symptoms}

The final model predicting depressive symptoms included only TREATMENT
and TIME, as well as their interaction as fixed effects (see Table 4).
The model was the following:

Value \textasciitilde{} treatment*time + (1\textbar{}vpn) +
(1\textbar{}item).

A post-hoc Tukey test of the interaction showed that symptom scores
significantly improved in the intervention period, \emph{d}~=~-0.25,
\emph{t}\textsubscript{\emph{ratio}}(1576)~=~-4.71,
\emph{p}~\textless{}~.001, but not in the wait-list period,
\emph{d}~=~0.05, \emph{t}\textsubscript{\emph{ratio}}(1567)~=~0.73,
\emph{p}~=~.46 (see Figure 4).

~

\textbf{Figure 4.} Course of depressive symptoms\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.42\columnwidth]{figures/fig4-bdi-small/fig4-bdi-small}

\end{center}
\end{figure}

Note. Colored lines indicate individual participants. Black lines
indicate the predicted interaction effect based on the mixed effect
model depicted in Table 4. The waitlist period occurred between sessions
W1 and T1, while the heart rate variability biofeedback intervention
period took place between T1 and T5. BDI -- Beck's Depression Inventory
II; *** -- p\textless{}.001; ns -- not significant.

\textbf{~}

\subsection*{3.4~~~~~~~~ Stress and Anxiety}\label{stress-and-anxiety}

The best model predicting DASS values included the RMSSD and the DASS
SCALE (anxiety, stress or depression) on top of TREATMENT, TIME and
their interaction (see Table 5). There was no three-way interaction of
TREATMENT~*~TIME~*~SCALE. The model was:

Value \textasciitilde{} treatment*time + scale + RMSSD +~
(1\textbar{}vpn) + (1\textbar{}item).

Post-hoc Tukey testing of the TREATMENT~*~TIME interactions showed a
significant improvement in the intervention period, \emph{d} = -0.19,
\emph{t\textsubscript{ratio}}(1573) = -3.88, \emph{p} \textless{} .001,
but not in the waitlist period, \emph{d}~=~0.09,
\emph{t}\textsubscript{\emph{ratio}}(1567)~=~1.37, \emph{p}~=~.17 (see
Figure 5).

\par\null

\textbf{Figure 5.} Course of Anxiety/Stress symptoms\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.42\columnwidth]{figures/fig5-dass-small/fig5-dass-small}

\end{center}
\end{figure}

Note. Colored lines indicate individual participants. Black lines
indicate the predicted interaction effect based on the mixed effect
model depicted in Table 5. The waitlist period occurred between sessions
W1 and T1, while the heart rate variability biofeedback intervention
period took place between T1 and T5. DASS -- Depression Anxiety and
Stress Scales; *** -- p\textless{}.001; ns -- not significant.

\textbf{~}

\subsection*{3.5~~~~~~~~ Heart rate
variability}\label{heart-rate-variability}

The RMSSD was log-transformed to approximate a normal distribution,
aligning it with the methodology used in other vmHRV research studies.
Two participants had to be excluded from the post measurement, as they
had acute respiratory tract infections.

The final model predicting the log(RMSSD) included only TREATMENT, TIME
and the TREATMENT * TIME interaction as fixed effects (see Table 6).

~

Value \textasciitilde{} treatment*time + (1\textbar{}vpn) +
(1\textbar{}item).

~

None of the fixed effects were significant predictors. A post-hoc Tukey
test of the interaction confirmed no significant improvement in the
intervention group, \emph{d}~=~0.11,
\emph{t}\textsubscript{\emph{ratio}}(44)~=~0.66, \emph{p}~=~.51, and no
significant pre-post difference in the waitlist group, \emph{d}~=~-0.14,
\emph{t}\textsubscript{\emph{ratio}}(43)~=~0.61, \emph{p}~=~.55 (see
Figure 6).

~

\textbf{Figure 6.} Course of vagally mediated heart rate variability\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.42\columnwidth]{figures/fig6-hrv-small/fig6-hrv-small}

\end{center}
\end{figure}

Note. Colored lines indicate individual participants. Black lines
indicate the predicted interaction effect based on the mixed effect
model depicted in Table 6. The waitlist period occurred between sessions
W1 and T1, while the heart rate variability biofeedback intervention
period took place between T1 and T5. RMSSD -- root mean square of
successive differences; ns -- not significant.

~

\subsection*{3.6~~~~~~~~ Attentional control}\label{attentional-control}

The reaction time prediction model included main effects for TIME,
TREATMENT, CUE, and FLANKER. Additionally, two three-way interactions
were included -- TREATMENT * TIME * CUE and TREATMENT * TIME * FLANKER.
Both of these three-way interactions revealed significant terms in the
model (see Table 7).

~

Reaction Time \textasciitilde{} treatment:time:cue +
treatment:time:flanker + time + treatment

~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~+ cue + flanker + (1\textbar{}vpn)
+ (1\textbar{}item).

~

We used linear trend estimates from the emmeans package (v1.8.8) to
compare valid vs. invalid (cue) and congruent vs. incongruent (flanker)
slopes in each pre-post comparison. This approximated the original score
calculations. Significant differences in slopes indicate changes in the
Orienting Score (invalid vs. valid cue trials) or Executive Score
(incongruent vs. congruent flanker trials) between pre and
post-assessment.

In the TIME * TREATMENT * CUE interaction, there were no significant
differences in pre-post slopes between valid and invalid trials during
both the intervention period, \emph{d}~=~-0.03,
\emph{z}\textsubscript{\emph{ratio}}~=~-0.67, \emph{p}~=~.91, and the
waitlist period, \emph{d}~=~0.00,
\emph{z}\textsubscript{\emph{ratio}}~=~0.03, \emph{p}~=~1, (see figure
7a).

In the TIME * TREATMENT * FLANKER interaction, there was a significant
difference in pre-post slopes between congruent and incongruent trials
during the intervention period, \emph{d}~=~-0.18,
\emph{z}\textsubscript{\emph{ratio}}~=~-4.60, \emph{p}~\textless{}~.001,
but no difference in the waitlist period, \emph{d}~=~-0.06,
\emph{z}\textsubscript{\emph{ratio}}~=~-1.34, \emph{p}~=~.54. The effect
in the intervention period indicated less difference between valid and
invalid trials post-treatment compared to pre-treatment, equivalent to
better Executive Scores (see figure 7b).

