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Emotional Reactivity to Daily Life Stress in Psychosis
Inez Myin-Germeys, PhD;
Jim van Os, MD, PhD, MRCPsych;
Joseph E. Schwartz, PhD;
Arthur A. Stone, PhD;
Philippe A. Delespaul, PhD
Arch Gen Psychiatry. 2001;58:1137-1144.
ABSTRACT
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Background The vulnerability-stress model of psychotic disorders describes, in
essence, an interaction between personal vulnerability and environmental stressors.
The present study investigated this interaction and studied emotional reactivity
to daily life stress as a vulnerability marker for psychotic illness.
Methods Patients with psychotic illness (n = 42), their first-degree relatives
(n = 47), and control subjects (n = 49) were studied with the Experience Sampling
Method (a structured diary technique assessing thoughts, current context,
and mood in daily life) to assess (1) appraised subjective stress of daily
events and smaller disturbances in daily life and (2) emotional reactivity
conceptualized as changes in both negative affect and positive affect.
Results Multilevel regression analyses showed that an increase in subjective
stress was associated with an increase in negative affect and a decrease in
positive affect in all groups. However, the groups differed quantitatively
in their pattern of reactions to stress. Patients with psychotic illness reacted
with more intense emotions to subjective appraisals of stress in daily life
than control subjects. The decrease in positive affect in the relatives was
similar to that of the patients, while the increase in negative affect in
this group was intermediary to that of patients and control subjects.
Conclusions Higher levels of familial risk for psychosis were associated with higher
levels of emotional reactivity to daily life stress in a dose-response fashion.
Subtle alterations in the way persons interact with their environment may
constitute part of the vulnerability for psychotic illness.
INTRODUCTION
THE VULNERABILITY-stress model1, 2, 3
has been widely accepted as a heuristically useful framework for the study
of the cause and clinical course of schizophrenia and other psychotic disorders.
According to this model, psychiatric symptoms emerge whenever a threshold
of stressors exceeds the individual's vulnerability level, with the latter
being a stable characteristic.4 The stress-vulnerability
concept is essentially interactional, and as such, it remains difficult to
investigate. To date, most research using this model has focussed on either
the indicator of vulnerability or the stressor; whereas their interplay has
rarely been examined.5 For example, cognitive
deficits,6 abnormalities in smooth-pursuit
eye movements,7 alterations of event-related
potentials,8 and cerebral structural abnormalities9 are more prevalent in the first-degree relatives of
patients with schizophrenia, which suggests that they are indicators of vulnerability.
Similarly, onset and relapse of schizophrenia and other psychotic disorders
are associated with minor daily hassles,10
life events,11, 12 exposure to
the stresses of urban life,13 or a hostile
family environment.12 However, these have mostly
been examined without acknowledgment of their specific effects on vulnerable
persons, and without acknowledging that reaction to stress is a continuous
process with important intraindividual variation over time.
In the current study, we used an intensive field method14, 15, 16
to examine subjective experience in the flow of daily life in order to address
the following questions: (1) how does the affect of persons vulnerable to
psychosis shift when they encounter a stressor in their natural environment,
and (2) in what way does the emotional reaction to a real life stressor vary
with differing degrees of vulnerability? Three groups were included on the
basis of differences in vulnerability for psychotic illness: patients (most
vulnerable), their first-degree relatives (intermediate vulnerability), and
control subjects (least vulnerable).
SUBJECTS AND METHODS
SUBJECTS
The sample consisted of 50 subjects with psychotic illness (patients),
50 first-degree relatives of individuals with a psychotic illness, and 50
control subjects. All patients were receiving treatment. Selection criteria,
assessed by a research physician or research psychologist, were a lifetime
occurrence of psychotic symptoms (according to Research Diagnostic Criteria)
for at least 2 weeks in clear consciousness for the patient group; no lifetime
history of psychotic symptoms for the first-degree relatives group; and neither
a family nor a personal history of psychosis, or current use of psychotropic
medication for the control group. Inclusion criteria were between 18 and 55
years, sufficient command of the Dutch language, and normal physical examination
results. Exclusion criteria were endocrine, cardiovascular, or brain disease;
use of alcohol in excess of 5 standard units per day; weekly use of illicit
drugs; and history of head injury with loss of consciousness. A fifth exclusion
criterion for patients included being in need of inpatient care, intensive
case management home care, or crisis intervention. Written informed consent,
conforming to the local ethics committee guidelines, was obtained from all
subjects. Patients were recruited through the inpatient and outpatient mental
health facilities in Maastricht, the Netherlands, and through patient associations
in the southern part of the Netherlands. Relatives were recruited through
participating patients and relatives' groups in the same area. Control subjects
were recruited from the general population in the local area through a random
mailing procedure.
