Copyright 1995 by the American Psychological Association, Inc.
0278-6133/95/S3.00
Health Psychology
1995, Vol. 14, No. 5,399-408
Arthritis and Perceptions of Quality of Life: An Examination of Positive
and Negative Affect in Rheumatoid Arthritis Patients
Craig A. Smith
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Alex J. Zautra and Mary H. Burleson
Arizona State University
Vanderbilt University
Susan J. Blalock
University of North Carolina
Kenneth A. Wallston
Robert F. DeVellis and Brenda M. DeVellis
Timothy W. Smith
University of Utah
Vanderbilt University
University of North Carolina
The utility of measuring both positive and negative affective states for assessing rheumatoid
arthritis (RA) patients was examined in 3 independent samples of male and female RA patients
(Sample A: 179 women, 48 men; Sample B: 177 women, 24 men; Sample C: 134 women, 38 men).
Confirmatory factor analyses of each sample indicated that positive and negative affect constituted
separate, negatively correlated factors. The relations among disease variables, coping, and affects
were consistent with a model in which coping mediates the relationship between disease variables
and positive and negative affect. Patients with higher pain and limitation from RA had higher levels
of maladaptive coping, and maladaptive coping was associated with lower positive affect and higher
negative affect. Those RAs with higher activity limitation also reported less adaptive coping, which
was associated with less positive affect.
Key words: arthritis, quality of life, coping, affect
been associated with both the collective good and individual
well-being. Initially, social research ignored subjective appraisals of life quality and relied instead on behavioral measures
and social statistics because such measures were seen as more
objective (Moynihan, 1970). Campbell and Converse (1972)
pointed out, however, that if the views of the individual are
ignored, researchers and policymakers presume a rank ordering of what constitutes a good life on the basis of their own
implicit values, which may not be shared by the individual. To
fill this gap, subjective indicators of well-being were developed
by a number of investigators (Andrews & Withey, 1976;
Campbell, Converse, & Rodgers, 1976). Typically, the respondents themselves were asked to rate their level of satisfaction
with various aspects of their lives; these ratings were then used
to map specific domains of perceived quality of life (PQOL).
Affective states were also assessed and related to global ratings
of PQOL (Zautra, Beier, & Cappel, 1977). In those studies,
both the level of positive and negative affect would contribute
to the prediction of PQOL (Andrews & Withey 1976).
The application of PQOL concepts to health psychology has
yet to fully benefit from the more generic research and
development efforts. In particular, investigators have not fully
appreciated the importance of subjective assessments. Much
work has focused on activity limitation as the central aspect of
well-being. Such an approach defines life quality as a utility
function—what individuals can do for themselves or others—
rather than how they appraise their own state of well-being.
Although functional measures are indeed critical outcomes,
without an assessment of the patient's state of emotional
well-being, it is difficult to fully evaluate the importance of loss
of function to the individual.
In a chronic, painful, and disabling illness such as rheumatoid arthritis (RA), both the patient and the clinician look for
ways to preserve the quality of life. Recent studies have
focused on some components of poor life quality that are of
concern to the patient, such as feelings of psychological
distress, pain, and loss of functioning (Beckham, Keefe,
Caldwell, & Roodman, 1991; Haythornthwaite, Sieber, &
Kerns, 1991; Kaplan et al, 1989), but have ignored the impact
of illness on positive states of well-being. The research we
report here examines both positive and negative affective
consequences of RA as means of furthering our understanding
of how those affective dimensions of a person's life quality may
be influenced by a chronic illness.
To refer to life quality means to be concerned with life's
"goodness," and there are a wide range of attributes that have
Alex J. Zautra and Mary H. Burleson, Department of Psychology,
Arizona State University; Craig A. Smith, Department of Psychology
and Human Development, Vanderbilt University; Susan J. Blalock,
Robert F. DeVellis, and Brenda M. DeVellis, Department of Psychology, University of North Carolina; Kenneth A. Wallston, School of
Nursing, Vanderbilt University; Timothy W. Smith, Department of
Psychology, University of Utah.
This research was supported in part by grants from the National
Center for Nursing Research, National Institutes of Health (Grant 5
RO1 NR01007); the National Institute of Arthritis, Musculoskeletal,
and Skin Diseases (Grant AR 307071); and the Office of the Vice
President for Research of Arizona State University.
Correspondence concerning this article should be addressed to Alex
J. Zautra, Department of Psychology, Arizona State University,
Tempe, Arizona 85287-1104.
399
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ZAUTRA ET AL.