~

\textbf{Figure 7.} Course of attentional control\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.84\columnwidth]{figures/fig7-att-small/fig7-att-small}

\end{center}
\end{figure}

Note. Colored lines indicate individual participants. Black lines
indicate the predicted interaction effect based on the mixed effect
model depicted in Table 5. The waitlist period occurred between sessions
W1 and T1, while the heart rate variability biofeedback intervention
period took place between T1 and T5. a) Orienting Score -- (reaction
time \textsubscript{incongruent spatial cues} - reaction time
\textsubscript{congruent spatial cues}); b) Executive Score -- (reaction
time \textsubscript{invalid flankers} - reaction time
\textsubscript{valid flankers}). *** -- p\textless{}.001; ns -- not
significant.

\textbf{~}

\section*{4~~~~~~~~~~~~~~ Discussion}

In this study, we investigated the impact of a 4-week
photoplethysmography smartphone-based HRVB intervention on premenstrual
and depressive symptoms using a waitlist-control design. Additionally,
we assessed its effects on stress symptoms, vmHRV, and attentional
control. Our findings revealed improvements in premenstrual, depressive,
and stress symptoms following the intervention, with no significant
changes observed during the waitlist period. However, we did not observe
any effects on vmHRV, and the results for attentional control were
mixed.

The positive effects on premenstrual, depressive, anxiety, and stress
symptoms found in our study highlight the feasibility and effectiveness
of a smartphone-based HRVB intervention without the need for external
devices. Laborde, Allen, Borges, Iskra, et al. (2022) reported similar
effects of a slow-paced breathing intervention, whether or not visual
feedback on current HRV was provided. This suggests that slow-paced
breathing is the primary driver of the intervention's effectiveness.
However, learning the technique of abdominal slow-paced breathing, which
allows for a relaxed resonance frequency activation, can be challenging.
The drastically slowed-down breathing can be uncomfortable and may even
lead to hyperventilation (Szulczewski \& Rynkiewicz, 2018). Providing
visual feedback on respiratory sinus arrhythmia can assist users in
correctly employing the method at home without extensive training.
Moreover, this biofeedback feature can enhance user engagement with the
app, as it offers immediate visual results of the breathing
intervention, providing a sense of immediate gratification. Essentially,
this form of feedback incorporates a simplified version of gamification
into the intervention, which can increase motivation and engagement
(Johnson et al., 2016).

Our primary objective was to investigate whether HRVB could help
alleviate PMS symptoms. We observed a significant improvement in the
premenstrual phase following 2-4 weeks of HRVB practice compared to the
preceding cycle in which no HRVB was practiced. The effect size we
observed is approximately 0.3, with a slightly larger effect of 0.4 in
the psychological symptom scale. In this context, the effect size is
similar to the moderate effect size found in meta-analyses of depressive
symptoms (Pizzoli et al., 2021). This finding supports the notion that
PMS may result from an altered reactivity within the ALLO/GABA system
(Hantsoo \& Epperson, 2020), affecting the CAN as proposed by Thayer and
Lane (2000). Consequently, interventions designed to target the
interconnectivity and functional capacity of the CAN may hold promise in
improving PMS symptoms.

However, further studies with expanded paradigms are needed to determine
whether this effect is mediated by a reduction in stress throughout the
menstrual cycle or by an increase in the capacity of the CAN, which may
buffer modulatory fluctuations throughout the cycle. It also remains
unclear whether there is a critical phase in the menstrual cycle when
HRVB has a more significant impact on subsequent premenstrual symptoms.
It is possible that practicing HRVB throughout the preceding follicular
phase reduces chronic stress (Goessl et al., 2017) and, consequently,
chronic ALLO exposure. This prolonged exposure has been suggested to
contribute to atypical GABA receptor reactivity to ALLO fluctuations
later in the cycle, which is assumed to cause PMS (Hantsoo \& Epperson,
2020). Another possibility is that during the premenstrual phase, the
acute stress-relieving effects (see e.g. Meier \& Welch, 2016; Prinsloo
et al., 2013) of HRVB directly alleviate the symptom burden.

Independently of mechanisms of action, HRVB is an easily learned
intervention with minimal to no side effects, offering PMS sufferers an
opportunity to enhance their symptom management self-efficacy.
Furthermore, it can lower the threshold for receiving treatment. When
delivered through a smartphone application, it becomes even more
accessible and, if its effectiveness is established, can be seamlessly
integrated into widely used menstrual cycle-related health apps.

Our results also demonstrated improvements in depressive symptoms, as
well as anxiety and stress symptoms, through the smartphone-based HRVB
intervention. This aligns with prior research using other HRVB
intervention methods (Goessl et al., 2017; Pizzoli et al., 2021),
although the effects we observed are somewhat smaller than the
meta-analytic effects on depression and anxiety/stress. This discrepancy
may be due to ceiling effects. The majority of studies investigating
HRVB effects typically focus on clinical populations with very high
values in the respective outcome measures. Our inclusion criteria, on
the other hand, involved individuals with above-average PMS symptoms or
slightly elevated depressive symptoms. 30\% of the participants were
included in our study due to only PMS symptoms. While clinically
significant affective premenstrual symptoms (premenstrual dysphoric
disorder) are often comorbid with anxiety and depression (Yonkers \&
McCunn, 2007), our subclinical sample likely had lower baseline scores
in both BDI-II and DASS values compared to the samples in most studies
included in the meta-analysis. This could account for the slightly
smaller effect sizes observed in our study.

We did not observe improvements in vmHRV (RMSSD) following the HRVB
intervention. Contrary to our findings, Laborde, Allen, Borges,
Dosseville, et al. (2022) reported consistent elevations in vmHRV
parameters after HRVB and slow-paced breathing interventions in their
meta-analysis. The effect size for this improvement was approximately
\emph{Hedges' g}~=~0.3. Notably, this effect size is smaller than the
meta-analytic effect sizes for depressive symptoms (\emph{g}~=~0.4;
Pizzoli et al., 2021) or anxiety/stress (\emph{g}~=~0.8; Goessl et al.,
2017). These findings suggest that improvements in vmHRV alone do not
fully account for the observed affective and cognitive effects. This is
consistent with other findings that demonstrate physiological and
clinical outcomes do not always change simultaneously in HRVB (Wheat \&
Larkin, 2010). Critically, our study may not have detected this effect
due to its relatively small sample size. A G*Power analysis indicates
that a sample size of more than 70 would be required to replicate the
effect of 0.3 with a power of .8. Our sample size was significantly
below this threshold. The reason why effects were found in other outcome
measures lies in the advantage of mixed model analysis, which allows us
to include each item/trial individually without the need to condense the
information into a composite score. This significantly increases the
number of data points included, by 20 to 144 times, depending on the
measurement. However, in the case of vmHRV, it is necessary compute a
single value per measurement, which drastically reduces statistical
power. This suggests the possibility that HRVB may have had a beneficial
impact on vmHRV, but our study may have been underpowered to detect this
effect.

