Psychology Research and Behavior Management
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ORIGINAL RESEARCH
Open Access Full Text Article
The relationship between emotion regulation
capacity, heart rate variability, and quality of life in
individuals with alcohol-related brain damage
Jean-Paul Steinmetz 1,2
Claus Vögele 3,4
Christiane Theisen-Flies 5
Carine Federspiel 1,2
Stefan Sütterlin 6,7
1
Department of Research and
Development, ZithaSenior, 2Centre
for Memory and Mobility, ZithaSenior,
3
Institute for Health and Behaviour,
Integrative Research Unit on
Social and Individual Development
(INSIDE), University of Luxembourg,
Luxembourg; 4Research Group
Health Psychology, University of
Leuven, Leuven, Belgium; 5Home St
Joseph, ZithaSenior, Luxembourg;
6
Department of Psychology,
Lillehammer University College,
Lillehammer, 7Division of Surgery and
Clinical Neuroscience, Department
of Psychosomatic Medicine, Oslo
University Hospital – Rikshospitalet,
Oslo, Norway
Correspondence: Jean-Paul Steinmetz
Department of Research and
Development, ZithaSenior, 30, rue Ste
Zithe, L-2763 Luxembourg
Tel +352 4 0144 2060
Fax +352 4 0144 2952
Email jean-paul.steinmetz@zitha.lu
Abstract: The reliable measurement of quality of life (QoL) presents a challenge in individuals
with alcohol-related brain damage. This study investigated vagally mediated heart rate variability
(vmHRV) as a physiological predictor of QoL. Self- and proxy ratings of QoL and dysexecutive
symptoms were collected once, while vmHRV was repeatedly assessed over a 3-week period
at weekly intervals in a sample of nine alcohol-related brain damaged patients. We provide
robustness checks, bootstrapped correlations with confidence intervals, and standard errors for
mean scores. We observed low to very low heart rate variability scores in our patients in comparison to norm values found in healthy populations. Proxy ratings of the QoL scale “subjective
physical and mental performance” and everyday executive dysfunctions were strongly related
to vmHRV. Better proxy-rated QoL and fewer dysexecutive symptoms were observed in those
patients with higher vmHRV. Overall, patients showed low parasympathetic activation favoring
the occurrence of dysfunctional emotion regulation strategies.
Keywords: heart rate variability, emotion regulation, alcohol-related brain damage, quality of life
Introduction
Healthy individuals without cognitive impairment can adapt flexibly to challenges in
their environment, while individuals with cognitive impairment may struggle with
behavioral, emotional, and cognitive adaptability. Success in these processes contributes to the person’s general well-being and enhances the individual’s experience of
quality of life (QoL). When evaluating QoL in those without cognitive impairment,
clinicians and researchers acknowledge that individuals are the experts of their own
QoL.1,2 Patients’ perceptions of their QoL are, therefore, assessed using self-report
measures of QoL, including subjective well-being and/or objective functioning.3
Assessing QoL in patients with severe cognitive deficits using such self-report measures, however, poses a challenge due to the nature of their impairment. In patients
with alcohol-related brain damage (ARBD), for example, self-report measures of QoL
may yield results with questionable reliability and are, therefore, often replaced in
clinical practice by expert ratings, that is, proxy measures. Such proxy ratings may
provide valuable information of QoL, especially with respect to concrete and observable aspects of QoL.1 In using self- and proxy ratings as a source of information, the
general phenomenon of judgmental inaccuracies between raters needs to be addressed.
Three perspectives on truth in terms of judgmental accuracy have been proposed:
consensus, correspondence, and pragmatic accuracy.4 Accuracy as consensus refers
to the consistency of ratings from at least two different judges. In a recent study
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http://dx.doi.org/10.2147/PRBM.S108322
Steinmetz et al
investigating QoL in ARBDs, we investigated the consensus of QoL evaluations using self- and proxy ratings, the
latter provided by health care professionals who were well
acquainted with the patients. We observed a significant lack
of concordance between both types of QoL ratings, suggesting very low inter-rater consistency.5 Interestingly, patients
judged their QoL similar to a large healthy normative
sample, while health care professionals assessed patients’
QoL significantly lower. On the one hand, this finding contradicts previous results in the sense that individuals with
alcohol use disorder generally score lower than the general
population on a number of QoL-related indices.6 On the
other hand, the observed gap between self-report and proxy
ratings have been reported in previous studies.1,7 Nevertheless, a review by Sneeuw et al8 show fair to moderate levels
of concordance between patient and physician ratings in
general, with physicians’ ratings of patients’ level of health
and functioning tending to be lower than those provided
by the patients themselves. The authors conclude from this
review that proxy ratings provided by health care providers
on several aspects of patients’ QoL are reasonably accurate.8
However, if patient and proxy ratings are at odds, it is generally accepted that patient ratings should be retained.1
It should be noted, however, that studies investigating
patient/proxy agreement in QoL include a wide variety of
clinical populations. The strength of the reported concordance between patients’ and proxy QoL ratings may probably
be a function of severity of patients’ cognitive impairments.