Some attention has focused on psychological maladjustment
as a subjective component of well-being of the chronically ill
patient (Meenan, Gertman, & Mason, 1980; Ware & Sherbourne, 1992). Along these lines, there is evidence that those
with more severe forms of arthritis often suffer from more
depressed affect than those with less severe forms of this illness
(Blalock, DeVellis, DeVellis, & Sauter, 1988; Blalock et al.,
1992) and that exacerbations of disease in the form of flare-ups
of joint inflammation and pain are often accompanied by
increases in psychological distress. The psychological adjustment approach is limited, however, to the assessment of the
presence or absence of negative affective states, such as
depression and anxiety (DeVellis, 1993); a differentiated
assessment of positive affective states as well as negative
affects is typically not provided (Zautra & Hempel, 1984).
Yet, considerable evidence suggests that positive affective
states are not simply the opposite of negative states. Bradburn
(1969) was among the first to find empirical evidence that
individual levels of positive affect were independent of levels
of negative affect. Watson and Tellegen (1985), following
Bradburn, factor analyzed a comprehensive sample of affect
terms and found further support for the idea that positive
affects were independent of negative affects. There is some
disagreement among researchers on the degree of independence between these affective states. Green, Goldman, and
Salovey (1993) found significant negative correlations between
positive and negative affect when they controlled for correlated measurement error among indicators of the two affective
states. Nevertheless, after controlling for measurement error,
there was still a sizable amount of variability in positive affect
not accounted for by reports of negative affect in Green et al.'s
data (see Green et al., 1993, p. 1037).
Positive affective states would appear to offer information
about PQOL not contained in measures of negative affective
states. Indeed, high ratings of PQOL, such as global ratings of
life satisfaction, have been found to derive from a mixture of
positive affect and the relative absence of negative affect
(McKennell & Andrews, 1983). Watson and his colleagues
(Clark & Watson, 1991; Clark, Watson, & Mineka, 1994) have
shown that depression is a mixture or combination of high
negative affect and low positive affect. Measures that combine
both affects, although comprehensive, do not allow much
specificity in the analysis of subjective outcomes.
For complex life experiences that result from a disabling
illness such as RA, both the positive and negative affects may
be altered, and in dissimilar ways. Furthermore,, different
aspects of the chronic illness may cause different effects. For
example, the pain that results from RA may increase negative
mood and challenge adjustment, as do other stressful events.
Function loss, whether from pain or joint damage, may not
only be stressful but such loss may also limit opportunities for
positive experiences and thus decrease PQOL by lowering
positive affect, as well as by increasing negative affective states
(Zautra & Hempel, 1984). A key issue that has not been
addressed as yet is whether chronically ill patients who
experience greater pain and disability also experience less
positive affect, in addition to an increase in negative affect.
Indeed, if a PQOL perspective on chronic illness is to be
useful, then an increment in the amount of variance accounted
for in the impact of RA on emotional well-being should be
gained through an assessment of positive emotions.
A logical extension of the concern with the individual's
assessment of his or her quality of life is the examination of the
individual's coping responses to the stresses associated with
chronic illness. Indeed, how the person copes with activity
limitation and pain may predict how their quality of life is
affected better than an accounting of the severity of the illness
alone. One of the consistent findings involving coping and
arthritis is that certain passive coping responses (e.g., sleeping
more or restricting one's daily activities) appear maladaptive
in that their use is associated with relatively poor psychological
adjustment (Zautra & Manne, 1992). More active coping
responses (e.g., maintaining one's activities in spite of the pain
or efforts to distract one's attention from the pain) are thought
to be more adaptive behaviors. Thus far, the evidence for
active coping related to adjustment to disease has been weaker
and less consistent (e.g., Smith & Wallston, 1992). This
inconsistent relationship may be due to the focus of most
outcome measures on negative affective states. Measures of
positive affect may detect both the impact of a chronic illness
such as RA and identify the outcomes of individual efforts at
coping and adaptation. Thus, adaptive coping efforts may show
a positive impact both by boosting positive affective states and
buffering the person from negative affective consequences of
the stresses of arthritis.
A mediational model may best characterize how disease
processes, coping, and affect are related (Baron & Kenny,
1986). The individual's coping response may be considered, in
part, a function of the illness parameters. For example, more
hopeless responses are likely when the disease is uncontrollable (Fry, 1991). Nevertheless, at any given level of disease
severity, there are likely to be substantial individual differences
in coping (Bolger, 1990). When differences in pain and activity
limitation are accounted for, the way the individual copes may
determine the affective consequences of the disease.