The influence of the HRVB on the attentional control domains yielded
mixed results. We chose to employ the Executive and Orienting Scores of
the ANT-R as outcome measures, given their documented correlations with
vmHRV (Blaser et al., 2023a; Quintana et al., 2017; Ram\selectlanguage{ngerman}írez et al.,
2015; Sørensen et al., 2019). Interestingly, despite previous studies
(Quintana et al., 2017; Sørensen et al., 2019) indicating a stronger
association between the Orienting Score and vmHRV (compared to the
Executive Score vmHRV association), we did not observe any improvements
in this measure. However, we did find enhancements in the Executive
Score following the intervention period. These varying outcomes align
with the findings of a meta-analysis conducted by Tinello et al. (2022),
who reported positive effects of HRVB on Executive Functions in
approximately half of the studies they reviewed, with a slightly higher
likelihood of effects in the attention domain. This highlights the need
for further research to elucidate the specific impact of HRVB on
cognitive outcomes.

Although we found significant beneficial effects of smartphone-based
HRVB on several mental health and some cognitive outcomes, our study has
several limitations that need to be considered. Firstly, the most
notable limitation is our small sample size. Although linear mixed model
analyses help to overcome the limited statistical power due to small
samples by including individual items/trials in the analysis, it is
important that the interpretation of the results and their applicability
to larger populations must be considered with caution. Another
limitation is related to the passive control group, which has
methodological weaknesses. In future studies, it would be beneficial to
implement active control groups to better distinguish the intervention's
effects from any potential placebo effects. Additionally, we did not
conduct clinical assessments of PMS or depression. However, it is
essential to note that our study was not aimed at identifying treatments
for a clinically relevant premenstrual dysphoric disorder. Instead, our
primary focus was on investigating a user-friendly intervention to help
individuals self-manage premenstrual symptoms, regardless of their
severity levels. Furthermore, our study included participants regardless
of their use of hormonal contraceptives and their current menstrual
cycle phase. For future studies, it is advisable to standardize these
criteria to ensure a more consistent assessment.

\section*{5~~~~~~~~~~~~~~ Conclusion}

{\label{902226}}

In summary, smartphone-based HRVB has proven to be effective in
enhancing both emotional and cognitive well-being, without the need for
external devices. This intervention holds promise as a novel approach
for self-managing premenstrual symptoms and provides a more accessible
solution for harnessing the known benefits of HRVB for depressive,
stress, and anxiety symptoms, as well as certain aspects of attention.

\par\null

\textbf{Acknowledgements}

The project was partly funded by the Funding of Knowledge and Technology
Transfer (FöWiTec) of the University of Potsdam. The first author was
partly supported by a PhD scholarship from the University of Potsdam
(Potsdam Graduate School) with fundings from the Graduate Fund of the
State of Brandenburg (Germany).~

During the preparation of this work, the first author utilized ChatGPT
3.5 to enhance the text's readability. Prompts used were aimed at tasks
such as ``check grammar and spelling in this paragraph.'' After
employing this tool/service, the author reviewed and edited the content
as necessary and assumes full responsibility for the publication's
content.

We would like to thank Dr. Michael Vesker and Kenkou GmbH for providing
the biofeedback App as well as Kenkou GmbH for partly providing the
participant compensation.

~

\section*{6~~~~~~~~~~~~~~ References}

{\label{335378}}

{}{}

{Allen,~S.~S., McBride,~C.~M., \& Pirie,~P.~L. (1991).}The shortened
premenstrual assessment form. \emph{The Journal of Reproductive
Medicine}, \emph{36}(11), 769--772.

{Beck,~A.~T., Epstein,~N., Brown,~G., \& Steer,~R.~A. (1988). An
inventory for measuring clinical anxiety: Psychometric properties.}
\emph{Journal of Consulting and Clinical Psychology}, \emph{56}(6),
893--897. https://doi.org/10.1037/0022-006X.56.6.893

{Beck,~A.~T., Steer,~R.~A., \& Brown,~G.~K. (2000).} \emph{BDI-II Fast
Screen for Medical Patients Manual}. The Psychological Corporation.

{Bibi,~A., Lin,~M., Zhang,~X.~C., \& Margraf,~J. (2020). Psychometric
properties and measurement invariance of Depression, Anxiety and Stress
Scales (DASS-21) across cultures.} \emph{International Journal of
Psychology : Journal International De Psychologie}, \emph{55}(6),
916--925. https://doi.org/10.1002/ijop.12671

{BioSign GmbH (Ed.). (2023).} \emph{Documentation for HRV-Scanner}.
https://www.biosign.de/download\_HRVScanner/HRV-Scanner\%20Dokumentation\%20Part\%201\%20EN.pdf

{Blaser,~B.~L., Weymar,~M., \& Wendt,~J. (2023a).}The effect of a
single-session heart rate variability biofeedback on attentional
control: Does stress matter? \emph{Frontiers in Psychology}, \emph{14.}
https://doi.org/10.3389/fpsyg.2023.1292983

{Blaser,~B.~L., Weymar,~M., \& Wendt,~J. (2023b). Ökonomische Erhebung
prämenstrueller Symptomatik -- Deutsche Übersetzung der Kurzversion der
Premenstrual Assessment Form und deren psychometrische Überprüfung
{[}Efficient assessment of premenstrual symptoms - German translation of
the shortened Premenstrual Assessment Form and its psychometric
evaluation{]}.} \emph{Psychotherapie, Psychosomatik, medizinische
Psychologie.} Advance online publication.
https://doi.org/10.1055/a-2136-6941

{Chapleau,~M.~W., \& Sabharwal,~R. (2011). Methods of assessing vagus
nerve activity and reflexes.} \emph{Heart Failure Reviews},
\emph{16}(2), 109--127. https://doi.org/10.1007/s10741-010-9174-6

{Chung,~A.~H., Gevirtz,~R.~N., Gharbo,~R.~S., Thiam,~M.~A., \&
Ginsberg,~J.~P.~J. (2021). Pilot Study on Reducing Symptoms of Anxiety
with a Heart Rate Variability Biofeedback Wearable and Remote Stress
Management Coach.} \emph{Applied Psychophysiology and Biofeedback},
\emph{46}(4), 347--358. https://doi.org/10.1007/s10484-021-09519-x