The diagnostic procedure included extensive screening with diagnostic
interviews that included the Life Chart,17
the Brief Psychiatric Rating Scale,18 and the
Positive and Negative Syndrome Scale,19 to
map psychiatric symptomatology. Interview data and clinical record data were
used to complete the Operational Criteria Checklist for Psychotic Illness,
yielding DSM-III-R diagnoses through the OPCRIT computer
program.20
PROCEDURE
The Experience Sampling Method (ESM) is a within-day self-assessment
technique. Previous applications of ESM in patients with schizophrenia14, 15, 16 have demonstrated
the feasibility, validity, and reliability of the method in this population.
Subjects were studied in their normal daily living environments. They each
received a digital wristwatch and a set of ESM self-assessment forms collated
in a booklet for each day. Ten times per day on 6 consecutive days, the watch
emitted a signal (beep) at unpredictable moments between 7:30 AM and 10:30
PM. After every signal, subjects were asked to stop their activity and fill
out the ESM self-assessment forms previously handed to them, collecting reports
of thoughts, current context (activity, persons present, location), appraisals
of the current situation, and mood. All self-assessments were rated on 7-point
Likert scales.
The ESM procedure was explained to the subjects during an initial briefing
session, and a practice form was completed to confirm that subjects were able
to understand the 7-point Likert scale format. Subjects were instructed to
complete their reports immediately after the beep, thus minimizing memory
distortions, and to record the time at which they completed the form. During
the actual sampling period, research staff repeatedly called the subjects
to assess whether they were complying with the instructions. To know whether
the subjects had completed the form within 15 minutes of the beep, the time
at which subjects indicated they completed the report was compared with the
actual time of the beep. All reports completed later than 15 minutes after
the signal were excluded from the analysis. Previous work14
has shown that reports completed after this interval are less reliable and
consequently less valid. Subjects with fewer than 20 valid reports were excluded
from the analysis.
MEASURES
Mood states were assessed with 10 mood adjectives rated on 7-point Likert
scales (1-7, indicating "not at all" to "very"). Factor analyses (principal
component analysis with Harris-Kaiser rotation) on the raw within-subject
scores identified 2 factors with eigenvalues greater than 1 explaining 41%
of the total variance. Two factor-based scales with equal weights for each
item were created. The mood adjectives "down," "guilty," "insecure," "lonely,"
and "anxious" formed the negative affect (NA) scale (Cronbach  = .79).
The mood adjectives "happy," "cheerful," "relaxed," and "satisfied" formed
the positive affect (PA) scale (Cronbach = .89). The item "angry"
had low loadings on both factors and was excluded to enhance differentiation
between the 2 factors.
Stress was conceptualized as the subjective
appraised stressfulness of distinctive events and minor disturbances that
continually happen in the natural flow of daily life. Four different stress
measures were computed. For event-related stress, subjects were asked to report
the most important event that happened between the current and the previous
reports. This event was subsequently rated on a 7-point bipolar scale (-3
= very unpleasant, 0 = neutral, 3 = very pleasant). Responses were recoded
to allow high scores to reflect stress (-3 = very pleasant, 0 = neutral,
3 = very unpleasant). For activity-related stress, subjects judged their current
activity on 3 self-report items (scored on 7-point Likert scales). The mean
of the scales "I am not skilled to do this activity," "I would rather do something
else," and "This activity requires effort," formed the activity-related stress
scale ( = .52). For thought-related stress, subjects judged their thoughts
at the moment of the beep on the 7-point Likert scale (ie, "My current thought
is unpleasant.") For social stress, subjects were asked to evaluate the social
context when other persons were present on two 7-point Likert scales "I don't
like the company" and " would rather be alone' ( = .59). The mean constituted
the social stress scale.
STATISTICAL ANALYSES
Experience Sampling Method data have a hierarchical structure, and multiple
observations are nested within subjects. Initial pairwise group comparisons
were performed on the subject averages for the independent and dependent variables
using 1-way analysis of variance with the Tukey multiple comparison procedure.
