Content uploaded by Marta Vigier
Author content
All content in this area was uploaded by Marta Vigier on Sep 27, 2023
Content may be subject to copyright.
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=hhth20
Health Communication
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/hhth20
An Investigation of Patients’ and Doctors’
Autonomic Nervous System Responses
Throughout News-Focused Medical Consultations
Marta Vigier, Katherine R. Thorson, Elisabeth Andritsch & Andreas R.
Schwerdtfeger
To cite this article: Marta Vigier, Katherine R. Thorson, Elisabeth Andritsch & Andreas R.
Schwerdtfeger (27 Sep 2023): An Investigation of Patients’ and Doctors’ Autonomic Nervous
System Responses Throughout News-Focused Medical Consultations, Health Communication,
DOI: 10.1080/10410236.2023.2261714
To link to this article: https://doi.org/10.1080/10410236.2023.2261714
© 2023 The Author(s). Published with
license by Taylor & Francis Group, LLC.
View supplementary material
Published online: 27 Sep 2023.
Submit your article to this journal
View related articles
View Crossmark data
An Investigation of Patients’ and Doctors’ Autonomic Nervous System Responses
Throughout News-Focused Medical Consultations
Marta Vigier
a,b
*, Katherine R. Thorson
c
*, Elisabeth Andritsch
d
, and Andreas R. Schwerdtfeger
a,e
a
Department of Psychology, University of Graz;
b
Department of Neurobiology, Linköping University;
c
Department of Psychology, Barnard College of
Columbia University;
d
Division of Oncology, Medical University Graz;
e
BioTechMed
ABSTRACT
Although it is clear that people experience physiological arousal in anticipation of news-focused medical
consultations, our knowledge of people’s experiences during and throughout these consultations is
scarce. We examine interbeat interval responses (IBI) of patients and doctors during real-life medical
consultations to understand how the experiences of both parties change throughout these encounters
and whether they dier from each other. We also examine how the type of news delivered aects
responses. We measured the IBI responses of patients and their oncologists throughout 102 consultations
in which providers delivered news (classied as good, bad, or status quo) to patients about a recent
computerized tomography scan. We observed two distinct phases of consultations: an initial “news”
delivery phase and a subsequent “information” phase. During the news phase, on average, patients’ IBI
responses rapidly increased–indicating less autonomic arousal over time – whereas doctors’ responses
did not change over time. In contrast, throughout the information phase, on average, both patients’ and
doctors’ responses remained steady. During the information phase, responses diered based on news
type: on average, status quo consultations involved an increase in autonomic arousal, whereas good and
bad news consultations involved no changes. Lastly, we observed signicant variability in patients’
responses during both phases. In sum, on average, patients (but not doctors) experience decreases in
autonomic arousal while news is being delivered, suggesting that anticipatory distress regarding these
consultations wanes quickly. However, our results also indicate that patients’ experiences vary from one
another, and future research should focus on factors explaining this variability.
Consultations between patients and their doctors can be psycho-
logically distressing for both parties, especially when doctors have
to deliver news to patients about their health (Bensing et al., 2008;
Del Piccolo et al., 2019; Van Dulmen & Bensing, 2002). Leading
up to such consultations, patients worry about the kind of news
they will receive, and doctors worry about how they will deliver
the news and manage patients’ emotions (Brown et al., 2009; Del
Piccolo et al., 2019; Howell & Sweeny, 2016; Shaw et al., 2013; Van
Dulmen et al., 2007). Although it is clear that people experience
distress and physiological arousal in anticipation of news-focused
medical consultations (Hoscheidt et al., 2014; Van Dulmen et al.,
2007), our knowledge of people’s experiences during and
throughout these consultations is scarce. Understanding these
experiences is important for developing techniques that support
patients’ accurate receipt of news, as well as patients’ and doctors’
emotional well-being both during and after such consultations
(Hoscheidt et al., 2014; Jagosh et al., 2011; Ong et al., 2000;
Schwabe et al., 2012; Street et al., 2009; Tyng et al., 2017).
Some research on this topic has examined simulated med-
ical consultations, during which real doctors or medical stu-
dents interact with standardized patients (people playing the
role of patients, potentially with scripted language) or are
viewed by analog patients (people instructed to take the per-
spective of real patients, while rating videotaped medical con-
sultations between doctors and actors in the roles of patients),
but these studies are limited. For one, the responses of stan-
dardized and analog patients are unlikely to be exactly the
same as those of real patients, given that people’s expectations
about how they will feel and act in response to hypothetical
social situations do not always align with their experiences and
behaviors in response to the real social situations themselves
(Eastwick et al., 2008; Kumar & Epley, 2021; Moore et al.,
2019). Secondly, past work on the experience of health care
providers when delivering news to standardized or analog
patients is unlikely to have fully captured the experiences of
health care providers when delivering real news to real
patients. Indeed, research on medical consultations in general
suggests that providers’ experiences are more intense when
interacting with real patients (Bokken et al., 2009).
Therefore, to improve our understanding of both patient
and provider experiences during news-focused medical con-
sultations, in the current work, we examine the autonomic
nervous system (ANS) activity of patients and their oncologists
throughout 102 consultations in which providers delivered
CONTACT Marta Vigier marta.vigier@edu.uni-graz.at Department of Psychology, University of Graz, Universitätsstraße 27/1, Graz 8036, Austria; Katherine R. Thorson
kthorson@barnard.edu Barnard College, Columbia University, 3009 Broadway, New York, NY 3009
The method section of this manuscript contains some parts that appeared in our previous work (Vigier et al., 2021).
*Shared first co-authorship.
Supplemental data for this article can be accessed online at https://doi.org/10.1080/10410236.2023.2261714
HEALTH COMMUNICATION
https://doi.org/10.1080/10410236.2023.2261714
© 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in
a repository by the author(s) or with their consent.
news to patients about a recent computerized tomography
(CT) scan. We focus on the ANS responses of patients and
doctors because they are responsive to quick changes in psy-
chological experiences. They can also be measured continu-
ously and unobtrusively throughout doctor-patient
consultations, and they do not require people to consciously
assess their psychological experiences, which would be parti-
cularly challenging while engaging in an ongoing social inter-
action (Blascovich et al., 2011). We measure cardiac interbeat
intervals (IBI), which indicate general autonomic arousal. We
investigate whether patients’ and doctors’ ANS responses
change over the course of these consultations and whether
response trajectories differ as a function of the type of news
being delivered (good, bad, or status quo). We elaborate on
these questions below.
Changes over the course of consultations
In this work, we examine whether and how patients’ and
doctors’ ANS responses change over the course of news-
focused medical consultations. Although it is clear that
patients and doctors experience significant physiological arou-
sal directly prior to consultations and during consultations,
relative to a baseline period (Deinzer et al., 2019; Pottier et al.,
2011; Van Dulmen et al., 2007), it is not clear how patients’ and
doctors’ ANS responses change throughout these consulta-
tions. We examine these temporal trends because knowledge
about the trajectory of patients’ physiological responses could
inform and guide providers during these consultations. For
example, if patients show similar levels of autonomic arousal
throughout an entire news-focused consultation, this may
suggest that they are not recovering psychologically from the
stress of anticipating or receiving news. Thus, providers may
find it best to simply deliver news and then help the patient
cope with the news rather than, for example, ask the patient to
make decisions about future treatment plans (Morgado et al.,
2015). Knowledge about the trajectory of doctors’ responses
could also be useful. For example, during high-arousal states,
doctors often tend to ignore patients’ emotional cues (Helft &
Petronio, 2007). Thus, understanding when and whether doc-
tors recover from the high-arousal moments of news delivery
might help structure the rest of these consultations in ways that
maximize doctors’ recognition of patients’ emotions.