{Da Silva,~V.~P., Oliveira,~N.~A.~de, Silveira,~H., Mello,~R.~G.~T., \&
Deslandes,~A.~C. (2015). Heart rate variability indexes as a marker of
chronic adaptation in athletes: A systematic review.} \emph{Annals of
Noninvasive Electrocardiology}, \emph{20}(2), 108--118.
https://doi.org/10.1111/anec.12237

{Delli Pizzi,~S., Chiacchiaretta,~P., Mantini,~D., Bubbico,~G.,
Edden,~R.~A., Onofrj,~M., Ferretti,~A., \& Bonanni,~L. (2017). Gaba
content within medial prefrontal cortex predicts the variability of
fronto-limbic effective connectivity.} \emph{Brain Structure \&
Function}, \emph{222}(7), 3217--3229.
https://doi.org/10.1007/s00429-017-1399-x

{Economides,~M., Lehrer,~P., Ranta,~K., Nazander,~A., Hilgert,~O.,
Raevuori,~A., Gevirtz,~R., Khazan,~I., \& Forman-Hoffman,~V.~L. (2020).
Feasibility and Efficacy of the Addition of Heart Rate Variability
Biofeedback to a Remote Digital Health Intervention for Depression.}
\emph{Applied Psychophysiology and Biofeedback}, \emph{45}(2), 75--86.
https://doi.org/10.1007/s10484-020-09458-z

{Eriksen,~B.~A., \& Eriksen,~C.~W. (1974). Effects of noise letters upon
the identification of a target letter in a nonsearch task.}
\emph{Perception \& Psychophysics}, \emph{16}(1), 143--149.
https://doi.org/10.3758/bf03203267

{Fan,~J., Gu,~X., Guise,~K.~G., Liu,~X., Fossella,~J., Wang,~H., \&
Posner,~M.~I. (2009).}Testing the behavioral interaction and integration
of attentional networks. \emph{Brain and Cognition}, \emph{70}(2),
209--220. https://doi.org/10.1016/j.bandc.2009.02.002

{Goessl,~V.~C., Curtiss,~J.~E., \& Hofmann,~S.~G. (2017). The effect of
heart rate variability biofeedback training on stress and anxiety: A
meta-analysis.} \emph{Psychological Medicine}, \emph{47}(15),
2578--2586. https://doi.org/10.1017/S0033291717001003

{Greene,~D.~J., Barnea,~A., Herzberg,~K., Rassis,~A., Neta,~M., Raz,~A.,
\& Zaidel,~E. (2008). Measuring attention in the hemispheres: The
lateralized attention network test (LANT).} \emph{Brain and Cognition},
\emph{66}(1), 21--31. https://doi.org/10.1016/j.bandc.2007.05.003

{Guede-Fernández,~F., Ferrer-Mileo,~V., Mateu-Mateus,~M.,
Ramos-Castro,~J., García-González,~M.~Á., \& Fernández-Chimeno,~M.
(2020). A photoplethysmography smartphone-based method for heart rate
variability assessment: device model and breathing influences.}
\emph{Biomedical Signal Processing and Control}, \emph{57}, 101717.
https://doi.org/10.1016/j.bspc.2019.101717

{Halbreich,~U., Endicott,~J., Schacht,~S., \& Nee,~J. (1982).}The
diversity of premenstrual changes as reflected in the Premenstrual
Assessment Form. \emph{Acta Psychiatrica Scandinavica}, \emph{65}(1),
46--65. https://doi.org/10.1111/j.1600-0447.1982.tb00820.x

{Hantsoo,~L., \& Epperson,~C.~N. (2020). Allopregnanolone in
premenstrual dysphoric disorder (PMDD): Evidence for dysregulated
sensitivity to GABA-A receptor modulating neuroactive steroids across
the menstrual cycle.} \emph{Neurobiology of Stress}, \emph{12}, 100213.
https://doi.org/10.1016/j.ynstr.2020.100213

{Heiss,~S., Vaschillo,~B., Vaschillo,~E.~G., Timko,~C.~A., \&
Hormes,~J.~M. (2021). Heart rate variability as a biobehavioral marker
of diverse psychopathologies: A review and argument for an ``ideal
range''.} \emph{Neuroscience and Biobehavioral Reviews}, \emph{121},
144--155. https://doi.org/10.1016/j.neubiorev.2020.12.004

{Henry,~J.~D., \& Crawford,~J.~R. (2005). The short-form version of the
Depression Anxiety Stress Scales (DASS-21): Construct validity and
normative data in a large non-clinical sample.} \emph{The British
Journal of Clinical Psychology}, \emph{44}(Pt 2), 227--239.
https://doi.org/10.1348/014466505X29657

{Herhaus,~B., Kalin,~A., Gouveris,~H., \& Petrowski,~K. (2022).}Mobile
Heart Rate Variability Biofeedback Improves Autonomic Activation and
Subjective Sleep Quality of Healthy Adults - A Pilot Study.
\emph{Frontiers in Physiology}, \emph{13}, 821741.
https://doi.org/10.3389/fphys.2022.821741

{Holzman,~J.~B., \& Bridgett,~D.~J. (2017). Heart rate variability
indices as bio-markers of top-down self-regulatory mechanisms: A
meta-analytic review.} \emph{Neuroscience and Biobehavioral Reviews},
\emph{74}(Pt A), 233--255.
https://doi.org/10.1016/j.neubiorev.2016.12.032

{Jiménez Morgan,~S., \& Molina Mora,~J.~A. (2017). Effect of Heart Rate
Variability Biofeedback on Sport Performance, a Systematic Review.}
\emph{Applied Psychophysiology and Biofeedback}, \emph{42}(3), 235--245.
https://doi.org/10.1007/s10484-017-9364-2
\selectlanguage{english}
{Johnson,~D., Deterding,~S., Kuhn,~K.-A., Staneva,~A., Stoyanov,~S., \&
Hides,~L. (2016). Gamification for health and wellbeing: A systematic
review of the literature.} \emph{Internet Interventions}, \emph{6},
89--106. https://doi.org/10.1016/j.invent.2016.10.002

{Jose,~A., Nayak,~S., Rajesh,~A., Kamath,~N., \& Nalini,~M. (2022).
Impact of relaxation therapy on premenstrual symptoms: A systematic
review.} \emph{Journal of Education and Health Promotion}, \emph{11}(1),
401. https://doi.org/10.4103/jehp.jehp\_586\_22