Our intention in this study, therefore, was to step past the
assessment of functional limitation and focus on assessments
of affective states as indicators of the subjective side of quality
of life for RA patients. In doing this we differentiated the
affective aspects of quality of life or PQOL into separate
components corresponding to positive and negative affect. We
sought to examine whether the separate assessment of positive
states would reveal more about the impact of the disease than
possible with a less differentiated assessment. These aims led
us to pose three principal questions. First, do positive affect
scores arise from a dimension of PQOL separate from negative
affective states for RA patients? Second, does positive affect
correlate with aspects of the disease of RA after accounting for
the patient's reports of negative affect? Third, how do individual differences in coping responses relate to positive and
negative affects? In addressing this third question we tested
whether individual differences in coping mediated the relationship between disease severity and affective outcomes.
We addressed these questions through the analysis of three
sets of data that focus on the psychological impact of RA on
the patient. By using multiple data sets, we could assess the
replicability of our findings. In addition, we could examine
whether our results were robust with respect to important
401
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SPECIAL SECTION: ARTHRITIS AND POSITIVE AFFECT
variations in the characteristics of the study sample and the
measures used to assess their coping and adaptation. For our
study the samples were similar in disease; all had RA. One of
the three samples, however, comprised recently diagnosed
patients, in contrast to the other two. Although no uniform
directional prediction on the effects of disease duration could
guide a priori hypotheses, it was conceivable that those who
had the disease longer had developed more adaptive coping
methods and would show fewer affective disturbances than
those new to the pain and limitations that accompany RA.
Variations in measures of coping and activity limitation also
would help test the uniformity of findings across samples. The
measures of positive and negative affect were the same across
the three samples; they composed our method of appraising
the PQOL of the individual in this study.
Method
Overview
To answer the questions posed above, we carried out a series of
analyses using three sets of data collected by different groups of
investigators: Blalock and her colleagues (Blalock et al., 1992; Sample
A), R. DeVellis and B. DeVellis (study ongoing; Sample B), and
Smith, Wallston, and colleagues (Smith & Wallston, 1992; Smith,
Wallston, & Dwyer, 1995; Sample C). Using conflrmatoiy factor
analyses, we first addressed the issue of the independence of positive
and negative affect in RA patients. We subsequently modeled the
relations between indicators of the disease process and affective
outcomes, with coping as a mediator in those data sets where it was
measured. Our general strategy in these analyses was to establish a
model using one data set and then cross-validate it by testing its fit to
one or more additional data sets.
The methods used to collect Samples A and C have been described
thoroughly in the published reports cited above, and details regarding
Sample B can be obtained from us. We present here a brief summary
of those aspects relevant to the current analyses.
Sample Recruitment and Characteristics
Participants in Sample B were recruited from three sources:
advertisement in the Arthritis Foundation Newsletter mailed to
members (61% of total), physician referral (28%), and patient support
groups (11%). Diagnosis with RA was confirmed by a physician to all
cases. Participants in both Sample A and Sample C were recruited
through practicing rheumatologists, who also provided diagnostic
confirmation. Characteristics of the three samples are shown in
Table 1.
Measures
All three of the studies measured pain and activity limitation from
arthritis, along with positive and negative affect. Two of the studies
measured mechanisms for coping with pain.
Arthritis pain. Two items from the Pain subscale of the Arthritis
Impact Measurement Scale (AIMS; Meenan et al., 1980) were used to
measure pain in all three samples. Using 6-point scales ranging from
none or never to very severe or always, participants rated the overall
severity of arthritis pain and frequency of severe arthritis pain over the
past month.
Activity limitation. Sample A measured activity limitation with
eight 4-point items from the Modified Health Assessment Questionnaire (Fries, Spitz, Kraines, & Holman, 1980). Respondents rated
their difficulty in carrying out daily activities requiring mobility,
physical activity, and manual dexterity. In Samples B and C, ongoing
level of activity limitation was measured using the Limitation subscale
of the AIMS. This subscale comprises six yes or no items addressing
mobility, physical activity, and manual dexterity, along with four
3-point items in which participants rate their ability to perform their
social roles and daily activities.
Positive and negative affect. All three studies measured positive and
negative affect using the Positive and Negative Affect Schedule
(PANAS; Watson, Clark, & Tellegen, 1988). This scale was developed
to assess the independent dimensions of positive and negative affect. It
comprises a list of 20 adjectives describing mood states. The respondents rate the extent to which they have felt each of the affective states
during the designated time frame, using a 5-point scale ranging from
very slightly or not at all to extremely. The 10 positively valenced affective
states included the following: interested, excited, strong, enthusiastic,
proud, alert, inspired, determined, attentive, and active. The list of
negative mood items includes distressed, upset, guilty, scared, hostile,
irritable, ashamed, nervous, jittery, and afraid.