Correlations between the independent variables and the dependent variables
were calculated per subject and subsequently analyzed as individual-level
variables, corrected with a Fisher z transformation.
One-sample 2-tailed t tests with an level
of .05 were conducted to test whether the mean across people of these individual-level
correlation coefficients significantly deviated from zero.
To estimate the effect of the independent variables (stress) on the
dependent variables (mood), a multilevel linear random regression model21 was used. Multilevel or hierarchical linear modelling
techniques are a variant of the more often used unilevel linear regression
analyses and are ideally suited for the analysis of ESM data consisting of
multiple observations in 1 person (ie, at 2 levels [ESM-beep level and subject
level]).22 In the ESM, observations from the
same subject are more similar than observations from different subjects; therefore,
the residuals are not independent. Conventional regression techniques do not
take into account the variance components at 2 different levels. Furthermore,
the variance explained by autocorrelation (observations from 1 subject that
are closer to each other in time will be more similar than those further apart)
was taken into account by including the autoregression factor in the model.
Data were analyzed with the SAS PROC MIXED module (SAS Technical Report
P-229, 1992) statistical software (SAS Institute Inc, Cary, NC). The ß
is the fixed regression coefficient of the predictor in the multilevel model
and can be interpreted identically to the estimate in a unilevel linear regression
analysis.
Multilevel linear regression analyses were conducted with standardized NA and PA scales as the dependent variables:
Thus, the effect of the independent variable (stress measure) was expressed
in SD units of the dependent variables (NA and PA). According to Cohen,23 0.8 SD can be considered a large effect size, and
0.2 SD, a small effect size. A group variable was constructed to reflect different
levels of vulnerability for psychosis. "Group" was analyzed as a 3-level categorical
variable with value labels (0 = control subjects, 1 = relatives, and 2 = patients).
Group and the different stress measures, as well as their interactions (stress
x group), were the independent variables. To assess the main effects
of group on mood, and to test whether the effect of stress on mood was modified
by group, F tests were conducted analyzing whether the differences in intercepts
and slopes were significant between the 3 categories of the group variable.
The control subjects were treated as the reference group in these analyses.
Analyses were conducted separately for each stress measure, followed by an
analysis with all stress measures entered jointly into the model to assess
the relative independence of their effects.
RESULTS
SUBJECTS
Of the 150 subjects who entered the study, 1 control subject was excluded
because of technical problems with the signalling device (see the "Procedure"
subsection). Two relatives stopped their collaboration after one day of sampling
because of objections on the part of their ill relatives. Two patients did
not return the diary booklets. One family member and 6 patients were unable
to comply with the research protocol (they had fewer than 20 valid reports
and were therefore excluded from the analyses, see the "Procedure" subsection).
The final study sample thus consisted of 138 subjects (Table 1).
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Table 1. Sociodemographic and Clinical Characteristics of the Research
Sample*
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STRESS AND MOOD MEASURES
Relatives and control subjects did not differ significantly on any of
the 4 stress measures (Table 2),
while patients scored significantly higher only on the event-related stress
measure and the social stress measure compared with the control subjects and
the relatives and control subjects, respectively. All 4 stress measures were
significantly correlated within subjects, but the correlations were low. The
highest mean correlation was 0.28 between the activity-related stress scale
and the social stress scale (95% confidence interval [CI], 0.23 0.34), and
the lowest mean correlation was 0.13 between the event-related stress scale
and the social stress scale (95% CI, 0.09-0.18).
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Table 2. Number of Valid Reports and the Independent and Dependent
Variables for Patients, Relatives, and Controls*
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The patient group reported significantly more NA and less PA than both
the relatives and the control subjects, who did not differ from each other
(Table 2). The 2 dependent variables
were significantly negatively correlated (mean r
= -0.47; 95% CI, -0.62 to -0.33).
PREDICTORS OF MOOD STATES
The multilevel random regression analyses (Table 3 and Table 4) showed that the 4 stress measures were all significantly associated with mood.
In addition, group was also significantly associated with both PA and NA.
In agreement with the unilevel analysis in Table 2, the relatives did not differ significantly from the control
subjects in prediction of mood, while the patients scored significantly higher
on NA and lower on PA than the control subjects in the multilevel model (results
not shown).
