Berenike Lisa Blaser1*, Mathias Weymar1,2 & Julia Wendt11 University of Potsdam, Department of Biological Psychology and Affective Science, Faculty of Human Sciences, Karl-Liebknecht Strasse 24/25, 14476 Potsdam, Germany2 Faculty of Health Sciences Brandenburg, University of Potsdam, Potsdam, Germany  * Correspondence:Berenike Lisa BlaserUniversity of PotsdamKarl-Liebknecht Strasse 24/2514476 Potsdam, Germany Email: [email protected] Running head: Heart Rate Variability and premenstrual symptoms Keywords: heart rate variability, vagal tone, premenstrual syndrome, premenstrual symptoms, menstrual cycle, ambulatory assessmentAbstractIntroductionPremenstrual syndrome (PMS) affects up to 90% of individuals with an active menstrual cycle. Several studies have observed reduced vagally mediated heart rate variability in a single assessment during the luteal phase compared to an assessment during the follicular phase, especially in participants experiencing strong PMS symptoms. The aim of this investigation was to initially assess the relationship between premenstrual symptoms and vagally mediated heart rate variability throughout the menstrual cycle, as well as to examine the feasibility of conducting a large-scale study to verify this association.MethodsThree participants completed daily ambulatory assessments of resting vagally mediated heart rate variability using mobile electrocardiographs and typical PMS symptoms. We calculated correlations between these measurements for each participant.ResultsPMS symptoms and vagally mediated heart rate variability showed medium to high correlations in each of the participants throughout the cycle. These associations were primarily driven by the relationship between vagally mediated heart rate variability and psychological symptoms rather than physiological symptoms. Visual inspection of the fluctuations confirmed the concurrent occurrence of a phasic reduction in vagally mediated heart rate variability parallel to the increases in PMS symptoms experienced during the mid to late luteal phase in each participant.DiscussionThe results support the notion of an association between PMS symptoms and vagally mediated heart rate variability. An ambulatory daily assessment paradigm proves to be feasible. Studies with larger samples are necessary to provide deeper insights into inter- and intra-individual differences as well as stronger knowledge on the mechanistic pathways of PMS. 1               IntroductionPremenstrual syndrome (PMS) encompasses a heterogeneous collection of symptoms that typically manifest in the week preceding menstruation, during the luteal phase of the menstrual cycle, and dissipate within a few days after menstruation begins. These symptoms can be of a physiological nature, such as bloating and water retention, or psychological, including feelings of stress, anxiety, or irritability. It is noteworthy that as many as 90% of menstruating individuals regularly experience at least one symptom associated with PMS (Tschudin et al., 2010).The etiology of PMS remains unclear. A common hypothesis is that varying sensitivities to fluctuations in gonadal hormones throughout the menstrual cycle play a role (Rapkin & Akopians, 2012). This differential sensitivity may involve several systems, including the Gamma-Aminobutyric Acid (GABA) and serotonin systems (Nappi et al., 2022; Rapkin & Akopians, 2012). Vagally mediated heart rate variability (vmHRV) serves as a potential physiological marker that could contribute to our understanding of PMS. VmHRV is regarded as a marker for cardiac vagal control (Laborde et al., 2023), and research has linked it to a wide range of psychopathological states (Heiss et al., 2021) and cognitive outcomes (Holzman & Bridgett, 2017; Zahn et al., 2016). The associations are so consistent, that Beauchaine and Thayer (2015) have proposed vmHRV as a transdiagnostic biomarker for psychopathology.In a meta-analysis conducted by Schmalenberger et al. (2019), consistent reductions in vmHRV of medium effect size were identified during the luteal phase when compared to measurements during the follicular phase. A limited number of studies, however, have explored the relationship between this vmHRV reduction and PMS. The fluctuations in vmHRV are found to be moderated by the extend of premenstrual symptomatology (Schmalenberger et al., 2023) . The observed effect indicates that high PMS groups experience more substantial reductions in vmHRV during the luteal phase, whereas control groups show either smaller fluctuations (Zambotti et al., 2013) or no discernible difference (Baker et al., 2008; Matsumoto et al., 2007) in vmHRV between the follicular and luteal phases.The consistent direction of the association between vmHRV and PMS is noteworthy, but it is important to acknowledge that all previous studies on this topic have involved only a single measurement during each cycle phase. Consequently, it is challenging to ascertain whether symptoms and vmHRV fluctuate in parallel throughout the menstrual cycle. In an effort to shed more light on the relationship between vmHRV and PMS and to assess feasibility, we therefore initiated a pilot diary study involving three participants. This study aimed to gather daily assessments of premenstrual symptoms alongside measurements of resting vmHRV to follow the course of their association.2               Methods and Materials2.1         ParticipantsWe tested three participants of different ages (ageparticipant 1 = 44, ageparticipant 2 = 27, ageparticipant 3 = 20) who were recruited within our department. These participants provided informed consent and received either course credit or no compensation for their participation. In line with the guidelines suggested by Laborde et al. (2017), the participants did not take medication that could affect vmHRV, had no