Due to the severity of cognitive impairment experienced
by a proportion of individuals with ARBD, patients are no
longer able to live independently and, therefore, require
long-term, 24-hour care, including basic functions such as
personal hygiene.9 In addition, most ARBD patients have a
history of failed psychiatric rehabilitation and unsuccessful
social reintegration, demonstrate a range of cognitive and
affective impairments10 and considerable levels of anosognosia.5 Given their advanced stage of cognitive impairment,
patients with ARBD are, therefore, no longer able to lead an
independent life. The level of cognitive/affective dysfunction and anosognosia, together with the previously observed
repetitive failure of intense therapeutic and rehabilitative
measures in these patients prior to their admission casts
doubt on the reliability – and hence validity – of self-reported
QoL in patients with ARBD.8,11 The applicability of the consensus concept of truth4 in evaluating QoL in these patient
populations may thus be compromised. This is critical, as in
addition to complex clinical tests assessing physical health
status per se, ratings on QoL and well-being constitute
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important outcome measures of medical and psychosocial
interventions. Improving the reliability of QoL measures
in this patient sample, therefore, seems mandatory for both
research and clinical practice.
A possible solution may be to refer to the correspondence
theory of truth,4 defined as the correspondence between a
judgment and a more objective criterion. The present study
aims at addressing this issue. We investigated vagally mediated heart rate variability (vmHRV), an objectively measurable physiological parameter, as an indicator of emotion
regulation capacity. vmHRV has recently been shown to be
related to QoL in individuals with compromised intellectual capacities,12 making vmHRV a promising candidate to
index QoL as a result of an individual’s capacity to regulate
emotions.
Offline analysis of variability of interbeat intervals in a
resting condition allows for the extraction of parameters of
prefrontally modulated vagal activation. Prefrontally modulated and vmHRV under resting conditions is considered a
marker for regulated emotional responding13 and a correlate
of prefrontal cortical functions.14 Recent research has shown
associations between vmHRV and self-reported measures
of emotion regulation and QoL in intellectually impaired
and visually challenged individuals.12 This interpretation
with relation to QoL is based on extensive research relating vmHRV to crucial predictors of mental well-being
such as successful emotion regulation capacity,13,15,16 better
self-regulatory skills,17 and better top-down modulation of
emotional responses due to higher prefrontal cortical inhibitory capacity.14,18,19 A lack of prefrontal inhibitory control
over subcortical brain regions that are involved in emotional
processing gives rise to emotional dysregulation such as
emotional instability and perseverative thinking, which are
in turn related to impaired life satisfaction and lower QoL.20
Low vmHRV is associated with a number of negative
health outcomes related to emotional dysregulation such as
depression,19,21 anxiety,21 and stress.22 Lower vmHRV has
further been related to a range of subclinical risk factors for
emotional instability such as less efficient safety learning and
extinction of previously learnt stress responses,23 increased
sensitivity to and dysfunctional cognitive processing of painrelated stimuli,24 suboptimal decision-making in risky and
emotionally challenging social situations,25,26 and emotional
instability in everyday life of healthy individuals.15
As vmHRV is typically derived from heart rate recordings
under resting conditions for a short period of 5 minutes,27
it is not affected by confounding influences of participants’ cooperation, their motivation, deception, and social
Psychology Research and Behavior Management 2016:9
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desirability, which makes it a candidate for an objective
biomarker of QoL and well-being in cognitively impaired
individuals. Importantly, vmHRV measures obtained at rest
are commonly interpreted as trait, but can be affected by state
components28 that might confound trait-based interpretations
(such as trait-QoL). With single measurements, which are
common in the vast majority of research on heart rate variability (HRV) and health, the trait component has recently
been estimated to be only 49%,28 which gives rise to the
assumption that the statistical power of analyses involving
measures of HRV assessment is generally overestimated.
In the present research, we thus implemented repeated (up
to three) measurements and calculated the mean, thereby
maximizing the trait-component of our measure in order
to compensate for unintended confounding factors by the
challenging characteristics of our sample.
Vagally mediated HRV has mostly been used in basic
research. In the present study, we aimed to investigate the
practicability of vmHRV under the complex conditions found
in a normally operating institution. The clinical environment
entails practical and methodological limitations that are
characteristic for clinical institutions caring for vulnerable
and chronic, multimorbid patients. All these characteristics
could pose serious limitations to the use of vmHRV as a
diagnostic tool in clinical practice. ARBD rarely occurs
alone, that is, these patients typically present with a wide
range of comorbidities. In the present research, we aimed
to investigate a naturalistic sample of patients with ARBD,
including the entire range of comorbidities, pharmacological and other treatments, extent of neurological damage, and
sociodemographic variables, to best reflect everyday clinical
practice. The present sample, therefore, combines a range of
patient characteristics that are commonly considered “exclusion criteria” and are not recommended for basic research.
Nevertheless, these characteristics are common and constitute
a threshold between basic science with high internal but
limited external validity and practical use. We are aware that
a demonstrated relationship between QoL and vmHRV does
not allow for conclusions on direct causal relationships. To
demonstrate causality, intervention studies using longitudinal
designs are required.
In summary, we investigate the practicability of vmHRV
measurement and interpretation for the assessment of wellbeing and QoL in a small naturalistic clinical sample of
patients with severe ARBD. More precisely, we conceptualize
vmHRV as an objective and trait-related measure of emotion regulation capacity and examine its associations with
1) self-reported and 2) proxy-reported QoL/well-being and
Psychology Research and Behavior Management 2016:9
Alcohol and quality of life
the occurrence of dysexecutive symptoms in everyday life,
using a correlative within-subject design. Hence, we aim to
investigate vmHRVs role as a potential biomarker for QoL
and emotion regulation in clinical settings.