Coping. In Sample A, coping was measured with an abridged
version of the Coping Strategies Questionnaire (CSQ; Rosenstiel &
Keefe, 1983). This is a 12-item scale that measures catastrophizing and
Table 1
Characteristics of the Three Samples
Characteristic
Source of sample
n
%male
Age range (years)
M age (years)
Mtime with RA diagnosis (years)
Range of years with
RA diagnosis
M CES-D (60-point
scale)
M self-report arthritis
pain"
Sample A
Sample B
Sample C
Blalock etal. (1992)
R. Devellis & B. DeVellis
(ongoing study)
201
11.9
20-92
53.4
Smith et al. (1995)
0.25
12.2
10.2
0-1
2-47
7-15
13.1
11.1
12.3
6.2
6.9
7.2
227
21.1
16-89
52.4
172
22.0
28-86
57.8
Note. RA = rheumatoid arthritis; CES-D = Center for Epidemiological Studies—Depression Scale
(Radloff, 1977).
"Possible scores range from 2 (less frequent, less severe pain) to 12 (more frequent, more severe pain).
402
ZAUTRA ET AL,
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positive self-statement modes of coping with pain. Sample C used the
Vanderbilt Pain Management Inventory (VPMI; Brown & Nicassio,
1987) to assess coping with arthritis-related pain. Respondents use a
5-point scale to rate how often they perform the indicated activity
when experiencing pain. Six items capture passive coping strategies,
such as "wish the doctor would prescribe better pain medication," and
five items measure more active coping, such as "engage in physical
exercise or physical therapy." Coping was not assessed in Sample B. To
simplify comparisons in the present article, we refer to positive
self-statements (from the CSQ) and active coping (from the VPMI) as
adaptive coping, whereas we refer to catastrophizing (from the CSQ)
and passive coping (from the VPMI) as maladaptive coping.
Procedure
In Sample B, the participants filled out self-report questionnaires in
conjunction with in-person interviews conducted in their homes. In
Samples A and C, questionnaires were mailed to the participants'
homes and were self-administered.
Results
Development of Measurement Model
Our first purpose was to explore the relationship between
positive and negative affect in RA patients. To arrive at a
statistical approximation of the true correlation between the
underlying mood constructs, rather than the scale scores
themselves, we first developed a measurement model of the
relations between the latent mood factors and the observed
indicators. We started with a confirmatory factor analysis
(CFA) of the PANAS data from Sample A, which was chosen
because it had the largest number of participants (N = 227).
All CFAs were carried out using maximum likelihood estimation in LISREL 7 (Joreskog & Sorbom, 1989).
The simplest model of affective response proposes that
positive and negative mood can be characterized as opposite
ends of the same scale—in other words, as aspects of a single
underlying construct. Therefore, we first tested a one-factor
model. All of the PANAS items were specified to load on this
single factor. Preliminary analysis led us to drop the "ashamed"
item, because its distribution was highly skewed (3.3) and
kurtotic (12.2). We also eliminated the "active" item from the
PANAS, because it was very similar to the indicators for
activity limitation, and we wished to avoid measurement
overlap during the later stages of structural analysis. Thus,
there were 18 items. We used a marker strategy (set the
loading to 1.0 for one indicator of the underlying factor) to set
the measurement scale. Error covariances were set to zero.
The modification indexes from the first CFA indicated that
the model would fit significantly better if we freed two error
covariances—between the scared and afraid items and between the nervous and jittery items. After freeing these
covariances, we evaluated the overall fit of the one-factor
model. Several of the standardized factor loadings fell very
close to or below the conventional cutoff value of .4, although
all were significant. The chi-square was 737.90 (N = 277, p <
.001), which is large for the degrees of freedom (133),
signifying a poor fit (Bollen, 1989). Both the Tucker-Lewis
index (TLI; Tucker & Lewis, 1973) and the comparative fit
index (CFI; Bentler, 1990; .57 and .62, respectively) also
indicated a poor fit to the data.
On the basis of previous work by Watson et al. (1988), we
next tested a model with two factors. We specified hedonically
positive items to load on one factor (Positive Affect) and
negative items to load on the other (Negative Affect). There
were nine positive and nine negative items. We again used a
marker strategy to set the measurement scale. We also allowed
the factors to correlate with one another. Error covariances
were set to zero, except for those between the scared and
afraid items and between the nervous and jittery items.
Using the two-factor specification, all of the standardized
factor loadings were high and significant. The chi-square of
282.90 (N = 227, p < .01), although significant, was small for
the degrees of freedom (132), signifying a good fit. Both the
TLI and CFI (.89 and .91, respectively) also indicated a good fit
to the data. The negative correlation between the factors was
relatively small (r = -.31).