Only a few studies examine how people’s responses change
over time during news-focused medical consultations. This
work has compared people’s physiological responses during
the “news” phase of consultations (the first few minutes when
news is being delivered) to the “information” phase of con-
sultations (the rest of consultations when doctors provide
more information about future treatment options). For
patients, there is one study that takes this approach, using
analog patients. Here, the evidence for changes across these
phases is mixed. Although skin conductance levels and systolic
blood pressure declined from the news phase to the informa-
tion phase, heart rate increased from the news phase to the
information phase (Visser et al., 2016). For doctors, two stu-
dies documented significant decreases in heart rate and skin
conductance between the news and information phases
(Meunier et al., 2013; Shaw et al., 2015). We aim to build on
this work by using a relatively large sample of real patients and
doctors during real consultations to examine how people’s
ANS responses change from minute-to-minute (and not just
from one phase of a consultation to the next) throughout these
interactions.
In addition, we build on past work by examining whether
the type of news that is being delivered affects the trajectory of
patients’ and doctors’ ANS responses. We investigate three
news types: 1) “good” news, when doctors informed patients
about positive evolution in their health status, like for example
tumor-shrinking; 2) “bad” news, when the disease progressed
or metastasized to other organs, often involving a transition to
palliative, instead of curative care, and 3) “status quo” news,
when the disease was classified as stable, for example, with
a tumor neither shrinking nor growing since the last scan.
These news types differ in the ways they are communicated
and received (e.g., Maynard et al., 2016; Shaw et al., 2013), but,
to our knowledge, no research has examined whether they
differ in patients’ autonomic experiences. Several studies on
simulated consultations have investigated doctors’ responses
when delivering bad news relative to delivering good news or
no news at all (for example, during history taking (Shaw et al.,
2013). In general, doctors tend to show stronger sympathetic
nervous system arousal (e.g., higher heart rate, higher cardiac
output, and higher systolic blood pressure) when they have to
report bad news (Hulsman et al., 2010). This occurs both in
anticipation of consultations and while delivering the news
(Brown et al., 2009; Cohen et al., 2003; Meunier et al., 2013;
Van Dulmen et al., 2007). We build on past work by examining
all three news types in comparison to each other, and we
examine the influence of news type for both patients and
doctors.
Overview of current research
As noted above, we examine the ANS activity of patients and
their oncologists throughout 102 real consultations in which
providers delivered news to patients about a recent CT scan.
We measure ANS responses via cardiac interbeat intervals
(IBI; the amount of time in between successive heartbeats).
In general, medical consultations have a well-established struc-
ture with a typical sequence of events: 1) openings/greetings, 2)
discussion of symptoms, 3) discussion of results, 4) discussion
of treatment options, and 5) closings/end (Byrne & Long, 1978;
Heritage & Maynard, 2006; Robinson, 2003). News-focused
medical consultations, in particular, tend to differ from the
typical structure of medical consultations more generally and
often include two phases: a “news phase”, in which doctors
deliver results from the CT scan and an “information phase”,
in which doctors provide further details about future treatment
options (Shaw et al., 2013; Visser et al., 2016). Two reasons for
this difference are that, in news-focused medical consulta-
tions, 1) the focus is explicitly on news delivery (and not on
symptoms, for example) and 2) both providers and patients are
eager for news delivery to occur (which means that openings
and greetings are often extremely brief (Espinosa et al., 1996;
Shaw et al., 2015). Therefore, mirroring past work on news-
focused medical consultations specifically, in the current work,
we anticipated two distinct portions of consultations: the news
2M. VIGIER ET AL.
and information phases. We examine changes in patients’ and
doctors’ physiological responses throughout the news and
information phases, as well as whether the trajectories of
responses in these phases differ from each other.
News phase
We assumed that both patients and doctors would begin consulta-
tions with greater autonomic arousal than is typical of a resting
level, given the anxiety and uncertainty that often characterizes
waiting periods (Shaw et al., 2015; Sweeny, 2018; Sweeny &
Cavanaugh, 2012). For patients, we predicted that this arousal
would quickly dissipate once the consultation actually began,
uncertainty was reduced, and patients were in the presence of
their doctor who could provide information and potentially help
regulate their emotions. Thus, we predicted increases in IBI
responses (corresponding to less ANS arousal) during the news
phase for patients. We explored whether these responses for
patients varied as a function of news type, but, given the lack of
prior data, we did not have strong hypotheses. For doctors, we
expected that their IBI responses would differ as a function of
news type during the news phase. Given prior work, we predicted
that doctors’ IBI responses would show an upward trajectory
(corresponding to less autonomic arousal) when delivering good
news relative to bad news or status quo news (Brown et al., 2009;
Cohen et al., 2003; Meunier et al., 2013; Van Dulmen et al., 2007),
as doctors might be able to relax a bit while delivering good news
that hopefully calmed patients’ worries.
Information phase
Given mixed evidence (Visser et al., 2016), we did not have
strong hypotheses about patients’ IBI responses during the
information phase. For doctors, we expected that, on average,
their IBI responses would increase (reflecting a decrease in
their arousal) during the information phase, knowing that
doctors’ arousal tends to decrease after they deliver news to
patients (Meunier et al., 2013; Shaw et al., 2015). We explored
whether patients’ and doctors’ responses during the informa-
tion phase varied as a function of news type, but, given the lack
of prior data, we did not have strong hypotheses.
Variability in temporal trends
Lastly, we examined whether there was significant variability
in how patients’ and doctors’ ANS responses unfolded over
time, after accounting for the roles of time and news type. In
other words, are there meaningful between-person differences
in how patients and doctors respond over time? For example,
do all patients follow a similar trajectory of IBI responses
throughout their consultations? Or do their trajectories vary
from one another? We used multilevel models to quantify
variability in responses over time (Bolger et al., 2019).
Understanding whether variability exists in average temporal
trends has several key benefits. One, it can indicate whether
other factors might be associated with how people’s responses
change over time (Dart et al., 2002; Hu et al., 2017). Identifying
those sources can then help improve the explanatory and
predictive power of theoretical models. Two, this information
provides useful context for applying knowledge. For example,
if patients vary in their arousal across the course of consulta-
tions, this suggests that some patients may need greater
regulatory help from their doctors. Identifying the factors
that distinguish patients who need extra regulatory help from
those who do not would then be an important next step before
applying knowledge about these temporal trends in real-life
consultations.
Method
Study method and results are reported following the
Strengthening the reporting of observational studies in epide-
miology checklist (STROBE).
Participants
Participants were recruited from the oncology unit of University
Hospital Graz, Austria. Inclusion criteria included fluency in
German or English and being 60 years of age or younger, given
age-related differences in ANS activity (Lipsitz & Novak, 2013).
Exclusion criteria included a diagnosis of cardiovascular disease,
diabetes, or pregnancy. We selected eligible patients from
a database of patients at the unit and sent them an informational
recruitment letter. We also called the patients a few days later to
ask if they were interested and/or had any questions. Interested
patients participated during the consultation that followed their
next CT scan. During a weekly doctors’ meeting, we also presented
information about the study to eligible doctors. Only doctor-
patient dyads in which both the doctor and patient agreed to
partake in the study were enrolled.
Doctors and patients were matched based on which doctor
was available at the time of the patient’s appointment. Patients
did not select a doctor when making an appointment, nor did
they know ahead of the appointment with whom they would
be meeting. Between April, 2017, and March, 2018, we
recruited and collected data from 150 patients and 18 doctors.