Methods
Sample
The initial sample consisted of 18 detoxified patients with
chronic alcohol abuse living in a specialized ward providing
24-hour care and support. Of these, four patients withdrew
their participation over the course of the investigation (ie,
more than one of three HRV assessments missing), and one
patient was excluded from the study because of deteriorating health status (benign tumor diagnosis). The remaining
sample consisted of 13 patients with a mean age of 58.3 years
(standard deviation [SD] =6.9; range =45.7–66.5). Given the
widespread nature of neurotoxic effects of chronic alcohol
abuse,5,10 these patients are characterized by significant levels
of cognitive and functional impairments requiring 24-hour
care and support in a specialized nursing ward. All patients
had a history of chronic, heavy alcohol use, repeated relapse,
and unsuccessful psychiatric rehabilitation prior to entering
the present ward. Due to equipment failures (eg, storage
failure, signal loss) an additional four patients were excluded
from analysis, resulting in a final sample of nine patients.
Table 1 summarizes sample characteristics, including
sociodemographic data, medical comorbidities, and current
pharmacological treatment. Due to unique characteristics of
this particular sample, an age-matched healthy control group
was deemed neither necessary nor helpful, as the unambiguous interpretation of group differences in terms of diagnosis
would not have been possible due to differences at multiple
levels (eg, pharmacotherapy, psychiatric and physical comorbidities, and general lifestyles).
Ethical considerations
The present study’s aims, design, procedure, and publication of
anonymous data were reviewed and approved by two independent ethical research boards (Ethics Review Panel, University
of Luxembourg [Reference ERP13-021 ALCOQUOL LB/vg];
National Ethical Research Board of Luxembourg [Approval
201310/01]) prior to recruitment and data collection. In
addition, the treating physician (ie, an external psychiatrist
independent of the present research institutions and without
potential conflict of interest) was asked to provide written
informed consent on the method, the goal of the study, and
the anonymous publication of the results for each participant
independently. Furthermore, the general practitioner and legal
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Steinmetz et al
Table 1 Sociodemographics, medical information, and relevant pharmacological treatment in each patient
Characteristics
Sociodemographics
Age at time of study (years)
Time living in present ward, at time of study
(months)
Marital status
Work status
Alcohol abuse >10 years
History of alcoholism in family
Medical information
ICD10 diagnosis, primary
ICD10 diagnosis, secondary
CIRS-G
Organ-speciic categories endorsed
Organ categories with moderate disabilities
Overall severity index
Atrophies on MRI
Mammillary bodies
Cerebellum
Cortex
Pharmacological treatment
Beta-blocker
BZD and related substances
Neuroleptic
Antidepressant
Thyroid extract
ARBD1
ARBD2
ARBD3
ARBD4
ARBD5
ARBD6
ARBD7
ARBD8
ARBD9
46.9
3
57.4
19
55.4
107
45.7
4
49.6
101
64.7
19
60.1
106
61.3
28
59.6
20
Divorced
Invalidity
X
X
Married
–
–
–
Single
Invalidity
X
–
Divorced
Invalidity
X
X
Divorced
Invalidity
X
–
Divorced
Retired
X
–
Divorced
–
X
–
Divorced
Invalidity
X
X
Divorced
Retired
X
X
F10.6
K86.0
F10.6
F32
F10.6
F32
F10.6
G62.1
F10.6
I62.1
F10.6
F32
F10.6
E11
F10.6
F32
F10.6
F32
4
2
2.0
4
2
2.0
3
1
2.0
4
2
2.0
3
2
2.3
6
3
1.8
4
3
2.3
6
4
2.0
5
2
2.2
–
–
–
X
–
X
X
–
X
X
X
X
–
–
–
X
–
X
X
–
X
X
–
–
X
X
–
–
–
–
X
–
–
X
–
X
–
–
–
–
X
–
–
X
X
–
–
–
–
X*
X
–
–
X
–
X*
X
–
–
X
–
–
X
X
X
X
–
X
–
–
–
–
Notes: CIRS-G.44 Organ-speciic categories rated on a Likert-type scale from 0 (no problem) to 4 (extremely severe, organ failure): heart, vascular, hematopoietic,
respiratory, eyes/ears/nose/throat/larynx, upper gastrointestinal tract, lower gastrointestinal tract, liver, renal, genitourinary, musculoskeletal-integument, neurological,
endocrine-metabolic-breast, and psychiatric illness. A moderate disability is deined by requiring a irst-line therapy, overall severity index represents the number of organspeciic categories endorsed/total score (unreported here). *If required.
Abbreviations: ARBD, alcohol-related brain damage; BZD, benzodiazepines; CIRS-G, Cumulative Illness Rating Scale for Geriatrics; ICD, International Classiication of
Diseases; MRI, magnetic resonance imaging; “–”, data not available or condition does not apply; “X”, condition applies.
guardians of each patient were informed about the study in
general and asked to raise objections if applicable. Prior to individual data collection, the health care professional in charge
informed the patient about the following procedure including
the possibility to withdraw from participation at any time and
without any further consequences. (The informed consent
process differed from the Declaration of Helsinki of 1975, as
revised in 2000. This is especially the case for §27, §28, §29,
and §30 of the Declaration of Helsinki. By Luxembourg law,
legally authorized representatives are not competent in providing written informed consent for the participant to participate
in a study [Luxembourg Law of August 11, 1982; see external
link {only available in French language}: http://www.legilux.
public.lu/leg/a/archives/1982/0072/a072.pdf].)