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Table 3. Multilevel Model Estimates for Positive Affect
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Table 4. Multilevel Model Estimates for Negative Affect
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Significant interaction effects were found between group and all 4 stress
measures for both NA and PA, indicating that the level of underlying vulnerability
modified the emotional reaction toward the different stressors. For example,
the effect of activity-related stress on PA was -0.22 for the patient
group, meaning that 1 unit of change in activity-related stress resulted in
a decrease in PA of 0.22 SD. The difference between the extremes of the scales
(from 1-7 on the 7-point Likert scale), therefore, was (6 x 0.22) 1.32
SDs. In the same model, 1 unit of change in activity-related stress resulted
in a -0.21 SD decrease in PA for the relatives group and a -0.12
SD decrease for the control group. This is depicted in Figure 1, where the predicted values of PA for each group are calculated
according to the formula:
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Effect of activity-related stress on positive affect (PA) in the
3 groups (patients, relatives, controls), as derived from the statistical
model.
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for each of the 7 levels of activity-related
stress. The 2 vulnerable groups (patients and relatives) reported a similar
decrease in PA that was associated with stress (nearly parallel lines), and
both were significantly larger compared with the decrease reported by the
control subjects. For NA, the relatives showed a significantly greater increase
in relation to stress than the control subjects, except for the social stress
scale. The patients reported an even larger increase in NA compared with the
control subjects. Thus, the effects of stress on NA varied in a dose-response
fashion with group; the higher the degree of vulnerability, the bigger the
increase in NA in response to stress.
The regression analyses with all stress measures entered together in
the model showed that all measures remained significant predictors of mood.
To determine which stress measure differentiated best between the 3 groups
in its effect on mood, the interaction effects were added to the model (Table 5). Thought-related stress and event-related
stress differentiated best between the 3 groups.
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Table 5. Multilevel Multivariate Model Estimates for Positive and Negative
Affect*
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In a final analysis, sex was included as a possible confounder in the
multivariate multilevel regression model. No significant effect of sex (0 = female, 1 = male) was found for either NA (ß[SE] = .002[.088]; P = .98) or PA (ß [SE] = -.21[.15]; P = .15), and the estimated effects of the stress measures differed
only by a very small amount.
COMMENT
The results show an overall association between the subjective appraisals
of events and small disturbances in the natural flow of daily life and concurrent
mood. The effect sizes were small but not negligible, especially since we
assessed frequently occurring exposures in daily life, the cumulative effects
of which may be considerable. These results extend the results reported in
several studies investigating the effects of daily events on mood24, 25, 26, 27, 28;
specifically, the increase in perceived stress was related to an increase
in NA and a decrease in PA.
Although the 4 stress measures were all independent predictors of mood
(in most analyses), we would not argue, for reasons of parsimony, for separate
vulnerabilities related to specific stressors. As the 4 stress measures are
weakly but significantly correlated, there may be a generalized sensitivity
to stress, which can be expressed in different ways.
Can stress reactivity, as defined in the present study, be considered
a marker of vulnerability for psychosis?29
Patients with psychotic illness deviated in emotional stress reactivity from
general population control subjects. The increased stress reactivity was not
likely owing solely to present psychopathology, as none of the patients was
in a florid psychotic state during the ESM period (as evidenced by the low
scores on the Brief Psychiatric Rating Scale), and all were in remission, defined as not in need of intensive inpatient or outpatient
care. Nor was it solely due to past or residual psychopathology, as the healthy
relatives group also reported excess stress reactivity compared with the control
group. For NA, the degree of stress reactivity paralleled the level of genetic
vulnerability, with relatives showing values that were intermediate to those
of patients and control subjects. For PA, an equal decrease was found for
patients and relatives. Positive affect may be a less sensitive outcome to
gauge subtle differences between patients and relatives. Taken together, the
results suggest that stress reactivity may be considered as a behavioral expression
of familial risk, and thus possibly qualify as a vulnerability marker for
psychotic illness.
The differences in stress reactivity, however, might also be understood
in terms of environmental and social circumstances differentially serving
as risk factors or protective factors in the 3 subject groups. It has been
reported that a lack of social support is associated with more emotional reactivity
toward daily stressors.24, 30 As
reduced social competence and social withdrawal are key characteristics of
schizophrenia, it seems self-evident that patients with psychotic illness
lack social support. The same could be true for the relatives group, as it
has been reported that relatives of patients with schizophrenia more often
display schizophrenialike or schizotypal traits,31
such as social anhedonia (an indifference to other people),32
social dysfunction,31 and interpersonal problems
(eg, lack of close friends).33 Another explanation
is that differences in stress appraisal and coping might mediate the effects
of stress on mood.34 Appraised stress is essentially
subjective and may not necessarily correspond with the objective situation.