The data from 48 doctor-patient combinations were
excluded because there were excessive artifacts in the physio-
logical data (see below), we experienced technical problems
obtaining the data, or the doctor-patient consultations lasted
fewer than five minutes. The final dataset includes 18 doctors
(M
age
= 41.06, SD
age
= 7.83; 61.1% male; 38.9% female; 100%
White European) and 102 patients (M
age
= 52.12, SD
age
= 6.42;
39.2% male; 60.8% female; 99% White European, 1% Asian),
yielding data for 102 unique doctor-patient dyads. Information
regarding sample socioeconomic status was not collected.
Procedure
Prior to consultations, participants were fitted with an electro-
cardiography (ECG) Holter monitor (Schiller Holter
MedilogAR). Three Ag/AgCl electrodes were placed on the
distal end of the right clavicle, lower left rib cage chest, and
lower abdomen. Given the nature of doctors’ schedules (fast-
paced, with little to no time in between consultations), we were
not able to collect resting “baseline” responses prior to con-
sultations. In addition, given the stress that patients feel even
upon arrival at the hospital (prior to any consultation even
starting), any responses collected prior to the consultations
would be unlikely to be true resting responses anyway. We
continuously recorded ANS responses from patients and
HEALTH COMMUNICATION 3
doctors during their consultations, which ranged between five
and thirty-three minutes. We allowed all consultations to
unfold naturally and did not intervene during the consulta-
tions at all–for example, we did not request or require that
doctors and patients follow a specific sequence (e.g., news
delivery and then discussion) during the consultations.
Senior doctors, who supervised all other doctors in the hospital
unit in which these data were collected, confirmed that all
consultations involved presentation of news regarding the
most recent scan within the first few minutes (the “news
phase”) and then discussion of that information for the rest
of the consultation (the “information” phase). On average,
consultations lasted 13.54 minutes (SD = 7.12; Min = 5.0,
Max = 33.0).
Both doctors and patients were seated during the interactions.
Doctors completed a demographics questionnaire upon enrolling
in the study, and patients completed this questionnaire after their
consultation. The ethics committee of the Medical University of
Graz approved the study (EK-Number: 29–287 ex 16/17).
Participants provided informed consent prior to data collection.
Measures
Interbeat interval responses
Data were sampled at a rate of 1,000 Hz. We analyzed the ECG
data with Kubios HRV Premium software (version 3.3.1
(Tarvainen et al., 2014)) in one-minute intervals. Visual arti-
facts correction was performed on the IBI series, and, if
needed, an automatic correction algorithm was applied.
Intervals containing more than 5% of artifacts or excessive
ectopic beats were excluded. In total, 12.6% of IBI responses
were marked as missing. We report raw IBI responses.
News valence
Doctors classified the news delivered in the consultation as bad
(11.8%), good (52.9%), or status quo (32.4%; 2.9% missing data).
Doctors categorized information as “bad” when a CT scan
revealed metastases (i.e, the spread of disease to another part of
the body) or that a patient’s status had changed from curative to
palliative (i.e., there was no chance for remission). The doctors
labeled the news as “good” when the results confirmed an
improvement in a patient’s health status (e.g., a positive change
in cancer staging due to a tumor shrinking). The doctors gave
status quo news to patients remaining in similar health status,
meaning that no change due to treatment or evolution of the
disease was observed.
Covariates
In a sensitivity analysis, we examined whether effects were robust
when adjusting for gender, age, smoking status, and exercise
status, all of which can influence ANS activity (Dart et al., 2002;
Hu et al., 2017; Lipsitz & Novak, 2013) We also adjusted for
patients’ cancer stage, cancer type, and the number of times that
a patient had met with a particular doctor.
Smoking status. Participants were identified as ex-smokers
(5.6% of doctors; 25.5% of patients), smokers (27.8% of doc-
tors; 24.5% of patients), or nonsmokers (66.7% of doctors;
50.0% of patients).
Exercise status. Participants answered the following ques-
tions: “During a normal week, do you practice regular physical
activity (e.g., brisk walking, jogging, cycling) long enough to
work up a sweat? If yes, how many hours on average per
week?” We categorized participants’ answers as no exercise at
all (16.7% of doctors; 53.9% of patients), fewer than 3 hours
weekly (44.4% of doctors; 0% of patients), between 3 and 6
hours weekly (27.8% of doctors; 26.5% of patients), more than
6 hours weekly (11.1% of doctors; 19.6% of patients).
Cancer stage. Patients’ cancer stages were classified by doc-
tors based on TNM (Rosen & Sapra, 2023) classification as
follows: stage 1 (44.1%), stage 2 (4.9%), stage 3 (17.6%), and
stage 4 (33.3%).
Cancer type. Doctors classified patients’ cancer types as fol-
lows: colorectal (46.1%), breast (39.2%), pancreatic (8.8%),
lung (3.9%), and prostate (2%).
Relationship length. We measured the number of times that
a patient had met with a particular doctor via patient records
(M = 3.5, SD = 2.8). The minimum relationship length was one
consultation, meaning that the consultation during which we
measured physiological responses was the first consultation
between a particular patient and doctor. The maximum rela-
tionship length was 12 consultations.
Results
Additional analytic details and results are provided in the
Supplemental Material (SM). At the request of doctors who
participated in the study, all participants were told that raw
data would remain confidential and would not be shared;
however, the analysis syntax for all models is available at
https://osf.io/qhuwt/. Higher numbers for IBI responses indi-
cate more time in between successive heartbeats and less
autonomic arousal; therefore, increases in raw IBI responses
indicate decreases in autonomic arousal and vice versa.
First, we visually examined mean IBI responses for doctors
and patients across each time point of the consultations.
Corresponding to our hypotheses, we noticed an initial trend
for patients (minutes 1 through 3) that clearly differed from the
trend throughout the rest of consultations (see SM). Doctors
also appeared to have this pattern (with an initial response that
peaked around minute 2), though it was not as strong. To best
approximate these different trends in our model, we used
a piecewise regression model, also called a spline, segmented,
or broken-stick regression, in which we estimated different
slopes for different phases of consultations. This approach is
useful when there are non-linear longitudinal trends in data that
cannot be approximated using polynomials alone.
While inspecting our data, we also noticed that responses
after 20 minutes seemed to follow a different pattern relative to
the initial phase, and we noted that fewer than 20% of partici-
pants had consultations lasting longer than 20 minutes. Given
this information, we chose to analyze the first 20 minutes of
consultations only. We did this to ensure that the different
trends which seemed to occur from minutes 21 to 33 (and
which represented data from fewer than 20% of our
4M. VIGIER ET AL.
participants) would not unduly influence the average estimate
that was provided for all participants across minutes 3
through 20.
Thus, given our visual inspection of the data, we estimated
one slope for minutes 1 through 3 (the “news” phase; three
timepoints) and one slope for minutes 3 through 20 (the “infor-
mation phase;” 18 time points). Instead of one linear term for
time, this means that we estimated two linear terms for time: one
during the news phase and one during the information phase.
We also conducted a sensitivity analysis to see whether our
results were consistent if we estimated the news slope from
minutes 1 through 2 and the information slope from minutes
2 through 20, given that this also seemed a plausible way to
differentiate the two temporal trends in the raw data. Results
were consistent across both specifications (see below).
We conducted two sets of analyses. First, we tested whether
the slopes for the news and information phases were signifi-
cantly different from zero (i.e., whether there was evidence of
change over time), as well as whether role (patient or doctor)
and type of news (bad, good, or status quo) affected these
slopes (see the SM for details on slope coding). We did this
by entering news phase, information phase, role, and news
type as predictors, as well as the following interaction terms:
role*news phase, role*information phase, news type*news
phase, news type*information phase, news type*role*news
phase, and news type*role*information phase. For ease of
understanding, we report the results for each phase separately.