The factor loadings and fit indexes suggest that the twofactor model provides a better fit to the data. We performed a
chi-square difference test to evaluate directly the relative fit of
the two models; AX2(1, N = 221) = 455.00,^ = .001, a highly
significant result. Thus, the two-factor model was superior.
Our next step was to test the fit of the two-factor model
specification in another data set. We chose the DeVellis and
DeVellis sample (Sample B), which had the next largest
number of participants (N = 201). Preliminary analyses
confirmed the high skewness and kurtosis of the ashamed item
from the PANAS, so we eliminated it from this data set along
with the active item. The specification was identical to the
modified two-factor model used for Sample A. The parameter
estimates and fit indexes indicated that the model fit the data
from Sample B very well. All of the parameter estimates were
significant and were identical in sign and very close in
magnitude to the analogous estimates from the first analysis.
The two factors were negatively correlated (r = —.29). There
was a good fit, x2(132, N = 201) = 207.27, p = .001. The TLI
(.94) and CFI (.94) were slightly better than those from the
two-factor model of the first data set, also signifying a good fit
to the data.
We also tested the identical model in the Smith and
Wallston data set (Sample C), which had the fewest participants (N = 172). The results were very similar to the first two
analyses. There was a good fit to the data, x2(132.N = 172) =
235.52,p = .001; TLI = .91; CFI = .92.
For the sake of completeness, we conducted chi-square
difference tests between the one-factor and two-factor models
for Samples B and C. Both were highly significant: Sample B,
X2(l, # = 201) = 402.34,/? < .001; Sample C, x2(l,# = 172) =
301.66, p < .001, indicating the superior fit of the two-factor
model.
In summary, the CFA results were parallel in all three of the
samples. Both the nature of the models and their high degree
of similarity support the existence of two separate but mildly
negatively correlated constructs underlying the PANAS scores.
On the basis of the pattern of item loadings, these constructs
can parsimoniously be referred to as positive affect and
negative affect. Thus, the question regarding the nature of the
relation between positive and negative affect in RA patients
403
SPECIAL SECTION: ARTHRITIS AND POSITIVE AFFECT
appears to be answered—they are negatively correlated but do
not account for the bulk of each other's variability. This
relationship is quite similar to that found in other populations
(Watson et al., 1988).
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Structural Models of Pain, Activity Limitation, Coping,
and Affect
Once we were confident that positive affect in RA patients
could not be accounted for by knowing the patients' negative
affect scores, we were ready to test whether positive affect
could add meaningfully to our understanding of the relations
among aspects of the disease, the individual's coping response
to them, and PQOL. Table 2 presents the correlations among
pain, activity limitation, coping, and scale scores for positive
and negative affect.
On the basis of previous research regarding symptoms,
coping, and well-being in other populations (e.g., Brown,
Nicassio, & Wallston, 1989; Felton & Revenson, 1984), we
predicted that high arthritis pain and activity limitation would
be associated with high negative affect and low positive affect.
We also expected coping to mediate the relations between the
signs and symptoms of arthritis and positive and negative
affect. The simple correlations provided initial support for
these relationships and led us to analyze the data using
structural equation methods.
Mediational effects of coping. To investigate the effects of
coping, we used the Baron and Kenny (1986) formulation for
testing mediation in structural models. In the first step of this
approach, the exogenous variables (in this case, pain and
activity limitation) are modeled as direct predictors of all of
the other variables. The size of the relationships between the
exogenous variables and the eventual outcome variables (positive and negative affect) is noted. The second step is to add
paths from the proposed mediating variables (adaptive and
maladaptive coping) to the outcome variables. If the magnitude of the direct paths from the exogenous variables to the
outcome variables is reduced by adding the proposed mediators, mediation is occurring.
Testing mediation: Step 1. We started with Sample A. Our
first step was to set up a model in which arthritis pain and
activity limitation predicted both of the coping variables
(positive coping self-statements and catastrophizing) and both
positive and negative affect. Pain, limitation, and coping were
modeled as observed rather than latent constructs. We again
used a marker strategy to set the measurement scale for affect.
The latent affect constructs were allowed to correlate with
each other, as were the pain and limitation scores and the
coping scores. The parameter estimates (shown in italics) and
fit indexes for this Step 1 model are shown in Figure 1.