{Laborde,~S., Ackermann,~S., Borges,~U., D'Agostini,~M., Giraudier,~M.,
Iskra,~M., Mosley,~E., Ottaviani,~C., Salvotti,~C., Schmausser,~M.,
Szeska,~C., van Diest,~I., Ventura-Bort,~C., Voigt,~L., Wendt,~J., \&
Weymar,~M. (2023). Leveraging Vagally Mediated Heart Rate Variability as
an Actionable, Noninvasive Biomarker for Self-Regulation: Assessment,
Intervention, and Evaluation.} \emph{Policy Insights from the Behavioral
and Brain Sciences}, \emph{10}(2), 212--220.
https://doi.org/10.1177/23727322231196789

{Laborde,~S., Allen,~M.~S., Borges,~U., Dosseville,~F., Hosang,~T.,
Iskra,~M., Mosley,~E., Salvotti,~C., Spolverato,~L., Zammit,~N., \&
Javelle,~F. (2022).}Effects of voluntary slow breathing on heart rate
and heart rate variability: A systematic review and a meta-analysis.
\emph{Neuroscience and Biobehavioral Reviews}, \emph{138}, 104711.
https://doi.org/10.1016/j.neubiorev.2022.104711

{Laborde,~S., Allen,~M.~S., Borges,~U., Iskra,~M., Zammit,~N., You,~M.,
Hosang,~T., Mosley,~E., \& Dosseville,~F. (2022). Psychophysiological
effects of slow-paced breathing at six cycles per minute with or without
heart rate variability biofeedback.} \emph{Psychophysiology},
\emph{59}(1), e13952. https://doi.org/10.1111/psyp.13952

{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.
https://doi.org/10.3389/fpsyg.2017.00213

{Lehrer,~P.~M., \& Gevirtz,~R. (2014). Heart rate variability
biofeedback: How and why does it work?} \emph{Frontiers in Psychology},
\emph{5}, 756. https://doi.org/10.3389/fpsyg.2014.00756

{Lehrer,~P.~M., Kaur,~K., Sharma,~A., Shah,~K., Huseby,~R., Bhavsar,~J.,
\& Zhang,~Y. (2020). Heart Rate Variability Biofeedback Improves
Emotional and Physical Health and Performance: A Systematic Review and
Meta Analysis.} \emph{Applied Psychophysiology and Biofeedback},
\emph{45}(3), 109--129. https://doi.org/10.1007/s10484-020-09466-z
\selectlanguage{english}
{Lehrer,~P.~M., Vaschillo,~E., Vaschillo,~B., Lu,~S.-E., Scardella,~A.,
Siddique,~M., \& Habib,~R.~H. (2004). Biofeedback treatment for asthma.}
\emph{Chest}, \emph{126}(2), 352--361.
https://doi.org/10.1378/chest.126.2.352
\selectlanguage{english}
{Lin,~I.-M. (2018). Effects of a cardiorespiratory synchronization
training mobile application on heart rate variability and
electroencephalography in healthy adults.} \emph{International Journal
of Psychophysiology : Official Journal of the International Organization
of Psychophysiology}, \emph{134}, 168--177.
https://doi.org/10.1016/j.ijpsycho.2018.09.005

{Liu,~I., Ni,~S., \& Peng,~K. (2020). Happiness at Your Fingertips:
Assessing Mental Health with Smartphone Photoplethysmogram-Based Heart
Rate Variability Analysis.} \emph{Telemedicine Journal and E-Health :
The Official Journal of the American Telemedicine Association},
\emph{26}(12), 1483--1491. https://doi.org/10.1089/tmj.2019.0283

{Lovibond,~P.~F., \& Lovibond,~S.~H. (1995). The structure of negative
emotional states: Comparison of the Depression Anxiety Stress Scales
(DASS) with the Beck Depression and Anxiety Inventories.}
\emph{Behaviour Research and Therapy}, \emph{33}(3), 335--343.
https://doi.org/10.1016/0005-7967(94)00075-U

{Matsumoto,~T., Ushiroyama,~T., Kimura,~T., Hayashi,~T., \& Moritani,~T.
(2007). Altered autonomic nervous system activity as a potential
etiological factor of premenstrual syndrome and premenstrual dysphoric
disorder.} \emph{BioPsychoSocial Medicine}, \emph{1}, 24.
https://doi.org/10.1186/1751-0759-1-24

{Meier,~N.~F., \& Welch,~A.~S. (2016). Walking versus biofeedback: A
comparison of acute interventions for stressed students.} \emph{Anxiety,
Stress, and Coping}, \emph{29}(5), 463--478.
https://doi.org/10.1080/10615806.2015.1085514

{Minen,~M.~T., Corner,~S., Berk,~T., Levitan,~V., Friedman,~S.,
Adhikari,~S., \& Seng,~E.~B. (2021). Heartrate variability biofeedback
for migraine using a smartphone application and sensor: A randomized
controlled trial.} \emph{General Hospital Psychiatry}, \emph{69},
41--49. https://doi.org/10.1016/j.genhosppsych.2020.12.008

{Noble,~D.~J., \& Hochman,~S. (2019). Hypothesis: Pulmonary Afferent
Activity Patterns During Slow, Deep Breathing Contribute to the Neural
Induction of Physiological Relaxation.} \emph{Frontiers in Physiology},
\emph{10}, 1176. https://doi.org/10.3389/fphys.2019.01176

{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
(Oxford, England)}, \emph{21}(3), 365--376.
https://doi.org/10.1046/j.1365-2281.2001.00337.x

{Pizzoli,~S.~F.~M., Marzorati,~C., Gatti,~D., Monzani,~D., Mazzocco,~K.,
\& Pravettoni,~G. (2021). A meta-analysis on heart rate variability
biofeedback and depressive symptoms.} \emph{Scientific Reports},
\emph{11}(1), 6650. https://doi.org/10.1038/s41598-021-86149-7

{Posner,~M.~I. (1980). Orienting of attention.} \emph{The Quarterly
Journal of Experimental Psychology}, \emph{32}(1), 3--25.
https://doi.org/10.1080/00335558008248231

{Price,~J., Cole,~V., \& Goodwin,~G.~M. (2009). Emotional side-effects
of selective serotonin reuptake inhibitors: Qualitative study.}
\emph{The British Journal of Psychiatry : The Journal of Mental
Science}, \emph{195}(3), 211--217.
https://doi.org/10.1192/bjp.bp.108.051110