Second, we tested whether the slopes for the news and
information phases were significantly different from each
other (i.e., whether there was a different trajectory over time
in the news phase relative to the information phase), as well as
whether role and type of news affected the difference between
these phases. To do this, we use the same terms as above, but
change the coding of the information phase slope (see the SM
for details).
Finally, we tested whether people exhibited significant
variability in their slopes during the news and information
phases – in other words, is there significant variability in the
temporal trajectory of people’s IBI responses over time?
We used multilevel modeling with PROC MIXED in SAS 9.4
to adjust for nonindependence in responses between patients of
the same doctor and between the same doctors (similar to the
reciprocal one-with-many-design with indistinguishable partners
described in (Hagiwara et al., 2014; Kenny & Kashy, 2011),
between members of the same doctor-patient dyad, and across
time for each person. We describe the variance-covariance para-
meters we used in the supplement. We use the results of these
variance parameters to address our questions about variability in
temporal trends of IBI responses (i.e., whether there is significant
variability in the extent to which IBI responses change over time).
We report this information for each phase separately.
News phase
Participants’ IBI responses significantly increased across the first
three minutes of consultations (b = 23.97, SE = 3.16,
t(112) = 7.58, p < .0001, R
2
= 33.9%; see Figure 1). However,
these trends varied significantly by role (patient vs. doctor; see
Figure 1; F(1, 70.7) = 43.84, p < .0001, R
2
= 38.3%). Patients’ IBI
responses significantly increased across the first three minutes of
consultations (b = 42.59, SE = 4.38, t(84.3) = 9.73, p < .0001, R
2
= 52.9%) but doctors’ responses did not change (b = 5.34, SE =
4.08, t(28.5) = 1.31, p = .20, R
2
= 5.7%). The trajectory of IBI
responses in the news phase did not vary by news type (F(2,
216) = 0.48, p = .62, R
2
= 0.4%) nor by an interaction between
news type and role (F(2, 137) = 0.52, p = .60, R
2
= 0.8%).
The variance parameters showed significant variability in
the temporal trajectory of patients’ IBI responses in the news
phase – in other words, patients varied significantly in the
extent to which their IBI responses changed across the first
three minutes of their consultations (τ = 471.84, SE = 159.15,
Z = 2.96, p = .002; see Figure 2). This result indicates that there
were differences, from patient to patient, in how IBI responses
changed over time during the news phase. There was no
Figure 1. Average model-predicted temporal trends for patients and doctors. Note. Bands indicate standard errors.
HEALTH COMMUNICATION 5
significant variability in how doctors responded over time
during the news phase but because our sample size of unique
doctors was low (n = 18), we advise caution with these find-
ings. There was no significant variability in doctors’ responses,
averaged across all of their patients, over time: τ = 54.30,
SE = 66.11, Z = 0.82, p = .21, nor was there significant variabil-
ity in doctors’ responses, from one dyadic interaction to
another dyadic interaction, over time: τ = 2.35, SE = 79.93,
Z = 0.03, p = .49, see Figure 2.
Information phase
Participants’ IBI responses, on average, did not significantly
change during the information phase of consultations
(b = −0.36, SE = 0.37, t(152) = −0.97, p = .33, R
2
= 0.6%). In
contrast to the news phase, this pattern did not vary by role
(patient vs. doctors, F(1, 95.7) = 0.81, p = .37, R
2
= 0.8%).
However, this trend did vary by news type (F(2, 156) = 5.88,
p = .004, R
2
= 7.0%). During consultations in which good or
bad news was reported, people’s IBI responses did not change
over time (good news: b = −0.34, SE = 0.56, t(194) = −0.60,
p = .55, R
2
= 0.2%; bad news: b = 1.26, SE = 0.74, t(118) = 1.71,
p = .09, R
2
= 2.4%). However, during consultations in which
status quo news was reported, people’s IBI responses declined
over time (indicating greater autonomic arousal; b = −1.99,
SE = 0.61, t(184) = −3.27, p = .001, R
2
= 5.4%). These pat-
terns did not differ as a function of role (meaning that
news type did not also interact with role to predict the
change in IBI responses across the information phase of
consultations [F(2, 98.4) = 2.07, p = .13, R
2
= 4.0%]). The
pattern of responses over time during status quo consul-
tations significantly differed those during good news con-
sultations (F(1, 189) = 4.04, p = .046, R
2
= 2.1%) and
during bad news consultations (F(1, 141) = 11.55, p
< .001, R
2
= 7.6%).
The variance parameters we estimated showed significant
variability in the temporal trajectory of patients’ IBI responses
in the information phase – in other words, patients varied
significantly in the extent to which their IBI responses changed
across the information phase of their consultations (τ = 4.88,
SE = 2.74, Z = 1.79, p = .037; see Figure 3). This result indicates
that there were differences, from patient to patient, in how IBI
responses changed over time during the information phase.
We did not find significant variability in how doctors
responded over time during the information phase; we could
not estimate variance in the information phase slope for doc-
tors as a parameter (neither for variability from doctor to
doctor nor for variability from one dyadic interaction to
another dyadic interaction) so, in the predicted model, all
doctors had the same slope over time in the information
phase. However, again, we caution against over-interpreting
these findings as our sample size of unique doctors was low (n
= 18).
News phase relative to information phase
In a second set of analyses, we examined whether the trajectory of
people’s IBI responses during the news phase differed from the
Figure 2. New phase: Spaghetti plot showing model-predicted temporal trends for individual patients and doctors. Note. Each line indicates the model-predicted
trajectory for an individual participant during the news phase. The slopes in color represent the most positive slopes over time (blue), median slopes over time (green),
and most negative slopes over time (red).
6M. VIGIER ET AL.
trajectory of people’s IBI responses during the information phase.
This is different from the prior set of analyses in that the following
analyses test whether there was a significant change in the slope
from the news phase to the information phase (i.e., are people’s
responses over time different in the news phase relative to the
information phase), whereas the prior analyses test whether the
news and information phase slopes were significantly different
from zero (i.e., do people experience significant changes over time
in either the news or information phases).
The average trajectory of IBI responses during the news
phase differed from the average trajectory of IBI responses
during the information phase (F(1, 262) = 62.85, p < .0001,
R
2
= 19.3%), and this difference was moderated by role (patient
vs. doctor: F(1, 167) = 43.55, p < .0001, R
2
= 20.6%). For
patients, the change in slope from the news phase to the
information phase was significant, b = −42.65, SE = 4.52,
t(85.1) = −9.42, p < .0001, R
2
= 51.0%; a positive slope charac-
terized the news phase (b = 42.59, p < .0001) whereas a steady
slope characterized the information phase (b = −0.07, p = .90).
For doctors, the change in slope from the news phase to the
information phase was close to a conventional level of statistical
significance but did not surpass it (b = −6.77, SE = 3.71, t(736) =
−1.83, p = .068, R
2
= 0.4%); a steady slope characterized both the
news phase (b = 5.34, p = .20) and the information phase (b =
−0.65, p = 0.15). The difference in the average trajectory of IBI
responses during the news phase relative to the average trajectory
during the information phase did not differ significantly as
a function of news type (F(2, 263) = 0.77, p = .46, R
2
= 0.5%) nor
as a function of news type by role (F(2, 168) = 0.82, p = .44, R
2
=
0.9%). In sum, the primary change that occurred from the news
phase to the information phase was that the trajectory of
responses for patients leveled out. Although we found that the
trajectory of IBI responses did not differ by news type in the news
phase but did differ by news type in the information phase, this
difference was not statistically significant.
