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Background : Psychiatric patients with adverse childhood experiences (ACE) tend to be dysfunctional in the interoceptive part of their emotional experience. The integration of interoceptive emotional activity in the insular and cingulate cortices is linked to the regulation of sympathovagal balance. This makes heart rate variability (HRV) an ideal measure for providing feedback on emotion regulation in real time. Methods : A sample of one hundred (n=100) outpatients was evaluated. Participants underwent eight 30-minutes ACE exposure sessions during which patients were guided to experience bodily sensations related to ACE while their HRV was monitored using a commercial biofeedback device. Results : Comparing the results of first to last therapeutic session, a significant decrease in heart rate and an increase in HRV at the onset of the session were observed. Conclusions : This study suggests physiological impact of therapeutic interventions on the autonomic balance and underlines the interest of HRV biofeedback as a clinical practice.
Open Peer Review
Heart rate variability biofeedback intero-nociceptive emotion
exposure therapy for adverse childhood experiences [version 1;
peer review: awaiting peer review]
StéphanieHahusseau , BrunoBaracat , ThierryLebey , LionelLaudebat ,
ZarelValdez , ArnaudDelorme 4-7
1 2 3 3
3 4-7
04May2020, :326First published: 9
04May2020, :326Latest published: 9
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F1000Research 2020, 9:326 Last updated: 04 MAY 2020
ArnaudDelorme( )Corresponding author:
:Conceptualization,DataCuration,Investigation,Methodology; :Resources; :Supervision;Author roles: Hahusseau S Baracat B Lebey T
:Software; :Software; :SupervisionLaudebat L Valdez Z Delorme A
Nocompetinginterestsweredisclosed.Competing interests:
TheresearchwasfundedbytheConseilRégionaldeMidi-Pyrénée[n°12052834].ThegrantingagencywasnotinvolvedinGrant information:
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
©2020HahusseauS .Thisisanopenaccessarticledistributedunderthetermsofthe ,Copyright: et al CreativeCommonsAttributionLicense
HahusseauS,BaracatB,LebeyT How to cite this article: et al. Heart rate variability biofeedback intero-nociceptive emotion exposure
F1000Research2020, :326therapy for adverse childhood experiences [version 1; peer review: awaiting peer review] 9
04May2020, :326First published: 9
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F1000Research 2020, 9:326 Last updated: 04 MAY 2020
Heart rate variability
Heart Rate Variability reflects an individual’s ability to
adaptively cope with stress. According to Thayer’s model1
(see also 2,3), orthosympathetic activity is associated with
higher central nervous system activity, in particular activity
within the limbic system, the amygdala, and the prefrontal and
frontal cortex4. One of the roles of these high-level
structures is to inhibit the parasympathetic system and activate
the sympathetic system. When a person faces a threat, this
may elicit a hyperarousal and flight or fight” response5,
which leads to an inhibition of the parasympathetic system
and an activation of the sympathetic system. This corresponds
to a decrease of HRV and often an increase in Heart Rhythm
(HR)6. Emotional events may have an influence on the general
stress level7, which in turn is visible in the sympathetic/
parasympathetic balance. Generally, these effects are transient
because the higher nervous structures (essentially amygdala
and prefrontal cortex) inhibit each other and, as soon as the
stressor disappears, the system returns to parasympathetic tonus
with low HR and high HRV.
For several decades, autonomic nervous system tests have
been used to identify the physiologic correlates of psychiatric
illnesses, particularly for affective and anxiety disorders8.
In studies of Post-Traumatic Stress Disorder (PTSD) for
example, decreased HRV are observed in PTSD patients
compared to matched controls9. The HRV of PTSD patients
indicates an increase in sympathetic activity and a reduction
in parasympathetic activity. Patients suffering from PTSD tend
to exhibit hyperactivity of the autonomous nervous system
at rest and have been shown to be unable to further mobilize
their orthosympathetic system when facing a stressful
situation10. In addition, the HRV profile after exposure to a
trauma has been shown to be predictive of future traumatic
episodes in PTSD11,12. PTSD is associated with the disruption
of the autonomic processes that maintain heartbeat regulation13.