{Prinsloo,~G.~E., Derman,~W.~E., Lambert,~M.~I., \& Rauch,~H.~G.~L.
(2013).}The effect of a single episode of short duration heart rate
variability biofeedback on measures of anxiety and relaxation states.
\emph{International Journal of Stress Management}, \emph{20}(4),
391--411. https://doi.org/10.1037/a0034777

{Quintana,~D.~S., Elvsashagen,~T., Zak,~N., Norbom,~L.~B.,
Pedersen,~P.~O., Quraishi,~S.~H., Bjornerud,~A., Malt,~U.~F.,
Groote,~I.~R., Kaufmann,~T., Andreassen,~O.~A., \& Westlye,~L.~T.
(2017). Diurnal Variation and Twenty-Four Hour Sleep Deprivation Do Not
Alter Supine Heart Rate Variability in Healthy Male Young Adults.}
\emph{PloS One}, \emph{12}(2), e0170921.
https://doi.org/10.1371/journal.pone.0170921

{Ramirez,~E., Ortega,~A.~R., \& Del Reyes Paso,~G.~A. (2015). Anxiety,
attention, and decision making: The moderating role of heart rate
variability.} \emph{International Journal of Psychophysiology : Official
Journal of the International Organization of Psychophysiology},
\emph{98}(3 Pt 1), 490--496.
https://doi.org/10.1016/j.ijpsycho.2015.10.007

{Richter,~P., Werner,~J., Heerlein,~A., Kraus,~A., \& Sauer,~H.
(1998).}On the validity of the Beck Depression Inventory. A review.
\emph{Psychopathology}, \emph{31}(3), 160--168.
https://doi.org/10.1159/000066239

{Robinson,~S.~A., Dowell,~M., Pedulla,~D., \& McCauley,~L. (2004). Do
the emotional side-effects of hormonal contraceptives come from
pharmacologic or psychological mechanisms?} \emph{Medical Hypotheses},
\emph{63}(2), 268--273. https://doi.org/10.1016/j.mehy.2004.02.013
\selectlanguage{english}
{Ryu,~A., \& Kim,~T.-H. (2015). Premenstrual syndrome: A mini review.}
\emph{Maturitas}, \emph{82}(4), 436--440.
https://doi.org/10.1016/j.maturitas.2015.08.010

{Schmalenberger,~K.~M., Eisenlohr-Moul,~T.~A., Wurth,~L., Schneider,~E.,
Thayer,~J.~F., Ditzen,~B., \& Jarczok,~M.~N. (2019).}A Systematic Review
and Meta-Analysis of Within-Person Changes in Cardiac Vagal Activity
across the Menstrual Cycle: Implications for Female Health and Future
Studies. \emph{Journal of Clinical Medicine}, \emph{8}(11).
https://doi.org/10.3390/jcm8111946

{Schuman,~D.~L., Lawrence,~K.~A., Boggero,~I., Naegele,~P.,
Ginsberg,~J.~P., Casto,~A., \& Moser,~D.~K. (2023). A Pilot Study of a
Three-Session Heart Rate Variability Biofeedback Intervention for
Veterans with Posttraumatic Stress Disorder.} \emph{Applied
Psychophysiology and Biofeedback}, \emph{48}(1), 51--65.
https://doi.org/10.1007/s10484-022-09565-z
\selectlanguage{english}
{Schumann,~A., Helbing,~N., Rieger,~K., Suttkus,~S., \& Bar,~K.-J.
(2022). Depressive rumination and heart rate variability: A pilot study
on the effect of biofeedback on rumination and its physiological
concomitants.} \emph{Frontiers in Psychiatry}, \emph{13}, 961294.
https://doi.org/10.3389/fpsyt.2022.961294
\selectlanguage{english}
{Schumann,~A., La Cruz,~F.~de, Kohler,~S., Brotte,~L., \& Bar,~K.-J.
(2021).}The Influence of Heart Rate Variability Biofeedback on Cardiac
Regulation and Functional Brain Connectivity. \emph{Frontiers in
Neuroscience}, \emph{15}, 691988.
https://doi.org/10.3389/fnins.2021.691988

{Sevoz-Couche,~C., \& Laborde,~S. (2022). Heart rate variability and
slow-paced breathing:When coherence meets resonance.} \emph{Neuroscience
and Biobehavioral Reviews}, \emph{135}, 104576.
https://doi.org/10.1016/j.neubiorev.2022.104576

{Skovlund,~C.~W., Morch,~L.~S., Kessing,~L.~V., \& Lidegaard,~O. (2016).
Association of Hormonal Contraception With Depression.} \emph{JAMA
Psychiatry}, \emph{73}(11), 1154--1162.
https://doi.org/10.1001/jamapsychiatry.2016.2387
\selectlanguage{english}
{Sorensen,~L., Wass,~S., Osnes,~B., Schanche,~E., Adolfsdottir,~S.,
Svendsen,~J.~L., Visted,~E., Eilertsen,~T., Jensen,~D.~A., Nordby,~H.,
Fasmer,~O.~B., Binder,~P.-E., Koenig,~J., \& Sonuga-Barke,~E. (2019).}A
psychophysiological investigation of the interplay between orienting and
executive control during stimulus conflict: A heart rate variability
study. \emph{Physiology \& Behavior}, \emph{211}, 112657.
https://doi.org/10.1016/j.physbeh.2019.112657

{Stein,~P.~K., \& Pu,~Y. (2012). Heart rate variability, sleep and sleep
disorders.} \emph{Sleep Medicine Reviews}, \emph{16}(1), 47--66.
https://doi.org/10.1016/j.smrv.2011.02.005

{Sundstrom-Poromaa,~I., Smith,~S., \& Gulinello,~M. (2003). Gaba
receptors, progesterone and premenstrual dysphoric disorder.}
\emph{Archives of Women's Mental Health}, \emph{6}(1), 23--41.
https://doi.org/10.1007/s00737-002-0147-1

{Szulczewski,~M.~T., \& Rynkiewicz,~A. (2018). The effects of breathing
at a frequency of 0.1 Hz on affective state, the cardiovascular system,
and adequacy of ventilation.} \emph{Psychophysiology}, \emph{55}(12),
e13221. https://doi.org/10.1111/psyp.13221

{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 : A Publication of the Society of Behavioral Medicine},
\emph{37}(2), 141--153. https://doi.org/10.1007/s12160-009-9101-z

{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.
https://doi.org/10.1016/S0165-0327(00)00338-4

{Thayer,~J.~F., \& Lane,~R.~D. (2009). Claude Bernard and the
heart-brain connection: further elaboration of a model of neurovisceral
integration.} \emph{Neuroscience and Biobehavioral Reviews},
\emph{33}(2), 81--88. https://doi.org/10.1016/j.neubiorev.2008.08.004