Sensitivity analyses
In one sensitivity analysis, we examined whether effects were
robust when adjusting for people’s gender, age, smoking status,
and exercise status, as well as patients’ cancer stage, patients’
cancer type, duration of the appointment, and the number of
times that a patient had met with a particular doctor. When
including these covariates, all results are consistent with the
ones presented above; full results are listed in the SM.
In a second sensitivity analysis, we estimated the news slope
from minutes 1 through 2 and the information slope from
minutes 2 through 20. We did this because this also appeared
to be a plausible way to differentiate the two temporal trends in
the data. All results were consistent with the ones presented
above; full results are listed in the SM.
Discussion
In the current work, we examined changes in ANS activity of
patients and their oncologists during real-life medical consul-
Figure 3. Information phase: Spaghetti plot showing model-predicted temporal trends for individual patients. Note. Each line indicates the model-predicted trajectory
for an individual patient during the information phase. The slopes in color represent the most positive change over time (blue), median change over time (green), and
the most negative change over time (red).
HEALTH COMMUNICATION 7
tations in which doctors reported and discussed patients’
recent scan results. We investigated whether responses chan-
ged over time during two phases of the consultations (the news
phase and the information phase) and whether the changes
varied as a function of role (patient vs. doctor) and the type of
news that patients received (bad, good, status quo). Three
primary findings emerged. One, patients’ arousal diminished
during the news phase and was then steady throughout the
information phase, whereas doctors’ responses were steady
during both of these phases. Two, people showed significant
differences in arousal trajectories in the information phase
based on news type. Responses declined (indicating greater
autonomic arousal) after the delivery of status quo news, but
remained steady after the delivery of good and bad news.
Three, there was significant heterogeneity in patients’
responses during both the news and information phases,
meaning that patients varied in the extent to which their
physiological responses changed across these phases; we did
not observe significant heterogeneity among doctors in their
responses in either phase of the consultations.
Regarding the first primary finding – that patients’ arousal
diminished during the news phase – we think this reflects
a change in patients’ anticipatory anxiety and uncertainty,
which dissipate once news is delivered. In combination with
other work documenting the anticipatory anxiety that patients
feel leading up to news-focused medical consultations
(Falkenstein et al., 2020; Sweeny & Falkenstein, 2015), these
data suggest that patients experience a rapid release from these
high-arousal, anticipatory states. It is almost as though patients
“recover” from their anticipatory stress during these first few
minutes, somewhat similar to the way that people recover from
a stressor in the lab (Buske-Kirschbaum et al., 2002; Childs
et al., 2006). Future research might try to more precisely target
the factors that cause these quick changes. For example, are
these decreases in ANS arousal facilitated by the presence of
patients’ doctors (after waiting alone) or by feeling more
certain once news is received? Regardless of the cause, these
results suggest that doctors should wait to deliver additional
information, for example, about changes in treatments, or to
ask patients to make important decisions until after this initial
arousal has subsided, as patients may better process informa-
tion at that point (Medendorp et al., 2017; Sep et al., 2014;
Visser et al., 2017).
For doctors, the steady trends throughout consultations
probably indicate that they remain at a similar level of atten-
tiveness and vigilance throughout the entire consultation.
Future work might consider whether these trends match peo-
ple’s subjective experiences and whether they are associated
with short- and long-term emotional well-being surrounding
treatment (for patients) and their profession (for doctors).
In the information phase, we observed that both patients’
and doctors’ responses did not significantly change over time.
We interpret this finding to mean that once initial news has
been delivered to patients, on average, patients’ and doctors’
arousal does not fluctuate during the rest of this type of con-
sultation. In addition, we found some evidence that people’s
experiences varied as a function of news type during the
information phase. Specifically, patients’ and doctors’ arousal
increased over time when status quo news was discussed,
compared to good and bad news, where arousal remained
steady. This may be because status quo news carries greater
uncertainty about what to do next, which can be anxiety-
provoking (Beach, 2021). Status quo news may also be asso-
ciated with greater cognitive load, as patients and doctors
carefully consider which path to take next. We are cautious
about overinterpreting these findings regarding news type
during the information phase, though, because we did not
observe that news type played a significantly different role in
the news phase vs. the information phase.
Our second primary finding – differences in arousal trajec-
tories in the information phase based on news type – aligns
with research showing that the delivery of status quo news
often differs from the delivery of both good news and bad
news. One reason for this is that good and bad news tend to be
perceived similarly by everyone: good news is desirable; bad
news is undesirable. Status quo news, on the other hand, tends
to be perceived differently by doctors and patients: doctors
consider status quo news as similar to good news, but patients
do not (Beach, 2021; Maynard et al., 2016; Singh et al., 2017).
Disagreement over the meaning of status quo news, along with
the inherent uncertainty of status quo news, mean that status
quo consultations can be difficult to navigate. For instance,
doctors try to present status quo news in a positive light, trying
to convince patients that it is positive, while recognizing that
patients are unlikely to see it that way (Maynard et al., 2016;
Singh et al., 2017). In response, patients can resist and question
doctors’ perspectives, expressing uncertainty and confusion
about the meaning of the news and future courses of action
(Beach, 2021). Thus, the finding we observed here – that
autonomic arousal increased over time for status quo consul-
tations but remained steady for good news and bad news
consultations – may be due to the increased interactional
complexity of status quo consultations. Future work might
attempt to directly tie certain interactional features (e.g.,
more question-asking on the part of patients) to changes in
autonomic responses to better understand this association. In
addition, future work might explore whether the level of agree-
ment between doctors and patients in their perception of news
shapes autonomic responses. In the current work, doctors
classified news, but the extent to which doctors and patients
differ in their classifications might also be tied to people’s
responses.
Regarding our third primary finding – specifically, that
patients exhibited significant variability in their responses
over time in both the news and information phases–there are
several factors that might contribute to this variability. Stress
appraisals, social support, length of illness, and uncertainty
tolerance may all play a role in how patients’ experiences
unfold during these consultations and are all worthwhile tar-
gets for future research on this topic (e.g., Blascovich &
Mendes, 2000; Goodyke et al., 2021). One goal of such research
might be to search for predictors of reduced arousal, in order
to develop interventions that, alongside reducing ANS arousal,
might also help improve patients’ recall of information
received during the consultation, their prognostic awareness,
and their emotional well-being (Danzi et al., 2018; McColl-
Kennedy et al., 2017; Van Osch et al., 2014). Regardless of the
factors associated with changes in ANS arousal, the finding
8M. VIGIER ET AL.
that there is significant variability in patients’ experiences is
important because it suggests that a “one-size-fits-all”
approach is not appropriate for doctors when thinking about
how to communicate news to patients (for example, see also
Lee et al., 2002; Van Dulmen, 2011). The variability shown
here means that patients react to news quite differently and
doctors must take that into consideration when approaching
these consultations.
Strengths and limitations
A major strength of this study is that we examined trends in
doctors’ and patients’ arousal throughout real-life consulta-
tions in a highly distressing context (oncology appointments).
To our knowledge, this is the first study that examined real
patients’ responses during real consultations and which exam-
ined real doctors’ responses while interacting with real
patients. We consider this worthwhile because what happens
in real social interactions where the stakes are high is often
very different from what happens in imagined, scripted, or
hypothetical social interactions (Carey et al., 2020; Kumar &
Epley, 2021; Moore et al., 2019).