Clinical impact and assessment of adverse childhood
Research in psychiatry indicates that adverse childhood
experiences leave durable physiological and neurophysi-
ological traces and that there is a strong relationship between
adverse childhood experiences and depression, suicide attempts,
alcoholism, drug abuse, and other negative health outcomes14.
Adverse childhood experiences (ACEs) are ubiquitous among
the adult patient population15. The damaging effects of ACEs
are nonspecific, thereby affecting a variety of functions and
behaviors. In fact, ACEs have been shown to be negatively
correlated with adult mental well-being16. Chronic traumatic
experiences in childhood that extend over several years – as in
cases with trauma and neglect – impair self-regulation function
such as mood regulation and constancy in relations, described
in “complex PTSD” and “developmental trauma disorder”.
Physiological impact of adverse childhood experiences
Clinically, autonomic nervous system (ANS) function and
emotional well-being are closely related17. Research has shown
that having experienced early-life adverse events was associated
with lasting effects on Heart Rate Variability (HRV)18, reveal-
ing complex interactions between traumatic experiences, ANS
functioning and psychopathology19.
In addition, psychiatric research has shown that having expe-
rienced early-life adverse events was associated with altered
interoception20. Interoception is crucial for well-being21 as it
mediates emotion regulation22. In fact, most psychiatric
disorders are sustained by a type of interoceptive phobia23.
Interoception require the interplay between perception of
body states and cognitive appraisal of these states to inform
emotional experience and motivating regulatory behavior24.
The insular cortex in humans processes interoceptive activity
and integrates and modulates cardiovascular, respiratory and
emotional signals in order to create an integrated emotional
Evidence-based treatment for adults
Neurophysiological impairments due to ACEs have been shown
to be reversible26,27. Evidence-based psychotherapy for adults
with ACE history typically involves a progression through
three phases: safety and stabilization; trauma processing;
consolidation of therapeutic gains28. The trauma processing
phase requires sensitive therapeutic guidance. The other phases
are best-practice approaches to all psychotherapeutic treatments,
with the focus on the unique impact of ACEs.
Evidence based psychotherapy models for adults with
ACEs-related disorders such as emotion-focused trauma therapy
and eye-movement-desensitization-reprocessing are useful in
the trauma processing phase. The efficacy of these approaches
may be related to interoception rather than cognitive focusing29.
Efficacy of psychotherapy with trauma patients may depend
on the patient being able to face and feel adverse sensory and
perceptual stimuli related to trauma-related memories in paced
Prolonged exposure therapy and cognitive processing therapy
have gathered a significant amount of empirical support for
PTSD treatment. However, they are not universally effective
with patients continuing to struggle with residual post adverse
childhood-traumatic symptoms. As such, other type of inter-
ventions such as biofeedback may be beneficial. When patients
with PTSD were assigned to receive HRV biofeedback
plus treatment, the results indicated that HRV biofeedback
significantly increased the HRV while reducing symptoms of
PTSD31. The present study intends to replicate these results
using commercially available biofeedback equipment within an
ecological therapeutic environment.
ACE therapy with interoceptive component
This procedure was developed by therapist MD SH (first author)
for over more than 10 years. The therapeutic protocol comprises
two parts. In the first part, which typically comprised eight
weekly sessions of half an hour each, the therapist (co-author
SH) identified the occurrence of adverse childhood experiences
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(psychological abuse, physical abuse, contact sexual abuse,
or exposure to household dysfunction during childhood, e.g.
exposure to substance abuse, mental illness, violent treatment
by parent or stepparent, criminal behavior in the household). To
do so, the therapist carried out clinical investigations, collected
anamnestic and diachronic data, and guided the patient to
specific breathing visualization exercises.