chronic diseases, and were not pregnant.2.2         Testing protocolEach participant received an introduction on how to use the mobile electrocardiography (ECG) device and a document with written instructions outlining the procedure. Measurements were taken each day at the same time, between 7 and 8 pm. The participants began by completing an online questionnaire assessing premenstrual symptoms, recording their last menstrual period, and responding to a number of control variables. Following the questionnaire, the ECG measurement was conducted. Participants attached the ECG device to their chest, set a timer for 6 minutes, initiated the ECG recording, and closed their eyes while the resting vmHRV measurement was taken. This measurement was performed with participants in a sitting position.Participants were requested to complete the assessments daily over 1.5 menstrual cycles to ensure the inclusion of one complete cycle. We employed the backward- and forward-counting method to assess the cycle phase (Schmalenberger et al., 2021). We included two weeks before a reported menstruation onset (luteal phase) and two to three weeks (depending on reported average cycle length) after a reported menstruation onset (follicular phase) for the analysis.2.3         Heart rate variabilityVagally mediated heart rate variability was assessed using a mobile 1-channel ECG device, the Bittium Faros 180. The device electrodes were attached to the chest, and data were collected at a sampling rate of 1000 Hz. Data preprocessing was performed using the Faros Software, which generated R-R interval and R peak timestamp series. The first and last 30 seconds of each measurement were removed, resulting in a 5-minute interval, to avoid artifacts caused by participant movement, as participants initiated and concluded the measurement themselves.The root mean square of successive differences (RMSSD) was derived from the time series as measurement of vmHRV. The time series were analyzed in R (version 4.2.2) using the RHRV package (https://rhrv.r-forge.r-project.org/), following the package documentation. We chose this measurement over the high-frequency component of power spectral analysis due to its robustness to breathing rate and its clearer indication of parasympathetic activity (Chapleau & Sabharwal, 2011).2.4         Premenstrual symptomsPremenstrual symptoms were assessed with the German version of the shortened Premenstrual Assessment Form (PAF20) (Allen et al., 1991; Blaser et al., 2023). The questionnaire comprises the 20 most endorsed items from the long form PAF, which includes nearly 100 items in total. Each item represents a specific symptom, and participants are asked to rate how strongly they experienced each symptom during the last premenstrual phase using a 6-point Likert scale, ranging from “not at all/no change from normal” to “extreme change from normal”. The German version of this questionnaire has demonstrated good validity and reliability and loads onto two distinct factors, creating psychological and physiological symptom scales. For this study, we adapted the questionnaire to a diary format, where participants reported how strongly they experienced each symptom in the previous 24 hours.2.5         Control variablesThe daily online questionnaire included several control variables that are known to influence vmHRV or PMS. Participants were asked to provide retrospective assessments of these variables for the last 24 hours. The control variables encompassed substance intake (alcohol, caffeine, nicotine), a one-item rating of the level of stress experienced that day on a Likert scale ranging from 1 to 9, a one-item rating of sleep quality on a 1-9 Likert scale, and reports of any physical health symptoms related to acute diseases, such as respiratory symptoms. 2.6         AnalysisAll statistical analyses were conducted with R (version 4.2.2). To assess the association between premenstrual symptoms and RMSSD over the menstrual cycle, we conducted Pearson correlations between the two measurements for each participant individually. Furthermore, separate correlations were calculated for the physiological and the psychological subscale of the PAF20 with the RMSSD.To test the association between PAF20 and RMSSD for all three participants, independently of the control variables, we conducted a linear mixed model predicting PAF20 sum scores. Participant intercepts were modeled as random effects to account for nesting of the data. The RMSSD and control variables were introduced as fixed effects.3               ResultsThe RMSSD values were subjected to a log transformation to approximate a normal distribution, aligning with the conventions of other vmHRV research (Laborde et al., 2017). A visual representation of the symptom course and RMSSD for each of the three participants is presented in Figure 1. Pearson correlations between log-transformed RMSSD and daily symptom scores were moderate to high, rparticipant1(25) = -.41, p < .05, rparticipant 2(35) = -.48, p < .01, rparticipant 3(29) = -.43, p < .05. The correlations were consistently higher in the psychological subscale than the physiological subscale (see Table 1). The associations between RMSSD and physiological symptoms were not significant in all three participants.Table 1. Pearson correlations of vagally mediated heart rate variability and premenstrual symptom scores     Psychological symptoms   Physiological symptoms       r df p r df p   Participant 1 -.50 25 .008 -.25 25 .21 log(RMSSD) Participant 2 -.57 35 <.001 -.07 35 .68   Participant 3 -.46 29 .010 -.31 29 .093 Notes. The symptom scores are the sum scores of the psychological and physiological subscale of the daily ratings of the short form of the premenstrual assessment form (PAF20). RMSSD – root mean square of successive differences; df – degrees of freedom.