Another strength of this work is that we evaluated how
people’s ANS responses change from minute-to-minute
throughout news-focused consultations. This is in contrast to
previous work which has averaged physiological responses
across the entire consultation or across different phases
(Meunier et al., 2013; Shaw et al., 2015; Visser et al., 2016). By
taking a more fine-grained approach, we were able to investigate
not only how people’s physiological responses changed between
different phases of the consultation but also how their responses
fluctuated throughout those phases as well. This is important
given that changes in people’s responses may be stronger pre-
dictors of their future affect and behavior than average levels of
those experiences (Carver & Scheier, 1990).
Lastly, by using a multilevel modeling approach to analyze
these data, we were also able to examine variability in the
primary effects that we reported. This is important because it
highlights important targets for future research: given that
patients exhibit significant variability in their physiological
responses over time during these consultations, future research
would do well to examine the kinds of factors that create this
variability.
Of course, our study also has several limitations. First, the
type of news that patients received was only classified by doc-
tors; thus, patients’ experience regarding the valence of this
information might differ (Beach, 2021; Christakis & Lamont,
2000; Cupit-Link et al., 2018; Singh et al., 2017). Second, we did
not evaluate subjective experiences – like stress, negative affect,
perseverative thinking, or prognostic awareness – and so we do
not know whether changes in physiological arousal were asso-
ciated with changes in subjective experiences as well (Blascovich
& Mendes, 2000; Brown et al., 2009). Third, due to the nature of
the clinical routine, we were not able to collect resting “baseline”
responses prior to consultations, however, given the stress that
patients feel even upon arrival at the hospital (prior to any
consultation even starting (Okazaki et al., 2009), any responses
collected prior to the consultations would be unlikely to be true
resting responses anyway. Fourth, in our data, 20% of
consultations lasted longer than 20 minutes. Although we did
not have the statistical power to examine what happened at the
ends of these longer consultations, future work might consider
doing that.
Lastly, based on research on news-focused medical consulta-
tions specifically, we anticipated two distinct phases of consulta-
tions: a news phase and an information phase. We observed
evidence of these two distinct phases within the average trajec-
tories of physiological responses over time. We also confirmed
with senior doctors in the hospital unit in which these data were
collected that all consultations focusing on the results of CT
scans include these two phases in that order. However, future
work should use additional methods (e.g., audio recordings or
detailed post-consultation reports from doctors and patients) to
get more specific information about what happened during the
consultations, the length of the different phases for each indivi-
dual consultation, and whether additional phases (e.g., discus-
sion of symptoms) existed and, if so, when.
Conclusion
In this study, we examined trends in patients’ and doctors’ ANS
activity throughout news-focused medical consultations. We
found that patients’ arousal decreased over the news phase and
remained steady during the information phase, whereas doctors’
physiological responses remained rather stable across both the
news and information phases of the consultations. Moreover,
patients showed significant heterogeneity in their responses dur-
ing both the news and information phases. These results indicate
that patients and doctors experience news-focused medical con-
sultations differently from each other and that patients’ experi-
ences change dynamically over the different phases of the
consultations. These data suggest that consultations should con-
sider how patients’ physiological responses change across ses-
sions in order to allow optimal delivery of information and to
support patients’ well-being. In addition, they suggest that
research into the factors that influence the wide range of patients’
experiences during such encounters is needed.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Credit author statement
Marta Vigier: Conceptualization, Methodology, Project administration,
Investigation, Data Curation, Visualization, Writing – Original Draft.
Katherine R. Thorson: Conceptualization, Methodology, Formal
Analysis, Resources, Data Curation, Visualization, Writing – Original
Draft. Elisabeth Andritsch: Resources. Andreas R. Schwerdtfeger:
Supervision, Writing – Review and Editing, Resources.
Funding
The authors acknowledge the financial support by the University of Graz.
ORCID
Marta Vigier http://orcid.org/0000-0002-2307-4411
Katherine R. Thorson http://orcid.org/0000-0003-1528-1071
HEALTH COMMUNICATION 9
References
Beach, W. A. (2021). Managing “stable” cancer news. Social Psychology
Quarterly, 84(1), 26–48. https://doi.org/10.1177/0190272520976133
Bensing, J., Verheul, W., Dulmen, A. M., & Li, H. Z. (2008). Patient
anxiety in the medical encounter: A study of verbal and nonverbal
communication in general practice. Health Education, 108(5),
373–383. https://doi.org/10.1108/09654280810899993
Blascovich, J., & Mendes, W. B. (2000). Challenge and threat appraisals:
The role of affective cues. In J. P. Forgas (Ed.), Feeling and thinking:
The role of affect in social cognition (pp. 59–82). Cambridge University
Press. https://psycnet.apa.org/record/2000-07085-002
Blascovich, J., Vanman, E. J., Mendes, W. B., & Dickerson, S. (2011).
Social psychophysiology for social and personality psychology. Sage
Publications Ltd. https://doi.org/10.4135/9781446287842
Bokken, L., Rethans, J. J., van Heurn, L., Duvivier, R., Scherpbier, A., & van der
Vleuten, C. (2009). Students’ views on the use of real patients and simu-
lated patients in undergraduate medical education. Academic Medicine, 84
(7), 958–963. https://doi.org/10.1097/ACM.0b013e3181a814a3
Bolger, N., Zee, K. S., Rossignac-Milon, M., & Hassin, R. R. (2019). Causal
processes in psychology are heterogeneous. Journal of Experimental
Psychology: General, 148(4), 601–618. https://doi.org/10.1037/
xge0000558
Boyd, E., & Heritage, J. (2006). Taking the history: Questioning during
comprehensive history-taking. In J. Heritage & D. Maynard (Eds.),
Communication in medical care: Interaction between primary care
physicians and patients (pp. 151–184). Cambridge University Press.
https://doi.org/10.1017/CBO9780511607172.008
Brown, R., Dunn, S., Byrnes, K., Morris, R., Heinrich, P., & Shaw, J.
(2009). Doctors’ stress responses and poor communication perfor-
mance in simulated bad-news consultations. Academic Medicine, 84
(11), 1595–1602. https://doi.org/10.1097/ACM.0b013e3181baf537
Buske-Kirschbaum, A., Geiben, A., Höllig, H., Morschhäuser, E., &
Hellhammer, D. (2002). Altered responsiveness of the
hypothalamus-pituitary-adrenal axis and the sympathetic adrenome-
dullary system to stress in patients with atopic dermatitis. The Journal
of Clinical Endocrinology and Metabolism, 87(9), 4245–4251. https://
doi.org/10.1210/jc.2001-010872
Byrne, P. S., & Long, B. E. (1978). Doctors talking to patients: A study of the
verbal behaviour of general practitioners consulting in their surgeries.
His Majesty’s Stationery Office.
Carey, A. L., Rentscher, K. E., & Mehl, M. R. (2020). Naturalistic observa-
tion of social interactions. In K. Sweeny, M. L. Robbins, & L. M. Cohen
(Eds.), The Wiley encyclopedia of health psychology (pp. 373–383). John
Wiley & Sons Ltd. https://doi.org/10.1002/9781119057840.ch87
Carver, C. S., & Scheier, M. F. (1990). Origins and functions of positive
and negative affect: A control-process view. Psychological Review, 97
(1), 19–35. https://doi.org/10.1037/0033-295X.97.1.19
Childs, E., Vicini, L. M., & De Wit, H. (2006). Responses to the Trier
Social Stress Test (TSST) in single versus grouped participants.