In order to characterize adverse childhood memories responsi-
ble for interoceptive phobia, the patient was asked to initially
focus his attention on his/her breath, then on nociceptive sensa-
tions, and finally on the childhood memories32. The therapist
asked the patient to focus his/her attention on his/her breathing
while describing the images associated with the memories and
specific body sensation or pain that might arise in detail. This
exercise was carried out with closed eyes. During this exercise
each uncomfortable physical sensation and negative thought
was rated in terms of intensity on a 10-point scale. Later, after
a meeting devoted to the conceptualization of the selected
traumatic memories and their influence on repetitive negative
emotions, the therapist helped the patient to establish a
coherent narrative within which to frame his/her difficulties. The
practitioner explained the therapeutic hypothesis, which would
be instantiated in the second phase of the therapy indicated as
described below.
The second phase of the therapy consisted of bi-monthly
one-hour therapeutic sessions. In each session, after five minutes
of rest, the therapist asked the patient to wear an ear device sen-
sor which is part of a HRV biofeedback device (Emwave2;
Heartmath, Inc.). The patient was then asked to focus his/her
attention on his/her breathing for two minutes. After two
minutes, the evocation of images related to the adverse
childhood memory chosen for this session started33. To avoid
dissociative processes and develop interoception and para-
sympathetic activation, the patient was asked to focus his/her
attention on the uncomfortable bodily sensations for about
30 minutes34. Feedback on the sympathetic-vagal balance was
directly affected by the sound delivered by the biofeedback
device. The sound of the biofeedback device is correlated with
the low frequency peak in the HRV spectrum (HeartMath
Emwave 2 device and associated software; US patent 6,358,201
and Australian patent 770323). The number of sessions depended
on the number of adverse childhood experiences to face – in
general about 6 sessions. During these meetings, the therapist
saved the series of heart beat intervals (R to R intervals) using
the biofeedback software. In this study, five minutes of data
at the beginning and at the end of the first session of phase
2 (session 1) and the last session of phase 2 (designated as
“session 2” even though there might be several sessions
between “session 1” and “session 2”) have been analyzed. Each
of these 5 minutes comprise 2 minutes of breathing plus the
evocation of traumatic imagery.
Patient’s inclusion
The most recent 100 outpatients of therapist SH having
experienced at least one type of adverse childhood experience
and having used the biofeedback method described above were
retained as study population. Only patients for whom more
than 3 consecutive sessions were collected were included.
These two conditions were the only inclusion criteria. 100
patients was judged appropriate for an HRV study of this nature
based on the literature35,36. In general, HRV studies require
about of 100 patients or subjects to observe links between
mental condition and HRV measures, although some studies
have observed significant effects in depressive subjects with
group sizes as low as 2737. Inclusion criteria included an
history of adverse childhood experience (therapist assessment).
Patient who required psychiatric medical treatment (therapist
assessment) were excluded from the study. Patients were
included regardless of DSM V guidelines for trauma since these
do not provide a definition for patients having experienced
chronic trauma over several years such as neglect. However,
sub-categories in the DSM V were considered as described in
a later section. The data was collected over one year. Table 1
summarizes the main features of the data sample.
Compliance with ethical standards
The local ethical committee (Comité de protection des
personnes Sud Ouest) approved the study and the use of the
data for research purposes. Since the study was performed
retrospectively, no patient consent was necessary. However, the
French national entity for the protection of public and medical
digital records (CNIL) authorized the retrospective use of the
clinical data for this research (authorization number 1685185).
The therapist associated a random number to each patient
which was then used to anonymize the questionnaire data,
the scanned notes of the therapist and the EKG files of each
patient. Except for the therapist (co-author SH), all other
investigators were blind to the identity of the patients. The
blinding procedure consisted in assigning a randomly gener-
ated code to patients, in compliance with CNIL requirements
(Commission nationale de l’informatique et des libertés). It was
performed at the therapist’s office by the therapist herself to
ensure that no identifiable document could inadvertently be
lost, stolen, or read by anyone else than the therapist. When a
paper form contained identifiable information, it was masked
by the therapist, a sticker with the anonymized patient ID was
temporarily placed on the form and the form was photocop-
ied for later digital transfer. The questionnaire data was not
integrated into the current report to focus on the interpretation of
heart beat intervals.