{Tinello,~D., Kliegel,~M., \& Zuber,~S. (2022). Does Heart Rate
Variability Biofeedback Enhance Executive Functions Across the Lifespan?
A Systematic Review.} \emph{Journal of Cognitive Enhancement : Towards
the Integration of Theory and Practice}, \emph{6}(1), 126--142.
https://doi.org/10.1007/s41465-021-00218-3

{Tschudin,~S., Bertea,~P.~C., \& Zemp,~E. (2010).}Prevalence and
predictors of premenstrual syndrome and premenstrual dysphoric disorder
in a population-based sample. \emph{Archives of Women's Mental Health},
\emph{13}(6), 485--494. https://doi.org/10.1007/s00737-010-0165-3

{van Dijk,~W., Huizink,~A.~C., Oosterman,~M., Lemmers-Jansen,~I.~L.~J.,
\& Vente,~W.~de (2023). Validation of Photoplethysmography Using a
Mobile Phone Application for the Assessment of Heart Rate Variability in
the Context of Heart Rate Variability-Biofeedback.} \emph{Psychosomatic
Medicine}, \emph{85}(7), 568--576.
https://doi.org/10.1097/psy.0000000000001236

{Wheat,~A.~L., \& Larkin,~K.~T. (2010). Biofeedback of heart rate
variability and related physiology: A critical review.} \emph{Applied
Psychophysiology and Biofeedback}, \emph{35}(3), 229--242.
https://doi.org/10.1007/s10484-010-9133-y

{Yonkers,~K.~A., \& McCunn,~K.~L. (2007). Comorbidity of premenstrual
syndrome and premenstrual dysphoric disorder with other psychiatric
conditions. In P. M. S. O'Brien, A. Rapkin, \& P. J. Schmidt (Eds.),}
\emph{The Premenstrual Syndromes} (pp.~49--54). CRC Press.
https://doi.org/10.3109/9781435628168-7

{Yuda,~E., Shibata,~M., Ogata,~Y., Ueda,~N., Yambe,~T., Yoshizawa,~M.,
\& Hayano,~J. (2020). Pulse rate variability: A new biomarker, not a
surrogate for heart rate variability.} \emph{Journal of Physiological
Anthropology}, \emph{39}(1), 21.
https://doi.org/10.1186/s40101-020-00233-x

{Zahn,~D., Adams,~J., Krohn,~J., Wenzel,~M., Mann,~C.~G.,
Gomille,~L.~K., Jacobi-Scherbening,~V., \& Kubiak,~T. (2016). Heart rate
variability and self-control--A meta-analysis.} \emph{Biological
Psychology}, \emph{115}, 9--26.
https://doi.org/10.1016/j.biopsycho.2015.12.007

{Zehravi,~M., Maqbool,~M., \& Ara,~I. (2023). Unfolding the mystery of
premenstrual syndrome (PMS): An overview.} \emph{International Journal
of Adolescent Medicine and Health}, \emph{35}(1), 9--13.
https://doi.org/10.1515/ijamh-2022-0023

~

\textbf{Table 1.} Sample description\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
~ & Group waitlist (n = 13) & Group only intervention (n =
14)\tabularnewline
~ & Mean \selectlanguage{ngerman}± SD & Range\tabularnewline
Age & 25.3 \selectlanguage{ngerman}± 7.0 & 18-37\tabularnewline
BMI in kg/m\textsuperscript{2} & 22.9 \selectlanguage{ngerman}± 4.5 & 18.3-32.2\tabularnewline
Gender & 12f, 1m & 14f\tabularnewline
Active menstrual cycle & 11 yes, 2 no & 14 yes\tabularnewline
HC usage & 36.4\% & 42.9\%\tabularnewline
PAF sum & 54.7 \selectlanguage{ngerman}± 13.4 & 23-70\tabularnewline
BDI-II sum & 12.9 \selectlanguage{ngerman}± 7.2 & 0-27\tabularnewline
Inclusion criterium & 4 BDI, 4 PAF, 5 both & 4 BDI, 4 PAF, 6
both\tabularnewline
DASS sum & 34.1 \selectlanguage{ngerman}± 7,1 & 27-54\tabularnewline
RMSSD in ms & 37.0 \selectlanguage{ngerman}± 10.9 & 19.6-51.5\tabularnewline
ANT-R Executive in ms & 148.1 \selectlanguage{ngerman}± 56.8 & 76.7-291.3\tabularnewline
ANT-R Orienting in ms & 85.0 \selectlanguage{ngerman}± 37.3 & 36.3-149.6\tabularnewline
\bottomrule
\end{longtable}

Note. The DASS, RMSSD, and ANT-R values were obtained during the first
laboratory session of each participant. All other values were assessed
during the screening. SD -- standard deviation; BMI -- body-mass-index;
HC -- hormonal contraceptives; PAF -- premenstrual assessment form;
BDI-II -- Becks Depression Inventory; DASS -- depression anxiety stress
scales; RMSSD -- root mean square of successive differences; ANT-R --
revised attention network test.

~

\textbf{Table 2.} Results of a linear mixed model predicting
premenstrual symptoms\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{~} & \textbf{value PAF20 item}\tabularnewline
\emph{Predictors} & \emph{Estimates}\tabularnewline
(Intercept) & 2.92\tabularnewline
group {[}W{]} & -0.28\tabularnewline
time & -0.30\tabularnewline
scale {[}psy{]} & 1.08\tabularnewline
group {[}W{]} * time & 0.40\tabularnewline
(group {[}I{]} * time) * scale {[}psy{]} & -0.36\tabularnewline
(group {[}W{]} * time )* scale {[}psy{]} & 0.13\tabularnewline
\textbf{Random Effects} &\tabularnewline
\selectlanguage{greek}σ\selectlanguage{english}\textsuperscript{2} & 1.40\tabularnewline
\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{00}~\textsubscript{participant} & 0.58\tabularnewline
\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{00}~\textsubscript{item} & 0.24\tabularnewline
ICC & 0.37\tabularnewline
N~\textsubscript{participant} & 24\tabularnewline
N~\textsubscript{item} & 20\tabularnewline
Observations & 1300\tabularnewline
Marginal R\textsuperscript{2}~/ Conditional R\textsuperscript{2} & 0.116
/ 0.442\tabularnewline
\bottomrule
\end{longtable}

Note. The random effect structure includes participant intercepts and
item intercepts. PAF20 -- premenstrual assessment form short version;
group -- treatment (biofeedback vs. waitlist); W -- waitlist; I --
intervention (biofeedback); psy -- psychological symptoms.