Materials and procedure
Heart rate recording
Measurement of heart rate was performed individually at
rest in a separate room with the patient sitting in a comfortable armchair using the heart rate monitor Polar RS800CX
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(Polar, Kempele, Finland).29,30 Patients were well accustomed
to the room as it is located in the ward and is regularly used
for relaxation purposes. On the ward, the three health care
professionals who were in charge of heart rate measurements
were well acquainted with the patients and experienced in
conducting relaxation sessions with them. For the purpose
of the present study, relaxation sessions were individually
performed with one health care professional present in the
same room. Patients were accustomed to these kinds of
relaxation sessions as they are regularly performed either
in a group or individual format. The measurement trial was
part of a regularly performed 30 minutes relaxation session,
with the patient performing relaxation exercises during the
first 15 minutes of the session under the supervision and
instruction of the health care professional in charge. For the
following 10 minutes, patients were instructed to lean back
in the chair, to relax, and to avoid any movements. A 5-minute period of this relaxation phase was used to record heart
rate to guarantee maximum relaxation. The last 5 minutes
of the 30 minutes session were used to exit the relaxation
Psychology Research and Behavior Management 2016:9
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training. Heart rate was recorded three times per participant
in identical settings as recommended by Bertsch et al,28 with
an interval of 1 week between measurements. Each heart rate
recording was standardized according to the same protocol,
with assessments per participant corresponding to the same
period of the day (eg, always in the morning) and with the
same health care professional as the preceding assessment.
Self- and proxy ratings
Self-ratings were collected with the help of a certified clinical
psychologist who interviewed each patient on the respective
items and who was acquainted with them. We implemented
this approach to rule out a possible lack of comprehension
given patients’ cognitive deficiencies described in Steinmetz
and Federspiel.10 Proxy ratings differ from self-ratings only
by health care professionals being instructed to base their
ratings on their personal experiences with and the impressions of the target patient.
QoL ratings
QoL was assessed using the QoL-profile for chronically ill
patients.31 This self-rating instrument is a 40-item generic
measure using a 5-point Likert scale to assess the degree of
accordance on six different QoL domains: “subjective physical and mental performance” (scale 1), “ability to have pleasure and relaxation” (scale 2), “presence of positive mood”
(scale 3), “absence of negative mood” (scale 4), “ability to
relate/contact/approach” (scale 5), and “sense of affiliation”
(scale 6). The QoL-profile instrument and data are described
in a previous publication,5 as they were collected in January
2013 and thus ∼10 to 12 months prior to the present study’s
data collection period (November 2013–January 2014).
Cognitive deicit ratings
Cognitive impairments were assessed using the Dysexecutive Questionnaire (DEX), a 20-item self- and expert-rating
questionnaire taken from the Behavioral Assessment of the
Dysexecutive Syndrome battery.32 Ratings are made on a
5-point Likert scale (0–4, ranging from “never” to “very
often”) with a maximum total score of 80. The DEX assesses
the frequency with which observable everyday manifestations
of executive dysfunction occur. The DEX was assessed in the
same period as vmHRV measurements (data collection period
ranging from November 2013 to January 2014).
Alcohol and quality of life
errors, these were deleted and substituted by means of cubic
spline interpolation, and statistical time and frequency domain
measures of vmHRV were obtained. Data processing and statistical analysis followed Task Force recommendations27 and
was carried out using ARTiiFACT software,33 which is based
on an error detection algorithm defining individual threshold
criteria for erroneously detected interbeat intervals developed
by Berntson et al.34 The root mean square successive difference (rMSSD) was calculated as a time domain measure.
The high frequency component (HF-HRV, 0.15–0.40 Hz) was
obtained via fast-Fourier transformation (interpolation rate 4
Hz, window width 256 seconds, window overlap 50%). Time
and frequency domain measures are based on very different
statistical approaches, but both are considered to be reliable
indicators of vagal activation14,27,28 and are usually highly correlated.35–38 To maximize the proportion of the trait component
and to minimize state-dependent effects on vmHRV,15,28 statistical analyses were limited to those participants from whom
data could be obtained from at least two measurement sessions. Reported 95% confidence intervals (CIs) and standard
errors (SEs) were estimated using bootstrapping with 1,000
replications. Given the challenging small sample size, significance tests for normalization are not considered sufficiently
reliable, therefore both parametric and nonparametric testing
procedures were conducted as a conservative and transparent
approach to investigate robustness of the findings.
Results
Table 2 provides descriptive statistics of vmHRV parameters.