Data collection and data processing
R to R intervals were collected during therapeutic sessions
using the biofeedback Emwave2© device. This system uses a
photoplethysmographic sensor located on the right ear lobe
Table 1. Data sample statistics.
Men Women
Number of participants 20 80
Mean age in years 37.5 37.8
Mean number of session 6.5 5.5
Mean duration of therapy (in days) 206 201
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and series of heart beats are automatically extracted by the
biofeedback software. The accuracy of this data was verified
in one subject by comparison to a simultaneously recorded
real EKG (Biopac MP36 unit and Acqknowledge© using
Einthoven Lead II derivation): the heart beats monitored by the
biofeedback system were delayed in comparison to the EKG
based on the time it takes for blood pressure to build up at
the ear lobe. Except for this delay, heart beat measurements
were accurate within millisecond precision in comparison to
those visible on the EKG. Using heart beat time intervals over
300 seconds, HRV calculations were carried out with the
Biomedical Toolkit used on Labview© version 2009. This
software performs HRV calculation in the same way as other
HRV software packages do – such as the popular Kubios software
(Kubios Oy, Finland). R to R intervals were resampled at
8hz, and the power spectrum was calculated over the whole
5-minute record using an FFT decomposition. Power was
obtained at each frequency by calculating the square value of
the FFT absolute amplitude. In the frequency domain, total
HRV was obtained by summing the total spectral power for
the low frequency band (LF) 0.05Hz-0.15Hz and the high
frequency band (HF) 0.15Hz-0.35Hz. The LF/HF ratio was
also calculated. Before performing statistical analyses, a log
(Ln) transformation was applied and values were subsequently
normalized across subjects. Other heart measures calculated
in the time domain were Heart Rate (RR), Root Mean Square
of Standard Deviation of R to R intervals (RMSSD), proportion
of R to R intervals larger than 50 msec (pNN50), and Triangular
Index of R to R intervals.
The clinical assessment of the therapist led to the creation
of the following categories mapped onto DSM V categories:
Substance abuse (SA); Somatoform Disorders (SD); Anxious
Disorders (AD); Serious Personality Disorder (SPD); Post
Traumatic Stress Syndrome (PTSS) (Data not included).
All the patients could be diagnosed with trauma complex or
developmental trauma disorder38. In addition to these catego-
ries, additional independent variables were retained: patient
age, patient sex, the number of meetings in phase 2, and the
number of days between the first and the last data recording
sessions. Data was collected by the therapist on custom forms
(available as extended data37) that were later transcribed into
digital form after the anonymization process.
Statistical procedure
Changes in the HRV between the two selected therapy sessions
and within each session between the beginning and the end of
each recording were analyzed using 2-way repeated measure
ANOVA. Measurements related to the first meeting of therapy
of phase 2 are indicated by “session 1” and a measurement at
the end of the session of phase 2 is indicated by “session 2”. For
each of these sessions, a measurement was taken at the
beginning of the session (indicated by “Measurement 1”) and
another at the end of the session (indicated by “Measurement
2”). There is about 25 minutes delay between “Measurement
1” and “Measurement 2” during which the patient was asked to
re-experience traumatizing events this time frame was not
Statistical analyses combine two within-subjects factors with
two levels; “Session” and “Measurement”. Additionally, other
between-subjects factors and independent variables described
in the previous section were included. All the analyses were
carried out with General Linear Model (GLM) module of SPSS©
(version 17) by using the statistics of Greenhouse-Geisser.
The existence of corrupted R to R series and/or incomplete data
associated with the statistical method used (within-subjects
measurement) implies that the number of subjects included in
the statistical analyses was lower than 100, and varies depending
on the type of analysis. R to R and demographic data are
available as underlying data37.
Significant changes in HR and HRV were observed. HR was higher
by 3.4 beat per minute (bpm) in session 1 compared to session 2
(D=4.99; DF=1,55; p=0.029). Within sessions HR increased by
1.6 bpm (D=23.53; DF=1,55; p <0.001). There was no interaction
between these two factors.