Berenike Lisa Blaser1*, Mathias Weymar1,2, & Julia Wendt11 University of Potsdam, Department of Biological Psychology and Affective Science, Faculty of Human Sciences, Karl-Liebknecht Strasse 24/25, 14476 Potsdam, Germany2Faculty of Health Sciences Brandenburg, University of Potsdam, Potsdam, Germany * Correspondence:Berenike Lisa BlaserUniversity of PotsdamKarl-Liebknecht Strasse 24/2514476 Potsdam, Germany Email: [email protected] Running head: Smartphone-based Heart Rate Variability Biofeedback for PMS Keywords: smartphone photoplethysmography, mHealth, heart rate variability, biofeedback, vagal tone, premenstrual syndrome, premenstrual symptoms, menstrual cycle, stress, depression, attentional control AbstractIntroductionHeart 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.MethodsTwenty-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.ResultsWe 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.DiscussionThese 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. 1               IntroductionHeart 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 (Jiménez 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.2               Methods2.1         ParticipantsA 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 ≥ 50), depressive symptoms that indicate at least minimal depression (Beck’s Depression Inventory, BDI-II ≥ 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.2.2         ProcedureThe 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. Figure 1. Study procedure

Vagally mediated heart rate variability is an index of autonomic nervous system activity that is associated with a large variety of outcome variables including psychopathology and self-regulation. While practicing heart rate variability biofeedback over several weeks has been reliably associated with a number of positive outcomes, its acute effects are not well known. Because the strongest association with heart rate variability has been found particularly within the attention-related subdomain of self-regulation, we investigated the acute effect of heart rate variability biofeedback on attentional control using the revised Attention Network Test (ANT-R). Fifty-six participants were tested in two sessions. In one session each subject received a heart rate variability biofeedback intervention, and in the other session a control intervention of paced breathing at a normal ventilation rate. After the biofeedback or control intervention, participants completed the ANT-R using the Orienting Score as a measure of attentional control. Mixed models revealed that higher resting baseline heart rate variability (RMSSD) was associated with better performance in attentional control, which suggests more efficient direction of attention to target stimuli. There was no significant main effect of the intervention on attentional control. However, an interaction effect indicated better performance in attentional control after biofeedback in individuals who reported higher current stress levels. The results point to acute beneficial effects of heart rate variability biofeedback on cognitive performance in highly stressed individuals. Although promising, the results need to be replicated in larger or more targeted samples in order to reach stronger conclusions about the effects.

The luteal phase of the menstrual cycle is accompanied by diminished vagally mediated heart rate variability (vmHRV). VmHRV is consistently linked to anxiety, a commonly experienced symptom during the luteal phase. However, fear conditioning, a laboratory model of anxiety, has received limited attention in the context of menstrual cycle fluctuations. This study therefore aims to explore the influence of menstrual cycle phases on instructed fear conditioning and its interactions with vmHRV.In this study, 58 healthy individuals with regular menstrual cycles, currently in the luteal or follicular phase, participated in a fear conditioning paradigm. During this experiment, two geometric figures were either paired (CS+) or not paired (CS-) with an electric shock. Linear mixed models were used to analyze the modulatory effects of the menstrual cycle phase on the startle magnitude and skin conductance responses (SCRs) to these conditioned stimuli.Results revealed higher fear differentiation (CS+ vs. CS-) during the luteal phase in the startle magnitude, driven by a startle potentiation to the conditioned stimulus (CS+). In terms of SCR, interacting effects with vmHRV revealed that individuals with high vmHRV exhibited a similar increased fear differentiation during the luteal phase, while low vmHRV individuals showed less fear differentiation. These findings suggest that during the luteal phase, individuals exhibit stronger fear-related differentiation, a pattern that is partly modulated by vmHRV. These insights shed light on potential origins of varying symptom experiences like increased anxiety during the luteal phase. However, further research is required to investigate associations between these fluctuations and symptomatology.