Compared to available norms in healthy adults,29 mean levels
of both vmHRV parameters (rMSSD and HF-HRV) in the
present patient sample were considerably lower. rMSSD
ranged from 3.9 (SD =0.5) to 17.5 (SD =5.9), with an overall mean of 10.7 (SD =4.5) (bootstrapped: SEMean =1.4; CI
[7.9,13.6]), whereas Nunan et al39 observed an overall mean
value of 42 (SD =15) ranging between 19 and 75. For the
frequency domain measure HF-HRV, we observed a similar
pattern in the present sample ranging from 3.9 (SD =2.1) to
163.6 (SD =70.8) and an overall mean of 45.5 (SD =29.2)
(bootstrapped: SEMean=13.8; CI [22.5,75.2]), whereas norm
values reported by Nunan et al39 are considerably higher,
ranging from 82 to 3,630 and a computed cross study overall
mean of 657 (SD =777).
Self- and proxy ratings
Data analysis
Interbeat intervals were retrieved via the software Polar Pro
Trainer 5.0 (Polar). All data were screened for measurement
Psychology Research and Behavior Management 2016:9
Results of the QoL measures on a similar but larger patient
sample are presented and discussed in Steinmetz et al.5
Although these preliminary findings from the present small
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Steinmetz et al
Table 2 QoL and dysexecutive failures as rated by patients and health carers, and heart rate variability results
ARBD1
S
ARBD2
P
S
ARBD3
P
QoL-proile
Subjective
–
– 2.0
0.9
physical
and mental
performance
(scale 1)
Ability to have –
– 2.5
1.0
pleasure and
relaxation
(scale 2)
Presence of
–
– 1.8
0.1
positive mood
(scale 3)
Absence of
–
– 2.4
0.8
negative mood
(scale 4)
Ability to
–
– 1.7
0.6
relate/contact/
approach
(scale 5)
Sense of
–
– 3.0
1.2
afiliation
(scale 6)
DEX
Total score
16
18 –
–
Vagally mediated heart rate variability
Number of
3
2
measurements
Parameters,
mean (SD)
Interbeat
826.4 (50.3) 643.8 (23.8)
interval
Heart rate
72.8 (4.5)
93.3 (3.4)
rMSSD
17.5 (5.9)
5.1 (2.5)
HF-HRV
163.6 (70.8) 8.6 (6.5)
ARBD4
ARBD5
ARBD6
ARBD7
ARBD8
ARBD9
P
S
P
S
P
S
P
S
P
S
P
S
P
S
P
3.2
1.7 –
–
2.4
2.2 2.2
1.6
2.6
2.4
1.8
1.6
3.2
2.4 0.014
3.0
2.2 –
–
3.0
2.6 2.7
2.4
3.1
2.9
2.4
1.7
2.4
1.6 0.006
3.0
1.5 –
–
2.8
1.8 1.6
1.7
3.2
2.4
1.8
0.7
2.2
1.3 0.004
2.1
2.9 –
–
4.0
3.3 3.6
2.9
3.6
3.4
2.7
2.2
3.1
2.2 0.098
3.8
1.8 –
–
2.8
1.6 2.3
1.8
1.7
2.7
2.5
1.2
2.7
1.7 0.059
3.4
2.2 –
–
3.4
2.6 3.4
2.5
3.4
3.1
2.2
2.5
1.4
1.4 0.050
22
35 7
41 28
31 –
–
8
29
–
–
–
–
3
3
3
3
2
2
3
0.071
n/a
683.6 (32.1) 612.3 (22.6) 673.5 (80.3) 794.7 (135.4) 794.1 (21.3) 730.2 (91.5)
729.9 (183.2) n/a
87.9 (4.0)
8.1 (2.2)
23.8 (10.0)
85.3 (18.8)
12.6 (15.4)
35.5 (56.6)
98.1 (3.6)
3.9 (0.5)
3.9 (2.1)
89.9 (10.2)
11.3 (4.2)
53.5 (40.4)
77.1 (14.4)
11.1 (1.9)
37.1 (20.9)
75.4 (2.0)
15.4 (1.5)
56.9 (32.1)
82.8 (10.4)
11.4 (6.6)
26.3 (23.4)
n/a
n/a
n/a
Notes: QoL-proile for chronically ill patients.31 For scale names, please refer to the text. Higher scores indicate a more positive QoL rating. DEX taken from the BADS.32
Higher DEX scores indicate higher ratings on the occurrence of everyday cognitive failures. HF-HRV (0.15–0.40 Hz) expressed in absolute values P of scale mean differences
(dependent t-tests, two-tailed) between self- and proxy ratings. Number of measurements, number of vagally mediated heart rate variability assessments available and taken
into account per person in the context of the present study.
Abbreviations: ARBD, alcohol-related brain damage; BADS, Behavioral Assessment of the Dysexecutive Syndrome; DEX, Dysexecutive Questionnaire; HF-HRV, high
frequency component; n/a, not available; P, proxy rating from acquainted health care professionals; QoL-proile, quality of life proile; rMSSD, root mean square of successive
difference; S, self-rating; SD, standard deviation; “–”, data not available or not provided.
sample have limited generalizability, results support these
previous findings with QoL ratings from patients being
higher on four of the six dimensions than those of the proxies
(Table 2). Similarly, proxies rate the frequency of everyday
cognitive impairments to be somewhat higher than self-raters
suggesting the occurrence of anosognosia in patients. Again,
these findings are in line with previous results,5 whereas confirmation of the findings is required in other larger samples
before firm conclusions on the occurrence of anosognosia
in patients with ARBD can be drawn.