Globally, total HRV estimated in the frequency domain showed
significant changes as well. Within a session, HRV decrease was
significant (D=10.97; DF=1,55; p=0.002). The total quantity
of transformed HRV decreased by 0.245 points between the
beginning and the end of the therapy but failed to reach signifi-
cance. The interaction between the two factors was significant
(D=13.32; DF=1,55; p=0.001). This is due to the fact that of
the decrease in HRV between Measurement 1 and Measure-
ment 2 was relatively large during the second session (0.476
points; DF=1,55; p<0.05), but relatively low for session 1 (0.014
points; ns). Table 2 summarizes mean HRV values and standard
errors of the mean. Figure 1 summarizes the variations in
HRV based on the two factors – the Z score of Ln(HRV) was
plotted where the difference were most striking.
All other analyses of measurements of HRV obtained in the
frequency domain (LF, HF, LF/HF) or time domain did not
lead to significant differences. Additional inclusion of factors
(“Clinical Opinion”, “Sex” as between subject factor, “Age”,
“Number of days between Session 1 and Session 2”, “Mean
number of meetings” and “Time between the two sessions” as
covariates in between-subject factor) in the ANOVA did not
lead to significant differences and did not modify the level of
significance of the differences mentioned above. Table 3 shows
the spectral LF and HF values for the different sessions and
Table 2. Mean heart rate variability (HRV) and
standard error of the mean (in parenthesis)
for all the sessions and measurements.
Measurement 1 Measurement 2
Session 1 9834 (1329) 10907 (1887)
Session 2 7889 (939) 10602 (1124)
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The present study demonstrates an effect of biofeedback
therapeutic interventions both in terms of heart rhythm and
heart rhythm variability measurements. Subjects’ HR showed
a significant decrease between session 1 and session 2 which
could indicate reduced chronic stress. The reduction in the
average HR in session 2 compared to session 1 can be interpreted
as an effect of therapeutic interventions.
Moreover the patient average HR increased between the
beginning and the end of each of the two sessions. This
increase in the HR is consistent with the model of Thayer17:
the patient experiences a change in emotional state due to the
recall of the traumatic experience, and the induced stress leads
to an increase in HR.
The analysis of the modifications of HRV partially confirms
this interpretation. At the onset of session 2, patients had
higher HRV than at the onset of session 1, which indicates
larger parasympathetic influences towards the end of the
therapy. Also, in the general population, HRV tends to be
lower in patients compared to controls. In the task force of
the European Society of Cardiology and the North American
Society of Pacing Electrophysiology39, HRV of control subjects
in decubitus dorsal at rest over 5 minutes were 3466 ms2/hz
1018 ms2/hz). Measurements for the present study were
approximately three times lower which could mean that HRV is
close to its minimum. 1085 ms2/hz (standard error of the mean
(s.e.) 1329 ms2/hz) were calculated for “Session 1-Measurement
1”, 1094 ms2/hz (s.e. 1887 ms2/hz) for “Session 1-Measurement
2”,1195 ms2/hz (s.e. 939 ms2/hz) for “Session 2-Mesurement 1”
and 1080 ms2/hz (s.e. 1123 ms2/hz) of “Session 2-Measurement
2”. A possible interpretation for the reduction in HRV within
session 2 (and not within session 1), is that the HRV at the
onset of session 2 was high enough to allow for a reduction
associated with the emotional trauma recall. This was not the
case in session 1 where the initial HRV was lower than in
session 2 and thus might not have allowed for further reduction
in HRV induced by trauma recall.
Differences in HRV total power but not in the Low Frequency
(LF) and High Frequency (HF) of the HRV were observed. The
absence of an effect on HF and LF across conditions could
be explained by the important inter-individual variability. HF
values were weak (average of all conditions 220 ms2/hz)
compared to LF values (average of all conditions 1123 ms2/hz).
This means that the major part of the total HRV power was due
to the LF component and the LF coefficient of variation was
large (ranging between 1.07 and 1.94). Finally, differences
of HRV between sessions and measurement times, calculated
at the individual level, ranged between -1105 ms2/hz and
+1151 ms2/hz which means that irrespective of the compari-
son, there were almost as many patients whose HRV varies
in one direction as ones whose HRV varies in the opposite
The absence of a control group and the naturalistic conditions of
this retrospective study, carried out with the constraints imposed
Table 3. Mean high frequency (HF) and low frequency (LF) spectral values and standard
error of the mean (in parenthesis) in ms2/hz for all the sessions and measures.