The fit of the model to the data was fairly good, as can be
seen from the fit indexes, x2(202, N = 227) = 455.54,p = .001;
TLI = .84; CFI = .86. More important for the Baron and
Kenny (1986) framework, the paths from arthritis pain and
limitation to the outcome variables (positive and negative
Table 2
Pearson Correlations Among Arthritis Pain, Activity Limitation, Coping Styles, and Positive and
Negative Affect Scores in the Three Samples
Variables
and source
1. Arthritis pain
Sample A
Sample B
Sample C
2. Activity limitation
Sample A
Sample B
Sample C
3. Adaptive coping*1
Sample A
Sample C
4. Maladaptive coping11
Sample A
Sample C
5. Positive affect"
Sample A
Sample B
Sample C
6. Negative affectd
Sample A
Sample B
Sample C
na
1
2
3
203
196
138
—
—
—
.60**
.41**
.50**
-.05
NA
-.12
.40**
NA
.36**
-.10
-.14*
-.23**
.23**
.21**
.22**
—
—
—
-.15*
NA
-.25**
.36**
NA
.42**
-.17**
-.21**
-.20**
.22**
.18*
.29**
-.27**
-.21*
.38**
.29**
-.16**
-.19*
-.33**
-.23**
.53**
.55**
203
196
138
203
138
203
138
218
196
138
218
196
138
—
—
4
—
—
5
—
—
—
6
-.25**
-.22**
-.28**
—
—
—
Note. NA = not assessed.
"Numbers of participants who provided data for each correlation, based on the subset used in the
structural models. bCoping was not assessed in Sample B. "The "active" item was omitted from this
scale (see text for explanation). dThe "ashamed" item was omitted from this scale (see text for
explanation).
*p < .05. **/? < .01.
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404
ZAUTRA ET AL,
Figure 1. Testing mediation: Step 1 model and Step 2 model, using Sample A (Blalock et al., 1992). The
Step 1 model is shown in solid lines, with parameter estimates in italics, x2(202, N = 227) = 455.54, p <
.01; Tucker-Lewis index = .84; comparative fit index = .86. For the Step 2 model, added paths are shown
in dashed lines, with parameter estimates in parentheses, x2(198, N = 227) = 369.69, p < .01;
Tucker-Lewis index = .90; comparative fit index = .91. *p < .05. **p < .01.
affect) were either significant or marginally significant, with
one exception—the path from pain to positive affect was close
to zero. Three of the four paths from the exogenous variables
to the proposed mediators (adaptive and maladaptive coping)
were also significant or marginally significant, the only exception being the path from pain to adaptive coping.
Testing mediation: Step 2. Our next step was to add paths
from adaptive and maladaptive coping to positive and negative
affect. The parameter estimates (shown in parentheses) and fit
indexes for this model also are illustrated in Figure 1. The fit of
the model was much better, as would be expected because of
the relaxation of model constraints. More important, all of the
direct paths from pain and limitation to affect were reduced in
magnitude; in fact, all became nonsignificant. Three of the four
paths from the proposed mediators to the outcome variables
were significant; only the relation between adaptive coping and
negative affect was nonsignificant. Thus, the data from Sample
A meet the criteria for mediation.
Model optimization. Our final step in developing a model of
the relations among symptoms, coping, and affective outcome
was to eliminate the original direct paths from the exogenous
variables to the outcomes. In addition, we dropped the other
two nonsignificant paths from the Step 2 model. This streamlined version of the model, along with parameter estimates and
fit indexes, is illustrated in Figure 2.
The fit of the model to the data was good. Positive and
negative affect were inversely correlated, as were positive and
negative coping statements. Pain and limitation were positively
correlated. Pain and activity limitation were both positively
correlated with maladaptive coping, which in turn was inversely related to positive affect and positively related to
negative affect. In other words, RA patients with high levels of
pain and limitation reported more use of catastrophizing
coping mechanisms, less positive affect, and more negative
affect than those with low pain and limitation. In addition,
activity limitation was inversely related to adaptive coping,
which was positively related to positive affect but unrelated to
negative affect. RA patients with high activity limitation
reported less use of positive self-statements and less positive
affect than those with low activity limitation.
Cross-validation of the model. Because Sample B (DeVellis
& DeVellis, ongoing study) included no measure of coping, we
turned to Sample C to cross-validate the mediated model. The
coping measure was different from that used in Sample A:
Instead of positive self-statements and catastrophizing, we
modeled active and passive coping. We carried out Steps 1 and
2 of the Baron and Kenny (1986) test for mediation. The
estimated path coefficients and fit indexes for the models
specified in Steps 1 and 2 are shown in Table 3.
The estimated correlations between the variables in the Step
1 model were as follows: pain and limitation, r = .49, p < .01;
adaptive and maladaptive coping, r = — .13, p < .05; and
positive and negative affect, r = — .31, p < .01. For Step 2, the
same correlations were r = .49,p < .01; r= —.10,^ < .10, and
r = — .20, p < .05, respectively.