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Relationships of vmHRV with QoL and
DEX
Bootstrapped correlation analyses between rMSSD and
self- and proxy-rated QoL revealed an interesting pattern of
relationships. Linear relationships that are discussed were
investigated using Pearson’s correlation coefficient r. When
using Spearman’s rank order correlation coefficient (rho), the
pattern of correlations was similar to Pearson’s r, except for
the QoL-profile scale 4 (absence of negative mood) (details
are presented in Table 3).
Psychology Research and Behavior Management 2016:9
Notes: DEX taken from the BADS.32 We report bootstrapped Pearson’s r, with SE for each correlation coeficient. Robustit is a robustness algorithm implemented in MATLAB® (The MathWorks, Inc., Natick, MA, USA) software. β Scores
for both least square regression and robust regression are checked for differences and the signiicance is reported as Z-statistic. *Relationship is signiicant at P<0.05.
Abbreviations: BADS, Behavioral Assessment of the Dysexecutive Syndrome; CI, conidence interval; DEX, Dysexecutive Questionnaire; LL, lower limit; P, proxy ratings; QoL-proile, quality of life proile; rMSSD, root mean square of
successive differences; S, self-ratings; SE, standard error; UL, upper limit; vmHRV, vagally mediated heart rate variability.
–0.01
–0.01
0.02
0.01
–0.04
–0.44
–0.01
–0.05
0.00
–0.03
0.24
1.98
0.00
0.02
0.00
0.00
0.00
–0.02
0.00
–0.01
0.00
–0.03
0.00
–0.01
0.00
–0.03
0.00
0.01
–1.00*
–0.94*
0.05
[–1,–0.83]
0.20
0.08
0.59
[–1,1]
0.58
0.66
0.42
[–0.68,1]
–0.20
–0.15
0.40
[–0.84,0.84]
0.51
0.84*
0.31
[–0.30,0.98]
–0.11
–0.15
0.59
[–0.98,0.96]
0.51
0.69
0.24
[–0.17,0.97]
0.46
0.76*
0.34
[–0.35,0.98]
0.78*
0.87*
0.14
[0.47,0.99]
0.06
0.27
0.42
[–0.79,0.86]
0.43
0.68
0.39
[–0.65,0.96]
0.40
0.36
0.40
[–0.69,0.88]
0.45
0.71
0.41
[–0.61,0.96]
P
S
P
S
P
S
P
S
P
0.25
0.20
0.39
[–0.56,0.83]
rMSSD
Spearman’s rho
Pearson’s r
SE
95% CI [LL,UL]
Robustit
β Differences
Z
S
P
S
S
P
Total score
Ability to relate/contact/ Sense of afiliation
approach
(scale 6)
(scale 5)
Presence of positive
mood (scale 3)
Absence of negative
mood (scale 4)
Alcohol and quality of life
Subjective physical and Ability to have
mental performance
pleasure and
(scale 1)
relaxation (scale 2)
QoL-proile
Parameters
Table 3 Parametric/nonparametric correlation coeficients and robustness for relationships among vmHRV, QoL, and everyday cognitive failures
DEX
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Psychology Research and Behavior Management 2016:9
Small- to moderate-sized correlations (total mean
r=|0.30|) were observed between rMSSD and self-rated
QoL (with one exception, scale 4 in Table 3), whereas
correlations between rMSSD and proxy-rated QoL were
considerably higher (total mean r=|0.75|) (Table 3). When
inspecting corresponding SEs and 95% CIs of these correlations, only proxy-rated QoL-profile scale “subjective
physical and mental performance” (scale 1) remained valid
with a CI ranging from 0.47 to 0.99 (Table 3). The remaining
correlations between self- and proxy-rated QoL with rMSSD
had large SEs and hence large 95% CIs and thus warrant
no further interpretation. Considering correlations between
self-rated everyday cognitive failures (DEX total score), a
weak relationship was observed with rMSSD (r=−0.08, rho
=0.20), whereas a nearly perfect negative relationship with
rMSSD was observed for the proxy rating (r=−0.94), with
95% CIs ranging from −1.00 to −0.84 (Table 3). Robustness
of correlations was checked using the robustfit algorithm
implemented in MATLAB® software (The MathWorks, Inc.,
Natick, MA, USA). The robustfit function computes the difference between ordinary least square regressions and robust
regressions, yielding nonsignificant β differences between
least square and robust regressions (−1.96<Z>+1.96), with
one exception (self-rated QoL scale 5 with rMSSD, Figure 1).
Nonsignificant β differences indicate that linear relationships
are relatively robust with no or only minor influences from
extreme scores (ie, outliers) on one or the other variable.
Discussion
The purpose of the present study was to investigate the usefulness of vmHRV as a correlate of well-being and QoL in a
clinical sample of severely deteriorated ARBD individuals.
We related the patient’s emotion regulation capacity with
self- and proxy-rated QoL/well-being. Additionally, we
investigated possible relationships of vmHRV with self- and
proxy-reported executive failures.