LF Measurement 1 Measurement 2 HF Measurement 1 Measurement 2
Session 1 9637 (1321) 10694 (1876) Session 1 197 (21) 214 (32)
Session 2 10972 (1102) 10409 (1117) Session 2 231 (23) 194 (18)
Figure 1. Changes in Z score of Ln(HRV (heart rate variability)) (Ln for Napierian Logarithm) for the first and last sessions (“session”)
and within session (“Measurement”).
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by clinical out-patient medical practice, are not ideal, and resulted
in large inter-individual measure variances. In this retrospective
study, a large number of variables not included in the analysis
might also have influenced outcome measures. For example, the
decision to follow the therapy could have been accompanied by
a change in lifestyle (i.e. general improvement of the hygiene
of life), which may affect both HR and HRV measures. In
addition, time alone could have been responsible for changes
in HRV. As this study has been conceived a posteriori, such
variables could not be controlled. However, the absence of
statistical effects associated with biographical variables indicates
that these types of effects are relatively unlikely.
The intra-individual differences in the emotional reactivity
following the evocation of the traumatic memory were
difficult to standardize. One possible solution could be the
consideration of an individual cardiovascular reactivity, which
may be modeled as influenced by several independent factors40.
One of these factors would depend on individual physiological
variables and be independent of the nature and the intensity of
the emotional trauma evoked during therapy. This factor could
be estimated separately using simple tests which have been
used to establish relationships between the variations of HRV
and the ability to regulate emotions6. Other factors, such as
the intensity of the trauma and the type of trauma could also
influence cardiac reactivity. This multi-factor type of modeling
could potentially help to reduce and understand inter-subject
variability and lead to HR and HRV measures with diagnostic
and therapeutic value.
In this protocol which includes two therapeutic components;
HRV biofeedback and intero-nociceptive exposure, it is impos-
sible to distinguish the impact of one component versus the
other. The hypothesis was that both components are important,
and that it is the combination of the two which maximizes the
therapeutic effect. Further studies will be necessary to investigate
this hypothesis.
The analysis of HRV is a simple and noninvasive method to
quantify the activity of the autonomous nervous system. The
sympathetic-parasympathetic balance of patients having undergone
important traumas is modified in favor of sympathetic
influences. This study shows that interoception exposure
therapy – combined with biofeedback - was able to increase
parasympathetic influences. Furthermore, progressive reduction
in the cardiac rhythm and an increase in HRV at rest over a
period of a few months were demonstrated. It is important to
note that these variations were independent of the disorder
diagnosed by the Psychiatrist, therefore the HRV might be
considered as a general indicator of health. These results
warrant further investigation of both therapeutic components
(HRV biofeedback and intero-nociceptive exposure) and their
comparison to other types of interventions.
Data availability
Underlying data
Zenodo: R-R HRV data from Biofeedback on 100 patients.
This project contains the following underlying data:
- Archive_RR_All_subjects (folder containing R R
interval data for all participants as .txt files. Participants
can be identified using the ID (e.g. n1799) in the file
- biographic_data.txt (Demographic data for participants)
Extended data
Zenodo: R-R HRV data from Biofeedback on 100 patients.
This project contains the following extended data:
- info_sheet.docx (Study data collection form, English)
- info_sheet_fr.docx (Study data collection form, French)
Data are available under the terms of the Creative Commons
Attribution 4.0 International license (CC-BY 4.0).
The authors wish to thank The Research and Editing Consulting
Program (RECP) for their editing services.