Overall, the data from Sample C met the Baron and Kenny
(1986) criteria for mediation; although the direct path from
pain to positive affect remained fairly large in the Step 2
model, it decreased from its Step 1 magnitude. We next tested
a fully mediated model in this data set. The direct paths from
pain and limitation to affect, along with the same two nonsignificant paths as in Sample A, were eliminated. The estimated
405
SPECIAL SECTION: ARTHRITIS AND POSITIVE AFFECT
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.77*S
Figure 2. Streamlined mediational model, using Sample A (Blalock et al, 1992), x2(204, N = 227)
371.56,/> < .01; Tucker-Lewis index = .90; comparative fit index = .91. *p < .05. **/> < .01.
path coefficients and fit indexes for the stripped-down model
are shown in Table 3. The estimated correlations between pain
and limitation, adaptive and maladaptive coping, and positive
and negative affect were very similar to those from the Step 2
model. The model fit the data well. Despite the differences in
measurement of coping, the path coefficients were all significant and in the same direction as those from Sample A. In both
samples, high pain and high limitation predicted more maladaptive coping, which in turn predicted more negative affect and
less positive affect. High limitation also predicted less adaptive
coping, which in turn predicted less positive affect but had no
relation with negative affect. Thus, in both Sample A and
Sample C, positive affect was a meaningful addition to the
model and added to our understanding of the relations
between disease aspects and PQOL.
Discussion
Several findings emerge clearly from our analyses of these
three separate data sets. The first is that positive and negative
affects represent two distinct emotional responses, both of
which are influenced by the disease processes associated with
Table3
Standardized Path Coefficients and Fit Indexes for Step 1, Step 2, and Streamlined Structural
Models in Sample C (Smith & Wallston, 1992)
Criterion
Model and
predictor
Step 1 model"
Arthritis pain
Activity limitation
Step 2 modelb
Arthritis pain
Activity limitation
Adaptive coping
Maladaptive coping
Streamlined model0
Arthritis pain
Activity limitation
Adaptive coping
Maladaptive coping
Adaptive
coping
Maladaptive
coping
Positive
affect
Negative
affect
.00
.20*
.33*
-.20*
-.11
.20*
.20*
.33*
-.17
-.25*
.00
-.25*
—
-.25*
.20*
.33*
.00
.13
.02
.01
.24*
-.17*
-.09
.58**
—
—
.23*
—
—
—
.60**
-.23*
Note. Dashes indicate paths omitted from streamlined model.
"X2(202, N = 138) = 364.45, p < .01; Tucker-Lewis index = .85; comparative fit index = .87. bx2(198,
N = 138) = 315.33,p < .01; Tucker-Lewis index = .89; comparative fit index = .91. Cx2(204, N = 138) =
320.20,p < .01; Tucker-Lewis index = .90; comparative fit index = .91.
*p < .05. **p < .01.
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406
ZAUTRA ET AL.
RA. The confirmatory factor analyses indicate that two inversely correlated factors, corresponding with positive and
negative affect, best described the patients' self-reported
experience of mood. The inverse relationship between the two
affective factors was significant and somewhat higher than that
found among the college student samples examined by Watson
and Tellegen (1985). Nevertheless, the relationship was modest in size, with substantial-variance in one variable unexplained by scores on the other variable. The use of latent
factors, along with replication across three studies, adds to our
confidence in the reliability of this finding. Thus, our results
support a bidimensional approach to measurement of mood in
the RA patient population, in which positive and negative
affective experience are regarded as separate but not necessarily independent.
We hypothesized that positive and negative affective states
would be related to disease processes in different ways, and the
data generally support that hypothesis. In the Step 1 models,
patients experiencing pain reported more negative affect, but
there was a smaller (in Sample A, nonsignificant) relationship
between pain and positive affect. Activity limitation, however,
showed more pervasive effects: Positive emotions were reduced and negative emotions were increased for RA patients
with more disability. The data are not identical in mapping
these relationships but as a whole do support different patterns
of correlation between the disease indicators and the two
underlying affective factors.
We further evaluated the utility of assessing both positive
and negative affect by examining how individual differences in
coping with pain and disability would be related to these
emotional states. The two forms of coping showed different
patterns of relationship with positive and negative affect. As
would be expected, patients who used more maladaptive
coping responses had more negative affect and less positive
affect. Furthermore, those higher in adaptive coping reported
more positive affect. Interestingly, however, negative affect
was not related to adaptive coping with pain in either sample
of patients. Thus, the previous weak and somewhat inconsistent findings regarding adaptive coping and adjustment to RA
have likely been at least partially due to the overly restrictive
focus on depression and negative affect. The clear relationship
between active coping and positive affect highlights the need to
include measures of positive affective states for a full understanding of disease effects on quality of life.