In general, we observed low to very low HRV scores
compared to healthy adults.39 Although preliminary, this
suggests a remarkably low tonic parasympathetic activation that might indicate diminished prefrontal inhibitory
control in these patients. Limited inhibitory control may
favor the occurrence of dysfunctional psychological adaptation to internal or external input and hence, emotional
dysregulation in daily life,15 reduced general self-regulatory
abilities,17,19 and overall reduced QoL.12 Previously, low
vmHRV has been associated with numerous negative health
outcomes related to emotional dysregulation. Our sample is
characterized by a relatively high prevalence of psychiatric
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225
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Steinmetz et al
A
3.2
Data
Ordinary least squares
Robust regression
R 2=0.04
3.0
QoL self scale 1
2.8
2.6
2.4
2.2
2.0
1.8
4
6
8
10
12
14
16
rMSSD
B
2.6
Data
Ordinary least squares
Robust regression
2
R =0.75
2.4
2.2
QoL proxy scale 1
2.0
1.8
1.6
1.4
1.2
1.0
0.8
4
6
8
10
12
14
16
rMSSD
Figure 1 (Continued)
226
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Psychology Research and Behavior Management 2016:9
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Alcohol and quality of life
C
3.2
Data
Ordinary least squares
Robust regression
R 2=0.07
3.1
3.0
QoL self scale 2
2.9
2.8
2.7
2.6
2.5
2.4
2.3
4
6
8
10
12
14
16
rMSSD
D
3.0
Data
Ordinary least squares
Robust regression
R 2=0.46
2.8
2.6
QoL proxy scale 2
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
4
6
8
10
12
14
16
rMSSD
Figure 1 (Continued)
Psychology Research and Behavior Management 2016:9
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227
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Steinmetz et al
E
3.2
Data
Ordinary least squares
Robust regression
R 2=0.13
3.0
QoL self scale 3
2.8
2.6
2.4
2.2
2.0
1.8
1.6
4
6
8
10
rMSSD
12
14
16
F
2.5
Data
Ordinary least squares
Robust regression
R 2=0.58
QoL proxy scale 3
2.0
1.5
1.0
0.5
0.0
4
6
8
10
12
14
16
rMSSD
Figure 1 (Continued)
228
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Psychology Research and Behavior Management 2016:9
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Alcohol and quality of life
G
4.0
Data
Ordinary least squares
Robust regression
R 2=0.48
3.8
3.6
QoL self scale 4
3.4
3.2
3.0
2.8
2.6
2.4
2.2
2.0
4
6
8
10
rMSSD
12
14
16
H
3.5
Data
Ordinary least squares
Robust regression
R 2=0.51
3.0
QoL proxy scale 4
2.5
2.0
1.5
1.0
0.5
4
6
8
10
rMSSD
12
14
16
Figure 1 (Continued)
Psychology Research and Behavior Management 2016:9
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229
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Steinmetz et al
I
4.5
Data
Ordinary least squares
Robust regression
R 2=0.02
4.0
QoL self scale 5
3.5
3.0
2.5
2.0
1.5
4
6
8
10
12
14
16
rMSSD
J
3.0
Data
Ordinary least squares
Robust regression
R 2=0.71
QoL proxy scale 5
2.5
2.0
1.5
1.0
0.5
4
6
8
10
12
14
16
rMSSD
Figure 1 (Continued)
230
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Psychology Research and Behavior Management 2016:9
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Alcohol and quality of life
K
3.5
Data
Ordinary least squares
Robust regression
R 2=0.02
QoL self scale 6
3.0
2.5
2.0
1.5
1.0
4
6
8
10
12
14
16
rMSSD
L
3.5
Data
Ordinary least squares
Robust regression
QoL proxy scale 6
3.0
R 2=0.43
2.5
2.0
1.5
1.0
4
6
8
10
12
14
16
rMSSD
Figure 1 (Continued)
Psychology Research and Behavior Management 2016:9
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Steinmetz et al
M
30
Data
Ordinary least squares
Robust regression
R 2=0.1
DEX self total
25
20
15
10
5
2
4
6
8
10
12
14
16
18
rMSSD
N
45
R 2=0.88
Data
Ordinary least squares
Robust regression
40
DEX proxy total
35
30
25
20
15
2
4
6
8
10
12
14
16
18
rMSSD
Figure 1 Comparison of ordinary least square regressions and robust regressions for self- and proxy ratings on the six QoL-proile scales (A–L) and the occurrence of
everyday executive dysfunctions (M and N).
Notes: QoL-proile for chronically ill patients.31 For scale names, please refer to the text. Higher scores indicate a more positive QoL rating. DEX taken from the BADS.32
Higher DEX scores indicate higher ratings on the occurrence of everyday cognitive dysfunctions. The red line represents the ordinary least squares regression line. The green
line represents the robust regression computed by the robustit algorithm implemented in MATLAB® software (The MathWorks, Inc., Natick, MA, USA).
Abbreviations: BADS, Behavioral Assessment of the Dysexecutive Syndrome; DEX, Dysexecutive Questionnaire; rMSSD, root mean square successive difference; QoLproile, quality of life proile.
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Psychology Research and Behavior Management 2016:9
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illnesses (eg, depressions, anxiety), with seven out of nine
individuals (78%) suffering from symptoms requiring
first-line treatment (unpublished data using the CIRS-G
instrument). Although causal relationships should not be
inferred from the present data, the present results may well
have clinical implications. HRV biofeedback interventions
have been shown to improve symptoms in patients with
major depression and anxiety disorders related to emotional
and behavioral dysregulation.40
The present findings support the notion that judgments
of patient’s QoL obtained from acquainted health care professionals are a valuable and reasonably accurate source of
information.8 Our findings further demonstrate that patients’
HRV is related to QoL ratings obtained from acquainted
health care professionals. HRV was observed to be higher
in patients that were rated as experiencing better QoL and
demonstrated fewer dysexecutive symptoms. In contrast, we
observed mostly weak associations between self-rated QoL
and patients’ vmHRV, suggesting a nil relationship between
both variables. Hence, by considering only patient ratings
in the present sample, important health-related information
on quality of care and QoL of the patient would have been
missed.