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Background: Adversity experienced during childhood manifests deleteriously across the lifespan. This study provides updated frequency estimates of ACEs using the most comprehensive and geographically diverse sample to date. Methods: ACEs data were collected via BRFSS (Behavioral Risk Factor Surveillance System). Data from a total of 211,376 adults across 34 states were analyzed. The ACEs survey is comprised of 8 domains: physical/emotional/sexual abuse, household mental illness, household substance use, household domestic violence, incarcerated household member, and parental separation/divorce. Frequencies were calculated for each domain and summed to derive mean ACE scores. Findings were weighted and stratified by demographic variables. Group differences were assessed by post-estimation F-tests. Results: Most individuals experienced at least one ACE (57.8%) with 21.5% experiencing 3+ ACEs. F-tests showed females had significantly higher ACEs than males (1.64 to 1.46). Multiracial individuals had a significantly higher ACEs (2.39) than all other races/ethnicities, while White individuals had significantly lower mean ACE scores (1.53) than Black (1.66) or Hispanic (1.63) individuals. The 25-to-34 age group had a significantly higher mean ACE score than any other group (1.98). Generally, those with higher income/educational attainment had lower mean ACE scores than those with lower income/educational attainment. Sexual minority individuals had higher ACEs than straight individuals, with significantly higher ACEs in bisexual individuals (3.01). Conclusion: Findings highlight that childhood adversity is common across sociodemographic, yet higher in certain categories. Identifying at-risk populations for higher ACEs is essential to improving the health outcomes and attainment across the lifespan.
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Introduction Early life stress is imprinting regulatory properties with life-long consequences. We investigated heart rate variability in a group of small children with height below the third percentile, who experienced an episode of early life stress due to heart failure or intra uterine growth retardation. These children appear to develop autonomic dysfunction in later life. Results Compared to the healthy control group heart rate variability (HRV) is reduced on average in a group of 101 children with short stature. Low HRV correlates to groups of children born small for gestational age (SGA), children with cardiac growth failure and children with congenital syndromes, but not to those with constitutional growth delay (CGD), who had normal HRV. Reduced HRV indicated by lower RMSSD and High Frequency (HF)-Power is indicating reduced vagal activity as a sign of autonomic imbalance. Conclusion It is not short stature itself, but rather the underlying diseases that are the cause for reduced HRV in children with height below the third percentile. These high risk children—allocated in the groups with an adverse autonomic imprinting in utero or infancy (SGA, congenital heart disease and congenital syndromes)—have the highest risk for ‘stress diseases’ such as cardiovascular disease in later life. The incidence of attention deficit disorder is remarkably high in our group of short children.
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We briefly review the evidence for distinct neuroanatomical substrates that underlie interoception in humans, and we explain how they substantialize feelings from the body (in the insular cortex) that are conjoined with homeostatic motivations that guide adaptive behaviours (in the cingulate cortex). This hierarchical sensorimotor architecture coincides with the limbic cortical architecture that underlies emotions, and thus we regard interoceptive feelings and their conjoint motivations as homeostatic emotions . We describe how bivalent feelings, emotions and sympathovagal balance can be organized and regulated efficiently in the bicameral forebrain as asymmetric positive/negative, approach/avoidance and parasympathetic/sympathetic components. We provide original evidence supporting this organization from studies of cardiorespiratory vagal activity in monkeys and functional imaging studies in healthy humans showing activation modulated by paced breathing and passively viewed emotional images. The neuroanatomical architecture of interoception provides deep insight into the functional organization of all emotional feelings and behaviours in humans. This article is part of the themed issue ‘Interoception beyond homeostasis: affect, cognition and mental health’.