Psychophysiology, 43(4), 366–371. https://doi.org/10.1111/j.1469-
8986.2006.00414.x
Christakis, N. A., & Lamont, E. B. (2000). Extent and determinants of
error in doctors’ prognoses in terminally ill patients: Prospective
cohort study. British Medical Journal, 320(7233), 469–473. https://
doi.org/10.1136/bmj.320.7233.469
Cohen, L., Baile, W., Henninger, E., Agarwal, S., Kudelka, A., Lenzi, R.,
Sterner, J., & Marshall, G. (2003). Physiological and psychological
effects of delivering medical news using a simulated physician–patient
scenario. Journal of Behavioral Medicine, 26(5), 459–471. https://doi.
org/10.1023/A:1025724118504
Cupit-Link, M., Syrjala, K. L., & Hashmi, S. K. (2018). Damocles’ syn-
drome revisited: Update on the fear of cancer recurrence in the com-
plex world of today’s treatments and survivorship. Hematology/
Oncology and Stem Cell Therapy, 11(3), 129–134. https://doi.org/10.
1016/j.hemonc.2018.01.005
Danzi, O. P., Perlini, C., Tedeschi, F., Nardelli, M., Greco, A.,
Scilingo, E. P., Valenza, G., & Del Piccolo, L. (2018). Affective com-
munication during bad news consultation. Effect on analogue patients’
heart rate variability and recall. Patient Education and Counseling, 101
(11), 1892–1899. https://doi.org/10.1016/j.pec.2018.06.009
Dart, A. M., Du, X. J., & Kingwell, B. A. (2002). Gender, sex hormones and
autonomic nervous control of the cardiovascular system.
Cardiovascular Research, 53(3), 678–687. https://doi.org/10.1016/
s0008-6363(01)00508-9
Deinzer, R., Kiupel, S., & Weik, U. (2019). Endocrine and psychological
stress response in simulated doctor-patient interactions in medical
education. Psychoneuroendocrinology, 105, 172–177. https://doi.org/
10.1016/j.psyneuen.2018.09.028
Del Piccolo, L., Mazzi, M. A., Mascanzoni, A., Lonardi, M., De Felice, M.,
Danzi, O. P., Buizza, C., Ghilardi, A., Bottacini, A., & Goss, C. (2019).
Factors related to the expression of emotions by early-stage breast
cancer patients. Patient Education and Counseling, 102(10),
1767–1773. https://doi.org/10.1016/j.pec.2019.04.002
Eastwick, P. W., Finkel, E. J., Krishnamurti, T., & Loewenstein, G. (2008).
Mispredicting distress following romantic breakup: Revealing the time
course of the affective forecasting error. Journal of Experimental Social
Psychology, 44(3), 800–807. https://doi.org/10.1016/j.jesp.2007.07.001
Espinosa, E., González Barón, M., Zamora, P., Ordóñez, A., & Arranz, P.
(1996). Doctors also suffer when giving bad news to cancer patients.
Supportive Care in Cancer, 4(1), 61–63. https://doi.org/10.1007/
BF01769878
Falkenstein, A., Dooley, M. D., & Sweeny, K. (2020). Waiting for health
news. In K. Sweeny, M. L. Robbins, & L. M. Cohen (Eds.), The Wiley
encyclopedia of health psychology (pp. 781–788). John Wiley & Sons Ltd.
Goodyke, M. P., Hershberger, P. E., Bronas, U. G., & Dunn, S. L. (2021).
Perceived social support and heart rate variability: An integrative
review. Western Journal of Nursing Research, 44(11), 1057–1067.
https://doi.org/10.1177/01939459211028908
Hagiwara, N., Kashy, D. A., & Penner, L. A. (2014). A novel analytical
strategy for patient-physician communication research: The one-with-
many design. Patient Education and Counseling, 95(3), 325–331.
https://doi.org/10.1016/j.pec.2014.03.017
Helft, P. R., & Petronio, S. (2007). Communication pitfalls with cancer
patients: “Hit-and-Run” deliveries of bad news. Journal of the
American College of Surgeons, 205(6), 807–811. https://doi.org/10.
1016/j.jamcollsurg.2007.07.022
Heritage, J., & Maynard, D. (2006). Communication in medical care:
Interaction between physicians and patients. Cambridge University
Press. https://doi.org/10.1017/CBO9780511607172
Hoscheidt, S. M., LaBar, K. S., Ryan, L., Jacobs, W. J., & Nadel, L. (2014).
Encoding negative events under stress: High subjective arousal is
related to accurate emotional memory despite misinformation
exposure. Neurobiology of Learning and Memory, 112, 237–247.
https://doi.org/10.1016/j.nlm.2013.09.008
Howell, J. L., & Sweeny, K. (2016). Is waiting bad for subjective health?
Journal of Behavioral Medicine, 39(4), 652–664. https://doi.org/10.
1007/s10865-016-9729-7
Hu, M. X., Lamers, F., de Geus, E. J. C., & Penninx, B. W. J. H. (2017).
Influences of lifestyle factors on cardiac autonomic nervous system
activity over time. Preventive Medicine, 94, 12–19. https://doi.org/10.
1016/j.ypmed.2016.11.003
Hulsman, R. L., Pranger, S., Koot, S., Fabriek, M., Karemaker, J. M., &
Smets, E. M. A. (2010). How stressful is doctor-patient communica-
tion? Physiological and psychological stress of medical students in
simulated history taking and bad-news consultations. International
Journal of Psychophysiology, 77(1), 26–34. https://doi.org/10.1016/j.
ijpsycho.2010.04.001
Jagosh, J., Donald Boudreau, J., Steinert, Y., MacDonald, M. E., &
Ingram, L. (2011). The importance of physician listening from the
patients’ perspective: Enhancing diagnosis, healing, and the doctor-
patient relationship. Patient Education and Counseling, 85(3), 369–374.
https://doi.org/10.1016/j.pec.2011.01.028
Kenny, D. A., & Kashy, D. A. (2011). Dyadic data analysis using multilevel
modeling. In J. J. Hox & J. K. Roberts (Eds.), Handbook for advanced
multilevel analysis (pp. 335–370). Routledge/Taylor & Francis Group.
https://doi.org/10.4324/9780203848852
Kumar, A., & Epley, N. (2021). It’s surprisingly nice to hear you:
Misunderstanding the impact of communication media can lead to
10 M. VIGIER ET AL.
suboptimal choices of how to connect with others. Journal of
Experimental Psychology: General, 150(3), 595–607. https://doi.org/
10.1037/xge0000962
Lee, S. J., Back, A. L., Block, S. D., & Stewart, S. K. (2002). Enhancing
physician-patient communication. Hematology, 2002(1), 464–483.
https://doi.org/10.1182/asheducation-2002.1.464
Lipsitz, L. A., & Novak, V. (2013). Ageing and the autonomic nervous
system. In C. J. Mathias & R. Bannister (Eds.), Autonomic failure:
A textbook of clinical disorders of the autonomic nervous system (5th
ed, pp. 847–859). Oxford University Press. https://doi.org/10.1093/
med/9780198566342.003.0072
Maynard, D. W., Cortez, D., & Campbell, T. C. (2016). ‘End of life’
conversations, appreciation sequences, and the interaction order in
cancer clinics. Patient Education and Counseling, 99(1), 92–100.
https://doi.org/10.1016/j.pec.2015.07.015
McColl-Kennedy, J. R., Danaher, T. S., Gallan, A. S., Orsingher, C.,
Lervik-Olsen, L., & Verma, R. (2017). How do you feel today?