~

\textbf{Table 3.} Post-Hoc Tukey effects of time*treatment*scale
interaction term predicting premenstrual symptoms\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
~ & Scale & Diff & \emph{p}\tabularnewline
Waitlist & Physio & 0.06 & .54\tabularnewline
Psych & 0.14 & .11 &\tabularnewline
Intervention & Physio & -0.19 & \textbf{.012}\tabularnewline
Psych & -0.42 & \textbf{\textless{}.0001} &\tabularnewline
\bottomrule
\end{longtable}

Note. Diff -- standardized pre-post treatment difference; psych --
psychological symptoms; physio -- physiological symptoms.

~

\textbf{Table 4.} Results of a linear mixed model predicting depressive
symptoms\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{~} & \textbf{value BDI item}\tabularnewline
\emph{Predictors} & \emph{Estimates}\tabularnewline
(Intercept) & 1.69\tabularnewline
group {[}W{]} & -0.09\tabularnewline
time & -0.17\tabularnewline
group {[}W{]} * time & 0.21\tabularnewline
\textbf{Random Effects} &\tabularnewline
\selectlanguage{greek}σ\selectlanguage{english}\textsuperscript{2} & 0.34\tabularnewline
\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{00}~\textsubscript{participant} & 0.10\tabularnewline
\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{00}~\textsubscript{item} & 0.03\tabularnewline
ICC & 0.27\tabularnewline
N~\textsubscript{participant} & 27\tabularnewline
N~\textsubscript{item} & 21\tabularnewline
Observations & 1617\tabularnewline
Marginal R\textsuperscript{2}~/ Conditional R\textsuperscript{2} & 0.010
/ 0.281\tabularnewline
\bottomrule
\end{longtable}

Note. The random effect structure includes participant intercepts and
item intercepts. BDI -- Beck's Depression Inventory II; group --
treatment (biofeedback vs. waitlist); W -- waitlist.

~

\textbf{Table 5.} Results of a linear mixed model predicting
stress/anxiety symptoms\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{~} & \textbf{value DASS item}\tabularnewline
\emph{Predictors} & \emph{Estimates}\tabularnewline
(Intercept) & 1.12\tabularnewline
time & -0.14\tabularnewline
group {[}W{]} & -0.11\tabularnewline
scale {[}D{]} & 0.11\tabularnewline
scale {[}S{]} & 0.44\tabularnewline
rmssd & 0.14\tabularnewline
time * group {[}W{]} & 0.21\tabularnewline
\textbf{Random Effects} &\tabularnewline
\selectlanguage{greek}σ\selectlanguage{english}\textsuperscript{2} & 0.35\tabularnewline
\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{00}~\textsubscript{participant} & 0.14\tabularnewline
\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{00}~\textsubscript{item} & 0.07\tabularnewline
ICC & 0.39\tabularnewline
N~\textsubscript{participant} & 27\tabularnewline
N~\textsubscript{item} & 21\tabularnewline
Observations & 1617\tabularnewline
Marginal R\textsuperscript{2}~/ Conditional R\textsuperscript{2} & 0.074
/ 0.432\tabularnewline
\bottomrule
\end{longtable}

Note. The random effect structure includes participant intercepts and
item intercepts. DASS -- Depression Anxiety and Stress Scale; group --
treatment (biofeedback vs. waitlist); W -- waitlist; D -- depression
scale; S -- stress scale; RMSSD -- root mean square of successive
differences.

~

\textbf{Table 6.} Results of a linear mixed model predicting vagally
mediated heart rate variability\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{~} & \textbf{log(RMSSD in ms)}\tabularnewline
\emph{Predictors} & \emph{Estimates}\tabularnewline
(Intercept) & 3.53\tabularnewline
group {[}W{]} & 0.07\tabularnewline
time & 0.06\tabularnewline
group {[}W{]} * time & -0.13\tabularnewline
\textbf{Random Effects} &\tabularnewline
\selectlanguage{greek}σ\selectlanguage{english}\textsuperscript{2} & 0.08\tabularnewline
\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{00}~\textsubscript{participant} & 0.21\tabularnewline
ICC & 0.71\tabularnewline
N~\textsubscript{participant} & 25\tabularnewline
Observations & 71\tabularnewline
Marginal R\textsuperscript{2}~/ Conditional R\textsuperscript{2} & 0.003
/ 0.711\tabularnewline
\bottomrule
\end{longtable}

Note. The random effect structure includes participant intercepts. RMSSD
-- root mean square of successive differences; group -- treatment
(biofeedback vs. waitlist); W -- waitlist.

~

\textbf{Table 7.} Results of a linear mixed model predicting attentional
control\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{~} & \textbf{Reaction time}\tabularnewline
\emph{Predictors} & \emph{Estimates}\tabularnewline
(Intercept) & 600.31\tabularnewline
time & -37.63\tabularnewline
group {[}W{]} & 39.00\tabularnewline
flanker {[}incongruent{]} & 130.14\tabularnewline
cue {[}valid{]} & -98.44\tabularnewline
time \selectlanguage{ngerman}× group {[}I{]} × cue {[}invalid{]} & 45.56\tabularnewline
time \selectlanguage{ngerman}× group {[}W{]} ×cue {[}invalid{]} & -0.22\tabularnewline
time \selectlanguage{ngerman}× group {[}I{]} × cue {[}valid{]} & 41.09\tabularnewline
time \selectlanguage{ngerman}× group {[}I{]} × flanker {[}incongruent{]} & -26.57\tabularnewline
time \selectlanguage{ngerman}× group {[}W{]} × flanker {[}incongruent{]} & -9.44\tabularnewline
\textbf{Random Effects} &\tabularnewline
\selectlanguage{greek}σ\selectlanguage{english}\textsuperscript{2} & 11369.21\tabularnewline
\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{00}~\textsubscript{participant} & 4325.41\tabularnewline
ICC & 0.28\tabularnewline
N~\textsubscript{participant} & 27\tabularnewline
Observations & 7040\tabularnewline
Marginal R\textsuperscript{2}~/ Conditional R\textsuperscript{2} & 0.267
/ 0.469\tabularnewline
\bottomrule
\end{longtable}

Note. The model predicts trial-based prediction times of the revised
attention network test. The random effect structure includes participant
intercepts. Group -- treatment (biofeedback vs. waitlist); W --
waitlist; I -- intervention (biofeedback).

~

~

~

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