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Traumatic experiences have severe impact on the autonomous nervous system. Heart rate variability (HRV) is a reliable psychophysiological marker for the autonomous nervous system functioning. Reduced vagally mediated HRV has been found in patients with post-traumatic stress disorder (PTSD) and, in some studies, in patients with borderline personality disorder (BPD). In this study, we compared HRV parameters of patients with PTSD, current BPD, and BPD in remission with healthy volunteers in a 5 min resting-state electrocardiogram recording. 91 unmedicated female participants took part in the study (18 with PTSD, 27 with the current BPD, 23 with BPD in remission, and 23 healthy volunteers). We found significant group differences in both time-domain and frequency-domain (total power, low-frequency and high-frequency power) HRV parameters. Root mean square of the successive differences (RMSSD) was lowest in patients with PTSD (M = 48.6 ms, SD = 23.5 ms) followed by patients with BPD in remission (M = 57.7 ms, SD = 31.5 ms) and patients with the current BPD (M = 71.1 ms, SD = 44.5 ms), while the highest RMSSD was found in healthy volunteers (M = 84.1 ms, SD = 41.7 ms). Variance of HRV was higher in patients with BPD than in patients with PTSD. In addition, RMSSD was significantly negatively correlated with self-reported early life maltreatment assessed with the Childhood Trauma Questionnaire. Our findings point out a complex interaction between traumatic experiences, the functioning of the autonomic nervous system, and psychopathology. Alterations in HRV might be related to early life maltreatment or associated psychological factors rather than diagnostic entities.
Traumatic experiences are common and linked to cardiovascular disease (CVD) risk, yet the mechanisms underlying these relationships is less well understood. Few studies have examined trauma exposure and its relation to autonomic influence over cardiac function, a potential pathway linking trauma exposure to CVD risk. Investigating autonomic influence over cardiac function during both wake and sleep is critical, given particular links of sleep autonomic function to cardiovascular health. Among midlife women, we tested whether trauma exposure would be related to lower high frequency heart rate variability (HF-HRV), an index of vagal influence over cardiac function, during wake and sleep. Three hundred and one nonsmoking midlife women completed physical measures, a 24-hr electrocardiogram, actigraphy sleep measurement, and questionnaires about trauma (Brief Trauma Questionnaire), childhood abuse (Child Trauma Questionnaire [CTQ]), mood, demographics, and medical/psychiatric history. Relations between trauma and HF-HRV were assessed in linear mixed effects models adjusting for covariates (age, race, education, body mass index, blood pressure, psychiatric history, medication use, sleep, mood, childhood abuse history). Results indicated that most women had experienced trauma. Any trauma exposure as well as a greater number of traumatic experiences were associated with lower HF-HRV during wake and particularly during sleep. Relations were not accounted for by covariates. Among midlife women, trauma exposure was related to lower HF-HRV during wake and sleep. Trauma may have an important impact on vagal influence over the heart, particularly during sleep. Decreased vagal influence over cardiac function may be a key mechanism by which trauma is associated with CVD risk.
The relationship between anxiety and cardiovascular function and symptoms has long been of interest, culminating in the recent emphasis on the modulation of cardiovascular response in patients with panic disorder. The relationship between panic disorder and mitral valve prolapse remains controversial. Panic disorder appears to be significantly associated with increased incidence of cardiovascular morbidity. The detection and treatment of panic disorder in patients with cardiovascular risk or diseases could have an important impact on prognosis and quality of life of the patients.
The calculation of heart rate variability (HRV) is a popular tool used to investigate differences in cardiac autonomic control between population samples. When interpreting effect sizes to quantify the magnitude of group differences, researchers typically use Cohen’s guidelines of small (0.2), medium (0.5), and large (0.8) effects. However, these guidelines were originally proposed as a fall back for when the effect size distribution (ESD) was unknown. Despite the availability of effect sizes from hundreds of HRV studies, researchers still largely rely on Cohen’s guidelines to interpret effect sizes and to perform power analyses to calculate required sample sizes for future research. This article describes an ESD analysis of 297 HRV effect sizes from between-group/case-control studies, revealing that the 25th, 50th, and 75th effect size percentiles correspond with effect sizes of 0.26, 0.51, and 0.88, respectively. The analyses suggest that Cohen’s guidelines may underestimate the magnitude of small and large effect sizes and that HRV studies are generally underpowered. Therefore, to better reflect the observed ESD, effect sizes of 0.25, 0.5, and 0.9 should be interpreted as small, medium, and large effects (after rounding to the closest 0.05). Based on power calculations using the ESD, suggested sample sizes are also provided for planning suitably powered studies that are more likely to replicate. Researchers are encouraged to use the ESD dataset or their own collected datasets in tandem with the provided analysis script to perform custom ESD and power analyses relevant to their specific research area.