As patients with psychotic illness tend to be more sensitive to environmental
stress,12, 35 they would more easily
report higher levels of appraised stress given an objective situation. However,
the present study used appraised stress as the primary independent measure,
and the patients did not report much higher levels of appraised stress. Apparently,
the patients were living a "normally stressful" life that was adjusted to
their impairment. Coping, on the other hand, may have little effect on mood
in within-day assessments,36 suggesting that
coping efforts have no immediate effect on mood, and therefore, not on stress
reactivity. A possible alternative explanation of the results is that living
with an ill family member might strongly influence stress levels and mood
in relatives. In the present study, however, only some family members were
living with their ill relatives (n = 11), and no significant differences were
found between relatives and control subjects on any dependent or independent
variable.
If stress reactivity is, in addition to being an indicator of familial
risk, also causally related to the development of the symptoms of schizophrenia,
some clinical implications would become apparent. Stress reactivity implies
an emotional reaction toward daily life stress, and as such, it leaves 2 options
for intervention: (1) reducing the stressfulness of the environment or (2)
altering personal reactivity. The first option has successfully been applied.
For example, in family intervention studies, reducing the stress in the social
environment of patients37 decreased the risk
of relapse. Altering personal stress reactivity, on the other hand, may be
more difficult. Stress reduction techniques such as physical exercise and
meditation decreased symptom severity in chronic schizophrenia38;
and emotional management therapy, including relaxation and distraction techniques,
improved emotional well-being in chronic schizophrenia but not in early psychosis.39 Cognitive-behavioral therapy for psychosis, which
is aimed at reducing emotional distress caused by psychotic symptomatology,40 could possibly be extended to emotional reactivity
during nonpsychotic periods to prevent relapse.
The present results should be viewed in the light of several potential
methodological issues. First, they are based on subjective reports. Although
subjective reports may not be highly reliable (eg, all subjects may not interpret
the questions identically), they can be valid. On the other hand, the validity
of objective approaches should not be taken for granted.41
Second, all results have been interpreted in terms of emotional reactivity
toward subjective stress. The cross-sectional analysis of the data, however,
makes it impossible to establish a causal relationship. Therefore, the reverse
may (also) be true. A worse mood may influence the subjective appraisal of
the environment. Either explanation, however, has clinical relevance. Third,
the results have been interpreted as supporting the hypothesis that stress
reactivity is a vulnerability marker for psychosis. However, as no psychiatric
control group was included, it is possible that stress reactivity is a vulnerability
marker for psychiatric disorders in general. Stress has been hypothesized
to play a role in the etiology of many psychiatric disorders.42
Fourth, patients showed significantly higher levels of the dependent variables
NA and PA. Mood levels per se could not be considered a vulnerability marker,
as relatives reported the same levels of NA and PA as the control subjects.
Higher levels of NA give rise to more variability, which in turn enhances
the detection of stress reactivity; however, this cannot explain the increase
in stress reactivity in relatives as compared with control subjects. Finally,
the increased stress reactivity in the relatives group could possibly be attributed
mainly to the 6 relatives with a lifetime diagnosis of major depression or
to the 11 relatives who lived with a patient. Post-hoc analyses, however,
showed that parameter estimates differed only slightly and remained highly
statistically significant when these subjects were excluded from the analyses.
AUTHOR INFORMATION
Accepted for publication June 26, 2001.
This study was made possible in part by grants from the Dutch Prevention
Fund and the Dutch Brain Society, the Netherlands. Inez Myin-Germeys, PhD,
thanks Nederlandse Organisatie voor Wetenschappelijk Onderzoek and the Van
Walree fund for travel grants to the State University of New York, Stony Brook.
From the Department of Psychiatry and Neuropsychology, European Graduate
School of Neuroscience, Maastricht University, Maastricht, the Netherlands
(Drs Myin-Germeys, van Os, and Delespaul); the Department of Psychiatry, State
University of New York, Stony Brook, (Drs Schwartz and Stone); and the Division
of Psychological Medicine, Institute of Psychiatry, London, England (Dr van
Os).
Corresponding author: Jim van Os, MD, Department of Psychiatry and
Neuropsychology, European Graduate School of Neuroscience, Maastricht University,
PO Box 616 (PAR 45), 6200 MD Maastricht, the Netherlands.
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