As pointed out previously, the present sample is atypical
for research studies investigating vmHRV and its relationship to other constructs. We investigated a small clinical
sample with a range of comorbidities requiring therapy and,
therefore, compromising internal validity of the study. We
cannot exclude that the observed deficits or relationships
are independent of other causes (eg, head injury, dementia
unrelated to alcoholism, liver diseases, etc). Importantly and
although a large body of research discusses the psychological correlates of vmHRV in healthy and clinical samples,
many studies suffer from limited ecological validity due to
restrictive inclusion and exclusion criteria. This study put
previous findings into practice and investigated QoL as a
previously reported correlate of QoL in a “naturalistic” setting in a challenging small and highly comorbid sample. A
range of measures have been applied to test for robustness
of the results. First, data were qualitatively inspected and
visualized to check for possible outliers. In this context,
robustness of the reported linear relationships was checked
and beta weight differences are reported. The reported
patterns of relationships between vmHRV and all six QoL
dimensions are consistent. That is, proxy ratings are positively and strongly related to vmHRV, whereas mostly weak
or nil relationships are observed between self-ratings and
vmHRV. In addition, vmHRV is strongly and consistently
Psychology Research and Behavior Management 2016:9
Alcohol and quality of life
related to the proxy-reported occurrence of executive dysfunctions (DEX total score), whereas again, weak/nil relationships are observed between the self-reported executive
dysfunctions and vmHRV. This pattern of relationships is
reproduced when inspecting correlations between the three
underlying DEX factors: cognition, behavior, and emotion.32,41–43 Second, we provide bootstrapped 95% CIs and
SEs for the computed central tendencies and correlations.
Third, the present study maximizes the trait-component of
baseline vmHRV measures by averaging over more than one
assessment. This contributes to the stability and internal
validity of the present findings.28 In the present study, no
significant findings based on single measurements were
found (unreported here).
To the best of our knowledge, the present study is the first
to investigate direct relationships between emotion regulation
capacity, the occurrence of everyday executive dysfunctions,
and QoL in a sample of chronic patients with severe multimorbid conditions. The present preliminary results suggest
that vmHRV demonstrates construct validity, given the
relative stability of correlations investigated. While internal
validity of the present study is limited, its external validity
would seem to be adequate. More precisely, external validity
is important as it demonstrates the applicability of the present
research design and approach to a highly complex multimorbid clinical sample. We were able to perform accurate and
repeated assessments of vmHRV in a complex clinical context with a wide variety of intervening confounding factors.
Thus, our paper contributes to the applicability of vmHRV
by stressing the necessity of multiple measurements to
ensure trait characteristics of HRV. Pending further research
and replication in larger samples, HRV could be useful in
situations where expert ratings are unclear (eg, disagreement
within a team, high degree of staff fluctuation in transition
periods). First studies on applying vmHRV assessments to
clinical samples have been published.12
Conclusion
The present study demonstrates that individuals with ARBD
have low to very low HRV scores compared to healthy adults
and that patients’ HRV is related to QoL ratings obtained
from acquainted health care professionals. We demonstrated
the construct validity of vmHRV, given the relative stability
of the investigated associations. We performed accurate and
repeated assessments of vmHRV in a complex clinical context
with many intervening factors. We thus see the contribution
of this research in the fact that we put a well-known and as
such well-established psychophysiological measure to the test
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233
Steinmetz et al
under challenging contextual situations in a natural setting
(low sample size, heterogeneous sample with multiple comorbidities, and intellectual impairment). Hence, we conclude
that demonstrating the practical use of a tool and applicability
of its measures in many diverse clinical settings is a crucial
and necessary step for this promising method to slowly move
from the laboratory to the clinical field.
Acknowledgments
The authors are grateful to the patients, their families, and nursing staff for their participation and support during the study.
They would like to thank Dr Tobias Kaufmann, Institute of
Clinical Medicine at the University of Oslo, for his assistance
during data analysis. This work was supported by continuous
funding from the Foundation Ste Zithe Luxembourg which
is gratefully acknowledged. The supporting source had no
involvement in the study design, data collection, data analysis,
interpretation of the data, in the writing of report, or in the
decision to submit the manuscript for publication. Parts of the
study were presented as a poster at the 22nd Nordic Congress
of Gerontology in Gothenburg, Sweden, May 26–28, 2014.
Author contributions
J-PS, CV, CT-F, CF, and SS conceived the design of the study.
J-PS and SS conducted the literature search. J-PS and SS were
responsible for the first draft of the manuscript. CT-F obtained
the study data. J-PS and CF acquired funding for the study.
J-PS and SS carried out the statistical analyses. J-PS and SS
contributed to the interpretation of the study findings. J-PS,
CV, CT-F, CF, and SS contributed to the critical revision of
the paper and approved the final manuscript.
Disclosure
The authors report no conflicts of interest in this work.
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