Not only do these findings endorse a differentiated view of
affective outcomes of chronic illness but they raise some
important challenges for theoretical models of coping. As
Zautra and Manne (1992) pointed out in their review of
empirical studies of coping with arthritis, stress and coping
models do not make separate predictions for how coping
would influence positive versus negative affective states. Moreover, because coping is conceptualized as a response to
distressing events (such as pain and disability), the primary
emotional effect of adaptive coping ought to be the reduction
of negative affect. In these two samples of patients, however,
adaptive coping did not reduce negative affect. In fact, the two
variables were unrelated in both data sets. Instead, adaptive
coping was associated with an increase in positive affect. Thus,
these data show that individual differences in adaptive coping
may affect psychological well-being and with it, quality of life,
but in a very different way from the standard stress and coping
perspective.
Although we have drawn the structural models as if causal
relationships were tested, no definitive claims of causation can
be made on the basis of this investigation. Only one assessment
was made, so lagged relationships could not be examined;
longitudinal data could have provided more evidence of causal
predominance between the variables. The order of the variables was presumed such that positive and negative affects
were treated only as outcomes. This presumptive ordering was
valuable to the primary purpose of the study, which was the
examination of the use of measuring affective responses as
indicators of disease impacts. Nevertheless, it seems likely that
the experience of these affects would influence the individual's
coping with his or her illness. The unidirectional causal path
assumed between maladaptive coping and negative affect is
particularly tenuous: Negative affects may lead to feelings of
helplessness that could result in passivity and other withdrawal
responses associated with maladaptive coping (Smith & Wallston, 1992). It is also conceivable that coping can influence
reports of pain and disability, rather than the way we have
drawn the model here. Future studies are needed to examine
the extent to which a rise in negative affect prompts increases
in maladaptive responses, in contrast to the hypothesis that
maladaptive coping precedes elevations in negative affect.
Such a reversal in causal direction may well occur for chronically ill patients when the pain from their illness is especially
intense. Investigation of the effects of coping on reported pain
and activity limitation would also be valuable.
Despite the limitation on causal inference arising from
cross-sectional data, the current report has several important
strengths. Because we examined three independent data sets,
collected by different investigators at different sites and using a
range of instrumentation, we can have a high level of confidence in the reliability of the findings. How might these
findings inform us about assessment of the impact of disease
and the development and testing of interventions in arthritis?
First, it seems apparent from the data that the disease has
important impacts on the quality of the emotional life of the
patient that are overlooked in the standard assessments of
disability and pain. A full evaluation of the effects of the
disease thus will depend on the use of subjective assessments
of well-being such as self-reported affect. The use of these
measures provides a broader spectrum of outcomes with which
to evaluate the efficacy of treatments, whether those treatments are pharmacological or behavioral in focus. They also
provide a more comprehensive means of identifying differences between treatments in their unintended negative (and
positive) effects.
Second, the identification of positive and negative affect as
separate dimensions of adjustment in chronic medical illness
provides the opportunity for more complete models of individual differences in response to such potentially stressful
conditions. Recent theory and research indicates that negative
affective responses are characteristic of persons high on the
dimension of neuroticism, whereas positive affective responsiveness is related to the dimension of introversion versus extraversion (Watson & Clark, 1992). Thus, these elements of the
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SPECIAL SECTION: ARTHRITIS AND POSITIVE AFFECT
five-factor model of personality (McCrae & John, 1992) could
provide important information in elucidating the determinants
of the psychosocial impact of chronic illness.
Third, the importance of coping as a mediator of the
relationships between illness parameters and affective outcomes directs our focus to the individual's response to illness
as another salient outcome. The study of coping mechanisms is
a consequence of an interest in designing behavioral treatments that may assist arthritis patients in preservation of
well-being. The goals of these treatments are often couched in
the language of mental health: Better coping will reduce the
intensity of episodes of pain and lower psychological distress.
A PQOL perspective would include other positive changes as
well, such as an increase in the frequency of positive emotions.
Although some may view such concerns for the quality of
subjective experience unimportant, we think such outcomes
play an important role in the decisions that patients make
about their treatments. Furthermore, there is evidence from
several studies that positive affect and emotion can restore
morale among those under stress (Reich & Zautra, 1988),
neutralize negative affective experiences (Tennant, Bebbington, & Hurry, 1981), and otherwise provide the person with a
breather from demands of coping with a chronic stressor like
RA. How such emotions may enhance the number of well
years of patients with chronic diseases such as RA is a question
that awaits further research.
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