Managing patient emotions during health care experiences to enhance
well-being. Journal of Business Research, 79, 247–259. https://doi.org/
10.1016/j.jbusres.2017.03.022
Medendorp, N. M., Visser, L. N. C., Hillen, M. A., de Haes, J. C. J. M., &
Smets, E. M. A. (2017). How oncologists’ communication improves
(analogue) patients’ recall of information. A randomized video-
vignettes study. Patient Education and Counseling, 100(7),
1338–1344. https://doi.org/10.1016/j.pec.2017.02.012
Meunier, J., Merckaert, I., Libert, Y., Delvaux, N., Etienne, A.-M., Liénard, A.,
Bragard, I., Marchal, S., Reynaert, C., Slachmuylder, J.-L., & Razavi, D.
(2013). The effect of communication skills training on residents’ physio-
logical arousal in a breaking bad news simulated task. Patient Education
and Counseling, 93(1), 40–47. https://doi.org/10.1016/j.pec.2013.04.020
Moore, M. M., Chan, R. C. K., Huang, J., & Martin, E. A. (2019). Affective
forecasting and accuracy in social anhedonia: Predicted and experi-
enced emotion for a social interaction. Journal of Clinical Psychology,
75(9), 1684–1700. https://doi.org/10.1002/jclp.22796
Morgado, P., Sousa, N., & Cerqueira, J. J. (2015). The impact of stress in
decision making in the context of uncertainty. Journal of Neuroscience
Research, 93(6), 839–847. https://doi.org/10.1002/jnr.23521
Okazaki, S., Iwamitsu, Y., Masaru, K., Todoroki, K., Suzuki, S.,
Yamamoto, K., Hagino, M., Watanabe, M., & Miyaoka, H. (2009).
The psychological responses of outpatient breast cancer patients before
and during first medical consultation. Palliative and Supportive Care, 7
(3), 307–314. https://doi.org/10.1017/S147895150999023X
Ong, L. M. L., Visser, M. R. M., Lammes, F. B., & de Haes, J. C. J. M.
(2000). Doctor-patient communication and cancer patients’ quality of
life and satisfaction. Patient Education and Counseling, 41(2), 145–156.
https://doi.org/10.1016/S0738-3991(99)00108-1
Pottier, P., Hardouin, J.-B., Dejoie, T., Bonnaud, A., Le Loupp, A.-G.,
Planchon, B., & LeBlanc, V. (2011). Stress responses in medical students
in ambulatory and in-hospital patient consultations. Medical Education,
45(7), 678–687. https://doi.org/10.1111/j.1365-2923.2011.03935.x
Robinson, J. D. (2003). An interactional structure of medical activities during
acute visits and its implications for patients’ participation. Health
Communication, 15(1), 27–59. https://doi.org/10.1207/
S15327027HC1501_2
Rosen, R. D., & Sapra, A. (2023). Tumor nodes metastasis classification.
StatPearls Publishing.
Schwabe, L., Joëls, M., Roozendaal, B., Wolf, O. T., & Oitzl, M. S. (2012).
Stress effects on memory: An update and integration. Neuroscience &
Biobehavioral Reviews, 36(7), 1740–1749. https://doi.org/10.1016/j.neu
biorev.2011.07.002
Sep, M. S. C., van Osch, M., van Vliet, L. M., Smets, E. M. A., &
Bensing, J. M. (2014). The power of clinicians’ affective communica-
tion: How reassurance about non-abandonment can reduce patients’
physiological arousal and increase information recall in bad news
consultations. An experimental study using analogue patients.
Patient Education and Counseling, 95(1), 45–52. https://doi.org/10.
1016/j.pec.2013.12.022
Shaw, J., Brown, R., & Dunn, S. (2015). The impact of delivery style on
doctors’ experience of stress during simulated bad news consultations.
Patient Education and Counseling, 98(10), 1255–1259. https://doi.org/
10.1016/j.pec.2015.08.023
Shaw, J., Brown, R., Heinrich, P., & Dunn, S. (2013). Doctors’ experience of
stress during simulated bad news consultations. Patient Education and
Counseling, 93(2), 203–208. https://doi.org/10.1016/j.pec.2013.06.009
Singh, S., Cortez, D., Maynard, D., Cleary, J. F., DuBenske, L., &
Campbell, T. C. (2017). Characterizing the nature of scan results
discussions: Insights into why patients misunderstand their
prognosis. Journal of Oncology Practice, 13(3), e231–e239. https://doi.
org/10.1200/JOP.2016.014621
Street, R. L., Makoul, G., Arora, N. K., & Epstein, R. M. (2009). How does
communication heal? Pathways linking clinician-patient communica-
tion to health outcomes. Patient Education and Counseling, 74(3),
295–301. https://doi.org/10.1016/j.pec.2008.11.015
Sweeny, K. (2018). On the experience of awaiting uncertain news. Current
Directions in Psychological Science, 27(4), 281–285. https://doi.org/10.
1177/0963721417754197
Sweeny, K., & Cavanaugh, A. G. (2012). Waiting is the hardest part:
A model of uncertainty navigation in the context of health news.
Health Psychology Review, 6(2), 147–164. https://doi.org/10.1080/
17437199.2010.520112
Sweeny, K., & Falkenstein, A. (2015). Is waiting the hardest part?
Comparing the emotional experiences of awaiting and receiving bad
news. Personality and Social Psychology Bulletin, 41(11), 1551–1559.
https://doi.org/10.1177/0146167215601407
Tarvainen, M. P., Niskanen, J. P., Lipponen, J. A., Ranta-Aho, P. O., &
Karjalainen, P. A. (2014). Kubios HRV- heart rate variability analysis
software. Computer Methods and Programs in Biomedicine, 113(1),
210–220. https://doi.org/10.1016/j.cmpb.2013.07.024
Tyng, C. M., Amin, H. U., Saad, M. N. M., & Malik, A. S. (2017). The
influences of emotion on learning and memory. Frontiers in
Psychology, 8, 1–22. https://doi.org/10.3389/fpsyg.2017.01454
Van Dulmen, A. M., & Bensing, J. M. (2002). Health promoting effects of
the physician-patient encounter. Psychology, Health & Medicine, 7(3),
289–300. https://doi.org/10.1080/13548500220139421
Van Dulmen, S. (2011). The value of tailored communication for
person-centred outcomes. Journal of Evaluation in Clinical Practice,
17(2), 381–383. https://doi.org/10.1111/j.1365-2753.2010.01586.x
Van Dulmen, S., Tromp, F., Grosfeld, F., ten Cate, O., & Bensing, J.
(2007). The impact of assessing simulated bad news consultations on
medical students’ stress response and communication performance.
Psychoneuroendocrinology, 32(8), 943–950. https://doi.org/10.1016/j.
psyneuen.2007.06.016
Van Osch, M., Sep, M., van Vliet, L. M., van Dulmen, S., & Bensing, J. M.
(2014). Reducing patients’ anxiety and uncertainty, and improving
recall in bad news consultations. Health Psychology, 33(11),
1382–1390. https://doi.org/10.1037/hea0000097
Visser, L. N. C., Tollenaar, M. S., Bosch, J. A., van Doornen, L. J. P., de
Haes, H. C. J. M., & Smets, E. M. A. (2016). Analogue patients’ self-
reported engagement and psychophysiological arousal in a video-
vignettes design: Patients versus disease-naïve individuals. Patient
Education and Counseling, 99(10), 1724–1732. https://doi.org/10.
1016/j.pec.2016.04.012
Visser, L. N. C., Tollenaar, M. S., Bosch, J. A., van Doornen, L. J. P., de
Haes, H. C. J. M., & Smets, E. M. A. (2017). Are psychophysiological
arousal and self-reported emotional stress during an oncological con-
sultation related to memory of medical information? An experimental
study. Stress, 20(1), 103–111. https://doi.org/10.1080/10253890.2017.
1286323
HEALTH COMMUNICATION 11
